The determination of the depth and boundary of the goaf is of great significance for the detection of illegal mining. However, determining the current location of unknown goafs mainly relies on low-efficiency, time-consuming, and labor-intensive physical detection methods such as geomagnetic field changes. Due to their large coverage and high degree of automation, research on remote sensing methods has been conducted to locate mining activities by monitoring surface deformation. This paper proposes a method that relies on the principle of the probability integration method (PIM) and on synthetic aperture radar interferometry (InSAR) to retrieve the location of an underground goaf. First, the relationship between ground subsidence and the location of the mined-out area was established according to PIM; then, the location of the mined-out area was obtained by the surface deformation acquired by InSAR. The proposed method does not rely on complex nonlinear models and has complete parameters; therefore, it has higher engineering application value. A test site in the Fengfeng mining area and 11 Radarsat-2 images were used to verify the proposed method. The experimental results showed that the average relative error of the proposed method is 6.35%, which is 27.56% higher than that of similar algorithms based on complex nonlinear models. Compared to algorithms that ignore the coal seam dip, the accuracy is improved to 98.27%.

中文翻译：

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基于InSAR和PIM的倾斜采空区确定，用于非法采矿检测

确定采空区的深度和边界对发现非法采矿具有重要意义。但是，确定未知采空区的当前位置主要取决于效率低，耗时且劳动强度大的物理检测方法，例如地磁场变化。由于其覆盖范围广且自动化程度高，因此已进行了遥感方法研究，以通过监测地表变形来定位采矿活动。本文提出了一种方法，该方法依赖于概率积分法（PIM）的原理和合成孔径雷达干涉法（InSAR）来检索地下采空区的位置。首先，根据PIM建立了地面沉降与采空区位置之间的关系。然后，通过InSAR获得的表面变形获得采空区的位置。该方法不依赖复杂的非线性模型，具有完整的参数。因此具有较高的工程应用价值。该方法在凤峰矿区的一个测试现场和11张Radarsat-2图像上进行了验证。实验结果表明，该方法的平均相对误差为6.35％，比基于复杂非线性模型的相似算法的平均相对误差高27.56％。与忽略煤层倾角的算法相比，其准确性提高到98.27％。该方法在凤峰矿区的一个测试现场和11张Radarsat-2图像上进行了验证。实验结果表明，该方法的平均相对误差为6.35％，比基于复杂非线性模型的相似算法的平均相对误差高27.56％。与忽略煤层倾角的算法相比，其准确性提高到98.27％。该方法在凤峰矿区的一个测试现场和11张Radarsat-2图像上进行了验证。实验结果表明，该方法的平均相对误差为6.35％，比基于复杂非线性模型的相似算法的平均相对误差高27.56％。与忽略煤层倾角的算法相比，其准确性提高到98.27％。