Evaluating the SAIPy Performance using a Local Seismic Network for Volcano-Tectonic Earthquakes Monitoring

TL;DR

This study assesses the performance of the SAIPy deep learning seismic analysis toolkit in a volcanic environment, demonstrating its ability to detect and analyze local volcano-tectonic earthquakes using a seismic network, and highlighting areas for future improvement.

Contribution

The paper extends SAIPy's application from single-station to network-based seismic monitoring in volcanic settings, evaluating its effectiveness on complex signals and proposing enhancements for broader use.

Findings

SAIPy detects more local events than previous catalogs.

Reliable phase picking and P-wave polarity estimation achieved.

Limitations in magnitude estimation for complex volcanic signals.

Abstract

In this study, we evaluated the performance of SAIPy, an open-source Python package for deep learning-based seismic data analysis, by applying its single-station monitoring tools and extending its use to a seismic network based approach, using data from a local seismic network deployed in a Caldera. Although the integrated models into SAIPy for earthquake detection,magnitude estimation, seismic phase picking, and P-wave polarity classification, were originally trained on tectonic signals, we assess their performance in a more complex seismic environment that includes volcano-tectonic events, along with signal interference from distant earthquakes.We also demonstrate the advantages of integrating outputs using multiple stations to improve event detection. SAIPy was able to identify a significantly larger number of local events than those included in previously published catalogs. SAIPy demonstrated reliable phase picking and P-wave polarity estimation, particularly for local volcano-tectonic events, with some limitations observed in the magnitude estimation for complex volcanic signals. These results support the utility of SAIPy for processing continuous seismic data and suggest that future retraining using data with physically standardized units, removing instrumental response, and including data from more diverse seismic sources, could improve its generalization for magnitude estimation to complex scenarios and different seismic networks and sensor types.