Results 251 to 260 of about 7,858,197 (288)

Seismic Random Noise Attenuation by Applying Multiscale Denoising Convolutional Neural Network

IEEE Transactions on Geoscience and Remote Sensing, 2022
Seismic prospecting is a common method used in oil and gas resource exploration. However, due to the limitations of current collection techniques, seismic records acquired in the field are typically contaminated by severe incoherent noise, which has ...
T. Zhong, Ming Cheng, Xintong Dong, N. Wu   +3 more
semanticscholar   +1 more source

Unsupervised 3-D Random Noise Attenuation Using Deep Skip Autoencoder

IEEE Transactions on Geoscience and Remote Sensing, 2021
Effective random noise attenuation is critical for subsequent processing of seismic data, such as velocity analysis, migration, and inversion. Thus, the removal of seismic random noise with an uncertainty level is meaningful. Attenuating 3-D random noise
Liuqing Yang, Shoudong Wang, Xiaohong Chen, O. Saad, Wei Chen, Y. A. S. I. Oboué, Yangkang Zhang   +6 more
semanticscholar   +1 more source

Unsupervised Deep Learning for Random Noise Attenuation of Seismic Data

IEEE Geoscience and Remote Sensing Letters, 2022
Random 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, Jinghang Yue, Zhiwu Zuo, Xinji Xu, Chao Fu, Senlin Yang, Peng Jiang   +6 more
semanticscholar   +1 more source

Seismic Random Noise Attenuation via Self-Supervised Transfer Learning

IEEE Geoscience and Remote Sensing Letters, 2022
Random noise attenuation is an important step in seismic data processing. Unfortunately, most conventional denoising methods heavily rely on specific prior knowledge and fine-tuning of the parameters.
Huimin Sun, Fangshu Yang, Jianwei Ma
semanticscholar   +1 more source

Self-supervised learning for random noise suppression in seismic data

First International Meeting for Applied Geoscience & Energy Expanded Abstracts, 2021
A staunch companion to seismic signals, noise consistently hinders processing and interpretation of seismic data. Bor-rowing ideas from the field of computer vision, we propose the use of self-supervised deep learning for the task of random noise ...
C. Birnie, M. Ravasi, T. Alkhalifah
semanticscholar   +1 more source

Seismic Random Noise Separation and Attenuation based on MVMD and MSSA

IEEE Transactions on Geoscience and Remote Sensing, 2021
Seismic noise separation and attenuation is a fundamental topic in the seismic signal processing and geological interpretation. Several kinds of algorithms are proposed for separating and attenuating seismic random noises.
Yijie Zhang, Haoran Zhang, Yang Yang, Naihao Liu, Jinghuai Gao   +4 more
semanticscholar   +1 more source

Deep denoising autoencoder for seismic random noise attenuation

, 2020
Attenuation of seismic random noise is considered an important processing step to enhance the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random noise based on a deep-denoising autoencoder (DDAE).
O. Saad, Yangkang Zhang
semanticscholar   +1 more source

Random noise

Electronics and Power, 1975
  +12 more sources

Seismic Random Noise Reduction Using Adaptive Threshold Combined Scale and Directional Characteristics of Shearlet Transform

IEEE Geoscience and Remote Sensing Letters, 2020
Random noise attenuation is an important step in seismic processing. A method with an adaptive threshold based on scale and directional characteristics of shearlet transform is proposed to attenuate seismic random noise.
Jicheng Liu, Ya Gu, Yongxin Chou, J. Gu
semanticscholar   +1 more source

Seismic Random Noise Attenuation Using Optimal Empirical Wavelet Transform With a New Wavelet Thresholding Technique

IEEE Sensors Journal
The most vital challenge in seismic signal processing is the attenuation of random noise in seismic data. Many attenuation methods are formulated to mitigate the random noise but fail to retain high accuracy.
K. Geetha, M. Hota
semanticscholar   +1 more source