融合DS-InSAR与THPF-LSTM的关闭矿井地表形变监测及预测

    Fusion of DS-InSAR and THPF-LSTM for monitoring and predicting surface deformation in closed mines

    • 摘要: 矿井关闭后,其上覆岩层及地表会再次发生变形,影响建(构)筑物安全运营。由于关闭矿井缺乏监管,导致地表形变时空演化规律及预测预警模型研究不足。为此,提出了一种分布式目标雷达干涉测量(Distributed scatter Interferometric synthetic aperture radar,DS-InSAR)、时间域高通滤波(Temporal High Pass Filtering,THPF)以及长短期记忆网络(Long Short Term Memory Network,LSTM)相结合的关闭矿井地表形变预测模型。以98景Sentinel-1A升轨影像为数据源,首先利用DS-InSAR方法联合PS(Persistent Scatterer)和DS点获取徐州西部关闭矿井2019−11—2022−12的时序地表沉降信息;然后利用THPF分解原始沉降序列获取高频和低频序列沉降信息;之后采用LSTM完成高低频子序列的形变预测,将高低频子序列预测值叠加获取最终的预测结果。结果表明:DS-InSAR监测点密度分布均匀,监测结果水准实测形变间的决定系数达到0.95;相较于LSTM模型,THPF-LSTM模型在预测点位上的最大均方根误差(RMSE,Root Mean Square Error)为3.0,最大平均绝对误差(Mean Absolute Error,MAE)为2.4,最大校正决定系数(Adjusted R-square)为0.9,优于传统LSTM模型的4.5、3.9和0.6,模型综合预测精度提升20%以上,能够准确反映出关闭矿井地表形变的趋势和波动性,可有效提高短期内关闭矿井沉降预测精度,实现了关闭矿井地表形变监测和预测的一体化分析。

       

      Abstract: After the closure of a mine, the overlying rock layer and the ground surface will deform again, this affects the safe operation of buildings (structures). Due to the lack of supervision of mine closure, the spatial and temporal evolution of surface deformation and prediction and warning models are not well studied. To this end, we proposed a prediction model for surface deformation of closed mines combining distributed scatter interferometric synthetic aperture radar (DS-InSAR), temporal high pass filtering (THPF), and a long short term memory network (LSTM). Taking the 98-view Sentinel-1A uptrack image as the data source, firstly, the DS-InSAR method combined with persistent scatterer (PS) and DS points was used to obtain the time-series surface subsidence information of the closed mines in western Xuzhou for the period from November 2019 to December 2022; then the THPF was used to decompose the original subsidence sequences to obtain the high frequency and low frequency, and then, LSTM was used to complete the deformation prediction of the high and low frequency sub-sequence, and the predicted values of the high and low frequency sub-sequence were superimposed to obtain the final prediction result. The results show that: the density of DS-InSAR monitoring points is uniformly distributed, and the coefficient of determination between the measured deformation and the monitoring results reached 0.95; compared with the LSTM model, the maximum RMSE (root mean square error) of the THPF-LSTM model in the prediction points is 3.0, and the mean absolute error (MAE) is 2.4, and the maximum Adjusted R-Square was 0.9, which is better than 4.5, 3.9, and 0.6 of the traditional LSTM model, and the comprehensive prediction accuracy of the model is improved by more than 20%, and it can accurately reflect the trend and volatility of the surface deformation of the closed mine, and can effectively improve the prediction accuracy of mine settlement in the short term. The method of this paper realizes the integrated analysis of monitoring and prediction of surface deformation in closed mines.

       

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