Visual texture for automated characterisation of geological features in borehole televiewer imagery

Detailed characterisation of the structure of subsurface fractures is greatly facilitated by digital borehole logging instruments, the interpretation of which is typically time-consuming and labour-intensive. Despite recent advances towards autonomy and automation, the final interpretation remains heavily dependent on the skill, experience, alertness and consistency of a human operator. Existing computational tools fail to detect layers between rocks that do not exhibit distinct fracture boundaries, and often struggle characterising cross-cutting layers and partial fractures. This paper presents a novel approach to the characterisation of planar rock discontinuities from digital images of borehole logs. Multi-resolution texture segmentation and pattern recognition techniques utilising Gabor filters are combined with an iterative adaptation of the Hough transform to enable non-distinct, partial, distorted and steep fractures and layers to be accurately identified and characterised in a fully automated fashion. This approach has successfully detected fractures and layers with high detection accuracy and at a relatively low computational cost.