Skip to main content

This website only uses technically necessary cookies. They will be deleted at the latest when you close your browser. To learn more, please read our Privacy Policy.

DE EN
Login
Logo, to home
  1. You are here:
  2. AI based 1D P & S-wave Velocity Models for the Greater Alpine Region from Local Earthquake Data
...

    Dataset: AI based 1D P & S-wave Velocity Models for the Greater Alpine Region from Local Earthquake Data

    • RADAR Metadata
    • Content
    • Statistics
    • Technical Metadata
    Alternate identifier:
    -
    Related identifier:
    -
    Creator/Author:
    Braszus, Benedikt [Geophysikalisches Institut]

    Rietbrock, Andreas [Geophysikalisches Institut]

    Haberland, Christian [Helmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum]

    Ryberg, Trond [Helmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum]
    Contributors:
    -
    Title:
    AI based 1D P & S-wave Velocity Models for the Greater Alpine Region from Local Earthquake Data
    Additional titles:
    -
    Description:
    (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 ... 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.

    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.

    Show all

    (Technical Remarks) # == This file is summarizing the content of the data files in this repository published together with the article: AI based 1D P & S-wave Velocity Models for the Greater Alpine Region from Local Earthquake Data When you are using the data provided please cite: Benedikt Braszus, Andreas Rietbrock... # == This file is summarizing the content of the data files in this repository published together with the article: AI based 1D P & S-wave Velocity Models for the Greater Alpine Region from Local Earthquake Data When you are using the data provided please cite: Benedikt Braszus, Andreas Rietbrock, Christian Haberland, Trond Ryberg, AI based 1D P & S-wave Velocity Models for the Greater Alpine Region from Local Earthquake Data, Geophysical Journal International, 2024;, ggae077, https://doi.org/10.1093/gji/ggae077 VELOCITY FILES AlpsLocPS_VEL.mod - VELEST model file of 'AlpsLocPS_VELEST' (red in Fig. 6 of Braszus et al., 2024) AlpsLocPS_McMC.mod - McMC model of 'AlpsLocPS_McMC' (orange in Fig. 6 of Braszus et al., 2024) GAR1D_PS_VEL.mod - VELEST model file of 'GAR1D_PS_VELEST' (lime in Fig. 6 of Braszus et al., 2024) GAR1D_PS_McMC.mod - McMC model of 'GAR1D_PS_McMC' (purple in Fig. 6 of Braszus et al., 2024) STATION FILES Station corrections have to be substracted from the synthetic travel times ! Only stations with >= 10 observations per phase are included. The column "station4char" contains the 4-character station name used for the VELEST inversions ( see GAR1D_PS.CNV ) 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 FILE events_VELEST.csv - Catalog of relocated events using VELEST PICK FILE GAR1D_PS.CNV - .CNV file of final VELEST run yielding the GAR1D_PS_VELEST model - the 4-character station names can be mapped back to the true names with the station file "GAR1D_sta_cors.csv"

    == This file is summarizing the content of the data files in this repository published together with the article:

    AI based 1D P & S-wave Velocity Models for the Greater Alpine Region from Local Earthquake Data When you are using the data provided please cite: Benedikt Braszus, Andreas Rietbrock, Christian Haberland, Trond Ryberg, AI based 1D P & S-wave Velocity Models for the Greater Alpine Region from Local Earthquake Data, Geophysical Journal International, 2024;, ggae077, https://doi.org/10.1093/gji/ggae077 VELOCITY FILES AlpsLocPS_VEL.mod
    - VELEST model file of 'AlpsLocPS_VELEST' (red in Fig. 6 of Braszus et al., 2024) AlpsLocPS_McMC.mod
    - McMC model of 'AlpsLocPS_McMC' (orange in Fig. 6 of Braszus et al., 2024) GAR1D_PS_VEL.mod
    - VELEST model file of 'GAR1D_PS_VELEST' (lime in Fig. 6 of Braszus et al., 2024) GAR1D_PS_McMC.mod
    - McMC model of 'GAR1D_PS_McMC' (purple in Fig. 6 of Braszus et al., 2024) STATION FILES
    Station corrections have to be substracted from the synthetic travel times ! Only stations with >= 10 observations per phase are included. The column "station4char" contains the 4-character station name used for the VELEST inversions ( see GAR1D_PS.CNV ) 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 FILE events_VELEST.csv - Catalog of relocated events using VELEST PICK FILE GAR1D_PS.CNV - .CNV file of final VELEST run yielding the GAR1D_PS_VELEST model - the 4-character station names can be mapped back to the true names with the station file "GAR1D_sta_cors.csv"

    Show all Show markdown
    Keywords:
    1D P & S-phase seismic velocity models
    Related information:
    -
    Language:
    -
    Publishers:
    Karlsruhe Institute of Technology
    Production year:
    2023
    Subject areas:
    Physics
    Resource type:
    Dataset
    Data source:
    -
    Software used:
    -
    Data processing:
    -
    Publication year:
    2024
    Rights holders:
    Braszus, Benedikt

    Rietbrock, Andreas

    Haberland, Christian

    Ryberg, Trond
    Funding:
    -
    Show all Show less
    Name Storage Metadata Upload Action
    Status:
    Published
    Uploaded by:
    kitopen
    Created on:
    2024-03-07
    Archiving date:
    2024-03-13
    Archive size:
    789.0 kB
    Archive creator:
    kitopen
    Archive checksum:
    eefc65b0a3a45e3a133119697c0aeb60 (MD5)
    Embargo period:
    -
    DOI: 10.35097/1965
    Publication date: 2024-03-13
    Download Dataset
    Download (789.0 kB)

    Download Metadata
    Statistics
    0
    Views
    0
    Downloads
    Rights statement for the dataset
    This work is licensed under
    CC BY-NC-SA 4.0
    CC icon
    Cite Dataset
    Braszus, Benedikt; Rietbrock, Andreas; Haberland, Christian; et al. (2024): AI based 1D P & S-wave Velocity Models for the Greater Alpine Region from Local Earthquake Data. Karlsruhe Institute of Technology. DOI: 10.35097/1965
    • About the Repository
    • Privacy Policy
    • Terms and Conditions
    • Legal Notices
    • Accessibility Declaration
    powered by RADAR
    1.22.10 (f) / 1.16.5 (b) / 1.22.4 (i)

    RADAR4KIT ist ein über das Internet nutzbarer Dienst für die Archivierung und Publikation von Forschungsdaten aus abgeschlossenen wissenschaftlichen Studien und Projekten für Forschende des KIT. Betreiber ist das Karlsruher Institut für Technologie (KIT). RADAR4KIT setzt auf dem von FIZ Karlsruhe angebotenen Dienst RADAR auf. Die Speicherung der Daten findet ausschließlich auf IT-Infrastruktur des KIT am Steinbuch Centre for Computing (SCC) statt.

    Eine inhaltliche Bewertung und Qualitätsprüfung findet ausschließlich durch die Datengeberinnen und Datengeber statt.

    1. Das Nutzungsverhältnis zwischen Ihnen („Datennutzerin“ bzw. „Datennutzer“) und dem KIT erschöpft sich im Download von Datenpaketen oder Metadaten. Das KIT behält sich vor, die Nutzung von RADAR4KIT einzuschränken oder den Dienst ganz einzustellen.
    2. Sofern Sie sich als Datennutzerin oder als Datennutzer registrieren lassen bzw. über Shibboleth legitimieren, kann Ihnen seitens der Datengeberin oder des Datengebers Zugriff auch auf unveröffentlichte Dokumente gewährt werden.
    3. Den Schutz Ihrer persönlichen Daten erklären die Datenschutzbestimmungen.
    4. Das KIT übernimmt für Richtigkeit, Aktualität und Zuverlässigkeit der bereitgestellten Inhalte keine Gewährleistung und Haftung, außer im Fall einer zwingenden gesetzlichen Haftung.
    5. Das KIT stellt Ihnen als Datennutzerin oder als Datennutzer für das Recherchieren in RADAR4KIT und für das Herunterladen von Datenpaketen keine Kosten in Rechnung.
    6. Sie müssen die mit dem Datenpaket verbundenen Lizenzregelungen einhalten.