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Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data

Kluvanec, Daniel; McCaffrey, Kenneth J.W.; Phillips, Thomas B.; Al Moubayed, Noura

Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data Thumbnail


Authors

Thomas B. Phillips



Abstract

We can represent the orientation of a plane in 3D by its normal vector. However, every plane has two normal vectors that are negatives of each other. We propose four novel representations of vectors in 3D that are negation invariant and can be used by a neural network to predict orientation. Our proposed solution is the first to introduce representations that are negation invariant, continuous and easily parallelisable on the GPU. We evaluate the representations by predicting the orientation of a plane on a toy task, and by applying them to synthetic seismic tomographic data where we predict the presence and orientation of faults for every voxel in the volume. We further make use of the orientation of the faults in a post-processing algorithm on the GPU that separates the faults into segments (i.e. instances) that do not intersect, which allows us to selectively visualise faults in 3D. We demonstrate the utility of the representations by deploying the model on the Laminaria 3D Seismic volume as a case study. We quantitatively compare the model’s prediction against human interpretations of slices through the volume as well as existing interpretations in literature. Our analysis shows good agreement (F1 score of 88%) of the model with human interpretation in the shallow levels, where the ambient noise is lower, but this agreement degrades at deeper levels (F1 score of 68%). We explore possible reasons for this degradation.

Citation

Kluvanec, D., McCaffrey, K. J., Phillips, T. B., & Al Moubayed, N. (2023). Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data. IEEE Transactions on Geoscience and Remote Sensing, https://doi.org/10.1109/tgrs.2023.3273329

Journal Article Type Article
Online Publication Date May 5, 2023
Publication Date 2023
Deposit Date May 16, 2023
Publicly Available Date Mar 28, 2024
Journal IEEE Transactions on Geoscience and Remote Sensing
Print ISSN 0196-2892
Electronic ISSN 1558-0644
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/tgrs.2023.3273329
Public URL https://durham-repository.worktribe.com/output/1174917

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