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This paper presents a web tool for the unsupervised retrieval of Earth’s surface deformation from Synthetic Aperture Radar (SAR) satellite data. The system is based on the implementation of the Differential SAR Interferometry (DInSAR) algorithm referred to as Parallel Small BAseline Subset (P-SBAS) approach, within the Grid Processing on Demand (G-POD) environment that is a part of the ESA’s Geohazards Exploitation Platform (GEP). The developed on-demand web tool, which is specifically addressed to scientists that are non-expert in DInSAR data processing, permits to set up an efficient on-line P-SBAS processing service to produce surface deformation mean velocity maps and time series in an unsupervised manner. Such results are obtained by exploiting the available huge ERS and ENVISAT SAR data archives; moreover, the implementation of the Sentinel-1 P-SBAS processing chain is in a rather advanced status and first results are already available. Thanks to the adopted strategy to co-locate both DInSAR algorithms and computational resources close to the SAR data archives, as well as the provided capability to easily generate the DInSAR results, the presented web tool may contribute to drastically expand the user community exploiting the DInSAR products and methodologies.

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New stricter regulations on lead (Pb) content in brass for use in certain applications is driving the industry from traditional leaded brass towards Pb-free alloys. However, machining induced surface integrity for such Pb-free alloys and related corrosion resistance are largely unknown. Two Pb-free brass alloys, CuZn38As and CuZn21Si3P, approved for use in drinking water applications, were machined under different cutting conditions, tool geometries and tool wear states. The resulting surface integrity and sub-surface deformation was characterized using nano-indentation, scanning electron (SEM) and ion microscopy, and electron backscatter diffraction (EBSD). The materials resistance to stress corrosion cracking (SCC) was assessed by exposing the machined samples to a corrosive substance in accordance with SIS 117102. The results show that tool wear is the most influencing parameter leading to stronger sub-surface deformation. This was especially pronounced for alloy CuZn38As, where for equivalent depth of deformation, the material exhibited higher degree of work-hardening compared to the other tested alloy. Subsequently, substantial stress corrosion cracking was registered for machined CuZn38As samples.

Traditional surface monitoring methods can only obtain discrete surface deformation values at individual monitoring points, while InSAR technology can only measure the projection values of three‐dimensional surface deformation along the LOS direction. Additionally, when monitoring surface deformation in mining areas, it may encounter issues of low coherence or even loss of coherence. Therefore, this paper proposes a method for predicting three‐dimensional surface deformation in filling mining based on InSAR‐IPIM. The results show that the proposed method effectively corrects the error caused by the use of empirical parameters to predict the deformation of NC1202. The deviation rates between the optimal parameters and the initial empirical parameters are 23.61%, 34.29%, 16.32%, 0%, 14%, 3.85%, 4%, and 7.4%, respectively. The predicted three‐dimensional surface deformation can obtain the complete mining subsidence area results. Compared with the maximum measured leveling data, the absolute error of the maximum vertical deformation field in the three‐dimensional deformation is only 1 cm, which is small, making it possible to predict the complete three‐dimensional surface deformation of the working face using single track SAR data. Aiming at a series of defects existing in the monitoring of mining area by InSAR technology, this paper proposes an insar‐ipim model to predict the three‐dimensional surface deformation of filling mining. The parameters of the model are inversed by using the group optimization algorithm slime bacteria algorithm proposed in 2020, and the empirical parameters are corrected to reduce the prediction error of surface deformation.

Measuring surface deformation is crucial for a better understanding of spatial-temporal evolution and the mechanism of mining-induced deformation, thus effectively assessing the mining-related geohazards, such as landslides or damage to surface infrastructures. This study proposes a method of retrieving surface deformation by combining multi-temporal digital surface models (DSMs) with image homonymous features using China’s ZY-3 satellite stereo imagery. DSM is generated from three-line-array images of ZY-3 satellite using a rational function model (RFM) as the imaging geometric model. Then, elevation changes in deformation are extracted using the difference of DSMs acquired at different times, while planar displacements of deformation are calculated using image homonymous features extracted from multi-temporal digital orthographic maps (DOMs). Scale invariant feature transform (SIFT) points and line band descriptor (LBD) lines are selected as two kinds of salient features for image homonymous features generation. Cross profiles are also extracted for deformation in typical regions. Four sets of stereo imagery acquired in 2012 to 2022 are used for deformation extraction and analysis in the Fushun coalfield of China, where surface deformation is quite distinct and coupled with rising and descending elevation together. The results show that 21.60% of the surface in the study area was deformed from 2012 to 2017, while a decline from 2017 to 2022 meant that 17.19% of the surface was deformed with a 95% confidence interval. Moreover, the ratio of descending area was reduced to 6.44% between 2017 and 2022, which is lower than the ratios in other years. The slip deformation area in the west open pit mine is about 1.22 km2 and the displacement on the south slope is large, reaching an average of 26.89 m and sliding from south to north to the bottom of the pit between 2012 and 2017, but elevations are increased by an average of about 16.35 m, involving an area of about 0.86 km2 between 2017 and 2022 due to the restoration of the open pit. The results demonstrate that more quantitative features and specific surface deformation can be retrieved in mining areas by combining image features with DSMs derived from ZY-3 satellite stereo imagery.

The analysis of the degree of surface deformation can be a relevant aspect in the study of surface stability conditions, as it provides added value in the construction of risk management plans. This analysis provides the opportunity to establish the behaviors of the internal dynamics of the earth and its effects on the surface as a prediction tool for possible future effects. To this end, this study was approached through the analysis of Synthetic Aperture Radar (SAR) images using the Differential Interferometry (DInSAR) technique, which, in turn, is supported by the Small Baseline Subset (SBAS) technique to take advantage of the orbital separation of the Sentinel-1 satellite images in ascending and descending trajectory between the years 2014 and 2021. As a result, a time series was obtained in which there is a maximum uplift of 117.5 mm (LOS-ascending) or 49.3 mm (LOS-descending) and a maximum subsidence of −86.2 mm (LOS-ascending) or −71.5 mm (LOS-descending), with an oscillating behavior. These deformation conditions are largely associated with the kinematics of the Bucaramanga Fault, but a recurrent action of deep seismic activity from the Bucaramanga Seismic Nest was also observed, generating a surface deformation of ±20 mm for the period evaluated. These deformations have a certain degree of impact on the generation of mass movements, evaluated by the correlation with the LOS-descending images. However, their action is more focused as an inherent factor of great weight, which makes it possible to respond to early care and allows real-time follow-up, giving positive feedback to the system.

Land surface deformation, including subsidence and uplift, has significant impacts on human life and the natural environment. In recent years, the city of Wuxi, China has experienced large-scale surface deformation following the implementation of a groundwater abstraction ban policy in 2005. To accurately measure the regional impacts and understand the underlying mechanisms, we investigated the spatiotemporal characteristics of surface deformation in Wuxi from 2015 to 2023 using 100 Sentinel-1A SAR images and the Persistent Scatterer InSAR (PS-InSAR) technique. The results revealed that surface deformation in Wuxi exhibited significant spatial and temporal variations, with some areas experiencing alternating trends of subsidence and uplift rather than consistent unidirectional change. To uncover the factors influencing this volatility, we conducted a comprehensive analysis focusing on groundwater, precipitation, and soil geology. This study found strong correlations between the groundwater level changes and surface deformation, with the soft soil geology of the area, characterized by alternating layers of sand and clay, further increasing the surface volatility. Moreover, we innovatively applied the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, typically used in financial analyses, to analyze the subsidence displacement time series in Wuxi. Based on this model, we propose a new “Amplitude Factor” index to evaluate overall surface deformation volatility in the city. Our qualitative assessment of surface stability based on the Amplitude Factor was consistent with research findings, demonstrating the accuracy and effectiveness of the proposed model. These results provide valuable insights for urban planning, construction, and safety control, highlighting the importance of continuous monitoring and analysis of surface deformation volatility for the city’s future development and safety.

The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data.

Subsidence over abandoned goaves is a primary trigger for secondary geological hazards such as surface collapse, landslides, and cracking. This threatens safe mining operations, impairs regional economic progress, and endangers local inhabitants and their assets. At present, goaf areas are mainly treated through grouting. However, owing to the deficiencies of traditional deformation monitoring methods (e.g., leveling and GPS), including their slow speed, high cost, and limited data accuracy influenced by the number of monitoring points, the surface deformation features of goaf zones treated with grouting cannot be obtained in a timely fashion. Therefore, this study proposes a method to analyze the spatio-temporal patterns of surface deformation in grout-filled goaves based on the fusion of Multi-temporal InSAR technologies, leveraging the complementary advantages of D-InSAR, PS-InSAR, and SBAS-InSAR techniques. An investigation was conducted in a coal mine located in Shandong Province, China, utilizing an integrated suite of C-band satellite data. This dataset included 39 scenes from the RadarSAT-2 and 40 scenes from the Sentinel missions, acquired between September 2019 and September 2022. Key results reveal a significant reduction in surface deformation rates following grouting operations: pre-grouting deformation reached up to −98 mm/a (subsidence) and +134 mm/a (uplift), which decreased to −11.2 mm/a and +18.7 mm/a during grouting, and further stabilized to −10.0 mm/a and +16.0 mm/a post-grouting. Time-series analysis of cumulative deformation and typical coherent points confirmed that grouting effectively mitigated residual subsidence and induced localized uplift due to soil compaction and fracture expansion. The comparison with the leveling measurement data shows that the accuracy of this method meets the requirements, confirming the method’s efficacy in capturing the actual ground dynamics during grouting. It provides a scientific basis for the safe expansion of mining cities and the safe reuse of land resources.

We identified and analyzed surface displacements associated with the 2018 Hokkaido Eastern Iburi Earthquake in northern Japan using satellite radar interferograms from the Advanced Land Observing Satellite 2. The data generally show elastic deformation caused by the main earthquake as well as numerous complex surface displacements that cannot be explained by the motion of the seismic source fault. We identified three distinct phenomena: linear surface displacements representing secondary earthquake faults west of the epicenter, surface deformation caused by liquefaction in urban and coastal areas, and coherence changes in the interferograms due to landslides in mountainous areas and liquefaction in urban areas. The linear surface displacements show reverse fault motion with low dip angles and appear to be a geographic extension of known active faults; however, it is unlikely that these displacements were directly connected to the source fault of the main earthquake. Although there is no evidence that they generated strong seismic waves at the time of the main earthquake, there is a possibility that they represent active fault traces and could be the sources of large earthquakes in the future. Therefore, such linear surface displacements can be used to identify potentially dangerous hidden active faults. The interferograms reveal that liquefaction in urban areas occurred in low areas artificially filled during past residential development. Coherence-change maps drawn from the interferograms were useful for detecting liquefaction, but their high sensitivity limited their application for landslide detection in mountainous areas; the phase noise deviation method was more practical for purposes such as rapid response or mitigation. Our methods have the potential to allow improved mapping of local hazards in other areas and can be applied to urban planning and/or safety assessments.