Articles | Volume 9, issue 2
https://doi.org/10.5194/se-9-385-2018
https://doi.org/10.5194/se-9-385-2018
Method article
 | 
06 Apr 2018
Method article |  | 06 Apr 2018

Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

Evren Pakyuz-Charrier, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, and Mark Jessell

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Evren Pakyuz-Charrier on behalf of the Authors (07 Feb 2018)  Author's response   Manuscript 
ED: Publish subject to minor revisions (review by editor) (19 Feb 2018) by Ylona van Dinther
AR by Evren Pakyuz-Charrier on behalf of the Authors (26 Feb 2018)  Author's response   Manuscript 
ED: Publish as is (05 Mar 2018) by Ylona van Dinther
ED: Publish as is (13 Mar 2018) by Federico Rossetti (Executive editor)
AR by Evren Pakyuz-Charrier on behalf of the Authors (17 Mar 2018)
Download
Short summary
MCUE is a method that produces probabilistic 3-D geological models by sampling from distributions that represent the uncertainty of the initial input dataset. This process generates numerous plausible datasets used to produce a range of statistically plausible 3-D models which are combined into a single probabilistic model. In this paper, improvements to distribution selection and parameterization for input uncertainty are proposed.