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Articles | Volume 12, issue 7
Articles | Volume 12, issue 7
https://doi.org/10.5194/se-12-1683-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-12-1683-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 7
Research article
 | 
29 Jul 2021
Research article |  | 29 Jul 2021

Accelerating Bayesian microseismic event location with deep learning

Alessio Spurio Mancini, Davide Piras, Ana Margarida Godinho Ferreira, Michael Paul Hobson, and Benjamin Joachimi

Data sets

3D velocity model Alessio Spurio Mancini, Davide Piras, Ana M G Ferreira, Michael P Hobson, Benjamin Joachimi https://github.com/alessiospuriomancini/seismoML

3D velocity model Alessio Spurio Mancini, Davide Piras, Ana M. G. Ferreira, Michael P. Hobson, and Benjamin Joachimi https://github.com/alessiospuriomancini/seismoML

Model code and software

Deep generative models implementation Alessio Spurio Mancini, Davide Piras, Ana M G Ferreira, Michael P Hobson, Benjamin Joachimi https://github.com/alessiospuriomancini/seismoML

Deep generative models implementation Alessio Spurio Mancini, Davide Piras, Ana M. G. Ferreira, Michael P. Hobson, and Benjamin Joachimi https://github.com/alessiospuriomancini/seismoML

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Short summary
The localization of an earthquake is affected by many uncertainties. To correctly propagate these uncertainties into an estimate of the earthquake coordinates and their associated errors, many simulations of seismic waves are needed. This operation is computationally very intensive, hindering the feasibility of this approach. In this paper, we present a series of deep-learning methods to produce accurate seismic traces in a fraction of the time needed with standard methods.
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