Interactive Fracture Segmentation Based on Optimum Connectivity Between Superpixels

Autor(es)

Marques Junior, Ademir ; Falcao, Alexandre Xavier ; Racolte, Graciela ; Menezes, Eniuce ; Bachi, Leonardo ; Cazarin, Caroline Lessio ; Gonzaga, Luiz, Jr. ; Veronez, Mauricio Roberto

• Tipo de documento: Artigo Científico
• Fonte: IEEE Geoscience and Remote Sensing Letters; v. 19, p. 5-pg., 2022-01-01.

Resumo

Oil and gas reservoirs are well studied in petroleum engineering, using seismic data to estimate fluid flow and well placement. However, seismic data cannot capture fractures due to their scale, and fractures may affect rock porosity and permeability. Consequently, rock fracture segmentation and quantification from aerial images of analogous outcrops can input essential information into those studies. This letter presents a new method, named interactive Forest Growing (iFG), for fracture segmentation. The image is initially segmented into superpixels, defining a superpixel graph. The user selects seed superpixels, a path-cost threshold, and fractures are delineated by growing one optimum-path tree from each seed with path costs limited to the selected threshold. iFG considerably increases efficiency while reducing human effort in fracture segmentation compared to pixel-by-pixel manual annotation. We evaluate iFG with three specialists and against an interactive Region Growing (iRG) method using 15 images to measure bias in user interpretations, verify efficiency gain over a similar approach, and generate a dataset with consolidated annotation for future work. The experiments show that iFG reduces user interventions from 19% to 33% compared to iRG, users with more experience in fracture analysis complete segmentation 4-5 times faster, and segmentation effectiveness is independent of user experience since the average F1 scores between users using both methods ranged from 0.966 to 0.979, allowing us to create a consolidated segmentation. (AU)

• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático