Self-Supervised Annotation of Seismic Images using Latent Space Factorization

• URL: http://arxiv.org/abs/2009.04631v2
• Date: Sat, 26 Sep 2020 02:30:07 GMT
• Title: Self-Supervised Annotation of Seismic Images using Latent Space Factorization
• Authors: Oluwaseun Joseph Aribido, Ghassan AlRegib and Mohamed Deriche
• Abstract summary: Our framework factorizes the latent space of a deep encoder-decoder network by projecting the latent space to learned sub-spaces. Details of the annotated image are provided for analysis and qualitative comparison is made with similar frameworks.
• Score: 14.221460375400692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Annotating seismic data is expensive, laborious and subjective due to the number of years required for seismic interpreters to attain proficiency in interpretation. In this paper, we develop a framework to automate annotating pixels of a seismic image to delineate geological structural elements given image-level labels assigned to each image. Our framework factorizes the latent space of a deep encoder-decoder network by projecting the latent space to learned sub-spaces. Using constraints in the pixel space, the seismic image is further factorized to reveal confidence values on pixels associated with the geological element of interest. Details of the annotated image are provided for analysis and qualitative comparison is made with similar frameworks.

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