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Proceedings Papers

Complete detection of small earthquakes uncovers intricate relation between injection and seismicity

Yangkang Chen, Alexandros Savvaidis, Omar M. Saad, Daniel Siervo, Dino Huang, Yunfeng Chen, Iason Grigoratos, Sergey Fomel, Caroline Breton
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4081978
https://doi.org/10.1190/image2024-4081978.1
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View Paper titled, Complete detection of small earthquakes uncovers intricate relation between injection and seismicity
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Proceedings Papers

Analyzing the impact of additive and multiplicative noise on seismic data analysis

Akshika Rohatgi, Andrey Bakulin, Sergey Fomel
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4086176
https://doi.org/10.1190/image2024-4086176.1
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View Paper titled, Analyzing the impact of additive and multiplicative noise on seismic data analysis
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Proceedings Papers

Residual noise attenuation of poststack seismic data using t-p-x decomposition

Sujith Swaminadhan, Shuvajit Bhattacharya, Sergey Fomel
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4101272
https://doi.org/10.1190/image2024-4101272.1
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View Paper titled, Residual noise attenuation of poststack seismic data using t-p-x decomposition
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Proceedings Papers

Scalable, efficient, and adaptive simulation of frequency-domain wave propagation

Jacob Badger, Andrey Bakulin, Sergey Fomel
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4101625
https://doi.org/10.1190/image2024-4101625.1
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View Paper titled, Scalable, efficient, and adaptive simulation of frequency-domain wave propagation
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Proceedings Papers

Generating high-quality labels for deep learning CO 2 monitoring using local orthogonalization

Shuang Gao, Sergey Fomel, Yangkang Chen
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4101934
https://doi.org/10.1190/image2024-4101934.1
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View Paper titled, Generating high-quality labels for deep learning CO 2 monitoring using local orthogonalization
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Proceedings Papers

Phase pilot recovery: A foundation for mitigating speckle scattering noise in seismic data

Akshika Rohatgi, Andrey Bakulin, Sergey Fomel
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 26–29, 2024
Paper Number: SEG-2024-4091774
https://doi.org/10.1190/image2024-4091774.1
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View Paper titled, Phase pilot recovery: A foundation for mitigating speckle scattering noise in seismic data
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Proceedings Papers

Automated hyper-parameter optimization for deep learning framework to simulate boundary conditions for wave propagation

Harpreet Kaur, Sergey Fomel, Nam Pham
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3743090
https://doi.org/10.1190/image2022-3743090.1
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View Paper titled, Automated hyper-parameter optimization for deep learning framework to simulate boundary conditions for wave propagation
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Proceedings Papers

Seismic data augmentation for automatic faults picking using deep learning

Nam Pham, Sergey Fomel
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3745790
https://doi.org/10.1190/image2022-3745790.1
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View Paper titled, Seismic data augmentation for automatic faults picking using deep learning
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Proceedings Papers

Automatic detection of SEG-Y sampling format errors using machine learning

Sahana Vinayak, Ray Abma, Sergey Fomel
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3745795
https://doi.org/10.1190/image2022-3745795.1
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View Paper titled, Automatic detection of SEG-Y sampling format errors using machine learning
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Proceedings Papers

Shortest-path multiple well-log correlation

Hector Corzo-Pola, Sergey Fomel
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3751452
https://doi.org/10.1190/image2022-3751452.1
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Proceedings Papers

Shaping regularization by fast explicit diffusion

Sergey Fomel
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3751764
https://doi.org/10.1190/image2022-3751764.1
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View Paper titled, Shaping regularization by fast explicit diffusion
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Proceedings Papers

Operator chains and seismic data decomposition

Sergey Fomel
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3751656
https://doi.org/10.1190/image2022-3751656.1
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View Paper titled, Operator chains and seismic data decomposition
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Proceedings Papers

Least-squares non-stationary triangle smoothing

Reem F. Alomar, Sergey Fomel
Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 28–September 1, 2022
Paper Number: SEG-2022-3751694
https://doi.org/10.1190/image2022-3751694.1
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View Paper titled, Least-squares non-stationary triangle smoothing
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Proceedings Papers

A deep learning framework for seismic facies classification

Harpreet Kaur, Nam Pham, Sergey Fomel, Zhicheng Geng, Luke Decker, Ben Gremillion, Michael Jervis, Ray Abma, Shuang Gao
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583072
https://doi.org/10.1190/segam2021-3583072.1
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View Paper titled, A deep learning framework for seismic facies classification
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Proceedings Papers

Channel facies and faults multisegmentation in seismic volumes

Nam Pham, Dallas Dunlap, Sergey Fomel
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3584046
https://doi.org/10.1190/segam2021-3584046.1
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View Paper titled, Channel facies and faults multisegmentation in seismic volumes
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Proceedings Papers

A continuation approach for avoiding local minima in seismic velocity picking

Luke Decker, Sergey Fomel
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3579484
https://doi.org/10.1190/segam2021-3579484.1
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View Paper titled, A continuation approach for avoiding local minima in seismic velocity picking
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Proceedings Papers

Streaming seismic attributes

Zhicheng Geng, Sergey Fomel, Yang Liu, Qinghan Wang, Zhisheng Zheng, Yangkang Chen
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3594814
https://doi.org/10.1190/segam2021-3594814.1
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View Paper titled, Streaming seismic attributes
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Proceedings Papers

Nonstretching NMO correction using deep learning

Harpreet Kaur, Sergey Fomel
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3580806
https://doi.org/10.1190/segam2021-3580806.1
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Proceedings Papers

Boundary conditions for acoustic and elastic wave propagation using deep learning

Harpreet Kaur, Sergey Fomel, Nam Pham
Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3580865
https://doi.org/10.1190/segam2021-3580865.1
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View Paper titled, Boundary conditions for acoustic and elastic wave propagation using deep learning
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Proceedings Papers

Separating primaries and multiples using hyperbolic Radon transform with deep learning

Harpreet Kaur, Nam Pham, Sergey Fomel
Paper presented at the SEG International Exposition and Annual Meeting, October 11–16, 2020
Paper Number: SEG-2020-3419762
https://doi.org/10.1190/segam2020-3419762.1
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View Paper titled, Separating primaries and multiples using hyperbolic Radon transform with deep learning
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