Articles | Volume 9, issue 2
https://doi.org/10.5194/se-9-385-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-9-385-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
CORRESPONDING AUTHOR
Centre for Exploration Targeting, The University of Western Australia,
35 Stirling Hwy, Crawley WA 6009, Australia
Mark Lindsay
Centre for Exploration Targeting, The University of Western Australia,
35 Stirling Hwy, Crawley WA 6009, Australia
Vitaliy Ogarko
The International Centre for Radio Astronomy Research, The University
of Western Australia, 35 Stirling Hwy, Crawley WA 6009, Australia
Jeremie Giraud
Centre for Exploration Targeting, The University of Western Australia,
35 Stirling Hwy, Crawley WA 6009, Australia
Mark Jessell
Centre for Exploration Targeting, The University of Western Australia,
35 Stirling Hwy, Crawley WA 6009, Australia
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Latest update: 21 Nov 2024
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.
MCUE is a method that produces probabilistic 3-D geological models by sampling from...