Results 1 to 10 of about 4,442 (191)
Some of the next articles are maybe not open access.
Study of Parameters in Dictionary Learning Method for Seismic Denoising
IEEE Transactions on Geoscience and Remote Sensing, 2022In seismic data processing, denoising is one of the important steps to get the earth subsurface layers' information accurately. The dictionary learning (DL) method is one of the prominent methods to denoise the seismic data. In the DL method, there are various parameters involved for denoising such as patch size, dictionary size, number of training ...
Lakshmi Kuruguntla +2 more
exaly +2 more sources
Generative Adversarial Network for Desert Seismic Data Denoising
IEEE Transactions on Geoscience and Remote Sensing, 2021Seismic exploration is a kind of exploration method for oil and gas resources. However, the disturbance of numerous random noise will decrease the quality and signal-to-noise ratio (SNR) of real seismic records, which brings difficulties to the following works of processing and interpretation.
Hongzhou Wang, Yue Li, Xintong Dong
exaly +2 more sources
Q-Compensated Denoising of Seismic Data
IEEE Transactions on Geoscience and Remote Sensing, 2021It is widely known that strong noise can decrease the quality of seismic data. However, the anelastic attenuation could be more important to account for the weak amplitude and low quality of seismic data. Here, we develop an inversion framework to simultaneously compensate for the attenuation of seismic data and remove noise, thereby enhancing the ...
Hang Wang +3 more
openaire +1 more source
DnResNeXt Network for Desert Seismic Data Denoising
IEEE Geoscience and Remote Sensing Letters, 2022In recent years, the denoising of low-frequency desert noise has been the significant and difficult point in processing seismic data. Traditional random noise suppression methods could not get a good result in processing seismic data in desert areas. Moreover, convolutional neural network (CNN) has made notable achievements in many fields recently.
Haiyang Yao +3 more
openaire +1 more source
Unsupervised Seismic Data Denoising Using Diffusion Denoising Model
IEEE Transactions on Geoscience and Remote SensingFuyao Sun, Hongbo Lin, Yue Li
exaly +2 more sources
Seismic denoising diffusion restoration model for seismic data processing
Engineering Applications of Artificial IntelligenceYimin Dou
exaly +2 more sources
Seismic denoising with nonuniformly sampled curvelets
Computing in Science & Engineering, 2006The authors present an extension of the fast discrete curvelet transform (FDCT) to nonuniformly sampled data. This extension not only restores curvelet compression rates for nonuniformly sampled data but also removes noise and maps the data to a regular ...
Gilles Hennenfent, Felix J. Herrmann
openaire +1 more source
Self-Supervised Learning for Seismic Data Reconstruction and Denoising
IEEE Geoscience and Remote Sensing Letters, 2022With their powerful feature extraction ability, convolutional neural network (CNN) models achieve excellent signal reconstruction and recovery performances compared with those of traditional methods. The CNN-based approaches mainly use supervised learning approaches; thus, they require large numbers of ground-truth labeled samples.
Fanlei Meng, Qinyin Fan, Yue Li 0003
openaire +1 more source
Fast empirical seismic denoising
14th International Congress of the Brazilian Geophysical Society & EXPOGEF, Rio de Janeiro, Brazil, 3-6 August 2015, 2015In this work we present a new strategy to accelerate the computation of the so-called complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a data-driven technique that can be used to denoise seismic data. The new implementation replaces the use of the cubic interpolation scheme, which is required to calculate the signal and ...
Julián L. Gómez* +2 more
openaire +1 more source
DiffSD: Diffusion models for seismic denoising
2023Seismic waves contain information about the earthquake (EQ) source and many forms of noise deriving from the seismometer, anthropogenic effects, background noise associated with ocean waves, and microseismic noise. Separating the noise from the EQ signal is a critical first step in EQ physics and seismic waveform analysis.
Daniele Trappolini +5 more
openaire +2 more sources