【摘要】 随着矿产资源的大规模开采，矿区地表沉降问题日益严重，对环境安全和矿区可持续发展构成了重大威胁。以贵州省开阳县洋水矿区内平安一矿和双阳磷矿为研究对象，融合时序合成孔径雷达干涉测量(interferometric synthetic aperture radar, InSAR)技术和机器学习算法模型，对该磷矿矿区地表沉降进行监测与预测研究。利用小基线集技术获取2020年9月—2023年5月的地表累积时间序列沉降，并通过全球定位系统(global positioning system, GPS)实测沉降数据进行精度评估，得出监测结果具有准确性，并基于该结果，选取研究区内重要沉降点E1和E2,分别建立自回归滑动平均(autoregressive integrated moving average, ARIMA)和蜣螂优化算法(dung beetle optimizes, DBO)优化的长短期记忆网络(long short term memory, LSTM)预测模型以及基于不同权重下的ARIMA-DBO-LSTM组合模型，对其沉降趋势进行预测分析。结果表明：基于残差倒数法权重分配下的ARIMA-DBO-LSTM组合模型预测精度在E1和E2点均为最高，组合模型在处理复杂时序数据时弥补了单一预测模型的不足。所提方法能够为该研究区地表沉降预测提供一定的参考价值。更多还原

【Abstract】 With the large-scale exploitation of mineral resources, surface subsidence in mining areas has become an increasingly serious issue, posing significant threats to environmental safety and the sustainable development of these regions. Taking Ping’an No.1 Mine and Shuangyang Phosphate Mine in Yangshui Mining Area, Kaiyang County, Guizhou Province as research objects. time-sequence synthetic aperture radar interferometry(InSAR) technology was integrated with machine learning algorithms to monitor and predict surface subsidence in this phosphorus mining area. The small baseline subset technique was employed to obtain cumulative time series data of surface subsidence from September 2020 to May 2023.Additionally, global positioning system(GPS) data were used to validate the accuracy of the monitoring results. Important subsidence points E1 and E2 in the study area were selected to establish autoregressive integrated moving average(ARIMA) and dung beetle optimization(DBO)-optimized long short-term memory(LSTM) prediction models. The ARIMA-DBO-LSTM combination model, using different weights, was employed to predict and analyze settlement trends. Results indicate that the prediction accuracy of the ARIMA-DBO-LSTM combination model, based on the weight assignment of the residual inverse method, is the highest at both E1 and E2 points. This combination model compensates for the limitations of individual prediction models when addressing complex time series data. The proposed method provides valuable insights for predicting surface subsidence in the study area.更多还原