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<p>The relentless advancement and integration of mechanization and intelligence within the coal mining and oil and gas industry, combined with the increasing depths of mining operations, necessitate significantly higher safety standards. This necessity arises due to the increasingly complex geological and coal and oil and gas seam conditions encountered as mining operations delve deeper into the earth. Traditionally, 3D seismic exploration technology has been fundamental in ensuring both operational efficiency and safety in the coal and oil and gas mining sector. However, despite its critical role, this technology still has limitations in detecting and characterizing small-scale geological structures which can pose substantial risks if not accurately identified and mitigated. To address these challenges, this paper introduces a sparsity-constrained adaptive hard thresholding algorithm designed to tackle the spectral inversion optimization problem with an L0 norm constraint for the enhanced retrieval of reflection coefficients, thereby establishing a comprehensive workflow for enhancing seismic data resolution through compressed sensing spectral inversion. Real data validation has demonstrated that this method significantly enhances the resolution of seismic data while maintaining the amplitude integrity and fidelity of the original signals. This improvement allows for a more detailed and accurate characterization of small-scale geological features, which are critical in identifying and mitigating potential hazards within coal and oil and gas mining operations, thus offering robust support for the implementation of more effective safety protocols and disaster prevention strategies in coal and oil and gas mines.</p>

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Compressive Sensing-Based Spectral Inversion Algorithm and Its Application in Detailed Fault Characterization

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Guoxu Shu,
  • Xiaohong Meng,
  • Mengcheng Shu,
  • Shengqiang Mu,
  • Shoudong Huo

摘要

The relentless advancement and integration of mechanization and intelligence within the coal mining and oil and gas industry, combined with the increasing depths of mining operations, necessitate significantly higher safety standards. This necessity arises due to the increasingly complex geological and coal and oil and gas seam conditions encountered as mining operations delve deeper into the earth. Traditionally, 3D seismic exploration technology has been fundamental in ensuring both operational efficiency and safety in the coal and oil and gas mining sector. However, despite its critical role, this technology still has limitations in detecting and characterizing small-scale geological structures which can pose substantial risks if not accurately identified and mitigated. To address these challenges, this paper introduces a sparsity-constrained adaptive hard thresholding algorithm designed to tackle the spectral inversion optimization problem with an L0 norm constraint for the enhanced retrieval of reflection coefficients, thereby establishing a comprehensive workflow for enhancing seismic data resolution through compressed sensing spectral inversion. Real data validation has demonstrated that this method significantly enhances the resolution of seismic data while maintaining the amplitude integrity and fidelity of the original signals. This improvement allows for a more detailed and accurate characterization of small-scale geological features, which are critical in identifying and mitigating potential hazards within coal and oil and gas mining operations, thus offering robust support for the implementation of more effective safety protocols and disaster prevention strategies in coal and oil and gas mines.