基于SBAS-InSAR和水准数据融合的采动沉陷盆地边界稳健识别方法

doi:10.13474/j.cnki.11-2246.2026.0210

测绘通报 ›› 2026, Vol. 0 ›› Issue (2): 60-67.doi: 10.13474/j.cnki.11-2246.2026.0210

基于SBAS-InSAR和水准数据融合的采动沉陷盆地边界稳健识别方法

陈元非^1,2, 王磊¹

1. 安徽理工大学深部煤炭安全开采与环境保护全国重点实验室, 安徽淮南 232001;
2. 池州学院地理与规划学院, 安徽池州 247000

收稿日期:2025-07-03 发布日期:2026-03-12
作者简介:陈元非(1992—),男,博士,讲师,从事变形监测技术研究工作。E-mail:18796258825@163.com
基金资助:
国家自然科学基金(52474194;52074010);安徽省教育厅自然科学研究项目(2024AH051370;2024AH051378)

A robust method for identifying the subsidence basin boundary caused by mining based on SBAS-InSAR and level data fusion

CHEN Yuanfei^1,2, WANG Lei¹

1. State Key Laboratory for Safe Mining of Deep Coal Resources and Environment Protection, Anhui University of Science and Technology, Huainan 232001, China;
2. School of Geography and Planning, Chizhou University, Chizhou 247000, China

Received:2025-07-03 Published:2026-03-12

摘要/Abstract

摘要： InSAR监测技术难以获取采动沉陷区大变形数据,且在沉陷边界易受干扰而影响边界范围的准确划定。为此,本文引入随机采样一致性(RANSAC)算法,提出了耦合玻尔兹曼(Boltzmann)函数的采动沉陷数据稳健融合方法(BRAN)。通过随机采样SBAS-InSAR像素点,以像素内点数量为评价准则,重复反演直至得到内点数量最多的稳健参数解,最后用模型预测值替代异常像素和失相干数据,完成沉降数据稳健融合。模拟试验和淮南朱集东矿案例表明,BRAN方法能够识别并剔除异常像素点的干扰,融合的沉降量相对均方根误差为6.4%,获取的沉陷盆地边界符合采动沉陷特征。该研究成果为矿区多元数据稳健融合提供了新思路,对矿区沉陷灾害的监测和预警具有一定应用价值。

关键词: 采动沉陷区, 沉陷边界, SBAS-InSAR, 随机采样一致性, 玻尔兹曼

Abstract: InSAR monitoring technology struggles to obtain large deformation data in areas of mining subsidence,and the boundary of subsidence is easily influenced by interference,affecting the accuracy of boundary delineation.To address this,a random sample consensus (RANSAC)algorithm is introduced,and a robust fusion method for mining subsidence data is proposed,which couples the Boltzmann function (BRAN).By randomly sampling SBAS-InSAR pixel points and using the number of inliers as an evaluation criterion,we repeatedly invert the model until a robust parameter solution with the maximum number of inliers is obtained.Finally,the predicted subsidence calculated by Boltzmann model and its robust parameter are used to replace outliers pixels and decoherent data,completing the robust fusion of subsidence data.Simulation experiments and a case study from the Zhujidong coal mine in Huainan show that the BRAN method can effectively identify and eliminate the interference of outliers pixel points,with the relative root mean square error of the fused subsidence values being 6.4%.The obtained subsidence basin boundary aligns with the characteristics of mining subsidence.The research findings provide new insights for robust fusion of multi-source data in mining areas and hold certain application value for monitoring and early warning of subsidence disasters in these regions.

Key words: mining subsidence area, subsidence boundary, SBAS-InSAR, random sample consistency, Boltzmann

中图分类号:

P237

陈元非, 王磊. 基于SBAS-InSAR和水准数据融合的采动沉陷盆地边界稳健识别方法[J]. 测绘通报, 2026, 0(2): 60-67.

CHEN Yuanfei, WANG Lei. A robust method for identifying the subsidence basin boundary caused by mining based on SBAS-InSAR and level data fusion[J]. Bulletin of Surveying and Mapping, 2026, 0(2): 60-67.

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基于SBAS-InSAR和水准数据融合的采动沉陷盆地边界稳健识别方法

A robust method for identifying the subsidence basin boundary caused by mining based on SBAS-InSAR and level data fusion

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