Recovery of Seismic Events by Time-Frequency Peak Filtering

Lin, Hongbo; Li, Yue; Yang, Banghua

doi:10.1109/icip.2007.4379860

Cited by 33 publications

(14 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The WL of the PWVD must be chosen such that the signal IF is as close to linear as possible while not sacrificing frequency resolution. Equation (8) gives the empirical maximum WL as a function of the dominant frequency f d and the sampling frequency f s in case of the worst bias of 5% [9], [12] 1…”

Section: Tfpf Algorithmmentioning

confidence: 99%

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

Xiong

2014

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

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).

show abstract

Section: Tfpf Algorithmmentioning

confidence: 99%

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

Xiong

2014

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…However, most of the practical signals are nonlinear and nonstationary, such as seismic signals; thus, we use a windowed version of WVD, called pseudo-WVD (PWVD), to satisfy the local linearity of signals. The formula of PWVD is as follows [5]:…”

Section: Principles Of Conventional Tfpfmentioning

confidence: 99%

“…Time-frequency peak filtering (TFPF) is an effective way to remove the random noise from the seismic record with low SNR [5]- [8]. However, there are two shortcomings in con- Manuscript ventional TFPF: 1) It uses a fixed window length (WL).…”

Section: Introductionmentioning

confidence: 99%

Varying-Window-Length TFPF in High-Resolution Radon Domain for Seismic Random Noise Attenuation

Zhuang

Liu

et al. 2015

IEEE Geosci. Remote Sensing Lett.

Self Cite

View full text Add to dashboard Cite

The time-frequency peak filtering (TFPF) algorithm is an effective method for seismic random noise attenuation. The conventional TFPF filters seismic data only along the channel direction, ignoring the spatial characteristics of the reflection events, which results in the loss of directional information. In order to improve the filtering performance of TFPF, we adopt a Radon transform to implement spatiotemporal 2-D TFPF, which is doing TFPF in the Radon domain. Since this method takes the spatial correlation of the reflection events into account, it could extract the reflection events better than the conventional TFPF. As the conventional Radon transform may produce a smearing phenomenon, we apply an improved least squares high-resolution Radon transform to assist TFPF in this letter. Thus, the events can be highly focused to energy points in different positions of the Radon domain. Then we identify the signal parts and process them by TFPF with short window length (WL). The other parts are considered as noise, and we process them by TFPF with long WL. By virtue of this new method, we can preserve the valid signal better and suppress the random noise more effectively. Through experiments on the synthetic seismic records and the field seismic data, the new method possesses a superior performance in random noise attenuation and seismic event preservation compared with the conventional TFPF and Radon TFPF methods.Index Terms-High-resolution Radon transform, reflection events preservation, seismic random noise attenuation, timefrequency peak filtering (TFPF), varying-window-length (VWL). 1545-598X

show abstract

“…It can be shown that the empirical maximum window length (WL) as a function of the dominant frequency

f_{d}

and the sampling frequency

f_{s}

in the case of the worst bias of 5% (Lin et al . ) is

1 \leq W L \leq \frac{0.384 f_{s}}{f_{d}} .

…”

Section: Theorymentioning

confidence: 99%

Seismic directional random noise suppression by radial‐trace time–frequency peak filtering using the Hurst exponent statistic

Zhang

Lin

et al. 2015

Geophysical Prospecting

Self Cite

View full text Add to dashboard Cite

A B S T R A C TRadial-trace time-frequency peak filtering filters a seismic record along the radialtrace direction rather than the conventional channel direction. It takes the spatial correlation of the reflected events between adjacent channels into account. Thus, radial-trace time-frequency peak filtering performs well in denoising and enhancing the continuity of reflected events. However, in the seismic record there is often random noise whose energy is concentrated in certain directions; the noise in these directions is correlative. We refer to this kind of random noise (that is distributed randomly in time but correlative in the space) as directional random noise. Under radial-trace time-frequency peak filtering, the directional random noise will be treated as signal and enhanced when this noise has same direction as the signal. Therefore, we need to identify the directional random noise before the filtering. In this paper, we test the linearity of signal and directional random noise in time using the Hurst exponent. The time series of signals with high linearity lead to large Hurst exponent value; however, directional random noise is a random series in time without a fixed waveform and thus its linearity is low; therefore, we can differentiate the signal and directional random noise by the Hurst exponent values. The directional random noise can then be suppressed by using a long filtering window length during the radial-trace timefrequency peak filtering. Synthetic and real data examples show that the proposed method can remove most directional random noise and can effectively recover the reflected events.

show abstract

Recovery of Seismic Events by Time-Frequency Peak Filtering

Cited by 33 publications

References 5 publications

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

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

Varying-Window-Length TFPF in High-Resolution Radon Domain for Seismic Random Noise Attenuation

Seismic directional random noise suppression by radial‐trace time–frequency peak filtering using the Hurst exponent statistic

Contact Info

Product

Resources

About