Interactive 3D fault prediction using a weighted 2D-CNN and multidirectional 3D-CNN-Reference-Cited by-同舟云学术

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Interactive 3D fault prediction using a weighted 2D-CNN and multidirectional 3D-CNN

Published:2022-08-15 Issue: Volume: Page:
ISSN:
Container-title:Second International Meeting for Applied Geoscience & Energy
language:
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Author:

Lomask Jesse¹,Chambers Samuel¹

Affiliation:

1. S&P Global

Publisher

Society of Exploration Geophysicists and American Association of Petroleum Geologists

Link

https://library.seg.org/doi/pdf/10.1190/image2022-3746971.1

Reference3 articles.

1. Attention-Based 3-D Seismic Fault Segmentation Training by a Few 2-D Slice Labels

2. Statistical imaging of faults in 3D seismic volumes using a machine learning approach

3. FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Boulder prediction for offshore windfarm site evaluation using an interactive 2D CNN and a unique weighting scheme on unmigrated seismic;Third International Meeting for Applied Geoscience & Energy Expanded Abstracts;2023-12-14

2. Horizon detection with CNN-based multiscale volumetric flattening;Third International Meeting for Applied Geoscience & Energy Expanded Abstracts;2023-12-14

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