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Geophysics, 2022
The physics that describes the seismic response of an interval of saturated porous rocks with known petrophysical properties is relatively well understood and includes rock physics, petrophysics and wave propagation models.
D. Grana +4 more
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The physics that describes the seismic response of an interval of saturated porous rocks with known petrophysical properties is relatively well understood and includes rock physics, petrophysics and wave propagation models.
D. Grana +4 more
semanticscholar +1 more source
Unsupervised Deep Learning for Random Noise Attenuation of Seismic Data
IEEE Geoscience and Remote Sensing Letters, 2022Random noise attenuation is an essential step to improve the signal-to-noise ratio (SNR) of seismic data. Deep learning for seismic data denoising is dominated by supervised methods that require noise-free data as training targets.
B. Liu +6 more
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DeepSeg: Deep Segmental Denoising Neural Network for Seismic Data
IEEE Transactions on Neural Networks and Learning Systems, 2022Noise attenuation is a crucial phase in seismic signal processing. Enhancing the signal-to-noise ratio (SNR) of registered seismic signals improves subsequent processing and, eventually, data analysis and interpretation.
Naveed Iqbal
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Deep Learning Prior Model for Unsupervised Seismic Data Random Noise Attenuation
IEEE Geoscience and Remote Sensing Letters, 2021Denoising is an indispensable step in seismic data processing. Deep-learning-based seismic data denoising has been recently attracting attentions due to its outstanding performance.
Chenyu Qiu +4 more
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A Convolutional Autoencoder Method for Simultaneous Seismic Data Reconstruction and Denoising
IEEE Geoscience and Remote Sensing Letters, 2021Petroleum geophysical exploration is based on seismic data and has been widely affected by deep learning technology in recent years. As a consequence of the high efficiency and nonlinear fitting ability of deep learning models, we propose an improved ...
Jinsheng Jiang, Haoran Ren, Meng Zhang
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SeismoGen: Seismic Waveform Synthesis Using GAN With Application to Seismic Data Augmentation
Journal of Geophysical Research: Solid Earth, 2021Detecting earthquake arrivals within seismic time series can be a challenging task. Visual, human detection has long been considered the gold standard but requires intensive manual labor that scales poorly to large data sets.
Tiantong Wang, D. Trugman, Youzuo Lin
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RECONSTRUCTION OF SEISMIC IMPEDANCE FROM MARINE SEISMIC DATA
Theoretical and Computational Acoustics 2005, 2006blank
Raymond Mabuza, Boy +3 more
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Poststack Seismic Data Denoising Based on 3-D Convolutional Neural Network
IEEE Transactions on Geoscience and Remote Sensing, 2020Deep learning has been successfully applied to image denoising. In this study, we take one step forward by using deep learning to suppress random noise in poststack seismic data from the aspects of network architecture and training samples.
Dawei Liu +5 more
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An unsupervised deep-learning method for porosity estimation based on poststack seismic data
Geophysics, 2020We propose to invert reservoir porosity from poststack seismic data using an innovative approach based on deep-learning methods. We develop an unsupervised approach to circumvent the requirement of large volumes of labeled data sets for a conventional ...
R. Feng +3 more
semanticscholar +1 more source
Ninth Annual International Phoenix Conference on Computers and Communications. 1990 Conference Proceedings, 1990
An investigation of low-rate seismic data compression using transform techniques is presented. This study concentrates on discrete orthogonal transforms such as the discrete Fourier transform (DFT), the discrete cosine transform (DCT), the Walsh-Hadamard transform (WHT), and the Karhunen-Loeve transform (KLT).
S.B. Jonsson, A.S. Spanias
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An investigation of low-rate seismic data compression using transform techniques is presented. This study concentrates on discrete orthogonal transforms such as the discrete Fourier transform (DFT), the discrete cosine transform (DCT), the Walsh-Hadamard transform (WHT), and the Karhunen-Loeve transform (KLT).
S.B. Jonsson, A.S. Spanias
openaire +1 more source