Deep learning for characterizing paleokarst features in 3D seismic images

Author:

Wu Xinming1,Yan Shangsheng1,Qi Jie2,Zeng Hongliu3

Affiliation:

1. University of Science and Technology of China

2. University of Oklahoma

3. University of Texas at Austin

Publisher

Society of Exploration Geophysicists

Reference57 articles.

1. 3D volumetric multispectral estimates of reflector curvature and rotation

2. Andre, P., and A. Doulcet, 1970, Rospo mare field, italy, apulian platform, adriatic sea, in Stratigraphic traps II: AAPG Treatise of Petroleum Geology, Atlas of Oil and Gas Fields, 29–54.

3. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

4. 3-D seismic discontinuity for faults and stratigraphic features: The coherence cube

5. Machine learning for data-driven discovery in solid Earth geoscience

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1. Sensing prior constraints in deep neural networks for solving exploration geophysical problems;Proceedings of the National Academy of Sciences;2023-06

2. Karst cave detection and prediction by using two fully convolutional neural networks;SEG 2021 Workshop: 4th International Workshop on Mathematical Geophysics: Traditional & Learning, Virtual, 17–19 December 2021;2022-02-24

3. Seismic Coherence for Discontinuity Interpretation;Surveys in Geophysics;2021-11

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