Show simple item record

dc.contributor.authorRiquelme, Alvaro I.
dc.contributor.authorOrtiz, Julian M.
dc.identifier.citationRiquelme AI, Ortiz JM (2019) Modeling the uncertainty in geologic volumes: the log-normal case, Predictive Geometallurgy and Geostatistics Lab, Queen’s University, Annual Report 2019, paper 2019-05, 54-72.en
dc.descriptionThis is a preprint version of a paper that is under consideration of publication. It does not contain changes and edits that will be made during peer review, or updates, edits and alterations by the authors and the publisher that may occur prior to acceptance and final publication.
dc.description.abstractThe aim of the study is to develop a methodology that allows the quantification of the uncertainty in an arbitrary volume conditioned by sampling data, without the use of the traditional geostatistical simulation, which is time consuming and hard to manage, specially for large grid sizes. For this, we have studied the behavior of simulations when the variable is distributed according to a log-normal distribution. We have successfully found a formulation that makes the uncertainty in the arbitrary volume dependent on the values within the volume, the spatial correlation and conditioning data. Without the use of geostatistical simulation, and only with a Kriging of the Gaussian values, we are able to obtain local means, full conditional local distributions, covariances and correlations.en
dc.publisherQueen's Universityen
dc.relationQueen’s University Research Initiation Granten
dc.relationMitacs Accelerateen
dc.titleModeling the Uncertainty in Geologic Volumes: The Log-Normal Caseen
dc.typejournal articleen
project.funder.nameQueen's Universityen
project.funder.nameSRK Consulting Canadaen

Files in this item


This item appears in the following Collection(s)

Show simple item record
Except where otherwise noted, this item's license is described as