2009ProceedingsRequires access

Regularisation for Wide Azimuth Datasets

G. Poole, Daniel O. Trad, Richard Wombell, G. Williams

Open publisher page 1 citations

Abstract

Wide-azimuth datasets allow us to incorporate more dimensions into data regularisation. Using a multi-dimensional Fourier transform that handles irregular data we can either output traces on a regular grid or interpolate additional source and receiver lines. This allows us to fill holes more effectively and regularise in the offset and azimuth directions whilst preserving AVO and AVAz. Data examples show an improvement in continuity and accurate reconstruction of missing data.

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What this paper is about

Wide-azimuth datasets allow us to incorporate more dimensions into data regularisation. Using a multi-dimensional Fourier transform that handles irregular data we can either output traces on a regular grid or interpolate additional source and receiver lines. This allows us to fill holes more effectively and regularise in the offset and azimuth directions whilst preserving AVO and AVAz. Data examples show an improvement in continuity and accurate reconstruction of missing data.

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Available abstract

Wide-azimuth datasets allow us to incorporate more dimensions into data regularisation. Using a multi-dimensional Fourier transform that handles irregular data we can either output traces on a regular grid or interpolate additional source and receiver lines. This allows us to fill holes more effectively and regularise in the offset and azimuth directions whilst preserving AVO and AVAz. Data examples show an improvement in continuity and accurate reconstruction of missing data.

Key concepts: Azimuth, Offset (computer science), Computer science, Fourier transform, Grid, Algorithm, Missing data, Data mining

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