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19 pages, 3950 KiB  
Article
Reawakening of Voragine, the Oldest of Etna’s Summit Craters: Insights from a Recurrent Episodic Eruptive Behavior
by Sonia Calvari and Giuseppe Nunnari
Remote Sens. 2024, 16(22), 4278; https://doi.org/10.3390/rs16224278 - 17 Nov 2024
Viewed by 349
Abstract
Paroxysmal explosive activity at Etna volcano (Italy) has become quite frequent over the last three decades, raising concerns with the civil protection authorities due to its significant impact on the local population, infrastructures, viability and air traffic. Between 4 July and 15 August [...] Read more.
Paroxysmal explosive activity at Etna volcano (Italy) has become quite frequent over the last three decades, raising concerns with the civil protection authorities due to its significant impact on the local population, infrastructures, viability and air traffic. Between 4 July and 15 August 2024, during the tourist season peak when the local population doubles, Etna volcano gave rise to a sequence of six paroxysmal explosive events from the summit crater named Voragine. This is the oldest and largest of Etna’s four summit craters and normally only produces degassing, with the previous explosive sequences occurring in December 2015 and May 2016. In this paper, we use thermal images recorded by the monitoring system maintained by the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (INGV–OE), and an automatic procedure previously tested in order to automatically define the eruptive parameters of the six lava fountain episodes. These data allowed us to infer the eruptive processes and gain some insights on the evolution of the explosive sequences that are useful for hazard assessment. Specifically, our results lead to the hypothesis that the Voragine shallow storage has a capacity of ~12–15 Mm3, which was not completely emptied with the last two paroxysmal events. It is thus possible that one or two additional explosive paroxysmal events could occur in the future. It is noteworthy that an additional paroxysmal episode occurred at Voragine on 10 November 2024, after the submission of this paper, thus confirming our hypothesis. Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
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<p>Photos of Etna’s summit craters taken from a helicopter on 9 February (<b>a</b>) and 9 July (<b>b</b>) 2024. NEC = North-East Crater; SEC = South-East Crater; BN-1 and BN-2 are the two pits of the Bocca Nuova crater; VOR = Voragine. The comparison between the two photos taken five months apart shows the growth of the Voragine owing to the eruptive activity occurring between February and July 2024. (<b>a</b>) Photo by Maria Catania, INGV-OE. (<b>b</b>) Photo by Stefano Branca, INGV-OE.</p>
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<p>(<b>a</b>) Google Earth map of Italy, with the yellow rectangle showing Sicily, framed in (<b>b</b>). (<b>b</b>) Google Earth map of Sicily, with the yellow rectangle showing Mt. Etna volcano, framed in (<b>c</b>). (<b>c</b>) Google Earth map of Etna volcano, with the summit, where the name Etna is displayed, and the position of the EBT and EMCT INGV-OE cameras.</p>
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<p>Delimiting a mask (blue line with blue dots) to outline the Region of Interest (ROI): the blue line traces the boundary of the area to be analyzed, with the black region above the mask representing the ROI. This mask serves to isolate the volcanic activity by excluding the lower regions, minimizing data noise from surrounding non-essential areas and enhancing the focus on the primary activity zone for subsequent analysis.</p>
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<p>(<b>a</b>) RGB frame showing an ongoing lava fountain and associated lava flows spreading in multiple directions. The frame also includes a color bar (<b>right</b>) and a bar with camera name, date and time (<b>bottom</b>). This original frame provides the full color context but contains extraneous elements that may interfere with a quantitative analysis. (<b>b</b>) The same image as in (<b>a</b>) after conversion to gray scale using an appropriate threshold. This step removes color information, focusing on the contrast between active volcanic features and the background, reducing color noise and isolating key areas for further analysis. (<b>c</b>) The cropped image with the right and bottom bars removed. This step eliminates graphical noise from the color bar and timestamp, retaining only the Region of Interest (ROI) and enhancing the accuracy in identifying volcanic phenomena. (<b>d</b>) The binarized image, which highlights contrast between active and inactive areas. This improves the visibility of high-intensity regions, making it easier to accurately identify and assess areas of volcanic activity. (<b>e</b>) The mask (white) to delimit the ROI and exclude the lava flow area. This mask further isolates the lava fountain and nearby active areas, excluding irrelevant regions to improve measurement accuracy within the defined ROI. (<b>f</b>) The final image after the preprocessing sequence, with extraneous elements filtered out to enhance data quality and consistency for subsequent analysis.</p>
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<p>(<b>a</b>) Pyroclast area (<b>above</b>) and mean altitude (<b>below</b>) obtained from 4 July to 22 August 2024 on the thermal images recorded by the EBT camera. (<b>b</b>) Details of the lava fountain (LF) area for the six episodes, as recorded by the EBT camera.</p>
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<p>Lava fountain (LF) area recorded by the EBT (blue line) and EMCT (red line) cameras. Explosive paroxysm of 4 July 2024 (<b>a</b>), 7 July 2024 (<b>b</b>), 15–16 July 2024 (<b>c</b>), 23 July 2024 (<b>d</b>), 4 August 2024 (<b>e</b>) and 14–15 August 2024 (<b>f</b>). The <span class="html-italic">x</span>-axis is time (UTC), the <span class="html-italic">y</span>-axis is graduated in <math display="inline"><semantics> <mrow> <msup> <mrow> <mi mathvariant="normal">p</mi> <mi mathvariant="normal">i</mi> <mi mathvariant="normal">x</mi> <mi mathvariant="normal">e</mi> <mi mathvariant="normal">l</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>. In this figure, for clarity, the signals were down-sampled at a rate of 1 min.</p>
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<p>Area (<span class="html-italic">y</span>-axis, in pixels squared) against time (<span class="html-italic">x</span>-axis, UTC) occupied by the pyroclastic portion of the lava fountains recorded by the station. The experimental area data are shown in blue, with red curves representing the fit obtained using a basis of Gaussian functions. The thin black vertical lines mark the start and end times of each episode, estimated using the threshold algorithm mentioned in <a href="#sec3-remotesensing-16-04278" class="html-sec">Section 3</a>. (<b>a</b>–<b>f</b>) Data of the episodes 1 to 6, respectively.</p>
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<p>Frames from the EBT camera showing the initial, climax and declining phases of each of the six paroxysmal episodes at the VOR crater between July and August 2024. In each image, north is on the left, south is on the right. The color bar on the right displays the scale of the apparent temperature. (<b>a</b>) Frame of 4 July 2024 at 16:50:00, with the warm ash plume bending to the right, and the blue cold cloud to the left of the image. (<b>b</b>) Frame of 4 July 2024 at 21:01:50. (<b>c</b>) Frame of 5 July 2024 at 00:32:58. (<b>d</b>) Frame of 7 July 2024 at 02:02:08. (<b>e</b>) Frame of 7 July 2024 at 03:01:34. (<b>f</b>) Frame of 7 July 2024 at 06:01:54, with the blue portion of the plume showing the ash cloud spreading north (<b>left</b>). (<b>g</b>) Frame of 15 July 2024 at 19:01:30. (<b>h</b>) Frame of 15 July 2024 at 20:31:30. (<b>i</b>) Frame of 15 July 2024 at 23:01:54, with a lava flow starting from the right base of the crater. (<b>j</b>) Frame of 23 July 2024 at 06:01:34, with a lava flow starting from the right base of the crater. (<b>k</b>) Frame of 23 July 2024 at 06:46:42, with a lava flow starting from the right base of the crater. (<b>l</b>) Frame of 23 July 2024 at 07:31:36, with a lava flow starting from the right base of the crater. (<b>m</b>) Frame of 4 August 2024 at 02:31:08. (<b>n</b>) Frame of 4 August 2024 at 03:31:44, with a lava flow starting from the base of the crater. (<b>o</b>) Frame of 4 August 2024 at 07:01:30, with a lava flow spreading from the right and central portions of the crater. (<b>p</b>) Frame of 14 August 2024 at 22:01:24, with two lava flows starting from the right and central parts of the crater. (<b>q</b>) Frame of 14 August 2024 at 23:31:40, with at least three lava flows spreading from the base of the crater, and ash plume fallout to the right. (<b>r</b>) Frame of 15 August 2024 at 01:01:30, with four lava branches expanding from the base of the crater. All times are expressed as UTC.</p>
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<p>Duration, volume, maximum height and time-averaged discharge rate versus time for the six lava fountain episodes.</p>
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23 pages, 16601 KiB  
Article
Adaptive Weighted Coherence Ratio Approach for Industrial Explosion Damage Mapping: Application to the 2015 Tianjin Port Incident
by Zhe Su and Chun Fan
Remote Sens. 2024, 16(22), 4241; https://doi.org/10.3390/rs16224241 - 14 Nov 2024
Viewed by 292
Abstract
The 2015 Tianjin Port chemical explosion highlighted the severe environmental and structural impacts of industrial disasters. This study presents an Adaptive Weighted Coherence Ratio technique, a novel approach for assessing such damage using synthetic aperture radar (SAR) data. Our method overcomes limitations in [...] Read more.
The 2015 Tianjin Port chemical explosion highlighted the severe environmental and structural impacts of industrial disasters. This study presents an Adaptive Weighted Coherence Ratio technique, a novel approach for assessing such damage using synthetic aperture radar (SAR) data. Our method overcomes limitations in traditional techniques by incorporating temporal and spatial weighting factors—such as distance from the explosion epicenter, pre- and post-event intervals, and coherence quality—into a robust framework for precise damage classification. This approach effectively captures extreme damage scenarios, including crater formation in inner blast zones, which are challenging for conventional coherence scaling. Through a detailed analysis of the Tianjin explosion, we reveal asymmetric damage patterns influenced by high-rise buildings and demonstrate the method’s applicability to other industrial disasters, such as the 2020 Beirut explosion. Additionally, we introduce a technique for estimating crater dimensions from coherence profiles, enhancing assessment in severely damaged areas. To support structural analysis, we model air pollutant dispersal using HYSPLIT simulations. This integrated approach advances SAR-based damage assessment techniques, providing rapid reliable classifications applicable to various industrial explosions, aiding disaster response and recovery planning. Full article
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<p>(<b>a</b>) Location of Tianjin Port, East China, displayed by the true-RGB-color Sentinel-2 photograph on 18 September 2019 (provided by the ESA). The coverage area of the ascending (Asc.) and descending (Desc.) SAR images employed in this study is depicted on the inset map. (<b>b</b>) One of the limitations of the traditional coherence change detection method—the change from a positive elevation (building) to a negative one (crater)—exceeds the measurement capabilities of traditional coherence change detection techniques.</p>
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<p>On-site damage images from the explosion as of 12 September 2015, approximately one month after the event (images sourced from Google Earth). Images (<b>a</b>,<b>c</b>) depict the explosion epicenter with a large crater measuring 97 m in diameter and 2.7 m in depth. The toxic liquid, visible as a brownish color in the crater, likely contributed to unexplained decorrelation effects. Image (<b>b</b>) illustrates the complete destruction of buildings and vehicles, while (<b>d</b>) shows containers displaced due to the shockwave. Image (<b>e</b>), taken on 13 August 2015, captures chemically induced fires, facing south. The incident had potential ecological impacts on Bohai Bay [<a href="#B37-remotesensing-16-04241" class="html-bibr">37</a>,<a href="#B38-remotesensing-16-04241" class="html-bibr">38</a>] and air quality in the region [<a href="#B8-remotesensing-16-04241" class="html-bibr">8</a>].</p>
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<p>Workflow diagram for estimating explosion damage severity and subsequent classification. The methodology follows the framework adapted from [<a href="#B41-remotesensing-16-04241" class="html-bibr">41</a>].</p>
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<p>Comparison of coherence changes before (<b>a</b>,<b>c</b>) and after (<b>b</b>,<b>d</b>) the Tianjin Port explosion. Descending SAR pairs: (<b>a</b>) 30 July–11 August 2015, and (<b>b</b>) 11 August–23 August 2015. Ascending SAR pairs: (<b>c</b>) 1 July–25 July 2015, and (<b>d</b>) 25 July–18 August 2015. White–black brightness scale indicates coherence amplitude, with darker pixels representing lower coherence. The light blue arrows highlight the areas significantly impacted by the explosion, indicating zones with observed coherence changes. The dotted lines outline these affected regions to visually emphasize the explosion’s spatial extent.</p>
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<p>Comparison of four ratio-based change approaches for explosive analysis: (<b>a</b>) normalized change ratios, (<b>b</b>) logarithmic change ratios, (<b>c</b>) coherence change ratios, and (<b>d</b>) direct coherence ratio.</p>
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<p>Pre- and post-explosion coherence change ratio distribution. (<b>a</b>) Descending (track no. 149). (<b>b</b>) Ascending (track no. 69).</p>
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<p>Timeline showing acquisition dates for (<b>a</b>) D149 and (<b>b</b>) A69, with explosion and pre- and post-event intervals marked.</p>
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<p>Schematic diagram illustrating integrated damage assessment factors: Distance Normalization Factor (DNF), Coherence Quality Factor (CQF), and Post-Event Temporal Factor (PETF) across pre- and post-event intervals. The diagram also shows the radial damage zones from the explosion, including the inner, outer, and peripheral zones.</p>
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<p>Weighted ratio classification for the Tianjin Port explosion, combining D149 and A69 data. The profile width we used here is 500 m. Red polygons represent tall buildings. Histogram shows weighted ratio distribution from 0.07 to &gt;1.0.</p>
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<p>Weighted ratio classification for the Tianjin Port explosion, with profiles from (<b>a</b>) the north to the south and (<b>b</b>) from the southwest to the northeast, showing damage distribution from the epicenter.</p>
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<p>Pre- and post-explosion coherence change ratio distribution. (<b>a</b>) Descending (track no. 21). (<b>b</b>) Ascending (track no. 87). (<b>c</b>) Ascending (track no. 14). (<b>d</b>) Weighted ratio classification for the Beirut explosion incident, combining D21, A87, and A14 data.</p>
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<p>Coherence profiles for the Tianjin Port explosion epicenter: (<b>a</b>,<b>b</b>) show west–east coherence profiles before (red) and after (blue) the explosion, revealing significant coherence reductions across the crater region; (<b>c</b>,<b>d</b>) display similar coherence changes along the south–north axis, highlighting the crater’s extent, with coherence values dropping noticeably post-explosion. Combined profiles outline the crater’s approximate dimensions, measuring approximately 90 m west–east and 80 m north–south.</p>
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<p>Seven-day forward trajectories for nearby regions at different altitudes on 13 August and 19 August 2015. The trajectories are color-coded by release height above ground level (AGL): red for 100 m, blue for 500 m, and green for 1000 m. The HYSPLIT model outputs the heights of the trajectory endpoints in meters above the model terrain level, specifying heights both above ground level and above mean sea level (MSL) [<a href="#B66-remotesensing-16-04241" class="html-bibr">66</a>]. N. Korea: North Korea; S. Korea: South Korea.</p>
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17 pages, 22026 KiB  
Article
Quantitative Analysis of the Sloping Terrain on Al-Biruni’s Floor and Implications for the Cratering Process
by Feng Liu, Yuanxu Ma and Guanghao Ha
Remote Sens. 2024, 16(19), 3645; https://doi.org/10.3390/rs16193645 - 29 Sep 2024
Viewed by 463
Abstract
Surface unloading due to impact cratering results in lava filling the crater floor. Elevation differences in the crater floor, a common geological phenomenon on the Moon, represent direct evidence of cratering processes. However, few studies have been conducted on mare-filled craters on the [...] Read more.
Surface unloading due to impact cratering results in lava filling the crater floor. Elevation differences in the crater floor, a common geological phenomenon on the Moon, represent direct evidence of cratering processes. However, few studies have been conducted on mare-filled craters on the Moon. Al-Biruni (81 km) is a farside impact crater with an inclined topographic profile on its floor. We quantitatively measure the morphology of Al-Biruni and model the basaltic lava emplacement to depict the cratering process. Differential subsidence due to melt cooling, wall collapse, impact conditions and structural failure were assessed as potential factors influencing the formation of the elevation differences on the floor. The results suggest that pre-impact topography is a plausible cause of the differences in floor elevation within Al-Biruni. Other factors may also play a role in this process, affecting lava flow by altering the topography of the crater floor after the impact. Thus, regardless of whether the lava inside the crater is impact-generated or comes from outside the crater, altering topography at different stages of the cratering process is an essential factor in creating the sloped terrain on the crater floor. Full article
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<p>Mare Marginis, located near the eastern limb on the Moon on the near side.</p>
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<p>Al-Biruni (81 km) is situated to the south of the crater Joliot and northeast of Goddard. The black polygon highlights the boundary of the mare in this region [<a href="#B14-remotesensing-16-03645" class="html-bibr">14</a>].</p>
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<p>(<b>a</b>) A LROC WAC image of the Moon showing the spatial distribution of mare basalt in Mare Marginis. (<b>b</b>) A zoomed-in view of the Al-Biruni crater and surrounding areas, presented as a LROC WAC image mosaic. The red polygon marks the mare boundary in this region [<a href="#B14-remotesensing-16-03645" class="html-bibr">14</a>].</p>
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<p>(<b>a</b>) FeO weight percent maps of the Mare Marginis derived from Kaguya data. (<b>b</b>) TiO<sub>2</sub> maps of the same region. The black polygon outlines the mare boundary [<a href="#B14-remotesensing-16-03645" class="html-bibr">14</a>].</p>
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<p>A zoomed-in 3D view of (<b>a</b>) FeO weight percent maps and (<b>b</b>) TiO₂ maps of the Al-Biruni crater and surrounding regions, derived from Kaguya data.</p>
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<p>(<b>a</b>) Kaguya SLDEM2015 image of the Al-Biruni crater. The four topographic profiles (B, C, D, and E) illustrate the crater’s topography, including wall terraces and a ridge on the floor, which were plotted in (<b>b</b>), (<b>c</b>), (<b>d</b>) and (<b>e</b>), respectively.</p>
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<p>(<b>a</b>) Hillshade image generated from SLDEM2015. The black and red lines indicate elevation profiles across the ridge and floor, respectively, of Al-Biruni. The green line shows the location of the slope input for the PyFLOWGO model, as shown in <a href="#remotesensing-16-03645-f008" class="html-fig">Figure 8</a>. The ellipse with a yellow dashed line indicates the location of the ridge. (<b>b</b>) 3D view highlighting the morphology of Al-Biruni. The white dashed line marks the ridge on the crater floor.</p>
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<p>(<b>a</b>) Elevation profile across the ridge on the floor (see P1 in <a href="#remotesensing-16-03645-f007" class="html-fig">Figure 7</a>a). (<b>b</b>) Elevation profile through Al-Biruni, illustrating its morphology. (<b>c</b>) Slope profile used for the PyFLOWGO model, with an average slope of approximately 2°.</p>
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<p>Plot showing the modeled lava flow core temperature and viscosity along the slope profile on the floor (see green line in <a href="#remotesensing-16-03645-f007" class="html-fig">Figure 7</a>a).</p>
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12 pages, 7913 KiB  
Article
SO2 Diffusion Features of the 2022 Hunga Tonga–Hunga Ha’apai Volcanic Eruptions from DSCOVR/EPIC Observations
by Yi Huang and Wentao Duan
Atmosphere 2024, 15(10), 1164; https://doi.org/10.3390/atmos15101164 - 29 Sep 2024
Viewed by 476
Abstract
Understanding the volcanic SO2 diffusive characteristics can enhance our knowledge of the impact of volcanic eruptions on climate change. In this study, the SO2 diffusion features of the Hunga Tonga–Hunga Ha’apai underwater volcano (HTHH) 2022 eruptions are investigated based on the [...] Read more.
Understanding the volcanic SO2 diffusive characteristics can enhance our knowledge of the impact of volcanic eruptions on climate change. In this study, the SO2 diffusion features of the Hunga Tonga–Hunga Ha’apai underwater volcano (HTHH) 2022 eruptions are investigated based on the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) dataset, which could provide longer term, more consistent, and higher temporal sampling rate observations to complement current low-orbit satellite-based research. SO2 plume major-direction profile analysis indicates that the SO2 diffusion extent of subaerial eruption initiating at 15:20/13 January 2022 was approximately 1500 km in the Southeast–Northwest major diffusive direction by 20:15/14 January 2022 (about 29 h after the HTHH subaerial eruption). All-direction SO2 plume analysis shows that the HTHH subaerial eruption-emitted SO2 plume could diffuse as far as 6242 km by 02:20/15 January 2022. Furthermore, these two analyses in terms of the HTHH major eruption initiating at 04:00/15 January 2022 imply that HTHH major eruption-emitted SO2 plume could diffuse as far as 8600 km in the Southeast–Northwest major diffusive direction by 02:24/18 January 2022 (about 70 h after the HTHH major eruption). It is also implied that HTHH major eruption-emitted SO2 plume could extend to approximately 14,729 km away from the crater by 13:12/18 January 2022. We believe that these findings could provide certain guidance for volcanic gas estimations, thus helping to deepen our understanding of volcanic impacts on climate change. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))
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<p>Geolocation of the Hunga Tonga–Hunga Ha’apai (HTHH) submarine volcano (The red triangle).</p>
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<p>Total workflow of this study.</p>
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<p>(<b>a</b>) Absolute SO<sub>2</sub> concentration (SOC) and (<b>b</b>) interpolated SOC along the distance from the HTHH crater in the north diffusive direction at 18:46 on Jan 15 2022 (15 h after the 2022 HTHH major eruption). The correlation coefficient (R<sup>2</sup>) and root mean square error (RMSE) between (<b>a</b>,<b>b</b>) are 0.88 and 0.97 DU, respectively.</p>
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<p>SO<sub>2</sub> spatial distributions (<b>a</b>) 15 h before, (<b>b</b>) 6 h after, (<b>c</b>) 9 h after, and (<b>d</b>) 29 h after the 15:20 13 January 2022 HTHH subaerial eruption, respectively. Black triangle represents the HTHH volcano location; the purple dash arrow line represents the major Southeast-to-Northwest SO<sub>2</sub> diffusion direction.</p>
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<p>(<b>a</b>) SO<sub>2</sub> concentration along the distance from the HTHH crater in the Southeast–Northwest major diffusive direction at 20:15 on 14 January 2022 (29 h after the 2022 HTHH subaerial eruption). Red line denotes the SO<sub>2</sub> concentration, purple frames indicate the SO<sub>2</sub> plumes. (<b>b</b>) Temporally dependent SO<sub>2</sub> diffusive extent of the HTHH subaerial eruption initiating at 15:20/13 January 2022 UTC.</p>
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<p>SO<sub>2</sub> spatial distributions (<b>a</b>) 0.5 h (<b>b</b>) 18 h, (<b>c</b>) 45 h, and (<b>d</b>) 70 h after the 04:00 15 January 2022 HTHH major eruption, respectively. The black triangle represents the HTHH volcano location; the purple dash arrow line represents the major Southeast-to-Northwest SO<sub>2</sub> diffusion direction.</p>
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<p>(<b>a</b>) SO<sub>2</sub> concentration along the distance from the HTHH crater in the Southeast–Northwest major diffusive direction at 02:24 on 18 January 2022 (70 h after the 2022 HTHH major eruption). (<b>b</b>) Temporally dependent SO<sub>2</sub> diffusive extent of the HTHH major eruption initiating at 04:00/15 January 2022 UTC. Red solid line in (<b>a</b>) denotes the SO<sub>2</sub> concentration, black solid line in (<b>b</b>) indicates the SO<sub>2</sub> diffusion extent.</p>
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23 pages, 20834 KiB  
Article
Inferring the Variability of Dielectric Constant on the Moon from Mini-RF S-Band Observations
by Shashwat Shukla, Gerald Wesley Patterson, Abhisek Maiti, Shashi Kumar and Nicholas Dutton
Remote Sens. 2024, 16(17), 3208; https://doi.org/10.3390/rs16173208 - 30 Aug 2024
Viewed by 742
Abstract
The physical properties of lunar regolith are crucial for exploration planning, hazard assessment, and characterizing scientific targets at global and polar scales. The dielectric constant, a key property, offers insights into lunar material distribution within the regolith and serves as a proxy for [...] Read more.
The physical properties of lunar regolith are crucial for exploration planning, hazard assessment, and characterizing scientific targets at global and polar scales. The dielectric constant, a key property, offers insights into lunar material distribution within the regolith and serves as a proxy for identifying volatile-rich regoliths. Miniature radio frequency (Mini-RF) on the Lunar Reconnaissance Orbiter (LRO) provides a potential tool for mapping the lunar regolith’s physical nature and assessing the lunar volatile repository. This study presents global and polar S-band Mini-RF dielectric signatures of the Moon, obtained through a novel deep learning inversion model applied to Mini-RF mosaics. We achieved good agreement between training and testing of the model, yielding a coefficient of determination (R2 value) of 0.97 and a mean squared error of 0.27 for the dielectric constant. Significant variability in the dielectric constant is observed globally, with high-Ti mare basalts exhibiting lower values than low-Ti highland materials. However, discernibility between the South Pole–Aitken (SPA) basin and highlands is not evident. Despite similar dielectric constants on average, notable spatial variations exist within the south and north polar regions, influenced by crater ejecta, permanently shadowed regions, and crater floors. These dielectric differences are attributed to extensive mantling of lunar materials, impact cratering processes, and ilmenite content. Using the east- and west-looking polar mosaics, we estimated an uncertainty (standard deviation) of 1.01 in the real part and 0.03 in the imaginary part of the dielectric constant due to look direction. Additionally, modeling highlights radar backscatter sensitivity to incidence angle and dielectric constant at the Mini-RF wavelength. The dielectric constant maps provide a new and unique perspective of lunar terrains that could play an important role in characterizing lunar resources in future targeted human and robotic exploration of the Moon. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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<p>Schematic overview of the proposed framework in the research.</p>
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<p>Scattering geometry of the lunar surface, consisting of regolith, embedded rocks, and underlying bedrock. Regolith grains are represented by a circular shape, whereas the buried inclusions (rocks) are denoted by shaded circles. These are randomly distributed across the regolith layer of thickness <math display="inline"><semantics> <mrow> <mi>d</mi> </mrow> </semantics></math>. <b>a</b> is scattering from the top rough surface, <b>b</b> is volume scattering due to scatterers within the layer, <b>c</b> is subsurface scattering from the bedrock, and <b>d</b> represents the scattering due to surface-volume interaction. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> is the dielectric constant of buried inclusions, whereas <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>r</mi> <mi>e</mi> <mi>g</mi> <mi>o</mi> <mi>l</mi> <mi>i</mi> <mi>t</mi> <mi>h</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>b</mi> <mi>e</mi> <mi>d</mi> <mi>r</mi> <mi>o</mi> <mi>c</mi> <mi>k</mi> </mrow> </msub> </mrow> </semantics></math> are for the regolith layer and subsurface, respectively.</p>
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<p>Sensitivity of radar backscatter to incidence angle for five scattering processes under S-band configuration.</p>
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<p>Sensitivity of radar backscatter to dielectric constant under S-band configuration and Mini-RF incidence angle of 49<math display="inline"><semantics> <mrow> <mo>°</mo> </mrow> </semantics></math>.</p>
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<p>Comparison of predicted dielectric constant (violin plot with median lines) with ground truth (filled and hollow dots) from Apollo sites. The spread is calculated by dielectric constant radially averaged around the landing site in the radius range of 0–40 m (up to 5 km<sup>2</sup> area).</p>
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<p>Real part of the dielectric constant, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>r</mi> <mi>e</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math>, from Mini-RF global S-band data at a spatial resolution of 64 pixels per degree. The map projection is equirectangular. AP is Aristarchus Plateau, K is Kepler crater, and C is Copernicus crater.</p>
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<p>Imaginary part of the dielectric constant, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>i</mi> <mi>m</mi> <mi>a</mi> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math>, from Mini-RF global S-band data at a spatial resolution of 64 pixels per degree. The map projection is equirectangular. AP is Aristarchus Plateau, K is Kepler crater, and C is Copernicus crater.</p>
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<p>Estimated average real part (top) and imaginary part (bottom) of the dielectric constant from Mini-RF polar S-band data at a spatial resolution of 512 pixels per degree. Cabeus (Ca), Schomberger-A (S-A), Wiechert-J (W-J), and Faustini (F) craters are marked in the South Pole (SP), whereas Rozhdestvenskiy (R), Plaskett (P), Hermite-A (H-A), and Erlanger (E) craters are in the North Pole (NP). The map projection is polar stereographic.</p>
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<p>Real (<b>a</b>–<b>d</b>) and imaginary (<b>e</b>–<b>h</b>) parts of dielectric constant, overlain on Mini-RF <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> image, for south polar craters Schomberger-A (<b>a</b>,<b>e</b>), Wiechert-J (<b>b</b>,<b>f</b>), Faustini (<b>c</b>,<b>g</b>), and Cabeus (<b>d</b>,<b>h</b>). The map projection is polar stereographic, and the spatial resolution is 512 pixels per degree.</p>
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<p>Real (<b>a</b>–<b>d</b>) and Imaginary (<b>e</b>–<b>h</b>) parts of the dielectric constant, overlain on Mini-RF <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> image, for North polar craters Plaskett (<b>a</b>,<b>e</b>), Hermite-A (<b>b</b>,<b>f</b>), Erlanger (<b>c</b>,<b>g</b>), and Rozhdestvenskiy (<b>d</b>,<b>h</b>). R-K is Rozhdestvenskiy-K crater. The map projection is polar stereographic, and the spatial resolution is 512 pixels per degree.</p>
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<p>Difference maps of the real part (top) and imaginary part (bottom) of the dielectric constant from Mini-RF polar S-band data at a spatial resolution of 512 pixels per degree. Cabeus (Ca), Schomberger-A (S-A), Wiechert-J (W-J), and Faustini (F) craters are marked in the South Pole (SP), whereas Rozhdestvenskiy (R), Plaskett (P), Hermite-A (H-A), and Erlanger (E) craters are in the North Pole (NP). The map projection is polar stereographic.</p>
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<p>Histogram of difference maps of the real (top) and imaginary (bottom) parts of the dielectric constant for the south pole (left) and north pole (right).</p>
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15 pages, 21052 KiB  
Article
Response of a Coral Reef Sand Foundation Densified through the Dynamic Compaction Method
by Linlin Gu, Weihao Yang, Zhen Wang, Jianping Wang and Guanlin Ye
J. Mar. Sci. Eng. 2024, 12(9), 1479; https://doi.org/10.3390/jmse12091479 - 26 Aug 2024
Viewed by 650
Abstract
Dynamic compaction is a method of ground reinforcement that uses the huge impact energy of a free-falling hammer to compact the soil. This study presents a DC method for strengthening coral reef foundations in the reclamation area of remote sea islands. Pilot tests [...] Read more.
Dynamic compaction is a method of ground reinforcement that uses the huge impact energy of a free-falling hammer to compact the soil. This study presents a DC method for strengthening coral reef foundations in the reclamation area of remote sea islands. Pilot tests were performed to obtain the design parameters before official DC operation. The standard penetration test (SPT), shallow plate-load test (PLT), and deformation investigation were conducted in two improvement regions (A1 and A2) with varying tamping energies. During the deformation test, the depth of the tamping crater for the first two points’ tamping and the third full tamping was observed at two distinct sites. The allowable ground bearing capacity at two disparate field sites was at least 360 kPa. The reinforcement depths were 3.5 and 3.2 m in the A1 and A2 zones, respectively. The DC process was numerically analyzed by the two-dimensional particle flow code, PFC2D. It indicated that the reinforcement effect and effective reinforcement depth were consistent with the field data. The coral sand particles at the bottom of the crater were primarily broken down in the initial stage, and the particle-crushing zone gradually developed toward both sides of the crater. The force chain developed similarly at the three tamping energies (800, 1500, and 2000 kJ), and the impact stress wave propagated along the sand particles primarily in the vertical direction. Full article
(This article belongs to the Special Issue Advances in Marine Geological and Geotechnical Hazards)
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<p>Field site coral reef sand under dynamic compaction.</p>
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<p>Grain size distribution in the two test zones.</p>
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<p>Layout of impact points and investigation points in two test zones.</p>
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<p>DC site photos.</p>
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<p>Average settlement of the two tamping zones in different passes.</p>
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<p>Shallow plate-load test at the field sites.</p>
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<p>Load-settlement curves for shallow plate-load test at the field sites.</p>
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<p>Variation in SPT blows against depth at the field sites before and after DC.</p>
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<p>Two-dimensional particle model of coral sand foundation.</p>
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<p>Comparison between numerical results and field data.</p>
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<p>Settlement curves of three tamping energies.</p>
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<p>Displacement vector of coral foundation after tamping under different energy levels.</p>
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<p>Development of particle breakage after tamping under different energy levels.</p>
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<p>Cloud chart of force chain after tamping under different working conditions.</p>
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18 pages, 7033 KiB  
Article
Pseudo-Spectral Spatial Feature Extraction and Enhanced Fusion Image for Efficient Meter-Sized Lunar Impact Crater Automatic Detection in Digital Orthophoto Map
by Huiwen Liu, Ying-Bo Lu, Li Zhang, Fangchao Liu, You Tian, Hailong Du, Junsheng Yao, Zi Yu, Duyi Li and Xuemai Lin
Sensors 2024, 24(16), 5206; https://doi.org/10.3390/s24165206 - 11 Aug 2024
Viewed by 1353
Abstract
Impact craters are crucial for our understanding of planetary resources, geological ages, and the history of evolution. We designed a novel pseudo-spectral spatial feature extraction and enhanced fusion (PSEF) method with the YOLO network to address the problems encountered during the detection of [...] Read more.
Impact craters are crucial for our understanding of planetary resources, geological ages, and the history of evolution. We designed a novel pseudo-spectral spatial feature extraction and enhanced fusion (PSEF) method with the YOLO network to address the problems encountered during the detection of the numerous and densely distributed meter-sized impact craters on the lunar surface. The illumination incidence edge features, isotropic edge features, and eigen frequency features are extracted by Sobel filtering, LoG filtering, and frequency domain bandpass filtering, respectively. Then, the PSEF images are created by pseudo-spectral spatial techniques to preserve additional details from the original DOM data. Moreover, we conducted experiments using the DES method to optimize the post-processing parameters of the models, thereby determining the parameter ranges for practical deployment. Compared with the Basal model, the PSEF model exhibited superior performance, as indicated by multiple measurement metrics, including the precision, recall, F1-score, mAP, and robustness, etc. Additionally, a statistical analysis of the error metrics of the predicted bounding boxes shows that the PSEF model performance is excellent in predicting the size, shape, and location of impact craters. These advancements offer a more accurate and consistent method to detect the meter-sized craters on planetary surfaces, providing crucial support for the exploration and study of celestial bodies in our solar system. Full article
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<p>(<b>a</b>) An illustration of an impact crater slice. (<b>b</b>) The number–diameter distribution of 34,876 impact craters with diameters less than 14 m in our cropped 59 slices from the CE-4 landing site.</p>
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<p>Schematic diagram of the PSEF method for detecting meter-sized impact craters. The parameter preceding the ‘@’ represents the number of channels in the image. The parameters <span class="html-italic">W</span> and <span class="html-italic">H</span> following ‘@’ denote the width and height of the image, respectively. In this figure, <span class="html-italic">W</span> and <span class="html-italic">H</span> are both set to 320 pixels, e.g., ‘1@4<span class="html-italic">W</span>×4<span class="html-italic">H</span>’ under the PHS image indicates that the PHS image is single-channel with a size of 1280 × 1280 pixels. The grey arrows indicate that the images connected by these arrows represent the same image at different stages of the process.</p>
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<p>Illustration of images in the pseudo-spectral space. (<b>a</b>) The original PHS image. (<b>b</b>) Incidence features of crater rims obtained by the Sobel operator along the direction of incident light on the PHS image. (<b>c</b>) Isotropic features of meter-sized craters obtained by the LoG operator on the PHS image. (<b>d</b>) Eigen frequency features of meter-sized craters obtained by the bandpass filter in the frequency domain on the PHS image. (<b>e</b>) The PMS image obtained by amalgamating the images (<b>b</b>–<b>d</b>). (<b>f</b>) The PSEF image obtained by panchromatic sharpening on the PMS and PHS images.</p>
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<p>Frequency domain maps of PHS images with various mask regions. (<b>a</b>) Only the central 1–11 pixels annular mask region in the frequency domain of the PHS image is allowed to pass through. (<b>b</b>) The pseudo-spectrum obtained after the inverse DFT exhibiting eigen frequency features of impact craters with diameters in the hundreds of meters. (<b>c</b>) The annular mask region of 25–50 pixels in the frequency domain. (<b>d</b>) Eigen frequency features of impact craters with diameters over 20 m. (<b>e</b>) The concentric dual annular masks of 215–270 pixels and 350–400 pixels in the frequency domain. (<b>f</b>) Eigen frequency features for meter-sized impact craters.</p>
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<p>Illustrations for the optimization of the confidence threshold (<span class="html-italic">σ</span>) and IoU threshold (<span class="html-italic">τ</span>) using the discrete exhaustive search (DES) method. The <span class="html-italic">F</span><sub>1</sub>-score in the best post-processing parameters for the PSEF model and the Basal model are highlighted with blue circles.</p>
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<p>Visualization of partial test dataset slices and their corresponding automatic identification results. (<b>a</b>–<b>c</b>) are images from the test dataset. (<b>d</b>–<b>f</b>) are the identification results from the PSEF model. (<b>g</b>–<b>i</b>) display the identification results of the Basal model. Among them, the green, red, yellow, and blue circles refer to true positives, ground truth, false negatives, and false positives, respectively.</p>
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<p>Statistical marginal distribution of the diameter relative error (<span class="html-italic">δ<sub>D</sub></span>), the eccentricity error (Δ<span class="html-italic">e</span>), and the location error (Δ<span class="html-italic">L</span>) between true positives and the corresponding ground truths in the test dataset under the optimal post-processing parameters for the Basal model (<b>a</b>–<b>c</b>) and the PSEF model (<b>d</b>–<b>f</b>). The scatter points represent the distribution of error metrics—diameter for each true positive. The top and bottom of each box plot represent 75% and 25% of the data, respectively. Within the box, the solid line indicates the median, while the dot denotes the mean value. The whiskers extending from the boxes represent the interval within one standard deviation of the mean. Outliers that exceed the whiskers are marked individually.</p>
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19 pages, 6568 KiB  
Article
Quantitative Analysis of Pb in Soil Using Laser-Induced Breakdown Spectroscopy Based on Signal Enhancement of Conductive Materials
by Shefeng Li, Qi Zheng, Xiaodan Liu, Peng Liu and Long Yu
Molecules 2024, 29(15), 3699; https://doi.org/10.3390/molecules29153699 - 5 Aug 2024
Viewed by 777
Abstract
Studying efficient and accurate soil heavy-metal detection technology is of great significance to establishing a modern system for monitoring soil pollution, early warning and risk assessment, which contributes to the continuous improvement of soil quality and the assurance of food safety. Laser-induced breakdown [...] Read more.
Studying efficient and accurate soil heavy-metal detection technology is of great significance to establishing a modern system for monitoring soil pollution, early warning and risk assessment, which contributes to the continuous improvement of soil quality and the assurance of food safety. Laser-induced breakdown spectroscopy (LIBS) is considered to be an emerging and effective tool for heavy-metal detection, compared with traditional detection technologies. Limited by the soil matrix effect, the LIBS signal of target elements for soil heavy-metal detection is prone to interference, thereby compromising the accuracy of quantitative detection. Thus, a series of signal-enhancement methods are investigated. This study aims to explore the effect of conductive materials of NaCl and graphite on the quantitative detection of lead (Pb) in soil using LIBS, seeking to find a reliable signal-enhancement method of LIBS for the determination of soil heavy-metal elements. The impact of the addition amount of NaCl and graphite on spectral intensity and parameters, including the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), and relative standard deviation (RSD), were investigated, and the mechanism of signal enhancement by NaCl and graphite based on the analysis of the three-dimensional profile data of ablation craters and plasma parameters (plasmatemperature and electron density) were explored. Univariate and multivariate quantitative analysis models including partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and extreme learning machine (ELM) were developed for the quantitative detection of Pb in soil with the optimal amount of NaCl and graphite, and the performance of the models was further compared. The PLSR model with the optimal amount of graphite obtained the best prediction performance, with an Rp that reached 0.994. In addition, among the three spectral lines of Pb, the univariate model of Pb I 405.78 nm showed the best prediction performance, with an Rp of 0.984 and the lowest LOD of 26.142 mg/kg. The overall results indicated that the LIBS signal-enhancement method based on conductive materials combined with appropriate chemometric methods could be a potential tool for the accurate quantitative detection of Pb in soil and could provide a reference for environmental monitoring. Full article
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<p>Spectra of soil with different contents of sodium chloride (NaCl). (<b>a</b>) Full spectra of soil samples; (<b>b</b>) Pb I 283.31 nm spectrum of soil samples; (<b>c</b>) Pb I 368.35 nm spectrum of soil samples; (<b>d</b>) Pb I 405.78 nm spectrum of soil samples.</p>
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<p>Spectra of soil samples with different contents of graphite. (<b>a</b>) Full spectra of soil samples; (<b>b</b>) Pb I 283.31 nm spectrum of soil samples; (<b>c</b>) Pb I 368.35 nm spectrum of soil samples; (<b>d</b>) Pb I 405.78 nm spectrum of soil samples; (<b>e</b>) C I 405.78 nm spectrum of soil samples; (<b>f</b>) CN 386.03 nm, CN387 nm and CN388.22 nm spectra of soil samples. G means graphite.</p>
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<p>Comparison of Pb spectral-line parameters of soils with different contents of NaCl and graphite. (<b>a</b>,<b>d</b>) SBR; (<b>b</b>,<b>e</b>) SNR; (<b>c</b>,<b>f</b>) RSD. G means graphite.</p>
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<p>Comparison of the three-dimensional profile of ablation craters of soil samples with different conductive materials. (<b>a</b>) NaCl; (<b>b</b>) graphite; (<b>c</b>) without additives.</p>
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<p>(<b>a</b>) Instrument-broadening fitting curve; (<b>b</b>) Collision coefficient versus plasma temperature curve; (<b>c</b>) Comparison of plasma temperature; (<b>d</b>) Comparison of electron density. G means graphite.</p>
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<p>Univariate detection models and prediction results of soil Pb content with the optimal addition of NaCl based on the three primary characteristic lines of the Pb element. (<b>a</b>) The univariate model of Pb I 283.31 nm; (<b>b</b>) Prediction results of the univariate model based on Pb I 283.31 nm; (<b>c</b>) The univariate model of Pb I 368.35 nm; (<b>d</b>) Prediction results of the univariate model based on Pb I 368.35 nm; (<b>e</b>) The univariate model of Pb I 405.78 nm; (<b>f</b>) Prediction results of the univariate model based on Pb I 405.78 nm.</p>
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<p>Univariate detection models and prediction results of soil Pb content with the optimal addition of graphite based on the three primary characteristic lines of the Pb element. (<b>a</b>) The univariate model of Pb I 283.31 nm; (<b>b</b>) Prediction results of the univariate model based on Pb I 283.31 nm; (<b>c</b>) The univariate model of Pb I 368.35 nm; (<b>d</b>) Prediction results of the univariate model based on Pb I 368.35 nm; (<b>e</b>) The univariate model of Pb I 405.78 nm; (<b>f</b>) Prediction results of the univariate model based on Pb I 405.78 nm.</p>
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<p>Univariate detection models and prediction results of soil Pb content without additives based on the three primary characteristic lines of the Pb element. (<b>a</b>) The univariate model of Pb I 283.31 nm; (<b>b</b>) Prediction results of the univariate model based on Pb I 283.31 nm; (<b>c</b>) The univariate model of Pb I 368.35 nm; (<b>d</b>) Prediction results of the univariate model based on Pb I 368.35 nm; (<b>e</b>) The univariate model of Pb I 405.78 nm; (<b>f</b>) Prediction results of the univariate model based on Pb I 405.78 nm.</p>
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<p>Schematic diagram of LIBS system.</p>
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14 pages, 4202 KiB  
Article
On Low-Velocity Impact Response and Compression after Impact of Hybrid Woven Composite Laminates
by Yumin Li, Yongxing Jin, Xueting Chang, Yan Shang and Deng’an Cai
Coatings 2024, 14(8), 986; https://doi.org/10.3390/coatings14080986 - 5 Aug 2024
Viewed by 1033
Abstract
This paper aims to study the low-velocity impact (LVI) response and compression after impact (CAI) performance of carbon/aramid hybrid woven composite laminates employed in marine structures subjected to different energy impacts. The study includes a detailed analysis of the typical LVI responses of [...] Read more.
This paper aims to study the low-velocity impact (LVI) response and compression after impact (CAI) performance of carbon/aramid hybrid woven composite laminates employed in marine structures subjected to different energy impacts. The study includes a detailed analysis of the typical LVI responses of hybrid woven composite laminates subjected to the impact with three different energies, as well as a comparative analysis of cracks and internal delamination damage within impact craters. Additionally, the influence of different impact energies on the residual compressive strength of hybrid woven composite laminate is investigated through CAI tests and a comparative analysis of internal delamination damage is also conducted. The results indicate that as the impact energy increases, the impact load and CAI strength show a decreasing trend, while impact displacement and impact dent show an increasing trend. The low-velocity impact tests revealed a range of failure modes observed in the hybrid woven composite laminates. Depending on the specific combination of fiber materials and their orientations, the laminates exhibited different failure mechanisms. Buckling failures were observed in the uppermost composite layers of laminates with intermediate modulus systems. In contrast, laminates with higher modulus systems showed early damage in the form of delamination within the top surface layers. Full article
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<p>Instron CEAST 9350 and specimen for LVI test.</p>
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<p>Test fixture and specimen of compression after impact.</p>
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<p>The UltraPAC<sup>TM</sup> immersion C-scan device BSN-C1285.</p>
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<p>Impact force–time curves. (<b>a</b>) 10 J; (<b>b</b>) 20 J; (<b>c</b>) 30 J.</p>
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<p>Impact force–displacement curves. (<b>a</b>) 10 J; (<b>b</b>) 20 J; (<b>c</b>) 30 J.</p>
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<p>Impact energy–time curves. (<b>a</b>) 10 J; (<b>b</b>) 20 J; (<b>c</b>) 30 J.</p>
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<p>Impact energy–time curves. (<b>a</b>) 10 J; (<b>b</b>) 20 J; (<b>c</b>) 30 J.</p>
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<p>Impact damages for specimens.</p>
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<p>The delamination damage morphologies of composite laminates after impacts. (<b>a</b>) 10 J frontside, (<b>b</b>) 10 J backside, (<b>c</b>) 20 J frontside, (<b>d</b>) 20 J backside, (<b>e</b>) 30 J frontside, (<b>f</b>) 30 J backside.</p>
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<p>Compression load–displacement curves.</p>
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<p>The damage morphologies of composite laminates after CAI.</p>
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14 pages, 6230 KiB  
Article
Study on Low-Velocity Impact and Residual Compressive Mechanical Properties of Carbon Fiber–Epoxy Resin Composites
by Xueyuan Qiang, Te Wang, Hua Xue, Jun Ding and Chengji Deng
Materials 2024, 17(15), 3766; https://doi.org/10.3390/ma17153766 - 31 Jul 2024
Cited by 2 | Viewed by 807
Abstract
Room temperature drop hammer impact and compression after impact (CAI) experiments were conducted on carbon fiber–epoxy resin (CF/EP) composites to investigate the variation in impact load and absorbed energy, as well as to determine the residual compressive strength of CF/EP composites following impact [...] Read more.
Room temperature drop hammer impact and compression after impact (CAI) experiments were conducted on carbon fiber–epoxy resin (CF/EP) composites to investigate the variation in impact load and absorbed energy, as well as to determine the residual compressive strength of CF/EP composites following impact damage. Industrial CT scanning was employed to observe the damage morphology after both impact and compression, aiding in the study of impact-damage and compression-failure mechanisms. The results indicate that, under the impact load, the surface of a CF/EP composite exhibits evident cratering as the impact energy increases, while cracks form along the length direction on the back surface. The residual compressive strength exhibits an inverse relationship with the impact energy. Impact damage occurring at an energy lower than 45 J results in end crushing during the compression of CF/EP composites, whereas energy exceeding 45 J leads to the formation of long cracks spanning the entire width of the specimen, primarily distributed symmetrically along the center of the specimen. Full article
(This article belongs to the Special Issue Dynamic Behavior of Advanced Materials and Structures)
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<p>Schematic diagram of vacuum infusion molding.</p>
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<p>Clamping diagram of low-velocity impact test specimen.</p>
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<p>Schematic diagram of the remaining compression experimental fixture after impact.</p>
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<p>CT damage atlas of sample after 45 J impact energy (<b>a</b>) specimen face, (<b>b</b>)specimen back.</p>
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<p>CT damage atlas of sample after 105 J impact energy (<b>a</b>) specimen face, (<b>b</b>)specimen back.</p>
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<p>CT damage atlas of sample after 125 J impact energy (<b>a</b>) specimen face, (<b>b</b>)specimen back.</p>
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<p>Load/energy-time curves and load-displacement curves of specimens under different impact energy levels; (<b>a</b>) load-time curves, (<b>b</b>) energy-time curves, (<b>c</b>) load-displacement curves.</p>
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<p>Residual compressive strength curves at different impact energies.</p>
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<p>CT atlas of compressive test specimen after 35 J impact energy; (<b>a</b>) specimen face, (<b>b</b>) specimen back, (<b>c</b>) specimen length direction end, (<b>d</b>) specimen width direction end.</p>
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<p>CT atlas of compressive test specimen after 45 J impact energy; (<b>a</b>) specimen face, (<b>b</b>) specimen back, (<b>c</b>,<b>d</b>) specimen length direction side, (<b>e</b>) specimen center of length direction, (<b>f</b>) specimen center of width direction.</p>
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<p>CT atlas of compressive test specimen after 105 J impact energy; (<b>a</b>) specimen face, (<b>b</b>) specimen back, (<b>c</b>,<b>d</b>) specimen length direction side, (<b>e</b>) specimen center of length direction, (<b>f</b>) specimen center of width direction.</p>
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<p>CT atlas of compressive test specimen after 125 J impact energy; (<b>a</b>) specimen face, (<b>b</b>) specimen back, (<b>c</b>,<b>d</b>) specimen length direction side, (<b>e</b>) specimen center of length direction, (<b>f</b>) specimen center of width direction.</p>
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<p>Residual compressive load-displacement curves of CF/EP composites.</p>
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22 pages, 13149 KiB  
Article
Experimental and Numerical Simulation of Ejecta Size and Velocity of Hypervelocity Impact Rubble-Pile Asteroid
by Wenjin Liu, Qingming Zhang, Renrong Long, Jiankang Ren, Juncheng Li, Zizheng Gong, Qiang Wu and Siyuan Ren
Aerospace 2024, 11(8), 621; https://doi.org/10.3390/aerospace11080621 - 29 Jul 2024
Viewed by 891
Abstract
Rubble-pile asteroids may be the type of near-Earth object most likely to threaten Earth in a future collision event. Small-scale impact experiments and numerical simulations for large-scale impacts were conducted to clarify the size ratio of the boulder/projectile diameter effects on ejecta size–velocity [...] Read more.
Rubble-pile asteroids may be the type of near-Earth object most likely to threaten Earth in a future collision event. Small-scale impact experiments and numerical simulations for large-scale impacts were conducted to clarify the size ratio of the boulder/projectile diameter effects on ejecta size–velocity distribution. A series of small-scale impact cratering experiments were performed on porous gypsum–basalt targets at velocities of 2.3 to 5.5 km·s−1. Three successive ejection processes were observed by high-speed and ultra-high-speed cameras. The momentum transfer coefficient and cratering size were measured. A three-dimensional numerical model reflecting the random distribution of the interior boulders of the rubble-pile structure asteroid is established. The size ratio (length to diameter) of the boulder size inside the asteroid to the projectile diameter changed from 0.25 to 1.7. We conducted a smoothed particle hydrodynamics numerical simulation in the AUTODYN software to study the boulder size effect on the ejecta size–velocity distribution. Simulation results suggest that the microscopic porosity on regolith affects the propagation of shock waves and reduces the velocity of ejecta. Experiments and numerical simulation results suggest that both excavation flow and spalling ejection mechanism can eject boulders (0.12–0.72 m) out of the rubble-pile asteroid. These experiments and simulation results help us select the potential impact site in a planetary defense scenario and reduce deflection risk. are comprised primarily of boulders of a range of sizes. Full article
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<p>Photographs and SEM of porous gypsum–basalt targets. (<b>a</b>) Anhydrite powder and basalt pebbles. (<b>b</b>) Photographs of targets. (<b>c</b>) SEM image of targets.</p>
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<p>Experimental devices for specimen compression. (<b>a</b>) Uniaxial compressive specimen with diameter and height 50 and 100 mm, respectively. (<b>b</b>) Brazilian test specimens with diameter and height are both 50 mm.</p>
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<p>Compressive engineering stress-strain curves of the GB at a strain rate of 1 × 10<sup>−4</sup> s<sup>−1</sup> at room temperature. (<b>a</b>) Uniaxial compressive; and (<b>b</b>) Brazilian test.</p>
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<p>Schematic figures of hypervelocity impact momentum transfer tests.</p>
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<p>Geometric diagram of ballistic pendulum motion.</p>
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<p>Impact craters formed in GB at different velocities.</p>
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<p>Silicone mold and release after the crater. (<b>a</b>) Filling silica gel. (<b>b</b>) Front face. (<b>c</b>) Back face. (<b>d</b>) Side face.</p>
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<p><span class="html-italic">p/D</span> at different impact velocities.</p>
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<p>Ejecta from Shot 10 by 6 mm projectile impacting on GB at 4.9 km·s<sup>−1</sup>. The projectile incidence direction is perpendicular to the surface of the target.</p>
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<p>Typical motion evolution progress of the ejecta by 6 mm projectile impacting on GB at 4.5 km·s<sup>−1</sup>.</p>
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<p>Collected ejection at Shot 4. (<b>a</b>) Photographs of ejecta; and (<b>b</b>) SEM image of ejecta.</p>
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<p>Momentum enhancement <math display="inline"><semantics> <mi>β</mi> </semantics></math> vs. impact velocity for target with porosity. The 6 mm projectile into basalts from Liu et al. [<a href="#B31-aerospace-11-00621" class="html-bibr">31</a>] and Gault et al. [<a href="#B44-aerospace-11-00621" class="html-bibr">44</a>].</p>
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<p>Al2024 sphere projectile impact basalt in 2D axial symmetry model.</p>
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<p>Random filling model of different size boulders: (<b>a</b>) 0.18 m; (<b>b</b>) 0.72 m; and (<b>c</b>) 1.2 m.</p>
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<p>FEM of the boulder and regolith: (<b>a</b>) FEM of the regolith; (<b>b</b>) boulder area; (<b>c</b>) FEM of the boulder; and (<b>d</b>) FEM of the boulder and regolith.</p>
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<p>Calculation model of 1/4 symmetry.</p>
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<p>The ejection velocity (left column) and pressure (right column) distribution at different times of aluminum projectile impact on homogeneous basalt at 6.15 km·s<sup>−1</sup>: (<b>a</b>) 0.002 ms; (<b>b</b>) 0.02 ms; and (<b>c</b>) 0.2 ms.</p>
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<p>Velocity (the upper part) and pressure (the lower part) cloud diagram aluminum sphere impact targets with different porosity at 6.15 km·s<sup>−1</sup>. (<b>a</b>) Basalt with 0% porosity. (<b>b</b>) Porous basalt with 30% porosity.(<b>c</b>) Pumice with 70% porosity.</p>
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<p>Velocity (left column) and pressure (right column) cloud diagram (front view). (<b>a</b>) Homogeneous asteroid with basalt. (<b>b</b>) Rubble-pile asteroid with porous basalt regolith where the boulder size is 0.18 m. (<b>c</b>) Rubble-pile asteroid with pumice regolith where boulder size is 0.18 m.</p>
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<p>Pressure cloud diagram impact rubble-pile asteroid with porous basalt regolith containing different sizes of boulder. (Top view, t = 1 ms). (<b>a</b>) Boulder size is 0.18 m. (<b>b</b>) Boulder size is 0.72 m. (<b>c</b>) Boulder size is 1.2 m.</p>
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<p>The ejection distribution after impacting the rubble-pile asteroid with different size boulders at 0.01 s, light green is regolith particles, black is boulder particles. (<b>a</b>) Boulder size is 0.18 m. (<b>b</b>) Boulder size is 0.72 m. (<b>c</b>) Boulder size is 1.2 m.</p>
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18 pages, 4576 KiB  
Article
Evaluation of Slurry-Eroded Rubber Surface Using Gloss Measurement
by Wichain Chailad and Liu Yang
Coatings 2024, 14(7), 915; https://doi.org/10.3390/coatings14070915 - 22 Jul 2024
Viewed by 860
Abstract
Slurry erosion testing is essential for evaluating the durability of materials under erosive conditions. This study examines the slurry erosion behaviours of chloroprene rubber (CR) under varying impact conditions to assess its durability. Traditional mass loss methods and qualitative techniques, including microscopy, SEM, [...] Read more.
Slurry erosion testing is essential for evaluating the durability of materials under erosive conditions. This study examines the slurry erosion behaviours of chloroprene rubber (CR) under varying impact conditions to assess its durability. Traditional mass loss methods and qualitative techniques, including microscopy, SEM, and AFM, were employed to analyse eroded CR samples. Results indicate that cumulative material loss in CR increases linearly with sand impingement after approximately 60 kg of sand and correlates with an impact energy of about 30 kJ. The highest erosion rate was found at an impact angle of 15°. Erosion mechanisms vary with impact angle, affecting surface topography from cutting and ploughing at lower angles to deformation and crater formation at higher angles. Despite their efficacy, these methods are time-intensive and costly. This paper presents a novel approach utilising gloss measurement for continuous, non-destructive monitoring of eroded rubber surfaces. Gloss measurements are 24 times faster than traditional mass loss methods. Correlating gloss values with cumulative material loss, steady-state erosion, and impact energy offers significant time savings and an enhanced understanding of the erosion process. Experimental results demonstrate the effectiveness of gloss measurement as a reliable tool in slurry erosion testing of rubbers. The quantitative output from gloss measurements could support proactive maintenance strategies to extend service life and optimise operational efficiency in industrial applications, particularly in the mining industry. Full article
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<p>Spectral reflection.</p>
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<p>(<b>a</b>) High diffuse reflection; (<b>b</b>) low diffuse reflection.</p>
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<p>Incident angle for gloss measurement [<a href="#B21-coatings-14-00915" class="html-bibr">21</a>].</p>
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<p>A schematic view of the SJET rig (not to scale) [<a href="#B10-coatings-14-00915" class="html-bibr">10</a>]. 1. Nozzle; A test piece with backing strip (adjustable angle 10° to 90°); 2. CR Sample; 3. Slurry collecting reservoir; 4. Main slurry pipe; 5. Main pump with inverter; 6. 500 L testing reservoir; 7. Sand slurry; 8. Overhead mixer; 9. Transfer pump; 10. Slurry storage reservoir; 11. Water inlet; 12. Splash cover; 13. Draining pipe points.</p>
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<p>Step-by-step gloss measurements and interpretation of gloss results.</p>
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<p>(<b>a</b>) Cumulative mass loss of CR with the amount of impingement sand; (<b>b</b>) impact energy in SJET at impact angles of 15°, 30°, 45°, and 90°; (<b>c</b>) slurry erosion rates obtained from the slope in <a href="#coatings-14-00915-f006" class="html-fig">Figure 6</a>a.</p>
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<p>Optical micrographs of the eroded surface of CR impacted by 120 kg of sand at angles of (<b>a</b>) 15°; (<b>b</b>) 30°; (<b>c</b>) 45°; (<b>d</b>) 90°.</p>
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<p>SEM images of the eroded surface of CR impacted by 120 kg of sand at angles of (<b>a</b>) 15°; (<b>b</b>) 30°; (<b>c</b>) 45°; (<b>d</b>) 90°.</p>
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<p>AFM topography images of the (<b>a</b>) uneroded and eroded surfaces of CR after 120 kg of sand impingement at impact angles of (<b>b</b>) 15°; (<b>c</b>) 30°; (<b>d</b>) 45°, (<b>e</b>) 90°.</p>
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<p>Average roughness of CR surfaces before and after 120 kg of sand at impact angles of 15° 30°, 45°, and 90°.</p>
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<p>Changes in gloss units of eroded CR with the amount of impingement sand in SJET at impact angles of 15°, 30°, 45°, and 90°.</p>
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<p>Changes in gloss units of eroded CR with the impact energy in SJET at impact angles of 15°, 30°, 45°, and 90°.</p>
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<p>Gloss units changes in CR samples at impact angles of 15°, 30°, 45°, and 90°.</p>
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<p>Gloss units for CR as a function of cumulative mass loss at impact angles of 15°, 30°, 45°, and 90°.</p>
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12 pages, 5115 KiB  
Article
Effect of Target Properties on Regolith Production
by Minggang Xie and Yan Li
Remote Sens. 2024, 16(14), 2650; https://doi.org/10.3390/rs16142650 - 20 Jul 2024
Viewed by 799
Abstract
Based on the measurements of regolith thicknesses on the lunar maria (basalts), the lunar regolith was determined to have accumulated at a rate of about 1 m/Gyr since the era of the late heavy bombardment. However, regolith production on porous targets (e.g., crater [...] Read more.
Based on the measurements of regolith thicknesses on the lunar maria (basalts), the lunar regolith was determined to have accumulated at a rate of about 1 m/Gyr since the era of the late heavy bombardment. However, regolith production on porous targets (e.g., crater ejecta deposits) is less studied, especially for Copernican units, and how target properties affect regolith production is not well understood. Here, we measured regolith thicknesses on the ejecta blanket of the Copernicus crater, showing that the regolith production rate sensitively depends on the initial target properties. The regolith production rate of the Copernicus ejecta blanket (3.0 ± 0.1 m/Gyr) is significantly larger than that of the Copernicus impact melt, which was previously estimated to be 1.2 ± 0.2 m/Gyr. Although crater production varies with different targets, our observed crater density of the Copernicus impact melt is indistinguishable from that of the Copernicus ejecta because impacts fracture the melt, causing it to resemble the ejecta. However, due to the fact that the formation of crater ejecta had already caused them to undergo fragmentation, ejecta require fewer fragmentation times to become regolith compared to impact melt; thus, the growth of regolith on the ejecta is faster than the melt. This indicates that similar observed size–frequency distributions do not indicate similar regolith production, especially for the targets with significant differences in initial physical properties. Full article
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<p>Measurements of regolith thickness distribution at Copernicus crater. (<b>a</b>) The location of regolith thickness distribution measurement at Copernicus ejecta (blue). The location of regolith measurement at Copernicus melt is also shown for comparison (red). The base map is LROC WAC Mosaic. (<b>b</b>) Mapped fresh craters (the circles) at the Copernicus ejecta. The red, green, and blue circles represent normal, flat-bottom, and concentric craters. The base map is LROC NAC M144768961 mosaic.</p>
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<p>(<b>a</b>) Diameter distributions of the three types of fresh craters. The red, green, and blue are normal, flat-bottom, and concentric craters, respectively. Each curve represents the fraction of one crater type to all crater types, and their corresponding 1σ uncertainties are shown. The squares represent the raw diameters. (<b>b</b>) Regolith thickness distributions (i.e., cumulative percentages of regolith thicker than a given value). The regolith thicknesses derived from the normal (red) and concentric (blue) craters are the diameters of panel (<b>a</b>) divided by <span class="html-italic">n</span> = 4 and 9, respectively. A median regolith thickness corresponds to the thickness with a 50% cumulative percentage. The squares represent the regolith thicknesses directly derived from the raw diameters.</p>
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<p>The relationship between regolith thickness and crater production function. The regolith thickness distribution panel (<b>a</b>) and median thickness panel (<b>b</b>) depend on SFD slope and crater density. The uncertainties are calculated by adopting 0.1 and 0.01% geometric saturations.</p>
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<p>Crater size–frequency measurements at Copernicus crater. Mapping (<b>a</b>) large and (<b>b</b>) smaller craters (the red circles) on Copernicus ejecta. Secondary crater fields are excluded (yellow). An interior nested small area (the green polygon in panel (<b>a</b>)) is the same as that in panel (<b>b</b>). Counting (<b>c</b>) large and (<b>d</b>) smaller craters (the red circles) on Copernicus impact melt. The blue rectangles in panels (<b>b</b>,<b>d</b>) are regolith mapping regions (see also <a href="#remotesensing-16-02650-f001" class="html-fig">Figure 1</a>). All panels are given in Mercator projection, and all base maps are Kaguya TC morning images.</p>
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<p>Observed crater SFDs of Copernicus crater by using the nested crater counting technique. (<b>a</b>) SFDs of the raw diameter data, and (<b>b</b>) constructed SFDs of Copernicus ejecta and impact melt. The data points in panel (<b>a</b>) correspond to the data with the same colors shown in panel (<b>b</b>). The 1, 2, and 10% geometric saturations (the dashed lines) are shown for comparison. The MPF<sub>T</sub> represents the modeled production population of craters on a 0.8 Ga-old target (Copernicus ejecta, Copernicus impact melt, or a competent-rock-like reference target) with consideration of target properties [<a href="#B40-remotesensing-16-02650" class="html-bibr">40</a>]. The MPF<sub>TD</sub> of Copernicus ejecta, which considers the effect of topographic degradation on the MPF<sub>T</sub> of Copernicus ejecta, matches with the observed Copernicus SFDs (as measurement error has a minor effect on SFDs; thus, here we neglect the measurement error when comparing SFDs). In contrast to the MPF<sub>T</sub> of a reference target (competent rock does not change with time), the MPF<sub>T</sub> of Copernicus melt accounts for the fragmentation by subsequent impacts, and hence, it gradually follows the trend in the MPF<sub>T</sub> of Copernicus ejecta for craters smaller than about 10 m.</p>
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24 pages, 24274 KiB  
Article
Multi-Platform Integrated Analysis of the Degradation Patterns of Impact Crater Populations on the Lunar Surface
by Meixi Chen, Xinyu Ma, Teng Hu, Zhizhong Kang and Meng Xiao
Remote Sens. 2024, 16(13), 2359; https://doi.org/10.3390/rs16132359 - 27 Jun 2024
Viewed by 773
Abstract
Following the processing of the Chang’e-4 satellite images, Chang’e-4 landing camera images, and Yutu-2 panoramic camera images, data were obtained in a variety of resolutions, including digital elevation model (DEM) and digital orthophoto map (DOM). By determining the morphological parameters, including the depths [...] Read more.
Following the processing of the Chang’e-4 satellite images, Chang’e-4 landing camera images, and Yutu-2 panoramic camera images, data were obtained in a variety of resolutions, including digital elevation model (DEM) and digital orthophoto map (DOM). By determining the morphological parameters, including the depths and diameters of impact craters in the study area, as well as their degradation classes based on surface texture features, we conducted a comprehensive analysis of the morphological parameters and population degradation patterns of impact craters from multiple platforms. The data from three platforms were employed to identify 12,089 impact craters with diameters ranging from 0.1 m to 800.0 m, which were then classified into five degradation classes based on their morphology in the images. This study indicates that as the size of impact craters increases, the population within them experiences a greater degree of degradation. However, the severe degradation of impact craters with diameters of less than 1 m or even 2 m is influenced by the rapid rate of degradation of the crater and the low solidity of the crater lips. The results of the equilibrium state of impact craters indicate that for sub-metre-sized impact craters (with diameters below 2.0 m), it is challenging to reach equilibrium. Furthermore, the smaller the impact crater, the more difficult it is to achieve equilibrium, which is probably the result of simpler generation conditions and the faster degradation of small impact craters. Full article
(This article belongs to the Special Issue Remote Sensing and Photogrammetry Applied to Deep Space Exploration)
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<p>Flow chart of impact craters’ data processing.</p>
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<p>Impact crater identification method. The red outlined areas correspond to impact craters identified.</p>
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<p>Crater morphological parameter extraction algorithm.</p>
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<p>PCAM images. (<b>a</b>) DOM image. (<b>b</b>) DEM image.</p>
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<p>Identified impact craters in DOM images.</p>
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<p>LCAM images and identified impact craters.</p>
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<p>Satellite images (M1298916428) and identified impact craters.</p>
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<p>Histogram of frequencies versus diameters of craters. (<b>a</b>) Results of PCAM image data. (<b>b</b>) Results of LCAM image data. (<b>c</b>) Results of satellite image data.</p>
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<p>Histogram of frequencies versus diameters of craters. (<b>a</b>) Results of PCAM image data. (<b>b</b>) Results of LCAM image data. (<b>c</b>) Results of satellite image data.</p>
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<p>Histogram of frequency versus depth-to-diameter ratio of craters. (<b>a</b>) Results of PCAM image data. (<b>b</b>) Results of LCAM image data. (<b>c</b>) Results of satellite image data. The red lines indicate the frequency curve of the data.</p>
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<p>Histogram of frequency versus depth-to-diameter ratio of craters. (<b>a</b>) Results of PCAM image data. (<b>b</b>) Results of LCAM image data. (<b>c</b>) Results of satellite image data. The red lines indicate the frequency curve of the data.</p>
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<p>Trend plot of the diameter versus the depth-to-diameter ratio of craters.</p>
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<p>Histogram of frequencies versus diameters of all craters.</p>
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<p>Depth versus diameter values of craters.</p>
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<p>Histogram of frequency versus depth-to-diameter ratio of craters.</p>
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<p>Crater degradation class.</p>
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<p>Histogram of percentage versus diameter and degradation class of craters.</p>
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<p>Histogram of percentage versus diameter and degradation class of craters.</p>
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<p>Examples of impact craters, including results of morphology and depth-to-diameter ratio in DOM images. The yellow outlined areas correspond to impact craters identified.</p>
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<p>Craterstats2-program-calculated equilibrium lines. (<b>a</b>) The three areas as a whole. (<b>b</b>) The satellite imagery area. (<b>c</b>) The landing area. (<b>d</b>) The area along the route of the rover. EF refer to the equilibrium function of Hartmann [<a href="#B36-remotesensing-16-02359" class="html-bibr">36</a>]. PF and CF refer to the production function and the chronology function of Neukum et al. [<a href="#B4-remotesensing-16-02359" class="html-bibr">4</a>], respectively.</p>
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<p>Craterstats2-program-calculated equilibrium lines of the area along the rover. EF refer to the equilibrium function of Hartmann [<a href="#B36-remotesensing-16-02359" class="html-bibr">36</a>]. PF and CF refer to the production function and the chronology function of Neukum et al. [<a href="#B4-remotesensing-16-02359" class="html-bibr">4</a>], respectively.</p>
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19 pages, 22731 KiB  
Article
Study on the Degradation Pattern of Impact Crater Populations in Yutu-2′s Rovering Area
by Xinyu Ma, Meixi Chen, Teng Hu, Zhizhong Kang and Meng Xiao
Remote Sens. 2024, 16(13), 2356; https://doi.org/10.3390/rs16132356 - 27 Jun 2024
Cited by 1 | Viewed by 677
Abstract
A detailed analysis of the panoramic camera data from the 27th to 33rd lunar days was conducted on the high-resolution scenes captured by the Yutu-2 rover stations. This analysis aimed to determine the detailed morphological parameters of the 2015 impact craters within the [...] Read more.
A detailed analysis of the panoramic camera data from the 27th to 33rd lunar days was conducted on the high-resolution scenes captured by the Yutu-2 rover stations. This analysis aimed to determine the detailed morphological parameters of the 2015 impact craters within the inspection area. The levels of degradation observed in the impact craters were determined alongside the surface features. Subsequently, the degradation patterns of the impact craters located within the Yutu-2’s roving area and the distribution patterns of the morphological parameters were analysed and investigated. The results of the analysis indicate that 94% of the impact craters exhibited severe degradation, 80% had depth-to-diameter ratios (DDRs) ranging from 0.07 to 0.17, and the remaining craters were moderately degraded. The DDRs of the impact craters exhibited a declining trend with an increase in the dimensions of the impact craters. Additionally, the degree of degradation of impact crater populations demonstrated a decreasing trend. In general, the impact craters along the rover’s route exhibited severe degradation, with the population of degradation degrees gradually decreasing with increasing diameter. Full article
(This article belongs to the Special Issue Planetary Geologic Mapping and Remote Sensing (Second Edition))
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<p>Parts of an original panoramic camera image.</p>
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<p>Step diagram of data processing.</p>
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<p>Impact crater identification method. (<b>a</b>) Impact crater marking with DOM as base map. (<b>b</b>) Contour-assisted recognition with DOM as base map. (<b>c</b>) Visual comparison of the original images of the rover. The red circle in the figure shows the approximate location of the impact crater, and the yellow line shows the contour lines.</p>
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<p>Impact crater identification method. (<b>a</b>) Impact crater marking with DOM as base map. (<b>b</b>) Contour-assisted recognition with DOM as base map. (<b>c</b>) Visual comparison of the original images of the rover. The red circle in the figure shows the approximate location of the impact crater, and the yellow line shows the contour lines.</p>
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<p>Examples of different types of impact crater degradation. The diagram shows the degradation levels of the impact crater. The levels are marked in the lower left corner.</p>
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<p>Generated images: (<b>a</b>) D0M image; (<b>b</b>) DEM image.</p>
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<p>Schematic of DOM image range.The red ring in the figure represents the effective range of the image, and the meanings represented by the other colours are marked in the figure.</p>
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<p>Distribution of impact craters in DOM images. The yellow circles represent the approximate locations of all extractable impact craters at the station in the diagram.</p>
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<p>Size–frequency distribution of the impact craters up to 2 m in diameter.</p>
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<p>Depth–diameter relationship.</p>
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<p>Frequency plot of depth-to-diameter ratios.</p>
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<p>Diameter–depth ratio relationship.</p>
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<p>Degradation level classifications as percentages.</p>
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<p>Diameter–degradation level relationship.</p>
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<p>Relationship between depth-to-diameter ratio and degradation classification.</p>
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<p>Results of the existing study. (<b>a</b>) shows a plot of diameter versus depth, and (<b>b</b>) shows a plot of diameter versus depth-to-diameter ratio.</p>
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<p>Craterstats2 programme-calculated equilibrium lines [<a href="#B26-remotesensing-16-02356" class="html-bibr">26</a>,<a href="#B28-remotesensing-16-02356" class="html-bibr">28</a>].</p>
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