The L-DVV method for the seismic signal extraction

Yu, Pengjun; Li, Yue; Cheng, Yuanlin; Lin, Hongbo

doi:10.1016/j.jappgeo.2015.03.016

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…The horizontal axis of the scatter diagram corresponds to the DVV plot of the surrogate time series and the vertical axis corresponds to that of the original time series. Quantification of the delay vector variance (QDV) is defined as follows (Yu et al 2015):…”

Section: Time-varying Wl Tfpf Algorithm Based On the L-dvv Methodsmentioning

confidence: 99%

“…The iAAFT of the surrogate has the same probability density distribution and roughly the same power spectrum as the original time series, while the noise (such as white Gaussian noise) has a roughly even power spectrum (Gautama et al 2004b). The proposed delay vector variance method based on the straight line sequence (L-DVV) is essentially an extension of the DVV method (Yu et al 2015). The straight line sequence is selected as the surrogate time series, meaning that this method is extremely sensitive to random noise and is able to accurately distinguish between the seismic signal and noise, providing a basis for time-varying WL TFPF processing.…”

Section: Introductionmentioning

confidence: 99%

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Removal of Random Noise in Seismic Data by Time-varying Window-length Time-frequency Peak Filtering

Lin

et al. 2016

Acta Geophys.

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Time-frequency peak filtering (TFPF) is an effective tool for the removal of random noise and can be used to process seismic data with a low signal-to-noise ratio. A crucial aspect of this algorithm is the choice of window length (WL) of the time-frequency distribution. Whereas a fixed WL cannot simultaneously preserve signal and attenuate noise, timevarying WLs can achieve this goal. We propose a new method, L-DVV (delay vector variance), which successfully processes non-stationary signals by using the surrogate to measure the non-linearity of a time series. This method is sensitive to random noise and can accurately recover seismic signal masked by noise. Since the linearity criterion also meets the unbiased estimation criterion of the TFPF algorithm, the L-DVV method can be used for time-varying WL TFPF processing. Analysis of synthetic and real seismic data shows that the time-varying WL TFPF algorithm is effective at removing noise and recovering seismic signal.

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Section: Time-varying Wl Tfpf Algorithm Based On the L-dvv Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Removal of Random Noise in Seismic Data by Time-varying Window-length Time-frequency Peak Filtering

Lin

et al. 2016

Acta Geophys.

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“…However, for intermediate frequency (around 40 Hz) seismic record discrimination, conventional TFPF would cause serious amplitude attenuation, even for the shortest window in PWVD (Wu et al, 2014). In other words, sometimes there is no guarantee of linearity (Yu et al, 2015) of the desired signal, even for extreme values of the parameter. The limitation of the conventional TFPF is that it views the signal only along the time direction where the rapid variation may cause serious distortion.…”

Section: Conventional Time-frequency Peak Filteringmentioning

confidence: 99%

Trace-transform-based time-frequency filtering for seismic signal enhancement in Northeast China

Tian

et al. 2016

Comptes Rendus. Géoscience

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“…In general, the effective signals in the microseismic records are often represented as the high-frequency reflection events with weak energy and short duration (Maxwell and Urbanic, 2001), (Shemeta and Anderson, 2010). Meanwhile, the recorded data is always contaminated by the intense background noise, bringing difficulty in extracting meaningful information (Yu et al, 2015), (Yu et al, 2016). Therefore, telling the desired signals from the unwanted noise has great significance in the microseismic data processing.…”

Section: Introductionmentioning

confidence: 99%

A three-dimensional microseismic downhole noise suppression based on polarization filtering method in Shearlet transform

Han

Wang

Yu³

2023

Front. Earth Sci.

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Microseismic noise suppression is widely used in the exploration of unconventional oil and gas resources. The effective microseismic downhole signals have extremely weak energy and are contaminated by strong interference, making data processing and interpretation difficult. The need for high-frequency effective signal reservation presents a basic problem in the design of noise suppression methods. The effective signals represent as the continuous reflection event and have more concentrated features in the transform domain, which can be used to tell the signal from the irregular microseismic noise. However, the high-frequency signal and extremely complex noise bring difficulty in accurately separating them by a single threshold. In this study, we propose a novel denoising method called Shearlet-polarization filtering to effectively suppress the microseismic noise. In general, Shearlet-polarization filtering is the combination of polarization filtering and conventional Shearlet transform. Specifically, the Shearlet transform can decompose the microseismic data into multi-directional and multi-scale information, providing a solid foundation for the separation of effective signals and background noise. From this basis, polarization filtering achieves signal reservation and noise attenuation by making full use of the three-dimensional information. To evaluate the performance, we also compare the proposed method with conventional Shearlet threshold filtering and polarization filtering. Experimental results both in synthetic and field data processing indicate that the Shearlet-polarization filtering is superior to the competing methods because it can significantly improve the continuity and smoothness of the microseismic events, even in low SNR conditions.

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The L-DVV method for the seismic signal extraction

Cited by 4 publications

References 12 publications

Removal of Random Noise in Seismic Data by Time-varying Window-length Time-frequency Peak Filtering

Removal of Random Noise in Seismic Data by Time-varying Window-length Time-frequency Peak Filtering

Trace-transform-based time-frequency filtering for seismic signal enhancement in Northeast China

A three-dimensional microseismic downhole noise suppression based on polarization filtering method in Shearlet transform

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