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Keywords: deep learning
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Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583288
... With the development of intelligent inversion technology, deep learning method has been successfully applied in geophysical inversion. Unlike the traditional neural network, this study uses the U-Net structure based on fully convolutional networks (FCN) to invert the physical properties...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583365
... Program but was not presented at IMAGE 2021 in Denver, Colorado. neural network seismic data artificial intelligence interperted horizon machine learning deep learning applied geoscience reservoir characterization upstream oil & gas accuracy seismic horizon extraction seismic sample...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583063
... machine learning deep learning artificial intelligence qc score bayesian optimization subtraction workflow neural network applied geoscience prediction optimization exploration geophysicist 10 algorithm label noise reservoir characterization upstream oil & gas automated...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3582266
... propose a form for a 2.5D residual neural network designed to detect faults in seismic volumes and examine its robustness and expense through tests on synthetic data. reservoir characterization upstream oil & gas machine learning deep learning neural network artificial intelligence pixel...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583248
... A staunch companion to seismic signals, noise consistently hinders processing and interpretation of seismic data. Borrowing ideas from the field of computer vision, we propose the use of self-supervised deep learning for the task of random noise suppression. These techniques require no clean...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583393
... As a robust feature extraction method, deep learning has made significant progress in attenuating noise from seismic datasets. One critical assumption of deep learning for prediction is that test and training data should arise from the same distribution. Poststack data from a given survey can...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583460
... distance, combining the geosteering inversion with the deep learning techniques may cause sensitivity issues and lead the training to fail. This paper proposes a cross-gradient based method for solving the highly sensitive geosteering inversion with a physics-driven deep network. By inspecting...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583602
... Fault interpretation is a critical step for building reservoir models, mitigating drilling and production hazards such as reservoir compartmentalization. Estimating fault probability from 3D seismic using Deep Learning based image segmentation techniques has been widely studied and applied...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583901
... Seismic data are often interfered by random noise. Noise attenuation is a significant effort in seismic data processing. Deep learning methods have been introduced in recent years by training a neural network with noisy data for input and noise-free data for output. In practice, it is difficult...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583667
... In this paper, we design a framework to combine deep neural network (DNN) and the traditional separate inversion workflow together and improve the joint inversion result iteratively for multi-physics data. Different from conventional end-to-end networks, our proposed deep learning enhanced (DLE...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583695
... deep learning applied geoscience linear extrapolation artificial intelligence upstream oil & gas autoencoder co 2 softmax extrapolation network latent space sleipner area loss function encoder network exploration geophysicist 10 seismic survey reservoir characterization neural...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3584041
... and non-stationary process, which is suitable for the popular deep learning (DL)-based image processing. Different from the most straightforward DL-based adaptive subtraction (i.e., the full wavefield and the advanced estimated primary training pair), we propose to include both the original full wavefield...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583662
... an accurate deblending process to separate the blended signals. Deep learning has been widely used in various seismic processing tasks. However, the disparity of seismic datasets is a challenge to a deep neural network’s adaptiveness (also known as generalization). The scarcity of labeled data...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583565
... We present a general scheme for 3D geophysical inversion using a deep learning convolutional neural network that enables three-dimensional inversions of useful size to be solved on laptops and desktops. A priori constraints of maximum smoothness or compactness on model parameters used during...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3584040
... noise-only data machine learning deep learning applied geoscience diffraction-only data gajewski application neural network corresponding label wavefield exploration geophysicist 10 reservoir characterization dataset reflection-only data epoch upstream oil & gas convolutional neural...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583675
... Even though deep neural networks are incredibly successful in speeding up seismic interpretation, they are frequently met with skepticism. Specifically, the main critique of deep learning remains the lack of explainable predictions and their poor generalization to difficult textures...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3584046
... artificial intelligence parihaka dataset submarine canyon system neural network encoder layer convolutional neural network geophysics high uncertainty machine learning deep learning model uncertainty interpretability exploration geophysicist 10 probability fomel channel facies...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583955
...Deep Learning for Joint Geophysical Inversion of Seismic and MT datasets AbhinavPratap Singh1*, Divakar Vashisth1, Shalivahan Srivastava1 1Department of Applied Geophysics, Indian Institute of Technology (Indian School of Mines) Dhanbad Summary (Sen and Stoffa, 2013). The solution to such a problem...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583872
... to mitigate the introduction of any edge artifacts, low or high-frequency artifacts, amplitudes shifts, and spurious kinks in the data. artificial intelligence applied geoscience geophysics machine learning deep learning prediction international conference noise neural network bottleneck...
Proceedings Papers

Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, September 26–October 1, 2021
Paper Number: SEG-2021-3583956
... clustering algorithm latent space factorization reservoir characterization deep learning annotation geologic structure alregib expanded abstract exploration geophysicist 10 application seismic structure alfarraj alaudah self supervised delineation data mining upstream oil & gas applied...

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