Geovariances expert consultant, Pedram Masoudi, is to present a technical paper at DIGMIN 2025,  the International Conference on Intelligence for Green Mining
and Industrial Networks (Dhanbad, India, Sept. 12-13), on:

Multivariate geostatistics and machine learning: complementary tools in modelling mineral resources

This presentation explores how geostatistics and machine learning complement each other. Through a real-world case study, Pedram will highlight multivariate geostatistical techniques, including Gaussian Mixture Models (GMM), imputation, cosimulations, and the Probabilistic Prediction Model (PPM). He will demonstrate how combining these approaches with machine learning can create new opportunities in resource modeling.

Abstract – Theories of geostatistics and machine learning were both developed in the 20th century. The former originates from earth sciences schools, while the latter has its roots in statistics and mathematics. Geostatistics offers a numerical framework for modelling mineral grades and zones through variographic analysis, kriging interpolation, and geostatistical simulations. It focuses on interpolating mineral grades in the space of coordinates (x, y, and z), conditioned by samples and a variogram model. In contrast, machine learning estimates target (output) variables based on input features by identifying and leveraging statistical relationships among data.
While geostatistics has long been a standard in mineral resource modelling, machine learning is rapidly gaining adoption. Rather than viewing them as rivals, this presentation highlights their complementarity through a case study. It also introduces multivariate geostatistical techniques such as Gaussian Mixture Model (GMM), imputation, cosimulations, and the Projection Pursuit Multivariate Transform (PPMT).