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

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 set with known spatial context, however, here it is used to classify 400 m of drillcore imagery into 12 distinct classes reflecting the dominant rock types and alteration features in the core. We modified two unsupervised learning models to incorporate spatial metadata and an average improvement of 25% was achieved over equivalent models that did not utilize metadata. Our semi‐supervised workflow involves unsupervised network training followed by semi‐supervised clustering where a support vector machine uses a subset of M expert labeled images to assign a pseudolabel to the entire data set. Fine‐tuning of the best performing model showed an f1 (macro average) of 90%, and its classifications were used to estimate bulk fresh and altered rock abundance downhole. Validation against the same information gathered manually by experts when the core was recovered during the Oman Drilling Project revealed that our automatically generated data sets have a significant positive correlation (Pearson's r of 0.65–0.72) to the expert generated equivalent, demonstrating that valuable geological information can be generated automatically for 400 m of core with only ∼24 hr of domain expert effort. Plain Language Summary: This work presents a novel method for using the spatial context of digital core images to improve the descriptive accuracy of unsupervised machine learning algorithms. The addition of spatial metadata improves model performance by an average of 25%, with the best performing model in this study achieving an accuracy score of 90%. The output of this model was then used to estimate the amount of fresh and altered rock within a 400 m long drill core, which was shown to be of comparable quality to the same estimations made by geologists on the cores themselves. Key Points: Use of spatial metadata improves state‐of‐the‐art unsupervised feature extractionSemi‐supervised methods using spatial metadata outperform supervised methods for the same expert labeling effortNew methods described enable the standardization of core‐logging processes and improve cross‐dataset comparisons [ABSTRACT FROM AUTHOR]