TY - JOUR

T1 - Retrieval and Prediction of Three-Dimensional Displacements by Combining the DInSAR and Probability Integral Method in a Mining Area

AU - Zhu, Chuanguang

AU - Wang, Zhengshuai

AU - Li, Peixian

AU - Motagh, Mahdi

AU - Zhang, Liya

AU - Jiang, Zongli

AU - Long, Sichun

N1 - Funding information: Manuscript received September 16, 2019; revised January 24, 2020; accepted March 1, 2020. Date of publication March 16, 2020; date of current version April 13, 2020. This work was supported in part by the National Natural Science Foundation of China under Grants 41901373 and 41877283, in part by the Natural Science Foundation of Hunan Province under Grant 2019JJ50190, and in part by the Key Laboratory of Coal Resources Clean-utilization and Mine Environment Protection of Hunan Province under Grants E21505 and E21706. (Corresponding author: Chuanguang Zhu.) Chuanguang Zhu, Liya Zhang, Zongli Jiang, and Sichun Long are with the Key Laboratory of Coal Resources Clean-Utilization & Mine Environment Protection of Hunan Province, Hunan University of Science & Technology, Xiangtan 411201, China (e-mail: zhucg@hnust.edu.cn; lyzhang47@163.com; jiangzongli@hnust.edu.cn; sclong@hnust.edu.cn).

PY - 2020/3/16

Y1 - 2020/3/16

N2 - 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.

AB - 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.

KW - Cultural algorithm and random particle swarm optimization (CA-rPSO)

KW - mining displacement

KW - probability integral method (PIM)

KW - three-dimensional (3-D) displacement

UR - http://www.scopus.com/inward/record.url?scp=85083780840&partnerID=8YFLogxK

U2 - 10.1109/JSTARS.2020.2978288

DO - 10.1109/JSTARS.2020.2978288

M3 - Article

AN - SCOPUS:85083780840

VL - 13

SP - 1206

EP - 1217

JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

SN - 1939-1404

M1 - 9037272

ER -