Articles | Volume 12, issue 7
© Author(s) 2021. This work is distributed underthe Creative Commons Attribution 4.0 License.
Accelerating Bayesian microseismic event location with deep learning
- Final revised paper (published on 29 Jul 2021)
- Preprint (discussion started on 22 Mar 2021)
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)
The manuscript (MS) develops deep learning models to compute wavefields from a point source in a microseismic setting to accelerate Bayesian waveform-based location inversion. The paper is well-written and easy to follow. I would like the authors to consider below points in improving the text.
On this note, the MS seems unaware of many PINN-based works in the field of Seismology that are quite relevant:
Song, C., Alkhalifah, T., & Waheed, U. B. (2021). Solving the frequency-domain acoustic VTI wave equation using physics-informed neural networks. Geophysical Journal International. doi.org/10.1093/gji/ggab010
Waheed, U., Haghighat, E., Alkhalifah, T., Song, C., & Hao, Q. (2020). Eikonal solution using physics-informed neural networks. arXiv preprint arXiv:2007.08330.
Waheed, U., Alkhalifah, T., Haghighat, E., Song, C., & Virieux, J. (2021). PINNtomo: Seismic tomography using physics-informed neural networks. arXiv preprint arXiv:2104.01588.