SeisPolar: Seismic Wave Polarity Module for the SeisBlue Deep Learning Seismology Platform

Responding to challenges from increasing seismic data, our study leverages deep learning to enhance seismic data processing's automation and efficiency. Recognizing Taiwan's unique geological structure, we have developed deep learning models using data from dense seismic arrays since 2018. We have integrated the Transformer model with GAN training techniques for phase picking. Our latest system, SeisBlue, has evolved from phase picking and earthquake location to include magnitude and focal mechanism estimation, primarily using SeisPolar, a CNN model for P-wave polarity classification, crucial for focal mechanism analysis. Additionally, our redesign of the seismic monitoring process emphasizes data pipelines and integrates software engineering technologies, including hardware, system environment, database, data pipelines, model version control, task monitoring, data visualization, and Web UI interaction. The model shows high proficiency in identifying P-wave polarity and deciphering focal mechanisms, with an accuracy of 85%, and precision and recall rates for three categories [positive, negative, undecidable] at [87%, 77%, 53%] and [84%, 80%, 54%], respectively. It notably achieves about 70% Kagan angle under 40 degrees for focal mechanism analysis. This semi-automated workflow, from data processing to phase picking, earthquake location, magnitude determination, focal mechanism estimation and Web UI, significantly boosts seismic monitoring's efficiency and accuracy. It facilitates quicker and more meaningful engagement for researchers in subsequent studies, marking a notable advancement in seismic monitoring and deep learning application.