Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach


Shear wave splitting (SWS) analysis is widely used to provide critical constraints on crustal and mantle structure and dynamic models. In order to obtain reliable splitting measurements, an essential step is to visually verify all the measurements to reject problematic measurements, a task that is increasingly time consuming due to the exponential increase in the amount of data. In this study, we utilized a convolutional neural network (CNN) based method to automatically select reliable SWS measurements. The CNN was trained by human-verified teleseismic SWS measurements and tested using synthetic SWS measurements. Application of the trained CNN to broadband seismic data recorded in south central Alaska reveals that CNN classifies 97.0% of human selected measurements as acceptable, and revealed ∼30% additional measurements. To our knowledge, this is the first study to systematically explore the potential of a machine-learning based technique to assist with SWS analysis.


Geosciences and Geological and Petroleum Engineering


IRIS Data Services are funded through the Seismological Facilities for the Advancement of Geoscience (SAGE) Award of the National Science Foundation under Cooperative Support Agreement EAR-1851048. The study was partially supported by the U.S. National Science Foundation under awards 1830644 and 1919789 to S.G.

Keywords and Phrases

Convolutional Neural Network; Data Mining; Machine Learning; Seismic Anisotropy; Shear Wave Splitting

International Standard Serial Number (ISSN)

1944-8007; 0094-8276

Document Type

Article - Journal

Document Version

Final Version

File Type





© 2022 Wiley; American Geophysical Union, All rights reserved.

Publication Date

28 Jun 2022

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