现代信息科技2026，Vol.10Issue(9)：152-157,162,7.DOI:10.19850/j.cnki.2096-4706.2026.09.027
基于InSAR和深度学习的珠海市沉降区域识别与预测
Identification and Prediction of Subsidence Areas in Zhuhai City Based on InSAR and Deep Learning
摘要
Abstract
This paper takes Zhuhai City as the research area,and integrates time-series InSAR technology with Deep Learning algorithms to conduct large-scale,high-precision research on surface subsidence identification and prediction.A dataset comprising 58 Sentinel-1A SAR images acquired from January 2022 to December 2023 is processed using a combined PS-InSAR and SBAS-InSAR approach to derive temporally dense surface deformation time series.Three distinct subsidence areas are identified,which are Baijiao Town in Doumen District,the under-construction segment of Xianghai Expressway in Jinwan District,and Pingsha Town,exhibiting average subsidence rates of-36.00 mm/yr,-37.20 mm/yr,and-37.94 mm/yr,respectively.The monitoring data indicate that Zhuhai City maintains overall geomechanical stability,with localized subsidence associated with infrastructure development,groundwater activities,and specific soil conditions.The LSTM-based Deep Learning model is innovatively developed using PS-InSAR-derived subsidence time series as input.The model is trained and validated in representative areas,achieving a relative error between predicted and actual values ranging from 0.52%to 5.83%.Both the MAE and RMSE on the test set remain below 4.7 mm.The model effectively captures subsidence trends and delivers high-precision predictions,providing a scientific basis and technical support for geological disaster prevention and planning in Zhuhai and other coastal cities.关键词
时序InSAR/PS-InSAR/SBAS-InSAR/深度学习/地表沉降预测/珠海市Key words
time-series InSAR/PS-InSAR/SBAS-InSAR/Deep Learning/ground subsidence prediction/Zhuhai City分类
信息技术与安全科学引用本文复制引用
陈燕奎,何骏杰,刘洋,谢作轮..基于InSAR和深度学习的珠海市沉降区域识别与预测[J].现代信息科技,2026,10(9):152-157,162,7.基金项目
广东省科技创新战略专项资金(大学生科技创新培育)项目(pdjh2024b350) （大学生科技创新培育）
嘉应学院校级教学质量与教学改革工程项目(ZLGC2023102) （ZLGC2023102）
