Articles | Volume 16, issue 23
Development and technical paper
29 Nov 2023
Development and technical paper |  | 29 Nov 2023

GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling

Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-290', Anonymous Referee #1, 24 May 2023
  • RC2: 'Comment on gmd-2022-290', Anonymous Referee #2, 06 Aug 2023
  • AC1: 'Response to both referees comments on gmd-2022-290', Michael Hillier, 17 Sep 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Michael Hillier on behalf of the Authors (18 Sep 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (21 Sep 2023) by Thomas Poulet
RR by Anonymous Referee #1 (03 Oct 2023)
ED: Publish as is (05 Oct 2023) by Thomas Poulet
AR by Michael Hillier on behalf of the Authors (09 Oct 2023)
Short summary
Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.