Advances in the identification of geological discontinuities in boreholes with deep learning

Geological discontinuities significantly influence rock mass behaviour. Understanding the origin, setting, and properties of discontinuities is of major relevance, especially in boreholes. Traditionally, manual interpretation of borehole logs is done by geologists, a process that is time-consuming, costly, and subject to variability based on the interpreter's expertise. Recent advancements in artificial intelligence have made it feasible to use machine learning models and automatically detect and differentiate various features in digital images. In this study, we employ a state-of-the-art semantic segmentation model to tackle domain-specific challenges, enabling the identification of discontinuity types (e.g., natural faults, fault zones) and rock mass behaviour features (e.g., breakouts, induced cracks). We applied the SegFormer semantic segmentation model, which integrates a hierarchically structured transformer encoder with a multilayer perceptron (MLP). The borehole data used in this study was collected from the Mont Terri underground rock laboratory. Specifically, we labelled several high-resolution optical logs from one borehole and divided the dataset into training and testing subsets. The borehole considered is an experimental borehole designed to investigate the spatial and temporal evolution of damage around an underground opening in faulted clay shale. Our strategy achieved robust and accurate segmentation results on borehole images. Following segmentation, post-processing techniques were employed to extract critical information such as the total length of induced cracks and the total area of breakouts, as well as their locations and frequencies. The experimental results demonstrate high performance, with the pixel accuracy of 96 % in under three minutes for a 10-meter borehole. Our study lays the groundwork for future research by introducing a powerful tool for extracting geological structures and demonstrating the potential of AI models in geological analysis. By reducing processing time and increasing consistency in the identification, mapping, and classification of geological features, our approach can reveal spatial and temporal patterns associated with the evolution of rock masses.