基于SMA-CNN-GRU-Attention组合模型的矿区地表三维形变预测  

3D deformation prediction of mine surface based on combined SMA-CNN-GRU-Attention modeling

作　　者：彭毅博 杨维芳 闫香蓉 高墨通 侯宇豪 张德龙 PENG Yibo;YANG Weifang;YAN Xiangrong;GAO Motong;HOU Yuhao;ZHANG Delong(Faculty of Geomatics,Lanzhou Jiaotong University,Lanzhou 730070,China;Nation-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring,Lanzhou 730070,China;Gansu Provincial Engineering Laboratory for National Geographic State Monitoring,Lanzhou 730070,China)

机构地区：[1]兰州交通大学测绘与地理信息学院,甘肃兰州730070 [2]地理国情监测技术应用国家地方联合工程研究中心,甘肃兰州730070 [3]甘肃省地理国情监测工程实验室,甘肃兰州730070

出　　处：《测绘通报》2025年第3期8-14,20,共8页Bulletin of Surveying and Mapping

基　　金：国家自然科学基金(42061076);兰州交通大学优秀平台支持项目(201806)。

摘　　要：矿区地表形变监测与预测研究对于矿区安全生产与灾害防治预警具有重要意义。现有研究偏向于对地面垂直沉降的监测与预测,对三维方向形变预测研究较少。针对以上问题,本文基于小基线集合成孔径雷达干涉测量(SBAS-InSAR)技术对金川矿区西二采区进行多轨道数据地表形变监测,并提出一种加入黏菌优化算法(SMA)的SMA-CNN-GRU-Attention组合网络模型,利用该模型对该区域地表三维形变进行预测研究。结果表明,加入SMA进行最优参数求解后,垂直向预测结果的平均绝对误差(MAE)与均方根误差(RMSE)较CNN-GRU网络模型分别降低30%和46%;南北向预测结果的MAE与RMSE分别降低37%、39%;东西向预测结果的精度提升较小,MAE、RMSE分别降低6%和10%。SMA算法不仅可以加快模型最优参数选取效率,还能较大程度地提升CNN-GRU-Attention模型预测性能。SMA-CNN-GRU-Attention多特征输入预测模型相较其他预测模型具有优越性,为地表三维形变预测研究提供了一种有效方法。Research on monitoring and prediction of surface deformation in mining areas is of great significance for safe production and disaster prevention and warning in mining areas.Existing studies tend to monitor and predict the vertical subsidence of the ground surface,and there are fewer studies on the prediction of 3D directional deformation.To address the above problems,this paper is based on the small baselines set synthetic aperture radar interferometry(SBAS-InSAR)technology to monitor the surface deformation of the west second mining area of Jinchuan mining area with multi-track data.A combined SMA-CNN-GRU-Attention network model with slime mould Algorithm(SMA)is proposed to predict the surface deformation in this area.The results show that adding SMA for optimal parameter solving reduces the MAE and RMSE of the vertical prediction results by 30%and 46%compared to the CNN-GRU network model;the MAE and RMSE of the north-south prediction results are 37%and 39%lower,respectively;and the accuracy of the east-west prediction results is lower,with the MAE and RMSE lower than those of the CNN-GRU network model by 6%and 10%,respectively.The SMA algorithm can accelerate the efficiency of the optimal parameter selection of the model,and it can also improve the prediction performance of the CNN-GRU-Attention model to a larger extent.The SMA-CNN-GRU-Attention multi-feature input prediction model has the superiority compared with other prediction models,and it provides an effective method for the research of 3D deformation prediction of the ground surface.

关 键 词：SBAS-InSAR 形变监测 三维形变预测 SMA优化算法 组合模型 

分 类 号：P237[天文地球—摄影测量与遥感]