Time series analysis and prediction of surface subsidence based on SBAS-InSAR technology

doi:10.13474/j.cnki.11-2246.2025.0409

Abstract

Abstract: Wanbailin district is listed as one of the high-risk areas of geological disasters in Taiyuan city.In order to explore the surface subsidence in Wanbailin district of Taiyuan city,it is difficult to obtain long-term and large-scale surface deformation information by traditional measurement methods. In this paper,Wanbailin district of Taiyuan city is taken as the research area. Based on 36 scenes of Sentinel-1A image data,SBAS-InSAR technology is used to obtain the cumulative settlement and change rate of the study area from July 2017 to June 2020.LSTM neural network model and GM(1,1) model are used to simulate and predict the monitoring results of surface feature points. The results show that: ①The uneven settlement of Wanbailin area in Taiyuan city is more and more serious from east to west. The most obvious area is located near the western factory village,and the maximum settlement rate can reach -60 mm/a; ②The GM(1,1) model cannot effectively predict the surface feature points with large fluctuations during the settlement monitoring period. The LSTM neural network model can better realize the urban settlement prediction,and the prediction accuracy is higher.

Key words: SBAS-InSAR technology, time series analysis, LSTM, GM(1,1)

CLC Number:

P237

SHAO Yaxuan, MA Jing, HAN Lili, YAO Guanyu. Time series analysis and prediction of surface subsidence based on SBAS-InSAR technology[J]. Bulletin of Surveying and Mapping, 2025, 0(4): 51-57,62.

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