Velocity analysis for multicomponent data is revised in order to improve the accuracy of the velocity estimate and to combine information from horizontal and vertical components into a single panel. Multicomponent OBC data are strongly contaminated by coherent noise such as torsional modes, mud rolls, multiples and ghosts. The reflections cannot be properly resolved by the standard semblance operator that is insensitive to coherent noises and is unable to distinguish between interfering events. Velocity estimation is improved by the use of a coherence measurement based on the decomposition into eigenstructures of the spatial covariance matrix as well as by the approximate a-priori knowledge of the wavelet amplitude spectrum. The multicomponent panel, in which the velocity analysis is performed, is obtained by adding in quadrature the horizontal and the vertical responses. One of the main advantages is that the velocity analysis carried out on a single gather allows to speed up the velocity picking that, otherwise, has to be repeated for orthogonal panels. Moreover, lithologic bounds on the Vp/Vs ratio can be checked because the trends of pure and converted waves are mapped together. Tests performed on synthetic and real data show that the multicomponent velocity analysis provides accurate velocity estimations even for data at the early steps of processing.

Multicomponent velocity analysis by means of covariance measures and complex matched filters

STUCCHI, EUSEBIO MARIA;MAZZOTTI, ALFREDO
2004-01-01

Abstract

Velocity analysis for multicomponent data is revised in order to improve the accuracy of the velocity estimate and to combine information from horizontal and vertical components into a single panel. Multicomponent OBC data are strongly contaminated by coherent noise such as torsional modes, mud rolls, multiples and ghosts. The reflections cannot be properly resolved by the standard semblance operator that is insensitive to coherent noises and is unable to distinguish between interfering events. Velocity estimation is improved by the use of a coherence measurement based on the decomposition into eigenstructures of the spatial covariance matrix as well as by the approximate a-priori knowledge of the wavelet amplitude spectrum. The multicomponent panel, in which the velocity analysis is performed, is obtained by adding in quadrature the horizontal and the vertical responses. One of the main advantages is that the velocity analysis carried out on a single gather allows to speed up the velocity picking that, otherwise, has to be repeated for orthogonal panels. Moreover, lithologic bounds on the Vp/Vs ratio can be checked because the trends of pure and converted waves are mapped together. Tests performed on synthetic and real data show that the multicomponent velocity analysis provides accurate velocity estimations even for data at the early steps of processing.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/890798
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