Automated stratigraphic classification and feature detection from images of borehole cores
This thesis describes techniques proposed for analysing images of borehole cores. We address two problems: firstly, the automated stratigraphic classification of cores based on texture and secondly, the location of thin chromitite layers hidden in pyroxenite cores. Texture features of different rock types are extracted using wavelets, the theory of which provides an intuitive and powerful tool for this purpose. A Bayesian classifier is trained and used to discriminate between different samples. Thin, planar chromitite layers are located using a shortest path algorithm. In order to estimate the physical orientation of any layer found, a sinusoidal curve is fitted. The proposed algorithms were implemented and tested on samples taken from photographed cores. A high success rate was obtained in rock classification, and thin planar layers were located and characterised.