Wavelet deconvolution

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...

Wavelet Estimation via Sparse Multichannel Blind Deconvolution

Efficient Deconvolution Algorithm

This thesis describes the design of a new deconvolution approach known as Efficient Deconvolution algorithm so that reconstruction image will be free from both blur and noise. This algorithm comprises two steps, a global blur compensation using generalized Wiener filter, followed by a pure local denoising algorithm using dual-tree complex wavelet transform (DT-CWT). The main advantage of this d...

Tackling mixed-phase wavelets in blind deconvolution using hybrid solver

By assuming the reflectivity series to be sparse rather than ”white”, we introduced the hybrid norm spiking deconvolution (Zhang, 2010). However one theoretical drawback of spiking deconvolution is that it assumes the source wavelet to be minimum-phase, which might not be true in practice. We propose a new formulation of spiking deconvolution with the hybrid solver that does not require such as...

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....

Data examples of logarithm Fourier-domain bidirectional deconvolution

Time-domain bidirectional deconvolution methods show great promise for overcoming the minimum-phase assumption in blind deconvolution of signals containing a mixed-phase wavelet, such as seismic data. However, usually one timedomain method is slow to converge (the slalom method) and the other one is sensitive to the initial point or preconditioner (the symmetric method). Claerbout proposed a lo...