Stable <i>Q</i> analysis on vertical seismic profiling data

Wang, Yanghua

doi:10.1190/geo2013-0273.1

Cited by 49 publications

(4 citation statements)

References 25 publications

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“…11(a). Figure 10(a) shows the input interval Q value, which was inverted using zero-offset vertical seismic profiling (VSP) data (Tonn 1989(Tonn , 1991Zhang et al 2014;Wang 2014) from the borehole. Figure 10(b) shows the input equivalent Q value that was calculated from the input interval Q value in Figure 10(a) and the corresponding optimized equivalent Q value for the stable-factor-method amplitude compensation with a 20-dB gain limit.…”

Section: Real Data Examplementioning

confidence: 99%

An efficient and self‐adaptive approach for Q value optimization

Zhang

Wang

2016

Geophysical Prospecting

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The time‐invariant gain‐limit‐constrained inverse Q‐filter can control the numerical instability of the inverse Q‐filter, but it often suppresses the high frequencies at later times and reduces the seismic resolution. To improve the seismic resolution and obtain high‐quality seismic data, we propose a self‐adaptive approach to optimize the Q value for the inverse Q‐filter amplitude compensation. The optimized Q value is self‐adaptive to the cutoff frequency of the effective frequency band for the seismic data, the gain limit of the inverse Q‐filter amplitude compensation, the inverse Q‐filter amplitude compensation function, and the medium quality factor. In the processing of the inverse Q‐filter amplitude compensation, the optimized Q value, corresponding gain limit, and amplitude compensation function are used simultaneously; then, the energy in the effective frequency band for the seismic data can be recovered, and the seismic resolution can be enhanced at all times. Furthermore, the small gain limit or time‐variant bandpass filter after the inverse Q‐filter amplitude compensation is considered to control the signal‐to‐noise ratio, and the time‐variant bandpass filter is based on the cutoff frequency of the effective frequency band for the seismic data. Synthetic and real data examples demonstrate that the self‐adaptive approach for Q value optimization is efficient, and the inverse Q‐filter amplitude compensation with the optimized Q value produces high‐resolution and low‐noise seismic data.

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Section: Real Data Examplementioning

confidence: 99%

An efficient and self‐adaptive approach for Q value optimization

Zhang

Wang

2016

Geophysical Prospecting

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“…The latter is a statistical quantity that can be evaluated practically from the discrete Fourier spectrum of the seismic data. In the published literature, some works implement the evaluation of the mean frequency using the power spectrum Lee, 1989, 1990;Barnes, 1993;Loughlin and Tacer, 1997;Loughlin and Davidson, 2001;Carter and Kendall, 2006;Wang, 2014), and other works use the amplitude spectrum (Quan and Harris, 1997;Hu et al, 2013). We derive expressions analytically for both statistical cases and prove that the central frequency is close to the mean frequency evaluated from the power spectrum rather than the amplitude spectrum.…”

Section: Introductionmentioning

confidence: 97%

Frequencies of the Ricker wavelet

Wang

2015

GEOPHYSICS

Self Cite

170

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The Ricker wavelet is theoretically a solution of the Stokes differential equation, which takes into account the effect of Newtonian viscosity, and is applicable to seismic waves propagated through viscoelastic homogeneous media. In this paper, we defined the time-domain breadth and the frequency-domain bandwidth of the Ricker wavelet and developed quantities analytically in terms of the Lambert W function. We determined that the central frequency, the geometric center of the frequency band, is close to the mean frequency statistically evaluated using the power spectrum, rather than the amplitude spectrum used in some of the published literature. We also proved that the standard deviation from the mean frequency is not, as suggested by the literature, the half-bandwidth of the frequency spectrum of the Ricker wavelet. Moreover, we established mathematically the relationships between the theoretical frequencies (the central frequency and the half-bandwidth) and the numerical measurements (the mean frequency and its standard deviation) and produced each of these frequency quantities analytically in terms of the peak frequency of the Ricker wavelet.

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“…Many methods have been proposed for Q estimation from seismic data, such as the spectral ratio method (McDonal et al, 1958;Hauge, 1981;Blias, 2012;Reine et al, 2012;Nakata et al, 2020), the matching method (White, 1980), the amplitude decay method (Tonn, 1991), the analytical signal method (Engelhard, 1996), the frequency shift method (Quan and Harris, 1997;Zhang and Ulrych, 2002;Gao and Yang, 2007;Hu et al, 2013;Matsushima et al, 2016;Li et al, 2020;Yang et al, 2020), the Q-tomography method (Brzostowski and McMechan, 1992;Dutta and Schuster, 2016) and the Q-analysis method (Wang, 2004;2014). Among the above-mentioned methods, the spectral ratio method and the frequency shift method are widely used in practice, which estimate Q values by comparing the frequency content of two individual waveforms at different depths or time levels.…”

Section: Introductionmentioning

confidence: 99%

Concurrent estimation of seismic reflectivity and Q by using an optimal dictionary learning method

Yan

Huang²,

Xu³

2023

Front. Earth Sci.

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The seismic reflectivity and quality factor Q play an important role in seismic processing and interpretation, such as improving the resolution of seismic data and enhancing the reservoir identification. Most methods estimate seismic reflectivity and Q separately. However, the error of Q model has a negative impact on the reflectivity estimation and the interference of reflectivity makes Q estimates less reliable. In this paper, we propose a new method for concurrent estimation of seismic reflectivity and Q by using optimal dictionary learning. This new method first constructs a complete dictionary based on the non-stationary convolution model, then computes the reflectivity series under different dictionary matrices with the corresponding referencing Q values, and finally selects the optimal dictionary matrix by comprehensively analyzing the residual and reflectivity sparsity so as to obtain seismic reflectivity and Q simultaneously. The results of synthetic and real data examples test confirm the effectiveness of the proposed method. The proposed method provides accurate estimation of seismic reflectivity and Q, improves the vertical resolution without losing weak events and offers more accurate information concerning stratigraphic features in great details.

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Stable Q analysis on vertical seismic profiling data

Cited by 49 publications

References 25 publications

An efficient and self‐adaptive approach for Q value optimization

An efficient and self‐adaptive approach for Q value optimization

Frequencies of the Ricker wavelet

Concurrent estimation of seismic reflectivity and Q by using an optimal dictionary learning method

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