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A quasi-real-time system for automatic local event monitoring in Germany

A quasi-real-time system for automatic local event monitoring in Germany

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

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Authors

Catalina Ramos , Stefanie Donner , Klaus Stammler

Abstract

We present TieBeNN, a wrapper that integrates open-source, state-of-the-art seismic monitoring tools, including advanced machine learning--based approaches, to enhance the German Federal Seismological Survey’s (EdB) automatic real-time earthquake monitoring system. TieBeNN extends the existing workflow by adding automatic, probabilistic focal depth estimation using NonLinLoc and introduces a Location Quality Score (LQS) to quantify location reliability with a single metric. In testing, TieBeNN’s automated locations approach the accuracy of human analyst solutions, demonstrating comparable performance in well-instrumented regions. By automating depth determination and providing immediate quality assessment, the system reduces analysts’ daily workload, allowing them to focus on events flagged as low-quality or complex. The LQS effectively distinguishes well-constrained event locations from those with large uncertainties or poor network geometry, enabling rapid identification of high-quality automatic results versus those requiring review. However, events below the Moho depth (i.e., deeper than approximately 30~km), which are rare in Germany, remain challenging: their uncertainties are larger, and LQS values tend to be lower, indicating limitations in the current calibration. Overall, these enhancements significantly advance real-time local seismic event monitoring in Germany by increasing both the speed and reliability of automatic event characterization.

DOI

https://doi.org/10.31223/X5774H

Subjects

Geophysics and Seismology

Keywords

seismic location, python, NonLinLoc, Germany, machine learning

Dates

Published: 2025-07-07 23:04

Last Updated: 2025-07-07 23:04

License

CC BY Attribution 4.0 International