GROUND ROLL ATTENUATION USING ADAPTIVE SINGULAR VALUE DECOMPOSITION FILTERING IN THE F-K DOMAIN

2016 ◽  
Vol 33 (3) ◽  
Author(s):  
Danilo S. Cruz ◽  
Milton J. Porsani

ABSTRACT. The land seismic data often have low signal-to-noise ratio due, among other factors, the presence of ground roll. It is a coherent noise present in seismograms that appears as linear events... RESUMO. Os dados sísmicos terrestres geralmente apresentam baixa razão sinal-ruído devido, entre outros fatores, à presença do ground roll . Trata-se de um ruído dominado por altas amplitudes...

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256700
Author(s):  
Olivia W. Stanley ◽  
Ravi S. Menon ◽  
L. Martyn Klassen

Magnetic resonance imaging radio frequency arrays are composed of multiple receive coils that have their signals combined to form an image. Combination requires an estimate of the radio frequency coil sensitivities to align signal phases and prevent destructive interference. At lower fields this can be accomplished using a uniform physical reference coil. However, at higher fields, uniform volume coils are lacking and, when available, suffer from regions of low receive sensitivity that result in poor sensitivity estimation and combination. Several approaches exist that do not require a physical reference coil but require manual intervention, specific prescans, or must be completed post-acquisition. This makes these methods impractical for large multi-volume datasets such as those collected for novel types of functional MRI or quantitative susceptibility mapping, where magnitude and phase are important. This pilot study proposes a fitted SVD method which utilizes existing combination methods to create a phase sensitive combination method targeted at large multi-volume datasets. This method uses any multi-image prescan to calculate the relative receive sensitivities using voxel-wise singular value decomposition. These relative sensitivities are fitted to the solid harmonics using an iterative least squares fitting algorithm. Fits of the relative sensitivities are used to align the phases of the receive coils and improve combination in subsequent acquisitions during the imaging session. This method is compared against existing approaches in the human brain at 7 Tesla by examining the combined data for the presence of singularities and changes in phase signal-to-noise ratio. Two additional applications of the method are also explored, using the fitted SVD method in an asymmetrical coil and in a case with subject motion. The fitted SVD method produces singularity-free images and recovers between 95–100% of the phase signal-to-noise ratio depending on the prescan data resolution. Using solid harmonic fitting to interpolate singular value decomposition derived receive sensitivities from existing prescans allows the fitted SVD method to be used on all acquisitions within a session without increasing exam duration. Our fitted SVD method is able to combine imaging datasets accurately without supervision during online reconstruction.


2015 ◽  
Vol 33 (3) ◽  
pp. 421
Author(s):  
Danilo S. Cruz ◽  
Milton J. Porsani

ABSTRACT. The land seismic data often have low signal-to-noise ratio due, among other factors, the presence of ground roll. It is a coherent noise present in seismograms that appears as linear events, with low frequencies and high amplitudes, low velocities and, in most cases, overlapping the reflections and harm both the processing and the interpretation of the data. In this work we present a filtering approach to attenuate the ground roll, which is based on the Singular Value Decomposition (SVD) method applied in the frequency (f-k) domain. Before filtering the data we applied the standard pre-processing procedure to the original seismograms: the static corrections, the spherical divergence and a gain to improving the quality of the seismic records. After application of the 2D Fourier transform, the SVD is applied to a small frequency range using a sliding window approach. The f-k filtered spectrum is obtained with the difference between the original spectrum and the predicted ones and the family of filtered traces is obtained by performing a 2D inverse Fourier transform. The method was applied on a land seismic line of Tacutu Basin locatedin northeastern part of Brazil. The results show that the method is effective for mitigating the ground roll and provides better results when compared to conventionalfiltering method f-k.Keywords: ground roll attenuation, seismic processing, SVD filtering, f-k filtering.RESUMO. Os dados sísmicos terrestres geralmente apresentam baixa razão sinal-ruído devido, entre outros fatores, à presença do ground roll . Trata-se de um ruído dominado por altas amplitudes, baixas frequências, baixas velocidades e de caráter dispersivo, representado no domínio x-t como eventos lineares. Este ruído se sobrepõe às reflexões e prejudica tanto o processamento quanto a interpretação dos dados. Para atenuação do ground roll , utilizamos uma técnica de filtragem adaptativa baseada no método Singular Value Decomposition (SVD) e aplicada no domínio f-k. Como parte do pré-processamento dos dados foram aplicadas a correção estática, a correção de divergência esférica além da aplicação de um ganho com intuito de melhorar a qualidade do registro sísmico. Em seguida, as famílias de ponto de tiro comum foram levadas para o domínio da frequência (f-k), através da transformada dupla de Fourier, e a filtragem SVD foi aplicada na forma de janelas deslizantes confinadas à faixa de frequências dominada pelo ground roll. O espectro f-k filtrado foi obtido tomando a diferença entre o espectro original e o espectro SVD predito com a primeira autoimagem. A família de traços filtrada no domínio x-t é obtida através da transformada inversa dupla de Fourier. O método foi aplicado sobre uma linha sísmica terrestre da Bacia do Tacutu, localizada na parte norte do Brasil. Os resultados mostram que o método proposto é eficaz para atenuar o ground roll e fornece resultados melhores quando comparados ao método convencional de filtragem f-k.Palavras-chave: atenuação do ground roll , processamento sísmico, filtragem SVD, filtragem f-k.


Geophysics ◽  
2021 ◽  
pp. 1-51
Author(s):  
Chao Wang ◽  
Yun Wang

Reduced-rank filtering is a common method for attenuating noise in seismic data. As conventional reduced-rank filtering distinguishes signals from noises only according to singular values, it performs poorly when the signal-to-noise ratio is very low, or when data contain high levels of isolate or coherent noise. Therefore, we developed a novel and robust reduced-rank filtering based on the singular value decomposition in the time-space domain. In this method, noise is recognized and attenuated according to the characteristics of both singular values and singular vectors. The left and right singular vectors corresponding to large singular values are selected firstly. Then, the right singular vectors are classified into different categories according to their curve characteristics, such as jump, pulse, and smooth. Each kind of right singular vector is related to a type of noise or seismic event, and is corrected by using a different filtering technology, such as mean filtering, edge-preserving smoothing or edge-preserving median filtering. The left singular vectors are also corrected by using the filtering methods based on frequency attributes like main-frequency and frequency bandwidth. To process seismic data containing a variety of events, local data are extracted along the local dip of event. The optimal local dip is identified according to the singular values and singular vectors of the data matrices that are extracted along different trial directions. This new filtering method has been applied to synthetic and field seismic data, and its performance is compared with that of several conventional filtering methods. The results indicate that the new method is more robust for data with a low signal-to-noise ratio, strong isolate noise, or coherent noise. The new method also overcomes the difficulties associated with selecting an optimal rank.


2016 ◽  
Vol 34 (3) ◽  
Author(s):  
Cristian D. Ariza A. ◽  
Milton J. Porsani

ABSTRACT. The ground-roll is a type of noise normally present in land seismic data. It strongly harms the signal-to-noise ratio, and interferes in several stages of the seismic data processing, strongly affecting the final quality of the obtained seismic images...Keywords: seismic noise, signal-to-noise ratio, adaptive filters, Burg algorithm, seismic signal decomposition.  RESUMO. O ground-roll é um tipo de ruído normalmente presente nos dados sísmicos terrestres. Ele prejudica muito a razão sinal-ruído e interfere em vários est´ágios do processamento de dados sísmicos, afetando fortemente a qualidade final das imagens sísmicas obtidas...Palavras-chave: ruídos sísmicos, relação sinal-ruído, filtragem adaptativo, algoritmo de Burg, decomposição do sinal sísmico.


2021 ◽  
pp. 356-362
Author(s):  
Rajesh Patil ◽  
Surendra Bhosale

Filtering noise to recreate a high-quality image in medical image processing is an important task. During acquisition, transmission, and retrieval from storage devices, generally images are getting corrupted. So, for further analysis images must get denoised. The noises can be categorised into different types based on their nature and origin. Researchers are still looking for the effective denoising technique. Wavelet Transform (WT) is an effective transform method for denoising. Similarly Singular Value Decomposition (SVD) is also an important tool for denoising. Combining WT with SVD results in further reduction of noise. This paper proposes use of WT along with SVD for medical image denoising. Performance of image denoising is evaluated on the basis of Signal to Noise Ratio (SNR) and Peak Signal-Noise Ratio (PSNR). In the proposed approach, experimental results of WT-SVD combination gives better SNR and PSNR values than WT and SVD, if used independently.


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