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AI based 1-D P- and S -wave velocity models for the greater alpine region from local earthquake data

Braszus, Benedikt 1; Rietbrock, Andreas ORCID iD icon 1; Haberland, Christian; Ryberg, Trond
1 Geophysikalisches Institut (GPI), Karlsruher Institut für Technologie (KIT)

Abstract:

The recent rapid improvement of machine learning techniques had a large impact on the way seismological data can be processed. During the last years several machine learning algorithms determining seismic onset times have been published facilitating the automatic picking of large data sets. Here we apply the deep neural network PhaseNet to a network of over 900 permanent and temporal broad-band stations that were deployed as part of the AlpArray research initiative in the Greater Alpine Region (GAR) during 2016–2020. We selected 384 well distributed earthquakes with M$_L$ ≥ 2.5 for our study and developed a purely data-driven pre-inversion pick selection method to consistently remove outliers from the automatic pick catalogue. This allows us to include observations throughout the crustal triplication zone resulting in 39 599 P and 13 188 S observations. Using the established VELEST and the recently developed McMC codes we invert for the 1-D P- and S-wave velocity structure including station correction terms while simultaneously relocating the events. As a result we present two separate models differing in the maximum included observation distance and therefore their suggested usage. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000169986
Veröffentlicht am 22.04.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Geophysikalisches Institut (GPI)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 05.2024
Sprache Englisch
Identifikator ISSN: 0956-540X, 0016-8009, 0952-4592, 0955-419X, 1365-246X, 2051-1965, 2051-1973, 2056-5216
KITopen-ID: 1000169986
Erschienen in Geophysical Journal International
Verlag Oxford University Press (OUP)
Band 237
Heft 2
Seiten 916 – 930
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 01.03.2024
Schlagwörter Machine learning, Body waves, Crustal imaging, Seismicity and tectonics
Nachgewiesen in Scopus
Web of Science
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