Mining Subsidence Prediction Parameter Inversion by Combining GNSS and DInSAR Deformation Measurements

Guorui Wang,Ximin Cui,Jia Zhang,Wei Tang,Yongfeng Gong,Qiang Wu,Peixian Li

doi:10.1109/access.2021.3089820

Abstract

Line of Sight (LOS) deformation based on Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques cannot be used in traditional probability integration method (PIM) parameter inversion. To improve the accuracy of parameter inversion, a model based on 3D deformation was proposed. The model simulates 3D deformation using PIM directly. The inverse of the Sum of the Squared Errors (SSE) of the PIM results and the measured deformation results was used as a fitting function within the GA. Reliable PIM parameters can be obtained based on this GA model. To identify the surface movement law of the Jinfeng coal mine, 6 Global Navigation Satellite System (GNSS) monitor points were established over the 011207 and 011809 working panels. Due to the limited number of points and the large distance between the points, it is not sufficient to obtain reliable PIM parameters using GNSS only. As a complement, 83 Sentinel-1A images were analyzed with small baseline subset (SBAS) DInSAR, and the LOS direction deformation was obtained. The reliable PIM parameters were calculated with the 3D inversion model based on the combination of LOS direction deformation and GNSS-monitored deformation. Then, those parameters were used to predict the coal mine deformation of panels 011207 and 011809, which demonstrated that the prediction results coincide with the measured results. The model can be used to study the laws of mining subsidence combined with DInSAR and GNSS, which can reduce the requirements of the number of GNSS points and the impact of radar decoherence. This provides a new technical approach for studying the law of surface movement in mining subsidence research.

Highlights

The large-scale development of coal resource production has made an indelible contribution to the development of China's national economy and provided a strong guarantee for the rapid development of China's economy and society
It is a bionic global search optimization algorithm belonging to the same group algorithm as particle swarm optimization (PSO), the GA can solve the discrete problem, has a mature convergence analysis method and can estimate the convergence speed
Genetic algorithms start with a population that represents a potential solution to the problem, while a population consists of a certain number of individuals encoded by genes

Summary

Introduction

The large-scale development of coal resource production has made an indelible contribution to the development of China's national economy and provided a strong guarantee for the rapid development of China's economy and society. The large-scale exploitation of coal resources will inevitably cause a series of ecological environment and social problems. The exploitation of underground coal resources has led to the large-scale subsidence of the surface and destruction of land resources. According to China's coal production and its subsidence rate, the annual land damage caused by the surface collapse of coal mining areas is approximately 33,000 to 47,000 hectares. The annual land damage of coal mining areas is equivalent to the average arable land ownership of 500,000 people in China. By the end of 2004, more than 700,000 hectares of land had collapsed [2], and the number is increasing by 133 hectares every year [3]

Objectives

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Conclusion