Shear Wave Splitting Analysis using Deep Learning (SWSNet)

Abstract

Teleseismic shear-wave splitting analyses are typically performed by reversing the splitting process through the application of frequency- or time-domain operations minimizing transverse-component waveforms. These operations yield two splitting parameters, φ (fast-axis orientation) and δt (delay time). In this study, we investigate the applicability of a recurrent neural network, SWSNet, for determining the splitting parameters from pre-selected waveform windows. Due to the scarcity of sufficiently labelled real waveform data, we generate our own synthetic training dataset. The model is capable of determining φ and δt with a root mean squared error (RMSE) of 9.58◦ and 0.143 s for noisy synthetic test data. The application to real data involves a deconvolution step to homogenize the waveforms. When applied to data from the USArray dataset, the results exhibit similar patterns to those found in previous studies with mean absolute differences of 11.12◦ and 0.25 s in the calculation of φ and δt, respectively.