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Deep Learning for Extracting Dispersion Curves

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Abstract

High-frequency surface-wave methods have been widely used for surveying near-surface shear-wave velocities. A key step in high-frequency surface-wave methods is to acquire dispersion curves in the frequency–velocity domain. The traditional way to acquire the dispersion curves is to identify the dispersion energy and manually pick phase velocities by following energy peaks at different frequencies. A large number of dispersion curves need to be extracted for inversion, especially for surveys with long two-dimensional sections or large three-dimensional (3D) coverages. Human–machine interaction-based dispersion curves extraction, however, is still common, which is time-consuming. We developed a deep learning model, termed Dispersion Curves Network (DCNet), that can rapidly extract dispersion curves from dispersion images by treating dispersion curves extraction as an instance segmentation task. The accuracy of the dispersion curves extracted by our DCNet model is demonstrated by theoretical data. We used a 3D field application of ambient seismic noise to demonstrate the effectiveness and robustness of our method. The real-world results showed that the accuracy of the dispersion curves extracted from the field data using our method can achieve human-level performance and our method can meet the requirement of geoengineering surveys in rapidly extracting massive dispersion curves of surface waves.

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Acknowledgements

The authors would like to thank associate editor Yu Jeffrey Gu and two anonymous reviewers for their constructive comments and suggestions. This study is supported by the National Natural Science Foundation of China (NSFC) under Grant No. 41774115 and Nanjing Center of China Geological Survey under Grant No. DD20190281. The authors appreciate Binbin Mi, Jingyin Pang, Changjiang Zhou, Hongyu Zhang, and Xinhua Chen for their help in field data collection. The authors also appreciate Xiaojun Chang of Nanjing Center of China Geological Survey for their assistance in field data collection and providing borehole data.

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Authors and Affiliations

  1. Hubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, 388 Lumo Rd., Wuhan, 430074, Hubei, China

    Tianyu Dai

  2. Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, Zhejiang, China

    Jianghai Xia, Ling Ning, Chaoqiang Xi & Ya Liu

  3. Nanjing Center of the Geological Survey, China Geological Survey, 534 Zhongshan East Road, Nanjing, 210016, Jiangsu, China

    Huaixue Xing

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  1. Tianyu Dai
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Dai, T., Xia, J., Ning, L. et al. Deep Learning for Extracting Dispersion Curves. Surv Geophys 42, 69–95 (2021). https://doi.org/10.1007/s10712-020-09615-3

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