Abstract

The time window for analyzing local shear-wave splitting (SWS) phases significantly affects the quality of measurements, revealing a noteworthy domain influence. In this study, an approach using convolutional neural network (CNN) is applied to determine the end of time window (e), which has a similar idea of the phase-picking CNNs. The start of time window is 0.5 s before e. Our data set contains 803 human-labeled measurements, recorded from three stations located in Ridgecrest, California. These measurements are foreshocks and aftershocks of an M 7.1 earthquake on 6 July 2019. After 21 times shifting on each measurement, 90% of the data set is applied as the training data set, with the remaining 10% as the testing data set. The performance of CNN with the testing data set is compared with a nonmachine learning method, multiple filter automatic splitting technique (MFAST). The results reveal that the CNN yields more similar results with human-labeled outcomes than MFAST, as evidenced by lower absolute error and standard deviation for e, SWS time, the orientation of fast-wave polarization, and more consistent results on the map. The CNN also performs well when applied to data recorded by a station in Parkfield, California. This study shows the outstanding performance of CNN in picking the time window and the reliable automatic determination of this time window, and it is also a crucial step for future development of automatic ranking methodologies.

Department(s)

Geosciences and Geological and Petroleum Engineering

Publication Status

Available Access

Comments

Missouri University of Science and Technology, Grant 1919789

International Standard Serial Number (ISSN)

1938-2057; 0895-0695

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Seismological Society of America, All rights reserved.

Publication Date

01 Nov 2024

Download

Full Text Link

Share

 
COinS