Stable seismic data recovery

  • Date: 03/02/2007

Felix Herrmann (University of British Columbia)


University of British Columbia


In this paper, directional frames, known as curvelets, are used to
recover seismic data and images from noisy and incomplete data.
Sparsity and invariance properties of curvelets are exploited to
formulate the recovery by a $\ell_1$-norm promoting program. It is
shown that our data recovery approach is closely linked to the recent
theory of 'compressive sensing' and can be seen as a first step towards
a nonlinear sampling theory for wavefields.

The second problem that will be discussed concerns the recovery of the
amplitudes of seismic images in clutter. There, the invariance of
curvelets is used to approximately invert the Gramm operator of seismic
imaging. In the high-frequency limit, this Gramm matrix corresponds to
a pseudo-differential operator, which is near diagonal in the curvelet

Other Information: 

Centre for Scientific Computing Seminar 2007