首页|融入坐标—时间函数(CT-PIM)的矿区时序InSAR形变预计—以淮安盐矿为例

融入坐标—时间函数(CT-PIM)的矿区时序InSAR形变预计—以淮安盐矿为例

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对矿区开展长期形变监测和后续形变预计对于预防矿山开采所引发的潜在安全问题和矿山生态环境保护具有十分重要的意义.利用合成孔径雷达干涉测量技术InSAR(Interferometric Synthetic Aperture Radar)与概率积分法PIM(Probability Integral Method)结合是较为有效的矿区形变预计方法.然而,目前矿区时序InSAR形变建模环节中大多数采用纯经验数学模型,未顾及开采沉陷机制,严重影响获取的形变观测值的精度;不准确的InSAR形变观测值将误差传递至后续形变预计,进而导致误差累积.本文将坐标—时间函数CT(Coordinate Time)引入时序InSAR建模环节,构建了坐标—时间函数预计模型(CT-PIM)以取代传统纯经验数学模型;再基于InSAR相位观测值求解未知参数,将构建的CT-PIM直接用于矿区形变预计,避免了误差的传递,以改善形变预计精度.分别开展了模拟实验和真实实验对提出预计方法进行验证.模拟实验显示:模型预计获取的时序形变与模拟真值的均方根误差(RMSE)为±4.6 mm.真实实验利用覆盖淮安市一盐矿区的35景Sentinel-1A SAR卫星影像开展,获取了研究区域2019年3月30日至2019年7月28日的预计结果,结果显示测区最大预计沉降量为152mm.本文所提方法相比传统多速率模型,建模精度提升了38.2％;相比传统静态概率积分预计方法获取的形变结果,精度提升了39.1％.本文方法为预防盐矿区采矿引起的地质灾害问题提供了有力的工具,为盐矿区生产安全和生态环境保护提供参考.
Incorporation of Coordinate-Time Function(CT-PIM)time-series InSAR deformation prediction for salt mining areas:Case study of the Huaian Salt Mine
Long-term monitoring and the subsequential prediction of deformation for salt mining areas is essential to the safety prevention and environmental protection of mining areas.The combination of the interferometric synthetic aperture radar(InSAR)technique with the Probability Integral Method(PIM)has proven to be powerful in predicting the deformation of mining areas.However,single multitemporal InSAR(MT-InSAR)is limited because it can only obtain the deformation sequences during SAR acquisition dates,and the subsequent future displacement beyond the span of the SAR observations cannot be acquired.In addition,traditional mathematical empirical models are mostly used in the time-series modeling of mining areas,ignoring the underground mining mechanisms,which seriously affect the accuracy of the observations.Inaccurate InSAR deformation monitoring results transmit errors to forward predicted subsidence,which may induce considerable errors.In this study,the Coordinate-Time(CT)function is introduced into time-series InSAR deformation modeling,and a CT function prediction model(CT-PIM),which can well describe the dynamic evolution disciplines of the underground mining subsidence in InSAR deformation modeling,is constructed to replace the traditional mathematical empirical models.The unknown CT-PIM parameters can be estimated directly via InSAR time-series phase observations,and the constructed CT-PIM is directly used in the deformation prediction of the mining area,which can avoid the error propagation from the InSAR-generated deformations and improve deformation prediction accuracy.The new approach is tested by simulation and real data experiments.The simulation results show that the root mean square error between the time-series deformation prediction of the model and the simulated true value is estimated to be±4.6 mm,which implies that the proposed method is of promising accuracy.The real experiment was conducted using a total of 35 Sentinel-IA SAR images covering the salt mining area in Huaian City,and the deformation prediction results of the study area from March 30,2019 to July 28,2019 were obtained.Results show that the maximum settlement of deformation prediction in the study area is 152 mm.The modeling accuracy showed an improvement of 38.2％compared with traditional SBAS-InSAR,and the deformation prediction accuracy exhibited an improvement of 39.1％compared with the traditional static PIM prediction method.CT-PIM was used as a substitute for traditional MT-InSAR pure empirical models and was applied for predicting the dynamic deformation over the salt mining area,which provides a more robust tool for the forecasting of mining-induced hazards.The above results show that CT-PIM can describe the temporal dynamic characteristics of the mining-induced subsidence more realistically,which can avoid the secondary error propagation,and can serve as a reference for safety management and ensuring environment protection.

remote sensingInSARmineCoordinate-Time Functionland subsidencedeformation prediction

张腾飞、邢学敏、彭葳、朱珺、刘祥彬、葛家旺、雷敏超

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长沙理工大学交通运输工程学院,长沙 410114

长沙理工大学交通测绘雷达遥感应用研究所,长沙 410114

遥感 InSAR 矿区 坐标—时间函数 地表沉陷 形变预计

国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金湖南省自然科学基金青年基金湖南省自然科学基金青年基金湖南省自然科学基金青年基金湖南省教育厅重点项目湖南省教育厅重点项目洞庭湖生态环境遥感监测湖南省重点实验室开放项目中国水利水电第八工程局有限公司科研项目长沙市杰出创新青年培养计划项目

42074033518780784170153641904003617010472017JJ33222019JJ506392020JJ557118A14819C00422021-0112023060kq2209011

2024

10.11834/jrs.20222172

遥感学报
中国地理学会环境遥感分会 中国科学院遥感应用研究所

遥感学报

CSTPCD北大核心
影响因子：2.921
ISSN：1007-4619
年,卷(期)：2024.28(6)