Journal ArticleDOI
Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis
TLDR
In this article, a rank reduction algorithm for simultaneous reconstruction and random noise attenuation of seismic records is proposed, which is based on multichannel singular spectrum analysis (MSSA).Abstract:
We present a rank reduction algorithm that permits simultaneous reconstruction and random noise attenuation of seismic records. We based our technique on multichannel singular spectrum analysis (MSSA). The technique entails organizing spatial data at a given temporal frequency into a block Hankel matrix that in ideal conditions is a matrix of rank k , where k is the number of plane waves in the window of analysis. Additive noise and missing samples will increase the rank of the block Hankel matrix of the data. Consequently, rank reduction is proposed as a means to attenuate noise and recover missing traces. We present an iterative algorithm that resembles seismic data reconstruction with the method of projection onto convex sets. In addition, we propose to adopt a randomized singular value decomposition to accelerate the rank reduction stage of the algorithm. We apply MSSA reconstruction to synthetic examples and a field data set. Synthetic examples were used to assess the performance of the method in two...read more
Citations
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Journal ArticleDOI
Simultaneous denoising and reconstruction of 5-D seismic data via damped rank-reduction method
TL;DR: The damped rank-reduction method can obtain a perfect reconstruction performance even when the observed data has extremely low signal-to-noise ratio (SNR), and is suggested for wide application in the industry.
Journal ArticleDOI
A tensor higher-order singular value decomposition for prestack seismic data noise reduction and interpolation
Nadia Kreimer,Mauricio D. Sacchi +1 more
TL;DR: In this paper, a rank-reduction process was used to reduce the rank of the prestack seismic tensor, and the higher-order singular value decompostion was used for rank reduction.
Journal ArticleDOI
Damped multichannel singular spectrum analysis for 3D random noise attenuation
TL;DR: In this paper, a damping factor was introduced into traditional multichannel singular spectrum analysis (MSSA) to dampen the singular values to distinguish between signal and noise in seismic data.
Journal ArticleDOI
Seismic Signal Denoising and Decomposition Using Deep Neural Networks
TL;DR: DeepDenoiser as discussed by the authors uses a deep neural network to learn a sparse representation of data in the time-frequency domain and a nonlinear function that maps this representation into masks that decompose input data into a signal of interest and noise.
Journal ArticleDOI
Deep denoising autoencoder for seismic random noise attenuation
Omar M. Saad,Yangkang Chen +1 more
TL;DR: The proposed algorithm to attenuate random noise based on a deep-denoising autoencoder (DDAE) succeeds in attenuating the random noise in an effective manner and is compared with several benchmark algorithms.
References
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A survey of collaborative filtering techniques
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