Modified Sparse Multichannel Blind Deconvolution

Wavelet Estimation via Sparse Multichannel Blind Deconvolution

Multichannel data: separating independent causes

The algorithm for blind deconvolution of a nonstationary time series of vector components (i.e. multichannel) has three stages: (1) Linear-least-squares multichannel prediction-error filtering, (2) Cholesky factorization of the zero-lag covariance matrix, and (3) Rotation angle scanning for maximum sparsity.

Single-frame multichannel blind deconvolution by nonnegative matrix factorization with sparseness constraints.

Single-frame multichannel blind deconvolution is formulated by applying a bank of Gabor filters to a blurred image. The key observation is that spatially oriented Gabor filters produce sparse images and that a multichannel version of the observed image can be represented as a product of an unknown nonnegative sparse mixing vector and an unknown nonnegative source image. Therefore a blind-deconv...

Multichannel Semi-blind Deconvolution (MSBD) of seismic signals

Seismic deconvolution is a general problem associated with recovering the reflectivity series from a seismic signal when the wavelet is known. In this paper, we solve the problem of semi-blind seismic deconvolution, where the wavelet is known up to some error. The Multichannel Semi-blind Deconvolution (MSBD) model was developed for cases where there is some uncertainty in the assumed wavelet. W...

Multichannel Semi-blind Sparse Deconvolution of Seismic Signals

Seismic deconvolution is associated with recovering the reflectivity series from a seismic signal when the wavelet is known. In this paper, we address the problem of multichannel semiblind seismic deconvolution, where the wavelet is unknown and there is some uncertainty in the assumed wavelet. We present a novel, two-stage iterative algorithm that recovers both the reflectivity and the wavelet....