Nothing Special   »   [go: up one dir, main page]

You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (5,674)

Search Parameters:
Keywords = geophysics

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
13 pages, 33523 KiB  
Article
Mapping Sulphide Mineralization in the Hawiah Area Using Transient Electromagnetic Methods
by Panagiotis Kirmizakis, Abid Khogali, Konstantinos Chavanidis, Timothy Eatwell, Tomos Bryan and Pantelis Soupios
Minerals 2025, 15(2), 186; https://doi.org/10.3390/min15020186 - 17 Feb 2025
Viewed by 151
Abstract
The Arabian–Nubian Shield (ANS) hosts numerous volcanogenic massive sulphide (VMS) deposits formed in submarine volcanic settings and enriched by hydrothermal processes, making it a critical region for mineral exploration due to the types of deposits it hosts and its geological complexity. The Wadi [...] Read more.
The Arabian–Nubian Shield (ANS) hosts numerous volcanogenic massive sulphide (VMS) deposits formed in submarine volcanic settings and enriched by hydrothermal processes, making it a critical region for mineral exploration due to the types of deposits it hosts and its geological complexity. The Wadi Bidah Mineral Belt (WBMB), located within the Arabian Shield, contains over 30 polymetallic VMS occurrences associated with an island arc system active between 950 and 800 million years ago. Despite its mineral potential, the WBMB still needs to be explored, with limited geophysical studies to support resource evaluation. This study focuses on the Hawiah area, a prominent VMS site within the WBMB, to delineate subsurface mineralization using transient electromagnetic (TEM) methods. TEM surveys were conducted to characterize the conductivity structure and identify potential zones of sulphide mineralization. Data were processed and inverted to generate 1D, 2D, and 3D resistivity models, providing critical insights into the depth, geometry, and continuity of the mineralized zones based on the final 3D resistivity distribution. The results revealed distinct conductive (very low resistivity) anomalies, correlating with known surface gossans and inferred sulphide-rich layers, and extended these features into the subsurface. The integration of TEM results with geological and geochemical data highlights the effectiveness of this approach in detecting and mapping concealed mineral deposits in complex geological environments. This study advances the understanding of VMS systems in the WBMB and demonstrates the potential of TEM surveys as a key tool for mineral exploration in the Arabian Shield. Full article
(This article belongs to the Special Issue Novel Methods and Applications for Mineral Exploration, Volume III)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) The location of the Hawiah VMS deposit within the Arabian Peninsula, (<b>b</b>) geological map of WBMB depicting the location of Hawiah VMS deposits (modified from Beziat and Donzeau [<a href="#B20-minerals-15-00186" class="html-bibr">20</a>]), and (<b>c</b>) the study area, including key features such as the Crossroads Lode, Central Area, and Camp Lode.</p>
Full article ">Figure 2
<p>Generalized cross-section of the main oxidation state zones (oxide, transitional, and fresh) in the Hawiah VMS deposit, based on Eatwell [<a href="#B21-minerals-15-00186" class="html-bibr">21</a>] and Ashley [<a href="#B22-minerals-15-00186" class="html-bibr">22</a>].</p>
Full article ">Figure 3
<p>(<b>a</b>) Extended area of interest around the Hawiah deposit, (<b>b</b>) survey area with TEM stations marked by green squares, and (<b>c</b>) acquisition setup for each TEM station showing the arrangements of the transmitter (100 × 100 m) and receiver (10 × 10 m) loops for data collection.</p>
Full article ">Figure 4
<p>Geological cross-section shows the lithological unit distribution and mineralization within the Hawiah VMS deposit. The TEM profile (red line) highlights resistivity variations associated with the mineralized zones.</p>
Full article ">Figure 5
<p>Resistivity depth slices from the TEM survey show variations within depth intervals ranging from 0-300 m. Each panel (<b>a</b>–<b>f</b>) represents a specific depth interval, highlighting the resistivity’s lateral and vertical distribution. The color scale reflects resistivity values in ohm-meters (Ohm.m), where lower values (blue color) indicate conductive zones and higher values (red/orange colors) represent resistive zones.</p>
Full article ">Figure 6
<p>Petrographic images of core samples showing various lithological and mineralogical features under plane-polarized transmitted light. All fields of view are 0.5 to 2 mm across.</p>
Full article ">
21 pages, 4777 KiB  
Article
Foundations for an Operational Earthquake Prediction System
by Angelo De Santis, Gianfranco Cianchini, Loredana Perrone, Maurizio Soldani, Habib Rahimi and Homayoon Alimoradi
Geosciences 2025, 15(2), 69; https://doi.org/10.3390/geosciences15020069 - 17 Feb 2025
Viewed by 148
Abstract
Earthquake prediction is one of the most challenging enterprises of science. Any prediction system must be based on the search for a precursor appearing during the preparation phase of an earthquake in the ground, atmosphere, or ionosphere that can anticipate its occurrence. We [...] Read more.
Earthquake prediction is one of the most challenging enterprises of science. Any prediction system must be based on the search for a precursor appearing during the preparation phase of an earthquake in the ground, atmosphere, or ionosphere that can anticipate its occurrence. We present methods to detect potential pre-earthquake anomalies. In particular, we show the analysis of lithospheric, atmospheric, and ionospheric data and the detection of anomalies under specific criteria. When we apply these methods retrospectively, we find that their accuracy goes from 69% to 83%. The combination of two or more methods is expected to improve the accuracy. Full article
(This article belongs to the Special Issue Earthquake Hazard Modelling)
Show Figures

Figure 1

Figure 1
<p>The <span class="html-italic">b</span>-value (bold curve with its statistical uncertainty given with dotted upper and lower curves) as estimated before and after the occurrence of the 2022 M6.8 Luding earthquake (adapted from [<a href="#B29-geosciences-15-00069" class="html-bibr">29</a>]). The pink rectangle depicts the <span class="html-italic">b</span>-value anomalous pattern. The corresponding time interval of the rectangle depicts the beginning of the <span class="html-italic">b</span>-value decrease, while it ends with the EQ occurrence.</p>
Full article ">Figure 2
<p>Cumulative reduced Benioff strain (in blue when far from the main fault, in red when close to the fault) for the 2019 M7.2 Kermadec Islands earthquake (adapted from [<a href="#B42-geosciences-15-00069" class="html-bibr">42</a>]). The main anomaly can be considered the point where the acceleration begins, detected automatically by the fitting algorithm, when C &gt; 0.6 (in this case, June 2018). The diagram at the bottom shows the temporal distribution of earthquake magnitudes.</p>
Full article ">Figure 3
<p>SKT for the case study of the M7.1 Ridgecrest (6 July 2019) earthquake [<a href="#B44-geosciences-15-00069" class="html-bibr">44</a>], showing the mean value of the historical background (blue trend), with 1 (light blue), 1.5 (green), and 2 (yellow) standard deviation bands. Single values with light blue circles exceed two standard deviations, while the red circle represents a pair of values surpassing the two standard deviations (persistent anomaly).</p>
Full article ">Figure 4
<p>The anomalies detected by the ionosonde provide a linear relationship between the logarithm of ΔT∙R and EQ magnitude M. ΔT is the time in advance in days of the ionosonde anomaly, and R is the distance in km between the ionosonde and EQ epicenter. The correlation coefficient r of the linear fit is very high (<span class="html-italic">r</span> = 0.96) [<a href="#B45-geosciences-15-00069" class="html-bibr">45</a>].</p>
Full article ">Figure 5
<p>The anomalies detected by the ionosonde provide a linear relationship between the logarithm of ΔT and EQ magnitude M. ΔT is the time in advance in days of the ionosonde anomaly. The correlation coefficient r of the linear fit is very high (<span class="html-italic">r</span> = 0.97) [<a href="#B45-geosciences-15-00069" class="html-bibr">45</a>].</p>
Full article ">Figure 6
<p>An example of anomaly detection (evidenced by a red circle) from Swarm satellite ten days before the 7 November 2019 M5.9 Iran EQ. <span class="html-italic">R</span> is the Dobrovolsky radius of 344 km. From left: residuals of <span class="html-italic">X</span>, <span class="html-italic">Y</span>, and <span class="html-italic">Z</span> magnetic field components, together with the total intensity (indicated as dBx, dBy, dBz, and dF) and geographical map with the epicenter as a green star. In the latter, the red line is the satellite orbit, as projected at the Earth’s surface, with satellite direction as shown by the black arrow. Please note that the vertical axes for the magnetic field components are geomagnetic latitudes, while that for the geographical map are geographic latitudes.</p>
Full article ">Figure 7
<p>General patterns of the anomalies before four earthquakes: (<b>a</b>) 5 September 2022 M6.8 Luding EQ [<a href="#B29-geosciences-15-00069" class="html-bibr">29</a>]; (<b>b</b>) 9 November 2022 M5.7 Fano EQ [<a href="#B48-geosciences-15-00069" class="html-bibr">48</a>]; (<b>c</b>) 6 February 2023 M7.7 Turkey EQ [<a href="#B49-geosciences-15-00069" class="html-bibr">49</a>]; (<b>d</b>) 22 January 2024 M7.0 Wushi EQ. In two cases (i.e., (<b>b</b>,<b>c</b>)), there is also an indication of the type of anomaly, which generally goes from the lithosphere to atmosphere and ionosphere. The red curve is an exponential fit. In (<b>c</b>), there is also the power-law fit in black.</p>
Full article ">Figure 8
<p>(<b>a</b>): Cumulative number of anomalies in their chronological order during the last 90 days before the 2023 Turkey EQ with C-factor = 0.5. (<b>b</b>): Cumulative number of shuffled anomalies with <span class="html-italic">C-factor</span> = 1.</p>
Full article ">Figure 9
<p>EQs (red circles) occurring in Greece in 2003–2015 within 350 km from the ionosonde of Athens (yellow star). Adapted from [<a href="#B52-geosciences-15-00069" class="html-bibr">52</a>].</p>
Full article ">Figure 10
<p>Map of earthquakes (stars) in Central Italy between July and October, from 1994 to 2016 (23 years), occurring in the selected (red) area (red stars) and in a close larger (black) area (black stars), whose effects can be detected based on the temperature in the red area (adapted from Figure 11 of [<a href="#B55-geosciences-15-00069" class="html-bibr">55</a>]).</p>
Full article ">
15 pages, 3219 KiB  
Article
Earthquake Forecasting Based on b Value and Background Seismicity Rate in Yunnan Province, China
by Yuchen Zhang, Rui Wang, Haixia Shi, Miao Miao, Jiancang Zhuang, Ying Chang, Changsheng Jiang, Lingyuan Meng, Danning Li, Lifang Liu, Youjin Su, Zhenguo Zhang and Peng Han
Entropy 2025, 27(2), 205; https://doi.org/10.3390/e27020205 - 15 Feb 2025
Viewed by 254
Abstract
Characterized by frequent earthquakes and a dense population, Yunnan Province, China, faces significant seismic hazards and is a hot place for earthquake forecasting research. In a previous study, we evaluated the performance of the b value for 5-year seismic forecasting during 2000–2019 and [...] Read more.
Characterized by frequent earthquakes and a dense population, Yunnan Province, China, faces significant seismic hazards and is a hot place for earthquake forecasting research. In a previous study, we evaluated the performance of the b value for 5-year seismic forecasting during 2000–2019 and made a forward prediction of M ≥ 5.0 earthquakes in 2020–2024. In this study, with the forecast period having passed, we first revisit the results and assess the forward prediction performance. Then, the background seismicity rate, which may also offer valuable long-term forecasting information, is incorporated into earthquake prediction for Yunnan Province. To assess the effectiveness of the prediction, the Molchan Error Diagram (MED), Probability Gain (PG), and Probability Difference (PD) are employed. Using a 25-year catalog, the spatial b value and background seismicity rate across five temporal windows are calculated, and 86 M ≥ 5.0 earthquakes as prediction samples are examined. The predictive performance of the background seismicity rate and b value is comprehensively tested and shown to be useful for 5-year forecasting in Yunnan. The performance of the b value exhibits a positive correlation with the predicted earthquake magnitude. The synergistic effect of combining these two predictors is also revealed. Finally, using the threshold corresponding to the maximum PD, we integrate the forecast information of background seismicity rates and the b value. A forward prediction is derived for the period from January 2025 to December 2029. This study can be helpful for disaster preparedness and risk management in Yunnan Province, China. Full article
(This article belongs to the Special Issue Time Series Analysis in Earthquake Complex Networks)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) The <span class="html-italic">b</span> value from January 2015 to December 2019 and earthquakes with M ≥ 5.0 from January 2020 to December 2024. The dot and star are scaled to the magnitude. (<b>b</b>) Temporal distribution of earthquakes in Yunnan Province from January 2000 to December 2024. (<b>c</b>) Temporal distribution of earthquakes in Yunnan Province from January 2020 to December 2024. (<b>d</b>) The MED of forecast performance based on the <span class="html-italic">b</span> value in (<b>a</b>). The marked numbers are the serial numbers in <a href="#entropy-27-00205-t001" class="html-table">Table 1</a>, and the size of the cross markers is scaled to the magnitude.</p>
Full article ">Figure 2
<p>The <span class="html-italic">b</span> value and background seismicity rate. (<b>a</b>–<b>e</b>) <span class="html-italic">b</span> value; (<b>f</b>,<b>j</b>) background seismicity rate. Results in (<b>a</b>,<b>f</b>) using catalog in 2000–2004 and forecasting moderate–large earthquakes in 2005–2009; (<b>b</b>,<b>g</b>) using catalog in 2005–2009 and forecasting moderate–large earthquakes in 2010–2014; (<b>c</b>,<b>h</b>) using catalog in 2010–2014 and forecasting moderate–large earthquakes in 2015–2019; (<b>d</b>,<b>i</b>) using catalog in 2015–2019 and forecasting moderate–large earthquakes in 2020–2024; (<b>e</b>,<b>j</b>) using catalog in 2020–2024. A dot represents an earthquake with 5.0 ≤ M &lt; 5.5. A star represents an earthquake with M ≥ 5.5. The sizes of the dots and stars are scaled to magnitude.</p>
Full article ">Figure 3
<p>Forecast performance based on <span class="html-italic">b</span> value and background seismicity rate during 2005–2024. (<b>a</b>–<b>c</b>) show the results of earthquakes with M ≥ 5.5. (<b>a</b>) MED; (<b>b</b>) <span class="html-italic">PG</span>; (<b>c</b>) <span class="html-italic">PD</span>. (<b>d</b>–<b>f</b>) are the results of earthquakes with M ≥ 5.0. (<b>d</b>) MED; (<b>e</b>) <span class="html-italic">PG</span>; (<b>f</b>) <span class="html-italic">PD</span>. The number of earthquake samples is <math display="inline"><semantics> <mrow> <mo>=</mo> <mn>29</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>M</mi> <mo>≥</mo> <mn>5.0</mn> </mrow> </msub> <mo>=</mo> <mn>86</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 4
<p>The variation in forecast performance with earthquake magnitude. (<b>a</b>) Variation in maximum <span class="html-italic">PG</span> with the forecast magnitude; (<b>b</b>) variation in maximum <span class="html-italic">PD</span> with the forecast magnitude; (<b>c</b>) variation in <span class="html-italic">S</span> with the forecast magnitude.</p>
Full article ">Figure 5
<p>Forecast performance by combining <span class="html-italic">b</span> value and background seismicity rate during 2005–2024. The x-axis is the alarming rate of background seismicity corresponding to <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>t</mi> <mi>h</mi> <mi>r</mi> </mrow> <mrow> <mi>μ</mi> </mrow> </msub> </mrow> </semantics></math>, and the y-axis is the alarming rate of the <span class="html-italic">b</span> value corresponding to <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>t</mi> <mi>h</mi> <mi>r</mi> </mrow> <mrow> <mi>b</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>a</b>) <span class="html-italic">PG</span> for M ≥ 5.5 earthquakes; (<b>b</b>) <span class="html-italic">PD</span> for M ≥ 5.5 earthquakes; (<b>c</b>) <span class="html-italic">PG</span> for M ≥ 5.0 earthquakes; (<b>d</b>) <span class="html-italic">PD</span> for M ≥ 5.0 earthquakes. The location of the maximum value (<span class="html-italic">PG</span> or <span class="html-italic">PD</span>) in each figure is marked with dots and detailed in <a href="#entropy-27-00205-t002" class="html-table">Table 2</a>. The cross in (<b>b</b>) is located at the alarming rate corresponding to the maximum <span class="html-italic">PD</span> in <a href="#entropy-27-00205-f003" class="html-fig">Figure 3</a>c.</p>
Full article ">Figure 6
<p>Alarmed regions for the period from January 2025 to December 2029 based on <span class="html-italic">b</span> value and background seismicity rate obtained during 2020–2024. (<b>a</b>) Alarmed area based on <span class="html-italic">b</span> value and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>t</mi> <mi>h</mi> <mi>r</mi> <mo>_</mo> <mi>P</mi> <mi>D</mi> </mrow> <mrow> <mi>b</mi> </mrow> </msub> </mrow> </semantics></math>, with 0.38 alarming rate; (<b>b</b>) alarmed area based on background seismicity rate and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>t</mi> <mi>h</mi> <mi>r</mi> <mo>_</mo> <mi>P</mi> <mi>D</mi> </mrow> <mrow> <mi>μ</mi> </mrow> </msub> </mrow> </semantics></math>, with 0.42 alarming rate; (<b>c</b>) alarmed area based on <span class="html-italic">b</span> value and background seismicity rate, with 0.20 alarming rate. The red edge squares show the alarmed grid cells.</p>
Full article ">
21 pages, 20678 KiB  
Article
Estimation of Equivalent Pore Aspect Ratio in Rock Physics Models and Validation Using Digital Rocks
by Luiz Eduardo Queiroz, Dario Grana, Celso Peres Fernandes, Tapan Mukerji, Leandro Passos de Figueiredo and Iara Frangiotti Mantovani
Geosciences 2025, 15(2), 67; https://doi.org/10.3390/geosciences15020067 - 15 Feb 2025
Viewed by 145
Abstract
Complex pore structures with multiple inclusions challenge the predictive accuracy of rock physics models. This study introduces a novel method for estimating a single equivalent pore aspect ratio that optimizes rock physics model predictions by minimizing discrepancies with experimental measurements in porous rocks [...] Read more.
Complex pore structures with multiple inclusions challenge the predictive accuracy of rock physics models. This study introduces a novel method for estimating a single equivalent pore aspect ratio that optimizes rock physics model predictions by minimizing discrepancies with experimental measurements in porous rocks with multiple inclusions with variable aspect ratios and proportions. The proposed methodology uses digital rock physics numerical simulations for validation. A comparative analysis is conducted between the equivalent aspect ratio derived from optimized rock physics models, numerical simulations, and the aspect ratio distribution estimated from digital rock samples. The approach is tested on both synthetic and real core samples, demonstrating its robustness and applicability to field data, including core samples and well log data. The validation results confirm that the method enhances predictive accuracy and offers a versatile framework for addressing pore complexity in subsurface rock formations. Full article
(This article belongs to the Section Geophysics)
Show Figures

Figure 1

Figure 1
<p>Sensitivity of equivalent pore aspect ratio (EPAR) to RPM parameters. The EPAR values are obtained by optimizing the aspect ratio value of a single inclusion. The EPAR values are plotted as function of the volume fraction of the inclusions with aspect ratio <math display="inline"><semantics> <msub> <mi>α</mi> <mn>2</mn> </msub> </semantics></math>. Their variations are shown with respect to multiple values of the following parameters: porosity <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math> (<b>a</b>), matrix bulk modulus <math display="inline"><semantics> <msub> <mi>K</mi> <mi>m</mi> </msub> </semantics></math> (<b>b</b>), matrix shear modulus <math display="inline"><semantics> <msub> <mi>μ</mi> <mi>m</mi> </msub> </semantics></math> (<b>c</b>), aspect ratio of inclusion 1 <math display="inline"><semantics> <msub> <mi>α</mi> <mn>1</mn> </msub> </semantics></math> (<b>d</b>), and aspect ratio of inclusion 2 <math display="inline"><semantics> <msub> <mi>α</mi> <mn>2</mn> </msub> </semantics></math> (<b>e</b>). In each plot, one parameter varies, whereas the others are kept constant and equal to the reference parameters.</p>
Full article ">Figure 2
<p>Equivalent pore aspect ratio (EPAR) for porous medium with inclusions of aspect ratios <math display="inline"><semantics> <msub> <mi>α</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>α</mi> <mn>2</mn> </msub> </semantics></math> and variable volumetric proportions for different rock physics models. The colored lines represent predictions for different values of (<b>a</b>) porosity, (<b>b</b>) <math display="inline"><semantics> <msub> <mi>α</mi> <mn>2</mn> </msub> </semantics></math>, and (<b>c</b>) <math display="inline"><semantics> <msub> <mi>α</mi> <mn>1</mn> </msub> </semantics></math>. In each plot, one parameter varies, whereas the others are kept constant and equal to the reference parameters.</p>
Full article ">Figure 3
<p>EPAR for porous rocks with 3 inclusion types: (<b>a</b>) EPAR variations for variable inclusion proportions and (<b>b</b>) EPAR variations in limiting cases with two inclusions only.</p>
Full article ">Figure 4
<p>Example of synthetic digital sample with ellipsoidal inclusions.</p>
Full article ">Figure 5
<p>Comparison between RPM predictions and DRP numerical simulations for synthetic digital images with constant aspect ratio for bulk and shear moduli. The stars represent the values of the numerical simulations, and the lines represent the RPM predictions.</p>
Full article ">Figure 6
<p>Relative error between RPM predictions and DRP numerical simulations for synthetic digital images for bulk and shear moduli.</p>
Full article ">Figure 7
<p>Comparison between RPM predictions and DRP numerical simulations for synthetic digital images with two inclusion types with aspect ratios <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics></math> for bulk and shear moduli. The stars represent the values of the numerical simulations, and the lines represent the RPM predictions.</p>
Full article ">Figure 8
<p>Bulk and shear moduli calculated using DRP numerical simulations in GeoDict. The stars represent the calculated porosity and elastic moduli of each sub-sample; the colors represent each sample type. The dashed-dotted lines represent the RPM predictions assuming different mineralogical compositions consistent with the samples for three different values of aspect ratios equal to 0.1, 0.2, and 0.3.</p>
Full article ">Figure 9
<p>Estimated EPAR of digital images of 80 sub-samples for four RPMs. Each color represents a different rock type.</p>
Full article ">Figure 10
<p>Estimated bulk and shear moduli of digital images of 80 sub-samples for four RPMs. Circles indicate values of elastic moduli numerically calculated using DRP; crosses indicate elastic moduli calculated using RPMs with the calculated EPARs. Each color represents a different rock type.</p>
Full article ">Figure 11
<p>Aspect ratio distribution calculated from digital image analysis: on the (<b>left</b>), we show the distribution of aspect ratios; on the (<b>right</b>), we show the volume-weighted distribution of aspect ratios for sample BVE from lower Barra Velha formation (blue histograms) and sample ITP from Itapema formation (orange histograms).</p>
Full article ">Figure 12
<p>Pore space separation into individual objects (3D and 2D views) or sample BVE from lower Barra Velha formation (<b>left</b>) and sample ITP from Itapema formation (<b>right</b>). Each color represents an object considered as an individual pore.</p>
Full article ">Figure 13
<p>Measured and computed log data. From (<b>left</b>) to (<b>right</b>): porosity, estimated EPAR, and bulk and shear moduli. Black lines represent well logs, blue lines represent RPM predictions using EPAR, and golden lines represent EPAR mean values for each formation. The background colors represent stratigraphic zones corresponding to the intervals upper and lower Barra Velha and Itapema, respectively.</p>
Full article ">Figure 14
<p>Crossplot of bulk modulus versus porosity for well log data. The smaller dots represent well log measurements, the larger dots represent the laboratory measurements in core samples, and the colored lines represent the rock physics model for different values of the aspect ratio. Two thin sections from core samples illustrate the different pore structure and shape in the stratigraphic zones. The colors represent three stratigraphic zones corresponding to the intervals upper and lower Barra Velha and Itapema.</p>
Full article ">
18 pages, 2607 KiB  
Article
A Multivariate and Geographic-Information-System Approach to Assess Environmental and Health Hazards of Fe, Cr, Zn, Cu, and Pb in Agricultural Soils of Western Saudi Arabia
by Hassan Alzahrani, Abdelbaset S. El-Sorogy, Abdulaziz G. Alghamdi, Zafer Alasmary and Thawab M. R. Albugami
Sustainability 2025, 17(4), 1610; https://doi.org/10.3390/su17041610 - 15 Feb 2025
Viewed by 184
Abstract
This study evaluates the environmental and health hazards associated with the presence of Fe, Cr, Zn, Cu, and Pb in agricultural soils from the Makkah region in western Saudi Arabia. Soil samples were collected from 32 farms predominantly cultivating dates and vegetables and [...] Read more.
This study evaluates the environmental and health hazards associated with the presence of Fe, Cr, Zn, Cu, and Pb in agricultural soils from the Makkah region in western Saudi Arabia. Soil samples were collected from 32 farms predominantly cultivating dates and vegetables and analyzed for heavy metals (HMs) using inductively coupled plasma-atomic emission spectrometry (ICP-AES). Multivariate statistical analysis, Geographic Information Systems (GIS), and various contamination indices were employed. The average HM concentrations were arranged in descending order as follows: Fe (35.138 mg/kg), Zn (69.59 mg/kg), Cu (55.13 mg/kg), Cr (47.88 mg/kg), and Pb (6.09 mg/kg). Contamination indices indicated considerable enrichment of Cu and deficient to minimal enrichment for the other HMs, though a few individual samples showed higher enrichment factor (EF) values. Risk assessments revealed a low-level risk associated with HMs in Makkah soils. Multivariate analyses suggested that the HMs primarily originated from natural geological processes, with anthropogenic contributions particularly evident for Cu. Hazard index (HI) values ranged from 0.0003 to 0.0691 for adults and 0.003 to 0.6438 for children, remaining below the threshold of 1.0, which indicates no significant non-carcinogenic risk. Lifetime cancer risk estimates for Pb were below 1 × 10−6, while those for Cr ranged from 1 × 10−6 to 1 × 10−4, indicating tolerable carcinogenic risk levels with a few exceptions for Cr in children. This study is significant as it provides critical baseline data on HM contamination in agricultural soils in the Makkah region, offering insights into natural and anthropogenic contributions to soil pollution. The findings contribute to the broader understanding of environmental risk assessments and serve as a foundation for developing sustainable agricultural practices and targeted mitigation strategies to minimize health risks in regions with similar environmental conditions. Full article
Show Figures

Figure 1

Figure 1
<p>Location map of the study area and sampling sites from agricultural soils of western Saudi Arabia.</p>
Full article ">Figure 2
<p>Spatial distribution of HHMs per sample locations in Makkah agricultural soil.</p>
Full article ">Figure 3
<p>Spatial distribution of HI for Cr, Fe, and Zn for children and adults per sampled location in Makkah agricultural soil.</p>
Full article ">Figure 4
<p>Spatial distribution of HI for Cu, and Pb for children and adults per sampled location in Makkah agricultural soil.</p>
Full article ">Figure 5
<p>Spatial distribution of LCR for Cr, and Pb per sampled location in Makkah agricultural soil.</p>
Full article ">
22 pages, 13790 KiB  
Article
A Non-Destructive Search for Holocaust-Era Mass Graves Using Ground Penetrating Radar in the Vidzgiris Forest, Alytus, Lithuania
by Philip Reeder and Harry Jol
NDT 2025, 3(1), 5; https://doi.org/10.3390/ndt3010005 - 14 Feb 2025
Viewed by 231
Abstract
The non-destructive geophysical testing method ground penetrating radar (GPR), along with satellite image and air photo assessment, a review of the existing literature sources, and Holocaust survivor testimony, was used to document the location of potential mass graves in Alytus, Lithuania. In World [...] Read more.
The non-destructive geophysical testing method ground penetrating radar (GPR), along with satellite image and air photo assessment, a review of the existing literature sources, and Holocaust survivor testimony, was used to document the location of potential mass graves in Alytus, Lithuania. In World War II, six million Jews were murdered, as were as many as five million other victims of Nazi Germany’s orchestrated persecution. In the summer of 1941, 8030 Jews (4.70 percent of Lithuania’s Jewish population) lived in Alytus County, where the town of Alytus is located. It is estimated that over 8000 Jews were murdered in Alytus County, including nearly the entire Jewish population of the town of Alytus. The murder of Jews from Alytus County accounts for approximately 4.2% of the total number of Lithuanian Jews killed in the Holocaust. Survivor testimony indicates that several thousand Jews from both the town and county were murdered and buried in the Vidzgiris Forest about 1000 m from the town center. In 2022, field reconnaissance at locations in the forest, which appeared to be disturbed in a 1944 German Luftwaffe air photograph, indicated that these disturbances were associated with natural geomorphic processes and not the Holocaust. Analysis of GPR data that was collected using a pulseEKKO Pro 500-megahertz groundpenetrating radar (GPR) system in 2022 in the vicinity of monuments erected in the forest to memorialize mass graves indicates that no mass graves were directly associated with these monuments. The 1944 air photograph contained two roads that traversed through and abruptly ended in the forest, which was the impetus for detailed field reconnaissance in that area. A segment of a 150 m long linear surface feature found in the forest was assessed using GPR, and based on the profile that was generated, it was determined that this feature is possibly a segment of a much more extensive mass grave. Testimony of a Holocaust survivor stated that as many as three burial trenches exist in this portion of the forest. Additional research using non-destructive GPR technology, air photograph and satellite image assessment, and the existing literature and testimony-based data are required for the Vidzgiris Forest to better define these and other potential mass graves and other Holocaust-related features. Full article
Show Figures

Figure 1

Figure 1
<p>The location of Alytus, Lithuania.</p>
Full article ">Figure 2
<p>Part of the research team collecting GPR data in the summer of 2022 near one of the nine pyramid-shaped monuments in the Vidzgiris Forest.</p>
Full article ">Figure 3
<p>The location of the seven GPR grids that were established as part of this project, placed on a 2022 Google Earth satellite image. The red-colored grids contain data anomalies.</p>
Full article ">Figure 4
<p>GPR grid 4 location looking longitudinally down a potential trench (<b>left</b>) and actively collecting GPR and topographic data along this grid (<b>right</b>).</p>
Full article ">Figure 5
<p>Base map image for Storyteller for Lithuania with the Alytus area indicated within the blue rectangle.</p>
Full article ">Figure 6
<p>A Storyteller image zoomed in on Alytus showing the 1944 air photographs where coverage exists. German air photo coverage ends just east of the central portion of the city of Alytus.</p>
Full article ">Figure 7
<p>Comparison of locations on the 2018 satellite image (<b>A</b>) and the 1944 air photo (<b>B</b>).</p>
Full article ">Figure 8
<p>GPR grid 1 (<b>A</b>) data slice (depth from 0.75 to 0.80 m) with anomalous features in the red box and (<b>B</b>) cross-sectional profile data with anomalous features in the blue rectangle.</p>
Full article ">Figure 9
<p>GPR grid 5 (<b>A</b>) data slice (depth from 0.45 to 0.50 m) with anomalous features in the red rectangle and (<b>B</b>) cross-sectional profile data with anomalous features in the blue rectangle.</p>
Full article ">Figure 10
<p>GPR grid 5 (<b>A</b>) data slice (depth from 1.55 to 1.60 m) with anomalous features in the red box and (<b>B</b>) cross-sectional profile data with anomalous features in the blue rectangle.</p>
Full article ">Figure 11
<p>GPR grid 4 (<b>A</b>) data slice (depth from 0.45 to 0.50 m) with anomalous features in the yellow rectangle and (<b>B</b>) cross-sectional profile data with anomalous features in the yellow box.</p>
Full article ">Figure 12
<p>GPR grid 4 (<b>A</b>) data slice (depth from 1.75 to 1.80 m) with anomalous features in the white box and (<b>B</b>) cross-sectional profile data with anomalous features in the white rectangle.</p>
Full article ">Figure 13
<p>The area of Vidzgiris Forest that potentially contains trenches that hold the remains of Jews murdered and buried in the forest.</p>
Full article ">Figure 14
<p>The remains of two cement objects in the Vidzgiris Forest that may be the remains of a gate near the feature that is interpreted to be a possible burial trench.</p>
Full article ">Figure 15
<p>ESRI Storyteller image with the 1944 air photo overlain on a 2018 satellite image, with the roads that extend east from the forest road, and the area that contains the three potential burial trenches and the GPR grid over the main trench, highlighted.</p>
Full article ">
31 pages, 55875 KiB  
Article
Ranked Mappable Criteria for Magmatic Units: Systematization of the Ossa-Morena Zone Rift-Related Alkaline Bodies
by José Roseiro, Noel Moreira, Daniel de Oliveira, Marcelo Silva, Luis Eguiluz and Pedro Nogueira
Minerals 2025, 15(2), 174; https://doi.org/10.3390/min15020174 - 13 Feb 2025
Viewed by 385
Abstract
The Ossa-Morena Zone (SW Iberian Massif) hosts the largest set of Cambro–Ordovician alkaline magmatic plutons related to the Palaeozoic rifting of the northern Gondwana margin so far described. An organized framework for their classification at different scales is proposed through data-driven ranks based [...] Read more.
The Ossa-Morena Zone (SW Iberian Massif) hosts the largest set of Cambro–Ordovician alkaline magmatic plutons related to the Palaeozoic rifting of the northern Gondwana margin so far described. An organized framework for their classification at different scales is proposed through data-driven ranks based on their distinctive petrological features relative to other rift-related magmatic rocks found throughout western Europe. The classification method aims to enhance geological mapping at different scales, regional- and continental-scale correlations, and, as such, facilitate the petrogenetic interpretation of this magmatism. The hierarchical scheme, from highest to lowest rank, is as follows: rank-1 (supersuite) assembles rocks that have distinctive characteristics from other magmatic units emplaced in the same magmatic event; rank-2 (suite) categorizes the units based on their major textural features, indicating if the body is plutonic, sub-volcanic, or a strongly deformed magmatic-derived unit; rank-3 (subsuite) clusters according to their spatial arrangement (magmatic centres) or association to larger structures (e.g., shear zones or alignments); rank-4, the fundamental mapping unit, characterizes the lithotype (alkaline granite, alkaline gabbro, syenite, albitite, etc.) by considering higher ranks (alkalinity and textural aspects); rank-5 characterizes the geometry of individual plutons (with several intrusions) or swarms; rank-6 (smallest mappable unit) corresponds to each intrusion or individual body from a swarm. Although this classification scheme is currently presented solely for the Ossa-Morena Zone, the scheme can be easily extended to incorporate other co-magmatic alkaline bodies, such as those in the NW Iberian allochthonous units or other peri-Gondwanan zones or massifs, in order to facilitate regional correlations of the rift-related magmatism. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Location of the Ossa-Morena Zone (OMZ) within the tectono-stratigraphic zonation of the Iberian Massif (adapted from [<a href="#B22-minerals-15-00174" class="html-bibr">22</a>,<a href="#B23-minerals-15-00174" class="html-bibr">23</a>]). (<b>b</b>) Major structural domains of the OMZ, adapted from [<a href="#B24-minerals-15-00174" class="html-bibr">24</a>,<a href="#B25-minerals-15-00174" class="html-bibr">25</a>], separated by the major shear zones from [<a href="#B24-minerals-15-00174" class="html-bibr">24</a>,<a href="#B26-minerals-15-00174" class="html-bibr">26</a>,<a href="#B27-minerals-15-00174" class="html-bibr">27</a>]. The grey area corresponds to the sinistral Tomar–Badajoz–Córdoba Shear Zone [<a href="#B28-minerals-15-00174" class="html-bibr">28</a>].</p>
Full article ">Figure 2
<p>Location and geological map of the Portuguese side of the study area, including the Alter do Chão–Elvas domain and a segment of the central unit/Tomar–Badajoz–Córdoba Shear Zone, adapted from [<a href="#B115-minerals-15-00174" class="html-bibr">115</a>,<a href="#B116-minerals-15-00174" class="html-bibr">116</a>,<a href="#B117-minerals-15-00174" class="html-bibr">117</a>,<a href="#B118-minerals-15-00174" class="html-bibr">118</a>,<a href="#B119-minerals-15-00174" class="html-bibr">119</a>,<a href="#B120-minerals-15-00174" class="html-bibr">120</a>,<a href="#B121-minerals-15-00174" class="html-bibr">121</a>]. References regarding ages are found throughout the text.</p>
Full article ">Figure 3
<p>Location and geological map of the Spanish side of the area with rift-related alkaline magmatic bodies, with segments of the Elvas–Cumbres Mayores, Sierra Albarrana, and Zafra–Alanís domains, as well as part of the central unit/Tomar–Badajoz–Córdoba Shear Zone (Central Unit). Adapted from [<a href="#B122-minerals-15-00174" class="html-bibr">122</a>]. References regarding ages are found throughout the text.</p>
Full article ">Figure 4
<p>Classification systems for stratigraphic, morphogenetic, and mixed-class units, partially adapted from BRUCS [<a href="#B2-minerals-15-00174" class="html-bibr">2</a>], with only some examples of rank 5 and rank 6 classifications. the terms in bold are used at a larger scale (&gt;1:50,000). Mixed class units include more than one genetic type and have lower ranks. Ranks 5 and 6 can be used for detailed mapping (&lt;1:50,000) and to characterize individual massifs or swarms.</p>
Full article ">Figure 5
<p>Schematic representation of the mappable morphological types and designation of groups with spatially associated bodies. (<b>a</b>) Circular or ovoidal simple pluton; (<b>b</b>) ring-intrusion, a unit comprising more than one related intrusion, with an inner body bounded by a ring-shaped distinct body; (<b>c</b>) sheet-intrusion, represented by a tabular plutonic body with two long parallel borders much larger than the lateral dimensions; (<b>d</b>) dyke (<b>left</b>) and sill (<b>right</b>), correspondently near vertical or near horizontal tabular volcanic bodies; (<b>e</b>) lensoidal body of orthogneiss (lens); (<b>f</b>) composite unit, embodied by two or more lithotypes (also referred to as ‘parcel’ if the tectono-metamorphic units are contiguous at outcrop); (<b>g</b>) swarm, a group of two or more related dispersed units; and (<b>h</b>) train, a group of two or more units in a linear disposition. Schemes were made following definitions for unit terms in the hierarchy of morphogenetic units, from [<a href="#B2-minerals-15-00174" class="html-bibr">2</a>].</p>
Full article ">Figure 6
<p>Ranked classification scheme for the alkaline magmatic bodies of the Ossa-Morena Zone, with the 6 ranks adapted and following the recommendations from [<a href="#B2-minerals-15-00174" class="html-bibr">2</a>,<a href="#B4-minerals-15-00174" class="html-bibr">4</a>,<a href="#B10-minerals-15-00174" class="html-bibr">10</a>], with locations of the suites and the clusters over the maps from <a href="#minerals-15-00174-f002" class="html-fig">Figure 2</a> and <a href="#minerals-15-00174-f003" class="html-fig">Figure 3</a>.</p>
Full article ">Figure 7
<p>(<b>a</b>) Regional lithological map of the elongated Alter do Chão cluster (rank 3 unit), partially adapted from [<a href="#B149-minerals-15-00174" class="html-bibr">149</a>]. The southwestern body of Vaiamonte (separated by a stripe of rocks from the Carbonate Fm) is the Santo António limb. (<b>b</b>) Detailed geological map of the major different units of the Alter Pedroso composite pluton (rank 5 unit), with the two distinct syenite intrusions (rank 6 units).</p>
Full article ">Figure 8
<p>Representative textural aspects of rocks from the Alter do Chão Cluster. Rocks from the intrusions of the Alter Pedroso pluton: (<b>a</b>) Leucocratic syenite. (<b>b</b>) Mesocratic aegirine-bearing syenite (“lusitanite” [<a href="#B146-minerals-15-00174" class="html-bibr">146</a>]). (<b>c</b>) Pematoid syenite with riebeckite megacrystals. (<b>d</b>) A pegmatoid rock solely composed of riebeckite (“pedrosite” [<a href="#B115-minerals-15-00174" class="html-bibr">115</a>]). Sheared intrusions from the Vaiamonte sheet-complex: (<b>e</b>) strongly foliated mesocratic syenite and (<b>f</b>) weakly foliated leucocratic syenite.</p>
Full article ">Figure 9
<p>(<b>a</b>) Regional lithological map of the Elvas Centre (rank 3 unit, adapted from [<a href="#B119-minerals-15-00174" class="html-bibr">119</a>,<a href="#B149-minerals-15-00174" class="html-bibr">149</a>]), with the tabular and sub-circular/ovoidal plutonic alkaline bodies distributed asymmetrically in a central point and stretched following a NW-SE trend. The large fault separating Varche and Falcato intrusions corresponds to the Messejana fault (mentioned in the text). (<b>b</b>) Detailed geological map of the concentric zonation of the Gebarela ring intrusion (a slightly similar zonation has previously been proposed in [<a href="#B137-minerals-15-00174" class="html-bibr">137</a>]). In this region, the pluton swarms and the ring intrusion are classified at rank 5, while the individual plutons and each unit from the Gebarela body are rank 6.</p>
Full article ">Figure 10
<p>Macroscopic features of rocks from the Elvas Centre: (<b>a</b>) Hedembergite-bearing granitoid from Alcamins, (<b>b</b>) Mesocratic syenite from Varche, (<b>c</b>) Mesocratic syenite from Falcato, (<b>d</b>) Albitite from the inner Gebarela core, (<b>e</b>) Mesocratic syenite from the Gebarela ring intrusion, (<b>f</b>) Perthosite from the Degola-folded pluton.</p>
Full article ">Figure 11
<p>(<b>a</b>) Regional lithological map of the Monesterio cluster (rank 3), adapted from [<a href="#B122-minerals-15-00174" class="html-bibr">122</a>,<a href="#B155-minerals-15-00174" class="html-bibr">155</a>,<a href="#B156-minerals-15-00174" class="html-bibr">156</a>,<a href="#B157-minerals-15-00174" class="html-bibr">157</a>,<a href="#B158-minerals-15-00174" class="html-bibr">158</a>]. (<b>b</b>) Detailed map of the Almendral composite pluton, comprising syenite/quartzsyenite and granite intrusions, from [<a href="#B159-minerals-15-00174" class="html-bibr">159</a>]. (<b>c</b>) The Barcarrota ring complex, composed of syenites and quartz syenites and alkaline granite ring intrusions, around the central mafic body [<a href="#B160-minerals-15-00174" class="html-bibr">160</a>,<a href="#B161-minerals-15-00174" class="html-bibr">161</a>]. (<b>d</b>) Zonation of the Castillo composite pluton, with subalkaline granites to the southeast, the main alkaline granite body, and the orthogneiss northwest rim, from [<a href="#B162-minerals-15-00174" class="html-bibr">162</a>].</p>
Full article ">Figure 12
<p>Macroscopic aspects of the rocks from the Monesterio cluster: (<b>a</b>) albitite from the Jerez de los Caballeros swarm. (<b>b</b>) Leucocratic quartz syenites and (<b>c</b>) gabbro–diorite rocks from the Barcarrota ring complex. (<b>d</b>) Hastingsite-bearing granite from the Castillo pluton.</p>
Full article ">Figure 13
<p>Lithological map of the Feria cluster, showing the Feria albitites and the Sierra Vieja hypabyssal syenite body, from [<a href="#B142-minerals-15-00174" class="html-bibr">142</a>].</p>
Full article ">Figure 14
<p>Rocks from the Sub-Volcanic Suite: (<b>a</b>) macroscopic features of the Feria and (<b>b</b>) the Sierra Vieja rocks. Rocks from the Pero Lobo pluton: (<b>c</b>) sheared microgranite from the southwestern intrusion and (<b>d</b>) quartz syenite from the northwestern intrusion.</p>
Full article ">Figure 15
<p>(<b>a</b>) Geological map of the region of the rank 5 Monte Safueiro trachytic/microsyenite dyke swarm and the Pero Lobo body (São Romão Cluster, Sub-volcanic Suite), intruding the Miaolingian succession, adapted from [<a href="#B119-minerals-15-00174" class="html-bibr">119</a>,<a href="#B120-minerals-15-00174" class="html-bibr">120</a>]. (<b>b</b>) Lithological map of the Pero Lobo petrographic zoning (alkali microgranite and quartz syenite). Each individual “unnamed” dyke from the Monte Safueiro swarm and intrusion type from the Pero Lobo body is a rank 6 unit.</p>
Full article ">Figure 16
<p>Localization of the bodies from the Mylonite Suite within the Central Unit in (<b>a</b>) the Portuguese segment and (<b>b</b>) the Spanish segment. Lithological maps of different alkaline orthogneisses: (<b>c</b>) the lens of Assumar and (<b>d</b>) lens from the Arronches–Fialha swarm.</p>
Full article ">Figure 17
<p>Distinct fabrics from the lensoidal body characteristics of the Mylonite Suite: (<b>a</b>) hastingsite-bearing granitic gneiss, (<b>b</b>) nepheline-syenite gneiss from the Arronches–Fialha swarm (Fialha area), (<b>c</b>) Riebeckite- and aegirine-bearing syenite gneiss from Cevadais (“cevadaisite” [<a href="#B115-minerals-15-00174" class="html-bibr">115</a>,<a href="#B171-minerals-15-00174" class="html-bibr">171</a>]), (<b>d</b>) Almendralejo hastingsite-bearing syenite gneiss, (<b>e</b>) granitic gneiss from Ribera del Fresno, and (<b>f</b>) granitic gneiss from Las Minillas.</p>
Full article ">
17 pages, 5463 KiB  
Article
Asymmetric Finite-Range Persistence in Time Series Generated by the Modified Discrete Langevin Model
by Zbigniew Czechowski
Symmetry 2025, 17(2), 287; https://doi.org/10.3390/sym17020287 - 13 Feb 2025
Viewed by 310
Abstract
The concept of asymmetric persistence in time series was proposed and an appropriate stochastic Langevin-type model was presented. The influence of this particular form of memory on the behavior of the generated time series was examined. It has been shown that asymmetry causes [...] Read more.
The concept of asymmetric persistence in time series was proposed and an appropriate stochastic Langevin-type model was presented. The influence of this particular form of memory on the behavior of the generated time series was examined. It has been shown that asymmetry causes a significant distortion of the effect of drift forces and has a weaker impact on stochastic diffusion forces. Due to this, the current known methods for reconstructing the Langevin-type model fail. The results of this work may help in deriving a new reconstruction method. Full article
(This article belongs to the Section Physics)
Show Figures

Figure 1

Figure 1
<p>An illustration of the <span class="html-italic">p</span>-order persistence concept (for <span class="html-italic">p</span> = 7); <span class="html-italic">y</span>(<span class="html-italic">k</span> − 7), …, <span class="html-italic">y</span>(<span class="html-italic">k</span> − 1)—values of the seven states of the time series preceding the current state <span class="html-italic">y</span>(<span class="html-italic">k</span>); symbols + and − denote the signs of the seven previous increments: red arrows—next possible increment. (<b>a</b>) A case where the last increment has the positive sign; then, the sign of the next stochastic increment will also be positive with probability <span class="html-italic">p</span>(15) and negative with probability 1 − <span class="html-italic">p</span>(15); (<b>b</b>) A case where the last increment has the negative sign; then, the sign of the next stochastic increment will also be negative with probability <span class="html-italic">q</span>(50) and positive with probability 1 − <span class="html-italic">q</span>(50). The values of <span class="html-italic">p</span>(15) and <span class="html-italic">q</span>(50) depend on the sequence of seven signs of previous increments that are listed in <a href="#app1-symmetry-17-00287" class="html-app">Appendix A</a>.</p>
Full article ">Figure 2
<p>The fluctuation functions <span class="html-italic">F</span>(<span class="html-italic">n</span>) (blue circles) obtained from the <span class="html-italic">p</span>-order persistent time series generated by Equation (2) with <span class="html-italic">a</span>(<span class="html-italic">y</span>) = 0, <span class="html-italic">b</span>(<span class="html-italic">y</span>) = 1 and symmetric parameters, <span class="html-italic">p</span>(<span class="html-italic">k</span>) = <span class="html-italic">q</span>(<span class="html-italic">k</span>). (<b>a</b>) Case <span class="html-italic">p</span> = 1; (<b>b</b>) <span class="html-italic">p</span> = 4; (<b>c</b>) <span class="html-italic">p</span> = 7; (<b>d</b>) <span class="html-italic">F</span>(<span class="html-italic">n</span>) is obtained from fBm with <span class="html-italic">H</span> = 0.8. The black line is a power law with exponent <span class="html-italic">α</span> = 1 + <span class="html-italic">H</span> where <span class="html-italic">H</span> = 0.8, while for green line <span class="html-italic">H</span> = 0.5.</p>
Full article ">Figure 3
<p>Persistence parameters <span class="html-italic">p</span>(<span class="html-italic">k</span>) (red points) and <span class="html-italic">q</span>(<span class="html-italic">k</span>) (black +) reconstructed from fBm with <span class="html-italic">H</span> = 0.7.</p>
Full article ">Figure 4
<p>Fluctuation functions <span class="html-italic">F</span>(<span class="html-italic">n</span>) (blue circles) obtained from <span class="html-italic">p</span>-order persistent time series generated by Equation (2) with <span class="html-italic">a</span>(<span class="html-italic">y</span>) = 0, <span class="html-italic">b</span>(<span class="html-italic">y</span>) = 1. (<b>a</b>) The case with asymmetric parameters given by Equations (5) and (6). The black line is a power law with exponent <span class="html-italic">α</span> = 1 + <span class="html-italic">H</span> where <span class="html-italic">H</span> = 0.686, while for green line <span class="html-italic">H</span> = 0.5. (<b>b</b>) The case with antisymmetric parameters given by Equations (9) and (10). The black and green lines are a power law with exponent <span class="html-italic">α</span> = 1 + <span class="html-italic">H</span>, where <span class="html-italic">H</span> = 0.5.</p>
Full article ">Figure 5
<p>(<b>a</b>) Time series generated by Equation (2) with <span class="html-italic">a</span>(<span class="html-italic">y</span>) = −<span class="html-italic">y</span>, <span class="html-italic">b</span>(<span class="html-italic">y</span>) = 1 and antisymmetric parameters, Equations (9) and (10). (<b>b</b>) Potential <span class="html-italic">V</span>(<span class="html-italic">y</span>) (black graph) and distribution function <span class="html-italic">f</span>(<span class="html-italic">y</span>) (red graph) of the time series shown in plot (<b>a</b>). For a comparison, the green graph represents the distribution function for a symmetric case. The blue dot indicates a particle in the potential field.</p>
Full article ">Figure 6
<p>Standard procedure reconstructions of drift functions from time series generated by Equation (2) for two symmetric forms of persistence parameters given by Equation (15) (blue points) and Equation (16) (red points). (<b>a</b>) Case M1; (<b>b</b>) case M2; (<b>c</b>) case M3. Black graphs represent the initial forms of <span class="html-italic">a</span>(<span class="html-italic">y</span>).</p>
Full article ">Figure 7
<p>Standard procedure reconstructions of drift functions from time series generated by Equation (2) for three antisymmetric forms of persistence parameters given by Equation (17) (blue points), Equation (18) (green points) and Equation (19) (red points). (<b>a</b>) Case M1; (<b>b</b>) case M2; (<b>c</b>) case M3. Black graphs represent the initial forms of <span class="html-italic">a</span>(<span class="html-italic">y</span>).</p>
Full article ">Figure 8
<p>Standard procedure reconstructions of diffusion functions from time series generated by Equation (2) for the antisymmetric form of persistence parameters given by Equation (17) (blue points). (<b>a</b>) Case M1; (<b>b</b>) case M2; (<b>c</b>) case M3. Black graphs represent the initial forms of <span class="html-italic">b</span>(<span class="html-italic">y</span>).</p>
Full article ">Figure 9
<p>Standard procedure reconstructions of drift functions from time series generated by Equation (2) for two asymmetric forms of persistence parameters given by Equations (20) and (21) (blue points) and Equations (22) and (23) (red points). (<b>a</b>) Case M1; (<b>b</b>) case M2; (<b>c</b>) case M3. Black graphs represent the initial forms of <span class="html-italic">a</span>(<span class="html-italic">y</span>).</p>
Full article ">Figure 10
<p>Standard procedure reconstructions of diffusion functions from time series generated by Equation (2) for two asymmetric forms of persistence parameters given by Equations (20) and (21) (blue points) and Equations (22) and (23) (red points). (<b>a</b>) Case M1; (<b>b</b>) case M2; (<b>c</b>) case M3. Black graphs represent the initial forms of <span class="html-italic">b</span>(<span class="html-italic">y</span>).</p>
Full article ">Figure 11
<p>The case with random persistence parameters. (<b>a</b>) Illustration of <span class="html-italic">p</span>(<span class="html-italic">k</span>) (red points) and <span class="html-italic">q</span>(<span class="html-italic">k</span>) (black +); (<b>b</b>) fluctuation functions <span class="html-italic">F</span>(<span class="html-italic">n</span>) (blue circles); the black line is a power law with exponent <span class="html-italic">α</span> = 1 + <span class="html-italic">H</span>, where <span class="html-italic">H</span> = 0.652; (<b>c</b>) standard procedure reconstruction of drift function (blue points); black line represents the initial form, <span class="html-italic">a</span>(<span class="html-italic">y</span>) = −<span class="html-italic">y</span>; (<b>d</b>) standard procedure reconstruction of diffusion function (blue points); black line represents the initial form, <span class="html-italic">b</span>(<span class="html-italic">y</span>) = 1.</p>
Full article ">Figure 12
<p>Persistence parameters <span class="html-italic">p</span>(<span class="html-italic">k</span>) (red points) and <span class="html-italic">q</span>(<span class="html-italic">k</span>) (black points) reconstructed from generated time series. (<b>a</b>) Symmetric case; (<b>b</b>) antisymmetric case. Continuous lines (blue, black and red) represent the input values of these parameters.</p>
Full article ">Figure 13
<p>Visualizations of successive steps of the modified reconstruction procedure. Points represent the results of the standard procedure. Continuous lines are the least-square fits for <span class="html-italic">a</span><sub>1</sub>(<span class="html-italic">y</span>) (blue), <span class="html-italic">a</span><sub>2</sub>(<span class="html-italic">y</span>) (red), and <span class="html-italic">a</span><sub>3</sub>(<span class="html-italic">y</span>) (green). The black dashed line shows the input <span class="html-italic">a</span>(<span class="html-italic">y</span>); the orange line is the final reconstruction <span class="html-italic">a</span><sub>R</sub>(<span class="html-italic">y</span>). Left plot—symmetric case; right plot—antisymmetric case.</p>
Full article ">
20 pages, 4673 KiB  
Article
Depositional History Reconstruction of the Miocene Formations in the Carpathian Foredeep Area Based on the Integration of Seismostratigraphic and Chemostratigraphic Interpretation
by Anna Łaba-Biel, Andrzej Urbaniec, Benedykt Kubik, Anna Kwietniak and Robert Bartoń
Appl. Sci. 2025, 15(4), 1927; https://doi.org/10.3390/app15041927 - 13 Feb 2025
Viewed by 292
Abstract
Detailed recognition of the paleoenvironment of sedimentation for the monotonous series of heterolithic sediments of the Machow Formation in the central part of the Carpathian Foredeep is still relatively poor. This study presents an unconventional approach of integrating results of seismostratigraphic interpretation with [...] Read more.
Detailed recognition of the paleoenvironment of sedimentation for the monotonous series of heterolithic sediments of the Machow Formation in the central part of the Carpathian Foredeep is still relatively poor. This study presents an unconventional approach of integrating results of seismostratigraphic interpretation with conclusions from analyses of chemostratigraphic profiles in boreholes. The results obtained from the studies allowed the resolution of the seismic data to be increased, enabling it to be accurately tied to the well data. The studies showed a high consistency between results obtained by the two methods mentioned above, and their combination provided a range of additional information and conclusions that could not be drawn from using a single method. The possibility of correlating interpreted sedimentary sequences with specific elements of the paleoenvironment and stages of the depositional history of the analyzed sedimentary basin was also demonstrated. An important benefit of the integrated interpretation methodology used is the possibility to recognize an apparently monotonous profile of heterolithic formations, which was previously not possible with standard interpretation methods. Full article
(This article belongs to the Special Issue Petroleum Exploration and Structural Geology)
Show Figures

Figure 1

Figure 1
<p>Generalized outline of the Central Europe region (<b>A</b>); location of the study area (pink rectangle—see <a href="#applsci-15-01927-f002" class="html-fig">Figure 2</a>) in relation to the Carpathians and the Pannonian Basin System (PBS) (<b>B</b>) (after Kováč et al. [<a href="#B17-applsci-15-01927" class="html-bibr">17</a>], Golonka et al. [<a href="#B18-applsci-15-01927" class="html-bibr">18</a>]).</p>
Full article ">Figure 2
<p>Location of the research area against the range of the Carpathian Foredeep in Poland; ranges of geological units are according to Porębski and Warchoł [<a href="#B13-applsci-15-01927" class="html-bibr">13</a>].</p>
Full article ">Figure 3
<p>Miocene lithostratigraphic units of the Carpathian Foredeep basin in Poland (on the basis of publications: Gaździcka [<a href="#B43-applsci-15-01927" class="html-bibr">43</a>]; Garecka et al. [<a href="#B44-applsci-15-01927" class="html-bibr">44</a>]; Garecka and Olszewska [<a href="#B45-applsci-15-01927" class="html-bibr">45</a>]; Jasionowski [<a href="#B38-applsci-15-01927" class="html-bibr">38</a>]; Olszewska [<a href="#B39-applsci-15-01927" class="html-bibr">39</a>]; Andreyeva-Grigorovich et al. [<a href="#B46-applsci-15-01927" class="html-bibr">46</a>]; Moryc [<a href="#B47-applsci-15-01927" class="html-bibr">47</a>]; Oszczypko et al. [<a href="#B48-applsci-15-01927" class="html-bibr">48</a>]; time scale after Oszczypko et al. [<a href="#B49-applsci-15-01927" class="html-bibr">49</a>]).</p>
Full article ">Figure 4
<p>The seismic and borehole data integration procedure used in the work.</p>
Full article ">Figure 5
<p>Detailed interpretation of sequence units in the profile of Machow Formation in the Well-1 area based on the chronostratigraphic image (<b>A</b>) and Wheeler diagram (<b>B</b>).</p>
Full article ">Figure 6
<p>Tectonostratigraphic interpretation of the profile of Machow Formation in the Well-1 area.</p>
Full article ">Figure 7
<p>Results of mineralogical and chemostratigraphic studies of the Machow Formation and subdivision into chemostratigraphic units in the Well-2 profile: tracks A and G—depth, tracks B and F—chemostratigraphic zones, track C—well logs, track D—mineralogical profile, track E—chemical composition analysis.</p>
Full article ">Figure 8
<p>Compilation of seismostratigraphic and chemostratigraphic interpretation results for the Well-1 analyzed: track A—depth, track B—chemostratigraphic zones, track C—well logs, track D—dipmeter, track E—mineralogical profile, track F—synthetic seismogram and stick plot in the background of seismic section (fragment of InLine), track G—synthetic seismogram and stick plot in the background of seismic section (fragment of XLine presented in <a href="#applsci-15-01927-f005" class="html-fig">Figure 5</a> and <a href="#applsci-15-01927-f006" class="html-fig">Figure 6</a>), track H—stick plot in the background of systems tracts interpretation (fragment of XLine presented in <a href="#applsci-15-01927-f005" class="html-fig">Figure 5</a> and <a href="#applsci-15-01927-f006" class="html-fig">Figure 6</a>), track I—gamma-ray in the background of systems tracts interpretation (fragment of XLine presented in <a href="#applsci-15-01927-f005" class="html-fig">Figure 5</a> and <a href="#applsci-15-01927-f006" class="html-fig">Figure 6</a>), track J—stick plot in the background of tectonostratigraphic (megasequences) interpretation (fragment of XLine presented in <a href="#applsci-15-01927-f005" class="html-fig">Figure 5</a> and <a href="#applsci-15-01927-f006" class="html-fig">Figure 6</a>), track K—depositional sequence numbers, track L—chemical composition analysis.</p>
Full article ">Figure 9
<p>Compilation of seismostratigraphic and chemostratigraphic interpretation results for the Well-2 analyzed: track A—depth, track B—chemostratigraphic zones, track C—well logs, track D—dipmeter, track E—mineralogical profile, track F—synthetic seismogram and stick plot in the background of seismic section (fragment of InLine), track G—synthetic seismogram and stick plot in the background of seismic section (fragment of XLine), track H—stick plot in the background of systems tracts interpretation (fragment of XLine), track I—gamma-ray in the background of systems tracts interpretation (fragment of XLine), track J—stick plot in the background of tectonostratigraphic (megasequences) interpretation (fragment of XLine), track K—depositional sequence numbers, track L—chemical composition analysis.</p>
Full article ">
19 pages, 11696 KiB  
Article
Gravity Data Fusion and Imaging of Geological Structures in the Red River Fault Zone and Adjacent Areas
by Guiju Wu, Fei Yu, Hongbo Tan, Jiapei Wang and Weihua Liu
Sensors 2025, 25(4), 1101; https://doi.org/10.3390/s25041101 - 12 Feb 2025
Viewed by 302
Abstract
The geological structure in the Red River fault zone (RRF) and adjacent areas is complex. Due to the lack of high-precision gravity data in the study area, it is difficult to obtain the distribution of materials within the Earth’s crust. In this study, [...] Read more.
The geological structure in the Red River fault zone (RRF) and adjacent areas is complex. Due to the lack of high-precision gravity data in the study area, it is difficult to obtain the distribution of materials within the Earth’s crust. In this study, a gravity data-fused method is proposed. The Moho depth model data are utilized to construct the gravity anomaly trend, and the mapping relation between the gravity field model data and the measured gravity data is established. Using 934 high-precision measured gravity data as control points, the bilinear interpolation method is used to calculate high-precision grid data of the RRF. Finally, the apparent density inversion method is used to obtain clear crustal density images across the RRF. The experimental results show that the fuses data not only reflect the regional anomaly trend but also maintain the local anomaly information; the root-mean-square error of the fused data is less than 5% and the correlation coefficient is greater than 90%. Through an in-depth comparative analysis of density images, it is found that the low-density anomalous zones, with depths of ~20 km in the northern and southern sections of the RRF, are shallower than those in the middle. The data-fused method provides a new way to process geophysical data more efficiently. Full article
(This article belongs to the Section Remote Sensors)
Show Figures

Figure 1

Figure 1
<p>Schematic map of the geological structure (modified after [<a href="#B26-sensors-25-01101" class="html-bibr">26</a>,<a href="#B27-sensors-25-01101" class="html-bibr">27</a>]). F1: Nujiang fault; F2: Nantinghe fault; F3: Lantsang fault; F4: Wuliangshan fault; F5: Weixi–Weishan fault; F6: Binchuan–Yongsheng fault; F7: Red River fault; F8: Huanan–Chuxiong–Jianshui fault; F9: Anninghe fault; F10: Puduhe fault; F11: Xiaojiang fault; SYB: Sichuan–Yunnan block; BKB: Bayankala block; QB: Qiangtang block; S-YB: South Yunnan block; W-YB: West Yunnan block.</p>
Full article ">Figure 2
<p>Topographic relief and distribution of observation stations map. The blue stars indicate the measured gravity profiles (709 points). The blue circles indicate the gravity base stations (225 points). Cities are marked with solid gray circles.</p>
Full article ">Figure 3
<p>The BGAs of EGM2008 in the study area.</p>
Full article ">Figure 4
<p>Diagram of the bilinear interpolation method.</p>
Full article ">Figure 5
<p>A comparison of the data before and after fusion along the measured profiles.</p>
Full article ">Figure 6
<p>The BGAs after fusion in the study area.</p>
Full article ">Figure 7
<p>Density contrast results along five profiles beneath crossing the RRF from north to south. Subfigures (<b>a</b>–<b>e</b>) sequentially represent the density contrast results for L1, L2, L<span class="html-italic">m</span>, L5, and L4. The black lines indicate faults. The red circles indicate the projected locations of earthquakes with M ≥ 6 that occurred along the profiles.</p>
Full article ">Figure 8
<p>The residual BGAs of the research region, obtained by subtracting the regional values. (<b>a</b>) The residual BGAs of EGM2008; (<b>b</b>) the residual BGAs of the fused BGAs.</p>
Full article ">Figure 9
<p>A simplified diagram of material distribution and crustal material flow along the RRF. (<b>a</b>) The reflection ratio of the residual BGAs; (<b>b</b>) the reflection ratio of the fused BGAs. The white lines with arrows indicate deep material flow in the middle of the RRF; (<b>c</b>) a cartoon diagram of crustal material flow along the RRF. The black lines with arrows mark the eastward-moving material. The red lines with arrows mark the upwelling mantle material, and the area with blue wavy lines indicates a high-density area. The purple lines with arrows mark indicate lower crust flow [<a href="#B14-sensors-25-01101" class="html-bibr">14</a>].</p>
Full article ">
15 pages, 4481 KiB  
Article
A Novel Time Domain Reflectometry (TDR) System for Water Content Estimation in Soils: Development and Application
by Alessandro Comegna, Simone Di Prima, Shawcat Basel Mostafa Hassan and Antonio Coppola
Sensors 2025, 25(4), 1099; https://doi.org/10.3390/s25041099 - 12 Feb 2025
Viewed by 346
Abstract
Nowadays, there is a particular need to estimate soil water content accurately over space and time scales in various applications. For example, precision agriculture, as well as the fields of geology, ecology, and hydrology, necessitate rapid, onsite water content measurements. The time domain [...] Read more.
Nowadays, there is a particular need to estimate soil water content accurately over space and time scales in various applications. For example, precision agriculture, as well as the fields of geology, ecology, and hydrology, necessitate rapid, onsite water content measurements. The time domain reflectometry (TDR) technique is a geophysical method that allows, in a time-varying electric field, the determination of dielectric permittivity and electrical conductivity for a wide class of porous materials. Measuring the volumetric water content in soils is the most frequent application of TDR in soil science and soil hydrology. TDR has grown in popularity over the last 40 years because it is a practical and non-destructive technique that provides laboratory and field-scale measurements. However, a significant limitation of this technique is the relatively high cost of TDR devices, despite the availability of a range of commercial systems with varying prices. This paper aimed to design and implement a low-cost, compact TDR device tailored for classical hydrological applications. A series of laboratory experiments were carried out on soils of different textures to calibrate and validate the proposed measuring system. The results show that the device can be used to obtain predictions for monitoring soil water status with acceptable accuracy (R2 = 0.95). Full article
(This article belongs to the Special Issue Sensors in Smart Irrigation Systems)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Time Domain Reflectometry (TDR) hardware setup; (<b>b</b>) voltage versus travel time for an idealized TDR waveform, highlighting distinctive signal characteristics resulting from multiple reflections (adapted from [<a href="#B6-sensors-25-01099" class="html-bibr">6</a>]).</p>
Full article ">Figure 2
<p>Example of a TDR output waveform, showing (i) the first peak, (ii) the reflection point, and (iii) tangent lines required for the determination of these two points.</p>
Full article ">Figure 3
<p>(<b>a</b>) Electrical circuit diagram of the PKTDR device and (<b>b</b>) the printed circuit board (PCB) layout generated using KiCad software (version 7.0).</p>
Full article ">Figure 4
<p>(<b>a</b>) The printed circuit board (PCB) of the PKTDR device, (<b>b</b>) PKTDR housed within its PLA enclosure, and (<b>c</b>) the additional components needed to complete the assembly.</p>
Full article ">Figure 5
<p>Example of a TDR signal acquired using PKTDR with the Hantek 6254BD DSO.</p>
Full article ">Figure 6
<p>Laboratory apparatus for calibration and validation of the PKTDR device (adapted from [<a href="#B4-sensors-25-01099" class="html-bibr">4</a>]).</p>
Full article ">Figure 7
<p>Bulk dielectric permittivity (ε<sub>b</sub>) values measured using the PKTDR device plotted against the estimated θ values, calculated using Equation (2) for the four investigated soils. The red lines and red text represent θ values determined using the thermo-gravimetric method. The graphic also highlights the range of θ variability among the soils in blue text.</p>
Full article ">Figure 8
<p>(<b>a</b>) Comparison of θ values estimated using the PKTDR device and the commercial TDR100 device and (<b>b</b>) θ-PKTDR estimated values vs. known θ values, with reference to the four investigated soils.</p>
Full article ">Figure A1
<p>The PKTDR prototype.</p>
Full article ">Figure A2
<p>Scheme of the required components and connections for the PKTDR system.</p>
Full article ">
26 pages, 17105 KiB  
Article
CNN-GRU-ATT Method for Resistivity Logging Curve Reconstruction and Fluid Property Identification in Marine Carbonate Reservoirs
by Jianhong Guo, Hengyang Lv, Qing Zhao, Yuxin Yang, Zuomin Zhu and Zhansong Zhang
J. Mar. Sci. Eng. 2025, 13(2), 331; https://doi.org/10.3390/jmse13020331 - 12 Feb 2025
Viewed by 367
Abstract
Geophysical logging curves are crucial for oil and gas field exploration and development, and curve reconstruction techniques are a key focus of research in this field. This study proposes an inversion model for deep resistivity curves in marine carbonate reservoirs, specifically the Mishrif [...] Read more.
Geophysical logging curves are crucial for oil and gas field exploration and development, and curve reconstruction techniques are a key focus of research in this field. This study proposes an inversion model for deep resistivity curves in marine carbonate reservoirs, specifically the Mishrif Formation of the Halfaya Field, by integrating a deep learning model called CNN-GRU-ATT, which combines Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and the Attention Mechanism (ATT). Using logging data from the marine carbonate oil layers, the reconstructed deep resistivity curve is compared with actual measurements to determine reservoir fluid properties. The results demonstrate the effectiveness of the CNN-GRU-ATT model in accurately reconstructing deep resistivity curves for carbonate reservoirs within the Mishrif Formation. Notably, the model outperforms alternative methods such as CNN-GRU, GRU, Long Short-Term Memory (LSTM), Multiple Regression, and Random Forest in new wells, exhibiting high accuracy and robust generalization capabilities. In practical applications, the response of the inverted deep resistivity curve can be utilized to identify the reservoir water cut. Specifically, when the model-inverted curve exhibits a higher response compared to the measured curve, it indicates the presence of reservoir water. Additionally, a stable relative position between the two curves suggests the presence of a water layer. Utilizing this method, the oil–water transition zone can be accurately delineated, achieving a fluid property identification accuracy of 93.14%. This study not only introduces a novel curve reconstruction method but also presents a precise approach to identifying reservoir fluid properties. These findings establish a solid technical foundation for decision-making support in oilfield development. Full article
(This article belongs to the Special Issue Research on Offshore Oil and Gas Numerical Simulation)
Show Figures

Figure 1

Figure 1
<p>The location of the study area in Halfaya oilfield and the lithologic column diagram of Mishrif Formation [<a href="#B34-jmse-13-00331" class="html-bibr">34</a>].</p>
Full article ">Figure 2
<p>Micro- and nano-scale pore types in the bioclastic limestone of the Mishrif Formation: (<b>a</b>) dissolution pores and moldic pores, CTS; (<b>b</b>) intergranular pores and biocavities, CTS; (<b>c</b>) intercrystalline pores, matrix pores, and dissolution pores, CTS; (<b>d</b>) intragranular dissolution pores and biocavities, SEM; (<b>e</b>) primary intragranular pores, SEM; (<b>f</b>) moldic pores and dissolution pores, SEM; (<b>g</b>) matrix micropores, SEM; (<b>h</b>) illite–smectite mixed-layer intergranular pores, SEM; (<b>i</b>) kaolinite intercrystalline pores, SEM.</p>
Full article ">Figure 3
<p>Fundamental data validation diagram: (<b>a</b>) well location maps of two wells with similar distances and measured by different instruments; (<b>b</b>) logging curves of WD1 well measured by ALRT instrument; (<b>c</b>) logging curves of WD2 well measured by DLL instrument; (<b>d</b>) the schematic diagram of the structure of the ALRT instrument and the schematic diagram of the relative position of the electrode; (<b>e</b>) the structure schematic diagram of the DLL instrument and the schematic diagram of the relative position of the electrode; (<b>f</b>) current flow diagram in deep resistivity measurement; (<b>g</b>) current flow diagram for shallow resistivity measurement; (<b>h</b>) WD1 well and WD2 well resistivity contrast diagram of the same horizon.</p>
Full article ">Figure 4
<p>Basic structure of CNN.</p>
Full article ">Figure 5
<p>Feature combination and extraction based on CNN.</p>
Full article ">Figure 6
<p>Basic structure of GRU network.</p>
Full article ">Figure 7
<p>Attention Mechanism structure.</p>
Full article ">Figure 8
<p>CNN-GRU-Attention Model network structure.</p>
Full article ">Figure 9
<p>Flowchart of the research in this paper.</p>
Full article ">Figure 10
<p>Rendezvous plots between experimental parameters: (<b>a</b>) porosity vs. rock resistivity rendezvous plot in water-saturated core samples; (<b>b</b>) correlation between GR response and RD response.</p>
Full article ">Figure 11
<p>Partial hyperparameter validation results: (<b>a</b>) number of convolutional kernels; (<b>b</b>) convolutional kernel size.</p>
Full article ">Figure 12
<p>Training and validation dataset loss function plots (red line with red positive triangles for training set data, blue line with blue inverted triangles for validation set data).</p>
Full article ">Figure 13
<p>Comparison of model performance between training and validation datasets: (<b>a</b>) training set data; (<b>b</b>) validation set data.</p>
Full article ">Figure 14
<p>Effectiveness of models constructed with different ratios of training data to validation data on test data not involved in model construction.</p>
Full article ">Figure 15
<p>Effectiveness of the constructed CNN-GRU-ATT model in the new well: (<b>a</b>) logging and calculation curves of M-05X; (<b>b</b>) production data of the shot hole section of well M-05X; (<b>c</b>) rendezvous plot of the inverted deep resistivity of the formation section with the instrumental measurements.</p>
Full article ">Figure 16
<p>Effectiveness of CNN-GRU-ATT method for practical application in reservoir resistivity inversion: (<b>a</b>) logging curve and application effect of well M-X74 using the CNN-GRU-ATT method for reservoir resistivity inversion; (<b>b</b>) logging curve and application effect of well M-137 using the CNN-GRU-ATT method for reservoir resistivity inversion; (<b>c</b>) production curve of well M-X74; (<b>d</b>) production curve of well M-137.</p>
Full article ">Figure 17
<p>The oil–water interface calculated based on the application of the method of this paper to the original well: (<b>a</b>) raw ODT; (<b>b</b>) raw WUT.</p>
Full article ">Figure 18
<p>Oil–water profiles calculated based on the methodology of this paper: (<b>a</b>) MA reservoir; (<b>b</b>) MB-MC reservoir.</p>
Full article ">Figure 19
<p>Comparison of the effectiveness of different methods for constructing models based on the same data in the new well: (<b>a</b>) CNN-GRU-ATT; (<b>b</b>) CNN-GRU; (<b>c</b>) GRU; (<b>d</b>) LSTM; (<b>e</b>) Multiple Regression; (<b>f</b>) Random Forest.</p>
Full article ">Figure 20
<p>Error waterfall plot for different methods.</p>
Full article ">
14 pages, 8944 KiB  
Article
Computation of the Digital Elevation Model and Ice Dynamics of Talos Dome and the Frontier Mountain Region (North Victoria Land/Antarctica) by Synthetic-Aperture Radar (SAR) Interferometry
by Paolo Sterzai, Nicola Creati and Antonio Zanutta
Glacies 2025, 2(1), 3; https://doi.org/10.3390/glacies2010003 - 12 Feb 2025
Viewed by 266
Abstract
In Antarctica, SAR interferometry has largely been used in coastal glacial areas, while in rare cases this method has been used on the Antarctic plateau. In this paper, the authors present a digital elevation and ice flow map based on SAR interferometry for [...] Read more.
In Antarctica, SAR interferometry has largely been used in coastal glacial areas, while in rare cases this method has been used on the Antarctic plateau. In this paper, the authors present a digital elevation and ice flow map based on SAR interferometry for an area encompassing Talos Dome (TD) and the Frontier Mountain (FM) meteorite site in North Victoria Land/Antarctica. A digital elevation model (DEM) was calculated using a double SAR interferometry method. The DEM of the region was calculated by extracting approximately 100 control points from the Reference Elevation Model of Antarctica (REMA). The two DEMs differ slightly in some areas, probably due to the penetration of the SAR-C band signal into the cold firn. The largest differences are found in the western area of TD, where the radar penetration is more pronounced and fits well with the layer structures calculated by the georadar and the snow accumulation observations. By differentiating a 70-day interferogram with the calculated DEM, a displacement interferogram was calculated that represents the ice dynamics. The resulting ice flow pattern clearly shows the catchment areas of the Priestley and Rennick Glaciers as well as the ice flow from the west towards Wilkes Basin. The ice velocity field was analysed in the area of FM. This area has become well known due to the search for meteorites. The velocity field in combination with the calculated DEM confirms the generally accepted theories about the accumulation of meteorites over the Antarctic Plateau. Full article
Show Figures

Figure 1

Figure 1
<p>Location map of the Talos Dome site in the western part of North Victoria Land. In red, the area covered by ERS-SAR images.</p>
Full article ">Figure 2
<p>Talos Dome region SAR magnitude image geocoded using the REMA DEM and the GPS network positioning [<a href="#B16-glacies-02-00003" class="html-bibr">16</a>].</p>
Full article ">Figure 3
<p>(<b>a</b>) Topography interferogram after phase scaling by subtracting <math display="inline"><semantics> <mrow> <mi>I</mi> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>)</mo> </mrow> </semantics></math> from <math display="inline"><semantics> <mrow> <mi>I</mi> <mo>(</mo> <mn>3</mn> <mo>,</mo> <mn>4</mn> <mo>)</mo> </mrow> </semantics></math>; (<b>b</b>) motion interferogram after phase scaling erasing the topographic component differencing <math display="inline"><semantics> <mrow> <mi>I</mi> <mo>(</mo> <mn>3</mn> <mo>,</mo> <mn>4</mn> <mo>)</mo> </mrow> </semantics></math> from the topographic interferogram. The two interferograms are in the satellite line of sight.</p>
Full article ">Figure 4
<p>Regional interferometric DEM of the Talos Dome region with TD and FM locations.</p>
Full article ">Figure 5
<p>Differences between the interferometric and REMA DEMs. Trace of the altitude profile TD–GPR23 [<a href="#B15-glacies-02-00003" class="html-bibr">15</a>].</p>
Full article ">Figure 6
<p>Dome region SAR 70-day coherence image geocoded using the REMA DEM and the GPS network positioning. White colour indicates a high coherence, black a low coherence. The white arrows indicate the layer reflecting the C-radar signal of the ERS satellite.</p>
Full article ">Figure 7
<p>Altitude profile TD–GPR23 [<a href="#B15-glacies-02-00003" class="html-bibr">15</a>].</p>
Full article ">Figure 8
<p>Rate of change of ice surface elevation from 8 years of altimetric data analysis (after [<a href="#B35-glacies-02-00003" class="html-bibr">35</a>]) with the REMA DEM 50 contour line superimposed.</p>
Full article ">Figure 9
<p>Ice flow pattern inferred by SAR interferometry for the same region as shown in <a href="#glacies-02-00003-f003" class="html-fig">Figure 3</a>. Ice movements with an eastward component tend toward the blue colour, movements with a westward component toward the red colour spectrum. Black areas have no data.</p>
Full article ">Figure 10
<p>Frontier Mountain’s radar interferometric velocity field and ice field interpretation. Idealised location map of Frontier Mountain in relation to the major meteorite concentration sites. The ”Meteorite Valley“ site is designated as M-MV; the meteorite concentration on the blue ice is designated as M-BI. Black area have no data.</p>
Full article ">
15 pages, 4699 KiB  
Article
Deep Exploration Porphyry Molybdenum Deposit in Dasuji, Inner Mongolia: Insight from Aeromagnetism and Controlled-Source Audio-Magnetotellurics
by Zhihe Xu, Xingguo Niu, Bin Shi, Zhongjie Yang, Haoyuan He, Weijing Fan, Guanwen Gu, Yingjie Wang and Ningning Yang
Minerals 2025, 15(2), 166; https://doi.org/10.3390/min15020166 - 11 Feb 2025
Viewed by 334
Abstract
Porphyry molybdenum deposits hold significant potential for deep exploration. However, in the Dasuji molybdenum deposit, quartz porphyry, granite porphyry, and syenogranite are sporadically exposed beneath low mountains and hilly terrain, limiting the effectiveness of traditional geological methods. Consequently, geophysical techniques have become essential [...] Read more.
Porphyry molybdenum deposits hold significant potential for deep exploration. However, in the Dasuji molybdenum deposit, quartz porphyry, granite porphyry, and syenogranite are sporadically exposed beneath low mountains and hilly terrain, limiting the effectiveness of traditional geological methods. Consequently, geophysical techniques have become essential in this region. This study provides new magnetism and resistivity data obtained through high-precision aeromagnetic surveys and controlled-source audio-magnetotellurics (CSAMT) profiles. These results reveal concealed deep porphyries, identify deep-seated molybdenum ore bodies, and establish a porphyry-type molybdenum metallogenic model. The porphyries exhibit the lowest magnetic values (about −200 to 370 nT), suggesting that molybdenum mineralization-related granitoids have exceeded the Curie temperature and undergone an intense magnetic weakening effect. Ferromagnetic or ferromagnetic substances have transformed into paramagnetic substances. The CSAMT results indicate that the mineralized granite porphyry generally has medium to high resistivity (300 Ω·m to 500 Ω·m) and dips southward with a 60° inclination angle. Additionally, an unclosed low-resistance anomaly in the deep region of site 0 indicates promising potential for further mineral exploration and the discovery of deeper mineralized porphyries. We interpret weak magnetic anomalies and variations in resistivity as caused by high crystallization temperatures, low oxygen fugacity, and hydrothermal alteration in the context of porphyry molybdenum deposit mineralization. Full article
Show Figures

Figure 1

Figure 1
<p>Schematic tectonic map of Inner Mongolia and locations of the typical porphyry deposits (modified after [<a href="#B24-minerals-15-00166" class="html-bibr">24</a>]).</p>
Full article ">Figure 2
<p>Geological and structural map of the Dasuji porphyry molybdenum deposit. (<b>a</b>) Schematic diagram of the North China Craton; (<b>b</b>) regional geological map of the Dasuji porphyry molybdenum deposit. 1. Quaternary deposits; 2. Neogene basalt; 3. Paleogene sediments; 4. Cretaceous volcanic rocks; 5. Cretaceous clastic rocks; 6. Jurassic volcanic rocks; 7. Jurassic clastic rocks; 8. Permian clastic rocks; 9. Neoarchaean Sertengshan group metamorphic rocks; 10. Mesoarchean ural mountain group metamorphic rocks; 11.Mesoarchean Jining group metamorphic rocks; 12. Paleoarchean Xinghe group metamorphic rocks; 13. Mesozoic granite; 14. Paleozoic granite; 15. Paleozoic diorite; 16. Neoproterozoic granite; 17. Mesoarchean metamorphic intrusive rocks; 18. faults; 19. Molybdenum deposit; 20. gold deposit; 21. lead zinc silver deposit; 22. major faults.</p>
Full article ">Figure 3
<p>Geologic map of the Dasuji porphyry molybdenum deposit. 1. Quaternary sediments; 2. Jining Group; 3 Mesoarchean granite; 4. Triassic granite porphyry; 5. Triassic quartz porphyry; 6. Jurassic granite porphyry; 7. diabase; 8. diorite; 9. CSAMT profile; 10. aeromagnetic zone.</p>
Full article ">Figure 4
<p>Photographs of magmatic hand specimens closely related to Dasuji porphyry molybdenum mineralization. (<b>a</b>) quartz porphyry; (<b>b</b>) granite porphyry; (<b>c</b>) alkaline granite.</p>
Full article ">Figure 5
<p>Magnetic data measuring instrument. (<b>a</b>) MAG-DN20G4 unmanned aerial vehicle aeromagnetic system; (<b>b</b>) airborne navigation and positioning system; (<b>c</b>) flux gate magnetometer; (<b>d</b>) GSM-19T type proton magnetometer; (<b>e</b>) field geomagnetism observation mooring system; 1. magnetometer host; 2. serial data cable; 3. USB cable; 4. lithium battery; 5. battery charger; 6. attachment package.</p>
Full article ">Figure 6
<p>Work principle sketch maps of the CSAMT method; <span class="html-italic">Tx</span> = transmitter; <span class="html-italic">Rx</span> = receiver; <span class="html-italic">Ex</span> = electric field; <span class="html-italic">Hy</span> = magnetic field; A and B represent the transmitter electrodes.</p>
Full article ">Figure 7
<p>Comprehensive magnetic anomaly map of the Dasuji molybdenum deposit; (<b>a</b>) magnetic anomaly; (<b>b</b>) Za (A) anomaly; (<b>c</b>) Hax (T) anomaly; (<b>d</b>) Hay (H) anomaly; (<b>e</b>) upward extension by 50 m; (<b>f</b>) upward extension by 100 m; (<b>g</b>) upward extension by 200 m; (<b>h</b>) upward extension by 300 m; (<b>i</b>) vertical first derivative of the results with upward extension by 50 m; (<b>j</b>) vertical first derivative of <a href="#minerals-15-00166-f007" class="html-fig">Figure 7</a>e; (<b>k</b>) vertical first derivative of <a href="#minerals-15-00166-f007" class="html-fig">Figure 7</a>f; (l) vertical first derivative of <a href="#minerals-15-00166-f007" class="html-fig">Figure 7</a>k; (l) observed data with magnetic flight route superposed; 1. observed data (red represents positive values and blue represents negative values); 2. aeromagnetic flight route.</p>
Full article ">Figure 8
<p>Comprehensive anomaly map of CSAMT. (<b>a</b>) Inversion resistivity model of CSAMT; (<b>b</b>) pseudosection map of Cagniard resistivity; (<b>c</b>) pseudosection map of impedance phase; 1. abnormal resistivity (the infrermolybdenum ore body); 2. drillings.</p>
Full article ">Figure 9
<p>Deep mineral exploration in the Dasuji molybdenum deposit; (<b>a</b>) CSAMT resistivity model; (<b>b</b>) geological interpretation; 1. Mesoarchean granite; 2. Triassic granite porphyry; 3. Triassic quartz porphyry; 4. Jurassic granite porphyry; 5. diabase; 6. current terrain; 7. Mo ore bodies; 8. drillings; 9. CSAMT observed area.</p>
Full article ">
24 pages, 1031 KiB  
Article
Flood Management Framework for Local Government at Shah Alam, Malaysia
by Haziq Sarhan Rosmadi, Minhaz Farid Ahmed, Neyara Radwan, Mazlin Bin Mokhtar, Chen Kim Lim, Bijay Halder, Miklas Scholz, Fahad Alshehri and Chaitanya Baliram Pande
Water 2025, 17(4), 513; https://doi.org/10.3390/w17040513 - 11 Feb 2025
Viewed by 547
Abstract
Flood disasters are common events in Malaysia, particularly during the monsoon seasons. Hence, disaster management in Malaysia is based on the framework following “Directive 20” by the National Security Council (MKN). This study gathered qualitative information in Shah Alam Municipality through informal interviews [...] Read more.
Flood disasters are common events in Malaysia, particularly during the monsoon seasons. Hence, disaster management in Malaysia is based on the framework following “Directive 20” by the National Security Council (MKN). This study gathered qualitative information in Shah Alam Municipality through informal interviews with 20 informants following the quadruple-helix multi-stakeholders model in 2023 for flood disaster management (FDM). Thematic analysis of the qualitative information was conducted following the four main priority of action themes of the Sendai Framework for United Nations Disaster Risk Reduction (2015–2030) using the Taguette software. This study found coordination and inter-agency data sharing are two major issues in Shah Alam that require immediate attention for FDM. Thus, this study suggests improving district-level flood management guidelines, especially the involvement of the National Disaster Management Agency (NADMA). The NADMA should have a close look at the flood management plan, which acts as Malaysia’s main disaster management coordinator, as they are usually the first agency on the scene when a disaster occurs. Hence, to prevent and lessen flood disaster impact, disaster risk preparedness and individual management through customized training are crucial in combining non-structural and structural measures for FDM. Full article
(This article belongs to the Special Issue Recent Advances in Flood Risk Assessment and Management)
Show Figures

Figure 1

Figure 1
<p>Shah Alam municipality boundary area [<a href="#B40-water-17-00513" class="html-bibr">40</a>].</p>
Full article ">Figure 2
<p>Flood risk zone of the Shah Alam based on the current study [<a href="#B46-water-17-00513" class="html-bibr">46</a>].</p>
Full article ">Figure 3
<p>Flood management framework for local government in Malaysia.</p>
Full article ">
Back to TopTop