Unraveling Spatial Associations of Surface Subsidence in Typical Oil Production Areas of Songyuan City by Integrating Time-Series InSAR and Deep Learning

Oilfield subsidence threatens infrastructure and sustainable development. Although facility detection, factor-association analysis, and deformation forecasting have each advanced considerably, their systematic integration remains insufficiently explored for oilfields in agricultural plains, where C-band SAR decorrelation over cropland and wetland yields sparse and uneven deformation observations. This constraint complicates facility extraction, quantitative analysis of associations among interacting factors, and robust temporal prediction. To address these challenges, this study develops and applies an integrated modular workflow for the monitoring, analysis and prediction of surface subsidence in a typical oil production area of Songyuan City, China. We first used PS-InSAR for high-precision deformation monitoring and validated the results with leveling survey data. Subsequently, the YOLOv12 model was utilized for automated oil well identification, and the Geodetector model was applied to quantify the spatial associations between subsidence and the examined natural and anthropogenic factors. Finally, a hybrid LSTM–Transformer model was developed for time-series prediction and applied to forecast deformation through November 2025, twelve months beyond the final SAR acquisition. Results indicate significant spatial differentiation in deformation, with a maximum subsidence rate of −26.0 mm/y. Analysis identifies oil well density and surface elevation as the leading spatial factors among those examined, and the buffer zone analysis indicates a distance-related association of approximately 700 m between oil wells and subsidence rates. The hybrid model demonstrated superior overall accuracy compared to standalone LSTM and Transformer models. The findings provide concrete scientific support for risk mitigation in oil cities.