Machine Learning Techniques for Geochemical Analysis Using Laser-Induced Breakdown Spectroscopy.

Area of Science:

• Geochemistry
• Analytical Chemistry
• Spectroscopy

Background:

• Accurate classification of multielement rock samples is crucial for geological studies.
• Traditional methods may lack the precision and efficiency required for complex sample analysis.
• Advancements in spectroscopy and machine learning offer new avenues for elemental analysis.

Purpose of the Study:

• To propose and evaluate machine learning techniques coupled with LIBS for effective multielement rock sample classification.
• To identify the most suitable emission lines for optimizing classification efficiency.
• To compare the performance of different machine learning algorithms in rock sample analysis.

Main Methods:

• Generated plasma on rock samples using a 532 nm Nd:YAG laser.
• Collected optical emission spectra using an Avantes spectrometer.
• Selected well-isolated signature emission lines for elements (Ca, Mg, Na, K, Fe, Ba, Sr, Si, Al, Li).
• Applied machine learning algorithms: ANOVA, PCA, and PCA-SVM on normalized spectral line intensities.

Main Results:

• ANOVA testing confirmed data suitability for machine learning.
• Laser-induced breakdown spectroscopy (LIBS) combined with Principal Component Analysis (PCA) enabled comprehensive rock sample classification.
• Support Vector Machine (SVM) enhanced PCA's linearity and efficiency, leading to accurate rock sample classification.

Conclusions:

• The appropriate selection of emission lines and machine learning techniques is critical for effective multielement rock sample classification.
• The proposed LIBS-PCA-SVM methodology provides more reliable results compared to conventional techniques.
• This integrated approach offers a powerful tool for geochemical analysis and material characterization.