Automated Detection of Beddings From Borehole Images: A New Improvement and First Comparative Study

Automatic bedding extraction from borehole images of shale beddings and cross beddings is essential for reducing labor-intensive manual tasks. Three main types of algorithms, including Autodip, random Hough transform (RHT), and pairing algorithm-based detection (PABD), have been proposed in the past decades but have never been directly compared. In this study, we first propose a novel method integrating a heuristic genetic algorithm (Autodip integrate with genetic algorithm, ADGA) to overcome the low efficiency of the Autodip algorithm. This method improves the efficiency by a factor of 3 and even by a factor of 21 with the addition of automatic constraints while maintaining the advantages of high accuracy. Furthermore, to verify the effectiveness of the improved algorithm, all extraction algorithms are compared and analyzed systematically for the first time. The results demonstrate that three algorithms have presented their unique advantages and adaptability. The ADGA has the best stability, extraction accuracy, and image fitness for shale beddings. The PBAD algorithm shows its superiority in cross bedding. The RHT has the best efficiency with moderate stability. In addition, the extraction results produced by RHT are of poor quality for images with high density and low amplitude bedding (less than about 10 pixels) due to the inadequate performance of the structure tensor image processing. This research will significantly promote the future development of automatic bedding extraction algorithms for industrial applications.