矿区开采会造成严重的地面沉降，这类沉降常常伴随着大范围、不均匀的特点，对矿区的生产生活产生了巨大的威胁，因此，精准的地面沉降预测对于矿区沉降灾害的防治有着重要的意义。针对传统的时序预测模型存在时空信息捕捉能力差，时空特征学习不充分的问题，本文将时序分解策略与深度学习网络模型相结合，基于地面沉降时序位移在时间维度上的特性提出考虑季节性位移特征的Seasonal-Feature-Focused PredRNN（SFF-PredRNN）模型。本文选取新密市的米村煤矿作为研究区，通过小基线集干涉技术算法获得了研究区2018-2021年的地面沉降信息，在此基础上构建了地面沉降时序数据集，利用构建的SSF-PredRNN模型对研究区地面沉降进行时空预测，并通过平均绝对误差（MAE）、均方根误差（RMSE）、峰值信噪比（PSNR）以及结构相似性指数（SSIM）进行模型的精度评价。实验结果证明，对比于CNN-LSTM、ConvLSTM以及PredRNN模型，本文提出的SFF-PredRNN模型在各项指标中均有最好的表现，表明这项研究可以为矿区的地面沉降灾害的预防预治提供有效的数据支撑。

Mining can cause severe ground subsidence, which is often accompanied by widespread and uneven characteristics, posing a great threat to the production and life of the mining community. Timely and accurate monitoring and prediction of ground subsidence in mining areas are crucial to mitigating its adverse effects. However, traditional spatio-temporal prediction models for ground subsidence often struggle with capturing comprehensive spatio-temporal information and learning the intricate features associated with this phenomenon. To address these challenges, this study incorporates a temporal decomposition strategy into a deep learning network model, resulting in the development of the Spatio-Temporal Forecasting Framework (SFF-PredRNN) model. This innovative approach takes into account seasonal displacement features, enhancing the model"s ability to capture complex spatio-temporal patterns accurately. By integrating this advanced methodology, the SFF-PredRNN model offers improved predictive capabilities, allowing for more effective mitigation measures against ground subsidence and its associated risks. In this study, the focus is on the Micun coal mine located in Xinmi City, a region characterized by extensive mineral resource extraction and distinct seasonal variations in rainfall. The summer season contributes significantly to the annual rainfall, accounting for 60.9%. Certain mining areas within this region have experienced notable ground subsidence issues. Using the small baseline set interference technique algorithm, ground subsidence data from 2018 to 2021 were collected for the study area. The analysis revealed distinct spatial differences in subsidence patterns, particularly in the Mengzhuang and Zhangpocun coal mines at the center and the Wangzhuang coal mine in the southwest. These areas exhibited severe ground subsidence problems, with the maximum subsidence reaching 256 mm, while the surrounding regions did not show significant ground subsidence. A spatio-temporal dataset of ground subsidence was constructed based on the collected information, and the developed SFF-PredRNN model was employed for prediction. The model"s accuracy was assessed using metrics such as MAE, RMSE, PSNR, and SSIM. The results demonstrated that the SFF-PredRNN model, as proposed in this study, exhibited superior accuracy in predicting subsidence for the years 2019, 2020, and 2021. This highlights the model"s strengths in both temporal and spatial predictions of ground subsidence. The predictions for the upcoming year indicated a continued trend of subsidence in the mining areas of Mengzhuang, Wangzhuang, and Zhangpocun, with an expected maximum cumulative subsidence of 274.3 mm. The spatial distribution of settlement in the study area remained consistent with previous patterns. In conclusion, the SFF-PredRNN model proposed in this paper shows better performance in spatio-temporal prediction of ground subsidence, and the model can provide effective data support for the prevention and pre-treatment of ground subsidence disasters in mining areas.