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Title: Improving the Retrieval of Offshore-Onshore Correlation Functions With Machine Learning
Authors: Viens, Loïc
Iwata, Tomotaka
Author's alias: 岩田, 知孝
Keywords: machine learning
correlation functions
long‐period ground motions
Nankai Trough
seismic interferometry
Issue Date: Aug-2020
Publisher: Blackwell Publishing Ltd
Journal title: Journal of Geophysical Research: Solid Earth
Volume: 125
Issue: 8
Thesis number: e2020JB019730
Abstract: The retrieval of reliable offshore‐onshore correlation functions is critical to improve our ability to predict long‐period ground motions from megathrust earthquakes. However, localized ambient seismic field sources between offshore and onshore stations can bias correlation functions and generate nonphysical arrivals. We present a two‐step method based on unsupervised learning to improve the quality of correlation functions calculated with the deconvolution technique (e.g., deconvolution functions, DFs). For a DF data set calculated between two stations over a long time period, we first reduce the data set dimensions using the principal component analysis and cluster the features of the low‐dimensional space with a Gaussian mixture model. We then stack the DFs belonging to each cluster together and select the best stacked DF. We apply our technique to DFs calculated every 30 min between an offshore station located on top of the Nankai Trough, Japan, and 78 onshore receivers. Our method removes spurious arrivals and improves the signal‐to‐noise ratio of DFs. Most 30‐min DFs selected by our clustering method are generated during extreme meteorological events such as typhoons. To demonstrate that the DFs obtained with our method contain reliable phases and amplitudes, we use them to simulate the long‐period ground motions from a Mw 5.8 earthquake, which occurred near the offshore station. Results show that the earthquake long‐period ground motions are accurately simulated. Our method can easily be used as an additional processing step when calculating offshore‐onshore DFs and offers a new way to improve the prediction of long‐period ground motions from potential megathrust earthquakes.
Rights: An edited version of this paper was published by AGU. Copyright 2020 American Geophysical Union.
The full-text file will be made open to the public on 7 February 2021 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
DOI(Published Version): 10.1029/2020JB019730
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