Retrieval and Prediction of Three-Dimensional Displacements by Combining the DInSAR and Probability Integral Method in a Mining Area

Abstract

Monitoring ground displacement produced by underground mining is essential to ensure the safety of infrastructure over mining areas. Differential synthetic aperture radar (DInSAR) can only obtain the 1-D [i.e., along the line-of-sight (LOS) direction] displacement component. In this study, we present an improved algorithm for retrieving and predicting 3-D displacement fields induced by underground mining based on the LOS displacement derived from DInSAR and the probability integral method (PIM). Whole parameters included in the standard PIM model are involved in the improved algorithm. In addition, the interaction between multiple working panels is considered and incorporated into the model. Next, a stochastic optimization technique hybridizing the cultural algorithm and random particle swarm optimization has been designed to retrieve model parameters, which can be used to retrieve and predict the 3-D displacement field. Simulated experiments show that the root mean square errors (RMSEs) are 10, 12, and 17 mm in the vertical, east-west, and north-south directions, respectively, by comparing the simulated and retrieved 3-D displacement. Furthermore, the capability of the proposed method is investigated and validated in the Xuehu mining area of China using three ALOS PALSAR acquisitions. Our results agree well with leveling measurements in the vertical direction with an RMSE of 38 mm. Although the retrieved horizontal displacement cannot be validated due to a lack of field surveys, these displacement fields coincide spatially with the evolution of mining excavation. Index Terms-Cultural algorithm and random particle swarm optimization (CA-rPSO), mining displacement, probability integral method (PIM), three-dimensional (3-D) displacement.

Prediction Parameters for Mining Subsidence Based on Interferometric Synthetic Aperture Radar and Unmanned Aerial Vehicle Collaborative Monitoring

“…The research on mining surface subsidence prediction parameter inversion comprises two main parts: the subsidence prediction model and the parameter inversion method [35]. The probability integration method stands as the most extensively employed approach for subsidence prediction in China.…”

Comparative Analysis of Theoretical, Observational, and Modeled Deformation of Ground Subsidence: The Case of the Alhada Pb-Zn Mine

“…Baochen Liu and Guohua Liao et al proposed the probability integral method based on the stochastic medium theory [25], which is a probabilistic method to represent the subsidence of the overlying strata caused by the underground mining area. The expression of the surface sinking basin is as follows [26,[41][42][43]:…”

A New Strategy for Extracting 3D Deformation of Mining Areas from a Single-Geometry Synthetic Aperture Radar Dataset

“…And the near-polar orbit configuration causes significantly poor accuracy for north-south horizontal displacement estimates [19][20][21]. Compared with the former method, InSAR/SAR measurements combined with external models/data can effectively reduce the requirements for SAR data, such as InSAR/SAR measurements combined with probability integral method [23][24][25] or the priori information to obtain 3D mining displacements [26][27][28]. The method proposed by Li et al [28] is the most representative, which obtains 3D deformation from a single InSAR pair (SIP).…”