Near-surface attenuation estimation using wave-propagation modeling

Yadari, N. El; Ernst, Fabian; Mulder, W.A.

doi:10.1190/1.2976548

Cited by 25 publications

(6 citation statements)

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“…Power-law was used to simulate the WIGED theoretical decay feature [14] . The Power-law can be expressed as follows:…”

Section: Power Law Modelmentioning

confidence: 99%

Coarse-scale characterization of seismic wave attenuation based on viscoelastic model

Zheng,

Shi,

Jin

2024

Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023)

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In rock physical modeling, WIGED mechanism is selected to guide and calibrate pore fluid modeling in reservoirs with low porosity and low permeability, based on the occurrence characteristics of micro-nano pore fluid in rocks in the target area. The viscoelastic model with fewer parameters was selected to represent the petrophysical model with more parameters, which can avoid the disadvantages of too many parameters and unavailable of the petrophysical model, so as to achieve the purpose of scale coarsing of the model. Model test and field data application demonstrate the feasibility and accuracy of this strategy.

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“…Power-law was used to simulate the WIGED theoretical decay feature [14] . The Power-law can be expressed as follows:…”

Section: Power Law Modelmentioning

confidence: 99%

Coarse-scale characterization of seismic wave attenuation based on viscoelastic model

Zheng,

Shi,

Jin

2024

Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023)

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“…), the first‐break method (Brzostowski and McMechan ; Jeng, Tsai and Chen ; Du et al . ; EI Yadari, Ernst and Mulder 2007, 2008; Albuquerque, Gaia and Cavalcanti ; Cavalca and Moore ; Liu et al . ; Zhang et al .…”

Section: Introductionmentioning

confidence: 99%

Estimation of near‐surface Q factor under constraint of layered velocity based on uphole survey data

Zhao

Meihou

et al. 2020

Near Surface Geophysics

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To improve the resolution of seismic data, it is important to accurately estimate the near‐surface quality factor, Q, which provides a measure of seismic wave attenuation. In view of the unique advantages provided by uphole surveys when investigating near‐surface structures, they are widely employed to estimate the near‐surface Q factor. However, the Q factor estimated using the traditional spectral ratio method is not always precise and provides larger oscillations in the estimated Q factors due to errors associated with first‐break picking and velocity estimation. Following on the traditional logarithmic spectral ratio method, a new method called the logarithmic spectral ratio integral method was proposed to estimate the layer Q factor using uphole survey data. It calculates first the weighted integral of the logarithmic spectral ratio in an effective frequency interval between non‐adjacent traces, then makes a linear regression between the inter‐trace travel moveout and the weighted integral of logarithmic spectral ratio under the constraint of velocity stratification. The result of model analysis shows that under an ideal condition (without first‐break picking errors), the layer Q values estimated by the logarithmic spectral ratio integral method are fairly consistent with the true layer‐specific Q values in the model. In addition, the Q values estimated from field‐measured data and data from forward modelling with 10% random noise added, both have smaller mean relative errors than the results using traditional spectral ratio method and the double‐linear regression method. A case study is employed and the results show that the layer Q factor estimated using the new method correlates well with the velocity stratification and is thus applicable for use with various uphole survey observation systems. Furthermore, all results indicate that the logarithmic spectral ratio integral method delivers a more precise and stable estimation of the layered Q than the other methods, and the anti‐noise characteristics are stronger.

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“…Zhang and Ulrych (2002) derived an analytical relation between Q factor and seismic data peak frequency variation both along offset and vertical time direction, and estimated Q factor from CMP gathers using a layer-stripping approach. Yadari et al (2008) inverted weathering layer absorption by using seismic wave propagation modelling. Reine et al (2012) developed a robust method for measuring attenuation from prestack surface seismic gathers by making use of a variable-window time-frequency analysis.…”

Section: Introductionmentioning

confidence: 99%