Separation of multi-mode surface waves by supervised machine learning methods
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Accepted manuscript
Type
ArticleAuthors
Li, Jing
Chen, Yuqing

Schuster, Gerard T.

KAUST Department
Center for Subsurface Imaging and Fluid ModelingEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Date
2020-01-09Online Publication Date
2020-01-09Print Publication Date
2020-05Embargo End Date
2020-12-19Submitted Date
2019-05-28Permanent link to this record
http://hdl.handle.net/10754/661452
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Show full item recordAbstract
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:10.1111/1365-2478.12927Sponsors
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
WileyJournal
Geophysical ProspectingAdditional Links
https://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2478.12927ae974a485f413a2113503eed53cd6c53
10.1111/1365-2478.12927