The construction of subsurface velocity models and reservoir characterization depend heavily on the resolution of seismic interpretation. In the field of seismic exploration, combining multiple stacks, e.g. multi-angle, multi-azimuth, multi-frequency, of seismic data is becoming more and more common as a way to improve resolution. With the help of these stacks, seismic data at different offset angles can be processed, revealing more details about underlying structures and improving the imaging of intricate geological features. Delineating faults through deep learning becomes an important step in building subsurface structures. When deep learning fault prediction is used on multi-offset-angle stacks, it can help with seismic interpretation by displaying distinct fault features along each offset-angle stack. Its still unclear, though, how to combine the outcomes of each prediction to produce the ultimate best-of-all output. In this abstract, we use the convolutional network to analyze each predicted fault in latent space and then combine them based on frequency analysis. The final output will mitigate break faults and compile the most dependable faults from each angle stack after combining. Combining multi-frequency or multi-azimuth faults is another application for this technique.
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SEG/AAPG International Meeting for Applied Geoscience & Energy
August 26–29, 2024
Houston, Texas
Improving fault resolution from multiple angle stacks by latent feature analysis with deep learning
Konstantin Osypov
Konstantin Osypov
Halliburton Landmark
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Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, Houston, Texas, August 2024.
Paper Number:
SEG-2024-4083103
Published:
August 26 2024
Citation
Jiang, Fan, and Konstantin Osypov. "Improving fault resolution from multiple angle stacks by latent feature analysis with deep learning." Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, Houston, Texas, August 2024. doi: https://doi.org/10.1190/image2024-4083103.1
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