In this study, a methodology is proposed to use dual-polarimetric synthetic aperture radar (SAR) to identify the spatial distribution of soil liquefaction. The latter is a phenomenon that occurs in conjunction with seismic events of a magnitude generally higher than 5.5-6.0 and which affects loose sandy soils located below the water table level. The methodology consists of two steps: first the spatial distributions of soil liquefaction is estimated using a constant false alarm rate method applied to the SPAN metric, namely the total power associated with the measured polarimetric channels, which is ingested into a bitemporal approach to sort out dark areas not genuine. Second, the obtained masks are read in terms of the physical scattering mechanisms using a child parameter stemming from the eigendecomposition of the covariance matrix-namely the degree of polarization. The latter is evaluated using the coseismic scenes and contrasted with the preseismic one to have rough information on the time-variability of the scattering mechanisms occurred in the area affected by soil liquefaction. Finally, the obtained maps are qualitatively contrasted against state-of-the-art optical and interferometric SAR methodologies. Experimental results, obtained processing a time-series of ascending and descending Sentinel-1 SAR scenes acquired during the 2023 Turkiye-Syria earthquake, confirm the soundness of the proposed approach.

Lunar exploration has attracted considerable attention, with the lunar poles emerging as the next exploration hot spot for the cold trapping of volatiles in the permanently shadowed regions (PSRs) at these poles. Remote sensing via the satellite's optical load is one of the most important ways to get the scientific data of PSRs. However, the illumination conditions at the lunar poles are quite different from the low latitude areas and how to get appropriate optical signal remains challenging. Thus, simulation of the optical remote sensing process, which provides reference for the choice of satellites' imaging parameters to ensure the implementation of lunar exploration project, is of great value. In this article, an optical imaging chain modeling for the PSRs at the lunar south pole, which includes lunar 3-D topography, observing satellite's orbit, instrument's parameters, and other environmental parameters, has been built. To demonstrate the physical accuracy, some PSRs' observations acquired by narrow angle cameras (NACs) equipped on the lunar reconnaissance orbiter (LRO) are compared with the corresponding images simulated by the proposed imaging chain model. The digital value's difference between the simulated images and real captured images is generally less than 50 for 12-bit images ranging from 0 to 4095, indicating a good fit considering the uncertainty of soil's absolute reflectance and the noise in the real captured images. In addition, the impact of the imaging chain's parameters is revealed with the proposed algorithm. The simulation method will provide reference and assist future optical imaging of PSRs.

Excavation of underground caverns, such as mountain tunnels and energy-storage caverns, may cause the damages to the surrounding rock as a result of the stress redistribution. In this influenced zone, new cracks and discontinuities are created or propagate in the rock mass. Therefore, it is effective to measure and evaluate the acoustic emission (AE) events generated by the rocks, which is a small elastic vibration, and permeability change. The authors have developed a long -term measurement device that incorporates an optical AE (O-AE) sensor, an optical pore pressure sensor, and an optical temperature sensor in a single multi -optical measurement probe (MOP). Japan Atomic Energy Agency has been conducting R&D activities to enhance the reliability of high -level radioactive waste (HLW) deep geological disposal technology. In a high -level radioactive disposal project, one of the challenges is the development of methods for long -term monitoring of rock mass behavior. Therefore, in January 2014, the long -term measurements of the hydro-mechanical behavior of the rock mass were launched using the developed MOP in the vicinity of 350 m below the surface at the Horonobe Underground Research Center. The measurement results show that AEs occur frequently up to 1.5 m from the wall during excavation. In addition, hydraulic conductivity increased by 2-4 orders of magnitude. Elastoplastic analysis revealed that the hydraulic behavior of the rock mass affected the pore pressure fluctuations and caused micro-fractures. Based on this, a conceptual model is developed to represent the excavation damaged zone (EDZ), which contributes to the safe geological disposal of radioactive waste. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Obtaining high-visibility images of the lunar polar permanently shadowed region (PSR) is quite important for internal landforms and material existence exploration. However, PSR images usually have poor quality due to a lack of sufficient illumination. Existing researches, that attempt to address this problem, face challenges caused by relying on virtual assumptions, manual processing, and paired data. To solve these problems, we aim to avoid using paired datasets and directly optimize PSR images, and accordingly propose a zero-shot parameter learning model (ZSPL-PSR) for PSR image enhancement. Our ZSPL-PSR, which enhances PSR images by estimating parameters to adjust image properties, consists of a parameter learning network and a parameter weight learning structure. Particularly, first, a parameter learning network that integrates robust information is constructed to separately estimate the midtone brightness parameters, shadow brightness parameters, and contrast parameters. Where these parameters are beneficial for iteratively improve the overall brightness, shadow brightness, and contrast of the image. Second, a parameter weight learning structure is exploited to coordinate the priority of different parameter maps. In addition, to highlight the terrain details in the enhanced PSR image, we use USM sharpening for postprocessing. The experimental results display the fully interpretable enhanced PSR maps of the lunar north and south poles and their sharpened versions, showcasing rich landforms in PSR. To validate the model performance, a benchmark PSR testing set has been constructed, and extensive comparisons conducted on it demonstrated that ZSPL-PSR exceeds other zero-shot learning methods significantly in image quality. Our code is available at https://github.com/dl-zfq/ZSPL-PSR.

The Karakoram Anomaly has been intensively investigated, but the factors that control this anomaly, such as the glacier velocity, topography, and mass balance, remain poorly understood. To improve our understanding of the velocity, topography, and mass balance of the Karakoram Glacier, in this study, the spatiotemporal variability of four glacier velocities in the Hunza Basin of the Karakoram range were surveyed using co-registration of optically sensed images and correlation (COSI-Corr) on Landsat imagery from 1993-2019. The results show that the velocity of the Gulmit Glacier increases with a rising altitude from the glacier terminal. The three other glaciers initially display high velocity, followed by a decrease from the glacier terminal, with the maximum velocity attained in the middle of the glacier. In addition, the Karakoram glaciers produced a slight mass gain, with all mountain glaciers exhibiting clear regional acceleration from 1993-2019. The ice deformation velocity of the Batura Glacier diminished at an average rate of 8.49 %. However, the topography of the glacier base and physical factors require further analysis to determine their contribution to the observed changes in glacier velocity. In the present work, multi-temporal remote sensing image interpretations were carried out to determine glacier kinematics, which could enhance our understanding of glacier change mechanisms.

The shape, size, and abundance of rocks on the Moon's surface are essential for understanding impact cratering and weathering processes, interpreting remote sensing observations, and ensuring landing safety and rover trafficability. In most previous studies, rock information was extracted from optical images using visual identification or automatic detection methods. However, optical images cannot provide 3-D information on rocks and cannot be used in lunar permanently shadowed regions (PSRs), where rock information is critical to deciphering anomalously high radar echoes in water ice deposit detection. In this study, we proposed an automatic method for extracting 3-D information about rocks from topography data based on the geometry and clustering tendency of lunar surface rocks. A geometric shape model for lunar surface rocks is first developed by analyzing 3196 rocks in elevation data. In the proposed approach, rocks are detected from topography data using multiscale 2-D continuous wavelet transform (2-D CWT) and Hopkins statistic, and then a 3-D shape parameter extraction method is introduced to obtain the shape information directly from the detected irregular rock boundary by a region growing-based algorithm. To demonstrate the accuracy of the method, we applied the proposed method to both the simulated and real-topography data with various spatial resolutions and vertical uncertainties. The results show that, compared with the ground truth and manual detection results, the detection rate of rocks >4 pixels in size varies from 50% to 90%, depending mainly on the vertical uncertainty of elevation data. In addition, for the first time, we provide 3-D information on surface rocks (>10 m) in lunar PSRs from topography data. Our analyses suggest that, for future missions to the lunar PSRs (e.g., China's Chang'E-7), the vertical uncertainty of elevation data needs to be better than 0.2 m in order to accurately gather 3-D information of rocks larger than 2 m. Our method can be utilized for extracting 3-D information on rocks from topography data, selecting landing sites, and guiding instrument design for future altimeters.

Direct sampling has never been performed in the permanently shadowed regions (PSRs) of lunar poles up to now. In the Chinese Chang'e-7 (CE-7) mission, a mini-flyer will fly from the lander in a solar illuminated region at the lunar south polar region to the nearby PSRs to collect samples for analysis. In this letter, four potential craters of the lunar south pole, including Shackleton, Shoemaker, de Gerlache, and Slater are discussed for this proposal. Design principles of the landing site, sampling site, and flight route are presented. The local surface slopes are calculated using a digital elevation model (DEM) to select a flat area as a potential landing site, which should allow ample time for solar illumination to support the rover from the lander and allow the flyer to reach the neighboring PSRs. Mini-RF data are applied for further validation of the flat landing and sampling sites, particularly for some rocky rough surfaces that are not identified in DEM and optical images of PSRs. The craters de Gerlache and Slater are found to be suitable for further analysis when high-resolution synthetic aperture radar (SAR) data are acquired by the new polarimetric SAR carried by CE-7.