Non-negative matrix factorization-assisted independent component analysis for land subsidence feature decomposition and attribution in Lubei Plain, and further integration with deep learning for subsidence prediction

• Land subsidence decomposition via ICA with NMF-guided direction optimization. • Attribution analysis of land subsidence driving factors. • Land subsidence prediction combining deep learning and ICA. Interferometric Synthetic Aperture Radar (InSAR) has become crucial for large-scale land subsidence monitoring, but its results represent complex superpositions of deformation signals from multiple compressible layers driven by various anthropogenic and natural factors. Decomposing subsidence patterns by different driving factors is vital for attribution and prediction. However, existing feature decomposition methods suffer from problems of incomplete decomposition and difficulties in direction determination. This study employs non-negative matrix factorization (NMF) assisted independent component analysis (ICA) to achieve feature decomposition and attribution of land subsidence in the Lubei Plain (LBP) from 2018 to 2023, and integration with deep learning to realize land subsidence prediction. Key contributions include: (1) Using NMF component directions as reference to determine three independent component directions for subsequent attribution and prediction analysis. (2) Groundwater wells in high-spatial-score regions were selected to analyze the relationship between ICs, deformation, and groundwater levels. IC1 showed a high correlation exceeding 0.8 with subsidence, reflecting near-linear patterns from deep confined aquifer over-exploitation and petroleum extraction. IC2 reached maximum correlations of 0.72 with subsidence and 0.56 with groundwater levels (GWL), reflecting inter-annual precipitation-driven variations. Similarly, IC3 exhibited maximum correlations of 0.63 and 0.71, respectively, characterizing seasonal cycles linked to agricultural irrigation and precipitation. (3) The proposed prediction model demonstrated excellent performance with RMSE of 0.06–0.69 and R 2 of 0.80–0.86 in test datasets. This study provides a new reference perspective for future driving factor analysis and regulation of land subsidence in the Lubei Plain, and offers a new method for land subsidence prediction.