Articles | Volume 11, issue 1
https://doi.org/10.5194/esurf-11-89-2023
https://doi.org/10.5194/esurf-11-89-2023
Research article
 | 
28 Feb 2023
Research article |  | 28 Feb 2023

Automated classification of seismic signals recorded on the Åknes rock slope, Western Norway, using a convolutional neural network

Nadège Langet and Fred Marcus John Silverberg

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esurf-2022-15', Anonymous Referee #1, 17 May 2022
    • AC1: 'Reply on RC1', Nadège Langet, 05 Jul 2022
  • RC2: 'Comment on esurf-2022-15', Wei-An Chao, 20 Jun 2022
    • AC2: 'Reply on RC2', Nadège Langet, 06 Jul 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Nadège Langet on behalf of the Authors (11 Aug 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (30 Nov 2022) by Claire Masteller
AR by Nadège Langet on behalf of the Authors (05 Dec 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (22 Dec 2022) by Claire Masteller
ED: Publish as is (30 Jan 2023) by Tom Coulthard (Editor)
AR by Nadège Langet on behalf of the Authors (07 Feb 2023)
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Short summary
Microseismic events recorded on the Åknes rock slope in Norway during the past 15 years are automatically divided into eight classes. The results are analysed and compared to meteorological data, showing a strong increase in the microseismic activity in spring mainly due to freezing and thawing processes.