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Proceedings Papers

Automatic computation of relative geologic time volume using self-supervised learning

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Dowan Kim, Joongmoo Byun
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3581832
https://doi.org/10.1190/segam2021-3581832.1
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View Paper titled, Automatic computation of relative geologic time volume using self-supervised learning
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Proceedings Papers

Domain adaptation-based acoustic impedance estimation with seismic data constraint

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Jeonghun Yoo, Dowan Kim, Junhwan Choi, Joongmoo Byun
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3594199
https://doi.org/10.1190/segam2021-3594199.1
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View Paper titled, Domain adaptation-based acoustic impedance estimation with seismic data constraint
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Proceedings Papers

Uncertainty estimation in impedance inversion using Bayesian deep learning

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Junhwan Choi, Dowan Kim, Joongmoo Byun
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3428098
https://doi.org/10.1190/segam2020-3428098.1
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View Paper titled, Uncertainty estimation in impedance inversion using Bayesian deep learning
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Proceedings Papers

Data augmentation using CycleGAN for overcoming the imbalance problem in petrophysical facies classification

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Dowan Kim, Joongmoo Byun
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3427510
https://doi.org/10.1190/segam2020-3427510.1
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View Paper titled, Data augmentation using CycleGAN for overcoming the imbalance problem in petrophysical facies classification
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Proceedings Papers

Predicting mineralogy using a deep neural network and fancy PCA

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Dokyeong Kim, Junhwan Choi, Dowan Kim, Joongmoo Byun
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3426151
https://doi.org/10.1190/segam2020-3426151.1
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View Paper titled, Predicting mineralogy using a deep neural network and fancy PCA
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