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Deghosting seismic data by sparse reconstruction

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If you have a question about this talk, please contact Mustapha Amrani.

Inverse Problems

In marine environments, seismic reflection data is typically acquired with acoustic sensors attached to multiple streamers towed relatively close to the sea surface. Upward going waves reflect from the sea surface and destructively interfere with the primary signal. Ideally we would like to deconvolve these ghost events from our data. However, their phase delay depends on the angle of propagation at the receiver, and unfortunately, streamer separation is such that most frequencies of interest are aliased, so this angle cannot be easily determined.

In this talk, I will show how the problem can be addressed with the machinery of compressed sensing. I will illustrate with data examples how the trade-offs involved in the choice of basis function, the choice of sparse solver, the dimensionality in which the problem is framed, and the accuracy of the physics in the forward model, all effect the quality and cost of the reconstruction.

This talk is part of the Isaac Newton Institute Seminar Series series.

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