Coal mining-induced subsidence poses environmental hazards, particularly when interacting with extreme rainfalls that trigger flooding and hydrogeological imbalances. InSAR enables monitoring of mining-induced deformation, but its application across extreme rainfall remains limited, constraining both operational hazard mitigation and the understanding of hydrologically-controlled deformation in intensively-mined regions. Here, we integrate deep learning-enhanced InSAR to investigate how the 2021 Henan “7.20″ extreme rainfall event reshaped ground deformation patterns in coal mine goafs. This approach identified an additional 15.123 km 2 of rapid subsidence, effectively filling data gaps within mining-induced subsidence funnels that traditional methods missed. The time-series deformation and surface-water maps from Sentinel-1 SAR images (2017–2023) reveal that the post-event water accumulation closely aligns with InSAR-detected subsiding funnels. We observe that the extreme rainfall shortly reversed the ground subsidence to uplift in mined areas, lasting ∼4‒5 months. Notably, an area near a fault has been experiencing cumulative uplift of ∼30 cm without sign of chasing by the end of 2023, highlighting the role of rainfall-driven stress redistribution in inducing both short- and long-term uplift through hydraulic gradients. This work establishes a deep learning-enhanced InSAR framework that provides a critical quantitative basis for flood-risk assessment before extreme rainfall in ongoing mining sites. The revealed deformation patterns offer new insights into the interplay between climate extremes and anthropogenic subsidence.
Duan et al. (Wed,) studied this question.
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