Results 21 to 30 of about 612 (172)

Rank-constrained seismic data interpolation and denoising

Brazilian Journal of Geophysics, 2022
Rank-constrained seismic data interpolation methods have been used to cope with spatial sampling deficiencies, but some fundamental aspects are often neglected. Understanding their underlying features is the first step for developing new solutions to overcome existing limitations.
Quézia Cavalcante, Milton Porsani
openaire   +1 more source

Denoising of Seismic Data Based on Block Dictionary Learning Theory

CT Lilun yu yingyong yanjiu, 2022
With the increasingly complex observation environment of oil and gas exploration, the seismic data collected are often mixed with various noise signals, resulting in the effective weak signal caused by the exploration target is covered, which seriously ...
Junjie ZHOU, Xiangling WU, Wenjie LI, Jinghe LI   +3 more
doaj   +1 more source

Multi-scale interactive network in the application of DAS seismic data processing

Frontiers in Earth Science, 2023
Distributed acoustic sensing (DAS) is regarded as a novel acquisition technology for seismic data. Compared with conventional electrical geophones, DAS has a series of obvious advantages including low-cost, high spatial resolution, good coverage, and ...
Hongzhou Wang, Jun Lin, Jun Lin, Dan Shao, Xintong Dong, Yue Li   +5 more
doaj   +1 more source

Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF

Applied Sciences, 2022
As rock fractures caused by micro-seismic events has potential safety hazards to underground workers, it is often necessary to accurately locate the micro-seismic source for hidden danger investigation.
Jianting Chen, Jianfei Fu, Hao Cheng, Sanshi Jia, Yuzeng Yao, Di Yan   +5 more
doaj   +1 more source

Random Noise Attenuation Based on Residual Convolutional Neural Network in Seismic Datasets

IEEE Access, 2020
Seismic random noise attenuation is a key step in seismic data processing. The random seismic data recorded by the detector tends to have strong noise, and this noisy seismic ratio can be seen as a low signal-to-noise ratio (SNR).
Liuqing Yang, Wei Chen, Wei Liu, Bei Zha, Linqi Zhu   +4 more
doaj   +1 more source

Removing multiple types of noise of distributed acoustic sensing seismic data using attention-guided denoising convolutional neural network

Frontiers in Earth Science, 2023
In recent years, distributed optical fiber acoustic sensing (DAS) technology has been increasingly used for vertical seismic profile (VSP) exploration. Even though this technology has the advantages of high spatial resolution, strong resistance to high ...
Cong Wang, Xingguo Huang, Yue Li, Kristian Jensen   +3 more
doaj   +1 more source

Diffusion Model for DAS-VSP Data Denoising

Sensors, 2023
Distributed acoustic sensing (DAS) has emerged as a transformational technology for seismic data acquisition. However, noise remains a major impediment, necessitating advanced denoising techniques. This study pioneers the application of diffusion models,
Donglin Zhu, Lei Fu, Vladimir Kazei, Weichang Li   +3 more
doaj   +1 more source

Seismic data denoising and deblending using deep learning

CoRR, 2019
An important step of seismic data processing is removing noise, including interference due to simultaneous and blended sources, from the recorded data. Traditional methods are time-consuming to apply as they often require manual choosing of parameters to obtain good results.
Alan Richardson, Caelen Feller
openaire   +2 more sources

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

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   +1 more source

Ground-Truth-Free 3D Seismic Denoising Based on Diffusion Models: Achieving Effective Constraints Through Embedded Self-Supervised Noise Modeling

Remote Sensing
Three-dimensional (3D) seismic data, essential for revealing subsurface structures and exploring oil and gas resources, are often contaminated by noise with an unknown prior distribution.
Zhonghan Zhang, Guihe Qin, Yanhua Liang, Minghui Sun, Yingqing Wang, Jiaru Song   +5 more
doaj   +1 more source