A Deep Convolutional Neural Network for Localization of Clustered Earthquakes Based on Multistation Full Waveforms
- Earthquake localization is both a necessity within the field of seismology, and a prerequisite for further analysis such as source studies and hazard assessment. Traditional localization methods often rely on manually picked phases. We present an alternative approach using deep learning that once trained can predict hypocenter locations efficiently. In seismology, neural networks have typically been trained with either single-station records or based on features that have been extracted previously from the waveforms. We use three-component full-waveform records of multiple stations directly. This means no information is lost during preprocessing and preparation of the data does not require expert knowledge. The first convolutional layer of our deep convolutional neural network (CNN) becomes sensitive to features that characterize the waveforms it is trained on. We show that this layer can therefore additionally be used as an event detector. As a test case, we trained our CNN using more than 2000 earthquake swarm events from WestEarthquake localization is both a necessity within the field of seismology, and a prerequisite for further analysis such as source studies and hazard assessment. Traditional localization methods often rely on manually picked phases. We present an alternative approach using deep learning that once trained can predict hypocenter locations efficiently. In seismology, neural networks have typically been trained with either single-station records or based on features that have been extracted previously from the waveforms. We use three-component full-waveform records of multiple stations directly. This means no information is lost during preprocessing and preparation of the data does not require expert knowledge. The first convolutional layer of our deep convolutional neural network (CNN) becomes sensitive to features that characterize the waveforms it is trained on. We show that this layer can therefore additionally be used as an event detector. As a test case, we trained our CNN using more than 2000 earthquake swarm events from West Bohemia, recorded by nine local three-component stations. The CNN successfully located 908 validation events with standard deviations of 56.4 m in east-west, 123.8 m in north-south, and 136.3 m in vertical direction compared to a double-difference relocated reference catalog. The detector is sensitive to events with magnitudes down to M-L = -0.8 with 3.5% false positive detections.…
Verfasserangaben: | Marius KriegerowskiGND, Gesa Maria PetersenORCiDGND, Hannes Vasyura-BathkeORCiDGND, Matthias OhrnbergerORCiDGND |
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DOI: | https://doi.org/10.1785/0220180320 |
ISSN: | 0895-0695 |
ISSN: | 1938-2057 |
Titel des übergeordneten Werks (Englisch): | Seismological research letters |
Verlag: | Seismological Society of America |
Verlagsort: | Albany |
Publikationstyp: | Wissenschaftlicher Artikel |
Sprache: | Englisch |
Datum der Erstveröffentlichung: | 19.12.2018 |
Erscheinungsjahr: | 2018 |
Datum der Freischaltung: | 25.03.2021 |
Band: | 90 |
Ausgabe: | 2 |
Seitenanzahl: | 7 |
Erste Seite: | 510 |
Letzte Seite: | 516 |
Fördernde Institution: | Deutsche Forschungsgemeinschaft (DFG) International Continental Scientific Drilling Program (ICDP) [CE 223/2-1]; Geo., the Research Network for Geosciences in Berlin and Potsdam X [SO_087_GeoX] |
Organisationseinheiten: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
Peer Review: | Referiert |
Name der Einrichtung zum Zeitpunkt der Publikation: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Erd- und Umweltwissenschaften |