AI/ML for Automated Critical Materials Resource Mapping
Ahamed – INL R&D Staff Scientist
Research Overview
Efficient exploration and development of critical mineral resources increasingly depend on integrating large volumes of geologic and geophysical data. Traditional log‑analysis workflows for solution‑mined deposits require detailed interpretation of lithology, stratigraphic boundaries, fluid‑flow‑controlled alteration zones, and grade‑related signals. These processes are often labor‑intensive and subject to interpreter variability.
This project seeks to apply modern machine learning methodologies to automate essential components of mineral resource mapping. Students will work with representative geophysical and lithologic datasets to:
- Clean and preprocess well‑log data (e.g., resistivity, natural gamma, spontaneous potential)
- Correct depth misalignments between lithologic and geophysical logs
- Build ML models for lithology prediction, stratigraphic correlation, and geologic zonation analysis
- Investigate pattern recognition methods to identify alteration fronts and mineralization indicators
- Explore workflow expansion using physics‑informed ML or sparse sensing approaches to improve mapping under limited‑data conditions
The overarching goal is to develop scalable data‑driven methods that enhance subsurface interpretation for critical materials and solution‑mining applications.
Student Research Experience
Students will engage in interdisciplinary work spanning geoscience, machine learning, and data engineering. Activities include:
- Data Integration & Preprocessing – preparing and aligning multi‑source well‑log datasets, correcting sample‑lag effects, and constructing geologically meaningful features.
- Machine Learning Model Development – implementing algorithms such as PCA, dynamic time warping, tree‑based models, gradient‑boosting methods, and neural networks to support automated interpretation.
- Geologic Interpretation – learning how lithologic variation, stratigraphic architecture, and fluid‑alteration signatures relate to mineral resource distribution within solution‑mined systems.
- Model Evaluation & Visualization – generating correlation panels, prediction plots, and zonation maps to assess machine‑learning results in a geologic context.
Students will collaborate with INL staff and gain experience applying AI/ML techniques to real‑world subsurface problems relevant to critical materials, energy security, and advanced resource extraction technologies.