TITLE:
Mineralogical Characterization of Subsurface Soils Using Machine Learning: Application of Support Vector Machines
AUTHORS:
Ahmed Babacar Sarr, Mapathe Ndiaye, Sabou Sarr, Ndiouga Camara
KEYWORDS:
Classification, Support Vector Machine, Kernel Function, Soil, Raw Materials
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.13 No.10,
October
28,
2025
ABSTRACT: Support vector machines are recognized as a powerful tool for supervised analysis and classification in different fields, particularly geophysics. In summary, SVMs are binary classifiers. Thus, for the multiclass study, the problem is divided into a series of binary classifications. At the end, all the results obtained from these binary classifications are combined into a one-to-one or one-to-all comparison. In this article, the strategy consists of classifying soils using their chemical composition as characteristics specific to a soil. The prediction consists of 5 classes, which are: White Clay, Red Clay, Black Clay, Granite, and Sand. The dataset is composed of basic oxides, which contribute to increasing soil salinity, acidic oxides such as silica, which do not influence soil fertility, and amphoteric oxides. These data are divided into training, test, and validation data. The one-vs-all strategy was used. The results obtained showed the strength of the one-vs-all associated with SVM on all classification metrics. The selection of the kernel as well as hyperparameters also played an important role in the prediction score. From the results obtained, the one-vs-all associated with SVM can be used for classification problems. For further studies, geolocation can be introduced to have knowledge of the evolution according to the different sectors of the same region.