Common-reflection-surface method in weakly anisotropic vertical transverse isotropic media

Bloot, Rodrigo; Coimbra, Tiago A.; Faccipieri, Jorge H.; Tygel, Martin

doi:10.1190/geo2017-0368.1

Cited by 7 publications

(7 citation statements)

References 48 publications

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“…The nonhyperbolic traveltime approximation with anisotropy parameter as reported in Koshnavaz et al (2016a, b) can describe the reflection moveouts with a high accuracy for large offsets or vertical transverse isotropy (VTI) media. Several nonhyperbolic traveltime approximations have been proposed for the description of the behaviors of reflection curves in stratified isotropic and anisotropic media, and such traveltimes are based on the shifted hyperbola (Malovichko 1978;de Bazelaire 1988;Castle 1994;Siliqi and Bousquié Edited by Jie Hao 1 3 2000), rational moveout approximation (Alkhalifah and Tsvankin 1995;Ursin and Stovas 2006) and the commonreflection-surface (CRS) traveltime for weakly VTI media (Bloot et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Automatic estimation of traveltime parameters in VTI media using similarity-weighted clustering

Liu

Zhang

et al. 2020

Pet. Sci.

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Compared with hyperbolic velocity estimation methods, nonhyperbolic methods (such as shifted hyperbola) are better choices for large offsets or vertical transverse isotropy (VTI) media. Since local seismic event slope contains subsurface information, they can be used to estimate zero-offset two-way traveltime and normal moveout velocity. The traditional velocity estimation methods require a great deal of manual work and are also prone to human error. In order to estimate the traveltime parameters for VTI media automatically, in this paper, we propose to use predictive painting and similarity-weighted clustering to obtain traveltime parameters. The predictive painting is used to estimate zero-offset two-way traveltime, and the shifted-hyperbola traveltime equation is used to obtain velocity and anisotropy attributes. We first map local slopes to zero-offset two-way traveltime and moveout-parameters domain and then use similarity-weighted k-means clustering to find the maximum likelihood anisotropy parameters of the main subsurface structures. In order to demonstrate that, we apply the similarity-weighted clustering method to synthetic and field data examples and the results are of higher accuracy when compared to the ones obtained using multiparameter semblance-based method. From estimation error section, it can be seen that the estimation error of multiparameter semblance-based method is about 3-5 times that of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Automatic estimation of traveltime parameters in VTI media using similarity-weighted clustering

Liu

Zhang

et al. 2020

Pet. Sci.

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“…In many respects, the CRS-GMA approximation as presented in Equation 5.10 closely resembles the proposed anisotropic CRS approximation developed by Bloot et al (2018).…”

Section: Comparison Of Crs-gma With Weakly-anisotropic-crs Approximationmentioning

confidence: 68%

“…The major difference is that the quartic-component is dependent on both offset and midpoint displacement as opposed to offset alone as in the model by Bloot et al (2018).…”

Section: Comparison Of Crs-gma With Weakly-anisotropic-crs Approximationmentioning

confidence: 99%

“…A caveat of these approximations is that they do not account for large offset travel time deviations associated with anisotropy and heterogeneity like the non-hyperbolic CMP-based approximations. In recent times some work has been done to bridge the gap between these non-hyperbolic approximations and CRS-based moveout approximations (Bloot et al 2018, Vanelle et al 2018, Xu and Stovas 2015.…”

Section: Moveout Correctionmentioning

confidence: 99%

“…Another difference is that the parameter C is set to 1 in Bloot et al (2018). This was done solely for the purpose of dimension reduction.…”

Section: Comparison Of Crs-gma With Weakly-anisotropic-crs Approximationmentioning

confidence: 99%

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A general framework for anisotropic and wavefront moveout parameter analysis

Wilson¹

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Seismic data processing is essential in the development of seismic images. Seismic images are utilised for interpretation purposes in which insights into the underlying geology and structure are obtained. In the search for resource plays, imaging and interpretation are essential. To correctly image the subsurface, there are many important steps in a typical seismic data processing workflow. Of particular interest in this thesis are the moveout analysis, moveout correction and stacking processes. Moveout analysis involves the determination of moveout parameters that optimise the performance of the moveout correction and stacking processes. Moveout correction utilises the moveout parameters by using them in a model which transforms the spatial and temporal location of reflection impulses in the underlying seismic gathers so that they are optimally aligned in preparation for stacking. Stacking involves applying a weighted sum (typically the arithmetic mean) along the spatial axis of the underlying seismic gather. Limitations in these processes can lead to erroneous results which ultimately affect the quality of the seismic imaging and interpretation. Moveout analysis performance is determined by the presence of amplitude variation with offset (AVO) anomalies, the fold and quality of the data, the choice of moveout approximation and the number of moveout parameters, the method of moveout parameter determination, and the resolution provided by the semblance operator. Moveout correction performance is controlled by choice of moveout approximation and the method of stretch removal utilised. Stacking performance is dependent on fold, noise, quality of moveout correction and moveout stretch. Financial support No financial support was provided to fund this research.

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Optimization of Fermentation Medium of Camphor Lucidum Based on Artificial Neural Network and Genetic Algorithm

Zheng

Gong

2020

Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019)

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Common-reflection-surface method in weakly anisotropic vertical transverse isotropic media

Cited by 7 publications

References 48 publications

Automatic estimation of traveltime parameters in VTI media using similarity-weighted clustering

Automatic estimation of traveltime parameters in VTI media using similarity-weighted clustering

A general framework for anisotropic and wavefront moveout parameter analysis

Optimization of Fermentation Medium of Camphor Lucidum Based on Artificial Neural Network and Genetic Algorithm

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