基于多重探测技术的煤矿井下采空区空隙空间感知方法

By fusing multi-source data from ground-penetrating radar, resistivity tomography, and acoustic detection, a three-dimensional void model was constructed, which solved the reliability problem of void space detection in underground coal mine goaf areas, achieved high-precision void identification and volume calculation, optimized filling design, and improved the surface subsidence control effect.

CN121091392BActive Publication Date: 2026-07-17中煤能源研究院有限责任公司 +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
中煤能源研究院有限责任公司
Filing Date
2025-08-22
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing technologies lack a multi-source data fusion mechanism for detecting void spaces in underground coal mine goaf areas, resulting in insufficient reliability of detection results and an inability to provide an accurate three-dimensional void distribution model, which affects the filling effect and surface subsidence control.

Method used

By fusing multi-source data from three technologies—ground penetrating radar, resistivity tomography, and acoustic detection—and constructing a three-dimensional void model using adaptive weighting of information entropy and a Bayesian maximum a posteriori estimation framework, high-precision identification and volume calculation of voids in goaf areas can be achieved.

Benefits of technology

It improved the accuracy and reliability of void detection in goaf areas, provided accurate void volume data, optimized filling design, and enhanced filling effect and surface subsidence control.

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Abstract

本发明公开的基于多重探测技术的煤矿井下采空区空隙空间感知方法，步骤为：利用探地雷达获取三维介电常数体，确定采空区空隙空间位置；通过优化布设的电极网络进行电阻率层析成像，反演三维电阻率体；采用声波探测构建三维波速体，识别速度低值区；将三种技术获得的介电常数体、电阻率体和波速体统一配准至预设分辨率的立方网格并归一化处理，采用基于信息熵的自适应加权融合和贝叶斯最大后验估计框架，构建垮落带空隙概率体，最后生成三维空隙模型并计算总体积。本发明通过探地雷达、电阻率层析成像和声波探测的多源数据融合，解决单一技术在采空区空间探测中的局限性，提高了空隙探测的精度，适用于采空区空间感知和治理。
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Citation Information

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