PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms

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.

A study on small magnitude seismic phase identification using 1D deep residual neural network

Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio.With improved seismometers and better global coverage,a sharp increase in the volume of recorded seismic data has been achieved.This makes handling seismic data rather daunting by using traditional approaches and therefore fuels the need for more robust and reliable methods.In this study,we develop 1D deep Residual Neural Network(ResNet),for tackling the problem of seismic signal detection and phase identification.This method is trained and tested on the dataset recorded by the Southern California Seismic Network.Results demonstrate that the proposed method can achieve robust performance for the detection of seismic signals and the identification of seismic phases.Compared to previously proposed deep learning methods,the introduced framework achieves around 4%improvement in earthquake detection and a slightly better performance in seismic phase identification on the dataset recorded by Southern California Earthquake Data Center.The model generalizability is also tested further on the STanford EArthquake Dataset.In addition,the experimental result on the same subset of the STanford EArthquake Dataset,when masked by different noise levels,demonstrates the model’s robustness in identifying the seismic phases of small magnitude.