On Time-Series InSAR by SA-SVR Algorithm: Prediction and Analysis of Mining Subsidence

Yun Shi,Tongkang Zhang,Jingjian Shi,Xin Meng,Qianwen Li,Hyung-Sup Jung

doi:10.1155/2020/8860225

Abstract

Given the increasingly serious geological disasters caused by underground mining in the Hancheng mining area in China and the existing problems with mining subsidence prediction models, this article uses the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology to process 109 Sentinel-1A images of this mining area from December 2015 to February 2020. The results show that there are three subsidences: one in Donganshang, one in south of Zhuyuan village, and one in Shandizhaizi village. In the basin, the maximum annual average subsidence rate is 300 mm/a, and the maximum cumulative subsidence is 1000 mm. The SBAS-InSAR results are compared with Global Positioning System (GPS) observation results, and the correlation coefficient is 74%. Finally, a simulated annealing (SA) algorithm is used to estimate the optimal parameters of a support vector regression (SVR) prediction model, which is applied for mining subsidence prediction. The prediction results are compared with the results of SVR and the GM (1, 1). The minimum value of the coefficient of determination for prediction with SA-SVR model is 0.57, which is significantly better than that those of the other two prediction methods. The results indicate that the proposed prediction model offers high subsidence prediction accuracy and fully meets the requirements of engineering applications.

Highlights

Ground subsidence in a mining area is a type of vertical deformation of the ground, which is prone to slow regional changes due to the destruction of the structure of the rock mass caused by mining
Differential interferometric synthetic aperture radar (D-InSAR) has been proven to be able to probe small surface deformations [2]; it is widely used in the monitoring of landslides [3], urban areas [4], mining area ground subsidence [5], and other geological disasters, but it is susceptible to limitations such as decoherence and atmospheric delay, and it cannot provide continuous time-series deformation information
The surface deformation information of the mining area was extracted based on SBAS-InSAR technology, Global Positioning System (GPS) data were used to verify the monitoring results, and the SBAS-InSAR monitoring results were used as training samples to develop a simulated annealing (SA)-support vector regression (SVR) model for predicting mining subsidence

Summary

Introduction

Ground subsidence in a mining area is a type of vertical deformation of the ground, which is prone to slow regional changes due to the destruction of the structure of the rock mass caused by mining. Yin et al [13], Yang et al [14], Saygin et al [15], and Mark et al [16] have applied time-series InSAR technology for mining area monitoring in Lengshuijiang, China; Datong, China; Zonguldak Province, Turkey; and Springfield, Illinois, USA, and have shown the applicability of SBAS-InSAR technology in mining surface subsidence monitoring applications. He et al [17] used ALOS-1 data to identify large-scale surface deformations near China’s Hancheng coal mine. The fifth part summarizes the whole paper and discusses the advantages and disadvantages of the SA-SVR algorithm

Basic Idea of the SA-SVR Algorithm

Time-Series InSAR Monitoring and Analysis

Dazhangling

Mining Subsidence Prediction

Findings

Conclusion