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February 5, 2026GeoHazards1 citationsOpen Access

Monitoring and Prediction of Subsidence in Mining Areas of Liaoyuan Northern New District Based on InSAR Technology

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MLMenghao LiYZY. X. ZhangJZJiquan Zhang

Key Points

  • This study aims to monitor and predict ground subsidence in mining areas using InSAR technology, ensuring safety and environmental integrity.
  • Utilized SBAS-InSAR technique to process Sentinel-1A satellite images from August 2022 to March 2025.
  • Validated results with UAV measurements to enhance accuracy.
  • Trained and applied prediction models (LSTM, GRU, TCN-GRU) on subsidence data from August 2022 to March 2024.
  • Observed deformation rates between −26.80 mm/year (subsidence) and 13.12 mm/year (uplift).
  • Maximum cumulative subsidence of 59.59 mm observed near Xi’an Sixth District.
  • TCN-GRU model predictions correlated with actual values exceeding 0.95, showcasing high accuracy.

Abstract

Ground subsidence in mined-out areas has irreversible impacts on residents’ lives and infrastructure, making its monitoring and prediction crucial for ensuring safety, protecting the ecological environment, and promoting sustainable development. This study employed the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to process Sentinel-1A satellite images of Liaoyuan’s Northern New District from August 2022 to March 2025, deriving ground deformation data. The SBAS-InSAR results were validated using unmanned aerial vehicle (UAV) measurements. Monitoring revealed deformation rates ranging from −26.80 mm/year (subsidence) to 13.12 mm/year (uplift) in the area, with a maximum cumulative subsidence of 59.59 mm observed near the Xi’an Sixth District. Based on spatiotemporal patterns, most mining-induced subsidence in the study area is in its late stage, primarily caused by progressive compaction of fractured rock masses and voids within the collapse and fracture zones. Using subsidence data from August 2022 to March 2024, three prediction models—LSTM, GRU, and TCN-GRU—were trained and subsequently applied to forecast subsidence from March 2024 to August 2025. Comparisons between the predictions and SBAS-InSAR measurements showed that all models achieved high accuracy. Among them, the TCN-GRU model yielded predictions closest to the actual values, with a correlation coefficient exceeding 0.95, validating its potential for application in time-series settlement monitoring.

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Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/698433c8f1d9ada3c1fb13fdhttps://doi.org/10.3390/geohazards7010017
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