Articles | Volume 16, issue 17
Model description paper
14 Sep 2023
Model description paper |  | 14 Sep 2023

AutoQS v1: automatic parametrization of QuickSampling based on training images analysis

Mathieu Gravey and Grégoire Mariethoz


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-229', Anonymous Referee #1, 17 Oct 2022
    • AC1: 'Reply on RC1', Mathieu Gravey, 25 Feb 2023
  • CEC1: 'Comment on gmd-2022-229', Juan Antonio Añel, 12 Dec 2022
    • AC3: 'Reply on CEC1', Mathieu Gravey, 25 Feb 2023
  • RC2: 'Comment on gmd-2022-229', Ute Mueller, 01 Jan 2023
    • AC2: 'Reply on RC2', Mathieu Gravey, 25 Feb 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Mathieu Gravey on behalf of the Authors (02 May 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (26 Jun 2023) by Rohitash Chandra
AR by Mathieu Gravey on behalf of the Authors (05 Jul 2023)
Short summary
Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.