Application of Total-Variation-Based Curvelet Shrinkage for Three-Dimensional Seismic Data Denoising

Tang, Gang; Ma, Jianwei

doi:10.1109/lgrs.2010.2052345

Cited by 70 publications

(18 citation statements)

References 16 publications

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“…Methods based on time-frequency denoising [20,21] form a large class of seismic denoising techniques. In this approach, noisy time series are first transformed into the timefrequency domain using a time-frequency transform, such as a wavelet transform [22,23,24,25,26,27], Short Time Fourier Transform (STFT) [28], S-transform [29], curvelet transform [30,31,32], dreamlet transform [33], contourlet zhuwq@stanford.edu transform [34], shearlet transform [35], empirical mode decomposition [36,37,38,37,39,40], etc. The resulting timefrequency coefficients are modified (thresholded) to attenuate the coefficients associated with noise and to find an estimate of the signal coefficients.…”

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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“…2) Vertical edges are detected by the HPLP (high pass along rows and low pass along columns) subband W (17), (18) and (19) are proposed to detect edges in color images using DWT subbands.…”

Section: -Quadrant Curvelet Transformmentioning

confidence: 99%

A 4-quadrant Curvelet Transform for Denoising Digital Images

Parlewar¹,

Bhurchandi

2013

Int. J. Autom. Comput.

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The conventional discrete wavelet transform (DWT) introduces artifacts during denoising of images containing smooth curves. Finite ridgelet transform (FRIT) solved this problem by mapping the curves in terms of small curved ridges. However, blind application of FRIT all over an image is computationally heavy. Finite curvelet transform (FCT) selectively applies FRIT only to the tiles containing small portions of a curve. In this work, a novel curvelet transform named as 4-quadrant finite curvelet transform (4QFCT) based on a new concept of 4-quadrant finite ridgelet transform (4QFRIT) has been proposed. An image is band pass filtered and the high frequency bands are divided into small non-overlapping square tiles. The 4QFRIT is applied to the tiles containing at least one curve element. Unlike FRIT, the 4QFRIT takes 4 sets of radon projections in all the 4 quadrants and then averages them in time and frequency domains after denoising. The proposed algorithm is extensively tested and benchmarked for denoising of images with Gaussian noise using mean squared error (MSE) and peak signal to noise ratio (PSNR). The results confirm that 4QFCT yields consistently better denoising performance quantitatively and visually.

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Section: Introductionmentioning

confidence: 99%

“…A variety of noise can be introduced during seismic data acquisition by various sources, and this noise will then have strong interference on the signal recovered for seismic exploration. Therefore, noise removal plays an important role in increasing the quality of seismic exploration data, and various noise removal methods have been designed according to the characteristics of the noise (Cao and Chen 2005;Shan, Ma, and Yang 2009;Elboth, Presterud, and Hermansen 2010;Tang and Ma 2011;Baddari et al 2011;Oropeza and Sacchi * E-mail: liyue@jlu.edu.cn 2011). Radial-trace time-frequency peak filtering (RT-TFPF) is a 2D method that filters a seismic record along the radialtrace direction rather than filtering each channel along the time direction.…”

Section: Introductionmentioning

confidence: 99%

Seismic directional random noise suppression by radial‐trace time–frequency peak filtering using the Hurst exponent statistic

Zhang

Lin

et al. 2015

Geophysical Prospecting

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A B S T R A C TRadial-trace time-frequency peak filtering filters a seismic record along the radialtrace direction rather than the conventional channel direction. It takes the spatial correlation of the reflected events between adjacent channels into account. Thus, radial-trace time-frequency peak filtering performs well in denoising and enhancing the continuity of reflected events. However, in the seismic record there is often random noise whose energy is concentrated in certain directions; the noise in these directions is correlative. We refer to this kind of random noise (that is distributed randomly in time but correlative in the space) as directional random noise. Under radial-trace time-frequency peak filtering, the directional random noise will be treated as signal and enhanced when this noise has same direction as the signal. Therefore, we need to identify the directional random noise before the filtering. In this paper, we test the linearity of signal and directional random noise in time using the Hurst exponent. The time series of signals with high linearity lead to large Hurst exponent value; however, directional random noise is a random series in time without a fixed waveform and thus its linearity is low; therefore, we can differentiate the signal and directional random noise by the Hurst exponent values. The directional random noise can then be suppressed by using a long filtering window length during the radial-trace timefrequency peak filtering. Synthetic and real data examples show that the proposed method can remove most directional random noise and can effectively recover the reflected events.

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Application of Total-Variation-Based Curvelet Shrinkage for Three-Dimensional Seismic Data Denoising

Cited by 70 publications

References 16 publications

Seismic Signal Denoising and Decomposition Using Deep Neural Networks

Seismic Signal Denoising and Decomposition Using Deep Neural Networks

A 4-quadrant Curvelet Transform for Denoising Digital Images

Seismic directional random noise suppression by radial‐trace time–frequency peak filtering using the Hurst exponent statistic

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