Scientific Quarterly Journal of Geosciences

Scientific Quarterly Journal of Geosciences

3D modeling of ore grade with an approximate Bayesian approach

Document Type : Original Research Paper

Authors
Ahmadreza Boskabadi 1 ΒΆ
Alireza Arab-Amiri 1
Behzad Tokhmechi 1
Hossein Baghishani 2
1 Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran
2 Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran
10.22071/gsj.2024.433476.2129
Abstract
Since the grade of elements in a mining area has spatial correlation, its statistical analysis is impossible with the usual statistical methods. Therefore, spatial statistics methods are used in their analysis to model the spatial correlation structure and predict the unknown grade values in arbitrary locations. For prediction, including dependence structures and trend following due to contextual factors (such as topography) helps improve the accuracy of response variable forecasting. In data analysis, the small number of observations, the presence of outlier observations, or data with a highly skewed distribution, causes an inaccurate estimation of the variogram. In this article, a Bayesian approach is used for the 3D modeling of the data, and an approximate Bayesian method known as Integrated Nested Laplace Approximations (INLA) is used to fit the proposed model. Since Geostatistical data are densely indexed, to ensure fast calculations using INLA, the spatial model defined on the study area was converted into a Gaussian Markov Random Field (GMRF) using triangulation and the Stochastic Partial Differential Equation (SPDE) approach. The implementation of the INLA+SPDE method on a 3D Geostatistical data set is a new topic in the field of mining data modeling.
Keywords
Geostatistics
Bayesian inference
Laplace approximation
differential equations
3D modeling

Subjects

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Scientific Quarterly Journal of Geosciences
Volume 34, Issue 4 - Serial Number 134
Winter 2024, Vol. 34, Issue 4, Serial No. 134
Autumn 2024
Pages 23-36