Denoising seismic data using the nonlocal means algorithm

Bonar, David; Sacchi, Mauricio D.

doi:10.1190/geo2011-0235.1

Cited by 170 publications

(35 citation statements)

References 19 publications

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“…Due to these limitations, numerous efforts have been made to develop more effective noise suppression in seismic data e.g. [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]. Methods based on time-frequency denoising [20,21] form a large class of seismic denoising techniques.…”

Section: Introductionmentioning

confidence: 99%

Seismic Signal Denoising and Decomposition Using Deep Neural Networks

Zhu

Mousavi

Beroza

2019

IEEE Trans. Geosci. Remote Sensing

347

122

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Denoising and filtering are widely used in routine seismic-data-processing to improve the signal-to-noise ratio (SNR) of recorded signals and by doing so to improve subsequent analyses. In this paper we develop a new denoising/decomposition method, DeepDenoiser, based on a deep neural network. This network is able to learn simultaneously a sparse representation of data in the time-frequency domain and a non-linear function that maps this representation into masks that decompose input data into a signal of interest and noise (defined as any nonseismic signal). We show that DeepDenoiser achieves impressive denoising of seismic signals even when the signal and noise share a common frequency band. Our method properly handles a variety of colored noise and non-earthquake signals. DeepDenoiser can significantly improve the SNR with minimal changes in the waveform shape of interest, even in presence of high noise levels. We demonstrate the effect of our method on improving earthquake detection. There are clear applications of DeepDenoiser to seismic imaging, micro-seismic monitoring, and preprocessing of ambient noise data. We also note that potential applications of our approach are not limited to these applications or even to earthquake data, and that our approach can be adapted to diverse signals and applications in other settings.

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Section: Introductionmentioning

confidence: 99%

Seismic Signal Denoising and Decomposition Using Deep Neural Networks

Zhu

Mousavi

Beroza

2019

IEEE Trans. Geosci. Remote Sensing

347

122

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“…Denoising methods based on the gradient sparsity of a signal, such as total variation (TV) [8] and total generalized variation [9], have also been hot topics in recent years. In addition, nonlocal means [10], dictionary learning methods [11] and antileakage least-squares spectral analysis [12,13] have also attracted wide attention.…”

Section: Introductionmentioning

confidence: 99%

“…In other words, there is a certain degree of similarity in the data. This assumption is valid in seismic data [10]. The basic step of the dictionary learning method is to construct a sparse representation dictionary by using a learning method.…”

Section: Introductionmentioning

confidence: 99%

Seismic Data Denoising Based on Sparse and Low-Rank Regularization

Yang

Liu

et al. 2020

Energies

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Seismic denoising is a core task of seismic data processing. The quality of a denoising result directly affects data analysis, inversion, imaging and other applications. For the past ten years, there have mainly been two classes of methods for seismic denoising. One is based on the sparsity of seismic data. This kind of method can make use of the sparsity of seismic data in local area. The other is based on nonlocal self-similarity, and it can utilize the spatial information of seismic data. Sparsity and nonlocal self-similarity are important prior information. However, there is no seismic denoising method using both of them. To jointly use the sparsity and nonlocal self-similarity of seismic data, we propose a seismic denoising method using sparsity and low-rank regularization (called SD-SpaLR). Experimental results showed that the SD-SpaLR method has better performance than the conventional wavelet denoising and total variation denoising. This is because both the sparsity and the nonlocal self-similarity of seismic data are utilized in seismic denoising. This study is of significance for designing new seismic data analysis, processing and inversion methods.

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“…This filter has seen usage in the geophysical community to denoise seismic data (Bonar and Sacchi, 2012). In image denoising, the NLM filter produces a filtered image pixel of interest by the weighted average of pixels in a search neighborhood, where the weighting factor connecting the two pixels in question (i.e., the pixel of interest and a pixel in the neighborhood) is determined by the 'similarity' between them.…”

Section: Introductionmentioning

confidence: 99%

Non-local means filter for trim statics

Huang

Wang

Schuster

2014

SEG Technical Program Expanded Abstracts 2014

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SUMMARYStructures will be mispositioned across prestack migration gathers in the presence of inaccuracies in the velocity model. Stacking these misaligned gathers runs the risk of destroying important structures in the stacked migration image. To mitigate this problem, we propose a trim statics inspired by the non-local means algorithm originally developed for image denoising. This method differs from the conventional one in two fundamental respects. First, the trim statics are computed by comparing image patches instead of individual image traces. Second, no global pilot trace is needed because only two migration images at a time participate in trim statics and are stacked into one image. A multitude of migration images are stacked recursively in this two-to-one fashion. Tests with a Gulf of Mexico dataset show a noticeable improvement in the feature coherency of the stacked migration image.

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Denoising seismic data using the nonlocal means algorithm

Cited by 170 publications

References 19 publications

Seismic Signal Denoising and Decomposition Using Deep Neural Networks

Seismic Signal Denoising and Decomposition Using Deep Neural Networks

Seismic Data Denoising Based on Sparse and Low-Rank Regularization

Non-local means filter for trim statics

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