基于SBAS-InSAR技术的地表沉降时间序列分析与预测

doi:10.13474/j.cnki.11-2246.2025.0409

摘要/Abstract

摘要： 万柏林区被太原市列为地质灾害高易发区之一。为探究太原市万柏林区的地表沉降情况,针对传统测量方法难以获取长时序、大范围的地表形变信息的问题,本文以太原市万柏林区为研究区,基于36景降轨Sentinel-1A影像数据,利用SBAS-InSAR技术获取了研究区2017年7月—2020年6月的累计沉降量及变化速率等形变数据,并分别采用LSTM神经网络模型和GM(1,1)模型对地表特征点的监测结果进行模拟预测。研究结果表明:①太原市万柏林地区出现自东向西沉降愈加严重的不均匀现象,最明显的区域位于西部磺厂村附近,最大沉降速率可达-60 mm/a;②GM(1,1)模型无法对沉降监测期间波动幅度大的地表特征点进行有效预测,LSTM神经网络模型能较好地实现城区沉降预测,且预测精度更高。

关键词: SBAS-InSAR技术, 时序分析, LSTM, GM(1,1)

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)

中图分类号:

P237

邵亚璇, 马晶, 韩莉莉, 姚冠宇. 基于SBAS-InSAR技术的地表沉降时间序列分析与预测[J]. 测绘通报, 2025, 0(4): 51-57,62.

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.

参考文献

[1] BERARDINO P,FORNARO G,LANARI R,et al.A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(11):2375-2383.
[2] WASOWSKI J,BOVENGA F.Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry:Current issues and future perspectives[J].Engineering Geology,2014,174:103-138.
[3] PEPE A,MANZO M,CASU F,et al.Surface deformation of active volcanic areas retrieved with the SBAS-DInSAR technique:an overview[J].Annals of Geophysics,2009,51(1):5466-7180.
[4] 雷倩芳.基于时序InSAR技术的上海城区地表沉降监测与预测分析[D].抚州:东华理工大学,2022.
[5] 马飞.矿区沉降InSAR监测与预测方法研究[D].西安:长安大学,2020.
[6] 徐子兴,季民,张过,等.基于SBAS-InSAR技术和Logistic模型的矿区沉降动态预测方法[J].自然资源遥感,2022,34(2):20-29.
[7] 张天.基于SBAS-InSAR城市地表沉降的预测建模及原因分析[D].北京:北京建筑大学,2020.
[8] WANG Rong,FENG Yongjiu,TONG Xiaohua,et al.Large-scale surface deformation monitoring using SBAS-InSAR and intelligent prediction in typical cities of Yangtze River Delta[J].Remote Sensing,2023,15(20):4942.
[9] GUO Hengliang,YUAN Yonghao,WANG Jinyang,et al.Large-scale land subsidence monitoring and prediction based on SBAS-InSAR technology with time-series Sentinel-1A satellite data[J].Remote Sensing,2023,15(11):2843.
[10] 丁伟,陈展鹏,许兵,等.基于SBAS-InSAR技术的广州市白云区长时序地表形变监测与成因分析[J].测绘通报,2023(4):167-171.
[11] 叶萍萍.基于SBAS-InSAR的城市地表形变监测技术研究:以兰州新区城区为例[J].矿山测量,2020,48(6):80-83.
[12] 李红梅,郭在洁,刘庆施,等.基于SBAS-InSA R技术的青岛地铁1号线西海岸新区段地表沉降监测与分析[J].西南大学学报(自然科学版),2022,44(2):146-153.
[13] 曾学宏,赵义花.利用SBAS-InSAR技术分析西宁市地面沉降监测及驱动因素[J].测绘通报,2022(6):137-142.
[14] GERS F A,SCHMIDHUBER J A,CUMMINS F A.Learning to forget:continual prediction with LSTM[J].Neural Computation,2000,12(10):2451-2471.
[15] HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[16] 刘青豪,张永红,邓敏,等.大范围地表沉降时序深度学习预测法[J].测绘学报,2021,50(3):396-404.
[17] 陈媛媛,赵秉琨,王慧,等.基于LSTM模型的时序InSAR地表形变预测[J].人民长江,2024,55(3):146-152.
[18] 肖海平,夏益强,刘小生,等.融合SBAS-InSAR技术与TSO-LSTM模型的矿区地表沉降预测方法[J].金属矿山,2023(1):126-133.
[19] 李如仁,孙加瑶.融合SBAS-InSAR与GS-LSTM的尾矿库沉降监测与预测[J].金属矿山,2023(1):102-109.
[20] CHEN Bingqian,YU Hao,ZHANG Xiang,et al.Time-varying surface deformation retrieval and prediction in closed mines through integration of SBAS InSAR measurements and LSTM algorithm[J].Remote Sensing,2022,14(3):788.
[21] 王华,李素敏,袁利伟,等.基于MT-InSAR和灰色预测模型的金属矿区地表变形研究[J].化工矿物与加工,2022,51(4):45-50.
[22] 吴伟强.基于CNN-LSTM的采空区地表沉降预测[D].绵阳:西南科技大学,2022.
[23] 毕凌宇,孙承志,乔申.基于SBAS-InSAR与MA-PSO-BP的南京河西地区地表沉降监测及预测分析[J].测绘通报,2024(4):48-53.
[24] 丁帮宁,黄海兰,邹进贵.基于SBAS-InSAR技术的武汉市地表形变监测研究[J].测绘通报,2022(S2):81-84.
[25] 王润泽,费敏,梁世川,等.基于SBAS-InSAR技术监测西安市地表形变特征[J].测绘通报,2023(1):173-178.
[26] LIU Yahong,ZHANG Jin.Integrating SBAS-InSAR and AT-LSTM for time-series analysis and prediction method of ground subsidence in mining areas[J].Remote Sensing,2023,15(13):3409.
[27] 李相如,苏超,袁荣耀.时间序列重构改进LSTM的大坝变形预测模型[J].水力发电,2024,50(6):67-71.
[28] 陈毅,何毅,张立峰,等.长短时记忆网络TS-InSAR地表形变预测[J].遥感学报,2022,26(7):1326-1341.