Seismic Random Noise Elimination by Adaptive Time-Frequency Peak Filtering

Lin, Hongbo; Li, Yue; Yang, Banghua; Ma, Haitao; Zhang, Chao

doi:10.1109/lgrs.2013.2257674

Cited by 39 publications

(7 citation statements)

References 13 publications

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“…Therefore, how to accurately detect 1/f noise under the background of strong white noise is the research topic of this article. Time-Frequency Peak Filter (TFPF) is a noise elimination algorithm widely used in the field of signal processing [7][8] . And it has been proved that the noise cancellation can still be achieved effectively under the condition of low SNR [9].…”

Section: Iintroductionmentioning

confidence: 99%

An Approach to 1/f Noise Detection Based on Adaptive T-ATFPF Algorithm

Wu¹

2021

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The generation of 1/f noise is closely related to the quality defects of IGBT devices. In the process of detecting IGBT single tube noise, thermal noise and shot noise show obvious white noise characteristics in the low frequency band, which are detected under the background of strong white noise 1/f noise can characterize the performance of IGBT devices. Therefore, on the basis of the Time-Frequency Peak Filtering (TFPF) algorithm, a two-dimensional time-domain adaptive T-ATFPF algorithm is proposed, and the adaptive segmentation is realized by means of the confidence interval crossing criterion based on Chebyshev’s inequality. Variable window length,use a small window length to process the signal section, which retains more detailed information of the effective signal.Use a larger window length to process the buffer section to ensure a smooth transition.Use the large window length to process the noise section, which more effectively suppresses randomness for noise, apply T-ATFPF to artificial synthesis model and actual model. Experimental results indicate that compared with the conventional algorithm, the improved method can better recover 1/f noise, and the ratio of signal to noise is greatly improved by about 1.3dB.

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Section: Iintroductionmentioning

confidence: 99%

An Approach to 1/f Noise Detection Based on Adaptive T-ATFPF Algorithm

Wu¹

2021

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“…have been designed for seismic random noise reduction to meet the demands of the development in seismic exploration, such as wavelet transform-based denoising methods [1], [2], time-frequency peak filters [3]- [5], sparse representation [6], PDE-based diffusion filters [7], [8]. Although these denoising methods highly improve the quality of seismic images, the denoising performances still need to be improved under the condition of low SNR and spatiotemporally variant seismic random noise.…”

Section: Introductionmentioning

confidence: 99%

A Patch Based Denoising Method Using Deep Convolutional Neural Network for Seismic Image

Zhang

Lin

et al. 2019

IEEE Access

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The deep convolutional neural networks (CNNs) have been shown excellent performances for image denoising. However, the denoising CNN model trained with a specific noise level cannot deal with the images which have spatiotemporally variant random noise and low signal-to-noise ratio (SNR), such as seismic images. To this end, we propose a patch-based denoising CNN method, namely PDCNN. Specifically, we cluster the overlapping patches of noisy image into K classes where the image patches have close noise levels in each class, and then choose a suitable model for denoising the corresponding class from a series of well-trained CNN models. By embodying the structural statistics, we propose a CNN model selection criterion with a structural-dependent parameter. In contrast to the manual model selection process, the more accurate CNN model is chosen automatically and effectively. The capability of the PDCNN is demonstrated on synthetic and field seismic images. Experimental results show that the proposed method largely benefits from using multiple CNN models to jointly denoise, and leads to the satisfactory denoising performance in spatiotemporally variant seismic random noise reduction and structural signal preservation. INDEX TERMS Convolutional neural networks (CNNs), clustering, patch, seismic image denoising, signal preservation, spatiotemporally variant random noise.

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“…This technique is likely to work out properly since it separates the signal from noise, whose components are in scales of higher resolution. Another usual strategy to filter out impulsive interference is the adoption of non-linear techniques, such as median filtering [26,27], stack filtering [28] or morphological filtering [17,29]. These approaches are effective and are also applied for purposes other than noise suppression, such as phasor estimation [30], or image processing [31].…”

Section: Introductionmentioning

confidence: 99%

An impulsive noise filter applied in wireless control of wind turbines

et al. 2016

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a b s t r a c tThis paper proposes a novel non-linear filter applied to wireless-transmitted reference signals in a deadbeat control strategy of a doubly-fed induction wind turbines. These signals are likely to be corrupted by spikes intrinsically imposed by the wireless channel. This impulsive noise is traditionally mitigated using classical error-correction schemes, and the proposed filter is an alternative that is simpler and has lower computational cost. The proposed technique, hereby designated as FunctionallyWeighted Moving Average (FWMA) filter, is based on a non-conventional weighting of the signal samples, which is carried out by a rectangular function. The filter realization is as straight as any linear technique. The generator control scheme, which includes the filter, is embedded in a microprocessor locally placed at the generator site, where it acts on the reference signals at the receiving end of the channel. The performance of both the filter and the control system are verified by simulations that include the wind turbine dynamics and the communication channel. The proposed technique is compared with a morphological filter, previously suggested for the same purpose. The results endorse the FWMA filter efficacy to clean out impulsive interferences with minor delays.

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Seismic Random Noise Elimination by Adaptive Time-Frequency Peak Filtering

Cited by 39 publications

References 13 publications

An Approach to 1/f Noise Detection Based on Adaptive T-ATFPF Algorithm

An Approach to 1/f Noise Detection Based on Adaptive T-ATFPF Algorithm

A Patch Based Denoising Method Using Deep Convolutional Neural Network for Seismic Image

An impulsive noise filter applied in wireless control of wind turbines

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