Leveraging Spatial Metadata in Machine Learning for Improved Objective Quantification of Geological Drill Core

Leveraging Spatial Metadata in Machine Learning for Improved Objective Quantification of Geological Drill Core

Abstract Here we present a method for using the spatial x–y coordinate of an image cropped from the cylindrical surface of digital 3D drill core images and demonstrate how this spatial metadata can be used to improve unsupervised machine learning performance. This approach is applicable to any data...

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Bibliographic Details
Main Authors: Lewis J. C. Grant, Miquel Massot‐Campos, Rosalind M. Coggon, Blair Thornton, Francesca C. Rotondo, Michelle Harris, Aled D. Evans, Damon A. H. Teagle
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2024-03-01
Series:Earth and Space Science
Subjects:
geoscience
machine learning
neural networks
mining
hydrothermal alteration
Oman drilling program
Online Access:https://doi.org/10.1029/2023EA003220

Internet

https://doi.org/10.1029/2023EA003220

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