Application of Time Series INSAR (SBAS) Method Using Sentinel-1 for Monitoring Ground Deformation of the Aegina Island (Western Edge of Hellenic Volcanic Arc)

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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Identification of Potential Landslide in Jianzha Counctry Based on InSAR and Deep Learning

Yang,

Chen,

Dong

et al. 2024

Preprint

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Landslide disasters have characteristics of frequent occurrence, widespread impact, and high destructiveness, posing serious threats to human lives, property, and the ecological environment. Timely and accurate early identification of landslides remains an urgent issue within the disaster prevention field. This study focuses on Jianzha County, Qinghai Province, integrating PS-InSAR、SBAS-InSAR and optical remote sensing techniques to delineate potential landslide-prone areas. Utilizing Google Earth imagery and existing landslide datasets, potential landslide points were identified through a deep learning model. The results indicate that: (1) In Jianzha County, the variation trend of the average surface velocity monitored by PS-InSAR and SBAS-InSAR technology is consistent, and the deformation monitoring results are reliable. (2) Utilizing the deep learning model, 56 potential landslide points were identified, comprising 39 high-risk points and 17 medium-risk points. By integrating the spatial distribution data of historical geological disaster points, it was found that 10 out of 13 previously occurred landslide disaster points were located at the identified high-risk landslide points, achieving a detection accuracy of 76.92%. (3) The spatial distribution of landslide points exhibits clustering, with slopes ranging from 10–40°, elevations between 15–30 m, and slope orientations predominantly towards the northeast. (4) Landslide formation is correlated with seasonal precipitation concentrations and temperature fluctuations. This method can provide a crucial basis for large-scale surface deformation monitoring and early identification of landslide risks.

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Application of Time Series INSAR (SBAS) Method Using Sentinel-1 for Monitoring Ground Deformation of the Aegina Island (Western Edge of Hellenic Volcanic Arc)

Cited by 4 publications

References 49 publications

Identification of potential landslide in Jianzha county based on InSAR and deep learning

Identification of potential landslide in Jianzha county based on InSAR and deep learning

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

Identification of Potential Landslide in Jianzha Counctry Based on InSAR and Deep Learning

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