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Incorporation of Coordinate-Time Function （CT-PIM） time-series InSAR deformation prediction for salt mining areas： Case study of the Huaian Salt Mine

  • role： First author第一作者
  • Affiliation：

    School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China

    Institute of Radar Remote Sensing Applications for Traffic Surveying and Mapping, Changsha University of Science & Technology, Changsha 410114, China

  • Email：tengfei@stu.csust.edu.cn
  • Introduction：张腾飞，研究方向为矿区时序InSAR建模与形变预计。E-mail： tengfei@stu.csust.edu.cn
ZHANG Tengfei,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China

    Institute of Radar Remote Sensing Applications for Traffic Surveying and Mapping, Changsha University of Science & Technology, Changsha 410114, China

  • Email：xuemin.xing@csust.edu.cn
  • Introduction：邢学敏，研究方向为时序InSAR技术在地表变形监测中的应用。E-mail： xuemin.xing@csust.edu.cn
XING Xuemin*,  
  • Affiliation：

    School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China

    Institute of Radar Remote Sensing Applications for Traffic Surveying and Mapping, Changsha University of Science & Technology, Changsha 410114, China

PENG Wei,  
  • Affiliation：

    School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China

    Institute of Radar Remote Sensing Applications for Traffic Surveying and Mapping, Changsha University of Science & Technology, Changsha 410114, China

ZHU Jun,  
  • Affiliation：

    School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China

    Institute of Radar Remote Sensing Applications for Traffic Surveying and Mapping, Changsha University of Science & Technology, Changsha 410114, China

LIU Xiangbin,  
  • Affiliation：

    School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China

    Institute of Radar Remote Sensing Applications for Traffic Surveying and Mapping, Changsha University of Science & Technology, Changsha 410114, China

GE Jiawang,  
  • Affiliation：

    School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China

    Institute of Radar Remote Sensing Applications for Traffic Surveying and Mapping, Changsha University of Science & Technology, Changsha 410114, China

LEI Minchao

реферат

Long-term monitoring and the subsequential prediction of deformation for salt mining areas is essential to the safety prevention and environmental protection of mining areas. The combination of the interferometric synthetic aperture radar (InSAR) technique with the Probability Integral Method (PIM) has proven to be powerful in predicting the deformation of mining areas. However, single multitemporal InSAR (MT-InSAR) is limited because it can only obtain the deformation sequences during SAR acquisition dates, and the subsequent future displacement beyond the span of the SAR observations cannot be acquired. In addition, traditional mathematical empirical models are mostly used in the time-series modeling of mining areas, ignoring the underground mining mechanisms, which seriously affect the accuracy of the observations. Inaccurate InSAR deformation monitoring results transmit errors to forward predicted subsidence, which may induce considerable errors.In this study, the Coordinate-Time (CT) function is introduced into time-series InSAR deformation modeling, and a CT function prediction model (CT-PIM), which can well describe the dynamic evolution disciplines of the underground mining subsidence in InSAR deformation modeling, is constructed to replace the traditional mathematical empirical models. The unknown CT-PIM parameters can be estimated directly via InSAR time-series phase observations, and the constructed CT-PIM is directly used in the deformation prediction of the mining area, which can avoid the error propagation from the InSAR-generated deformations and improve deformation prediction accuracy.The new approach is tested by simulation and real data experiments. The simulation results show that the root mean square error between the time-series deformation prediction of the model and the simulated true value is estimated to be ±4.6 mm, which implies that the proposed method is of promising accuracy. The real experiment was conducted using a total of 35 Sentinel-1A SAR images covering the salt mining area in Huaian City, and the deformation prediction results of the study area from March 30, 2019 to July 28, 2019 were obtained. Results show that the maximum settlement of deformation prediction in the study area is 152 mm. The modeling accuracy showed an improvement of 38.2% compared with traditional SBAS-InSAR, and the deformation prediction accuracy exhibited an improvement of 39.1% compared with the traditional static PIM prediction method.CT-PIM was used as a substitute for traditional MT-InSAR pure empirical models and was applied for predicting the dynamic deformation over the salt mining area, which provides a more robust tool for the forecasting of mining-induced hazards. The above results show that CT-PIM can describe the temporal dynamic characteristics of the mining-induced subsidence more realistically, which can avoid the secondary error propagation, and can serve as a reference for safety management and ensuring environment protection.

ключеви́че слова́

remote sensing;InSAR;mine;Coordinate-Time Function;land subsidence;deformation prediction

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