Geovariances expert consultants, Pedram Masoudi, Jean Langanay, and Roberto Rolo, are to present a technical paper at the 12th International Conference on Mining, Materials, and Metallurgical Engineering (MMME’2025) in Paris (August 2025) on :

Coherent modeling of mineral grades and zones by coupling cokriging and Support Vector Machine

Join them to explore how combining geostatistics and Machine Learning can overcome key challenges in mineral resource modeling.
This talk introduces a hybrid workflow that improves classification in complex categorical datasets and preserves data integrity during interpolation.
📍 Don’t miss the case study on a synthetic porphyry copper deposit.

Abstract – The geostatistics and Machine Learning theories originate from statistics but are rooted in different applications. They are sometimes considered as competing theories, sometimes as complementary. This article’s latter perspective is a foundation that tries to use them jointly to cover their shortcomings in mineral resource modeling. The theory of geostatistics provides methods for a robust spatial modeling of mineral resources from univariate and multivariate datasets. However, it demands much effort and experience to generate a coherent model if the dataset contains many categories, either with a single categorical variable or because of crossing two or more categorical variables. Machine learning could cover this limitation to establish the classification rules between continuous variables and categorical variables in the space of drillholes. The proposed workflow is applied to a synthetic porphyry copper dataset to verify and illustrate its performance in a multivariate application. The dataset consists of five mineral grades within five mineral zones. The concluding remark is that the choice of geostatistical interpolator should be made cautiously, not altering the input core data’s statistical distribution (variance and dimension support change) in the drillhole space while interpolating to the block space.