A contrario dip picking for borehole imaging

Joris Costes (1) , Gabriele Facciolo (1) , Rafael Grompone von Gioi (1) , Josselin Kherroubi (2) , Enric Meinhardt-Llopis (1) , Jean-Michel Morel (1)

1 CB - CB - Centre Borelli - UMR 9010
2 Schlumberger

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.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Modélisation et simulation; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]

Joris Costes, Gabriele Facciolo, Rafael Grompone von Gioi, Josselin Kherroubi, Enric Meinhardt-Llopis, et al.. A contrario dip picking for borehole imaging. 2021. ⟨hal-03203943⟩