Random noise reduction by 3‐D spatial prediction filtering

Chase, Michael

doi:10.1190/1.1821933

Cited by 18 publications

(5 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has proven fruitful to extend noise suppression methods to two or more spatial dimensions, in part because it allows more data to be accessed from a more localized area (Chase, 1992;Ozdemir, et al, 1999;Soubaras, 2000). In general this allows harsher noise reduction and better signal preservation.…”

Section: Methodsmentioning

confidence: 99%

F‐xy Cadzow noise suppression

Trickett¹

2008

SEG Technical Program Expanded Abstracts 2008

151

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

F‐xy Cadzow noise suppression

Trickett¹

2008

SEG Technical Program Expanded Abstracts 2008

151

View full text Add to dashboard Cite

show abstract

“…In the case of non‐linear events in

t - x

, the assumption of dipping linear events can be approximated by applying proper windowing strategies to the data. This same technique can also be implemented in three dimensions using two‐dimensional convolutions under the assumption of planar events in time (Chase ). Furthermore, one can attenuate random noise in the case of multicomponent measurements via vector‐autoregressive (VAR) models (Naghizadeh and Sacchi ; Kamil et al .…”

Section: Theorymentioning

confidence: 99%

Widely linear denoising of multicomponent seismic data

Bahia

Sacchi

2019

Geophysical Prospecting

View full text Add to dashboard Cite

Seismic data processing is a challenging task, especially when dealing with vector‐valued datasets. These data are characterized by correlated components, where different levels of uncorrelated random noise corrupt each one of the components. Mitigating such noise while preserving the signal of interest is a primary goal in the seismic‐processing workflow. The frequency‐space deconvolution is a well‐known linear prediction technique, which is commonly used for random noise suppression. This paper represents vector‐field seismic data through quaternion arrays and shows how to mitigate random noise by proposing the extension of the frequency‐space deconvolution to its hypercomplex version, the quaternion frequency‐space deconvolution. It also shows how a widely linear prediction model exploits the correlation between data components of improper signals. The widely linear scheme, named widely‐linear quaternion frequency‐space deconvolution, produces longer prediction filters, which have enhanced signal preservation capabilities shown through synthetic and field vector‐valued data examples.

show abstract

“…The extension of this method to incorporate multiple spatial dimensions is similar to the extension of f − x deconvolution to f − x − y deconvolution where rather than incorporating only one spatial dimension to predict the reflectivity series, two spatial dimensions are considered (Chase 1992; Gulunay et al 1993; Gulunay 2000). Thus, a two‐dimensional prediction filter P l , m is required rather than a one‐dimensional prediction filter P l to allow for spatial prediction among multiple azimuths.…”

Section: Spectral Decomposition With F−x−y Preconditioningmentioning

confidence: 99%

“…By representing the seismic trace as a multi‐wavelet convolutional process, its time‐frequency representation can be considered to be a set of wavelet dependent reflectivities (Bonar and Sacchi 2010). Through considering the time‐frequency representation as a set of reflectivities, concepts such as f − x or f − x − y deconvolution (Canales 1984; Chase 1992; Gulunay et al 1993; Wang 1999; Gulunay 2000) can be incorporated into the spectral decomposition algorithm via preconditioning of the inversion problem similar to the structure‐and‐amplitude‐preserving multichannel deconvolution algorithm proposed in Wang and Sacchi (2009). The incorporation of f − x or f − x − y deconvolution into the spectral decomposition algorithm allows for the structure of neighbouring seismic traces to influence the time‐frequency representation of a seismic trace during its generation through spatial prediction filtering, helping to eliminate the effects of noise.…”

Section: Introductionmentioning

confidence: 99%

Spectral decomposition with f−x−y preconditioning

Bonar

Sacchi

2013

Geophysical Prospecting

View full text Add to dashboard Cite

A B S T R A C TSpectral decomposition, or local time-frequency analysis, tries to enhance the amount of information one can obtain from a seismic volume by finding the frequency content of the seismic data at each time sample. However, if a small amount of noise is present within the seismic amplitude volume, it has the potential to become more prominent in the spectrally decomposed data especially if high-resolution or sparsity promoting methods are utilized. To combat this problem post-processing noise removal has commonly been employed, but these techniques can potentially degrade the resolution of small-scale geological structures in their attempt to remove this noise. Rather than de-noising the spectrally decomposed data after they are generated, we propose to incorporate the ideas of f −x−y deconvolution within the spectral decomposition process to create an algorithm that has the ability to de-noise the timefrequency representation of the data as they are being generated. By incorporating the spatial prediction error filters that are utilized for f −x−y deconvolution with the spectral decomposition problem, a spatially smooth time-frequency representation that maintains its sparsity, or high-resolution characteristics, can be obtained. This spatially smooth high-resolution time-frequency representation is less likely to exhibit the random noise that was present in the more conventionally obtained timefrequency representation. Tests on a real data set demonstrate that by de-noising while the time-frequency representation is being constructed, small-scale geological structures are more likely to maintain their resolution since the de-noised time-frequency representation is specifically built to reconstruct the data.

show abstract

Random noise reduction by 3‐D spatial prediction filtering

Cited by 18 publications

References 1 publication

F‐xy Cadzow noise suppression

F‐xy Cadzow noise suppression

Widely linear denoising of multicomponent seismic data

Spectral decomposition with f−x−y preconditioning

Contact Info

Product

Resources

About