• views: 255
• date: 2 July 2026
• posted by: voska89

Analyze soil data using univariate, bivariate, and multivariate unsupervised machine learning
Define the background, threshold and anomaly values for surface geochemical data
Variography and Geostatistical analysis for geochemical data using ArcGIS Pro
Interpolate geochemical data using Ordinary Kriging to generate anomaly maps

Requirements

Primary and Secondary geochemical dispersion processes
Basic knowledge of Statistics and Geostatistics
Geochemical zonation of ore deposits

Description

This course focuses on teaching a workflow that enables geologists and geochemists to perform a geochemical data analysis from zero to hero. While this course uses soil data, the same workflow can be applied to any type of geochemical data used in mineral exploration. Geochemical analysis goes hand in hand with a thorough statistical analysis that allows each variable under study to be characterized from a univariate perspective—that is, to understand the distribution of the data—as well as through bivariate, trivariate, and multivariate analyses using a powerful software such as ioGAS. Bivariate analysis reveals the relationship between two chemical elements, trivariate analysis extends this to three, and multivariate analysis captures the relationships among a larger number of elements. These chemical elements can be properly handled once it is understood that the data is compositional in nature and that all elements share geochemical affinities that can be detected using unsupervised classification methods through machine learning. Finally, all of these analyses must be reflected spatially, which is why a GIS software—ArcGIS Pro—has been incorporated into the workflow. After conducting a variographic analysis, the data can be interpolated using Ordinary Kriging to produce a geochemical anomaly map that describes the anisotropy of the regionalized variables.

Who this course is for

Geologists and geochemists who want to learn how to work with geochemical data and deliver robust results




   
   
   

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