Velocity Analysis of Multicomponent Seismic Data of a Model with Thin Salt Layer

Author(s):  
N.R.C.F. Zuniga
2019 ◽  
Vol 37 (4) ◽  
pp. 397
Author(s):  
Nelson Ricardo Coelho Flores Zuniga ◽  
Eder Cassola Molina ◽  
Renato Luiz Prado

AbstractThe processing of multicomponent seismic data is already a challenge concerning the velocity analysis. When it is performed for offshore survey, the difficulty increases a lot more with the use of OBN (Ocean Bottom Nodes) technology. The ray tracing asymmetry generated by the wave conversion and the difference of datum between source and receptor are not the only factors which contribute for a strongly nonhyperbolic travel-time event. The layered subsurface models and the large offsets employed in the offshore surveys make the nonhyperbolicity even stronger. Aiming to solve this problem, eight approximations to perform the velocity analysis were tested for two models. The complexity analysis of each nonhyperbolic multiparametric approximation was also studied to understand their behaviors during the optimization process. The relative error between the observed curve and the calculated curve with each approximation was computed for PP and PS reflection events of two models. With these informations, it was possible to determine which approximation is the most reliable one for this kind of models.Keywords: multicomponent, OBN, nonhyperbolic, multiparametric. ResumoO processamento de dados sísmicos multicomponentes já é um desafio com relação à análise de velocidades. Quando realizado para levantamentos marítimos, a dificuldade aumenta muito mais com o uso da tecnologia OBN (Ocean Bottom Nodes). A assimetria no traçado de raios gerada pela conversão de onda e pela diferença de profundidade entre fonte e receptor não são os únicos fatores que contribuem para um evento de tempos de trânsito fortemente não-hiperbólico. Os modelos estratificados de subsuperfície e os grandes afastamentos aplicados nos levantamentos marítimos tornam a não-hiperbolicidade ainda mais forte. Visando resolver este problema, oito aproximações para realizar a análise de velocidades foram testadas para dois modelos. A análise de complexidade de cada aproximação não-hiperbólica multiparamétrica também foi estudada para entender seus comportamentos durante o processo de otimização. Os erros relativos entre as curvas observadas e calculadas com cada aproximação foram calculados para os eventos de reflexão PP e PS dos dois modelos. Com estas informações, foi possível determinar qual aproximação é a mais confiável para estes tipos de modelos.Palavras-chave: multicomponente, OBN; não-hiperbólico, multiparamétrico.


2007 ◽  
Author(s):  
Zhongping Qian ◽  
Xiang‐Yang Li ◽  
Mark Chapman ◽  
Yonggang Zhang ◽  
Yanguang Wang

2021 ◽  
Vol 14 (13) ◽  
Author(s):  
Jianguang Han ◽  
Zhiwei Liu ◽  
Yun Wang ◽  
Jiayong Yan ◽  
Bingluo Gu

1996 ◽  
Author(s):  
Dennis Corrigan ◽  
Robert Withers ◽  
Jim Darnall ◽  
Tracey Skopinski

2021 ◽  
Author(s):  
A.G. Yaroslavtsev ◽  
M.V. Tarantin ◽  
T.V. Baibakova

2021 ◽  
Vol 11 (1) ◽  
pp. 78
Author(s):  
Jianbo He ◽  
Zhenyu Wang ◽  
Mingdong Zhang

When the signal to noise ratio of seismic data is very low, velocity spectrum focusing will be poor., the velocity model obtained by conventional velocity analysis methods is not accurate enough, which results in inaccurate migration. For the low signal noise ratio (SNR) data, this paper proposes to use partial Common Reflection Surface (CRS) stack to build CRS gathers, making full use of all of the reflection information of the first Fresnel zone, and improves the signal to noise ratio of pre-stack gathers by increasing the number of folds. In consideration of the CRS parameters of the zero-offset rays emitted angle and normal wave front curvature radius are searched on zero offset profile, we use ellipse evolving stacking to improve the zero offset section quality, in order to improve the reliability of CRS parameters. After CRS gathers are obtained, we use principal component analysis (PCA) approach to do velocity analysis, which improves the noise immunity of velocity analysis. Models and actual data results demonstrate the effectiveness of this method.


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