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Geosciences
Volume 14
Issue 10
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Geosciences, Volume 14, Issue 10 (October 2024) – 32 articles

Cover Story (view full-size image): Benthic (or seafloor) habitats are spatially recognizable areas with physical, chemical, and/or biological characteristics that differ from surrounding areas. This study applies a common approach to defining benthic geologic habitats using acoustic side-scan sonar validated by ground-truth data (sediment samples and underwater video imagery) for portions of Acadia National Park, Maine, USA. Habitats were classified using the Coastal and Marine Ecological Classification Standard (CMECS) Substrate, Geoform and Biotic components. Study sites focused on rocky intertidal habitats and demonstrate the effectiveness of the CMECS classification, including the framework’s ability to be flexible in communicating information. View this paper
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16 pages, 6610 KiB  
Article
Landslide Hazard and Rainfall Threshold Assessment: Incorporating Shallow and Deep-Seated Failure Mechanisms with Physics-Based Models
by Roberto J. Marin, Julián Camilo Marín-Sánchez, Johan Estiben Mira, Edwin F. García, Binru Zhao and Jeannette Zambrano
Geosciences 2024, 14(10), 280; https://doi.org/10.3390/geosciences14100280 - 21 Oct 2024
Viewed by 587
Abstract
Landslides pose a significant threat worldwide, leading to numerous fatalities and severe economic losses. The city of Manizales, located in the Colombian Andes, is particularly vulnerable due to its steep topography and permeable volcanic ash-derived soils. This study aims to assess landslide hazards [...] Read more.
Landslides pose a significant threat worldwide, leading to numerous fatalities and severe economic losses. The city of Manizales, located in the Colombian Andes, is particularly vulnerable due to its steep topography and permeable volcanic ash-derived soils. This study aims to assess landslide hazards in Manizales by integrating shallow planar and deep-seated circular failure mechanisms using physics-based models (TRIGRS and Scoops3D). By combining hazard zonation maps with rainfall thresholds calibrated through historical data, we provide a refined approach for early warning systems (EWS) in the region. Our results underscore the significance of the landslide hazard maps, which combine shallow planar and deep-seated circular failure scenarios. By categorizing urban areas into high, medium, and low-risk zones, we offer a practical framework for urban planning. Moreover, we developed physics-based rainfall thresholds for early landslide warning, simplifying their application while aiming to enhance regional predictive accuracy. This comprehensive approach equips local authorities with essential tools to mitigate landslide risks, refine hazard zoning, and strengthen early warning systems, promoting safer urban development in the Andean region and beyond, as the physics-based methods used are well-established and implemented globally. Full article
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Figure 1

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<p>Study Site Overview: (<b>a</b>) Elevation model showcasing the topography; (<b>b</b>) Geological map of the area.</p> Full article ">Figure 2
<p>(<b>a</b>) Mean factor of safety (FS) and (<b>b</b>) failure probability (<span class="html-italic">Pf</span>) using TRIGRS. Landslide hazard for urban areas in Manizales: (<b>c</b>) planar failure (TRIGRS); (<b>d</b>) circular failure (Scoops3D).</p> Full article ">Figure 3
<p>Landslide hazard for urban areas in Manizales: map overlay of planar and circular failure hazard maps.</p> Full article ">Figure 4
<p>Power-law equation of ID thresholds for different critical failure areas.</p> Full article ">Figure 5
<p>Suggested Rainfall Thresholds (1, 2, and 3) for Manizales (Colombia): (<b>a</b>) Overlaid with triggering rainfall data and thresholds previously proposed by IDEA in 2013: Medium, High, and Very High Threshold. (<b>b</b>) Overlaid with thresholds of five zones of the EWS in Emilia Romagna (Italy) [<a href="#B47-geosciences-14-00280" class="html-bibr">47</a>]. Threshold equations are presented in <a href="#geosciences-14-00280-t005" class="html-table">Table 5</a>.</p> Full article ">Figure 6
<p>Relationships of Thresholds 1 and 2 to Threshold 3.</p> Full article ">
19 pages, 5888 KiB  
Article
Effects of CO2 on the Mechanical Properties of Hanna Sandstone
by Ehsan Dabbaghi and Kam Ng
Geosciences 2024, 14(10), 279; https://doi.org/10.3390/geosciences14100279 - 21 Oct 2024
Viewed by 425
Abstract
Possible deterioration of a rock’s structure and mechanical properties due to chemical reactions between the host rock, formation water, and CO2 requires due attention. In this study, cylindrical sandstone specimens obtained from the Hanna Formation, Wyoming, were prepared under three treatment conditions: [...] Read more.
Possible deterioration of a rock’s structure and mechanical properties due to chemical reactions between the host rock, formation water, and CO2 requires due attention. In this study, cylindrical sandstone specimens obtained from the Hanna Formation, Wyoming, were prepared under three treatment conditions: dry, submerged in water, and treated with water + CO2 for one week at a pressure of 5 MPa and room temperature. Specimens were subjected to three effective confining pressures of 5, 15, and 25 MPa. The mechanical test results show that water + CO2 treatment, on average, decreases the peak strength and elastic modulus of the specimens by 36% and 20%, respectively, compared to dry specimens. For all three effective confining pressures, the dry specimens exhibited higher compressive strengths, larger Young’s moduli, and more brittle behavior. CO2-treated specimens showed significantly lower calcite contents. Full article
(This article belongs to the Special Issue Computational Geodynamic, Geotechnics and Geomechanics)
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<p>Location map of the Hanna Basin in Wyoming—after [<a href="#B31-geosciences-14-00279" class="html-bibr">31</a>].</p> Full article ">Figure 2
<p>Photos show (<b>a</b>) a wheel saw, (<b>b</b>) a surface grinder, and (<b>c</b>) a prepared rock specimen.</p> Full article ">Figure 3
<p>Images illustrate (<b>a</b>) the rock specimens immersed in water within the vessel prior to treatment and (<b>b</b>) the setup designed for high-pressure and high-temperature CO<sub>2</sub> treatment.</p> Full article ">Figure 4
<p>Photos show (<b>a</b>) the NER Autolab 3000 polyaxial equipment at the University of Wyoming and (<b>b</b>) the completed specimen setup for the triaxial compression experiment.</p> Full article ">Figure 5
<p>An illustration of an example TC test process and timing.</p> Full article ">Figure 6
<p>XRD patterns comparing specimens with two varying treatment specifications (Ca = calcite; Ka = kaolinite; Q = quartz).</p> Full article ">Figure 7
<p>Photos illustrate specimens’ SEM images (<b>a</b>) in dry condition and (<b>b</b>) treated with water + CO<sub>2</sub>.</p> Full article ">Figure 8
<p>Variations of volumetric strain (ε<sub>v</sub>) versus the effective confining pressure (P<sub>d</sub>) under a target P<sub>d</sub> of 5 MPa.</p> Full article ">Figure 9
<p>Variations of volumetric strain (ε<sub>v</sub>) against the effective confining pressure (P<sub>d</sub>) under a target P<sub>d</sub> of 15 MPa.</p> Full article ">Figure 10
<p>Variations of volumetric strain (ε<sub>v</sub>) against the effective confining pressure (P<sub>d</sub>) under a target P<sub>d</sub> of 25 MPa.</p> Full article ">Figure 11
<p>The comparison of bulk modulus (K) and the effective confining pressure (P<sub>d</sub>) for all specimens.</p> Full article ">Figure 12
<p>Relationship between the deviatoric stress (Δσ) and axial, radial, and volumetric strains under (<b>a1</b>–<b>a3</b>) P<sub>d</sub> = 5 MPa, (<b>b1</b>–<b>b3</b>) P<sub>d</sub> = 15 MPa, and (<b>c1</b>–<b>c3</b>) P<sub>d</sub> = 25 MPa.</p> Full article ">Figure 13
<p>Variations of (<b>a</b>) elastic modulus and (<b>b</b>) Poisson’s ratio against the effective confining pressure.</p> Full article ">Figure 14
<p>Changes in the major effective principal stress (P′<sub>a</sub>) compared to minor effective principal stress (P<sub>d</sub>).</p> Full article ">Figure 15
<p>The comparison of c′ and φ′ for all specimens in three treatment conditions.</p> Full article ">
16 pages, 25832 KiB  
Article
Identifying Potential Landslides in Low-Coherence Areas Using SBAS-InSAR: A Case Study of Ninghai County, China
by Jin Xu, Shijie Ge, Chunji Zhuang, Xixuan Bai, Jianfeng Gu and Bingqiang Zhang
Geosciences 2024, 14(10), 278; https://doi.org/10.3390/geosciences14100278 - 19 Oct 2024
Viewed by 636
Abstract
The southeastern coastal regions of China are characterized by typical hilly terrain with abundant rainfall throughout the year, leading to frequent geological hazards. To investigate the measurement accuracy of surface deformation and the effectiveness of error correction methods using the small baselines subset–interferometry [...] Read more.
The southeastern coastal regions of China are characterized by typical hilly terrain with abundant rainfall throughout the year, leading to frequent geological hazards. To investigate the measurement accuracy of surface deformation and the effectiveness of error correction methods using the small baselines subset–interferometry synthetic aperture radar (SBAS-InSAR) method in identifying potential geological hazards in such areas, this study processes and analyzes 129 SAR images covering Ninghai County, China. By processing coherence coefficients using the Stacking technique, errors introduced by low-coherence images during phase unwrapping are mitigated. Subsequently, interferograms with high coherence are selected for time-series deformation analysis based on the statistical parameters of coherence coefficients. The results indicate that, after mitigating errors from low-coherence images, applying the SBAS-InSAR method to only high-coherence SAR datasets provides reliable surface deformation results. Additionally, when combined with field geological survey data, this method successfully identified landslide boundaries and potential landslides not accurately detected in previous geological surveys. This study demonstrates that using the SBAS-InSAR method and selecting high-coherence SAR images based on interferogram coherence statistical parameters significantly improves measurement accuracy and effectively identifies potential geological hazards. Full article
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<p>(<b>a</b>) The red rectangle in the figure indicates the area of Ninghai County; (<b>b</b>) the topography of Ninghai County is shown, with black stars marking the locations of Sangzhou Town, including the Nanshanzhang and Liufeng landslides.</p> Full article ">Figure 2
<p>The temporal and spatial baseline distribution plot for the first set of SAR images. The horizontal axis represents the acquisition time of the radar images, with the numbers after the decimal point indicating a decimal division of the year for greater precision.</p> Full article ">Figure 3
<p>Stacking coherence coefficient and standard deviation after weighted averaging. (<b>a</b>) Coherence coefficient map for SAR image interferograms of Ninghai County and surrounding areas; (<b>b</b>) coherence coefficient map for regions including the Nanshanzhang and Liufeng landslides in Nanling Village; (<b>c</b>,<b>d</b>) standard deviations of the weighted averages for the corresponding areas. The pentagrams in the figure indicate the positions of sliding masses identified through field geological surveys. In panel (<b>d</b>), the locations marked A and B represent the core deformation areas identified through field geological surveys and InSAR observations and were thus designated as feature points for subsequent deformation extraction.</p> Full article ">Figure 4
<p>Partial interferograms and the corresponding root-mean-square (RMS) of coherence coefficients.</p> Full article ">Figure 5
<p>Statistical parameters of interferometric coherence in SAR images.</p> Full article ">Figure 6
<p>Cumulative deformation of characteristic points in the study area. (<b>a</b>) Time-series deformation at feature points for the first dataset; (<b>b</b>) time-series deformation at feature points for the first dataset after removing interferograms with high noise, based on a coherence RMS threshold; (<b>c</b>) time-series deformation at feature points for the second dataset; (<b>d</b>) time-series deformation at feature points for the third dataset.</p> Full article ">Figure 6 Cont.
<p>Cumulative deformation of characteristic points in the study area. (<b>a</b>) Time-series deformation at feature points for the first dataset; (<b>b</b>) time-series deformation at feature points for the first dataset after removing interferograms with high noise, based on a coherence RMS threshold; (<b>c</b>) time-series deformation at feature points for the second dataset; (<b>d</b>) time-series deformation at feature points for the third dataset.</p> Full article ">Figure 7
<p>Temporal and spatial baseline distribution plot for the second set of SAR images. The horizontal axis represents the acquisition time of the radar images, with the numbers after the decimal point indicating a decimal division of the year for greater precision.</p> Full article ">Figure 8
<p>Statistical parameters of interferometric coherence corresponding to the second set of SAR images.</p> Full article ">Figure 9
<p>Temporal and spatial baseline distribution plot for the third set of SAR images. The horizontal axis represents the acquisition time of the radar images, with the numbers after the decimal point indicating a decimal division of the year for greater precision.</p> Full article ">Figure 10
<p>Statistical parameters of interferometric coherence corresponding to the third set of SAR images.</p> Full article ">Figure 11
<p>Rate of LOS deformation to the surface in the study area. (<b>a</b>) Surface deformation rate in the radar line of sight (LOS) in Ninghai County; (<b>b</b>) Surface deformation rate in the radar line of sight (LOS) in the research area of Sangzhou Town.</p> Full article ">Figure 12
<p>Cumulative surface deformation along the line of sight (LOS) in the study area. (<b>a</b>) Cumulative surface deformation in the LOS in Ninghai County; (<b>b</b>) Cumulative surface deformation in the LOS in the research area of Sangzhou Town.</p> Full article ">Figure 13
<p>Deformation and geomorphological characteristics of Nanzhang landslide and Liufeng landslide. (<b>a</b>) On-site topography from the field geological survey of the potential landslide at Nanshanzhang (mirror NW); (<b>b</b>) On-site topography from the field geological survey of the potential landslide at Nanshanzhang (mirror N); (<b>c</b>) Optical image of the study area; (<b>d</b>) Cumulative surface deformation of the study area; (<b>e</b>) On-site topography from the field geological survey of the potential landslide at Liufeng (mirror E); (<b>f</b>) On-site topography from the field geological survey of the potential landslide at Liufeng (mirror N).</p> Full article ">
21 pages, 11833 KiB  
Article
Ductile Versus Brittle Tectonics in the Anatolian–Aegean–Balkan System
by Enzo Mantovani, Marcello Viti, Daniele Babbucci, Caterina Tamburelli, Massimo Baglione and Vittorio D’Intinosante
Geosciences 2024, 14(10), 277; https://doi.org/10.3390/geosciences14100277 - 19 Oct 2024
Viewed by 582
Abstract
It is hypothesized that the present tectonic setting of the Anatolian, Aegean and Balkan regions has been deeply influenced by the different deformation styles of the inner and outer belts which constituted the Oligocene Tethyan system. Stressed by the Arabian indenter, this buoyant [...] Read more.
It is hypothesized that the present tectonic setting of the Anatolian, Aegean and Balkan regions has been deeply influenced by the different deformation styles of the inner and outer belts which constituted the Oligocene Tethyan system. Stressed by the Arabian indenter, this buoyant structure has undergone a westward escape and strong bending. The available evidence suggests that in the Plio–Pleistocene time frame, the inner metamorphic core mainly deformed without undergoing major fragmentations, whereas the orogenic belts which flanked that core (Pontides, Balkanides, Dinarides and Hellenides) behaved as mainly brittle structures, undergoing marked fractures and fragmentations. This view can plausibly explain the formation of the Eastern (Crete–Rhodes) and Western (Peloponnesus) Hellenic Arcs, the peculiar time-space features of the Cretan basins, the development of the Cyprus Arc, the North Aegean strike-slip fault system, the southward escapes of the Antalya and Peloponnesus wedges and the complex tectonic setting in the Balkan zone. These tectonic processes have mostly developed since the late Late Miocene, in response to the collision of the Tethyan belt with the Adriatic continental domain, which accelerated the southward bending of the Anatolian and Aegean sectors, at the expense of the Levantine and Ionian oceanic domains. The proposed interpretation may help us to understand the connection between the ongoing tectonic processes and the spatio-temporal distribution of major earthquakes, increasing the chances of estimating the long-term seismic hazard in the study area. In particular, it is suggested that seismic activity in the Serbo–Macedonian zone may be favored by the post-seismic relaxation that develops after seismic crises in the Epirus thrust front and inhibited/delayed by the activations of the North Anatolian fault system. Full article
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<p>Tectonic scheme of the central and eastern Mediterranean area. (1) European continental domain, (2) Nubia–Adriatic continental domain, (3) Ionian–Levantine oceanic domain, (4, 5) inner belts of the Tethyan system, constituted by ophiolitic units and metamorphic massifs, respectively, (6) outer orogenic belts, (7) Calabrian and Mediterranean ridges, (8) Rhodope and Serbo–Macedonian (SMM) massifs, (9) Black Sea thinned domain, (10) Cenozoic basins, (11, 12, 13) extensional, transcurrent and compressional features, (14) outer fronts of the orogenic belts (references in the text). Al = Albanides, Am = Ambracique trough, An = Antalya peninsula, Bi = Biga peninsula, CA = Cilicia–Adana basin, Ce = Cephalonia fault, Co = Corinth trough, CR = Calabrian ridge, CyA = Cyclades Arc, EAF = Eastern Anatolian fault, EC = Ecemis faults, ECA = External Calabrian Arc, ECB = Eastern Cretan Basin, Ed = Edremit fault, EHA = Eastern Hellenic Arc, Eu = Eubea, FB = Fethiye–Burdur fault, Ga–Ge = Ganos–Gelibolu thrust fault, Ka = Karphatos, Ma = Marmara trough, MR = Mediterranean ridge, NAF = North Anatolian fault, NAT = North Aegean trough, NH = Northern Hellenides (Epirus), Pa = Parnassus, Pe = Peloponnesus wedge, Pl = Pliny fault, Rh = Rhodes, St = Strabo fault, The = Thessaly, Thf = Thrace fault, Tr = Trapezona, UTG = Upper Thrace graben, Va = Vardar zone, WCB = Western Cretan basin, WHA = Western Hellenic Arc.</p> Full article ">Figure 2
<p>Proposed evolutionary reconstruction of the central–eastern Mediterranean region. (<b>a</b>) <b>Late Oligocene.</b> The Eurasian and African domains are separated by the Tethyan belt, formed by an inner core (ophiolitic and metamorphic units, pink and violet, respectively), flanked by orogenic belts of European and African affinity (yellow). Al = Alcapa block, TD = Tisza-Dacia block, TESZ = Trans European Suture Zone (separating the European deformed sector from the East European Craton, [<a href="#B43-geosciences-14-00277" class="html-bibr">43</a>]). (<b>b</b>) <b>Early Miocene.</b> Stressed by the Arabian indenter, the Tethyan belt migrates NW wards, at the expense of the oceanic and the thinned continental domains in the Magura zone. MH = Middle Hungarian fault, TAF = Trans Anatolian fault system. (<b>c</b>) <b>Middle-Late Miocene.</b> DSF = Dead Sea Fault. (1) Thinned continental Eurasian domain, (2) thinned continental Nubia/Adriatic domain. The kinematics with respect to a Eurasian reference frame [<a href="#B23-geosciences-14-00277" class="html-bibr">23</a>,<a href="#B44-geosciences-14-00277" class="html-bibr">44</a>] is tentatively indicated by the red arrows (scale in the inset). Present geographical contours (thin black lines) are reported for reference. Other colors and symbols as in <a href="#geosciences-14-00277-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 3
<p>(<b>a</b>) <b>Late Miocene</b>. (<b>b</b>) <b>Upper Pliocene</b>. (<b>c</b>) <b>Pleistocene</b>. An = Antalia wedge, CaA = Calabrian Arc, Ce = Cephalonia thrust fault, Cr = Crete, Cy = Cyprus Arc, CyA = Cyclades arc, ECB = Eastern Cretan Basin, EHA = Eastern Hellenic Arc, Eu = Eubea, FB = Fethyie–Burdur fault, GG = Ganos–Gelibolu thrust zone, Ka = Karphatos, LP = Libyan promontory, Pa = Parnassus, Pe = Peloponnesus, Pl = Pliny fault, Rh = Rhodes, Rdp = Rhodope massif, Sim = Simitli graben, SMM = Serbo–Macedonian massif, St = Strabo fault, SP = Scutari–Pec fault, Thr = Thrace trough, Tr = Trapezona, VE = Vlora–Elbasan fault, WCB = Western Cretan basin, WHA = Western Hellenic Arc. The small red triangles along the southern border of the Cyclades massif indicate the calc-alkaline volcanic arc. Color, symbols and other abbreviations as in <a href="#geosciences-14-00277-f001" class="html-fig">Figure 1</a> and <a href="#geosciences-14-00277-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 4
<p>Time patterns of main earthquakes that occurred in the Epirus thrust zone (<b>a</b>), the Serbo–Macedonian massif (<b>b</b>) and NAF (<b>c</b>) zones since 1800. The geometries of the zones considered are shown on the map. Seismicity data by [<a href="#B104-geosciences-14-00277" class="html-bibr">104</a>,<a href="#B105-geosciences-14-00277" class="html-bibr">105</a>,<a href="#B106-geosciences-14-00277" class="html-bibr">106</a>,<a href="#B107-geosciences-14-00277" class="html-bibr">107</a>,<a href="#B108-geosciences-14-00277" class="html-bibr">108</a>,<a href="#B109-geosciences-14-00277" class="html-bibr">109</a>,<a href="#B110-geosciences-14-00277" class="html-bibr">110</a>,<a href="#B111-geosciences-14-00277" class="html-bibr">111</a>,<a href="#B112-geosciences-14-00277" class="html-bibr">112</a>,<a href="#B113-geosciences-14-00277" class="html-bibr">113</a>,<a href="#B114-geosciences-14-00277" class="html-bibr">114</a>,<a href="#B115-geosciences-14-00277" class="html-bibr">115</a>,<a href="#B116-geosciences-14-00277" class="html-bibr">116</a>].</p> Full article ">Figure 5
<p>(<b>a</b>) Major earthquakes (M &gt; 5) that occurred since 1600 A.D. Seismicity data as in <a href="#geosciences-14-00277-f004" class="html-fig">Figure 4</a> and by [<a href="#B117-geosciences-14-00277" class="html-bibr">117</a>,<a href="#B118-geosciences-14-00277" class="html-bibr">118</a>,<a href="#B119-geosciences-14-00277" class="html-bibr">119</a>,<a href="#B120-geosciences-14-00277" class="html-bibr">120</a>,<a href="#B121-geosciences-14-00277" class="html-bibr">121</a>,<a href="#B122-geosciences-14-00277" class="html-bibr">122</a>,<a href="#B123-geosciences-14-00277" class="html-bibr">123</a>,<a href="#B124-geosciences-14-00277" class="html-bibr">124</a>]. (<b>b</b>) Strain fields in the main seismic zones inferred from focal mechanisms and geological data (See the references in the text). Abbreviations as in <a href="#geosciences-14-00277-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 6
<p>(<b>a</b>) Contours of the Aegean zone (blue line) that can be tectonically connected with the westward motion of Anatolia. (<b>b</b>) Time pattern (yellow bars) of the number of major earthquakes with M &gt; 5.5 in the Aegean area during the 1800–2024 time interval. The red segments over the yellow bars indicate the time pattern of major earthquakes along the NAF (as in <a href="#geosciences-14-00277-f004" class="html-fig">Figure 4</a>). (<b>c</b>) Time pattern of the strain energy release rate in the central Aegean zone, triggered by a displacement of 7 m at the eastern Anatolia zone in 1939 (taken from [<a href="#B11-geosciences-14-00277" class="html-bibr">11</a>]). The time interval during which the strain energy rate overcomes 70% of its maximum value is evidenced by the red bar along the time axis. Seismicity data as in <a href="#geosciences-14-00277-f004" class="html-fig">Figure 4</a> and <a href="#geosciences-14-00277-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 7
<p>Time pattern of sub-crustal earthquakes (depth greater than 50 km) under the Aegean zone. Seismicity data as in <a href="#geosciences-14-00277-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 8
<p>(<b>a</b>) Tectonic sketch of the collision zone between the Arabian indenter (grey) and the extruding Anatolian wedge (brown). BZ = Bitlis–Zagros thrust front, CyA = Cyprus Arc, DSF = Dead Sea fault system, EAF = East Anatolian fault, LC = Lesser Caucasus, NAF = North Anatolian fault. Tectonic symbols as in <a href="#geosciences-14-00277-f001" class="html-fig">Figure 1</a>. (<b>b</b>) Scale of magnitudes. (<b>c</b>–<b>f</b>) Major earthquakes (M ≥ 6.5) in the time intervals reported in the pictures. Seismicity data as in <a href="#geosciences-14-00277-f005" class="html-fig">Figure 5</a>.</p> Full article ">
25 pages, 4217 KiB  
Article
Development of Potential Slip Surface Identification Model for Active Deep-Seated Landslide Sites: A Case Study in Taiwan
by Shih-Meng Hsu, Chun-Chia Hsiung, Yu-Jia Chiu, Yi-Fan Liao and Jie-Ru Lin
Geosciences 2024, 14(10), 276; https://doi.org/10.3390/geosciences14100276 - 18 Oct 2024
Viewed by 536
Abstract
Identifying locations of landslide slip surfaces provides critical information for understanding the volume of landslides and the scale of disasters, both of which are essential for formulating disaster preparedness and mitigation strategies. Based on hydrogeological survey data from 24 deep-seated landslide-prone sites in [...] Read more.
Identifying locations of landslide slip surfaces provides critical information for understanding the volume of landslides and the scale of disasters, both of which are essential for formulating disaster preparedness and mitigation strategies. Based on hydrogeological survey data from 24 deep-seated landslide-prone sites in Taiwan’s mountainous regions, this study developed the hydraulic conductivity potential index (HCPI) using principal component analysis to quantify the hydraulic properties of disturbed rock formations with six geological factors. Then, regression analysis was performed to construct a permeability estimation model for the geological environment of landslides. Finally, the established model was utilized to develop an identification method for potential slip depths in landslide-prone sites. Results indicated a strong relation between HCPI and hydraulic conductivity with a determination coefficient of 0.895. The relation equation confirmed that the data it generated concerning the depths of significant changes in hydraulic conductivity could be used to identify potential slip surfaces. Additionally, this study successfully established a rule for identifying potential slip zones by summarizing data concerning the generated hydraulic conductivity profiles, stratigraphic lithology, existing inclinometer slip depth records, and groundwater level of landslide sites. This identification method was then applied to predict potential slip depths for ten landslide sites where slip surfaces have not yet occurred. These findings offer a new alternative to having early information on potential sliding depths for timely disaster management and control implementation. Full article
(This article belongs to the Section Natural Hazards)
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<p>Locations of the 24 landslide sites.</p> Full article ">Figure 2
<p>Schematic diagram for comparison of the hydraulic conductivity profile and the observed inclinometer data.</p> Full article ">Figure 3
<p>A scree plot.</p> Full article ">Figure 4
<p>The regression model for estimating hydraulic conductivity.</p> Full article ">Figure 5
<p>Comparison of HCPI and NHCB2 models.</p> Full article ">Figure 6
<p>An abrupt changing zone (the red rectangle in the figure) for the hydraulic conductivity profile at the depths between 34 m and 36 m (an example of the Wanda landslide site).</p> Full article ">Figure 7
<p>Comparison diagram for hydraulic conductivity profile and observed inclinometer data (an example of the Longhua landslide site).</p> Full article ">Figure 8
<p>Comparison diagram for hydraulic conductivity profile and observed S-shaped inclinometer data (an example of the Yixing landslide site).</p> Full article ">Figure 9
<p>Evaluation method and procedure for the identification of potential sliding surfaces.</p> Full article ">
12 pages, 1226 KiB  
Article
UNESCO Global Geoparks vs. Generative AI: Challenges for Best Practices in Sustainability and Education
by Jesús Enrique Martínez-Martín, Emmaline M. Rosado-González, Beatriz Martínez-Martín and Artur A. Sá
Geosciences 2024, 14(10), 275; https://doi.org/10.3390/geosciences14100275 - 17 Oct 2024
Viewed by 764
Abstract
Artificial intelligence (AI) has become one of the most controversial tools of recent times. Offering an extremely simple operating system, users can generate texts, images, videos and even human voices. The possibility of using such a powerful tool creates new paths and challenges [...] Read more.
Artificial intelligence (AI) has become one of the most controversial tools of recent times. Offering an extremely simple operating system, users can generate texts, images, videos and even human voices. The possibility of using such a powerful tool creates new paths and challenges in the field of environmental education: How does it influence natural heritage protection? Is it considered positive within sustainability and quality education? The reality is very different, showing algorithms trained with information of dubious quality and, on many occasions, obtained without permission from authors and artists around the world. UNESCO Global Geoparks (UGGps) are international references in education at all levels, related to territorial development and geoscience education. This article discusses if generative AI is, nowadays, an effective and applicable educational tool for the strategies developed and promoted by UGGps. This designation exists for people’s opportunities. The use of these tools in their current state could make the UGGp figure change its values and fundamental pillars in the future. Full article
(This article belongs to the Section Geoheritage, Geoparks and Geotourism)
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<p>Graphic summary of the methodology steps used for this study development.</p> Full article ">Figure 2
<p>Ethical requirements from the educational framework for the reliable use of AI [<a href="#B17-geosciences-14-00275" class="html-bibr">17</a>].</p> Full article ">Figure 3
<p>Strengths, weaknesses, opportunities and threats (SWOT) analysis of AI implementation in its current state in UGGps.</p> Full article ">
15 pages, 2513 KiB  
Article
The Evaluation of Rainfall Warning Thresholds for Shallow Slope Stability Based on the Local Safety Factor Theory
by Ya-Sin Yang, Hsin-Fu Yeh, Chien-Chung Ke, Lun-Wei Wei and Nai-Chin Chen
Geosciences 2024, 14(10), 274; https://doi.org/10.3390/geosciences14100274 - 16 Oct 2024
Viewed by 648
Abstract
Rainfall-induced shallow slope instability is a significant global hazard, often triggered by water infiltration that affects soil stability and involves dynamic changes in the hydraulic behavior of unsaturated soils. This study employs a hydro-mechanical coupled analysis model to assess the impact of rainfall [...] Read more.
Rainfall-induced shallow slope instability is a significant global hazard, often triggered by water infiltration that affects soil stability and involves dynamic changes in the hydraulic behavior of unsaturated soils. This study employs a hydro-mechanical coupled analysis model to assess the impact of rainfall on slope stability, focusing on the dynamic hydraulic behavior of unsaturated soils. By simulating the soil water content and slope stability under four different rainfall scenarios based on observational data and historical thresholds, this study reveals that higher rainfall intensity significantly increases the soil water content, leading to reduced slope stability. The results show a strong correlation between the soil water content and slope stability, with a 20 mm/h rainfall intensity threshold emerging as a reliable predictor of potential slope instability. This study contributes to a deeper understanding of slope stability dynamics and emphasizes the importance of proactive risk management in response to changing rainfall patterns while also validating current management practices and providing essential insight for improving early warning systems to effectively mitigate landslide risk. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping II)
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<p>The location of the stations in Babaoliao area.</p> Full article ">Figure 2
<p>Mohr circle-based conceptual illustration of Local Factor of Safety [<a href="#B66-geosciences-14-00274" class="html-bibr">66</a>].</p> Full article ">Figure 3
<p>Flowchart of the modeling analysis process.</p> Full article ">Figure 4
<p>Conceptual model, boundary condition, and mesh configuration: (<b>a</b>) zone A, (<b>b</b>) zone D.</p> Full article ">Figure 5
<p>Comparison of SWCC obtained from pressure plate tests and SWCC used in the model.</p> Full article ">Figure 6
<p>Results of simulated and observed values for (<b>a</b>) groundwater level in zone A, (<b>b</b>) soil water content in zone A, and (<b>c</b>) soil water content in zone D.</p> Full article ">Figure 7
<p>Four hypothetical rainfall scenarios: (<b>a</b>) extreme intensity, (<b>b</b>) high intensity, (<b>c</b>) moderate intensity, and (<b>d</b>) low intensity.</p> Full article ">Figure 8
<p>Simulation results for (<b>a</b>) soil water content in zone A, (<b>b</b>) LFS in zone A, (<b>c</b>) soil water content in zone D, and (<b>d</b>) LFS in zone D.</p> Full article ">
22 pages, 23991 KiB  
Article
Conceptual and Applied Aspects of Water Retention Tests on Tailings Using Columns
by Fernando A. M. Marinho, Yuri Corrêa, Rosiane Soares, Inácio Diniz Carvalho and João Paulo de Sousa Silva
Geosciences 2024, 14(10), 273; https://doi.org/10.3390/geosciences14100273 - 16 Oct 2024
Viewed by 577
Abstract
The water retention capacity of porous materials is crucial in various geotechnical and environmental engineering applications such as slope stability analysis, landfill management, and mining operations. Filtered tailings stacks are considered an alternative to traditional tailings dams. Nevertheless, the mechanical behaviour and stability [...] Read more.
The water retention capacity of porous materials is crucial in various geotechnical and environmental engineering applications such as slope stability analysis, landfill management, and mining operations. Filtered tailings stacks are considered an alternative to traditional tailings dams. Nevertheless, the mechanical behaviour and stability of the material under different water content conditions are of concern because these stacks can reach considerable heights. The water behaviour in these structures is poorly understood, particularly the effects of the water content on the stability and potential for liquefaction of the stacks. This study aims to investigate the water retention and flow characteristics of compacted iron ore tailings in high columns to better understand their hydromechanical behaviour. The research used 5 m high columns filled with iron ore tailings from the Quadrilátero Ferrífero region in Minas Gerais, Brazil. The columns were prepared in layers, compacted, and instrumented with moisture content sensors and suction sensors to monitor the water movement during various stages of saturation, drainage, infiltration, and evaporation. The sensors provided consistent data and revealed that the tailings exhibited high drainage capacity. The moisture content and suction profiles were effectively established over time and revealed the dynamic water retention behaviour. The comparison of the data with the theoretical soil water retention curve (SWRC) demonstrated a good correlation which indicates that there was no hysteresis in the material response. The study concludes that the column setup effectively captures the water retention and flow characteristics of compacted tailings and provides valuable insights for the hydromechanical analysis of filtered tailings stacks. These findings can significantly help improve numerical models, calibrate material parameters, and contribute to the safer and more efficient management of tailings storage facilities. Full article
(This article belongs to the Special Issue Advances in Thermo-Hydro-Mechanical Characterization and Modelling of Unsaturated Soils)
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<p>(<b>a</b>) Ore-pile draining and (<b>b</b>) water content variation along the pile.</p> Full article ">Figure 2
<p>Relationship between the water content and the amount of fines.</p> Full article ">Figure 3
<p>(<b>a</b>) Physical model of a soil column with a water table (<b>b</b>) Relationships between free energy and water content in a soil column with a fixed water table (<b>c</b>) Variation of water content with the height of the column (modified from Edlefesen and Anderson [<a href="#B7-geosciences-14-00273" class="html-bibr">7</a>]).</p> Full article ">Figure 4
<p>Suction (<b>a</b>) and water content (<b>b</b>) profile in the field.</p> Full article ">Figure 5
<p>(<b>a</b>) PVC column; (<b>b</b>) schematic drawing of the column; (<b>c</b>) suction equilibrium profile, and (<b>d</b>) water content profiles for three hypothetical materials [<a href="#B15-geosciences-14-00273" class="html-bibr">15</a>].</p> Full article ">Figure 6
<p>Soil water retention curve of the material (data from Jesus et al. [<a href="#B22-geosciences-14-00273" class="html-bibr">22</a>]).</p> Full article ">Figure 7
<p>Segments for the column assembly.</p> Full article ">Figure 8
<p>Drainage segment. Placement of (<b>a</b>) gravel and (<b>b</b>) medium sand.</p> Full article ">Figure 9
<p>Column compaction process: (<b>a</b>) Details of the compaction; (<b>b</b>) column at its 6th segment.</p> Full article ">Figure 10
<p>First completed column: (<b>a</b>) Image of the completed column; (<b>b</b>) sensor positions.</p> Full article ">Figure 11
<p>Time lag graphical analysis between sensors WC6 and TE6 during (<b>a</b>) saturation, (<b>b</b>) drainage, (<b>c</b>) infiltration, and (<b>d</b>) evaporation.</p> Full article ">Figure 12
<p>Stages imposed in the columns.</p> Full article ">Figure 13
<p>Profiles at the end of construction and before saturation: (<b>a</b>) Volumetric water content and (<b>b</b>) suction.</p> Full article ">Figure 14
<p>Profiles during saturation: (<b>a</b>) Volumetric water content and (<b>b</b>) suction.</p> Full article ">Figure 15
<p>Profiles during drainage: (<b>a</b>) Volumetric water content and (<b>b</b>) suction.</p> Full article ">Figure 16
<p>Responses of the TE6 (<b>a</b>) and WC6 (<b>b</b>) sensors to the first infiltration and evaporation.</p> Full article ">Figure 17
<p>Responses of the TE6 (<b>a</b>) and WC6 (<b>b</b>) sensors to the second infiltration and evaporation.</p> Full article ">Figure 18
<p>Profiles during the first infiltration: (<b>a</b>) volumetric water content and (<b>b</b>) suction.</p> Full article ">Figure 19
<p>Profiles during the first evaporation: (<b>a</b>) volumetric water content and (<b>b</b>) suction.</p> Full article ">Figure 20
<p>Profiles during the second infiltration: (<b>a</b>) volumetric water content and (<b>b</b>) suction.</p> Full article ">Figure 21
<p>Profiles during the second evaporation: (<b>a</b>) Volumetric water content and (<b>b</b>) suction.</p> Full article ">Figure 22
<p>Measured water flux at the base of the column.</p> Full article ">Figure 23
<p>A closer look at the sensor readings plotted with the retention curve.</p> Full article ">Figure 24
<p>Water retention curve with the sensor readings.</p> Full article ">Figure 25
<p>SWRC versus infiltration and evaporation data.</p> Full article ">
27 pages, 22748 KiB  
Article
A Methodologic Approach to Study Large and Complex Landslides: An Application in Central Apennines
by Massimo Mangifesta, Domenico Aringoli, Gilberto Pambianchi, Leonardo Maria Giannini, Gianni Scalella and Nicola Sciarra
Geosciences 2024, 14(10), 272; https://doi.org/10.3390/geosciences14100272 - 15 Oct 2024
Viewed by 804
Abstract
The evaluation of landslide hazards in seismic areas is based on a deterministic analysis, which is unable to account for various uncertainties in the analysis process. This paper focuses on the probabilistic local seismic hazard analysis and extends the results to the landslide [...] Read more.
The evaluation of landslide hazards in seismic areas is based on a deterministic analysis, which is unable to account for various uncertainties in the analysis process. This paper focuses on the probabilistic local seismic hazard analysis and extends the results to the landslide hazard analysis to consider both the uncertainties of the ground deformations and the strengths. The work studies the areas between Nibbiano and Sant’Erasmo hamlets in the Camerino municipality located in central Italy, where all constructions present evidence of damage caused by both the seismic sequence of 2016–2017 and the slope instability. An exhaustive geological and geophysical investigation has clarified the geological, geomorphological, and hydrogeological characteristics of the area, enabling a new characterization of material stress-strain behaviour. The study reveals that the low stiffness of the debris covers, and their fair degree of permeability contribute to potential instability scenarios triggered by both intense rainfall and the effects of strong earthquakes. The goal was to utilize the results to support local urban planning because in-depth knowledge of the possible evolutionary scenarios of the slopes is fundamental to the management of the degree of danger for structures, especially for people. Moreover, it was shown once again how a multi-source approach, with different investigation techniques, cannot be ignored for the study of the evolution of complex landslides. Full article
(This article belongs to the Special Issue Landslides Runout: Recent Perspectives and Advances)
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<p>Outline of the analysed area with the main gravitational phenomena, red circles, along the overthrust line, in yellow (background image from Google Earth, modified).</p> Full article ">Figure 2
<p>Geological-structural scheme of the Apennine region under study (after [<a href="#B59-geosciences-14-00272" class="html-bibr">59</a>], modified).</p> Full article ">Figure 3
<p>Geological map (<b>a</b>) of the Mt. Igno sector, including the Nibbiano-Sant’Erasmo area and geological cross-sections (<b>b</b>) showing the geometry of the thrust (after [<a href="#B59-geosciences-14-00272" class="html-bibr">59</a>], modified).</p> Full article ">Figure 4
<p>(<b>a</b>) Geological Map of the area extracted from the Marche Region inventory. (<b>b</b>) Interpretative cross-section of the bedrock under investigation: below the debris cover, in red, the probable thrust planes overlapping several lithologies (SAA-Scaglia Rosata, VAS-Scaglia Variegata, SCC-Scaglia cinerea, BIS-Bisciaro, SCH-Schlier).</p> Full article ">Figure 5
<p>Map of the scarps with gradients greater than 35° (<b>a</b>) and 40° (<b>b</b>).</p> Full article ">Figure 6
<p>Map of the mechanical drilling location, seismic and electric ubication lines, and HVSR positions. The number identify the sequence of the acquisitions HVSR.</p> Full article ">Figure 7
<p>Distribution of RQD values calculated on each meter of the core according to the single survey and representation of the average value.</p> Full article ">Figure 8
<p>RQD values Distribution calculated for each meter of core and as a function of depth for each single survey. Different colour indicates different borehole.</p> Full article ">Figure 9
<p>Seismic profiles in Vp waves. Line L1, L2, L3 and L4. Along the L1-L1′ line are evident some discontinuities that can probably be attributable to the gravitational deformation of the slope.</p> Full article ">Figure 10
<p>Geoelectric line A-A’ with a geometric indication of low resistivity areas and identification of a discontinuity mountainward the Sant’Erasmo hamlet.</p> Full article ">Figure 11
<p>Location of the HVSR recordings and clustering of maximum H/V ratios. The number identify the sequence of the acquisitions HVSR.</p> Full article ">Figure 12
<p>Earthquake waveforms used in seismic numerical modelling.</p> Full article ">Figure 13
<p>Average peak acceleration recorded in the seismic analysis.</p> Full article ">Figure 14
<p>3D numerical model and correlation between the Hoek-Brown criterion and the equivalent values of the Mohr-Coulomb criterion for the Bedrock.</p> Full article ">Figure 15
<p>Static analysis. (<b>a</b>) Shear strain increment was calculated as a global stability analysis. (<b>b</b>) Colour-Map of the local safety factor. (<b>c</b>,<b>d</b>) 2D sections with the distribution of the local safety factor.</p> Full article ">Figure 16
<p>Contours of Safety Factor. (<b>a</b>) The water table at −4.0 m from ground level. (<b>b</b>) The water table at −4.0 m from ground level.</p> Full article ">Figure 17
<p>Three-dimensional reconstruction of the morphology of the area and the direction of the movement (red arrows). (<b>a</b>) Hillshade with the movement indications. (<b>b</b>) slopes scarps (35°). (<b>c</b>) slope scarps (40°).</p> Full article ">Figure 18
<p>Details of the drilling of the bedrock. (<b>a</b>–<b>c</b>) Slickenside kinematic indicator. (<b>d</b>) Extraction of an intact core in the S1 survey at −48.0 m from ground level.</p> Full article ">Figure 19
<p>Seismic line L1, where the interpretation confirms the thrusts assumed by the geological survey (<a href="#geosciences-14-00272-f004" class="html-fig">Figure 4</a>b) and highlights the probable gravitational deformation surfaces (discontinuities).</p> Full article ">
42 pages, 631 KiB  
Review
Electromagnetic and Radon Earthquake Precursors
by Dimitrios Nikolopoulos, Demetrios Cantzos, Aftab Alam, Stavros Dimopoulos and Ermioni Petraki
Geosciences 2024, 14(10), 271; https://doi.org/10.3390/geosciences14100271 - 14 Oct 2024
Viewed by 1358
Abstract
Earthquake forecasting is arguably one of the most challenging tasks in Earth sciences owing to the high complexity of the earthquake process. Over the past 40 years, there has been a plethora of work on finding credible, consistent and accurate earthquake precursors. This [...] Read more.
Earthquake forecasting is arguably one of the most challenging tasks in Earth sciences owing to the high complexity of the earthquake process. Over the past 40 years, there has been a plethora of work on finding credible, consistent and accurate earthquake precursors. This paper is a cumulative survey on earthquake precursor research, arranged into two broad categories: electromagnetic precursors and radon precursors. In the first category, methods related to measuring electromagnetic radiation in a wide frequency range, i.e., from a few Hz to several MHz, are presented. Precursors based on optical and radar imaging acquired by spaceborne sensors are also considered, in the broad sense, as electromagnetic. In the second category, concentration measurements of radon gas found in soil and air, or even in ground water after being dissolved, form the basis of radon activity precursors. Well-established mathematical techniques for analysing data derived from electromagnetic radiation and radon concentration measurements are also described with an emphasis on fractal methods. Finally, physical models of earthquake generation and propagation aiming at interpreting the foundation of the aforementioned seismic precursors, are investigated. Full article
(This article belongs to the Special Issue Precursory Phenomena Prior to Earthquakes (2nd Edition))
26 pages, 35353 KiB  
Article
New Insights into the Understanding of High-Pressure Air Injection (HPAI): The Role of the Different Chemical Reactions
by Dubert Gutiérrez, Gord Moore, Don Mallory, Matt Ursenbach, Raj Mehta and Andrea Bernal
Geosciences 2024, 14(10), 270; https://doi.org/10.3390/geosciences14100270 - 13 Oct 2024
Viewed by 508
Abstract
High-pressure air injection (HPAI) is an enhanced oil recovery process in which compressed air is injected into deep, light oil reservoirs, with the expectation that the oxygen in the injected air will react with a fraction of the reservoir oil at an elevated [...] Read more.
High-pressure air injection (HPAI) is an enhanced oil recovery process in which compressed air is injected into deep, light oil reservoirs, with the expectation that the oxygen in the injected air will react with a fraction of the reservoir oil at an elevated temperature to produce carbon dioxide. The different chemical reactions taking place can be grouped into oxygen addition, thermal cracking, oxygen-induced cracking, and bond scission reactions. The latter reactions involve the combustion of a flammable vapor as well as the combustion of solid fuel, commonly known as “coke”. Since stable peak temperatures observed during HPAI experiments are typically below 300 °C, it has been suggested that thermal cracking and combustion of solid fuel may not be important reaction mechanisms for the process. The objective of this work is to assess the validity of that hypothesis. Therefore, this study makes use of different oxidation and combustion HPAI experiments, which were performed on two different light oil reservoir samples. Modeling of those tests indicate that thermal cracking is not an important reaction mechanism during HPAI and can potentially be ignored. The work also suggests that the main fuel consumed by the process is a flammable vapor generated by the chemical reactions. This represents a shift from the original in situ combustion paradigm, which is based on the combustion of coke. Full article
(This article belongs to the Topic Enhanced Oil Recovery Technologies, 3rd Volume)
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<p>Schematic flow diagram of ramped temperature system.</p> Full article ">Figure 2
<p>Schematic view of high-pressure combustion tube.</p> Full article ">Figure 3
<p>Photo of experimental core holder of high-pressure ramped temperature reactor along with its 3D simulation grid representation.</p> Full article ">Figure 4
<p>Photo of experimental core holder of high-pressure combustion tube along with its 3D simulation grid representation.</p> Full article ">Figure 5
<p>Simulated core temperatures–HPRTC Oil I.</p> Full article ">Figure 6
<p>Simulated core temperatures–HPRTC Oil K.</p> Full article ">Figure 7
<p>Simulated residual phases in post-test core and produced mole fraction of gas pseudo-component–HPRTC Oil I.</p> Full article ">Figure 8
<p>Simulated residual phases in post-test core and produced mole fraction of gas pseudo-component–HPRTC Oil K.</p> Full article ">Figure 9
<p>Simulated injection pressure–HPRTO Oil I without TCR and CSR.</p> Full article ">Figure 10
<p>Simulated core temperatures–HPRTO Oil I without TCR and CSR.</p> Full article ">Figure 11
<p>Simulated fluid production–HPRTO Oil I without TCR and CSR.</p> Full article ">Figure 12
<p>Simulated gas concentrations of CO, CO<sub>2</sub>, and O<sub>2</sub>–HPRTO Oil I without TCR and CSR.</p> Full article ">Figure 13
<p>Simulated gas concentrations of gas and nitrogen–HPRTO Oil I without TCR and CSR.</p> Full article ">Figure 14
<p>Simulated produced oil properties–HPRTO Oil I without TCR and CSR.</p> Full article ">Figure 15
<p>Simulated residual phases in post-test core–HPRTO Oil I without TCR and CSR.</p> Full article ">Figure 16
<p>Simulated injection pressure–HPRTO Oil K without TCR and CSR.</p> Full article ">Figure 17
<p>Simulated core temperatures–HPRTO Oil K without TCR and CSR.</p> Full article ">Figure 18
<p>Simulated fluid production–HPRTO Oil K without TCR and CSR.</p> Full article ">Figure 19
<p>Simulated gas concentrations of CO, CO<sub>2</sub>, and O<sub>2</sub>–HPRTO Oil K without TCR and CSR.</p> Full article ">Figure 20
<p>Simulated gas concentrations of gas and nitrogen–HPRTO Oil K without TCR and CSR.</p> Full article ">Figure 21
<p>Simulated produced oil properties–HPRTO Oil K without TCR and CSR.</p> Full article ">Figure 22
<p>Simulated residual phases in post-test core–HPRTO Oil K without TCR and CSR.</p> Full article ">Figure 23
<p>Simulated injection pressure–HPCT Oil I without TCR and CSR.</p> Full article ">Figure 24
<p>Simulated core temperatures (Zones 1–11)–HPCT Oil I without TCR and CSR.</p> Full article ">Figure 25
<p>Simulated core temperatures (Zones 12–22)–HPCT Oil I without TCR and CSR.</p> Full article ">Figure 26
<p>Simulated core temperatures (Zones 23–33)–HPCT Oil I without TCR and CSR.</p> Full article ">Figure 27
<p>Simulated fluid production–HPCT Oil I without TCR and CSR.</p> Full article ">Figure 28
<p>Simulated gas composition (CO, CO<sub>2</sub>, and O<sub>2</sub>)–HPCT Oil I without TCR and CSR.</p> Full article ">Figure 29
<p>Simulated gas composition (nitrogen and gas)–HPCT Oil I without TCR and CSR.</p> Full article ">Figure 30
<p>Simulated produced oil properties–HPCT Oil I without TCR and CSR.</p> Full article ">Figure 31
<p>Simulated residual phases in post-test core–HPCT Oil I without TCR and CSR.</p> Full article ">Figure 32
<p>Simulated injection pressure–HPCT Oil K without TCR and CSR.</p> Full article ">Figure 33
<p>Simulated core temperatures (Zones 1–11)–HPCT Oil K without TCR and CSR.</p> Full article ">Figure 34
<p>Simulated core temperatures (Zones 12–22)–HPCT Oil K without TCR and CSR.</p> Full article ">Figure 35
<p>Simulated core temperatures (Zones 23–33)–HPCT Oil K without TCR and CSR.</p> Full article ">Figure 36
<p>Simulated fluid production–HPCT Oil K without TCR and CSR.</p> Full article ">Figure 37
<p>Simulated gas composition (CO, CO<sub>2</sub>, and O<sub>2</sub>)–HPCT Oil K without TCR and CSR.</p> Full article ">Figure 38
<p>Simulated gas composition (nitrogen and gas)–HPCT Oil K without TCR and CSR.</p> Full article ">Figure 39
<p>Simulated produced oil properties–HPCT Oil K without TCR and CSR.</p> Full article ">Figure 40
<p>Simulated residual phases in post-test core–HPCT Oil K without TCR and CSR.</p> Full article ">
36 pages, 4601 KiB  
Article
Investigating Sense of Place and Geoethical Awareness among Educators at the 4th Summer School of Sitia UNESCO Global Geopark: A Quasi-Experimental Study
by Alexandros Aristotelis Koupatsiaris and Hara Drinia
Geosciences 2024, 14(10), 269; https://doi.org/10.3390/geosciences14100269 - 12 Oct 2024
Viewed by 848
Abstract
Anthropogenic global challenges and environmental pressures are increasingly significant. Developing pro-environmental behavior and geoethics is crucial for enhancing awareness, action capability, and respect for natural systems. UNESCO Global Geoparks (UGGps) play a vital role in conserving geological and biological diversity while aligning with [...] Read more.
Anthropogenic global challenges and environmental pressures are increasingly significant. Developing pro-environmental behavior and geoethics is crucial for enhancing awareness, action capability, and respect for natural systems. UNESCO Global Geoparks (UGGps) play a vital role in conserving geological and biological diversity while aligning with the United Nations’ Sustainable Development Goals. This quasi-experimental study, conducted during the 4th Summer School of Environmental Education on Geotopes and Sustainability at the Sitia UGGp, uses a pre–post design and comprehensive questionnaire to explore changes in participants’ sense of place and geoethical awareness. Results indicate significant improvements in place attachment, place meaning, and geoethical awareness. These findings suggest that stronger emotional bonds and deeper personal meanings related to the Sitia UGGp correlate with increased geoethical awareness. This research highlights the role of psychological connections in influencing geoenvironmental ethics and underscores the importance of place-based emotional and cognitive bonds in fostering geoethical thinking. However, this study’s limited sample size and the specific geographic context of Sitia UGGp may limit the generalizability of the findings. Despite these limitations, this study provides insights into the interplay of emotions, meanings, and geoethics within the sustainability and resilience spectrum. Full article
(This article belongs to the Section Geoheritage, Geoparks and Geotourism)
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<p>Distribution of total place attachment scores among summer school participants.</p> Full article ">Figure 2
<p>Distribution of total place meaning scores among summer school participants.</p> Full article ">Figure 3
<p>Distribution of total geoethical awareness scores among summer school participants.</p> Full article ">Figure 4
<p>Correlation of place attachment before the summer school with the years of teaching experience.</p> Full article ">Figure 5
<p>Correlation of geoethical awareness before the summer school with the years of teaching experience.</p> Full article ">Figure 6
<p>Correlation of place meaning after the summer school with the years of teaching experience.</p> Full article ">Figure 7
<p>Correlation of geoethical awareness after the summer school with the years of teaching experience.</p> Full article ">Figure 8
<p>Correlation of place attachment before the summer school with involvement in workshops or training sessions related to the Sitia UGGp.</p> Full article ">Figure 9
<p>Correlation of place meaning before the summer school with involvement in workshops or training sessions related to the Sitia UGGp.</p> Full article ">Figure 10
<p>Correlation of place attachment after the summer school with involvement in workshops or training sessions related to the Sitia UGGp.</p> Full article ">Figure 11
<p>Correlation of place attachment before the summer school with the development of environmental education programs related to the Sitia UGGp.</p> Full article ">Figure 12
<p>Correlation of place meaning before the summer school with the development of environmental education programs related to the Sitia UGGp.</p> Full article ">Figure 13
<p>Correlation of place attachment before the summer school with the number of visits to the Sitia UGGp.</p> Full article ">Figure 14
<p>Correlation of place meaning after the summer school with the membership in environmental organizations or groups.</p> Full article ">Figure 15
<p>Histogram of standardized residuals.</p> Full article ">Figure 16
<p>Q-Q plot of standardized residuals.</p> Full article ">Figure 17
<p>Homoscedasticity test of residuals.</p> Full article ">
35 pages, 14944 KiB  
Article
Simulating Compaction and Cementation of Clay Grain Coated Sands in a Modern Marginal Marine Sedimentary System
by James E. Houghton, Thomas E. Nichols and Richard H. Worden
Geosciences 2024, 14(10), 268; https://doi.org/10.3390/geosciences14100268 - 12 Oct 2024
Viewed by 640
Abstract
Reservoir quality prediction in deeply buried reservoirs represents a complex challenge to geoscientists. In sandstones, reservoir quality is determined by the extent of compaction and cementation during burial. During compaction, porosity is lost through the rearrangement and fracture of rigid grains and the [...] Read more.
Reservoir quality prediction in deeply buried reservoirs represents a complex challenge to geoscientists. In sandstones, reservoir quality is determined by the extent of compaction and cementation during burial. During compaction, porosity is lost through the rearrangement and fracture of rigid grains and the deformation of ductile grains. During cementation, porosity is predominantly lost through the growth of quartz cement, although carbonate and clay mineral growth can be locally important. The degree of quartz cementation is influenced by the surface area of quartz available for overgrowth nucleation and thermal history. Clay grain coats can significantly reduce the surface area of quartz available for overgrowth nucleation, preventing extensive cementation. Using a coupled-effect compaction and cementation model, we have forward-modelled porosity evolution of surface sediments from the modern Ravenglass Estuary under different maximum burial conditions, between 2000 and 5000 m depth, to aid the understanding of reservoir quality distribution in a marginal marine setting. Seven sand-dominated sub-depositional environments were subject to five burial models to assess porosity-preservation in sedimentary facies. Under relatively shallow burial conditions (<3000 m), modelled porosity is highest (34 to 36%) in medium to coarse-grained outer-estuary sediments due to moderate sorting and minimal fine-grained matrix material. Fine-grained tidal flat sediments (mixed flats) experience a higher degree of porosity loss due to elevated matrix volumes (20 to 31%). Sediments subjected to deep burial (>4000 m) experience a significant reduction in porosity due to extensive quartz cementation. Porosity is reduced to 1% in outer estuary sediments that lack grain-coating clays. However, in tidal flat sediments with continuous clay grain coats, porosity values of up to 30% are maintained due to quartz cement inhibition. The modelling approach powerfully emphasises the value of collecting quantitative data from modern analogue sedimentary environments to reveal how optimum reservoir quality is not always in the coarsest or cleanest clastic sediments. Full article
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<p>Porosity vs. depth cross plot for sandstones, modified after Worden and Utley [<a href="#B1-geosciences-14-00268" class="html-bibr">1</a>]. A secondary Y-axis displays the temperature for a geothermal gradient of 30 °C/km where the surface temperature is 10 °C. A third Y-axis shows the effective stress for normally pressured porous rocks. The initial porosity of a rock (indicated by the yellow circle) is controlled as a function of sorting and matrix content, where well-sorted and matrix-free sands can have porosities of up to 43% [<a href="#B5-geosciences-14-00268" class="html-bibr">5</a>]. The orange region represents ‘clean’ sandstones that contain little matrix or ductile framework grains, where porosity is reduced to ~26% due to grain rearrangement. The green region represents sandstones with higher matrix or ductile framework grain content. Sandstones in this region lose porosity more rapidly as a result of ductile compaction. Once the temperature exceeds 80 °C, chemical compaction and quartz cementation further reduce porosity. The degree of quartz cementation can be reduced or inhibited by chlorite, siderite, or micro quartz coatings on quartz grains and possibly by early oil emplacement or early overpressure, leading to anomalously high porosity in deeply buried sandstones. Compaction can be prevented due to overpressure or small amounts of cement, such as siderite, that stabilise the sandstone framework, leading to high porosity at intermediate depths (red region).</p> Full article ">Figure 2
<p>A schematic illustrating the effect of different detrital clay volumes on the development of diagenetic clay coats upon burial and diagenesis. &lt;3.5% total clay volume is present; continuous clay coats cannot form, and, therefore, extensive quartz cementation takes place at temperatures &gt; 80 °C. When total clay volume is between 3.5% and 13%, continuous clay coatings can form that can prevent quartz cementation due to a lack of free quartz surface area available for cement nucleation [<a href="#B38-geosciences-14-00268" class="html-bibr">38</a>]. Where total clay volume exceeds 13%, continuous clay coatings can form, but clay can also occlude pore space and block pore throats, significantly reducing the permeability of the rock. Shown on the right are examples of detrital clay coats from the Ravenglass Estuary, UK, and diagenetic clay coats from the Tilje Formation, Smørbukk Field, Norway.</p> Full article ">Figure 3
<p>(<b>a</b>) A map of the United Kingdom with a yellow box indicating the location of the Ravenglass Estuary. (<b>b</b>) Arial imagery of the Ravenglass Estuary, with the outline of the estuary indicated with the Land polygon. The sample locations of the 158 surface samples (&lt;2 cm depth) are indicated with yellow circles. Ravenglass Village is displayed in grey. (<b>c</b>) a map of the surface sub-depositional environments in the Ravenglass Estuary (modified after [<a href="#B99-geosciences-14-00268" class="html-bibr">99</a>,<a href="#B103-geosciences-14-00268" class="html-bibr">103</a>]). De1 = Saltmarsh, De2 = Mud flat, De3 = Mixed flat, De4 = Sand flat, De5 = Tidal bars, De6 = Tidal inlet, N-De8 = Northern foreshore, S-De8 = Southern foreshore, De9 = Pro-ebb delta, De10 = Gravel bed.</p> Full article ">Figure 4
<p>A time versus depth and temperature plot of the different modelled scenarios of the Ravenglass Estuary surface sediments. The red line is a constant burial rate of 10 m∙ma<sup>−1</sup> (Model 1), the orange line is a constant burial rate of 15 m∙ma<sup>−1</sup> (Model 2), the blue line is a constant burial rate of 20 m∙ma<sup>−1</sup> (Model 3), the green line is a constant burial rate of 25 m∙ma<sup>−1</sup> (Model 4), and the black line is a simulated burial history of the Tilje formation in well 6505/12-1 from the Smørbukk field (Model 5) [<a href="#B121-geosciences-14-00268" class="html-bibr">121</a>].</p> Full article ">Figure 5
<p>Time versus porosity plot for Model 1 (10 m∙ma<sup>−1</sup> burial rate). Each subplot represents a different sub-depositional environment from the Ravenglass Estuary. The dot-dash line represents the P90 (best case) scenario, the solid line represents the P50 (median case) scenario, and the dashed line represents the P10 (worst case) scenario.</p> Full article ">Figure 6
<p>Time versus porosity plot for Model 2 (15 m∙ma<sup>−1</sup> burial rate). Each subplot represents a different sub-depositional environment from the Ravenglass Estuary. The dot-dash line represents the P90 (best case) scenario, the solid line represents the P50 (median case) scenario, and the dashed line represents the P10 (worst case) scenario.</p> Full article ">Figure 7
<p>Time versus porosity plot for Model 3 (20 m∙ma<sup>−1</sup> burial rate). Each subplot represents a different sub-depositional environment from the Ravenglass Estuary. The dot-dash line represents the P90 (best case) scenario, the solid line represents the P50 (median case) scenario, and the dashed line represents the P10 (worst case) scenario.</p> Full article ">Figure 8
<p>Time versus porosity plot for Model 4 (25 m∙ma<sup>−1</sup> burial rate). Each subplot represents a different sub-depositional environment from the Ravenglass Estuary. The dot-dash line represents the P90 (best case) scenario, the solid line represents the P50 (median case) scenario, and the dashed line represents the P10 (worst case) scenario.</p> Full article ">Figure 9
<p>Time versus porosity plot for Model 5 (Tilje Formation burial history [<a href="#B121-geosciences-14-00268" class="html-bibr">121</a>]). Each subplot represents a different sub-depositional environment from the Ravenglass Estuary. The dot-dash line represents the P90 (best case) scenario, the solid line represents the P50 (median case) scenario, and the dashed line represents the P10 (worst case) scenario.</p> Full article ">Figure 10
<p>Maps of the Ravenglass Estuary sub depositional environments, where the environment polygons are coloured by the remaining porosity in the P50 scenario in Model 1 (10 m∙ma<sup>−1</sup> burial rate). Salt marsh (De1), Mud flat (De2), and Gravel beds (De10) have not been modelled as they are not sandstones and, therefore, considered non-reservoir.</p> Full article ">Figure 11
<p>Maps of the Ravenglass Estuary sub depositional environments, where the environment polygons are coloured by the remaining porosity in the P50 scenario in Model 2 (15 m∙ma<sup>−1</sup> burial rate). Salt marsh (De1), Mud flat (De2), and Gravel beds (De10) have not been modelled as they are not sandstones and, therefore, are considered non-reservoir.</p> Full article ">Figure 12
<p>Maps of the Ravenglass Estuary sub depositional environments, where the environment polygons are coloured by the remaining porosity in the P50 scenario in Model 3 (20 m∙ma<sup>−1</sup> burial rate). Salt marsh (De1), Mud flat (De2), and Gravel beds (De10) have not been modelled as they are not sandstones and, therefore, considered non-reservoir.</p> Full article ">Figure 13
<p>Maps of the Ravenglass Estuary sub depositional environments, where the environment polygons are coloured by the remaining porosity in the P50 scenario in Model 4 (25 m∙ma<sup>−1</sup> burial rate). Salt marsh (De1), Mud flat (De2), and Gravel beds (De10) have not been modelled as they are not sandstones and, therefore, considered non-reservoir.</p> Full article ">Figure 14
<p>Maps of the Ravenglass Estuary sub depositional environments, where the environment polygons are coloured by the remaining porosity in the P50 scenario in Model 5 (Tilje Formation burial history [<a href="#B121-geosciences-14-00268" class="html-bibr">121</a>]). Salt marsh (De1), Mud flat (De2), and Gravel beds (De10) have not been modelled as they are not sandstones and, therefore, considered non-reservoir.</p> Full article ">Figure 15
<p>Schematic summary to facilitate quantitative reservoir quality predictions in the sand-dominated sub-depositional environments of the Ravenglass Estuary, modified after [<a href="#B96-geosciences-14-00268" class="html-bibr">96</a>]. The likely mechanical compaction and cementation processes are shown after [<a href="#B25-geosciences-14-00268" class="html-bibr">25</a>,<a href="#B134-geosciences-14-00268" class="html-bibr">134</a>]. For each sand-dominated sub-depositional environment, generalised outputs of the coupled-effect models are illustrated. Schematic petrographic images of the surface sediment represent the input data for the coupled-effect models. Schematic petrographic images of mechanical compaction represent processes occurring at temperatures &lt; 80 °C and mechanical + chemical compaction images represent processes occurring at temperatures &gt; 80 °C. For each sub-depositional environment, modelled P50 porosity values are shown in the top left corner. The block diagram is modified after [<a href="#B135-geosciences-14-00268" class="html-bibr">135</a>].</p> Full article ">
26 pages, 8635 KiB  
Review
The Mundeck Salt Unit: A Review of Aptian Depositional Context and Hydrocarbon Potential in the Kribi-Campo Sub-Basin (South Cameroon Atlantic Basin)
by Mike-Franck Mienlam Essi, Eun Young Lee, Mbida Yem, Jean Marcel Abate Essi and Joseph Quentin Yene Atangana
Geosciences 2024, 14(10), 267; https://doi.org/10.3390/geosciences14100267 - 11 Oct 2024
Viewed by 504
Abstract
The Kribi-Campo sub-basin, located in the Gulf of Guinea, constitutes the southeastern segment of the Cameroon Atlantic Margin. Drilling in the Aptian salt unit revealed a sparse hydrocarbon presence, contrasting with modest finds in its counterparts like the Ezanga Salt in Gabon and [...] Read more.
The Kribi-Campo sub-basin, located in the Gulf of Guinea, constitutes the southeastern segment of the Cameroon Atlantic Margin. Drilling in the Aptian salt unit revealed a sparse hydrocarbon presence, contrasting with modest finds in its counterparts like the Ezanga Salt in Gabon and the Rio Muni Salt in Equatorial Guinea. This discrepancy prompted a reassessment of the depositional context and hydrocarbon potential of the Mundeck salt unit. By integrating 2D seismic reflection and borehole data analysis, this study established the structural and stratigraphic framework of the area, emphasizing the salt unit’s significance. Borehole data indicate a localized salt unit offshore Kribi, with seismic reflection data revealing distinct forms of diapir and pillow. This salt unit displays a substantial lateral extent with thicknesses ranging from 4000 m to 6000 m. The depositional context is linked to the following two major geological events: a significant sea-level drop due to margin uplift during the Aptian and thermodynamic processes driven by transfer faults related to mid-oceanic ridge formation. These events were crucial in forming and evolving the Mundeck Salt. Regarding hydrocarbon prospects, this study identifies the unit as being associated with potential petroleum plays, supported by direct hydrocarbon indicators and fault-related structures. The findings suggest that untapped hydrocarbon resources may still exist, underscoring the need for further exploration and analysis. Full article
(This article belongs to the Section Sedimentology, Stratigraphy and Palaeontology)
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<p>Map of the study area showing the Gulf of Guinea and the Cameroon Atlantic Margin (CAM) in western Africa (revised from [<a href="#B14-geosciences-14-00267" class="html-bibr">14</a>]). The CAM is segmented into the Northern Cameroon Atlantic Basin (NCAB; Rio Del Rey Basin) and the Southern Cameroon Atlantic Basin (SCAB; Douala and Kribi-Campo sub-basins) [<a href="#B15-geosciences-14-00267" class="html-bibr">15</a>], with respect to the mainland of Cameroon and Africa (inset). The Cameroon Volcanic Line (grey dashed line) indicates the boundary between the Northern and Southern CABs.</p> Full article ">Figure 2
<p>Extent of the South Atlantic Salt Unit (pink range) across the Southwest African Margin from the Benguela Basin (Angola) to the Rio-Muni Basin (Equatorial Guinea) [<a href="#B43-geosciences-14-00267" class="html-bibr">43</a>].</p> Full article ">Figure 3
<p>Stratigraphic framework of the study area showing major formations, unconformities, and tectono-sedimentary phases (modified from [<a href="#B12-geosciences-14-00267" class="html-bibr">12</a>]).</p> Full article ">Figure 4
<p>Geodynamic and general paleogeographic evolution of the Cameroon Atlantic Basin from the Late Jurassic to Cretaceous (revised from [<a href="#B53-geosciences-14-00267" class="html-bibr">53</a>]). (<b>1</b>–<b>4</b>) Paleogeographic evolution between Brazil and Africa; (<b>A</b>–<b>D</b>): Tectonostratigraphic evolution of the Cameroon Atlantic Basin. CAO—Central Atlantic Ocean, TO—Tethys Ocean, SA—South Atlantic, NA—North Atlantic.</p> Full article ">Figure 5
<p>Location of the (<b>A</b>) SCAB in the Atlantic Ocean and (<b>B</b>) boreholes and seismic sections in the southeastern segment of the Cameroon Atlantic Margin.</p> Full article ">Figure 6
<p>Salt structures and traps [<a href="#B36-geosciences-14-00267" class="html-bibr">36</a>]: (<b>a</b>) dome trap formed by salt pillow; (<b>b</b>) turtle structure trap; (<b>c</b>) dome trap formed by trusting; (<b>d</b>) rollover anticline; (<b>e</b>) trap formed by diapirism on two flanks; (<b>f</b>,<b>g</b>) trap formed by fault sealing; (<b>h</b>) lithological trap or lithological–structural trap; (<b>i</b>) unconformity trap; (<b>j</b>) lithological trap formed by diapir collapse.</p> Full article ">Figure 7
<p>Lithostratigraphic profiles of boreholes (<b>a</b>) B1, (<b>b</b>) B2, (<b>c</b>) B3, (<b>d</b>) B4, and (<b>e</b>) B5, focusing on the Aptian to Albian strata (see <a href="#geosciences-14-00267-f005" class="html-fig">Figure 5</a> for location). (<b>1</b>) Sandstone interbedded siltstones, (<b>2</b>) dark shales, (<b>3</b>) anhydrite traces, (<b>4</b>) salt, (<b>5</b>) Late Cretaceous to recent, (<b>6</b>) Albian, (<b>7</b>) Aptian–Albian, (<b>8</b>) Aptian.</p> Full article ">Figure 8
<p>Seismic interpretation of 38 km long NE–SW section SS1: (<b>a</b>) seismic profile of SS1; (<b>b</b>) major seismic sequences (SE) with boundaries and horizons (G, H, I, and J) (SR: seafloor reflector, SE1: Late Jurassic to Barremian, SE2: early to middle Aptian, SE3: middle Aptian to late Albian); (<b>c</b>) indication of salt distribution (pink-shaded area) based on seismic characteristics (R: regression, T: transgression, DLS: downlap surface, FS: flooding surface, SB: sequence boundary). Blue arrows indicate basement uplift.</p> Full article ">Figure 9
<p>Seismic interpretation of 45-km long NE–SW section SS2: (<b>a</b>) seismic profile of SS2; (<b>b</b>) major seismic sequences (SE) with boundaries and horizons (H, I, and J) (SR: seafloor reflector; SE1: Late Jurassic to Barremian; SE2: early to middle Aptian; SE3: middle Aptian to late Albian); (<b>c</b>) indication of salt distribution (pink-shaded area) based on seismic characteristics (R: regression; T: transgression; DLS: downlap surface; FS: flooding surface; SB: sequence boundary). Blue arrows indicate basement uplift.</p> Full article ">Figure 10
<p>Compiled stratigraphic and structural setting with salt distribution in the study area: (<b>a</b>) SS1 profile and (<b>b</b>) SS2 profile. KCH: Kribi-Campo High. Blue arrows indicate basement uplift.</p> Full article ">Figure 11
<p>A fence diagram showing the spatial distribution of salt units (pink layer) on NW- and NE-trending seismic profiles in the Kribi-Campo sub-basin, based on an integrated seismic borehole interpretation (revised from [<a href="#B14-geosciences-14-00267" class="html-bibr">14</a>]). From the bottom upwards, black surface indicates the top Precambrian basement; yellow surface indicates the Early Aptian flooding surface covering the Precambrian basement; blue surface indicates the top salt horizon of Aptian age; red surface indicates the Top Albian unconformity. The ① light brown, ② blue, and ③ green layers represent the LST (SE1), TST (SE2), and HST (SE3), respectively.</p> Full article ">Figure 12
<p>Regional geological map of the middle Atlantic Ocean, showing the salt basin spanning from Angola to Cameroon and on the Brazilian margin (revised from [<a href="#B76-geosciences-14-00267" class="html-bibr">76</a>]). The MOR and associated transform fault lines are outlined. SCAB: South Cameroon Atlantic Basin; E.G.: Equatorial Guinea; C.A.R: Central Africa Republic; D.R.C: Democratic Republic of Congo.</p> Full article ">
14 pages, 3655 KiB  
Article
Analyzing Public Interest in Geohazards Using Google Trends Data
by Dmitry Erokhin and Nadejda Komendantova
Geosciences 2024, 14(10), 266; https://doi.org/10.3390/geosciences14100266 - 11 Oct 2024
Viewed by 510
Abstract
This study investigates public interest in geological disasters by analyzing Google Trends data from 2023. This research focuses on earthquakes, hurricanes, floods, tornadoes, and tsunamis to understand how search behaviors reflect public awareness and concern. This study identifies temporal and geographical patterns in [...] Read more.
This study investigates public interest in geological disasters by analyzing Google Trends data from 2023. This research focuses on earthquakes, hurricanes, floods, tornadoes, and tsunamis to understand how search behaviors reflect public awareness and concern. This study identifies temporal and geographical patterns in search trends. Key findings reveal that public interest spikes during significant disaster events, such as the February 2023 earthquake in Turkey and Syria and the August 2023 hurricanes in the United States. This study highlights the importance of timely and accurate information dissemination for disaster preparedness and response. Google Trends proves to be a valuable tool for monitoring public interest, offering real-time insights that can enhance disaster management strategies and improve community resilience. This study’s insights are essential for policymakers, disaster management agencies, and educational efforts aimed at mitigating the impacts of natural disasters. Full article
(This article belongs to the Section Natural Hazards)
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<p>“Earthquake” Google Trends global scores in 2023. (In this and the subsequent figures, the vertical axis represents the Google Trends score, while the horizontal axis displays the timeline).</p> Full article ">Figure 2
<p>“Earthquake” Google Trends geographical distribution in 2023. (In this and the subsequent figures, the darker blue color indicates a higher Google Trends score).</p> Full article ">Figure 3
<p>“Hurricane” Google Trends global scores in 2023.</p> Full article ">Figure 4
<p>“Hurricane” Google Trends geographical distribution in 2023.</p> Full article ">Figure 5
<p>“Flood” Google Trends global scores in 2023.</p> Full article ">Figure 6
<p>“Flood” Google Trends geographical distribution in 2023.</p> Full article ">Figure 7
<p>“Tornado” Google Trends global scores in 2023.</p> Full article ">Figure 8
<p>“Tornado” Google Trends geographical distribution in 2023.</p> Full article ">Figure 9
<p>“Tsunami” Google Trends global scores in 2023.</p> Full article ">Figure 10
<p>“Tsunami” Google Trends geographical distribution in 2023.</p> Full article ">Figure 11
<p>Google Trends global scores in 2023 across various geohazards.</p> Full article ">Figure 12
<p>Google Trends geographical distribution in 2023 across various geohazards.</p> Full article ">
23 pages, 10461 KiB  
Article
Effects of Anthropic Structures on Morphodynamic Beach Evolution along the Gulf of Roses (Northwestern Mediterranean, Spain)
by Antoni Calafat, Manel Salvador, Marta Guinau and José L. Casamor
Geosciences 2024, 14(10), 265; https://doi.org/10.3390/geosciences14100265 - 10 Oct 2024
Viewed by 595
Abstract
This study conducts a morphodynamic analysis of beaches located in the northern sector of the Gulf of Roses (NW Mediterranean, Spain). The primary objective is to investigate mid-short (2004–2020) term spatial and temporal variations in shoreline position and sedimentological behaviour. The study area [...] Read more.
This study conducts a morphodynamic analysis of beaches located in the northern sector of the Gulf of Roses (NW Mediterranean, Spain). The primary objective is to investigate mid-short (2004–2020) term spatial and temporal variations in shoreline position and sedimentological behaviour. The study area covers the northern part of the gulf, spanning 9.86 km, and includes both natural beaches and heavily anthropized ones. The following GIS methodologies were employed to study the variations in the coastline: QGIS for areas and DSAS-ArcGIS for transects, quantifying coastal changes from 2004 to 2020. Sediment samples were collected from both the dry beach and swash areas for each profile. The results reveal minor discrepancies in shoreline evolution data, depending on the method used (transects or areas). Profile-based analysis shows an average annual rate of −0.11 m·y−1 (ranging between 0.53 and −0.55 m·y−1), while areal-based results (2004–2020) indicate a total loss of −20,810 m2 (−1300 m2·y−1). Sediment grain size decreases northward (from 745 to 264 µm in the swash zone). Changes in shoreline position and grain size illustrate the impact of various anthropogenic structures on morphodynamic behaviour. These structures preferentially deposit specific grain sizes and impede sediment transport, which will cause an advance in the position of the shoreline and sediment grain sizes upstream and a reverse process downstream. This study underscores the influence of coastal anthropization on beach morphology and sedimentology, generating distinct morphodynamic behaviour. Full article
(This article belongs to the Section Sedimentology, Stratigraphy and Palaeontology)
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<p>Location of the study area. Beaches studied and sampling stations (red dots) used for the sedimentological analysis. Mouth of two rivers and the breakwaters ordered numerically according to their distance from the Fluvià River mouth.</p> Full article ">Figure 2
<p>Historical evolution of the coastline of the study area. In the 1945 photo, a pristine condition of the beach–dune system is observed. Numbers indicate the mouths of the Muga River (1), the Salins Channel (2), the former Grau River (3), and the Ginjolers stream (4). In the 1987 photo, the presence of protection jetties at the entrances to the Marinas of Empuriabrava (5) and Santa Margarida (6) is observed, as well as the groin between the Nova and Rastrell beaches (7). Finally, in the 2020 orto-photomap, we observe the disappearance of the groin and the construction of jetties at the mouth of the Ginjolers stream (8), as well as the breakwaters of the Port of Roses (9).</p> Full article ">Figure 3
<p>Representative views of the different beaches in the study area. (<b>A</b>) On the natural beach of Can Comes, you can observe the presence of nebkha-type dunes, originating from the presence of wooden logs deposited during storms. (<b>B</b>) Empuriabrava beach, marked to prevent the trampling of the embryonic dunes and to protect the nests of the Kentish plover (<span class="html-italic">Charadrius alexandrinus</span>). (<b>C</b>) The natural beach of La Rovina, shown from a southerly direction, where you can see the breakwater at the entrance to the Marina of Roses. (<b>D</b>,<b>E</b>) Urban beaches of El Ristrell and La Punta, respectively, where you can observe the accumulations of sand above the promenade, produced by wind transport. (<b>F</b>) View of La Punta beach in an easterly direction, where in the background, you can see the breakwater of the Port of Roses.</p> Full article ">Figure 4
<p>Results of the two methods used to study the shoreline position. (<b>A</b>) Example of the DSAS analysis for La Punta beach. Orange lines indicate the transect location spaced every 100 m. (<b>B</b>) Example of the analysis carried out with QGIS software on the same beach. The red area is the receding (eroded) area, while the green area is the advanced area between 2004 and 2020.</p> Full article ">Figure 4 Cont.
<p>Results of the two methods used to study the shoreline position. (<b>A</b>) Example of the DSAS analysis for La Punta beach. Orange lines indicate the transect location spaced every 100 m. (<b>B</b>) Example of the analysis carried out with QGIS software on the same beach. The red area is the receding (eroded) area, while the green area is the advanced area between 2004 and 2020.</p> Full article ">Figure 5
<p>EPRan value graph (see <a href="#geosciences-14-00265-t003" class="html-table">Table 3</a>) of the study area.</p> Full article ">Figure 6
<p>Relationship between the End Point Rate (EPR) data on the horizontal axis and the Linear Regression Rate (LRR) data on the vertical axis.</p> Full article ">Figure 7
<p>Evolution of shoreline variation rates, EPR (open circles) and LRR (black triangles), along the study area. Blue transects, for river inputs, and grey bars, for jetties, are enumerated in ascending order based on their distance from the Fluvià River mouth.</p> Full article ">Figure 8
<p>Relationship between the EPR (horizontal x-axis) values obtained through the transect study and the EPRna (vertical y-axis) obtained through the areal study.</p> Full article ">Figure 9
<p>Relationship graph between the mean grain size, mean (x-axis), and the standard deviation, SD (y-axis), for the 36 samples from the wet beaches. The general trend implies better-sorted sediments for smaller grain sizes.</p> Full article ">Figure 10
<p>Evolution of the mean grain size with respect to distance for sediment samples from the wet and dry beaches. The river mouths are marked in blue, and the presence of groins is marked in grey. The numbers are in ascending order according to the mouth of the Fluvià River.</p> Full article ">Figure 11
<p>Graph of the relationship between grain size and the evolutionary trend in the coastline, represented by EPR (m·y<sup>−1</sup>). The regression line shows the general relationship trend between the two variables.</p> Full article ">Figure A1
<p>View of the southern area of Can Comes beach. The image shows the study results carried out through transects and by areas. The results show how the deposition (in green) and erosion (in red) surfaces intersperse, giving rise to the sediment waves described in the text.</p> Full article ">
25 pages, 10202 KiB  
Article
The Coefficient of Earth Pressure at Rest K0 of Sands up to Very High Stresses
by Maurizio Ziccarelli
Geosciences 2024, 14(10), 264; https://doi.org/10.3390/geosciences14100264 - 7 Oct 2024
Viewed by 781
Abstract
The mechanical behaviour of soils subjected to any stress path in which deviatoric stresses are present is heavily characterised by non-linearity, irreversibility and is strongly dependent on the initial state of stress. The latter, for the majority of geotechnical applications, is normally determined [...] Read more.
The mechanical behaviour of soils subjected to any stress path in which deviatoric stresses are present is heavily characterised by non-linearity, irreversibility and is strongly dependent on the initial state of stress. The latter, for the majority of geotechnical applications, is normally determined by the at-rest earth pressure coefficient K0, even though this state is valid, strictly speaking, for axisymmetric conditions and for zero-lateral deformations only. Many expressions are available in the literature for the determination of this coefficient for cohesive and granular materials both for normal consolidated and over-consolidated conditions. These relations are available for low to medium stress levels. Results of an extensive experimental investigation on two sands of different mineralogy up to very high stress (120 MPa) are reported in the paper. For reach very high vertical stresses, a special oedometer has been realised. In the loading phase (normal consolidated sands), the coefficient K0n depends on the stress level. It passes from values of about 0.8 to values of about 0.45 in the range of effective vertical stress σ′v = 0.5–4 MPa. Subsequently, K0n is about constant and varies between 0.45 to 0.55 up to very high vertical effective stresses (120 MPa). For the sands employed in the tests, Jaki’s relation did not lead to reliable results at relatively low pressures, while at high pressures, the same relationship seems to lead to reliable predictions if it refers to the constant volume angle of shear strength. For the over-consolidated sands, K0C strongly depends on the OCR, and for very high values of OCR, K0C could be greater than Rankine’s passive coefficient of earth pressure, Kp. This result is due to the very locked structure of the sands caused by the grain crushing, with intergranular contact of sutured and sigmoidal, concavo-convex and inter-penetrating type, that confer to the sand a sort of apparent cohesion and make it similar to weak sandstone. Full article
(This article belongs to the Section Geomechanics)
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<p>Carbonate sand C. Optical microscope photo of initial sand 0.25 &lt; d &lt; 0.42 mm.</p> Full article ">Figure 2
<p>Carbonate sand C. Scanning electron microscope (SEM) photos at different scales. (<b>a</b>) Sand 0.84 &lt; d &lt; 1.18 mm, (<b>b</b>,<b>c</b>) sand 0.42 &lt; d &lt; 0.60 mm. Intragranular pores are visible.</p> Full article ">Figure 3
<p>Initial grading of the sands utilized in the experimentation. (a) Natural C sand; (b) 0.075 &lt; d &lt; 0.106 mm; (c) 0.15 &lt; d &lt; 0.18 mm; (d) 0.18 &lt; d &lt; 0.25 mm; (e) 0.30 &lt; d &lt; 0.42 mm; (g) 0.42 &lt; d &lt; 0.60 mm; (h) 0.60 &lt; d &lt; 0.84 mm. The sand (f) was composed by 25% (in weight) of sand with 0.18 &lt; d &lt; 0.25 mm, 25% with 0.30 &lt; d &lt; 0.42 mm and 50% with 0.42 &lt; d &lt; 0.60 mm. Tests on Q sand have been performed on (e) and (g) sands, while tests on C sand have been carried out on all the sands.</p> Full article ">Figure 4
<p>(<b>a</b>,<b>b</b>) Quartz sand Q (0.42 &lt; d &lt; 0.60 mm). Scanning electron microscope (SEM) photos at different scales.</p> Full article ">Figure 5
<p>Scheme of the special oedometer utilised for the experimentation. The oedometer was equipped with strain gauges to measure circumferential strains. Measurements in mm.</p> Full article ">Figure 6
<p>Scheme for the determination, by axisymmetric FEM calculation, of the relation between the applied pressure (σ′<sub>h</sub>)* = 1 MPa (applied on a variable height h<sub>s</sub>) and the mean circumferential deformation (ε<sub>θ</sub>)*.</p> Full article ">Figure 7
<p>Relation between mean circumferential deformation (ε<sub>θ</sub>)* and height of the applied pressure h<sub>s</sub> (expressed in mm) and for (σ′<sub>h</sub>)* = 1 MPa. All tests carried out fall within the 12–20 mm range of height h<sub>s</sub> of load (see <a href="#geosciences-14-00264-t001" class="html-table">Table 1</a>).</p> Full article ">Figure 8
<p>Scheme of the current state of tests: (<b>a</b>) real situation, (<b>b</b>) vertical and horizontal stresses applied on the contour of the specimen, (<b>c</b>) scheme for the determination of the horizontal stress σ′<sub>h</sub> of the special oedometer utilised for the experimentation. Obviously, the horizontal stress σ′<sub>h</sub> of Figure (<b>b</b>) is the same of that of Figure (<b>c</b>).</p> Full article ">Figure 9
<p>Typical results of measured mean circumferential strain ε<sub>θm</sub> in function of the applied effective vertical stress σ′<sub>v</sub> for sand C. Note that in the unloaded phase at the end of the test for σ′<sub>v</sub> =0 the circumferential strain ε<sub>θm</sub> is greater than zero.</p> Full article ">Figure 10
<p>Typical results of measured effective horizontal stress σ′<sub>h</sub> in function of applied effective vertical stress σ′<sub>v</sub> for sand C (<b>a</b>,<b>b</b>) and sand Q (<b>c</b>,<b>d</b>). Note that at the end of the tests σ′<sub>h</sub> is greater than zero.</p> Full article ">Figure 11
<p>Specimens of sand C at the end of the test. (<b>a</b>) Test 7, initial sand 0.42 mm &lt; d &lt; 0.60 mm; (<b>b</b>) test 5, initial sand 0.60 mm &lt; d &lt; 0.84 mm.</p> Full article ">Figure 12
<p>Vertical section of specimen 7 of sand C at the end of the test. The final height of the specimen was 13 mm.</p> Full article ">Figure 13
<p>Evolution of grain size composition of sand C for σ′<sub>v</sub> = 80 MPa.</p> Full article ">Figure 14
<p>Typical results of specific volume <span class="html-italic">v</span> in function of σ′<sub>v</sub>. Sand C (<b>a</b>,<b>b</b>); sand Q (<b>c</b>,<b>d</b>); only a cycle of load–unload (<b>a</b>,<b>c</b>); more cycles of load–unload–reload (<b>b</b>,<b>d</b>).</p> Full article ">Figure 15
<p>Typical results in the plane s′–t (s′ = 0.5(σ′<sub>v</sub> + σ′<sub>h</sub>); t = 0.5(σ′<sub>v</sub> − σ′<sub>h</sub>)). Sand C (<b>a</b>,<b>b</b>); sand Q (<b>c</b>,<b>d</b>). (Wroth, 1975 [<a href="#B9-geosciences-14-00264" class="html-bibr">9</a>]).</p> Full article ">Figure 16
<p>Typical results of ratio (q/p′) in function of ln (p′/p′<sub>max</sub>). Sand C (<b>a</b>,<b>b</b>); sand Q (<b>c</b>,<b>d</b>). (Wroth, 1972 [<a href="#B8-geosciences-14-00264" class="html-bibr">8</a>]).</p> Full article ">Figure 17
<p>Typical trends of coefficient K<sub>0</sub> in function of applied σ′<sub>v</sub> for sand C (<b>a</b>,<b>b</b>) and for sand Q (<b>c</b>,<b>d</b>).</p> Full article ">Figure 18
<p>Trend of coefficient K<sub>0n</sub> with applied σ′<sub>v</sub> for sand C (<b>a</b>) and for sand Q (<b>b</b>) in the loading phase (normal consolidated sands).</p> Full article ">Figure 19
<p>Trend of shear strength angle φ′ in the function of mean effective stress p′ for sands of different mineralogical compositions and different initial relative densities, D<sub>R</sub>.</p> Full article ">Figure 20
<p>Relation between coefficient K<sub>0,C</sub> and the overconsolidation ratio OCR for sand C (<b>a</b>,<b>b</b>) and sand Q (<b>c</b>,<b>d</b>).</p> Full article ">Figure 21
<p>Coefficients <span class="html-italic">a</span> (<b>a</b>) and <span class="html-italic">b</span> (<b>b</b>) in function of initial void ratio e<sub>0</sub> of sands C and Q.</p> Full article ">Figure 22
<p>Relation between coefficient K<sub>0,C</sub> and overconsolidation ratio OCR for sand C (<b>a</b>) and sand Q (<b>b</b>) considering all data.</p> Full article ">Figure 23
<p>Trend of coefficient ξ (Daramola, 1980 [<a href="#B23-geosciences-14-00264" class="html-bibr">23</a>]) with the initial void ratio e<sub>0</sub> for sands C and Q.</p> Full article ">Figure 24
<p>Trend of coefficients ν′ (<b>a</b>) and <span class="html-italic">m</span> (<b>b</b>) (Wroth, 1975 [<a href="#B9-geosciences-14-00264" class="html-bibr">9</a>]) with the initial void ratio e<sub>0</sub> for sands C and Q.</p> Full article ">
24 pages, 55271 KiB  
Article
Santorini Volcanic Complex (SVC): How Much Has the Crustal Velocity Structure Changed since the 2011–2012 Unrest, and at What Point Are We Now?
by Andreas Karakonstantis and Filippos Vallianatos
Geosciences 2024, 14(10), 263; https://doi.org/10.3390/geosciences14100263 - 4 Oct 2024
Viewed by 1564
Abstract
This study is focused on one of the most active features of the Hellenic Volcanic Arc Southern Aegean Sea, the Santorini Island Volcanic Complex (SVC). The recent volcano-tectonic crisis in the intracalderic area has emerged the need for closer monitoring of the region. [...] Read more.
This study is focused on one of the most active features of the Hellenic Volcanic Arc Southern Aegean Sea, the Santorini Island Volcanic Complex (SVC). The recent volcano-tectonic crisis in the intracalderic area has emerged the need for closer monitoring of the region. The 2011–2012 unrest has been attributed to the augmentation of fluid flow inside local mapped fracture zones. After March 2012, the seismic activity dropped significantly, raising questions about whether we would have a long period of quiescence or be on a break before the next period of unrest. In this research, a re-examination of the seismic outbreak of 2011–2012 was conducted by adding more travel-time data from 2013 while we further analyzed the waveform data from 2014 to May 2024 to explore the differences of the SVC body-wave velocity structure by performing seismic tomography in these two time windows. The new dataset serves to identify the state of the Santorini Volcanic Complex. The results show a significant reduction in Vp and Vs anomalies at shallow depths since the period of unrest. At the same time, the distribution of Vp/Vs ratio remains high (>1.87) in the area NNE of Kameni at a shallower depth (2 km). The areas of Christiana Islands and Columbo volcano are mainly characterized by negative body-wave anomalies and low Vp/Vs ratio (1.56–1.64) at shallow depths for the study period, while a possible explanation to results in the submarine volcano may be explained by dry steam/gas phases that may have resulted in the generation of the swarms that occurred in the region. Full article
(This article belongs to the Special Issue Earthquake Geology of Plate Margins and Plate Interiors: Integrating Classical Methods with New Approaches)
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Figure 1

Figure 1
<p>Main tectonic features in Southern Aegean with the location of focal mechanisms of the significant earthquakes (<a href="#app1-geosciences-14-00263" class="html-app">Table S1</a>) that occurred between 1950 and 2020 (M &gt; 6.0) [<a href="#B6-geosciences-14-00263" class="html-bibr">6</a>]. The color of the focal mechanism represents the depth (km) of each event. Abbreviations—SAVA: South Aegean Volcanic Arc. Fault traces (red lines) derived by [<a href="#B16-geosciences-14-00263" class="html-bibr">16</a>].</p> Full article ">Figure 2
<p>Main tectonic features in the broader area of Santorini Volcanic Complex. The red triangles represent the active volcanic centers of the area. Fault traces (red lines) derived by [<a href="#B34-geosciences-14-00263" class="html-bibr">34</a>,<a href="#B35-geosciences-14-00263" class="html-bibr">35</a>].</p> Full article ">Figure 3
<p>Histogram of the distribution of earthquakes that were analyzed in this study between 2011 and 2024.</p> Full article ">Figure 4
<p>Distribution of the most important earthquake clusters identified between 2011 and 2013. White triangles represent the active volcanic centers, and the red lines represent the mapped fault traces [<a href="#B34-geosciences-14-00263" class="html-bibr">34</a>,<a href="#B35-geosciences-14-00263" class="html-bibr">35</a>].</p> Full article ">Figure 5
<p>Distribution of the most important earthquake clusters identified between 2014 and 2024. White triangles represent the active volcanic centers, and the red lines represent the mapped fault traces [<a href="#B34-geosciences-14-00263" class="html-bibr">34</a>,<a href="#B35-geosciences-14-00263" class="html-bibr">35</a>].</p> Full article ">Figure 6
<p>Distribution of the initial catalogue’s seismic events that were used in this study. The selected earthquakes for (<b>a</b>) 2011–2013 and (<b>b</b>) 2014–2024 time window. Seismic stations are depicted in purple triangles. Fault traces (red lines) derived by [<a href="#B34-geosciences-14-00263" class="html-bibr">34</a>,<a href="#B35-geosciences-14-00263" class="html-bibr">35</a>].</p> Full article ">Figure 7
<p>Residuals of P− (<b>left</b>) and S−wave (<b>right</b>) arrival times as a function of hypocentral distance before (blue dots) and after the tomographic inversion (red dots).</p> Full article ">Figure 8
<p>Location of the performed cross-sections. Red triangles represent the active volcanic centers and the red lines the mapped fault traces [<a href="#B34-geosciences-14-00263" class="html-bibr">34</a>,<a href="#B35-geosciences-14-00263" class="html-bibr">35</a>].</p> Full article ">Figure 9
<p>Tomograms of lateral Vp (%) variations at 2, 4, 8, and 12 km depths. Black dots represent the seismicity at each depth slice. Regions with low resolution are manually masked based on the resolution tests. Red triangles represent the volcanic centers of the SVC.</p> Full article ">Figure 10
<p>Tomograms of lateral Vs (%) variations at 2, 4, 8, and 12 km depths. Black dots represent the seismicity at each depth slice. Regions with low resolution are manually masked based on the resolution tests. Red triangles represent the volcanic centers of the SVC.</p> Full article ">Figure 11
<p>Tomograms of lateral Vp/Vs ratio at 2, 4, 8, and 12 km depths. Black dots represent the seismicity at each depth slice. Regions with low resolution are manually masked based on the resolution tests. Red triangles represent the volcanic centers of the SVC.</p> Full article ">Figure 12
<p>Distribution of Vp (%) variations. Regions with low resolution are manually masked based on the resolution tests. Map projection of cross-sections in <a href="#geosciences-14-00263-f008" class="html-fig">Figure 8</a>.</p> Full article ">Figure 13
<p>Distribution of Vs (%) variations. Regions with low resolution are manually masked based on the resolution tests. Map projection of cross-sections in <a href="#geosciences-14-00263-f008" class="html-fig">Figure 8</a>.</p> Full article ">Figure 14
<p>Distribution of Vp/Vs ratio values. Regions with low resolution are manually masked based on the resolution tests. Map projection of cross-sections in <a href="#geosciences-14-00263-f008" class="html-fig">Figure 8</a>.</p> Full article ">Figure 15
<p>Tomograms of lateral Vp (%) variations at 2, 4, 8, and 12 km depths. Black dots represent the seismicity at each depth slice. Regions with low resolution are manually masked based on the resolution tests. Red triangles represent the volcanic centers of the SVC.</p> Full article ">Figure 16
<p>Tomograms of lateral Vs (%) variations at 2, 4, 8, and 12 km depths. Black dots represent the seismicity at each depth slice. Regions with low resolution are manually masked based on the resolution tests. Red triangles represent the volcanic centers of the SVC.</p> Full article ">Figure 17
<p>Tomograms of lateral Vp/Vs ratio at 2, 4, 8, and 12 km depths. Black dots represent the seismicity at each depth slice. Regions with low resolution are manually masked based on the resolution tests. Red triangles represent the volcanic centers of the SVC.</p> Full article ">Figure 18
<p>Distribution of Vp (%) variations. Regions with low resolution are manually masked based on the resolution tests. Map projection of cross-sections in <a href="#geosciences-14-00263-f008" class="html-fig">Figure 8</a>.</p> Full article ">Figure 19
<p>Distribution of Vs (%) variations. Regions with low resolution are manually masked based on the resolution tests. Map projection of cross-sections in <a href="#geosciences-14-00263-f008" class="html-fig">Figure 8</a>.</p> Full article ">Figure 20
<p>Distribution of Vp/Vs ratio values. Regions with low resolution are manually masked based on the resolution tests. Map projection of cross-sections in <a href="#geosciences-14-00263-f008" class="html-fig">Figure 8</a>.</p> Full article ">Figure 21
<p>Interpretation of the tomography results of profiles AA’ and DD’ based on the results of Vp/Vs ratio for (<b>a</b>) 2011–2013 and (<b>b</b>) 2014–2024 temporal window.</p> Full article ">
34 pages, 10557 KiB  
Article
Possible Indication of the Impact of the Storegga Slide Tsunami on the German North Sea Coast around 8150 cal BP
by Andreas Vött, Hanna Hadler, Timo Willershäuser, Aron Slabon, Lena Slabon, Hannah Wahlen, Peter Fischer, Friederike Bungenstock, Björn R. Röbke, Manfred Frechen, Alf Grube and Frank Sirocko
Geosciences 2024, 14(10), 262; https://doi.org/10.3390/geosciences14100262 - 3 Oct 2024
Viewed by 1011
Abstract
The Storegga slide tsunami (SST) at ca. 8100 ± 100–250 cal BP is known to be the largest tsunami that affected the North Sea during the entire Holocene. Geological traces of tsunami landfall were discovered along the coasts of Norway, Scotland, England, Denmark, [...] Read more.
The Storegga slide tsunami (SST) at ca. 8100 ± 100–250 cal BP is known to be the largest tsunami that affected the North Sea during the entire Holocene. Geological traces of tsunami landfall were discovered along the coasts of Norway, Scotland, England, Denmark, the Faroes and Shetland Islands. So far, the German North Sea coast has been considered as being well protected due to the wide continental shelf and predominant shallow water depths, both assumed to dissipate tsunami wave energy significantly, thus hindering SST propagation dynamics. The objective of our research was to clarify if the SST reached the German Bight and if corresponding sediment markers can be found. Our research was based on the in-depth investigation of a 5 m long section of the research core Garding-2 from Eiderstedt Peninsula near Garding in North Frisia known from a previous study. For this, we newly recovered sediment core Garding-2A at exactly the same coring location as core Garding-2. Additionally, high-resolution Direct Push sensing data were collected to gain undisturbed stratigraphic information. Multi-proxy analyses of sediment material (grain size, geochemical, geochronological and microfaunal data) were carried out to reconstruct palaeoenvironmental and palaeogeographical conditions. We identified a high-energy event layer with sedimentological (e.g., erosional unconformity, rip-up clasts, fining-upward), microfaunal (e.g., strongly mixed foraminiferal assemblage) and other features typical of tsunami influence and identical in age with the SST, dated to ca. 8.15 ka cal BP. The event layer was deposited at or maximum ca. 1–1.5 m below the local contemporary relative sea level and several tens of kilometers inland from the coastline within the palaeo-Eider estuarine system beyond the reach of storm surges. Tsunami facies and geochronological data correspond well with SST signatures identified on the nearby island of Rømø. SST candidate deposits identified at Garding represent the southernmost indications of this event in the southeastern North Sea. They give evidence, for the first time, of high-energy tsunami landfall along the German North Sea coast and tsunami impact related to the Storegga slide. SST deposits seem to have been subsequently reworked and redeposited over centuries until the site was affected by the Holocene marine transgression around 7 ka cal BP (7.3–6.5 ka cal BP). Moreover, the transgression initiated energetically and ecologically stable shallow marine conditions within an Eider-related tidal channel, lasting several millennia. It is suggested that the SST was not essentially weakened across the shallow continental shelf of the North Sea, but rather caused tsunami run-up of several meters (Rømø Island) or largely intruded estuarine systems tens of kilometers inland (North Frisia, this study). We, therefore, assume that the southern North Sea coast was generally affected by the SST but sedimentary signals have not yet been identified or have been misinterpreted. Our findings suggest that the German North Sea coast is not protected from tsunami events, as assumed so far, but that tsunamis are also a phenomenon in this region. Full article
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<p>(<b>a</b>) Location of the Storegga submarine slide off western Norway and the North Sea basin with sites where geological evidence of tsunami landfall associated with the Storegga event is reported by different groups of authors (white and black dots; modified after [<a href="#B11-geosciences-14-00262" class="html-bibr">11</a>,<a href="#B12-geosciences-14-00262" class="html-bibr">12</a>]). The black dot directly north of the framed study area marks Storegga event deposits at Rømø Island reported by [<a href="#B13-geosciences-14-00262" class="html-bibr">13</a>]. The asterisk marks vibracoring site Garding-2A. (<b>b</b>) Topographical map of the wider area around Eiderstedt Peninsula in North Frisia, Germany. Maps based on EMODnet Bathymetry World Base Layer data and DTM derived from SRTM data. See <a href="#sec4-geosciences-14-00262" class="html-sec">Section 4</a> for further information.</p> Full article ">Figure 2
<p>(<b>a</b>) Depth topography of the base of Palaeogene and Neogene sediments in Schleswig-Holstein including Cenozoic tectonic faults (adapted from [<a href="#B57-geosciences-14-00262" class="html-bibr">57</a>]). Coring site Garding-2A is situated where Cenozoic deposits are thickest. (<b>b</b>) Recent vertical crust movements based on the GIS-based analysis of repeated precise leveling data in Schleswig-Holstein from 1923 to 1985 [<a href="#B58-geosciences-14-00262" class="html-bibr">58</a>].</p> Full article ">Figure 3
<p>(<b>a</b>) Aerial photograph of Eiderstedt Peninsula with inlay maps b and c showing details of the study site south of the village of Garding. (<b>b</b>) Location of vibracoring site Garding-2A and ERT transects GAR ERT 1 and 2. Direct push (DP) measurements were conducted at site Garding-2A. (<b>c</b>) Lidar map of the study area of the coring location. Site Garding-2A is located in the area of the former tidal inlet (displayed by green colours) that separated, until AD 1213, the two main islands of Eiderstedt, namely, Everschop and Utholm. An old dike to the east of the vibracoring site is displayed in dark brown colours. Maps in (<b>a</b>,<b>b</b>) are based on GoogleEarth aerial images. The DTM in (<b>c</b>) is derived from SRTM data.</p> Full article ">Figure 4
<p>Depth sections of electrical resistivity transects GAR ERT 1 and 2 measured in the immediate northern environs of coring site Garding-2A. Dashed line indicates approximate level of unit IIa considered to be accumulated within the course of the SST. See text for further details.</p> Full article ">Figure 5
<p>Photograph of the Garding-2 research core section between 15 m and 21 m below ground surface with the location of radiocarbon (yellow asterisks) and OSL samples (red boxes with OSL sample IDs), modified after [<a href="#B55-geosciences-14-00262" class="html-bibr">55</a>]. Core Garding-2A, presented in this paper, was drilled at exactly the same location (<a href="#geosciences-14-00262-f001" class="html-fig">Figure 1</a> and <a href="#geosciences-14-00262-f003" class="html-fig">Figure 3</a>). Ages are summarized in <a href="#geosciences-14-00262-t001" class="html-table">Table 1</a> and <a href="#geosciences-14-00262-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 6
<p>Stratigraphic units I to III of sediment core Garding-2A, section 15–20 m b.s., compared with grain size data, loss on ignition (LOI) and Ca concentrations. See text for further details. csa—coarse sand (2000–630 µm); msa—medium sand (630–200 µm); fsa—fine sand (200–125 µm); ffsa—finest fine sand (125–63 µm); si—silt (63–2 µm); c—clay (&lt;2 µm). Grain size scale adapted from [<a href="#B71-geosciences-14-00262" class="html-bibr">71</a>,<a href="#B101-geosciences-14-00262" class="html-bibr">101</a>,<a href="#B102-geosciences-14-00262" class="html-bibr">102</a>]. Open triangles next to stratigraphic log mark sampling depths.</p> Full article ">Figure 7
<p>Frequency distributions of grain size data (black curves) as well as cumulative grain size data (red curves) obtained for sediment samples from core Garding-2A, classified after sedimentary units (<b>I</b>–<b>VI</b>). Samples are grouped according to stratigraphic units I to III. See text for further information. csa—coarse sand (2000–630 µm); msa—medium sand (630–200 µm); fsa—fine sand (200–125 µm); vfsa—very fine sand (125–63 µm); csi—coarse silt (63–20 µm); msi—medium silt (20–6.3 µm); fsi—fine silt (6.3–2 µm); c—clay (&lt;2 µm).</p> Full article ">Figure 8
<p>Detail photo and grain size data of section 20–18.82 m b.s. of sediment core Garding-2A. See text for further details. csa—coarse sand (2000–630 µm); msa—medium sand (630–200 µm); fsa—fine sand (200–125 µm); vfsa—very fine sand (125–63 µm); si—silt (63–2 µm); c—clay (&lt;2 µm).</p> Full article ">Figure 9
<p>Results of direct Push (DP) in situ sensing at coring site Garding-2(A) based on Hydraulic Profiling Tools (HPT) and Cone Penetration Testing (CPT). Variables EC (electrical conductivity), HPT (maximum) pressure and Est K (estimated hydraulic conductivity) are derived from HPT sensing, variables u<sub>2</sub> (pore pressure), q<sub>c</sub> (cone resistance) and f<sub>s</sub> (sleeve friction) are based on CPT data.</p> Full article ">Figure 10
<p>Results of microfossil analyses conducted for sediment samples from sediment core Garding-2A (black dots). The small figures to the right of the core photo represent sample numbers and sampling depths. Core loss is indicated by lowercase c. Abundance and diversity of foraminifera were found highest for samples of stratigraphic units IIa. The increase is interpreted as the effect of a tsunami impact.</p> Full article ">Figure 11
<p>Detail photographs of foraminifers and ostracods found in sediment samples from sediment core Garding-2A. 1—<span class="html-italic">Haynesina germanica</span>; 2—<span class="html-italic">Ammonia</span> sp.; 3—<span class="html-italic">Elphidium williamsonii</span>; 4—<span class="html-italic">Elphidium excavatum</span>; 5—<span class="html-italic">Ammonia</span> sp. (deformed). 6—<span class="html-italic">Elphidium williamsonii</span> (deformed); 7—<span class="html-italic">Lagena</span> sp.; 8—<span class="html-italic">Textularia</span> sp.; 9—<span class="html-italic">Orbulina</span> sp., 10—<span class="html-italic">Leptocythere</span> sp.; 11—<span class="html-italic">Pontocythere</span> sp.</p> Full article ">Figure 12
<p>High-resolution results of Direct Push (DP) in situ sensing compared to stratigraphic units derived from sediment core Garding-2A and geochronological data. Radiocarbon ages are displayed as scenario a and b ages in cal BP (see <a href="#geosciences-14-00262-t001" class="html-table">Table 1</a>); OSL ages are given in ka (before 2012; in italics; see <a href="#geosciences-14-00262-t002" class="html-table">Table 2</a>). The stratigraphy corrected according to DP data is illustrated in red lines and letters. As DP allows obtaining stratigraphic information without any vertical compaction and/or distortion effects, the true thickness of unit II is calculated with 83 cm based on DP data.</p> Full article ">Figure 13
<p>Palaeogeographical maps showing the coastline as transition from blue to green colours for the time slices 8300 cal BP, 8000 cal BP and 7000 cal BP based on palaeo-sea levels of 22 m, 17 m and 10 m below present sea level (m b.s.l.) after [<a href="#B94-geosciences-14-00262" class="html-bibr">94</a>], respectively, and topographical and bathymetrical data for the German North Sea coast. Thin black lines depict the present shoreline. Coring site Garding-2A on Eiderstedt Peninsula is marked by an asterisk. Please note that the scenario for 7000 cal BP shows more topographic details than the other ones. This is due to resolution issues of the underlying bathymetric model data used. Scenarios (<b>a</b>–<b>c</b>) only represent rough estimates of the palaeogeographical constellation.</p> Full article ">Figure 14
<p>Local relative sea level data for the German North Sea coast predicted by [<a href="#B94-geosciences-14-00262" class="html-bibr">94</a>] for the southern North Sea coast based on ice and earth models showing a good correlation with observational sea level data. Curves 14 and 15 are relevant for the Eiderstedt Peninsula (this study). The SST age range based on [<a href="#B3-geosciences-14-00262" class="html-bibr">3</a>] (ages recalibrated, see <a href="#geosciences-14-00262-t001" class="html-table">Table 1</a>) is marked as the red zone. Calibrated radiocarbon age scenarios a and b obtained for unit IIa of core Garding-2A are marked in green.</p> Full article ">
10 pages, 3646 KiB  
Article
Non-Destructive Methods for Assessing the Condition of Reinforcement Materials in Soil
by Naoki Tatta and Hideo Sakai
Geosciences 2024, 14(10), 261; https://doi.org/10.3390/geosciences14100261 - 1 Oct 2024
Viewed by 668
Abstract
A reinforced earth wall is a structure in which reinforcement materials are placed in an embankment to build a vertical or nearly vertical wall surface. Such walls have been widely used in roads and in developed land since around 1960. Reinforcement materials have [...] Read more.
A reinforced earth wall is a structure in which reinforcement materials are placed in an embankment to build a vertical or nearly vertical wall surface. Such walls have been widely used in roads and in developed land since around 1960. Reinforcement materials have a set service life of 100 years and fall into two types: steel and geosynthetics. To ensure long-term durability, steel reinforcement materials are plated, while geosynthetics are designed with a limit strength designed to resist fracture for 100 years under the conditions of a given load placed on the reinforcement materials. However, owing to the difficulty of assessing the condition of reinforcement materials in soil, this paper proposes solutions based on non-destructive methods. Specifically, it proposes a method of assessing the amount of strain through an embedded optical fiber in the case of geosynthetic reinforcement materials, or magnetic surveying to investigate the degree of corrosion in the case of steel reinforcement materials. This paper demonstrates that it is possible to non-destructively assess the state of either type of reinforcement material. Full article
(This article belongs to the Special Issue Computational Geodynamic, Geotechnics and Geomechanics)
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<p>Geosynthetics with embedded optical fiber.</p> Full article ">Figure 2
<p>State of corrosion due to corrosion accelerant (using a 10% aqueous solution of inorganic halides).</p> Full article ">Figure 3
<p>States of corrosion due to electrical corrosion.</p> Full article ">Figure 4
<p>Magnetic field measurement using an optically pumped magnetometer.</p> Full article ">Figure 5
<p>Steel plates in which deterioration due to corrosion was simulated.</p> Full article ">Figure 6
<p>Arrangement of iron plates in burial experiments.</p> Full article ">Figure 7
<p>Cross section of a reinforced earth wall with geosynthetics [<a href="#B14-geosciences-14-00261" class="html-bibr">14</a>].</p> Full article ">Figure 8
<p>Distribution of strain in geosynthetics (L = 6.0 m optical fiber and S1–5 strain gauge) [<a href="#B14-geosciences-14-00261" class="html-bibr">14</a>].</p> Full article ">Figure 9
<p>Reduction in magnetization due to volume (mass) reduction.</p> Full article ">Figure 10
<p>Magnetic field (magnetic flux) of 3rd plate.</p> Full article ">Figure 11
<p>Changes in magnetic flux density with mass.</p> Full article ">Figure 12
<p>Distribution of magnetic flux density in buried iron plates.</p> Full article ">
31 pages, 83141 KiB  
Article
The Relationships between the Internal Nappe Zone and the Regional Mylonitic Complex in the NE Variscan Sardinia (Italy): Insight from a New Possible Regional Interpretation?
by Franco Marco Elter and Federico Mantovani
Geosciences 2024, 14(10), 260; https://doi.org/10.3390/geosciences14100260 - 28 Sep 2024
Viewed by 934
Abstract
This study presents an updated interpretation of geological data collected between 1984 and 2022. The area under consideration holds significant regional importance as it is located between the Internal Nappe Zone (INZ) and the Regional Mylonitic Complex (RMC). Re-evaluation of the geological data [...] Read more.
This study presents an updated interpretation of geological data collected between 1984 and 2022. The area under consideration holds significant regional importance as it is located between the Internal Nappe Zone (INZ) and the Regional Mylonitic Complex (RMC). Re-evaluation of the geological data has highlighted a more intricate structural framework than what is currently documented in the existing literature. This paper aims to illustrate, through structural analysis, that the Posada Valley Shear Zone (PVSZ) does not serve as the transitional boundary between the Inner Nappe Zone and the Regional Mylonitic Complex or High-Grade Metamorphic Complex (HGMC) as traditionally thought. Instead, the authors’ findings indicate that the transition boundary is confined to a shear band with a variable thickness ranging from 10 to 70 m at its widest points. The development of the Posada Valley Shear Zone is characterized by a series of transitions from mylonite I S-C to mylonite II S-C, extending over approximately 5 km. The formation of the Posada Valley Shear Zone is chronologically confined between the development of the East Variscan Shear Zone (EVSZ) and the emplacement of the Late Variscan granites. The differing orientations of Sm and S3 observed in the mylonitic events of the Posada Valley Shear Zone and the Regional Mylonitic Complex, respectively, are likely attributable to an anticlockwise rotation of the shortening directions during the upper Carboniferous period. Furthermore, this study proposes that the Condensed Isogrades Zone (CIZ), despite its unclear formation mechanism, should be recognized as the true transition zone between the Inner Nappe Zone and the Regional Mylonitic Complex or High-Grade Metamorphic Complex. This new interpretation challenges the previously accepted notion of increasing Variscan metamorphic zonation toward the northeast. This conclusion is supported by the identification of the same NE–SW orientation of the D2 tectonic event in both the Old Gneiss Complex (OGC in the Regional Mylonitic Complex) and the lithologies of the Inner Nappe Zone and the Condensed Isogrades Zone. The comprehensive analysis and new insights provided in this paper contribute to a refined understanding of the geological relationships and processes within this region, offering significant implications for future geological studies and interpretations. Full article
(This article belongs to the Special Issue Metamorphism and Tectonic Evolution of Metamorphic Belts)
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Figure 1

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<p>Sketch map of the Variscan Belt in Sardinia and its zones.</p> Full article ">Figure 2
<p>Simplified map showing the metamorphic zonation of the studied area (red rectangle) with the locations of sampling and field investigations (modified from [<a href="#B27-geosciences-14-00260" class="html-bibr">27</a>]).</p> Full article ">Figure 3
<p>Geological map of the study area showing the locations mentioned in this paper; modified after [<a href="#B28-geosciences-14-00260" class="html-bibr">28</a>].</p> Full article ">Figure 4
<p>S. Lucia complex: (<b>A</b>) Grt-Ab-Olg-bearing micaschist (seen from SW); (<b>B</b>) contact between porphyroids and paragneiss (seen from E).</p> Full article ">Figure 5
<p>Stereonet (equal area, lower hemisphere) of the S. Lucia complex: (<b>A</b>) rose diagram of 52 S2 planes; (<b>B</b>) contours calculation of 40 mineralogical lineations (L2) on the S2 surface.</p> Full article ">Figure 6
<p>S. Lucia complex: (<b>A</b>) rare S1 relics transposed by S2 folds (seen from SW); (<b>B</b>) crenulation Cleavage (seen from W); (<b>C</b>) open fold in porphyroid (seen from SW).</p> Full article ">Figure 7
<p>La Caletta-Siniscola augen gneiss: (<b>A</b>) augen gneiss (seen from N); (<b>B</b>) mylonite I S-C [<a href="#B33-geosciences-14-00260" class="html-bibr">33</a>,<a href="#B34-geosciences-14-00260" class="html-bibr">34</a>,<a href="#B35-geosciences-14-00260" class="html-bibr">35</a>] top-to-right sense of shear on the XZ plane (seen from S); (<b>C</b>) thin section of mylonitic I S-C [<a href="#B33-geosciences-14-00260" class="html-bibr">33</a>,<a href="#B34-geosciences-14-00260" class="html-bibr">34</a>,<a href="#B35-geosciences-14-00260" class="html-bibr">35</a>] with a clast showing top-to-right sense of shear (oriented sample, seen from S).</p> Full article ">Figure 8
<p>The Lodè-Mamone leucocratic orthogneiss: (<b>A</b>) leucocratic orthogneiss (seen from S); (<b>B</b>) thin section (oriented sample; seen from S).</p> Full article ">Figure 9
<p>Rio Mannu granodioritic orthogneiss: (<b>A</b>) orthogneiss (seen from E); (<b>B</b>) xenolith embedded in the granodioritic orthogneiss (seen from SE); (<b>C</b>) basic enclave embedded in the granodioritic orthogneiss (seen from W); (<b>D</b>) aplite enclave embedded in the granodioritic orthogneiss (seen from W); (<b>E</b>) shear band in the granodioritic orthogneiss (seen from SE).</p> Full article ">Figure 10
<p>Stereonet (equal area, lower hemisphere) of the Rio Mannu granodioritic orthogneiss: (<b>A</b>) rose diagram of 52 Sm planes; (<b>B</b>) contour calculation of 41 mylonitic lineations (Lm) on the Sm surface.</p> Full article ">Figure 11
<p>The Rio Mannu granodioritic orthogneiss: (<b>A</b>) drag folds with a top-to-left sense of shear on the XZ plane (seen from SE); (<b>B</b>) σ-type Kfs porphyroclasts showing a top-to right sense of shear (seen from S); (<b>C</b>) thin section of a σ-type Qz porphyroclasts showing a top-to right sense of shear (oriented sample; seen from S); (<b>D</b>) σ-type Kfs porphyroclasts showing a top-to right sense of shear (seen from S); (<b>E</b>) rare sheath fold (seen from S).</p> Full article ">Figure 12
<p>Simplified geological map of the area highlighting marble and calc-silicate lenses.</p> Full article ">Figure 13
<p>The contact aureoles: (<b>A</b>) static Grt + Ves-bearing marble related to the late Variscan Punta Tepilora granite contact aureole (seen from SW); (<b>B</b>) contact (dotted black line) between blue marble and the deformed Grt-Bearing calc-silicate related to the Ordovician Rio Mannu granodioritic orthogneiss (seen from S); (<b>C</b>) static And-bearing hornfel related to the late Variscan Punta Tepilora granite contact aureole (seen from SW).</p> Full article ">Figure 14
<p>Punta Gortomedda complex: (<b>A</b>) St-Grt-bearing micaschist (seen from S); (<b>B</b>) dark gray marble (seen from SE).</p> Full article ">Figure 15
<p>Stereonet (equal area, lower hemisphere) of the Punta Gortomedda complex: (<b>A</b>) rose diagram of 43 S2 planes; (<b>B</b>) contour calculation of 38 mineralogical lineations (L2) on the S2 surface; (<b>C</b>) rose diagram of 48 Sm planes; (<b>D</b>) contour calculation of 35 milonitic lineations (Lm) on the Sm surface.</p> Full article ">Figure 16
<p>Punta Gortomedda complex: (<b>A</b>) thin section of a mica fish showing a top-to-right sense of shear in the type I S-C mylonite [<a href="#B33-geosciences-14-00260" class="html-bibr">33</a>,<a href="#B34-geosciences-14-00260" class="html-bibr">34</a>,<a href="#B35-geosciences-14-00260" class="html-bibr">35</a>] (oriented sample; seen from S); (<b>B</b>) thin section of a mica fish showing a top-to-right sense of shear in the I S-C mylonite [<a href="#B33-geosciences-14-00260" class="html-bibr">33</a>,<a href="#B34-geosciences-14-00260" class="html-bibr">34</a>,<a href="#B35-geosciences-14-00260" class="html-bibr">35</a>] (oriented sample; seen from SW); (<b>C</b>) thin section of a deformed Grt wrapped by type B3 ribbon Qz [<a href="#B45-geosciences-14-00260" class="html-bibr">45</a>,<a href="#B46-geosciences-14-00260" class="html-bibr">46</a>] (oriented sample; seen from SE).</p> Full article ">Figure 17
<p>The Fruncu Nieddu complex: (<b>A</b>) Ky-bearing micaschist (seen from SE); (<b>B</b>) Ky-Grt-bearing micaschist; (<b>C</b>) Qz ribbon in the I S-C mylonite [<a href="#B33-geosciences-14-00260" class="html-bibr">33</a>,<a href="#B34-geosciences-14-00260" class="html-bibr">34</a>,<a href="#B35-geosciences-14-00260" class="html-bibr">35</a>] paragneiss (seen from S).</p> Full article ">Figure 18
<p>Stereonet (equal area, lower hemisphere) of the Fruncu Nieddu complex: (<b>A</b>) rose diagram of 33 S2 planes; (<b>B</b>) contour calculation of 39 mineralogical lineations (L2) on the S2 surface; (<b>C</b>) rose diagram of 52 Sm planes; (<b>D</b>) contour calculation of 44 mylonitic lineations (Lm) on the Sm surface.</p> Full article ">Figure 19
<p>Fruncu Nieddu complex: (<b>A</b>) Thin section showing a top-to-right Ky fish with Ms rim and Grt with snowball structures (oriented sample; seen from S); (<b>B</b>) thin section of II S–C mylonite [<a href="#B33-geosciences-14-00260" class="html-bibr">33</a>,<a href="#B34-geosciences-14-00260" class="html-bibr">34</a>,<a href="#B35-geosciences-14-00260" class="html-bibr">35</a>] showing a top-to-right Ky fish with Ms rim (oriented sample; seen from S); (<b>C</b>) thin section of II S–C [<a href="#B33-geosciences-14-00260" class="html-bibr">33</a>,<a href="#B34-geosciences-14-00260" class="html-bibr">34</a>,<a href="#B35-geosciences-14-00260" class="html-bibr">35</a>] mylonite showing relics of Ky, Ms and Grt (oriented sample; seen from S).</p> Full article ">Figure 20
<p>Punta Figliacoro complex (sensu stricto): (<b>A</b>) thin section of laminated amphibolite (oriented sample; seen from S); (<b>B</b>) laminated orthoderivates with σ-type Kfs porphyroclasts (seen from S).</p> Full article ">Figure 21
<p>Stereonet (equal area, lower hemisphere) of the Punta Figliacoro complex sensu stricto: (<b>A</b>) rose diagram of 28 S2 planes; (<b>B</b>) contour calculation of 37 mineralogical lineations (L2) on the S2 surface; (<b>C</b>) rose diagram of 27 Sm planes; (<b>D</b>) contour calculation of 25 mylonitic lineations (Lm) on the Sm surface.</p> Full article ">Figure 22
<p>The Pedra su Gattu complex: (<b>A</b>) phyllonite with Ab porphyroclasts (seen from E); (<b>B</b>) thin section of the phyllonite with a σ-type Ab porphyroclasts deformed by Sm with a top-to-right sense of shear (oriented sample; seen from S).</p> Full article ">Figure 23
<p>Stereonet (equal area, lower hemisphere) of the Pedra su Gattu complex: (<b>A</b>) rose diagram of 24 Sm planes; (<b>B</b>) contours calculation of 29 mylonitic lineations (Lm) on the Sm surface.</p> Full article ">Figure 24
<p>The Sedda Eneas complex: (<b>A</b>) paragneiss with a leucosome (Qz + Pl) ribbon (seen from S); (<b>B</b>) Sil-Ms-bearing gneiss with stromatitic structure [<a href="#B101-geosciences-14-00260" class="html-bibr">101</a>] (seen from S).</p> Full article ">Figure 25
<p>Stereonet (equal area, lower hemisphere) of the Sedda Eneas complex: (<b>A</b>) rose diagram of 39 S2 planes (green petals) and 34 S3 planes (pink petals); (<b>B</b>) contours calculation of 42 mineralogical lineations (L2) on the S2 surface; (<b>C</b>) rose diagram of 41 Sm planes; (<b>D</b>) contours calculation of 36 mylonitic lineations (Lm) on the Sm surface.</p> Full article ">Figure 26
<p>Hypothetical sketch of the studied area and a simplified scheme showing structural relationship between the Inner Nappe Zone (INZ), the Condensed Isogrades Zone (CIZ), the Posada Valley Shear Zone (PVSZ) and the Regional Mylonitic Complex (RMC); both seen from above.</p> Full article ">
23 pages, 6593 KiB  
Article
Multitemporal Quantification of the Geomorphodynamics on a Slope within the Cratère Dolomieu at the Piton de la Fournaise (La Réunion, Indian Ocean) Using Terrestrial LiDAR Data, Terrestrial Photographs, and Webcam Data
by Kerstin Wegner, Virginie Durand, Nicolas Villeneuve, Anne Mangeney, Philippe Kowalski, Aline Peltier, Manuel Stark, Michael Becht and Florian Haas
Geosciences 2024, 14(10), 259; https://doi.org/10.3390/geosciences14100259 - 28 Sep 2024
Viewed by 487
Abstract
In this study, the geomorphological evolution of an inner flank of the Cratère Dolomieu at Piton de La Fournaise/La Réunion was investigated with the help of terrestrial laser scanning (TLS) data, terrestrial photogrammetric images, and historical webcam photographs. While TLS data and the [...] Read more.
In this study, the geomorphological evolution of an inner flank of the Cratère Dolomieu at Piton de La Fournaise/La Réunion was investigated with the help of terrestrial laser scanning (TLS) data, terrestrial photogrammetric images, and historical webcam photographs. While TLS data and the terrestrial images were recorded during three field surveys, the study was also able to use historical webcam images that were installed for the monitoring of the volcanic activity inside the crater. Although the webcams were originally intended to be used only for visual monitoring of the area, at certain times they captured image pairs that could be analyzed using structure from motion (SfM) and subsequently processed to create digital terrain models (DTMs). With the help of all the data, the geomorphological evolution of selected areas of the crater was investigated in high temporal and spatial resolution. Surface changes were detected and quantified on scree slopes in the upper area of the crater as well as on scree slopes at the transition from the slope to the crater floor. In addition to their quantification, these changes could be assigned to individual geomorphological processes over time. The webcam photographs were a very important additional source of information here, as they allowed the observation period to be extended further into the past. Besides this, the webcam images made it possible to determine the exact dates at which geomorphological processes were active. Full article
(This article belongs to the Section Natural Hazards)
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<p>Location of the study area Cratère Dolomieu of the PLF. (Source of the overview base map: ASTER DEM. Source of the overview map of the Cratère Dolomieu is a 1 m DEM based on terrestrial laser scanning data acquired in 2014).</p> Full article ">Figure 2
<p>Riegl VZ4000 laser scanner located on the crater rim and dGNSS measurement of tie points (Riegl reflector) (own photographs captured during fieldwork in 2014).</p> Full article ">Figure 3
<p>The entire workflow for processing TLS data, digital terrestrial, and webcam photographs is illustrated. The particular processing steps are demonstrated for each relevant software (RiSCAN PRO (Version 2.4), Agisoft Metashape Pro (Version 1.5.5), Laserdata SAGA LIS (Version 3.0.7, 3.1.0)).</p> Full article ">Figure 4
<p>Investigated slope and stable areas for ICP adjustment. The location of the AoI can be seen in <a href="#geosciences-14-00259-f001" class="html-fig">Figure 1</a> on the overview map of the crater.</p> Full article ">Figure 5
<p>Different examples of photographs that were not usable for further SfM processing due to insufficiencies regarding differences in the quality. (<b>A</b>) Investigated slope was either completely or partially in clouds. (<b>B</b>) Camera lens was fogged. (<b>C</b>) Contamination on the camera lens. (<b>D</b>) Light reflections lead to a poor contrast. (<b>E</b>) Existing ground fog does not allow data processing. (<b>F</b>) Strong shadows especially during summer in the southern hemisphere led to contrast differences. (<b>G</b>) Existing fog in the crater. (<b>H</b>) Volcanic eruption occurred on 4 January 2010, moving lava prevented use of this image pair.</p> Full article ">Figure 6
<p>Mapped areas with visible surface changes within the different time steps between 2010 and 2016 that lie inside the derivable DTM. Both highlighted profile lines (grey, red) for the years 2010 and 2016 are analyzed in Figure 11.</p> Full article ">Figure 7
<p>Derived surface changes (digital terrain model of differences: DoDs) for the two rockfall hotspots 1 and 2 between 2010 and 2016 (shaded relief in the background is derived on the base of the 2016 DTM). Also shown are the positive surface changes [cm] and the accumulated volume [m<sup>3</sup>] of the two areas for the corresponding periods.</p> Full article ">Figure 8
<p>Derived surface changes (DoDs) on two selected debris cones between 2010 and 2016 (shaded relief in the background is derived on the base of the 2016 DTM). Also shown are the positive surface changes [cm] and the accumulated volume [m<sup>3</sup>] of the two areas for the corresponding periods.</p> Full article ">Figure 9
<p>Clearly visible linear patterns on the debris zone II.</p> Full article ">Figure 10
<p>The white arrows show visually detectable surface changes (DoDs) in rock zone II between 13 June 2011 and 19 June 2011.</p> Full article ">Figure 11
<p>(<b>A</b>) The two lines are showing the slope development as a swath profile of debris zone II between 2010 and 2016. The location of the profile lines can be found in <a href="#geosciences-14-00259-f006" class="html-fig">Figure 6</a>. (<b>B</b>) Statistical range of the slope inclination for the years 2010 until 2016 showing a flattening of approximately 1°.</p> Full article ">
27 pages, 39557 KiB  
Article
Application of Experimental Configurations of Seismic and Electric Tomographic Techniques to the Investigation of Complex Geological Structures
by Vasileios Gkosios, John D. Alexopoulos, Konstantinos Soukis, Ioannis-Konstantinos Giannopoulos, Spyridon Dilalos, Dimitrios Michelioudakis, Nicholas Voulgaris and Thomas Sphicopoulos
Geosciences 2024, 14(10), 258; https://doi.org/10.3390/geosciences14100258 - 28 Sep 2024
Viewed by 522
Abstract
The main purpose of this study is the subsurface investigation of two complex geological environments focusing on the improvement of data acquisition and processing parameters regarding electric and seismic tomographic techniques. Two different study areas, in central–east Peloponnese and SE Attica, were selected, [...] Read more.
The main purpose of this study is the subsurface investigation of two complex geological environments focusing on the improvement of data acquisition and processing parameters regarding electric and seismic tomographic techniques. Two different study areas, in central–east Peloponnese and SE Attica, were selected, where detailed geological mapping and surface geophysical survey were carried out. The applied geophysical survey included the application of electrical resistivity tomography (ERT), seismic refraction tomography (SRT) and ground penetrating radar (GPR). The geoelectrical measurements were acquired with different arrays and electrode configurations. Moreover, various types of seismic sources were used at seventeen shot locations along the seismic arrays. For the processing of geoelectrical data, clustered datasets were created, increasing the depth of investigation and discriminatory capability. The seismic data processing included the following: (a) the creation of synthetic models and seismic records to determine the effectiveness and capabilities of the technique, (b) spectral analysis of the seismic records to determine the optimal seismic source type and (c) inversion of the field data to create representative subsurface velocity models. The results of the two techniques successfully delineated the complex subsurface structure that characterizes these two geological environments. The application of the ERT combined with the SRT are the two dominant, high-resolution techniques for the elucidation of complex subsurface structures. Full article
(This article belongs to the Section Geophysics)
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<p>Detailed geological map of the first study area (<span class="html-italic">Kleisoura Valley</span>, <span class="html-italic">Ano Doliana</span>).</p> Full article ">Figure 2
<p>(<b>a</b>) General view of the west flank of the <span class="html-italic">Kleisoura Valley</span> where the “Arcadian nappe” (Pindos limestones—<b>Pl</b>) overlies the Tripolitza Unit; (<b>b</b>) close-up view of Tripolitza flysch (<b>fl</b>); (<b>c</b>,<b>d</b>) the transition from the Tripolitza limestones (<b>Tl</b>) to the flysch (<b>fl</b>) through transitional marly limestone (<b>ml</b>) beds (<b>c</b>) or high-angle fault (<b>d</b>); (<b>e</b>) thick tectonic breccia located at the structurally highest levels of the Tripolitza limestones; (<b>f</b>) Phyllite–Quartzite series: boudinaged light-colored quartzitic layers (<b>Q</b>) surrounded by gray–green phyllites (<b>Ph</b>).</p> Full article ">Figure 3
<p>Detailed geological map of the second study area (<span class="html-italic">Plaka</span>). Modified after [<a href="#B29-geosciences-14-00258" class="html-bibr">29</a>].</p> Full article ">Figure 4
<p>(<b>a</b>) Panoramic view of the <span class="html-italic">Plaka</span> study area where the geophysical survey was conducted. The trace of the detachment fault that juxtaposes the overlying Pounta Marble (<b>PM</b>) against the underlying Kamariza Schists (<b>KS</b>) is marked with the yellow line; (<b>b</b>) closer view of the study area, where the mining debris (<b>Md</b>) is illustrated; (<b>c</b>) contact metamorphosed marble lenses (<b>Ml</b>) in the Kamariza Schists (<b>KM</b>); (<b>d</b>) strongly weathered granodiorite; (<b>e</b>) Pounta Marble (<b>PM</b>); (<b>f</b>) ultra-mylonitic Upper Kamariza Marble (<b>UM</b>); (<b>g</b>) Kamariza Schists hornfels (<b>KS</b>).</p> Full article ">Figure 5
<p>Snapshots from data acquisition on the <span class="html-italic">Kleisroua Valley</span>, <span class="html-italic">Ano Doliana</span> (<b>left</b>) and <span class="html-italic">Plaka</span> (<b>right</b>) study areas.</p> Full article ">Figure 6
<p>Detailed geological map of the 1st study area (<span class="html-italic">Kleisoura Valley</span>, <span class="html-italic">Ano Doliana</span>), along with the acquisition layout of the geophysical techniques.</p> Full article ">Figure 7
<p>Detailed geological map of the second study area (<span class="html-italic">Plaka</span>), along with the acquisition layout of the geophysical techniques. Modified after [<a href="#B29-geosciences-14-00258" class="html-bibr">29</a>].</p> Full article ">Figure 8
<p>(<b>a</b>) ERT results of the first study area, based on which the synthetic velocity model was created; (<b>b</b>) synthetic velocity model with the seismic wavefronts generated at the outshot normal 1 position (120 m offset), with the geophones located between 157.5–392.5 m distance (red marks); (<b>c</b>) creation of the synthetic seismic record for the outshot normal 1 source location. First arrival times are noted by the red arrows.</p> Full article ">Figure 9
<p>(<b>a</b>) ERT results of the first study area in relation to the (<b>b</b>) seismic tomogram generated after the inversion of the 17 synthetic seismic records.</p> Full article ">Figure 10
<p>Superimposing of the synthetic first arrival times (red marks) on the field seismic records for the (<b>a</b>) outshot normal 1, (<b>b</b>) tomo shot 1 and (<b>c</b>) tomo shot 4 seismic source locations (<a href="#geosciences-14-00258-f006" class="html-fig">Figure 6</a>). The blow arrows and the blue dashed lines indicate the divergence between the synthetic and true first arrivals.</p> Full article ">Figure 11
<p>Seismic records along with their corresponding amplitude spectra acquired with four (4) different seismic sources at the outshot normal 2 source location (<a href="#geosciences-14-00258-f006" class="html-fig">Figure 6</a>). AWD: 20 kg—accelerated weight drop; SH: 6.5 kg—sledgehammer; SD—seismic detonator; BG—buffalo gun. The selected time window for the analysis is represented by the grey shaded box with red outline. The maximum energy amplitudes of the BG, SD and SH 6.5 kg sources are marked by the blue, yellow and green horizontal lines, correspondingly.</p> Full article ">Figure 12
<p>(<b>a</b>) ERT and (<b>b</b>) SRT profiles and their (<b>c</b>) geological interpretation derived from their combined evaluation for the first study area (<span class="html-italic">Kleisoura Valley</span>, <span class="html-italic">Ano Doliana</span>). The white dashed lines delimit formations with different geophysical parameters. The red arrow indicates the fault kinematics.</p> Full article ">Figure 13
<p>(<b>a</b>) ERT and (<b>b</b>) SRT profiles and their (<b>c</b>) geological interpretation derived from their combined evaluation for the second study area (<span class="html-italic">Plaka</span>). The white dashed lines delimit formations with different geophysical parameters.</p> Full article ">Figure 14
<p>GPR profile of the second study area. Underground air voids (possible mining galleries) are noted by the red dashed circles.</p> Full article ">
16 pages, 54958 KiB  
Article
Seismotectonic Setting of the Andes along the Nazca Ridge Subduction Transect: New Insights from Thermal and Finite Element Modelling
by Sara Ciattoni, Stefano Mazzoli, Antonella Megna and Stefano Santini
Geosciences 2024, 14(10), 257; https://doi.org/10.3390/geosciences14100257 - 28 Sep 2024
Viewed by 741
Abstract
The structural evolution of Andean-type orogens is strongly influenced by the geometry of the subducting slab. This study focuses on the flat-slab subduction of the Nazca Ridge and its effects on the South American Plate. The process of flat slab subduction impacts the [...] Read more.
The structural evolution of Andean-type orogens is strongly influenced by the geometry of the subducting slab. This study focuses on the flat-slab subduction of the Nazca Ridge and its effects on the South American Plate. The process of flat slab subduction impacts the stress distribution within the overriding plate and increases plate coupling and seismic energy release. Using the finite element method (FEM), we analyse interseismic and coseismic deformation along a 1000 km transect parallel to the ridge. We examine stress distribution, uplift patterns, and the impact of megathrust activity on deformation. To better define the crust’s properties for the model, we developed a new thermal model of the Nazca Ridge subduction zone, reconstructing the thermal structure of the overriding plate. The results show concentrated stress at the upper part of the locked plate interface, extending into the Coastal and Western Cordilleras, with deeper stress zones correlating with seismicity. Uplift patterns align with long-term rates of 0.7–1 mm/yr. Cooling from flat-slab subduction strengthens the overriding plate, allowing far-field stress transmission and deformation. These findings provide insights into the tectonic processes driving stress accumulation, seismicity, and uplift along the Peruvian margin. Full article
(This article belongs to the Special Issue New Trends in Earthquake Engineering and Seismotectonics)
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Figure 1
<p>Map of the study area (Esri World Imagery basemap). The red line (A–A’) is the trace of the studied transect; the white lines are the 100 km contour line indicating the depth of the slab from Hayes et al. [<a href="#B15-geosciences-14-00257" class="html-bibr">15</a>]. The grey area shows the Nazca Ridge, while the light-grey area represents the subducted portion of the Nazca Ridge.</p> Full article ">Figure 2
<p>Geological map of the study area based on the 1:100,000 scale geological maps from INGEMMET database. Modified after Ciattoni et al. [<a href="#B34-geosciences-14-00257" class="html-bibr">34</a>]. The black line (A–A’) is the trace of the studied transect, and the white dots indicate the pseudowell location.</p> Full article ">Figure 3
<p>Sketch of the layers used in the analytical procedure (the parameters are listed in <a href="#geosciences-14-00257-t001" class="html-table">Table 1</a>).</p> Full article ">Figure 4
<p>Sketch of the FEM model, showing the layers used in modelling.</p> Full article ">Figure 5
<p>Detail of the FEM model, showing the constructed mesh pattern. The cells are of smaller size in areas that are more critical for modelling deformation and stress accumulation.</p> Full article ">Figure 6
<p>Computed geotherms for the 12 pseudo-wells.</p> Full article ">Figure 7
<p>The values of heat flow <span class="html-italic">Qs</span> (blue dots) are interpolated using a sixth-degree polynomial (solid blue line) to discern the trend across the crustal cross-section. A graphical comparison is made with the observed data (black dots) and their associated relative errors (black bars), taking into account the maximum and minimum variations in radiogenic heat production, heat flow, and slab dip during the heat-flow calculations (dashed blue lines). The isotherms are displayed in the section at intervals of 50 °C (dashed and solid black lines). For comparative purposes, the lithospheric section, provided by Ciattoni et al. [<a href="#B43-geosciences-14-00257" class="html-bibr">43</a>], is included in the background.</p> Full article ">Figure 8
<p>Modelled results for coseismic, interseismic, and cumulative vertical displacement.</p> Full article ">Figure 9
<p>Von Mises stress results. White fault segment is unlocked (i.e., free to slip).</p> Full article ">Figure 10
<p>(<b>a</b>) Equivalent total strain results. White fault segment is unlocked (i.e., free to slip). (<b>b</b>) Earthquake hypocenters of events with M<sub>w</sub> ≥ 5 that occurred in the last 40 years, projected from a 100 km wide region along the study section. Data from the USGS catalogue [<a href="#B22-geosciences-14-00257" class="html-bibr">22</a>] and ISC catalogue [<a href="#B71-geosciences-14-00257" class="html-bibr">71</a>].</p> Full article ">
34 pages, 40857 KiB  
Article
Application of the Coastal and Marine Ecological Classification Standard (CMECS) to Create Benthic Geologic Habitat Maps for Portions of Acadia National Park, Maine, USA
by Bryan Oakley, Brian Caccioppoli, Monique LaFrance Bartley, Catherine Johnson, Alexandra Moen, Cameron Soulagnet, Genevieve Rondeau, Connor Rego and John King
Geosciences 2024, 14(10), 256; https://doi.org/10.3390/geosciences14100256 - 28 Sep 2024
Viewed by 848
Abstract
The Coastal and Marine Ecological Classification Standard (CMECS) was applied to four portions of Acadia National Park, USA, focusing on intertidal rocky and tidal flat habitats. Side-scan sonar coupled with multi-phase echo sounder bathymetry are the primary data sources used to map the [...] Read more.
The Coastal and Marine Ecological Classification Standard (CMECS) was applied to four portions of Acadia National Park, USA, focusing on intertidal rocky and tidal flat habitats. Side-scan sonar coupled with multi-phase echo sounder bathymetry are the primary data sources used to map the seafloor, coupled with underwater video imagery and surface grab samples for grain size and macrofaunal analysis. The CMECS Substrate, Geoform, and Biotic components were effective in describing the study areas. However, integrating the CMECS components to define Biotopes was more challenging due to the limited number of grab samples available and because the dominant species within a given map unit is largely inconsistent. While Biotopes ultimately could not be defined in this study, working within the CMECS framework to create statistically significant biotopes revealed the complexity of these study areas that may otherwise have been overlooked. This study demonstrates the effectiveness of the CMECS classification, including the framework’s ability to be flexible in communicating information. Full article
(This article belongs to the Special Issue Progress in Seafloor Mapping)
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<p>Location map of the four field areas mapped in Acadia National Park. The crosshatch polygons show the extent of the National Park on Mount Desert Island and adjacent areas (NPS, 2019).</p> Full article ">Figure 2
<p>Side-scan sonar mosaics for each of the four study areas. (<b>A</b>) Thompson Island; (<b>B</b>) Compass Harbor; (<b>C</b>) Ship Harbor; (<b>D</b>) Frazer Creek.</p> Full article ">Figure 3
<p>Benthic depth zones of the four study areas. (<b>A</b>) Thompson Island; (<b>B</b>) Compass Harbor; (<b>C</b>) Ship Harbor; (<b>D</b>) Frazer Creek. Note the added subclass of the Shallow infralittoral zone (0 to 1 m MLLW) added to better emphasize the intertidal flats, particularly in Frazer Creek and at Thompson Island. The inner portion of Ship Harbor was not able to be tidally corrected.</p> Full article ">Figure 4
<p>Location of sediment samples attempted in the four study areas. Pie charts show the percentage of sand, silt, and clay for measured samples. Green dots show the location of samples where no sediment was recovered. All stations were imaged using underwater video imagery. Yellow lines show the location of video drifts. (<b>A</b>) Thompson Island; (<b>B</b>) Compass Harbor; (<b>C</b>) Ship Harbor; (<b>D</b>) Frazer Creek. See <a href="#geosciences-14-00256-f001" class="html-fig">Figure 1</a> for the locations. 2018 NAIP Image Basemap.</p> Full article ">Figure 5
<p>Example underwater video images collected from (See <a href="#geosciences-14-00256-f004" class="html-fig">Figure 4</a> for station locations). The Ponar sampler visible in panels (<b>A</b>,<b>C</b>–<b>E</b>) is 20 cm across. The white PVC frame in panels B and F is 25 cm across. A. Thompson Island station 16. Muddy sand with Blue Mussels (Mytilus edulis) and an isolated boulder. (<b>B</b>) Thompson Island drift 1. Gravelly sediment within a tidal channel. (<b>C</b>) Thompson Island station 4 Slightly gravelly fine sand with abundant blue mussels (Mytilus edulis). (<b>D</b>) Compass Harbor station 3. Gravelly sediment sheet with boulders and bedrock outcrop. (<b>E</b>) Compass Harbor station 4. Gravelly sediment with boulders. (<b>F</b>) Compass Harbor drift 4. Gravelly sand with abundant sand dollars (Echinarachnius parma).</p> Full article ">Figure 6
<p>CMECS Substrate (subgroup). (<b>A</b>) Thompson Island; (<b>B</b>) Compass Harbor; (<b>C</b>) Ship Harbor; (<b>D</b>) Frazer Creek. See <a href="#geosciences-14-00256-f001" class="html-fig">Figure 1</a> for the locations. 2018 NAIP Image Basemap.</p> Full article ">Figure 7
<p>CMECS Geoforms mapped for the four study areas. (<b>A</b>) Thompson Island; (<b>B</b>) Compass Harbor; (<b>C</b>) Ship Harbor; (<b>D</b>) Frazer Creek. See <a href="#geosciences-14-00256-f001" class="html-fig">Figure 1</a> for the locations. 2018 NAIP Image Basemap.</p> Full article ">Figure 8
<p>(<b>A</b>) Thompson Island side-scan sonar mosaic, showing the representative hydraulic dredge trails visible on the side-scan sonar mosaic (2018 NAIP Basemap). (<b>B</b>) Side-scan sonar mosaic showing cross-cutting hydraulic dredge trails (1) and faint (older) hydraulic dredge trails (2). The blotchy sonar return on the side-scan sonar mosaic (3) is produced by the presence of mussels.</p> Full article ">Figure 9
<p>Benthic geologic habitats of Compass Harbor; see <a href="#geosciences-14-00256-f001" class="html-fig">Figure 1</a> for the locations. 2018 NAIP Image Basemap.</p> Full article ">Figure 10
<p>Benthic geologic habitats of Frazer Creek; see <a href="#geosciences-14-00256-f001" class="html-fig">Figure 1</a> for the locations. 2018 NAIP Image Basemap.</p> Full article ">Figure 11
<p>Benthic geologic habitats of Ship Harbor; see <a href="#geosciences-14-00256-f001" class="html-fig">Figure 1</a> for the locations. 2018 NAIP Image Basemap.</p> Full article ">Figure 12
<p>Benthic geologic habitats of Thompson Island; see <a href="#geosciences-14-00256-f001" class="html-fig">Figure 1</a> for the locations. 2018 NAIP Image Basemap.</p> Full article ">Figure 13
<p>nMDS plot of macrofaunal community composition for each grab sample site (n = 30), excluding samples that contained 0–5 individuals (n = 13). The plot shows samples separated out according to the study area, indicating macrofaunal communities are more similar within a given study area and more distinct across study areas.</p> Full article ">Figure 14
<p>Resulting CMECS classification outputs for (<b>A</b>) Compass Harbor, (<b>B</b>) Ship Harbor, and (<b>C</b>) Frazer Creek. The map units are defined by the Geoform Component (Geoform classification), and the grab sample locations are defined by the Biotic Component (Biotic Community named according to dominant species). The development of Biotopes in these study areas was not possible due to an insufficient number of benthic macrofaunal samples.</p> Full article ">Figure 15
<p>Resulting CMECS classification outputs for Thompson Island. The map units are defined by the Geoform Component (Geoform classification), and the grab sample locations are defined by the Biotic Component (Biotic Community named according to dominant species). The development of Biotopes in this study area was not possible due to the inconsistency of dominant species among samples within a given map unit type.</p> Full article ">Figure 16
<p>Comparison of the Geoform units from this study (<b>A</b>,<b>C</b>) with those mapped Timson [<a href="#B4-geosciences-14-00256" class="html-bibr">4</a>] (<b>B</b>,<b>D</b>) for Thompson Island (<b>A</b>,<b>B</b>) and Ship Harbor (<b>C</b>,<b>D</b>).</p> Full article ">
16 pages, 757 KiB  
Review
Deterministic Physically Based Distributed Models for Rainfall-Induced Shallow Landslides
by Giada Sannino, Massimiliano Bordoni, Marco Bittelli, Claudia Meisina, Fausto Tomei and Roberto Valentino
Geosciences 2024, 14(10), 255; https://doi.org/10.3390/geosciences14100255 - 27 Sep 2024
Viewed by 484
Abstract
Facing global warming’s consequences is a major issue in the present times. Regarding the climate, projections say that heavy rainfalls are going to increase with high probability together with temperature rise; thus, the hazard related to rainfall-induced shallow landslides will likely increase in [...] Read more.
Facing global warming’s consequences is a major issue in the present times. Regarding the climate, projections say that heavy rainfalls are going to increase with high probability together with temperature rise; thus, the hazard related to rainfall-induced shallow landslides will likely increase in density over susceptible territories. Different modeling approaches exist, and many of them are forced to make simplifications in order to reproduce landslide occurrences over space and time. Process-based models can help in quantifying the consequences of heavy rainfall in terms of slope instability at a territory scale. In this study, a narrative review of physically based deterministic distributed models (PBDDMs) is presented. Models were selected based on the adoption of the infinite slope scheme (ISS), the use of a deterministic approach (i.e., input and output are treated as absolute values), and the inclusion of new approaches in modeling slope stability through the ISS. The models are presented in chronological order with the aim of drawing a timeline of the evolution of PBDDMs and providing researchers and practitioners with basic knowledge of what scholars have proposed so far. The results indicate that including vegetation’s effects on slope stability has raised in importance over time but that there is still a need to find an efficient way to include them. In recent years, the literature production seems to be more focused on probabilistic approaches. Full article
(This article belongs to the Section Natural Hazards)
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<p>Flow chart of the methodology. * One of the reviewed models, SOSlope, uses the Discrete Element Method, but the ISS was adopted for the geometry definition. ** With “similar approaches”, it is intended that the model was extending its previous version.</p> Full article ">Figure 2
<p>PBDDMs’ normalized number of citations obtained (derived by Google Scholar). Models are in chronological order [<a href="#B99-geosciences-14-00255" class="html-bibr">99</a>,<a href="#B102-geosciences-14-00255" class="html-bibr">102</a>].</p> Full article ">
15 pages, 5438 KiB  
Article
The Relationships between Greenstone Belts and the Kryvyi Rih–Kremenchuk Basin in the Middle Dnieper Domain of the Ukrainian Shield Revealed by Detrital Zircon
by Hennadii Artemenko, Leonid Shumlyanskyy, David Chew, Foteini Drakou, Bruno Dhuime, Hugo Moreira and Valeryi Butyrin
Geosciences 2024, 14(10), 254; https://doi.org/10.3390/geosciences14100254 - 27 Sep 2024
Viewed by 545
Abstract
Detrital zircons from two samples of metasandstones from the Lykhmanivka Syncline, Middle Dnieper Domain of the Ukrainian Shield (Skelevate Formation of the Kryvyi Rih Group), have been dated by the LA-ICP-MS U-Pb method. Metasandstones from the northern part of the syncline yield zircons [...] Read more.
Detrital zircons from two samples of metasandstones from the Lykhmanivka Syncline, Middle Dnieper Domain of the Ukrainian Shield (Skelevate Formation of the Kryvyi Rih Group), have been dated by the LA-ICP-MS U-Pb method. Metasandstones from the northern part of the syncline yield zircons belonging to four age groups: 3201 ± 12 Ma, 3089 ± 11 Ma, 2939 ± 8 Ma, and 2059 ± 4 Ma. All three Archean groups originated from similar rock types that crystallized at different times from the same mafic source (lower crust) with a 176Lu/177Hf ratio of about 0.020. In contrast, zircon from metasediments from the southern end of the Lykhmanivka Syncline fall within two age groups: 3174 ± 13 Ma, and 2038 ± 9 Ma. In terms of Hf isotope compositions, the detrital zircons from the two oldest age groups in both samples are very similar. The source area was dominated by rocks of the Auly Group (3.27–3.18 Ga) and the Sura Complex (3.17–2.94 Ga). The proportion of zircons dated at 2.07–2.03 Ga, which reflects the timing of metamorphism, is 5%. The metamorphic nature of the Paleoproterozoic zircon allows us to define the maximum depositional age of the metasandstones of the Lykhmanivka Syncline at ca. 2.9 Ga, which is in good agreement with the earlier results from the metaterrigenous rocks of the Kryvyi Rih–Kremenchuk Basin. Our data also indicate the local nature of sedimentation and the absence of significant transport and mixing of detrital material within the basin. Full article
(This article belongs to the Section Geochemistry)
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<p>(<b>A</b>) Simplified tectonic map of the Ukrainian Shield, modified after the tectonic map of the basement of the Ukrainian Shield [<a href="#B18-geosciences-14-00254" class="html-bibr">18</a>]. (<b>B</b>) Schematic geological map of the Middle Dnieper Domain of the Ukrainian Shield. I—Kryvyi Rih Basin; II—Lykhmanivka Syncline; III—Vysokopillya greenstone belt; IV—Verkhivtseve greenstone belt; V—Zhovta Richka structure; VI—East Hannivka Syncline. The locations of the drill holes sampled for detrital zircon dating are indicated. For the approximate ages of various stratigraphic units and intrusive complexes, see <a href="#geosciences-14-00254-f002" class="html-fig">Figure 2</a>. Sampling sites and corresponding maximum depositional ages: 1—this work; 2—[<a href="#B10-geosciences-14-00254" class="html-bibr">10</a>]; 3—[<a href="#B11-geosciences-14-00254" class="html-bibr">11</a>]; 4—[<a href="#B12-geosciences-14-00254" class="html-bibr">12</a>,<a href="#B13-geosciences-14-00254" class="html-bibr">13</a>]; 5—[<a href="#B14-geosciences-14-00254" class="html-bibr">14</a>]; 6—[<a href="#B15-geosciences-14-00254" class="html-bibr">15</a>]. (<b>C</b>) Schematic geological map of the southern part of the Kryvyi Rih Basin.</p> Full article ">Figure 2
<p>Schematic stratigraphy of the Middle Dnieper Domain of the Ukrainian Shield (after [<a href="#B9-geosciences-14-00254" class="html-bibr">9</a>], with authors’ corrections). The wavy lines indicate unconformities. The ages of the stratigraphic units are given according to [<a href="#B5-geosciences-14-00254" class="html-bibr">5</a>,<a href="#B10-geosciences-14-00254" class="html-bibr">10</a>,<a href="#B11-geosciences-14-00254" class="html-bibr">11</a>,<a href="#B12-geosciences-14-00254" class="html-bibr">12</a>,<a href="#B13-geosciences-14-00254" class="html-bibr">13</a>,<a href="#B19-geosciences-14-00254" class="html-bibr">19</a>,<a href="#B20-geosciences-14-00254" class="html-bibr">20</a>,<a href="#B21-geosciences-14-00254" class="html-bibr">21</a>].</p> Full article ">Figure 3
<p>Schematic logs of drillholes 20803 (Mykolo-Kozelsk area, [<a href="#B27-geosciences-14-00254" class="html-bibr">27</a>]) and 20586 (Vysokopillya area, [<a href="#B28-geosciences-14-00254" class="html-bibr">28</a>]).</p> Full article ">Figure 4
<p>U-Pb concordia diagram and age distribution diagrams plotted as probability density curves for detrital zircon from the metasandstone samples 87-551 and 87-222.</p> Full article ">Figure 5
<p>Hafnium isotope composition of zircon from two metasandstone samples. Panel (<b>A</b>) demonstrates variations in <sup>176</sup>Hf/<sup>177</sup>Hf<sub>(t)</sub> plotted against <sup>207</sup>Pb/<sup>206</sup>Pb in individual zircon grains, whereas panel (<b>B</b>) shows variations in εHf values calculated against the U-Pb ages of each zircon group (see <a href="#geosciences-14-00254-f004" class="html-fig">Figure 4</a>). The regression line with <sup>176</sup>Lu/<sup>177</sup>Hf = 0 reflects the evolution of hafnium isotopic composition due to a simple loss of radiogenic lead (zircons lying on this line have the same hafnium isotopic composition regardless of age). The regression line with <sup>176</sup>Lu/<sup>177</sup>Hf = 0.02 reflects the evolution of the hafnium isotopic composition in the 3.56 Ga old mafic source.</p> Full article ">Figure 6
<p>Variations in the Th/U ratio between zircons of different ages from the two metasandstone samples.</p> Full article ">
25 pages, 3229 KiB  
Review
Evaluation of Strength Anisotropy in Foliated Metamorphic Rocks: A Review Focused on Microscopic Mechanisms
by Umer Waqas, Mohsin Usman Qureshi, Shahab Saqib, Hafiz Muhammad Awais Rashid and Ali Murtaza Rasool
Geosciences 2024, 14(10), 253; https://doi.org/10.3390/geosciences14100253 - 26 Sep 2024
Viewed by 1091
Abstract
This review paper addresses the recent and past advancements in investigating the anisotropic behavior of foliated metamorphic rock strength subjected to uniaxial or triaxial compression loading, direct or indirect tensile loading, and shear loading. The experimental findings published in the literature show that [...] Read more.
This review paper addresses the recent and past advancements in investigating the anisotropic behavior of foliated metamorphic rock strength subjected to uniaxial or triaxial compression loading, direct or indirect tensile loading, and shear loading. The experimental findings published in the literature show that the strength of foliated rocks is significantly affected by varying the angle β between weak planes and major principal stress. A higher value of strength is reported at β = 0° or 90°; whereas a low strength value is noted at intermediate angles between β = 0° and 90°. The strength anisotropy depends on the degree of schistosity or gneissosity, which is the result of the preferred arrangement of phyllosilicate minerals under differential pressures. The failure of foliated rocks starts at the microscopic scale because of the dislocation slip, plastic kinking, and fracturing in phyllosilicate minerals such as mica. Tensile wing cracks at the tip of the mica propagate parallel to the deviatoric stress. Then, intergranular and intragranular shear-tensile cracks coalesce and lead to rock failure. The weak planes’ orientation controls the mode of failure such that tensile splitting, slip failure, and shear failure across foliations are observed at β = 0°–30°, β = 30°–60°, β = 60°–90° respectively. In the past, several attempts have been made to formulate failure criteria to estimate rock strength using different mathematical and empirical approaches. Over the years, the trend has shifted towards discontinuum modeling to simulate rock failure processes and to solve problems from laboratory to upscaled levels. Full article
(This article belongs to the Section Geomechanics)
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<p>Strength variation curve with orientation angle modified after [<a href="#B16-geosciences-14-00253" class="html-bibr">16</a>].</p> Full article ">Figure 2
<p>Variation in tensile strength with anisotropic index [<a href="#B35-geosciences-14-00253" class="html-bibr">35</a>].</p> Full article ">Figure 3
<p>Strength variations in anisotropic foliated rocks including slate [<a href="#B26-geosciences-14-00253" class="html-bibr">26</a>], phyllite [<a href="#B54-geosciences-14-00253" class="html-bibr">54</a>], schist [<a href="#B47-geosciences-14-00253" class="html-bibr">47</a>], and gneiss [<a href="#B55-geosciences-14-00253" class="html-bibr">55</a>] with confining pressures. The Anisotropic Index (σz/σx45z) of rock types can be defined as the ratio of differential stresses (σz = σ1–σ3) measured in the z direction and x45z orientations (σx45z) [<a href="#B48-geosciences-14-00253" class="html-bibr">48</a>]. The σx45z refers to the differential stress measured in a 45-degree inclined direction relative to the vertical (z) axis.</p> Full article ">Figure 4
<p>Cracks in the matrix of foliated rocks [<a href="#B58-geosciences-14-00253" class="html-bibr">58</a>].</p> Full article ">Figure 5
<p>Stress–strain curve evaluating the rock behavior [<a href="#B58-geosciences-14-00253" class="html-bibr">58</a>,<a href="#B62-geosciences-14-00253" class="html-bibr">62</a>].</p> Full article ">Figure 6
<p>Damage characteristics of mica and granular minerals (<b>a</b>) slipping, (<b>b</b>) kinking, (<b>c</b>) fracturing, and (<b>d</b>) transgranular cracking [<a href="#B58-geosciences-14-00253" class="html-bibr">58</a>].</p> Full article ">Figure 6 Cont.
<p>Damage characteristics of mica and granular minerals (<b>a</b>) slipping, (<b>b</b>) kinking, (<b>c</b>) fracturing, and (<b>d</b>) transgranular cracking [<a href="#B58-geosciences-14-00253" class="html-bibr">58</a>].</p> Full article ">Figure 7
<p>(<b>a</b>) Post-failure photographs illustrating the failure modes in foliated rocks at varying orientation angles. The rock cores of gneiss under compression tests show their plane of failure as indicated by the yellow lines [<a href="#B78-geosciences-14-00253" class="html-bibr">78</a>]. (<b>b</b>) The disc samples of phyllite subjected to Brazilian tests show their failure surface. The green line signifies the direction of foliations [<a href="#B44-geosciences-14-00253" class="html-bibr">44</a>].</p> Full article ">Figure 7 Cont.
<p>(<b>a</b>) Post-failure photographs illustrating the failure modes in foliated rocks at varying orientation angles. The rock cores of gneiss under compression tests show their plane of failure as indicated by the yellow lines [<a href="#B78-geosciences-14-00253" class="html-bibr">78</a>]. (<b>b</b>) The disc samples of phyllite subjected to Brazilian tests show their failure surface. The green line signifies the direction of foliations [<a href="#B44-geosciences-14-00253" class="html-bibr">44</a>].</p> Full article ">Figure 8
<p>The illustrations (<b>a</b>–<b>c</b>) show the failure modes simulated by DEM. Whereas illustrations (<b>d</b>–<b>f</b>) exhibit post-failure microcrack distribution [<a href="#B141-geosciences-14-00253" class="html-bibr">141</a>].</p> Full article ">
23 pages, 103115 KiB  
Article
Miocene Petit-Spot Basanitic Volcanoes on Cretaceous Alba Guyot (Magellan Seamount Trail, Pacific Ocean)
by Igor S. Peretyazhko, Elena A. Savina and Irina A. Pulyaeva
Geosciences 2024, 14(10), 252; https://doi.org/10.3390/geosciences14100252 - 25 Sep 2024
Viewed by 558
Abstract
New data obtained from core samples of two boreholes and dredged samples from the Alba Guyot in the Magellan Seamount Trail (MST), Western Pacific, including the 40Ar/39Ar age determinations of basanite, and the mineralogy of basanite, tuff, tuffite, mantle-derived inclusions [...] Read more.
New data obtained from core samples of two boreholes and dredged samples from the Alba Guyot in the Magellan Seamount Trail (MST), Western Pacific, including the 40Ar/39Ar age determinations of basanite, and the mineralogy of basanite, tuff, tuffite, mantle-derived inclusions in basanite and tuff (lherzolite xenolith and Ol, Cpx, and Opx xenocrysts), and calcareous nannofossil biostratigraphy, have implications for the guyot′s development and history. Volcanic units in the upper part of the Alba Guyot main edifice and its Oma Vlinder satellite, at sea depths between 3600 and 2200 m, were deposited during the Cretaceous 112 to 86 Ma interval. In the following ~60 myr, the Alba Guyot became partly submerged and denuded with the formation of a flat summit platform while the respective fragment of the Pacific Plate was moving to the Northern Hemisphere. Volcanic activity in the northeastern part of the guyot summit platform was rejuvenated in the Miocene (24–15 Ma) and produced onshore basanitic volcanoes and layers of tuff in subaerial and tuffite in shallow-water near-shore conditions. In the Middle-Late Miocene (10–6 Ma), after the guyot had submerged, carbonates containing calcareous nannofossils were deposited on the porous surfaces of tuff and tuffite. Precipitation of the Fe-Mn crust (Unit III) recommenced during the Pliocene–Pleistocene (<1.8 Ma) when the guyot summit reached favorable sea depths. The location of the MST guyots in the northwestern segment of the Pacific Plate near the Mariana Trench, along with the Miocene age and alkali-basaltic signatures of basanite, provide first evidence for petit-spot volcanism on the Alba Guyot. This inference agrees with the geochemistry of Cenozoic petit-spot basaltic rocks from the Pacific and Miocene basanite on the Alba Guyot. Petit-spot volcanics presumably originated from alkali-basaltic melts produced by decompression partial melting of carbonatized peridotite in the metasomatized oceanic lithosphere at the Lithosphere–Asthenosphere Boundary level. The numerous volcanic cones with elevations of up to 750 m high and 5.1 km in basal diameter, discovered on the Alba summit platform, provide the first evidence of voluminous Miocene petit-spot basanitic volcanism upon the Cretaceous guyots and seamounts of the Pacific. Full article
(This article belongs to the Section Geochemistry)
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<p>Bathymetry map of the Kocebu and Alba Guyots (<b>a</b>); enlarged fragments of the main Alba Guyot edifice (<b>b</b>), modified from Peretyazhko et al. [<a href="#B9-geosciences-14-00252" class="html-bibr">9</a>]. Panel (<b>b</b>) shows inferred faults (dotted lines) and cones of petit-spot volcanoes. Location of sampling sites (<a href="#geosciences-14-00252-t001" class="html-table">Table 1</a>): 15D21, 15D18, and 15D266 dredges; 15B13 and 15B17 boreholes. Bold lines in panels (<b>b</b>) are isobaths at 100 m intervals.</p> Full article ">Figure 2
<p>Biostratigraphy core sample 15B13. Calcareous nannofossils were found and identified in the Fe-Mn crust (<span class="html-italic">Unit III</span>, points 1 and 2) and in the tuff section at points 3, 4, and 5. Colored zones show age ranges for different samples based on extant periods for identified nannoplankton species. The Cenozoic time scale is based on Neogene (NN), Martini [<a href="#B16-geosciences-14-00252" class="html-bibr">16</a>], (CN), Okada and Bukry [<a href="#B22-geosciences-14-00252" class="html-bibr">22</a>], and Palaeogene (NP), Martini [<a href="#B16-geosciences-14-00252" class="html-bibr">16</a>], and (CP) Okada and Bukry [<a href="#B22-geosciences-14-00252" class="html-bibr">22</a>] zonal scales correlated to ages by Shumenko [<a href="#B24-geosciences-14-00252" class="html-bibr">24</a>]. Numbers 1 to 21 (NP/NN), and 1 to 15 (CP/CN) refer to biozone numbers.</p> Full article ">Figure 3
<p>Biostratigraphy core sample 15B17. Calcareous nannofossils were identified and their respective extant periods were determined for the Fe-Mn crust (<span class="html-italic">Unit III</span>, point 1); tuffite (point 2); carbonate inclusions in buried nodules (<span class="html-italic">Unit I-2b</span>, point 3); and carbonate inclusions at the contact between <span class="html-italic">Units I-1</span> and <span class="html-italic">I-2b</span> (point 4). Colored zones show age ranges for different samples. The Cenozoic time scale is based on zonal scales (see <a href="#geosciences-14-00252-f002" class="html-fig">Figure 2</a> for explanations).</p> Full article ">Figure 4
<p>Matrix fragments of basanite 15D266A (<b>a</b>–<b>c</b>), lherzolite xenolith 15D266B (<b>d</b>–<b>f</b>), and tuff 15B13 (<b>g</b>–<b>i</b>). Abbreviations stand for Ol = olivine, Opx = orthopyroxene, Cpx = clinopyroxene, Sp = spinel, Ti–Mag = Ti-magnetite, Gl = glass, Fe-Mn = ore crust, Pgn = palagonite, Phy = phyllipsite. Scale bars: 20 µm for (<b>a</b>–<b>c</b>), and 100 µm for (<b>e</b>–<b>i</b>).</p> Full article ">Figure 5
<p>(<b>a</b>–<b>d</b>): Major-element compositions of olivine and pyroxene in Alba Guyot volcanic and mantle 362 rocks. Mantle olivine arrays in (<b>a</b>) are quoted from Takahashi et al. [<a href="#B25-geosciences-14-00252" class="html-bibr">25</a>]. The TiO<sub>2</sub> vs. Mg# clinopyroxene classification shown in (<b>c</b>) is after Wass [<a href="#B26-geosciences-14-00252" class="html-bibr">26</a>].</p> Full article ">Figure 6
<p>Major element compositions of spinel-group minerals (<b>a</b>) in Alba Guyot volcanic and mantle rocks. Dashed lines in panels (<b>b</b>,<b>c</b>) contour the fields of oceanic spinel from the Mariana Trough, Mariana Trench, Parece Vela Basin, and Yap Trench, after Chen et al. [<a href="#B27-geosciences-14-00252" class="html-bibr">27</a>].</p> Full article ">Figure 7
<p>Total alkali vs. silica (TAS) diagram for volcanic rocks, after Le Bas et al. [<a href="#B28-geosciences-14-00252" class="html-bibr">28</a>] and Irvine and Baragar [<a href="#B29-geosciences-14-00252" class="html-bibr">29</a>]. Bulk compositions of basaltic rocks are shown for the Alba Guyot, after Peretyazhko et al. [<a href="#B9-geosciences-14-00252" class="html-bibr">9</a>]; NW Pacific and unusual petit-spot compositions are marked by triangles, after Hirano and Machida [<a href="#B30-geosciences-14-00252" class="html-bibr">30</a>], and Mikuni et al. [<a href="#B31-geosciences-14-00252" class="html-bibr">31</a>]. The data are plotted as the total 100 wt%.</p> Full article ">Figure 8
<p><sup>40</sup>Ar/<sup>39</sup>Ar dating of basanite sample 15D266A.</p> Full article ">
12 pages, 2536 KiB  
Article
Uncovering Interdecadal Pacific Oscillation’s Dominance in Shaping Low-Frequency Sea Level Variability in the South China Sea
by Bijoy Thompson, Pavel Tkalich, Daiane G. Faller and Johnson Zachariah
Geosciences 2024, 14(10), 251; https://doi.org/10.3390/geosciences14100251 - 25 Sep 2024
Viewed by 539
Abstract
The low-frequency sea level variability in the South China Sea (SCS) is examined using high-resolution regional ocean model simulations that span the last six decades. The analysis reveals interdecadal oscillations with a periodicity of 12–13 years as the dominant mode of sea level [...] Read more.
The low-frequency sea level variability in the South China Sea (SCS) is examined using high-resolution regional ocean model simulations that span the last six decades. The analysis reveals interdecadal oscillations with a periodicity of 12–13 years as the dominant mode of sea level variability in the SCS. The fluctuations in the Luzon Strait transport (LST) are identified as primary drivers of interannual to interdecadal sea level variability, rather than atmospheric forcing within the SCS. Fourier spectrum analysis is employed to investigate the association between SCS sea level variability and the Interdecadal Pacific Oscillation (IPO), using principal components of SCS sea surface height anomalies, wind stress curl, wind stress components, net short wave flux, as well as the LST and various climate indices. The variations in the SCS sea level are driven by the IPO, which modifies the LST and ocean heat content, impacting the steric sea level. Full article
(This article belongs to the Section Climate)
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Figure 1

Figure 1
<p>(<b>a</b>) Model domain and bathymetry in metres (GEBCO 2020). The locations of tide gauges used in the analysis are shown as red squares. SSH standard deviation (in metres) derived from the model (<b>b</b>,<b>c</b>) and satellite observations (<b>d</b>,<b>e</b>) for the period 1993–2022. The left panel displays the standard deviation including the seasonal cycle, while the right panel presents the estimation derived from 13-month low-pass filtered data.</p> Full article ">Figure 2
<p>SSH standard deviation (in metres) computed from the model for the period 1961–2022. The left panel displays the standard deviation including the seasonal cycle (<b>a</b>), while the right panel presents the estimation derived from 13-month low-pass filtered data (<b>b</b>).</p> Full article ">Figure 3
<p>Time series of sea surface height anomaly (in metres) from the model (red line), tide gauge (black line), and satellite observations (blue line) at selected stations within the model domain. The panels also display the correlations between the tide gauge and model (r<sub>TN</sub>) as well as between the satellite and model (r<sub>SN</sub>).</p> Full article ">Figure 4
<p>(<b>a</b>,<b>b</b>) Maps of regression coefficients of the model SSHA against the SCS SSHA PC1 and PC2, respectively. Maps of regression coefficients of the ORAS5 SSHA against the (<b>c</b>) SCS SSHA PC1, (<b>d</b>) Nino3.4 index, (<b>e</b>) PDO index, and (<b>f</b>) IPO Tripolar index over the eastern Indian–Pacific Ocean region. Normalised principal components and indices are used for the regression analysis. Units are in metres.</p> Full article ">Figure 5
<p>(<b>a</b>) Normalised Fourier amplitude spectrum of PC1 (black line) and PC2 (red line) of model SSHA in the SCS. (<b>b</b>) Normalised Fourier amplitude spectrum of Nino3.4 index (black line), IPO tripolar index (red line), PDO index (blue line), and DMI (purple line) for 1961 to 2022 period. (<b>c</b>) and (<b>d</b>) Normalised Fourier amplitude spectrum of PC1 and PC2, respectively, of wind stress curl (Tau_curl, black line), zonal wind stress (Taux, red line), and meridional wind stress curl (Tauy, blue line) over the SCS during 1961–2022. (<b>e</b>) Normalised Fourier amplitude spectrum of LST (black line), and the PC1 (red line) and PC2 (blue line) of Q<sub>SW</sub> averaged over the SCS. (<b>f</b>) SSHA (red line) averaged over the SCS and LST (black line) estimated in the section shown in <a href="#geosciences-14-00251-f001" class="html-fig">Figure 1</a>. (<b>g</b>) Q<sub>SW</sub> (black line) averaged over the SCS and Pacific North Equatorial Current Bifurcation Latitude (NECBL) (red line) estimated using the model SSHA using Equation (2) from [<a href="#B39-geosciences-14-00251" class="html-bibr">39</a>]. (<b>h</b>) NECBL (red line) and IPO Tripolar index (black line).</p> Full article ">
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