Next Article in Journal
Natural Radioactivity of Intrusive-Metamorphic and Sedimentary Rocks of the Balkan Mountain Range (Serbia, Stara Planina)
Next Article in Special Issue
Relating Topological and Electrical Properties of Fractured Porous Media: Insights into the Characterization of Rock Fracturing
Previous Article in Journal
The Petrography, Mineralogy and Geochemistry of Some Cu- and Pb-Enriched Coals from Jungar Coalfield, Northwestern China
Previous Article in Special Issue
Integrated 3D Geological Modeling to Gain Insight in the Effects of Hydrothermal Alteration on Post-Ore Deformation Style and Strain Localization in the Flin Flon Volcanogenic Massive Sulfide Ore System
Open AccessArticle

Geological Modelling and Validation of Geological Interpretations via Simulation and Classification of Quantitative Covariates

by 1,2,3, 1,2,* and 4
1
Department of Mining Engineering, University of Chile, Santiago 8370448, Chile
2
Advanced Mining Technology Center, University of Chile, Santiago 8370448, Chile
3
CSIRO-Chile International Center of Excellence in Mining and Mineral Processing, Santiago 8370448, Chile
4
School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide 5000, Australia
*
Author to whom correspondence should be addressed.
Minerals 2018, 8(1), 7; https://doi.org/10.3390/min8010007
Received: 30 November 2017 / Revised: 19 December 2017 / Accepted: 27 December 2017 / Published: 29 December 2017
(This article belongs to the Special Issue Geological Modelling)
This paper proposes a geostatistical approach for geological modelling and for validating an interpreted geological model, by identifying the areas of an ore deposit with a high probability of being misinterpreted, based on quantitative coregionalised covariates correlated with the geological categories. This proposal is presented through a case study of an iron ore deposit at a stage where the only available data are from exploration drill holes. This study consists of jointly simulating the quantitative covariates with no previous geological domaining. A change of variables is used to account for stoichiometric closure, followed by projection pursuit multivariate transformation, multivariate Gaussian simulation, and conditioning to the drill hole data. Subsequently, a decision tree classification algorithm is used to convert the simulated values into a geological category for each target block and realisation. The determination of the prior (ignoring drill hole data) and posterior (conditioned to drill hole data) probabilities of categories provides a means of identifying the blocks for which the interpreted category disagrees with the simulated quantitative covariates. View Full-Text
Keywords: geological uncertainty; geological modelling; geological misinterpretation; geostatistical simulation; classification geological uncertainty; geological modelling; geological misinterpretation; geostatistical simulation; classification
Show Figures

Figure 1

MDPI and ACS Style

Adeli, A.; Emery, X.; Dowd, P. Geological Modelling and Validation of Geological Interpretations via Simulation and Classification of Quantitative Covariates. Minerals 2018, 8, 7. https://doi.org/10.3390/min8010007

AMA Style

Adeli A, Emery X, Dowd P. Geological Modelling and Validation of Geological Interpretations via Simulation and Classification of Quantitative Covariates. Minerals. 2018; 8(1):7. https://doi.org/10.3390/min8010007

Chicago/Turabian Style

Adeli, Amir; Emery, Xavier; Dowd, Peter. 2018. "Geological Modelling and Validation of Geological Interpretations via Simulation and Classification of Quantitative Covariates" Minerals 8, no. 1: 7. https://doi.org/10.3390/min8010007

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop