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Eifel Large-N Experiment: Detection and Localization of Seismic Events using Stacking and Migration Approach combined with Neural Network Phase Characterization

Authors
/persons/resource/isken

Isken,  Marius Paul
2.1 Physics of Earthquakes and Volcanoes, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Reiss,  Miriam
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

/persons/resource/cesca

Cesca,  Simone
2.1 Physics of Earthquakes and Volcanoes, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Hensch,  Martin
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Schmidt,  Bernd
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

/persons/resource/dahm

Dahm,  T.
2.1 Physics of Earthquakes and Volcanoes, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Münchmeyer,  Jannes
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Isken, M. P., Reiss, M., Cesca, S., Hensch, M., Schmidt, B., Dahm, T., Münchmeyer, J. (2023): Eifel Large-N Experiment: Detection and Localization of Seismic Events using Stacking and Migration Approach combined with Neural Network Phase Characterization, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-4193


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5021632
Abstract
We present the detection and localization of local seismic events from a seismic network consisting of more than 350 short-period and broadband stations deployed in the Eifel volcanic region, centered around the Laacher See. The network spans an area of 180 km by 120 km. Using a combination of stacking and migration method and characteristic function generated by a re-trained neural network which are imaging P and S wave phase arrivals, we are able to detect and automatically locate seismicity in the network from continuous waveform data, including those events associated with dormant volcanic processes. The Eifel region has been geologically active for millions of years, with the most recent volcanic explosive eruption 13,000 years ago at the Laacher See. While the area is currently considered volcanic dormant, the ongoing seismic activity provides insight into the magmatic activity at depth. Our results can help to improve our understanding of the transcrustal magmatic system beneath intraplate volcanic fields and potential for future volcanic activity in the Eifel. The method and example presented here can be applied to volcanic provinces elsewhere support volcanic monitoring and fast, automatic processing of seismicity.