Results 261 to 270 of about 7,858,197 (288)

Seismic random noise suppression using deep convolutional autoencoder neural network

, 2020
Due to human or environmental factors, random noise will inevitably be introduced during seismic data acquisition. Contaminated seismic data seriously affect subsequent seismic data processing and imaging.
Huijuan Song, Yang Gao, Wei Chen, Yajuan Xue, Hua Zhang, Xiang Zhang   +5 more
semanticscholar   +1 more source

Residual Learning of Deep Convolutional Neural Network for Seismic Random Noise Attenuation

IEEE Geoscience and Remote Sensing Letters, 2019
Over the last decades, seismic random noise attenuation has been dominated by transform-based denoising methods over the last decades. However, these methods usually need to estimate the noise level and select an optimal transformation in advance, and ...
Feng Wang, Shengchang Chen
semanticscholar   +1 more source

Nanosecond Random Telegraph Noise in In-Plane Magnetic Tunnel Junctions.

Physical Review Letters, 2021
We study the timescale of random telegraph noise (RTN) of nanomagnets in stochastic magnetic tunnel junctions (MTJs). From analytical and numerical calculations based on the Landau-Lifshitz-Gilbert and the Fokker-Planck equations, we reveal mechanisms ...
K. Hayakawa, S. Kanai, T. Funatsu, J. Igarashi, B. Jinnai, W. A. Borders, H. Ohno, S. Fukami   +7 more
semanticscholar   +1 more source

Seismic Random Noise Attenuation Using Sparse Low-Rank Estimation of the Signal in the Time–Frequency Domain

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019
Suppression of random noise in seismic data is a challenging preprocessing task. We propose a new denoising method, which includes the following steps. First, the short-time Fourier transform of the noisy seismic signal is computed.
R. Anvari, A. R. Kahoo, M. Mohammadi, Nabeel Ali Khan, Yangkang Chen   +4 more
semanticscholar   +1 more source

Attenuation of random noise using denoising convolutional neural networks

Interpretation, 2019
Random noise often contaminates seismic data and reduces its signal-to-noise ratio. Therefore, the removal of random noise has been an essential step in seismic data processing.
Xu Si, Yijun Yuan, Tinghua Si, Shiwen Gao   +3 more
semanticscholar   +1 more source

Dictionary learning based on dip patch selection training for random noise attenuation

Geophysics, 2019
In recent years, sparse representation is seeing increasing application to fundamental signal and image-processing tasks. In sparse representation, a signal can be expressed as a linear combination of a dictionary (atom signals) and sparse coefficients ...
S. Zu, Hui Zhou, R. Wu, Maocai Jiang, Yangkang Chen   +4 more
semanticscholar   +1 more source

Random-noise suppression in seismic data: What can deep learning do?

SEG technical program expanded abstracts, 2018
In the past few years, deep learning has gained great success in image signal and information processing. What are the challenges of seismic denoising compared to image denoising when using deep learning?
Dawei Liu, Wei Wang, Wenchao Chen, Xiaokai Wang, Yanhui Zhou, Shi Zhensheng   +5 more
semanticscholar   +1 more source

Streaming orthogonal prediction filter in the t-x domain for random noise attenuation

Geophysics, 2018
In seismic exploration, there are many sources of random noise, for example, scattering from a complex surface. Prediction filters (PFs) have been widely used for random noise attenuation, but these typically assume that the seismic signal is stationary.
Yang Liu, Bingxiu Li
semanticscholar   +1 more source

SeisGAN: Improving Seismic Image Resolution and Reducing Random Noise Using a Generative Adversarial Network

Mathematical Geosciences, 2023
Lei Lin, Zhi Zhong, Chuyang Cai, Chenglong Li, Heng Zhang   +4 more
semanticscholar   +1 more source

Unsupervised 3-D Random Noise Attenuation Using Deep Skip Autoencoder

IEEE Transactions on Geoscience and Remote Sensing, 2022
Liuqing Yang, Shoudong Wang, Xiaohong Chen   +2 more
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