Results 11 to 20 of about 382,825 (168)

Research on Seismic Signal Denoising Model Based on DnCNN Network

Applied Sciences
Addressing the noise in seismic signals, a prevalent challenge within seismic signal processing, has been the focus of extensive research. Conventional algorithms for seismic signal denoising often fall short due to their reliance on manually determined ...
Li Duan, Jianxian Cai, Li Wang, Yan Shi
doaj   +2 more sources

Application of 2D Variational Mode Decomposition Method in Seismic Signal Denoising [PDF]

Elektronika ir Elektrotechnika
Seismic data are typical nonlinear and nonstationary data. In the acquisition and processing of seismic data, many factors interfere with it. Seismic data contain both effective waves and random noises, seriously affecting the quality of seismic data and
Chao Liu, Ziang Wang, Yaping Huang, Aiping Zeng, Hongming Fan   +4 more
doaj   +2 more sources

Three-dimensional seismic denoising based on deep convolutional dictionary learning

Results in Applied Mathematics
Dictionary learning (DL) has been widely used for seismic data denoising. However, it is associated with the following challenges. First, learning a dictionary from one dataset cannot be applied to another dataset and requires setting learning and ...
Yuntong Li, Lina Liu
doaj   +2 more sources

Design &implementation of complex-valued FIR digital filters with application to migration of seismic data [PDF]

, 2006
One-dimensional (I-D) and two-dimensional (2-D) frequency-space seismic migration FIR digital filter coefficients are of complex values when such filters require special space domain as well as wavenumber domain characteristics. In this thesis, such FIR
Mousa, Wail Abdul-Hakim
core   +6 more sources

Seismic Random Noise Removal Based on a Multiscale Convolution and Densely Connected Network for Noise Level Evaluation

IEEE Access, 2022
Traditional denoising methods for seismic exploration data design a corresponding mathematical denoising model batch according to the different properties of different random noises, which is a tedious and time-consuming process.
Liang Guo, Renze Luo, Xingyu Li, Yang Zhou, Tuo Juanjuan, Canru Lei   +5 more
doaj   +1 more source

Research on Deep Convolutional Neural Network Time-Frequency Domain Seismic Signal Denoising Combined With Residual Dense Blocks

Frontiers in Earth Science, 2021
Deep Convolutional Neural Networks (DCNN) have the ability to learn complex features and are thus widely used in the field of seismic signal denoising with low signal-to-noise ratio (SNR).
Zhitao Gao, Zhitao Gao, Song Zhang, Jianxian Cai, Jianxian Cai, Li Hong, Li Hong, Jiangshan Zheng   +7 more
doaj   +1 more source

Structure-Preserving Random Noise Attenuation Method for Seismic Data Based on a Flexible Attention CNN

Remote Sensing, 2022
The noise attenuation of seismic data is an indispensable part of seismic data processing, directly impacting the following inversion and imaging. This paper focuses on two bottlenecks in the AI-based denoising method of seismic data: the destruction of ...
Wenda Li, Tianqi Wu, Hong Liu
doaj   +1 more source

Imaging Domain Seismic Denoising Based on Conditional Generative Adversarial Networks (CGANs)

Energies, 2022
A high-resolution seismic image is the key factor for helping geophysicists and geologists to recognize the geological structures below the subsurface.
Hao Zhang, Wenlei Wang
doaj   +1 more source

Seismic random noise suppression using improved CycleGAN

Frontiers in Earth Science, 2023
Random noise adversely affects the signal-to-noise ratio of complex seismic signals in complex surface conditions and media. The primary challenges related to processing seismic data have always been reducing the random noise and increasing the signal-to-
Shimin Sun, Guihua Li, Renwei Ding, Lihong Zhao, Lihong Zhao, Yujie Zhang, Shuo Zhao, Jinwei Zhang, Junlin Ye   +8 more
doaj   +1 more source

Seismic Data Reconstruction Based on Double Sparsity Dictionary Learning With Structure Oriented Filtering

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
In seismic data processing, denoising and reconstruction are the two steps for identification of resources in the earth subsurface layers. The seismic data quality is affected by random noise and interference during acquisition.
Lakshmi Kuruguntla, Vineela Chandra Dodda, Anup Kumar Mandpura, Sunil Chinnadurai, Karthikeyan Elumalai   +4 more
doaj   +1 more source