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A spatio-temporal prediction method of large-scale ground subsidence considering spatial heterogeneity

  • role： First author第一作者
  • Affiliation：

    College of Earth Sciences and Information Physics, Central South University, Changsha 410083, China

    Chinese Academy of Surveying and Mapping, Beijing 100830, China

  • Email：1580748414@qq.com
  • Introduction：刘青豪，研究方向为InSAR形变监测与分析、时空数据挖掘。E-mail： 1580748414@qq.com
LIU Qinghao12,  
  • Affiliation：

    College of Earth Sciences and Information Physics, Central South University, Changsha 410083, China

LIU Huimin1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Chinese Academy of Surveying and Mapping, Beijing 100830, China

  • Email：yhzhang@casm.ac.cn
  • Introduction：张永红，研究方向为InSAR形变监测原理与应用。E-mail： yhzhang@casm.ac.cn
ZHANG Yonghong2*,  
  • Affiliation：

    Chinese Academy of Surveying and Mapping, Beijing 100830, China

WU Hong’an2,  
  • Affiliation：

    College of Earth Sciences and Information Physics, Central South University, Changsha 410083, China

DENG Min1

resumen

The rapid and uneven ground subsidence has threatened human production activities, and high-precision subsidence prediction results are of great significance for the precise prevention and control of geological disasters. In order to grasp the evolution law of ground subsidence, a number of prediction studies have been carried out using field observation data or InSAR data. However, due to the existence of spatial heterogeneity, accurate prediction of large-scale ground subsidence is still a challenge.In this study, a spatio-temporal prediction method considering spatial heterogeneity for large-scale ground subsidence STLSTM (Spatio-temporal Long Short-Term Memory) is proposed from a data-driven perspective. First, clustering is used to identify homogenous subregions in geographic space; then, in each subregion, a special Long Short-Term Memory (LSTM) networks are used to capture the nonlinearity features of local locations; Finally, the pre-trained network is used to quantitatively predict the ground subsidence at the future time.In the experimental part, the sentinel-1 image data was used to compare the performance of STLSTM with the other 8 benchmark methods, and the effectiveness of STLSTM was analyzed using spatial statistical indicators. The results show that STLSTM achieves the highest prediction accuracy (71.4%) within 152 secs, and can effectively weaken the effect of spatial heterogeneity on large-scale subsidence prediction tasks.In conclusion, this paper integrates the spatial heterogeneity processing strategy into the deep learning model, and large-scale subsidence prediction is realized with high precision and time efficiency.

palabra clave

remote sensing;ground subsidence;spatio-temporal prediction;heterogeneity;LSTM;InSAR

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