Seismic Picking Attention Module

Automatic data-driven earthquake event detection and seismic phase-picking techniques have gained significant momentum and advancement in recent years. However, prevailing data-driven models tend to rely on an encoding-decoding structure that employs large-step convolution or pooling operations for feature extraction in the encoding region. While this operation is efficient, it inevitably sacrifices the spatial information of seismic data and obstructs the establishment of long-range dependencies between them. This spatial information is crucial for precise seismic phase picking. To tackle this issue, we propose the seismic picking attention (SPA) module as a plug-and-play component for earthquake event detection and seismic phase picking models. The SPA module collaborates with the base model, facilitating the aggregation of spatial contextual information and enabling the model to concentrate on task-relevant features. In this study, we establish a consistent experimental framework to evaluate the efficacy of the SPA model across three deep learning models, utilizing four publicly available seismic datasets. The results demonstrate a significant enhancement in the accuracy of both phase picking and event detection through the incorporation of the SPA module into the base model.