Open Access

Megha Chakraborty, Claudia Quinteros Cartaya, Wei Li, Johannes Faber, Georg Rümpker, Horst Stöcker, Nishtha Srivastava

• Highlights • We present PolarCAP, a deep learning model that can classify the polarity of a waveform with a 98% accuracy. • The first-motion polarity of seismograms is a useful parameter, but its manual determination can be laborious and imprecise. • We demonstrate that in several cases the model can assign trace polar-ity more accurately than a human analyst. 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 earth-quake 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 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.