Locating earthquakes with a network of seismic stations via a deep learning method.
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TLDR
In this paper, a convolutional neural network was applied to detect earthquakes from single-station waveforms and approximately map events across several large surface areas, within an error range of approximately 4.9 km on average to the epicenter and 1.0 km to the depth in catalogs.Abstract:
The accurate and automated determination of earthquake locations is still a challenging endeavor. However, such information is critical for monitoring seismic activity and assessing potential hazards in real time. Recently, a convolutional neural network was applied to detect earthquakes from single-station waveforms and approximately map events across several large surface areas. In this study, we locate 194 earthquakes induced during oil and gas operations in Oklahoma, USA, within an error range of approximately 4.9 km on average to the epicenter and 1.0 km to the depth in catalogs with data from 30 network stations by applying the fully convolutional network. The network is trained by 1,013 historic events, and the output is a 3D volume of the event location probability in the Earth. The trained system requires approximately one hundredth of a second to locate an event without the need for any velocity model or human interference.read more
Citations
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Journal ArticleDOI
Deep learning for geophysics: Current and future trends
Siwei Yu,Jianwei Ma +1 more
TL;DR: A new data-driven technique, i.e., deep learning (DL), has attracted significantly increasing attention in the geophysical community and the collision of DL and traditional methods has had an impact on traditional methods.
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LOC-FLOW: An End-to-End Machine Learning-Based High-Precision Earthquake Location Workflow
TL;DR: Loc-FLOW as mentioned in this paper is an end-to-end machine learning-based location workflow that can be applied directly to continuous waveforms and build high-precision earthquake catalogs at local and regional scales.
Posted ContentDOI
INSTANCE – the Italian seismic dataset for machine learning
Alberto Michelini,S. Cianetti,Sonja Gaviano,Sonja Gaviano,Carlo Giunchi,Dario Jozinović,Dario Jozinović,Valentino Lauciani +7 more
TL;DR: The waveform dataset is accompanied by metadata consisting of more than 100 parameters providing comprehensive information on the earthquake source, the recording stations, the trace features, and other derived quantities, which allows the users to target the data selection for their own purposes.
Journal ArticleDOI
Real-Time Earthquake Early Warning With Deep Learning: Application to the 2016 M 6.0 Central Apennines, Italy Earthquake
TL;DR: In this paper, a deep learning earthquake early warning system that utilizes fully convolutional networks to simultaneously detect earthquakes and estimate their source parameters from continuous seismic waveform streams is presented.
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Machine learning and fault rupture: A review
TL;DR: This work reviews recent advances in the application of machine learning in the study of fault rupture, ranging from the laboratory to the Earth, and covers applications of ML to geophysical data in solid Earth, with an emphasis on seismology, that has a long history of data-driven approaches.
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