TransQuake: A transformer-based deep learning approach for seismic P-wave detection

Abstract

Recent years, we have witnessed the increasing research interest in developing machine learning, especially deep learning which provides approaches for enhancing the performance of microearthquake detection. While considerable research efforts have been made in this direction, most of the state-of-the-art solutions are based on Convolutional Neural Network (CNN) structure, due to its remarkable capability of modeling local and static features. Indeed, the globally dynamic characteristics contained within time series data (i.e., seismic waves), which cannot be fully captured by CNN-based models, have been largely ignored in previous studies. In this paper, we propose a novel deep learning approach, TransQuake, for seismic P-wave detection. The approach is based on the most advanced sequential model, namely Transformer. To be specific, TransQuake can exploit the STA/LTA algorithm for adapting the three-component structure of seismic waves as input, and take advantage of the multi-head attention mechanism for conducting explainable model learning. Extensive evaluations of the aftershocks following the 2008 Wenchuan MW 7.9 earthquake clearly demonstrates that TransQuake is able to achieve the best detection performance which excels the results obtained using other baselines. Meanwhile, experimental results also validate the interpretability of the results obtained by TransQuake, such as the attention distribution of seismic waves in different positions, and the analysis of the optimal relationship between coda wave and P-wave for noise identification.