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<p>Ensuring underground coal mine safety is challenging due to undetected faults, fractured zones, and unstable strata. Conventional Ground Penetrating Radar systems often fail to detect these features accurately due to limited depth penetration, noise interference, and weak predictive capabilities. This study introduces an improved Sub Surface Profiler (SSP) that addresses these issues using advanced noise filtering, enhanced reflection coefficient modeling, and predictive fault detection. The SSP performed comprehensive scans along the 1Dip W and 1Dip E directions. The reflection coefficient model analyzed radar wave interactions at geological boundaries, distinguishing coal from rock or fault zones. Notably, the correlation between higher reflection coefficients and unstable formations provided a reliable predictive mechanism for identifying potential roof fall regions. Adaptive noise reduction techniques like wavelet transform and Kalman filtering minimized electromagnetic interference, enhancing data clarity. Borehole data validated the accuracy of the SSP, aligning scan results with physical core samples. The SSP effectively identified unstable formations, detecting the Roof Fall Zone (65–90&#xa0;m) with a reflection coefficient of 0.31, confirming severe instability. Distal Fault Zones were found between 90&#xa0;m–145&#xa0;m and 145&#xa0;m–227&#xa0;m, with reflection coefficients of 0.18 and 0.19. Stable zones beyond 227&#xa0;m showed minimal signal attenuation and a reflection coefficient of 0.12, indicating safe regions. The system achieved a prediction accuracy of ± 0.4&#xa0;m compared to borehole data. The improved SSP’s enhanced fault detection and depth resolution significantly improved underground safety. Future integration with machine learning is recommended to boost predictive capabilities and automate fault detection, enhancing operational efficiency in coal mines.</p>

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Enhanced Subsurface Profiling for Fault Detection in Underground Coal Mines using Advanced SSPGPR Algorithms with Integrated Validation Techniques

  • 期刊论文

  • 发表日期：

    2025年8月6日

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  • Mohd Ahtesham Hussain Siddiqui,
  • Somnath Chattopadhyaya,
  • Saurabh Dewangan

摘要

Ensuring underground coal mine safety is challenging due to undetected faults, fractured zones, and unstable strata. Conventional Ground Penetrating Radar systems often fail to detect these features accurately due to limited depth penetration, noise interference, and weak predictive capabilities. This study introduces an improved Sub Surface Profiler (SSP) that addresses these issues using advanced noise filtering, enhanced reflection coefficient modeling, and predictive fault detection. The SSP performed comprehensive scans along the 1Dip W and 1Dip E directions. The reflection coefficient model analyzed radar wave interactions at geological boundaries, distinguishing coal from rock or fault zones. Notably, the correlation between higher reflection coefficients and unstable formations provided a reliable predictive mechanism for identifying potential roof fall regions. Adaptive noise reduction techniques like wavelet transform and Kalman filtering minimized electromagnetic interference, enhancing data clarity. Borehole data validated the accuracy of the SSP, aligning scan results with physical core samples. The SSP effectively identified unstable formations, detecting the Roof Fall Zone (65–90 m) with a reflection coefficient of 0.31, confirming severe instability. Distal Fault Zones were found between 90 m–145 m and 145 m–227 m, with reflection coefficients of 0.18 and 0.19. Stable zones beyond 227 m showed minimal signal attenuation and a reflection coefficient of 0.12, indicating safe regions. The system achieved a prediction accuracy of ± 0.4 m compared to borehole data. The improved SSP’s enhanced fault detection and depth resolution significantly improved underground safety. Future integration with machine learning is recommended to boost predictive capabilities and automate fault detection, enhancing operational efficiency in coal mines.