A contrario dip picking for borehole imaging

We describe an algorithm to perform automatic dip picking on borehole images. One key element of the proposed method is a statistical validation, based on the a contrario theory, which is used to decide whether each candidate dip is to be accepted or not. The proposed method also uses a randomized Hough transform, which greatly improves the processing speed, allowing for a real-time detection of dips during image visualization. In addition, the same algorithm can be applied at different scales to provide a multi-resolution analysis of the structures. Our experiments show that the proposed algorithm produces reliable dip picking by an evaluation on three manually annotated boreholes: the proposed method detects from 60% to 90% of the dips annotated by an expert, depending on the complexity of the data.