[S-SS04] New trends in data acquisition, analysis and interpretation of seismicity

[SSS04-01] Earthquake sequence analysis usin SAIPy: a deep learning based python package for earthquake monitoring

*Nishtha Srivastava^1,2, Claudia Quinteros Cartaya¹, Johannes Faber^1,2 (1.Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany, 60438, 2.Goethe University, Frankfurt am Main, Germany, 60438)

Keywords:Earthquake monitoring, Deep learning, Phase picking, event detection

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. 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.