融合SBAS-InSAR与CS-SVM的矿区地表残余沉降预测模型

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万方数据
维普

中文摘要：煤矿开采地表残余形变可能对地表建(构)筑物、道路、地下管线等基础设施造成潜在威胁,有必要对其进行准确预测.融合SBAS-InSAR监测方法,提出了一种布谷鸟搜索算法改进支持向量机回归(CS-SVM)的预测模型,利用 2017 年 11 月—2020 年 6 月的 60 景Sentinel-1A SAR影像对安徽省某矿 7221 工作面进行开采沉陷长时序监测,获取了该工作面回采过程中与停采后 2 a内地表年均形变速率与累计形变.结果表明:该工作面最大年均形变速率为-56 mm/a,最大累计沉降为 151 mm.利用水准测量数据对InSAR结果进行验证,两者残差均小于 5 mm,证明了两者具有较好的一致性.为比较优化前后SVM预测模型的精度,引入平均绝对误差和均方根误差进行精度评价.结果显示:优化模型的 2 种误差均在 4 mm以内,相比传统模型,误差分别降低了 59%和 60%,预测精度明显提高.研究反映出,所提模型具有较好的预测能力,可为废弃煤矿区防灾减灾提供参考.

外文标题：Surface Residual Subsidence Prediction Model for Mining Area Based on the Fusion of SBAS-InSAR and CS-SVM

外文摘要：The surface residual deformation caused by coal mining activities may pose a potential threat to surface struc-tures,roads,underground pipelines,and other infrastructure.Accurate prediction of this deformation is necessary.This study proposes an improved predictive model using the Cuckoo Search algorithm to enhance Support Vector Machine regression(CS-SVM)based on the monitoring results of SBAS-InSAR.Sixty Sentinel-1A SAR images from November 2017 to June 2020 were utilized to monitor the long-term subsidence of the 7221 mining face in a coal mine in Anhui Province.The surface average an-nual deformation rates and cumulative deformation within 2 years after cessation of mining were obtained.The results indicate that the maximum average annual deformation rate of the mining face is-56 mm/a,and the maximum cumulative subsidence is 151 mm.Validation of the InSAR results was performed using leveling measurement data,and the residual values were both less than 5 mm,demonstrating good consistency between the two methods.To evaluate the prediction model's accuracy before and after optimization,the average absolute error and root mean square error were introduced as evaluation criteria.The results dem-onstrate that both errors of the optimized model were within 4 mm,representing a 59%and 60%reduction in errors compared to the traditional model,resulting in a significantly improved prediction accuracy.This study shows that the proposed method exhibits strong predictive capability and can serve as a reference for disaster prevention and mitigation in abandoned coal min-ing areas.

外文关键词：

mining subsidenceSBAS-InSARsubsidence monitoringsurface residual subsidenceoptimal parametersprediction model

作者：

刘增波、徐良骥、张坤、刘潇鹏、曹宗友、徐阳

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作者单位：

安徽理工大学空间信息与测绘工程学院,安徽淮南 232001

深部煤矿采动响应与灾害防控国家重点实验室,安徽淮南 232001

矿山采动灾害空天地协同监测与预警安徽普通高校重点实验室,安徽淮南 232001

矿山环境与灾害协同监测煤炭行业工程研究中心,安徽淮南 232001

合肥综合性国家科学中心能源研究院,安徽合肥 230031

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关键词：

开采沉陷 SBAS-InSAR 沉陷监测地表残余沉降最优参数预测模型

基金：

安徽省重点研发计划项目深部煤矿采动响应与灾害防控国家重点实验室开放基金项目安徽理工大学青年教师科学研究基金项目安徽理工大学研究生创新基金项目淮北矿业集团科研项目淮北矿业集团科研项目安徽建工集团科研项目

项目编号：

2022h11020024SKLMRDPC21KF19QNYB2021-022023cx217820221012023067SG2025Q11

出版年：

2024

DOI：

10.19614/j.cnki.jsks.202407018

金属矿山

中钢集团马鞍山矿山研究院中国金属学会

金属矿山

CSTPCD北大核心

影响因子：0.935

ISSN：1001-1250

年,卷(期)：2024.(8)