Using machine learning classifiers together with discrimination diagrams for validation of rock classification labels

Malte Mues, Dennis Kraemer, David M. Ernst Styn,
Using machine learning classifiers together with discrimination diagrams for validation of rock classification labels,
Applied Computing and Geosciences,
Volume 28,
2025,
100288,
ISSN 2590-1974,
https://doi.org/10.1016/j.acags.2025.100288.
(https://www.sciencedirect.com/science/article/pii/S2590197425000709)
Abstract: Rock classification based on chemical components is a common task in the geochemical domain. Literature recommends the Total Alkali and Silica (TAS) discrimination diagram for classifying igneous volcanic rocks by the sum of Na2O and K2O in relation to SiO2 contents. This paper comparatively applies the TAS diagram and machine learning classification techniques to a collection of volcanic rocks from the GEOROC database. The study demonstrates a mismatch between the rock type labeled by experts in the database and rock types assigned by the TAS diagram. Despite this discrepancy, the experiments show that support vector machines are particularly promising for building decision systems for rock classification. Random forests, multi-layer perceptrons and K nearest neighbors are less suitable as rock classifiers in the study.
Keywords: Rock classification; Geochemical analysis support systems; Supervised machine learning classification; Discrimination diagrams