--> Abstract: Field Data Performance of Curvelet-Based Noise Attenuation, by Ramesh Neelamani, Anatoly Baumstein, Mohamed Hadidi, and Warren Ross; #90077 (2008)
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Field Data Performance of Curvelet-Based Noise Attenuation

Ramesh Neelamani1*, Anatoly Baumstein1, Mohamed Hadidi2, and Warren Ross1
1ExxonMobil, USA
2ADCO
*[email protected]

This study presents the efficacy of curvelets, a recently developed mathematical transform, in attenuating random and coherent linear noises in a stacked dataset from the Middle East. Our main motivation to seek a new, advanced noise-attenuation tool is that even though conventional Previous HitfilteringNext Hit techniques such as median Previous HitfilteringNext Hit and FX-deconvolution remove respectable amounts of noise, they also harm the signal. The curvelet transform is a recently developed mathematical tool that represents an image using a linear, weighted combination of special elementary functions that resemble small pieces of a band-limited seismic reflector (Candes and Donoho, 1999). Each curvelet elementary function has a characteristic dip, Previous HitfrequencyNext Hit (thickness), and location. The localized nature of curvelet functions, along with their dip and Previous HitfrequencyNext Hit characteristics, makes the curvelet transform particularly suitable for attenuating noises in seismic data. In seismic data, most noises differ from the underlying geological signal in terms of the dip, Previous HitfrequencyTop, and/or location. Consequently, signal and noise separate more effectively in the curvelet transform domain than in other conventional transform domains that do not simultaneously exploit all these attributes. This powerful property enables us to separate the geologic signal from the noise by carefully muting appropriate curvelet components of noisy data. Our results demonstrate that the curvelet-based approach provides superior noise attenuation, with minimal impact on the desirable signal components. In conjunction with the preceding multiple attenuation steps that were employed on the dataset (see presentation by Baumstein et al for details), the noise suppression significantly improved the structural and quantitative interpretability of the dataset, thereby validating the efficacy of our approach.

 

AAPG Search and Discovery Article #90077©2008 GEO 2008 Middle East Conference and Exhibition, Manama, Bahrain