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Toward Science‐Led Publishing

Learned Publishing, Volume 38, Issue 3, July 2025.
Damian Pattinson, George Currie
wiley   +1 more source

Ocean-bottom seismometers reveal surge dynamics in Earth's longest-runout sediment flows. [PDF]

Commun Earth Environ
Kunath P, Talling PJ, Lange D, Chi WC, Baker ML, Urlaub M, Berndt C.   +6 more
europepmc   +1 more source

Dual-driving of data and knowledge to reduce uncertainty in lithofacies interpolation. [PDF]

Sci Rep
Dou M, Li S, Chang L, Wang K, Li J, Dai M.   +5 more
europepmc   +1 more source

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Seismic Coherence for Discontinuity Interpretation

Surveys in Geophysics, 2021
Seismic coherence is of the essence for seismic interpretation as it highlights seismic discontinuity features caused by the deposition process, reservoir boundaries, tectonic movements, etc. Since its appearance in 1995, seismic coherence has become one of the most popular and highly recognized interpretation tools.
Fangyu Li, Bin Lyu, Jie Qi, Sumit Verma, Bo Zhang   +4 more
openaire   +3 more sources

Imposing interpretational constraints on a seismic interpretation convolutional neural network

Geophysics, 2021
With 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, Cen Li, Stewart Smith, Zhun Li, A. Abubakar   +4 more
semanticscholar   +1 more source

DeepSeismic: a Deep Learning Library for Seismic Interpretation

, 2020
Summary 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, M. Kaznady, V. Paunic, I. Karmanov, A. Bhatia, W. Tok, S. Chikkerur   +6 more
semanticscholar   +1 more source

ChannelSeg3D: Channel simulation and deep learning for channel interpretation in 3D seismic images

Geophysics, 2021
Seismic 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
semanticscholar   +1 more source

Applications of supervised deep learning for seismic interpretation and inversion

The Leading Edge, 2019
Recent 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, Qie Zhang, Anar Yusifov, Yunzhi Shi   +3 more
semanticscholar   +1 more source

Convolutional neural networks for automated seismic interpretation

The Leading Edge, 2018
Deep-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, A. C. Jensen, L. Gelius, A. Solberg   +3 more
semanticscholar   +1 more source

Full-volume 3D seismic interpretation methods: A new step towards high-resolution seismic stratigraphy

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, J. Bourget, B. Durot, S. Lacaze, T. Payenberg, A. George, S. Lang   +6 more
semanticscholar   +1 more source