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Alteration Assemblage Characterization Using Machine Learning Applied to High-resolution Drill-core Images, Hyperspectral Data and Geochemistry by McLean Trott; Cole Mooney; Shervin Azad; Sam Sattarzadeh; Britt Bluemel; Matthew Leybourne; Daniel Layton-Matthews is a Computer Science article available to read on EtoBox.
What is Alteration Assemblage Characterization Using Machine Learning Applied to High-resolution Drill-core Images, Hyperspectral Data and Geochemistry about?
Integration of multiple data types is beneficial for prediction of geological characteristics. From the perspective that geochemistry characterizes the composition of a rock mass, hyperspectral data characterizes alteration mineralogy and image feature extraction characterizes texture, most geological classifications would be well-informed by the combination of these three features. The process of meaningfully integrating distinctly sourced datasets and producing scale-relevant predictions for geological classifications involves several steps. We demonstrate a workflow to comprehensively structure and integrate these three feature families, refine training data, predict alteration classes and mitigate noise derived from scale mismatch in output predictions. The dataset, compiled from the Josemaria porphyry copper–gold deposit in Argentina, is comprised of more than 14 000 intervals of approximately 2 m, taken from 36 drillholes, where geochemistry was merged with hyperspectral mineralogy represented as tabular pixel abundances, and textural metrics extracted from core imagery, structured into the geochemical interval. Feature engineering and principal component analysis provided in
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It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- McLean Trott; Cole Mooney; Shervin Azad; Sam Sattarzadeh; Britt Bluemel; Matthew Leybourne; Daniel Layton-Matthews
- Publisher
- Geological Society of London
- Published
- 2023
- Language
- EN
- Field
- Computer Science (Physical Sciences)