Anisotropic P-wave velocity analysis and seismic imaging in onshore Kutch sedimentary basin of India

Behera, Laxmidhar; Khare, Prashant; Sarkar, Dipankar

doi:10.1016/j.jappgeo.2011.06.004

Cited by 10 publications

(1 citation statement)

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“…It is important to note that a large range of η was used to test the spectrum over a broad spectrum space. In relation to a range of theoretical intrinsic and effective anellipticities, a range of -0.1 to 0.3 for intrinsic anellipticities and 0.0 to 0.5 for effective would be considered more acceptable however one should note that 0.5 is still considered huge and even a value of 0.3 is considered very large (Behera et al 2011). For practical purposes an intrinsic anellipticity range of -0.1 to 0.2 and an effective anellipticity range of 0.1 -0.3 may be considered.…”

Section: Nonhyperbolic Moveout Casementioning

confidence: 99%

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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Section: Nonhyperbolic Moveout Casementioning

confidence: 99%