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    Separation of multi-mode surface waves by supervised machine learning methods

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    Name:
    Geophysics_prospecting_ML_picking.final.pdf
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    1.848Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Li, Jing cc
    Chen, Yuqing cc
    Schuster, Gerard T. cc
    KAUST Department
    Center for Subsurface Imaging and Fluid Modeling
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2020-01-09
    Online Publication Date
    2020-01-09
    Print Publication Date
    2020-05
    Embargo End Date
    2020-12-19
    Submitted Date
    2019-05-28
    Permanent link to this record
    http://hdl.handle.net/10754/661452
    
    Metadata
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    Abstract
    Logistic regression, neural networks and support vector machines are tested for their effectiveness in isolating surface waves in seismic shot records. To distinguish surface waves from other arrivals, we train the algorithms on three distinguishing features of surface-wave dispersion curves in the (Formula presented.) domain: spectrum coherency of the trace's magnitude spectrum, local dip and the frequency range for a fixed wavenumber k in the spectrum. Numerical tests on synthetic data show that the kernel-based support vector machines algorithm gives the highest accuracy in predicting the surface-wave window in the (Formula presented.) domain compared to neural networks and logistic regression. This window is also used to automatically pick the fundamental dispersion curve. The other two methods correctly pick the low-frequency part of the dispersion curve but fail at higher frequencies where there is interference with higher-order modes.
    Citation
    Li, J., Chen, Y., & Schuster, G. T. (2020). Separation of multi-mode surface waves by supervised machine learning methods. Geophysical Prospecting. doi:10.1111/1365-2478.12927
    Sponsors
    We are grateful to the sponsors of the Center for SubsurfaceImaging and Modeling (CSIM) Consortium for their financialsupport. This work was supported by the Natural ScienceFoundation of China (41874134) and Jilin Excellent YouthFund of China (20190103142JH).
    Publisher
    Wiley
    Journal
    Geophysical Prospecting
    DOI
    10.1111/1365-2478.12927
    Additional Links
    https://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2478.12927
    ae974a485f413a2113503eed53cd6c53
    10.1111/1365-2478.12927
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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