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

Seismology has witnessed significant advancements in recent years with the application of deep
learning methods to address a broad range of problems. These techniques have demonstrated their
remarkable ability to effectively extract statistical properties from extensive datasets, surpassing the
capabilities of traditional approaches to an extent. In this study, we present SAIPy, an open-source
Python package specifically developed for fast data processing by implementing deep learning.
SAIPy offers solutions for multiple seismological tasks, including earthquake detection, magnitude
estimation, seismic phase picking, and polarity identification. We introduce upgraded versions
of previously published models such as CREIME_RT capable of identifying earthquakes with an
accuracy above 99.8% and a root mean squared error of 0.38 unit in magnitude estimation. These
upgraded models outperform state-of-the-art approaches like the Vision Transformer network. SAIPy
provides 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 be
used for real-time earthquake monitoring to enable timely actions to mitigate the impact of seismic
events.