We consider the task of channel detection as a regression problem and propose a workflow to identify the complex channels in 3D seismic volumes with complex structure using an end-to-end 3D convolutional neural network. To train the network, we automatically generate a training dataset containing more than 19000 3D synthetic seismic volumes and the corresponding channel labels, which are shown to be sufficient to train a good channel identification network. After training with only the synthetic dataset, the network automatically learns useful features that are important for channel identification. Multiple synthetic and field examples show that the network can much more accurately and efficiently predict channels in 3D seismic volumes with complex structure.
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SEG/AAPG International Meeting for Applied Geoscience & Energy
August 28–September 1, 2022
Houston, Texas, USA
3D CNN for channel identification in seismic volume
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, Houston, Texas, USA, August 2022.
Paper Number:
SEG-2022-3737089
Published:
November 01 2022
Citation
Li, Haishan, Yang, Wuyang, Zhang, Xiangyang, Wei, Xinjian, and Xin Xu. "3D CNN for channel identification in seismic volume." Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, Houston, Texas, USA, August 2022. doi: https://doi.org/10.1190/image2022-3737089.1
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