PolarCAP -- A Deep Learning approach for First Motion Polarity
Classification of Earthquake Waveforms
Abstract
The polarity of first P-wave arrivals plays a significant role in the
effective determination of focal mechanisms specially for smaller
earthquakes. Manual estimation of polarities is not only time-consuming
but also prone to human errors. This warrants a need for an automated
algorithm for first motion polarity determination. We present a deep
learning model - PolarCAP that uses an autoencoder architecture to
identify first-motion polarities of earthquake waveforms. PolarCAP is
trained in a supervised fashion using more than 130,000 labelled traces
from the Italian seismic dataset (INSTANCE) and is cross-validated on
$\sim$22,000 traces to choose the most optimal set of
hyperparameters. We obtain an accuracy of 0.98 on a completely unseen
test dataset of almost 33,000 traces. Furthermore, We check the model
generalizability by testing it on the datasets provided by previous
works and show that our model achieves a higher recall on both positive
and negative polarities.