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Earthquake detection and hypocenter relocation in Central Pyrenees: the case of l'Alt Urgell-Andorra seismic sequence (2021-2022)

Authors

Sánchez-Roldán,  Jose L.
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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

Herrero-Barbero,  Paula
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Álvarez-Gómez,  José A.
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Walter,  Jacob I.
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Martínez-Díaz,  José J.
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Sánchez-Roldán, J. L., Echeverria, A., Herrero-Barbero, P., Álvarez-Gómez, J. A., Walter, J. I., Martínez-Díaz, J. J. (2023): Earthquake detection and hypocenter relocation in Central Pyrenees: the case of l'Alt Urgell-Andorra seismic sequence (2021-2022), XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-3108


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5020534
Abstract
On 2022-02-01, the mainshock of a seismic sequence that started in 2021 struck between l'Alt Urgell (Catalonia) and Andorra la Vella (Andorra) in the Central Pyrenees area. This magnitude (Mw) 4.0 earthquake, which alerted the population of the vicinities, was followed by low-magnitude aftershocks. In this work, we aim to obtain a new catalog through machine-learning procedures. We used the easyQuake python package, which allowed us to build new catalogs after detecting earthquake arrival times within seismograms recorded by several stations in the study area. Using different pickers, we obtain those machine-learning catalogs and compare them against data from regional agencies. We observed that, depending on the deep-learning model, the new catalog matched a high percentage of the original dataset recorded by the agencies. Besides that, we noted that some earthquakes passed undetected by the routine processing of these agencies. Then, using the new arrival times, we relocated the hypocenters using a 1D velocity model of the area following a non-linear location inversion approach. Our results show low uncertainties, which suggest that the arrival times detected by the machine-learning software are accurate enough to obtain constrained hypocenters. We conclude that this procedure could be advantageous for agencies and organizations that run a regional network in addition to the standard routine procedure carried out by expert scientists and technicians.