Machine learning for recognizing minerals from multispectral data.

Area of Science:

• Geoscience
• Spectroscopy
• Machine Learning

Background:

• Machine learning (ML) is increasingly applied to spectroscopy for mineral analysis.
• Existing ML methods often focus on single spectroscopic techniques like Raman, VNIR, or LIBS.
• There is a need for improved accuracy in automatic mineral identification.

Purpose of the Study:

• To review and test ML approaches for mineral classification.
• To develop a novel method for automatic mineral identification using combined spectroscopic data.
• To evaluate the performance of fused spectral data compared to single sources.

Main Methods:

• Reviewed existing ML algorithms for mineral classification.
• Developed a Deep Learning approach using Convolutional Neural Networks (CNN) for Raman and VNIR spectra.
• Implemented cosine similarity for Laser-Induced Breakdown Spectroscopy (LIBS) data.
• Evaluated data fusion strategies combining Raman + VNIR, Raman + LIBS, and VNIR + LIBS spectra.

Main Results:

• Deep learning on Raman spectra outperformed previous state-of-the-art methods for mineral classification.
• Combining multiple spectroscopic methods (Raman, VNIR, LIBS) with ML significantly improved classification accuracy compared to single methods.
• The novel approach demonstrated high performance across diverse spectral libraries (RRUFF, USGS, RELAB, ECOSTRESS, NIST).

Conclusions:

• Multi-method spectroscopy combined with ML offers a powerful approach for rapid and accurate rock and mineral characterization.
• Future integration of Deep Learning Algorithms and data fusion from multi-method spectroscopy promises substantial gains in automatic mineral recognition accuracy.