ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN THE ANALYSIS OF MULTI-SOURCE GEOLOGICAL DATA: OPPORTUNITIES AND CHALLENGES
DOI:
https://doi.org/10.47390/ydif-y2026v2i17/n02Keywords:
Artificial intelligence, machine learning, geological data, remote sensing, geochemistry, Random Forest, XGBoost, neural networks.Abstract
This study examines the role of artificial intelligence and machine learning methods in the analysis of multi-source geological data (remote sensing, geochemical, and geophysical data). The stages of data preparation are described, and various models (Random Forest, XGBoost, SVM, CNN, LSTM) are compared, with a justification of the criteria for selecting an appropriate model.
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