基于InSAR和深度学习的韩城市地表形变监测与预测

doi:10.13474/j.cnki.11-2246.2026.0416

摘要/Abstract

摘要： 韩城市作为一座因煤而生的城市,矿产资源丰富,地质构造和生态环境多样,地质灾害频发,对该地区全域近年来的地表稳定性进行监测分析具有重要意义。目前对韩城市的研究主要集中于对局部矿区的短期监测,对全域近年来地表稳定性的监测预测研究方面存在不足。本文结合时序InSAR技术和深度学习算法处理2019—2023年覆盖韩城市全域的Sentinel-1卫星SAR数据集,获得韩城市地表时序形变及其未来发展趋势预测结果,并对当地典型的矿山开采导致的地表失稳进行重点分析。基于成因可将形变诱发机制归为矿区开采、滑坡和农业种植3类,局部区域形变速率超过-50 mm/a,最大累计沉降量达250 mm;建立的深度学习预测模型在韩城市形变拟合和预测方面性能较好,MAE、RMSE和MAPE分别为0.30、1.08和11.76 cm。本文研究成果可为韩城市地表失稳和地质灾害普查防治提供方法借鉴和数据支撑。

关键词: 韩城市, 时序InSAR, 深度学习, 形变监测, 形变预测

Abstract: Hancheng city,a city born out of coal,is rich in mineral resources and has diverse geological structures and ecological environments.It is also prone to frequent geological disasters.Monitoring and analyzing the surface stability of the entire region over the past few years is of great significance.Currently,research on Hancheng primarily focuses on short-term studies of local mining areas,with insufficient attention paid to monitoring and predicting surface stability across the entire region.In this study,we utilize advanced time-series InSAR technology and deep learning algorithms to process the SAR dataset from the Sentinel-1 satellite that covers the entire Hancheng from 2019 to 2023,to obtain the surface deformation of the whole Hancheng and predict its future development trend.It will also conduct a focused analysis on the surface instability caused by typical mining activities in the area.The results indicate that deformation areas are highly correlated with geological conditions and human activities in terms of spatiotemporal distribution.The local deformation rate exceeds -50 mm/a,and the maximum cumulative settlement reaches approximately 250 mm.The established deep learning model performs well in deformation fitting and prediction for the study area.The mean absolute error,root mean squared error,and mean absolute percentage error are 0.30,1.08,and 11.76 cm,respectively.The research results can provide methodological references and data support for the census and prevention of surface instability and geological disasters in Hancheng city.

Key words: Hancheng city, time-series InSAR, deep learning, deformation monitoring, deformation prediction

中图分类号:

P237

梁卫涛, 王跃东, 谢亚奇, 丁武, 杨红磊, 吴波, 薛启浪. 基于InSAR和深度学习的韩城市地表形变监测与预测[J]. 测绘通报, 2026, 0(4): 112-118,139.

LIANG Weitao, WANG Yuedong, XIE Yaqi, DING Wu, YANG Honglei, WU Bo, XUE Qilang. Surface deformation monitoring and prediction of Hancheng city based on time-series InSAR and deep learning[J]. Bulletin of Surveying and Mapping, 2026, 0(4): 112-118,139.

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