Qseek: A data-driven Framework for Automated Earthquake Detection, Localization and Characterization

Authors

  • Marius Paul Isken Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences, Potsdam, Germany https://orcid.org/0000-0003-2464-1630
  • Peter Niemz University of Utah, Seismograph Stations, Salt Lake City, UT, USA
  • Jannes Münchmeyer Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IRD, Univ. Gustave Eiffel, ISTerre, Grenoble, France https://orcid.org/0000-0002-4006-9673
  • Pinar Büyükakpınar Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences, Potsdam, Germany
  • Sebastian Heimann University of Potsdam, Germany, Institute of Geosciences, Potsdam, Germany https://orcid.org/0000-0003-0096-7193
  • Simone Cesca Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences, Potsdam, Germany
  • Hannes Vasyura-Bathke Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences, Potsdam, Germany https://orcid.org/0000-0002-3826-0663
  • Torsten Dahm Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences, Potsdam, Germany https://orcid.org/0000-0001-6432-7422

DOI:

https://doi.org/10.26443/seismica.v4i1.1283

Keywords:

Seismology, Earthquake detection, Machine learning, Earthquake Location, Large-N arrays

Abstract

We introduce a data-driven method and software for detecting and locating earthquakes in large seismic datasets. By combining seismic phase arrival annotations, delivered by neural network phase pickers, and waveform stacking with an adaptive octree search, we can automatically detect and locate seismic events even in noise-dominant seismic data. The resolution of the search volume is iteratively refined toward the seismic source location; this strategy facilitates an efficient, fast, and accurate search. We present a user-friendly and high-performance open-source software framework based on established frameworks, featuring event detection in layered 1D and complex 3D velocity models and event feature extraction capabilities, such as moment and local magnitude calculation from peak ground motions. We incorporated station-specific corrections and source-specific station terms into the search to enhance the location accuracy. We demonstrate and validate our approach by extracting extensive earthquake catalogs from large seismic datasets in different regions and geological settings: (1) Reykjanes Peninsula, Iceland; (2) Eifel volcanic region, Germany; and (3) Utah FORGE, USA. We capture seismic events from tectonic activity, volcanic swarms, and induced microseismic activity with magnitudes ranging from -1 to 5. Such precise and complete earthquake catalogs contribute to the interpretation and understanding of otherwise hidden subsurface processes.

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2025-02-19

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Isken, M., Niemz, P., Münchmeyer, J., Büyükakpınar, P., Heimann, S., Cesca, S., Vasyura-Bathke, H., & Dahm, T. (2025). Qseek: A data-driven Framework for Automated Earthquake Detection, Localization and Characterization. Seismica, 4(1). https://doi.org/10.26443/seismica.v4i1.1283

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Copyright (c) 2024 Marius Paul Isken, Sebastian Heimann, Peter Niemz, Jannes Münchmeyer, Simone Cesca, Hannes Vasyura-Bathke, Torsten Dahm

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