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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
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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
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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
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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
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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 0003, Xintong Dong
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Denoising of seismic signals with oversampled filter banks
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004In several applications such as denoising, when signal expansion is not crucial, oversampled filter banks may outperform critically decimated filter banks. We study the performance of the recently proposed GLPBT (Generalized Lapped Pseudo-Biorthogonal Transform), a class of oversampled filter banks, on noise removal in seismic data, using controlled ...
Laurent Duval, Toshihisa Tanaka
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A Multispectral Denoising Framework for Seismic Random Noise Attenuation
IEEE Transactions on Geoscience and Remote Sensing, 2022Random noise attenuation plays an important role in the seismic data processing. The global coherency among different spectral segments is often neglected in the traditional denoising methods, even though the seismic data are naturally broadband in the frequency spectrum.
Yi Lin, Jinhai Zhang
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Curvelet Transform and its Application in Seismic Data Denoising
2009 International Conference on Information Technology and Computer Science, 2009Curvelet transform is a new multi-scale transform developed upon wavelet transform. Beside scale and position, its constructive factors still include directions. All these make curvelet transform have a better directional characteristic. Based on these properties, we transform seismic data into curvelet domain, apply a window-shrinking algorithm to ...
Junhua Zhang +4 more
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Weighted Multisteps Adaptive Autoregression for Seismic Image Denoising
IEEE Geoscience and Remote Sensing Letters, 2018We devised a new filtering technique for random noise attenuation by weighted multistep adaptive autoregression (WMAAR). We first obtain a series of denoised results by means of different steps adaptive AR, and then we sum these results with different weights.
Guochang Liu +3 more
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Adaptive Dictionary Learning for Blind Seismic Data Denoising
IEEE Geoscience and Remote Sensing Letters, 2020The data-driven tight frame (DDTF) method is a dictionary learning method which has been used widely in the adaptive sparse representation and the seismic random noise attenuation. In the DDTF method, the thresholding operator setting plays a significant role on balancing the noise removal and preservation of detail information with high frequency. The
Xiaojing Wang, Jianwei Ma
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