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

Abstract

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.

Geostatistical Estimation of Multi-Domain Deposits with Transitional Boundaries: A Sensitivity Study for the Sechahun Iron Mine

“…Geologists usually determine geologic domains at drilling, assessing features such as lithology, mineralogy and drill core alteration. Deposit genesis and the type of geologic boundaries are also useful factors [10,13]. In multi-domain deposits, rock-type classification presents a high degree of uncertainty, when the boundaries between geologic domains are not evident (in soft boundaries).…”

“…There are several tools to identify hard or soft boundaries between geologic domains: contact plots, variogram ratios and preferential relationship schemes. They were developed to detect preferential contacts and assess the spatial transition at the limits of the different domains [13,22,29]. Boundaries are identified with the aid of geologic information, which serve as indicators alongside any kind of grade data.…”

“…Several resource estimation techniques exist depending on the geologic information available for the area in question and the study target. Many attempts have been made to improve estimation accuracy in the case of transitional boundaries [8][9][10], and to model geologic domains [3,[11][12][13].…”

Stochastic Modeling of Chemical Compounds in a Limestone Deposit by Unlocking the Complexity in Bivariate Relationships

“…To address these complexities, another geostatistical approach, such as stepwise conditioning transformation (SCT) [20], flow anamorphosis [21], or projection pursuit multivariate transform [22,23], can be an option for unlocking the complexity among the variables. The latter honors the complex multivariate normality after transformation and allows back-transformation of the simulated variables to the original dataset as well as restoring the multivariate characteristics [24,25]. Applications of PPMT in 3D multivariate geosciences mapping include petroleum reservoirs [23], environmental studies [26], grade control [27], and mineral resource classification [24].…”

Quantitative 3D Association of Geological Factors and Geophysical Fields with Mineralization and Its Significance for Ore Prediction: An Example from Anqing Orefield, China

“…Spatial analysis that is used for inquiring spatial information and revealing the characteristic of geological objects, is widely applied in evaluating and predicting mineral resources by combining with 3D GIS. Nowadays more and more quantitative mathematical methods have been developed to appraise the association of geological factors, geochemistry field and geophysics field with mineralization, and carry out 3D prospectivity mapping [33][34][35][36][37][38][39][40]. These methods are divided into two classes, knowledge-driven methods and data-driven methods [41,42].…”