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

TL;DR

SAIPy is an open source Python package that leverages deep learning for rapid, accurate earthquake detection, magnitude estimation, and seismic phase picking, improving upon existing models and enabling real-time monitoring.

Contribution

The paper introduces SAIPy, a comprehensive Python package integrating upgraded deep learning models for seismological tasks, outperforming previous approaches and facilitating real-time earthquake monitoring.

Findings

CREIMERT achieves over 99.8% accuracy in earthquake detection

Magnitude estimation has a root mean squared error of 0.38 units

Models outperform state-of-the-art approaches like Vision Transformer

Abstract

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 CREIMERT capable of identifying earthquakes with an accuracy above 99.8 percent 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 CREIMERT, DynaPickerv2, 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. Ongoing development efforts aim to enhance the performance of SAIPy and incorporate additional features that enhance exploration efforts, and it also would be interesting to approach the retraining of the whole package as a multi-task learning problem.