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Application of the CIPW Normative Method Using Artificial Intelligence

Completed academic study combining CIPW normative mineralogy, geochemical quality control, and domain-specific machine learning for igneous rock classification.

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Overview

This project integrates classical CIPW normative petrology with modern machine learning techniques for the systematic classification of igneous rocks.

Methodology

  • CIPW normative calculations following the Cross–Iddings–Pirsson–Washington formulation
  • Compilation and QA/QC of major-oxide geochemical datasets
  • Feature extraction from normative mineral assemblages
  • Training of Random Forest, XGBoost, and SVM models
  • Dimensionality reduction using PCA
  • Stratified cross-validation

Dataset

  • Initial compilation: 3,358 igneous rock samples from Peru
  • Final validated dataset: 2,707 samples after QA/QC and standardization

Results

  • Volcanic rocks: classification accuracy ≈ 0.80–0.82
  • Plutonic rocks: classification accuracy ≈ 0.52–0.56

Performance differences reflect mineralogical complexity and compositional overlap.

Repository

Contributions

The project demonstrates the integration of petrological reasoning with data-driven classification approaches.