Application of Conditional Random Fields for Seismic Segmentation

Conventionally, computer vision tasks such as semantic segmentation are handled by probabilistic models such as the Conditional Random Fields (CRFs). The wide usage of CRF models in most modern semantic segmentation pipelines is because of their ability in modelling the structural information. Despite CRFs’ successful application in natural and medical data, the application to seismic data, however, is limited. In this paper, it is shown how CRFs can be incorporated into deep learning pipelines to improve automatic seismic interpretation by acknowledging that we are predicting a structured output and thus by including our prior knowledge about the spatial image architecture.