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Correction of seismic attribute-based small-structure prediction errors using GPR data—A case study of the Shuguang Coal Mine, Shanxi

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Abstract

Small structures in coal mine working face is one of the main hidden dangers of safe and efficient production in coal mine. Currently, seismic exploration is often used as the main method for detecting such structures. However, limited by the accuracy of seismic data processing and interpretation, the interpreted location of small structures is often deviated. Ground-penetrating radar (GPR) can detect small structures accurately, but the exploration depth is shallow. The combination of the two methods can improve the exploration accuracy of small structures in coal mine. Aiming at the 1226# working face of Shuguang coal mine, we propose a method of seismic-attributes based small-structure prediction error correction using GPR data. First, we extract the coherence, curvature, and dip attributes from seismic data, that are sensitive to small structures, then by considering factors such as the effective detection range of GPR and detection environment, we select two structures from the prediction results of seismic attributes for GPR detection. Finally, based on the relationship between the positions of small structures predicted by the two methods, we use statistical methods to determine the overall offset distance and azimuth of the small structures in the entire study area and use the results as a standard for correcting each structure position. The results show that the GPR data can be used to correct the horizontal position errors of small structures predicted by seismic attribute analysis. The accuracy of the prediction results is greatly improved, with the error controlled within 5 m and reduced by more than 80%. Therefore, the feasibility of the method proposed in this study is verified.

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Acknowledgments

This study work is supported by the Directly Managed Scientific Research Project of Huainan Mining (Group) Co. Ltd. (No. HNKYJTJS(2018)181), the Major Project of Shaanxi Coal and Chemical Industry Group Co. Ltd. (No. 2018SMHKJ-A-J-03), China Energy Investment Corporation 2030 Pilot Project (No. GJNY2030XDXM-19-03.2), State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology (Beijing). I also would like to thank the editorial department and the review experts for their valuable comments and suggestions, and thank the Compagnie Générale de Géophysique (CGG) for the Jason software support.

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Authors and Affiliations

  1. College of Geoscience and Surveying Engineering, CUMTB, Beijing, 100083, China

    Cui Fan, Zhao Zhi-Rong & Du Yun-Fei

  2. State Key Laboratory of Coal Resources and Safe Mining, CUMTB, Beijing, 100083, China

    Cui Fan

  3. Beijing Key Laboratory for Precise Mining of Intergrown Energy and Resources, Beijing, 100083, China

    Cui Fan

  4. Beijing YuBo Innovation Geophysics Technology Co., Ltd, Beijing, 100083, China

    Bai Yu

  5. Shuguang Mine, Shanxi Fenxi Mining (Group) Co., Ltd., Jiexiu, 032000, China

    Xu Chang-Qing & Jia Xiao-Feng

Authors
  1. Cui Fan
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  2. Zhao Zhi-Rong
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  3. Du Yun-Fei
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  4. Bai Yu
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  5. Xu Chang-Qing
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  6. Jia Xiao-Feng
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Corresponding author

Correspondence to Cui Fan.

Additional information

Cui Fan, PhD, associate professor, he graduated from the Anhui University of Science and Technology with a bachelor’s degree in Resource Management in 2006, from the China University of Mining and Technology, Beijing, with a master’s degree in Earth Exploration and Information Technology in 2009, and from the China University of Mining and Technology, Beijing, with a doctorate degree in Earth Exploration and Information Technology in 2012. His research interests include applications of ground penetration, geophysical methods for electromagnetic applications and geophysics for coal fields. Email: cuifan@cumtb.edu.cn

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Fan, C., Zhi-Rong, Z., Yun-Fei, D. et al. Correction of seismic attribute-based small-structure prediction errors using GPR data—A case study of the Shuguang Coal Mine, Shanxi. Appl. Geophys. 17, 489–500 (2020). https://doi.org/10.1007/s11770-020-0841-7

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  • DOI: https://doi.org/10.1007/s11770-020-0841-7

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