Version 1
: Received: 19 June 2024 / Approved: 20 June 2024 / Online: 21 June 2024 (05:35:55 CEST)
How to cite:
Shu, G.; Meng, X.; Liao, L.; Shu, M.; Mu, S. Compressive Sensing-Based Spectral Inversion Algorithm and Its Application in Detailed Fault Characterization. Preprints2024, 2024061432. https://doi.org/10.20944/preprints202406.1432.v1
Shu, G.; Meng, X.; Liao, L.; Shu, M.; Mu, S. Compressive Sensing-Based Spectral Inversion Algorithm and Its Application in Detailed Fault Characterization. Preprints 2024, 2024061432. https://doi.org/10.20944/preprints202406.1432.v1
Shu, G.; Meng, X.; Liao, L.; Shu, M.; Mu, S. Compressive Sensing-Based Spectral Inversion Algorithm and Its Application in Detailed Fault Characterization. Preprints2024, 2024061432. https://doi.org/10.20944/preprints202406.1432.v1
APA Style
Shu, G., Meng, X., Liao, L., Shu, M., & Mu, S. (2024). Compressive Sensing-Based Spectral Inversion Algorithm and Its Application in Detailed Fault Characterization. Preprints. https://doi.org/10.20944/preprints202406.1432.v1
Chicago/Turabian Style
Shu, G., Mengcheng Shu and Shengqiang Mu. 2024 "Compressive Sensing-Based Spectral Inversion Algorithm and Its Application in Detailed Fault Characterization" Preprints. https://doi.org/10.20944/preprints202406.1432.v1
Abstract
The relentless advancement and integration of mechanization and intelligence within the coal mining 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 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 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 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 mining operations, thus offering robust support for the implementation of more effective safety protocols and disaster prevention strategies in coal mines.
Environmental and Earth Sciences, Geophysics and Geology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
