Land Subsidence and Groundwater Storage Assessment Using ICOPS, GRACE, and Susceptibility Mapping in Pekalongan, Indonesia

Mine operational safety is an important aspect of maintaining the operational continuity of a mining area. In this study, we used the InSAR time series to analyze land surface changes using the ICOPS (improved combined scatterers with optimized point scatters) method. This ICOPS method combines persistent scatterers (PS) with distributed scatterers (DS) to increase surface deformation analysis’s spatial coverage and quality. One of the improvements of this study is the use of machine learning in postprocessing, based on convolutional neural networks, to increase the reliability of results. This study used data from the Sentinel-1 SAR C-band satellite during the 2016–2022 observation period at the Musan mine, North Korea. In the InSAR surface deformation time analysis, the maximum average rate of land subsidence was approximately > 15.00 cm per year, with total surface deformation of 170 cm and 70 cm for the eastern dumping area and the western dumping area, respectively. Analyzing the mechanism of land surface changes also involved evaluating the geological conditions in the Musan mining area. Our research findings show that combining machine learning and statistical methods has great potential to enhance the understanding of mine surface deformation.

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Revealing Large-Scale Surface Subsidence in Jincheng City's Mining Clusters Using MT-InSAR and VMD-SSA-LSTM Time Series Prediction Model

Yang,

Zhi,

2024

Preprint

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Jincheng City's mining areas have long been plagued by surface subsidence, posing significant threats to local residents' safety and impacting the region's economic and social stability. Understanding and effectively monitoring the driving factors and mechanisms of surface subsidence are crucial for devising scientific prevention measures and promoting the sustainable development of mining areas. This article aims to comprehensively reveal the large-scale surface subsidence phenomenon in Jincheng City's mining clusters by utilizing advanced remote sensing technology and machine learning models, identifying its main driving forces, and predicting future subsidence trends to provide scientific evidence for geological disaster prevention in mining areas. The study employs Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology, using both Permanent Scatterer Interferometric SAR (PS-InSAR) and Small Baseline Subset Interferometric SAR (SBAS-InSAR) techniques for cross-validation, to confirm the existence of surface subsidence. Further, by integrating Variational Mode Decomposition (VMD), Singular Spectrum Analysis (SSA), and Long Short-Term Memory (LSTM) networks, a high-precision time series prediction model (VMD-SSA-LSTM) was developed. The results indicate that from 2018 to 2021, the surface subsidence rates in Jincheng City ranged from − 34 to 34 millimeters per year, with significant variations in subsidence levels across different areas. Gaoping City exhibited the highest subsidence, with rates ranging from − 34 to 5 mm per year, while Yangcheng County showed the most pronounced subsidence changes. These variations are primarily attributed to mining activities, land use changes, and adverse geological conditions in Jincheng City. This study unveils the large-scale surface subsidence phenomenon in Jincheng City's mining clusters, marking the first comprehensive ground deformation monitoring analysis of small mining clusters across four cities in Jincheng. The development of a high-precision surface subsidence prediction model provides new insights for scientifically understanding geological disasters in mining areas. These findings are significant for formulating effective geological disaster prevention measures and land management policies.

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Land Subsidence and Groundwater Storage Assessment Using ICOPS, GRACE, and Susceptibility Mapping in Pekalongan, Indonesia

Cited by 5 publications

References 94 publications

Monitoring activity in Mount Melbourne, Antarctica, by multi-temporal SAR interferometry based on the ICOPS algorithm

Monitoring activity in Mount Melbourne, Antarctica, by multi-temporal SAR interferometry based on the ICOPS algorithm

Multitemporal analysis of land subsidence induced by open-pit mining activity using improved combined scatterer interferometry with deep learning algorithm optimization

Revealing Large-Scale Surface Subsidence in Jincheng City's Mining Clusters Using MT-InSAR and VMD-SSA-LSTM Time Series Prediction Model

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