Seismic Attenuation Estimation Using the Centroid Frequency Shift and Divergence

Yan, Ke; Gao, Jinghuai; Zhang, Bing; Wang, Qian; Yang, Yang

doi:10.1109/lgrs.2019.2912190

Cited by 16 publications

(2 citation statements)

References 14 publications

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“…Due to the large variation in the upper segment of the record, when extracting the wavelet spectrums, we divide the seismic record above the target area into eight parts at different times (Yan et al., 2019), which are 840–1040, 850–1050, 860–1060, …, 910–1110 ms. The corresponding seismic spectrums can be obtained by the Fourier transform.…”

Section: Data Applicationmentioning

confidence: 99%

“…The Q factor of the LSR method is obtained by the change of the amplitude spectrum through the logarithmic ratio of a source spectrum to an attenuated spectrum. Nevertheless, the width of the frequency band affects the accuracy of the LSR method significantly (Yan et al., 2019). For the PFS method, Zhang and Ulrych (2002) estimate the Q factor only by the movement between a dominant frequency and a peak frequency (the frequency corresponding to the maximum value of the spectrum).…”

Section: Introductionmentioning

confidence: 99%

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Q estimation based on the centroid frequency and standard deviation of frequency‐weighted‐exponential function

Zhang

Meng

et al. 2022

Geophysical Prospecting

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The quality factor Q is a vital parameter for quantitatively describing the attenuation information of underground reservoirs, which is of great significance for hydrocarbon detection and reservoir characterization. A frequency-weighted-exponential (FWE) method utilizing the symmetry index and the characteristic frequency can obtain this parameter. Unfortunately, the constant symmetry index assumption of it reduces the accuracy of Q values under the non-standard FWE shape. Selecting an optimal symmetry index is also a problem for this method. Hence, the basic idea of a novel Q estimation method is to substitute the symmetry index with the standard deviation of the source and attenuated wavelet spectrums. The added standard deviation varies with the degree of attenuation under different wavelet shapes, which can reduce the effects of the constant assumption. Meanwhile, it is simple to calculate this parameter directly from the spectrums. In this way, the proposed method has wider applicability to various wavelets. The synthetic records show the better performance of the novel method in improving the accuracy of Q values and the resistance to random noise than the FWE method. Furthermore, the results of matching wavelets exhibit the applicability of the proposed method in real data, and the quadratic spectrum simulation method improves the stability of the spectrums. Finally, real data experiments indicate the effectiveness of the proposed method after the above two processing means.

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Section: Data Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%