Multichannel sparse deconvolution of seismic data with shearlet–Cauchy constrained inversion

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....

Constrained inversion of P-S seismic data

A method to estimate S-wave interval velocity, using P-S seismic data is presented. The method is composed of three steps. First, the P-S data are converted to a relative change in S-velocity section. The section is then linearized. Constrained inversion is used to convert this last section to S-interval velocity. To illustrate the method, the S velocity of a field data set was derived, and a V...

Multichannel Sparse Spike Inversion

In this paper, we address the problem of sparse multichannel seismic deconvolution. We introduce Multichannel Sparse Spike Inversion (MSSI) as an iterative procedure, which deconvolves the seismic data and recovers the earth twodimensional (2D) reflectivity image, while taking into consideration the relations between spatially-neighboring traces. We demonstrate the improved performance of the p...

Modified Sparse Multichannel Blind Deconvolution

Multichannel Blind Deconvolution of Seismic Signals

A new algorithm for simultaneous wavelet estimation and deconvolution of seismic reflection signals is given. To remove the inherent ambiguity in this blind deconvolution problem, we introduce relevant a priori information. Our major assumption is sparseness of the reflectivity, which corresponds to a layered earth model. This allows non-minimum phase wavelets to be recovered reliably and close...