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Intelligent identification method for near-surface ground fissures based on seismic data

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Abstract

Taking a study area in Jinzhong Basin in Qixian County, Shanxi Province, as an example, this work performs an intelligent interpretation of ground fissures. On the basis of a complete analysis of the regional geological background in the study area, dip-steering cube operation and median filtering of seismic data were performed using fast Fourier transform to improve the continuity of seismic events and eliminate random noise. A total of 200 stratigraphic continuous sample training points and 500 discontinuous training points were obtained from the processed seismic data. Thereafter, a variety of attributes (coherence, curvature, amplitude, frequency, etc.) were extracted as the input for the multilayer perceptron neural network training. During the training period, the training results were traced by normalized root mean square error (RMSE) and misclassification. The training results showed a downward trend during the training period. The misclassification curve was stable at 0.3, and the normalized RMSE curve was stable at 0.68. When the value of the normalized RMSE curve reached the minimum, the training was terminated, and the training results were extended to the whole data volume to obtain the attribute cube of intelligent ground fissure detection. The characteristics of ground fissures were analyzed and identified from the sections and slices. A total of 11 ground fissures were finally interpreted. The interpretation results showed that the dip angles were 60°–85°, the fault throws were 0–43 m, and the extension lengths were 300–1,100 m in the whole area. The strike of 73% of the ground fissures was consistent with the direction of the regional tectonic settings. Specifically, four ground fissures coincided with the surface disclosed, and the verification rate reached 100%. In conclusion, the intelligent ground fissure detection attribute based on the dip-steering cube is effective in predicting the spatial distribution of ground fissures.

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Authors and Affiliations

  1. State Key Laboratory for Coal Resources and Safety Mining, China University of Mining and Technology (Beijing), Beijing, 100083, China

    Su-Zhen Shi

  2. College of Geoscience and Surveying Engineering, China University of Mining Technology (Beijing), Beijing, 100083, China

    Jian Feng, Pei-fei Duan, You-chao Qi & Qi Han

  3. Zhongshui North Survey, Design and Research Co. Ltd., Changchun, China

    Jian-Ying Gu

Authors
  1. Su-Zhen Shi
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  2. Jian-Ying Gu
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  3. Jian Feng
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  4. Pei-fei Duan
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  5. You-chao Qi
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  6. Qi Han
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Corresponding author

Correspondence to Su-Zhen Shi.

Additional information

The study was supported by Open Fund of State Key Laboratory of Coal Resources and Safe Mining(Grant No. SKLCRSM19ZZ02) and the National Natural Science Foundation of China (No. 41702173).

Shi Suzhen is an associate professor and master supervisor at China University of Mining and Technology (Beijing). Her main work is seismic data interpretation and inversion. Her contact information is State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology (Beijing), 11 Ding Xueyuan Road, Haidian District, Beijing 100083, China. Her email address is ssz@cumtb.edu.cn.

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Shi, SZ., Gu, JY., Feng, J. et al. Intelligent identification method for near-surface ground fissures based on seismic data. Appl. Geophys. 17, 639–648 (2020). https://doi.org/10.1007/s11770-020-0877-8

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  • DOI: https://doi.org/10.1007/s11770-020-0877-8

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