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Hennenfent, Gilles; Herrmann, Felix J.; Neelamani, Ramesh

European Association of Geoscientists & Engineers

We propose an efficient iterative curvelet-regularized deconvolution algorithm that exploits continuity along reflectors in seismic images. Curvelets are a new multiscale transform that provides sparse representations for images (such as seismic images) that comprise smooth objects separated by piece-wise smooth discontinuities. Our technique combines conjugate gradient-based convolution operator inversion with noise regularization that is performed using non-linear curvelet coefficient shrinkage (thresholding). The shrinkage operation leverages the sparsity of curvelets representations. Simulations demonstrate that our algorithm provides improved resolution compared to the traditional Wiener-based deconvolution approach.

Curvelet; Deconvolution; Inversion; Noise; Wiener Theory; Curvelet transform

Text

eng

Vancouver : University of British Columbia Library

10.14288/1.0107397

Science, Faculty of; Earth and Ocean Sciences, Department of

Unreviewed

Herrmann, Felix J.