Deep Learning for Extracting Dispersion Curves
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Deep Learning for Extracting Dispersion Curves Tianyu Dai1 · Jianghai Xia2 · Ling Ning2 · Chaoqiang Xi2 · Ya Liu2 · Huaixue Xing3 Received: 22 January 2020 / Accepted: 25 August 2020 © Springer Nature B.V. 2020
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 realworld 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. Keywords Surface waves · Dispersion curves · Deep learning · Convolutional networks
1 Introduction High-frequency surface-wave methods (Xia et al. 1999, 2002) have been widely used for near-surface shear (S)-wave velocity survey among active (e.g., Xia et al. 2003, 2012; Xia 2014; Ivanov et al. 2006; Luo et al. 2007; Socco et al. 2010; Foti et al. 2011; Pan et al. * Jianghai Xia [email protected]; [email protected] 1
Hubei Subsurface Multi‑scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, 388 Lumo Rd., Wuhan 430074, Hubei, China
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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
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Nanjing Center of the Geological Survey, China Geological Survey, 534 Zhongshan East Road, Nanjing 210016, Jiangsu, China
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Vol.:(0123456789)
Surveys in Geophysics
2016a; Zhang and Alkhalifah 2019a) and passive seismic investigations (e.g., Louie 2001; Okada 2003; Park and Miller 2008; Cheng et al. 2015, 2016; Zhang et al. 2020). By using dispersion imaging methods, such as the τ–p transformation (McMechan and Yedlin 1981), the F–K transformation (Yilmaz 1987), the phase shift (Park et al. 1998), the frequency decomposition and slant stacking (Xia et al. 2007), and the high-resolution linear Radon transformation (HRLRT) (Luo et al
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