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.