EGU24-19502, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Nishtha Srivastava1,2, Wei Li1, Megha Chakraborty1,2, Claudia Quinteros Cartaya1, Jonas Köhler1,2, Johannes Faber1,3, and Georg Rümpker1,2
Nishtha Srivastava et al.
  • 1Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany (
  • 2Institute of Geosciences, Goethe Universität, Germany
  • 3Institute for Theoretical Physics, Goethe Universität, Germany

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

How to cite: Srivastava, N., Li, W., Chakraborty, M., Cartaya, C. Q., Köhler, J., Faber, J., and Rümpker, G.: SAIPy: A Python Package for single station Earthquake Monitoring using Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19502,, 2024.