SAIPy: A Python Package for single station Earthquake Monitoring using Deep Learning

Abstract

Seismology has witnessed significant advancements in recent years with the application of deeplearning methods to address a broad range of problems. These techniques have demonstrated theirremarkable ability to effectively extract statistical properties from extensive datasets, surpassing thecapabilities of traditional approaches to an extent. In this study, we present SAIPy, an open-sourcePython package specifically developed for fast data processing by implementing deep learning.SAIPy offers solutions for multiple seismological tasks, including earthquake detection, magnitudeestimation, seismic phase picking, and polarity identification. We introduce upgraded versionsof previously published models such as CREIME_RT capable of identifying earthquakes with anaccuracy above 99.8% and a root mean squared error of 0.38 unit in magnitude estimation. Theseupgraded models outperform state-of-the-art approaches like the Vision Transformer network. SAIPyprovides an API that simplifies the integration of these advanced models, including CREIME_RT,DynaPicker_v2, and PolarCAP, along with benchmark datasets. The package has the potential to beused for real-time earthquake monitoring to enable timely actions to mitigate the impact of seismicevents.