The radial trace transform: An effective domain for coherent noise attenuation and wavefield separation

Henley, David C.

doi:10.1190/1.1820721

Cited by 28 publications

(16 citation statements)

References 3 publications

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“…The RTT techniques for the elimination of coherent seismic noise were introduced by Henley [14], [15], based on earlier work by Claerbout [16], [17], who introduced RTT for the first time. Zhu et al presented local radial-trace median filtering [18] which overcame the problem of data loss in the traditional RTT techniques.…”

Section: Rttmentioning

confidence: 99%

Random-Noise Attenuation for Seismic Data by Local Parallel Radial-Trace TFPF

Xiong

2014

IEEE Trans. Geosci. Remote Sensing

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Time-frequency peak filtering (TFPF) is a new and effective tool for random-noise attenuation in the time-frequency domain. The conventional TFPF processes each channel of the seismic record independently with a fixed window length (WL). However, different frequency signals have different optimal WLs. Obviously, a fixed WL cannot effectively attenuate random noise for all frequency components at the same time. Depending on the geometry of the reflection, we can assume that the moveout of the reflected event is locally linear. With this in mind and taking the spatial correlation of the reflection events between adjacent channels in different layers into account, we propose a novel approach which is to do the TFPF along the local direction of the reflection event instead of along the channel. This method is called the local parallel radial-trace TFPF. It not only has the advantage of TFPF in denoising but also reduces the sensitivity of WL, making the filtering more flexible and effective. Both the synthetic model and seismic data have proved its better performance in noise attenuation and effective component preservation.Index Terms-Local parallel radial-trace time-frequency peak filtering (TFPF), radial-trace transform (RTT), random noise, seismic record processing, window length (WL).

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Section: Rttmentioning

confidence: 99%

Random-Noise Attenuation for Seismic Data by Local Parallel Radial-Trace TFPF

Xiong

2014

IEEE Trans. Geosci. Remote Sensing

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“…Furthermore, different decompositions can be cascaded to improve their denoising abilities. Idealized ground roll is transformed to small temporal frequency by the RT transform and can be eliminated by applying the RT transform, followed by high- pass filtering and the inverse RT transform (Claerbout 1983;Henley 1999). Figure 9(a) shows that the high-pass filter fails in removing noise, a larger filter window can damage the seismic signal.…”

Section: A P P L I C a T I O N T O G R O U N D -R O L L A T T E N U Amentioning

confidence: 99%

Seismic data analysis using local time‐frequency decomposition

Yang

Fomel

2012

Geophysical Prospecting

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Many natural phenomena, including geologic events and geophysical data, are fundamentally nonstationary ‐ exhibiting statistical variation that changes in space and time. Time‐frequency characterization is useful for analysing such data, seismic traces in particular. We present a novel time‐frequency decomposition, which aims at depicting the nonstationary character of seismic data. The proposed decomposition uses a Fourier basis to match the target signal using regularized least‐squares inversion. The decomposition is invertible, which makes it suitable for analysing nonstationary data. The proposed method can provide more flexible time‐frequency representation than the classical S transform. Results of applying the method to both synthetic and field data examples demonstrate that the local time‐frequency decomposition can characterize nonstationary variation of seismic data and be used in practical applications, such as seismic ground‐roll noise attenuation and multicomponent data registration.

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“…Henley, 1999, used the radial transform for seismic processing, transforming the data, from the distance-time domain (XT) to the velocity-time or angle-time domain (RT), making linear events show up as vertical events with low frequency and conserving the structure and frequency of the sub-horizontal events. Because of this change in the frequency of the data, Henley, 2000, used the low cut filtering in the RT domain for coherent noise attenuation.…”

Section: Introductionmentioning

confidence: 99%