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Imposing interpretational constraints on a seismic interpretation convolutional neural network
Geophysics, 2021With the expanding size of 3D seismic data, manual seismic interpretation becomes time-consuming and labor-intensive. For automating this process, recent progress in machine learning, in particular the convolutional neural network (CNN), has been ...
H. Di +4 more
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DeepSeismic: a Deep Learning Library for Seismic Interpretation
, 2020Summary We introduce DeepSeismic, an open source Github repository (https://github.com/microsoft/seismic-deeplearning) that provides implementation of deep learning algorithms for seismic facies interpretation.
M. Salvaris +6 more
semanticscholar +1 more source
ChannelSeg3D: Channel simulation and deep learning for channel interpretation in 3D seismic images
Geophysics, 2021Seismic channel interpretation involves detecting channel structures, which often appear as meandering shapes in 3D seismic images. Many conventional methods are proposed for delineating channel structures using different seismic attributes.
Hang Gao, Xinming Wu, Guofeng Liu
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Applications of supervised deep learning for seismic interpretation and inversion
The Leading Edge, 2019Recent advances in machine learning and its applications in various sectors are generating a new wave of experiments and solutions to solve geophysical problems in the oil and gas industry.
York Zheng +3 more
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Convolutional neural networks for automated seismic interpretation
The Leading Edge, 2018Deep-learning methods have proved successful recently for solving problems in image analysis and natural language processing. One of these methods, convolutional neural networks (CNNs), is revolutionizing the field of image analysis and pushing the state
A. U. Waldeland +3 more
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Interpretation, 2019
Following decades of technological innovation, geologists now have access to extensive 3D seismic surveys across sedimentary basins. Using these voluminous data sets to better understand subsurface complexity relies on developing seismic stratigraphic ...
V. Paumard +6 more
semanticscholar +1 more source
Following decades of technological innovation, geologists now have access to extensive 3D seismic surveys across sedimentary basins. Using these voluminous data sets to better understand subsurface complexity relies on developing seismic stratigraphic ...
V. Paumard +6 more
semanticscholar +1 more source
Virtual seismic interpretation
Proceedings SIBGRAPI'98. International Symposium on Computer Graphics, Image Processing, and Vision (Cat. No.98EX237), 2002This paper presents an application of the virtual reality paradigm to scientific visualization. We describe how the seismic interpretation task performed in oil and gas companies can be facilitated by using immersion techniques inherent to virtual reality.
R. Bastos, Luiz Lima
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Quantification and interpretation of seismicity
International Journal of Rock Mechanics and Mining Sciences, 2004Abstract Methods based on acoustic emission and microseismicity (AE/MS) have proven to be valuable for monitoring microcracking at the Underground Research Laboratory (URL) in Canada. The source locations of the seismic events induced by the excavation have helped to map out the extent of the excavation damage/disturbed zone (EDZ) around tunnels ...
D.S. Collins +3 more
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Probabilistic Seismic Interpretation Using Bayesian Neural Networks
81st EAGE Conference and Exhibition 2019, 2019Summary Recent progress in deep learning and especially convolutional neural networks has brought new advances in the automatic interpretation of seismic datasets.
L. Mosser, R. Oliveira, M. Steventon
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International Journal of Scholarly Research in Science and Technology
Sustainable exploration in the energy industry necessitates innovative approaches that address environmental concerns without compromising the efficiency (or robustness) of subsurface modeling.
Obobi Ume Onwuka, Akinsola Adu
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Sustainable exploration in the energy industry necessitates innovative approaches that address environmental concerns without compromising the efficiency (or robustness) of subsurface modeling.
Obobi Ume Onwuka, Akinsola Adu
semanticscholar +1 more source