Aliasing in the tau-p transform and the removal of spatially aliased coherent noise

Geophysics ◽  
1990 ◽  
Vol 55 (11) ◽  
pp. 1496-1503 ◽  
Author(s):  
Greg Turner
Keyword(s):  
Time Series ◽  
Radon Transform ◽  
Seismic Data ◽  
Graphical Method ◽  
Temporal Frequency ◽  
Coherent Noise ◽  
P Values ◽  
One Dimensional ◽  
Noise Rejection ◽  
P Domain

The tau-p transform is a discretized Radon transform. The choice of discretization parameters is a very important part of performing the transform. Insufficient sampling in the tau direction leads to aliasing problems equivalent to those encountered in any one‐dimensional time series. A simple graphical method illustrates that too coarse sampling in the p direction results in reconstructions containing data duplicated incorrectly at different spatial positions. The spacing of these duplications is dependent on the temporal frequency of the data. Insufficient spatial sampling of the original seismic data causes events to plot at multiple p values in tau-p domain, again dependent on temporal frequency. Therefore, to velocity filter spatially aliased noise efficiently, multiple p values must be filtered. The use of appropriate filters in the p-f domain can substantially improve the noise rejection capabilities of velocity filters on spatially aliased noise while having little effect on desired reflection signals.

Wavelet-Radon domain dealiasing and interpolation of seismic data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. V41-V49 ◽  
Author(s):  
Zhou Yu ◽  
John Ferguson ◽  
George McMechan ◽  
Phil Anno
Keyword(s):  
Time Series ◽  
Radon Transform ◽  
Seismic Data ◽  
High Frequency ◽  
Transform Domain ◽  
Original Time Series ◽  
Spatial Aliasing ◽  
Multichannel Data ◽  
And Migration ◽  
Original Time

Spatial aliasing is unavoidable in some seismic data and has serious effects on the performance of multichannel data processing and migration. Antialias filtering produces distortion of the signal through the removal of high-frequency information. In contrast, dealiasing produces an unaliased estimate of the signal at all frequencies present in the original time series. A new dealiasing algorithm is developed by exploiting the properties of seismic wavefields in the wavelet-Radon transform domain, specifically the overlap of information between wavelet scales at the same frequency. The effectiveness of the wavelet-Radon dealiasing algorithm is demonstrated through the processing of both synthetic and field seismic data.

Use of the slant stack for geologic or geophysical map lineament analysis

Geophysics ◽  
1997 ◽  
Vol 62 (6) ◽  
pp. 1774-1778 ◽  
Author(s):  
Robert S. Pawlowski
Keyword(s):  
Plane Wave ◽  
Radon Transform ◽  
Seismic Data ◽  
Seismic Events ◽  
Seismic Data Processing ◽  
Coherent Noise ◽  
Plane Wave Decomposition ◽  
Lineament Analysis ◽  
The Individual ◽  
Wave Decomposition

The slant‐stack technique (also known as Radon transform, τ-p transform, and plane‐wave decomposition) used in seismic data processing for discriminating between and separating seismic events of differing dips (or moveout) is applied here to the problem of geologic or geophysical map lineament analysis. The latter problem is analogous to the seismic coherent noise problem in the sense that lineaments associated with one geologic event or episode are often underprinted by the lineaments of preceding geologic disturbances and overprinted by the lineaments of subsequent disturbances. Consequently, it can be difficult to distinguish between the individual lineament sets.

Slowness adaptive f-k filtering of prestack seismic data

Geophysics ◽  
1994 ◽  
Vol 59 (1) ◽  
pp. 140-147 ◽  
Author(s):  
Guy Duncan ◽  
Greg Beresford
Keyword(s):  
Seismic Data ◽  
Seismic Signal ◽  
Signal Distortion ◽  
Coherent Noise ◽  
One Dimensional ◽  
Minimal Distortion ◽  
Velocity Filtering ◽  
High Pass ◽  
Attenuation Characteristics ◽  
Space Variant

Frequency‐wavenumber velocity filtering is often applied to prestack seismic data for the attenuation of coherent noise. Although the process often gives excellent results, it can sometimes result in signal smoothing and distortion and poor attenuation of coherent noise. A slowness adaptive f-k filter reduces signal distortion and improves the attenuation characteristics of the filter. The technique uses a time‐ and space‐variant narrow reject‐band f-k filter. Optionally, coherent noise is compressed before application of the filter. The apparent slowness of coherent noise events is estimated using local t-x slant stacks weighted by coherence. A two‐dimensional (2-D) window is moved across the shot record, and at each point on the record slant stacks are taken through the central sample of the window. The slowness value that produces the maximum stack is assigned to the central sample of the window. In this way, an instantaneous slowness image of the shot record is produced. A one‐dimensional (1-D), high‐pass, finite‐duration impulse‐response (FIR) filter is applied in a spatially and temporally varying way across the record on the basis of the instantaneous slowness values. Before filter application, trace‐to‐trace static and amplitude effects are estimated and removed from the data. This results in compression of coherent noise and improved attenuation after filtering. The filtering process has been applied to low‐fold prestack dynamite data from the Surat Basin, Australia. The results indicate that the technique has good attenuation characteristics and produces minimal distortion of seismic signal. The process, however, is computationally expensive.

A Single Bit Matters: Coherent Noise of Seismic Data Loggers

2015 ◽  
Vol 86 (3) ◽  
pp. 901-907 ◽  
Author(s):  
R. Takagi ◽  
K. Nishida ◽  
Y. Aoki ◽  
T. Maeda ◽  
K. Masuda ◽  
...  
Keyword(s):  
Seismic Data ◽  
Coherent Noise ◽  
Data Loggers

scholarly journals Evaluating the performance of multivariate indicators of resilience loss

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Els Weinans ◽  
Rick Quax ◽  
Egbert H. van Nes ◽  
Ingrid A. van de Leemput
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Multivariate Time Series ◽  
Tipping Points ◽  
Physical And Mental Health ◽  
One Dimensional ◽  
Slowing Down ◽  
The Past ◽  
Technological Developments ◽  
Resilience Indicators

AbstractVarious complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These ‘tipping points’ are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system.

Coherent noise in marine seismic data

Geophysics ◽  
1983 ◽  
Vol 48 (7) ◽  
pp. 854-886 ◽  
Author(s):  
Ken Larner ◽  
Ron Chambers ◽  
Mai Yang ◽  
Walt Lynn ◽  
Willon Wai
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Seismic Source ◽  
Coherent Noise ◽  
Critical Data ◽  
Predictive Deconvolution ◽  
Scattered Energy ◽  
Marine Seismic ◽  
The Many ◽  
Water Bottom

Despite significant advances in marine streamer design, seismic data are often plagued by coherent noise having approximately linear moveout across stacked sections. With an understanding of the characteristics that distinguish such noise from signal, we can decide which noise‐suppression techniques to use and at what stages to apply them in acquisition and processing. Three general mechanisms that might produce such noise patterns on stacked sections are examined: direct and trapped waves that propagate outward from the seismic source, cable motion caused by the tugging action of the boat and tail buoy, and scattered energy from irregularities in the water bottom and sub‐bottom. Depending upon the mechanism, entirely different noise patterns can be observed on shot profiles and common‐midpoint (CMP) gathers; these patterns can be diagnostic of the dominant mechanism in a given set of data. Field data from Canada and Alaska suggest that the dominant noise is from waves scattered within the shallow sub‐buttom. This type of noise, while not obvious on the shot records, is actually enhanced by CMP stacking. Moreover, this noise is not confined to marine data; it can be as strong as surface wave noise on stacked land seismic data as well. Of the many processing tools available, moveout filtering is best for suppressing the noise while preserving signal. Since the scattered noise does not exhibit a linear moveout pattern on CMP‐sorted gathers, moveout filtering must be applied either to traces within shot records and common‐receiver gathers or to stacked traces. Our data example demonstrates that although it is more costly, moveout filtering of the unstacked data is particularly effective because it conditions the data for the critical data‐dependent processing steps of predictive deconvolution and velocity analysis.

A method of ground‐roll elimination

Geophysics ◽  
1988 ◽  
Vol 53 (7) ◽  
pp. 894-902 ◽  
Author(s):  
Ruhi Saatçilar ◽  
Nezihi Canitez
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Wave Motion ◽  
Frequency Interval ◽  
Vertical Resolution ◽  
Matched Filters ◽  
One Dimensional ◽  
Frequency Bands ◽  
Ground Roll ◽  
Seismic Reflections

Amplitude‐ and frequency‐modulated wave motion constitute the ground‐roll noise in seismic reflection prospecting. Hence, it is possible to eliminate ground roll by applying one‐dimensional, linear frequency‐modulated matched filters. These filters effectively attenuate the ground‐roll energy without damaging the signal wavelet inside or outside the ground roll’s frequency interval. When the frequency bands of seismic reflections and ground roll overlap, the new filters eliminate the ground roll more effectively than conventional frequency and multichannel filters without affecting the vertical resolution of the seismic data.

Local Geometric Projection-Based Noise Reduction for Vibration Signal Analysis in Rolling Bearings

2008 ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao ◽  
Kang B. Lee ◽  
Steven E. Fick
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Noise Reduction ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Multifractal Spectrum ◽  
Vibration Signal ◽  
Rolling Bearings ◽  
One Dimensional ◽  
Vibration Signal Analysis

This paper presents a noise reduction technique for vibration signal analysis in rolling bearings, based on local geometric projection (LGP). LGP is a non-linear filtering technique that reconstructs one dimensional time series in a high-dimensional phase space using time-delayed coordinates, based on the Takens embedding theorem. From the neighborhood of each point in the phase space, where a neighbor is defined as a local subspace of the whole phase space, the best subspace to which the point will be orthogonally projected is identified. Since the signal subspace is formed by the most significant eigen-directions of the neighborhood, while the less significant ones define the noise subspace, the noise can be reduced by converting the points onto the subspace spanned by those significant eigen-directions back to a new, one-dimensional time series. Improvement on signal-to-noise ratio enabled by LGP is first evaluated using a chaotic system and an analytically formulated synthetic signal. Then analysis of bearing vibration signals is carried out as a case study. The LGP-based technique is shown to be effective in reducing noise and enhancing extraction of weak, defect-related features, as manifested by the multifractal spectrum from the signal.

Radon Transform Removes Correlation Noise Produced by Vibroseis

2014 ◽  
Vol 672-674 ◽  
pp. 1964-1967
Author(s):  
Jun Qiu Wang ◽  
Jun Lin ◽  
Xiang Bo Gong
Keyword(s):  
Radon Transform ◽  
Seismic Data ◽  
Cross Correlation ◽  
Random Noise ◽  
Seismic Record ◽  
Electromagnetic Drive ◽  
Seismic Records ◽  
Correlation Noise

Vibroseis obtained the seismic record by cross-correlation detection calculation. compared with dynamite source, cross-correlation detection can suppress random noise, but produce more correlation noise. This paper studies Radon transform to remove correlation noise produced by electromagnetic drive vibroseis and impact rammer. From the results of processing field seismic records, we can see that Radon transform can remove correlation noise by vibroseis, the SNR of vibroseis seismic data is effectively improved.

scholarly journals ATENUASI WATER-BOTTOM MULTIPLE DENGAN METODE TRANSFORMASI PARABOLIC RADON

2016 ◽  
Vol 12 (3) ◽  
pp. 145
Author(s):  
Subarsyah Subarsyah ◽  
Tumpal Benhard Nainggolan
Keyword(s):  
Radon Transform ◽  
Seismic Data ◽  
Wide Distribution ◽  
Seismic Section ◽  
Optimal Separation ◽  
Multiple Attenuation ◽  
Parabolic Radon Transform ◽  
Water Bottom

Interferensi water-bottom multipel terhadap reflektor primer menimbulkan efek bersifat destruktif yang menyebabkan penampang seismik menjadi tidak tepat akibat kehadiran reflektor semu. Teknik demultiple perlu diaplikasikan untuk mengatenuasi multipel. Transformasi parabolic radon merupakan teknik atenuasi multipel dengan metode pemisahan dalam domain radon. Multipel sering teridentifikasi pada penampang seismik. Untuk memperbaiki penampang seismik akan dilakukan dengan metode transformasi parabolic radon. Penerapan metode ini mengakibatkan reflektor multipel melemah dan tereduksi setelah dilakukan muting dalam domain radon terhadap zona multipel. Beberapa reflektor primer juga ikut melemah akibat pemisahan dalam domain radon yang kurang optimal, pemisahan akan optimal membutuhkan distribusi offset yang lebar. Kata kunci: Parabolic radon, multipel, atenuasi Water-bottom mutiple interference often destructively interfere with primary reflection that led to incorrect seismic section due to presence apparent reflector. Demultiple techniques need to be applied to attenuate the multiple. Parabolic Radon transform is demultiple attenuation technique that separate multiple and primary in radon domain. Water-bottom mutiple ussualy appear and easly identified on seismic data, parabolic radon transform applied to improve the seismic section. Application of this method to data showing multiple reflectors weakened and reduced after muting multiple zones in the radon domain. Some of the primary reflector also weakened due to bad separation in radon domain, optimal separation will require a wide distribution of offsets. Keywords: Parabolic radon, multiple, attenuation

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