中国地质灾害与防治学报2025，Vol.36Issue(6)：107-118,12.DOI:10.16031/j.cnki.issn.1003-8035.202408023
引入EEMD的SBAS-InSAR时序分析及其在地面沉降预测中的应用
Data ananlysis of SBAS-InSAR using EEMD series model and its application in land subsidence prediction:A case study of Taihe County,Anhui Province
李超 1田静 2张震 3孙何凌 3丁静 3康佳1
作者信息
- 1. 安徽省地质环境监测总站(安徽省地质灾害应急技术指导中心),安徽 合肥 230001||安徽省皖北地下水高效开发利用和灾害防治工程研究中心,安徽 合肥 230001||地面沉降机理与防控教育部重点实验室安徽研究基地,安徽 合肥 230001
- 2. 湖北省地质局第四地质大队,湖北 咸宁 437000
- 3. 安徽理工大学空间信息与测绘工程学院,安徽 淮南 232001
- 折叠
摘要
Abstract
Ground subsidence has a negative impact on urban development,public safety,and property.Accurate characterization and prediction of ground subsidence patterns are critical for effective disaster prevention and mitigation.This study focuses on the southern region of Taihe County,Anhui Province,utilizing 175 Sentinel-1 images and the MintPy time-series analysis tool,with SBAS-InSAR as the core,to derive time-series subsidence data from 2018 to 2023.To effectively capture the non-linear and non-stationary features of the data,the study uses the ensemble empirical mode decomposition(EEMD)algorithm to perform multi-scale decomposition of the data,effectively isolating subsidence trends across different frequencies.The TimesNet time series model was then applied to capture the characteristics and periodic distributions of the non-stationary time series data,enabling high-precision land subsidence predictions.The results indicate that the study area is basically stable,though localized subsidence phenomena exist,with subsidence rates ranging from 5 to 50 mm/a.In short-term prediction task,the model achieved root mean square error(RMSE)and mean absolute error(MAE)values of 0.54 mm and 0.31 mm,respectively,demonstrating excellent performance in short-term prediction.For long-term predictions,the model achieved an MAE of 0.83 mm on the validation set,effectively addressing the common challenge of underfitting in long-term tasks.This indicates that the model successfully captures time-series characteristics and provide reliable predictions of land subsidence using time-series InSAR data.关键词
SBAS-InSAR/地面沉降/时间序列预测/EEMD/TimesNetKey words
SBAS-InSAR/land subsidence/time series prediction/EEMD/TimesNet分类
天文与地球科学引用本文复制引用
李超,田静,张震,孙何凌,丁静,康佳..引入EEMD的SBAS-InSAR时序分析及其在地面沉降预测中的应用[J].中国地质灾害与防治学报,2025,36(6):107-118,12.
