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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on se-2021-24', Anonymous Referee #1, 22 Apr 2021
    • AC1: 'Reply on RC1', Alessio Spurio Mancini, 01 Jun 2021
  • RC2: 'Comment on se-2021-24', Anonymous Referee #2, 30 Apr 2021
    • AC2: 'Reply on RC2', Alessio Spurio Mancini, 01 Jun 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Alessio Spurio Mancini on behalf of the Authors (01 Jun 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (07 Jun 2021) by Michal Malinowski
RR by Anonymous Referee #1 (13 Jun 2021)
ED: Publish as is (20 Jun 2021) by Michal Malinowski
ED: Publish subject to technical corrections (21 Jun 2021) by CharLotte Krawczyk(Executive Editor)
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.