Automated Borehole Core Analysis with Report-Derived Weak Labels and Supervised Crack Segmentation

2026-08-12

AI Summary

This study addresses the challenge posed by the absence of pixel-level fracture annotations in core images, which hinders automated extraction of fracture spacing and associated geological features. To overcome this limitation, the authors propose a multimodal weakly supervised learning framework that leverages digital log reports to generate weak labels for fracture spacing classification and integrates these with limited human-annotated strong labels to train a fully supervised segmentation model. The approach innovatively incorporates a learnable spatial gating mechanism, combining a DINO self-supervised encoder, PiDiNet for edge detection, and Mask R-CNN for instance segmentation, alongside rule-based modules for estimating bedding angle and lithology color. Experimental results demonstrate a fracture segmentation F1 score of 0.860 (IoU 0.754), with bedding angle and lithology color predictions achieving 75.4% and 84.7% consistency, respectively, with expert log reports.

Abstract

Borehole archives commonly contain core tray photographs and corresponding digital log reports, but no native pixel-level crack annotations. We investigate two complementary approaches for extracting defect-spacing information from these archives. First, structured spacing categories recovered from the report text layer provide weak interval-level labels for classification. A DINO encoder trained on unlabeled core crops supplies domain-specific representations, and a manually verified subset is used to identify label inconsistencies. Second, we manually annotate 5,087 extracted core-row images and evaluate fully supervised crack-segmentation models. Our gated U-Net combines PiDiNet edge maps with Mask R-CNN masks through a learned spatial gating mechanism. This configuration achieves an F1 score of 0.860 and a crack-class IoU of 0.754, the highest result among the evaluated segmentation configurations. Deterministic post-processing converts predicted crack locations into defect-spacing categories. Separate rule-based branches estimate core-relative bedding angles and lithological color descriptors; their predictions agree with log-report references on 75.4% and 84.7% of 1,200 evaluated images, respectively. Because these references are extracted from existing reports, the reported values measure agreement with recorded geological observations rather than independent physical accuracy. The resulting framework combines report-derived weak supervision for spacing classification with fully supervised segmentation for image-based crack localization.

Problem

• borehole core analysis
• crack segmentation
• weak supervision
• defect spacing
• geological image interpretation

Innovation

• weak supervision
• crack segmentation
• gated U-Net
• DINO encoder
• borehole core analysis