TY  - JOUR
A1  - Kriegerowski, Marius
A1  - Petersen, Gesa Maria
A1  - Vasyura-Bathke, Hannes
A1  - Ohrnberger, Matthias
T1  - A Deep Convolutional Neural Network for Localization of Clustered Earthquakes Based on Multistation Full Waveforms
JF  - Seismological research letters
N2  - 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 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.
Y1  - 2018
U6  - https://doi.org/10.1785/0220180320
SN  - 0895-0695
SN  - 1938-2057
VL  - 90
IS  - 2
SP  - 510
EP  - 516
PB  - Seismological Society of America
CY  - Albany
ER  -