Results 211 to 220 of about 2,212,553 (293)

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

Using relative geologic time to constrain CNN-based seismic interpretation and property estimation

Geophysics, 2021
Three-dimensional seismic interpretation and property estimation is essential to subsurface mapping and characterization, in which machine learning, particularly supervised convolutional neural network (CNN) has been extensively implemented for improved ...
A. Abubakar, H. Di, Zhun Li
semanticscholar   +1 more source

MTL-FaultNet: Seismic Data Reconstruction Assisted Multitask Deep Learning 3-D Fault Interpretation

IEEE Transactions on Geoscience and Remote Sensing, 2023
Seismic fault interpretation is of extraordinary significant for hydrocarbon reservoir characterization and drilling hazard mitigation. In recent years, deep learning-based seismic fault detection methods have been conducted actively.
Weihua Wu, Yang Yang, Bangyu Wu, D. Ma, Zhanxin Tang, Xia Yin   +5 more
semanticscholar   +1 more source

Seismic Stratigraphic Interpretation Based on Deep Active Learning

IEEE Transactions on Geoscience and Remote Sensing, 2023
Seismic stratigraphic interpretation plays an important role in geophysics and geosciences. Recently, deep learning has been explored for seismic stratigraphic interpretation. However, deep-learning-based interpretation methods usually require sufficient
Xiaofeng Gu, Wenkai Lu, Y. Ao, Yinshuo Li, Cao Song   +4 more
semanticscholar   +1 more source

Seismic attribute-assisted seismic fault interpretation

GEOPHYSICS, 2023
Fault mapping is one of the main tasks of 3D seismic interpretation, and seismic discontinuity attributes are often used to support fault mapping in vertical sections and time slices. The most commonly used fault mapping procedure involves three passes/generations. First, the approximate positions of faults (generation I) on some vertical sections are
Bo Zhang, Yanghua Wang
openaire   +1 more source

Virtual seismic interpretation

Proceedings SIBGRAPI'98. International Symposium on Computer Graphics, Image Processing, and Vision (Cat. No.98EX237), 2002
This 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.
L.A. Lima, R. Bastos
openaire   +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

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

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

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