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 catalog. 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 1D 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. The model AlpsLocPS is based on arrivals from = 10 observations per phase are included.
AlpsLocPS_sta_cors.csv
- File listing station data and P- & S-phase station correction terms for the "AlpsLocPS_VELEST" and "AlpsLocPS_McMC" models after relocating all events ( see Table 2 'run2' in Braszus et al., 2024 )
GAR1D_sta_cors.csv
- File listing station data and P- & S-phase station correction terms for the final "GAR1D_PS_VELEST" and "GAR1D_PS_McMC" models
EVENT FILES
events_VELEST.csv
- Catalog of relocated events using VELEST
PICK CATALOG
pick_catalog.csv
The following describes the header entries of "pick_catalog.csv" in some more detail
"network name,station name,station latitude,station longitude, station elevation in m, event origin time, event latitude, event longitude, event depth in km, pick_type, pick_phase, pick_time, res in s"
