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Gomes et al. (2017)

V. M. Gomes, H. B. Santos, J. Schleicher, A. Novais, and M. A. C. Santos: Curvelet denoising for preconditioning of 2D poststack seismic data inversion

Gomes et al. analyse how seismic denoising using the curvelet transform as a conditioning step affects acoustic poststack seismic inversion. Their experiments involve both white and coloured noise with a standard hard thresholding technique for denoising and a Bayesian approach to constructing the objective function for inversion. Even though the minimum converges to solutions with reduced noise, curvelet filtering helps to reduce the misfit error considerably. However, they find that for high levels of white noise, and even for rather low levels of coloured noise, curvelet filtering by the tested method fails. In these situations, a more robust filtering technique is required.

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