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 p...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.展开更多
文摘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.
