A novel artificial intelligence algorithm for the interpretation of laser-induced breakdown spectroscopy (LIBS) data for transition mineral identification in rock cuttings
Year: 2026
Type: article
Abstract: The integration of laser-induced breakdown spectroscopy (LIBS) with advanced machine learning algorithms, particularly XGBoost, offers an effective and interpretable framework for identifying transition minerals and analyzing their compositions. By modeling the non-linear relationships and spectral interferences inherent in LIBS data, XGBoost significantly improves classification accuracy, robustness, and computational efficiency. The incorporation of dimensionality reduction, feature selection, and lithological context further enhances interpretability, enabling the identification of key spectral features critical for mineralogical differentiation. Model evaluations demonstrate high predictive performance for most elements—including Li, Ce, Nd, Pr, Eu, Er, Yb, Y, and Sc—evidenced by strong R² values and low mean squared errors. In contrast, moderate accuracy for La, Sm, Gd, Dy, and poor performance for Lu underscore the need for targeted model refinement. Overall, the results validate the potential of machine learning–based ensemble methods to advance LIBS-driven geochemical quantification, offering scalable, real-time solutions for automated, high-throughput elemental analysis in mineral exploration and sustainable resource development.
Institutions: Saudi Aramco (Saudi Arabia), Saudi Aramco (United States)
Language: English
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7130311667