Monitoring and predicting mining subsidence in mining areas through SBAS-InSAR and CNN-LSTM
In response to the limitations of conventional mining subsidence monitoring methods,which often require significant human and financial resources and lack robust prediction and warning models,this paper presents an innovative approach.It combines Synthetic Aperture Radar Interferometry(SBAS-InSAR)technology with Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)networks to develop a predictive framework for monitoring mining subsidence.To begin,40 observations of Sentinel-1A image data captured between September 4,2021,and September 18,2023,were analyzed using SBAS-InSAR technology to monitor the subsidence of Jianxin Coal Mine.This analysis yielded the annual average subsidence rate and cumulative subsidence value for the mining area.The findings indicate a maximum annual average subsidence rate of 131 mm/a and a maximum cumulative subsidence of 379 mm over the study period.A comparison between GNSS monitoring data and SBAS-InSAR results demonstrates a satisfactory correlation.To address the absence of some images in the study area,a three-time spline interpolation method is employed.This yields a comprehensive time series dataset,comprising 63 intervals,capturing the cumulative morphology of the study area at 12-day intervals from September 4,2021,to September 18,2023.Initially,the first 57 periods of data are allocated for training,while the remaining six periods are designated for testing.Subsequently,the CNN-LSTM model is deployed to forecast the sedimentation data for these six periods,and the outcomes are contrasted with the predictions from the individual CNN and LSTM models.Notably,the CNN-LSTM model exhibits a minimum reduction of 44.8％in Mean Absolute Error(MAE)and a decrease of 40.6％in Root Mean Square Error(RMSE)compared to the standalone CNN and LSTM models,respectively.Additionally,both the coefficients of determination surpass 98％.Subsequent predictions of the first six and middle six periods of subsidence data affirmed the temporal consistency of the CNN-LSTM prediction model.Consequently,the integration of SBAS-InSAR with the CNN-LSTM model demonstrates significant potential for monitoring and predicting subsidence in similar mining environments.
safety engineeringSmall Baseline Subset-Interferometry Synthetic Aperture Radar(SBAS-InSAR)mining subsidenceConvolutional Neural Networks-Long Short Term Memory(CNN-LSTM)settlement prediction
万方数据
维普
