The monitoring and accurate identification of coal mining subsidence areas are crucial for protecting surface structures, controlling soil erosion, facilitating ecological restoration, and regulating illegal mining activities. However, existing identification models often heavily depend on observational data from specific mining basin, resulting in poor generalization capability. To achieve large-scale, high-precision, and efficient identification of coal mining subsidence areas, this paper proposes a fine-scale identification method that integrates time-series InSAR (TS-InSAR) technology and surface subsidence curve characteristics. By extracting features from typical time-series subsidence curves, we constructed a time-series feature dataset to distinguish between deformation caused by mining and non-mining factors. Subsequently, a novel identification model was developed to improve its transferability and adaptive capability. Once trained, the model can automatically identify large-scale coal mining subsidence basins without requiring prior InSAR data from the target mining area. Identification experiments were conducted in three typical mining areas located in eastern, central, and western China. The identified subsidence boundaries were validated and compared using field measurement data obtained after panel extraction. The results show that the model can rapidly and accurately identify large-scale coal mining subsidence areas in the absence of prior data for the target region. The identified subsidence boundaries align closely with the field measurement data. The boundary extraction accuracy has been improved by approximately 80% compared to existing methods, providing high-precision technical support for coal mine safety production, coal pillar design, and mining disturbance identification.
Huang et al. (Fri,) studied this question.