Articles | Volume 23, issue 1
https://doi.org/10.5194/nhess-23-91-2023
https://doi.org/10.5194/nhess-23-91-2023
Research article
 | 
13 Jan 2023
Research article |  | 13 Jan 2023

On the calculation of smoothing kernels for seismic parameter spatial mapping: methodology and examples

David Montiel-López, Sergio Molina, Juan José Galiana-Merino, and Igor Gómez

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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 nhess-2022-191', Anonymous Referee #1, 16 Aug 2022
    • AC1: 'Reply on RC1', David Montiel, 27 Aug 2022
  • RC2: 'Comment on nhess-2022-191', Anonymous Referee #2, 31 Aug 2022
    • AC2: 'Reply on RC2', David Montiel, 13 Sep 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (review by editor) (19 Sep 2022) by Filippos Vallianatos
AR by David Montiel on behalf of the Authors (21 Sep 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (26 Nov 2022) by Filippos Vallianatos
AR by David Montiel on behalf of the Authors (27 Nov 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (10 Dec 2022) by Filippos Vallianatos
AR by David Montiel on behalf of the Authors (12 Dec 2022)  Manuscript 
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Short summary
One of the most effective ways to describe the seismicity of a region is to map the b-value parameter of the Gutenberg-Richter law. This research proposes the study of the spatial cell-event distance distribution to define the smoothing kernel that controls the influence of the data. The results of this methodology depict tectonic stress changes before and after intense earthquakes happen, so it could enable operational earthquake forecasting (OEF) and tectonic source profiling.
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