Abstract
Structural interpretation tasks require the step of fault segmentation, which is mostly performed manually, in seismic samples. Recent approaches represent seismic samples as 3D images and utilize a variety of methods, including Deep Learning. In this research, the authors propose a 3D bi-stream convolutional neural network, derived from U-Net, as an end-to-end model to segment seismic faults. Empirical results prove the power of the 3D bi-stream U-Net whose accuracy reaches 96.30% which outperforms recent works. The proposed network is potential for practical applications in seismic data analysis.
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Van-Ha, T.D., Thanh-An, N. (2022). 3D-FaultSeg-UNet: 3D Fault Segmentation in Seismic Data Using Bi-stream U-Net. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_32
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DOI: https://doi.org/10.1007/978-981-19-8069-5_32
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