Articles with "erratic noise" as a keyword



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MoG-Based Robust Sparse Representation for Seismic Erratic Noise Suppression

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Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"

DOI: 10.1109/lgrs.2020.3022985

Abstract: By modeling the noise as Gaussian distribution, quite a lot of methods, such as basis pursuit denoising (BPDN), have demonstrated their great effectiveness in suppressing commonly random seismic noise. However, when it comes to complex… read more here.

Keywords: noise; sparse representation; robust sparse; distribution ... See more keywords

Erratic Noise Attenuation Using Double Sparsity Dictionary Learning Method

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Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2022.3164460

Abstract: In seismic data processing, attenuation of erratic noise is a challenging task due to the unknown noise distribution. Erratic noise consists of high amplitude peaks and conventional sparse transforms based on least-square (LS) approach that… read more here.

Keywords: double sparsity; noise; noise attenuation; method ... See more keywords
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Attention-Based Neural Network for Erratic Noise Attenuation From Seismic Data With a Shuffled Noise Training Data Generation Strategy

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Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2022.3197929

Abstract: The supervised neural network-based method provides an effective way for seismic data denoising. The noise level of seismic erratic noise, i.e., outlier, varies from traces, time windows, and shot gathers. The networks with popular structures… read more here.

Keywords: neural network; network; noise; strategy ... See more keywords
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Erratic noise suppression using iterative structure‐oriented space‐varying median filtering with sparsity constraint

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Published in 2020 at "Geophysical Prospecting"

DOI: 10.1111/1365-2478.13032

Abstract: ABSTRACT Erratic noise often has high amplitudes and a non‐Gaussian distribution. Least‐squares–based approaches therefore are not optimal. This can be handled better with non–least‐squares approaches, for example based on Huber norm which is computationally expensive.… read more here.

Keywords: erratic noise; least squares; median filtering; noise suppression ... See more keywords