LASERGAME: Leveraging Advanced Spectroscopy and Ensemble Regression for Geochemical Analysis and Model Evaluation

Hostrup, Ivik Lau Dalgas ; Houmann, Christian Bager Bach ; Østergaard, Patrick Frostholm

4. term

Software, Master

2024

2024-06-14

81

This thesis advances the analysis of LIBS data for predicting major oxide compositions in geological samples. By integrating machine learning techniques and ensemble regression models, the study addresses challenges like high dimensionality, multicollinearity, and limited data availability. Key innovations include the use of stacked generalization for improved model performance and an automated hyperparameter optimization framework. The research contributes a comprehensive catalog of models and preprocessing techniques, and integrates findings into the PyHAT by the USGS, enhancing its scientific capabilities. This work aims to establish a robust foundation for future advancements in geochemical analysis and planetary exploration using LIBS data.

libs ; machine learning ; chemometrics ; mars ; nasa ; usgs

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