time-varying deconvolution operator in gabor domain

Gabor deconvolution

The Gabor transform decomposes a 1-D temporal signal onto a time-frequency plane. Temporal localization is accomplished by windowing the signal with a Gaussian analysis window translated to any particular time. The Fourier transform of the windowed signal provides frequency information for the time of the window centre. The inverse Gabor transform is an integration over the time-frequency plane...

An update on Gabor deconvolution

Gabor deconvolution has been updated and a new ProMAX module is released. The updates are: (1) a new method of spectral smoothing called hyperbolic smoothing; (2) a Gabor transform using compactly supported windows that improves run times by one to two orders of magnitude; and (3) a post-deconvolution time-variant bandpass filter whose maximum frequency tracks along a hyperbola in the time-freq...

Reflectivity Color Correction in Gabor Deconvolution

Summary White reflectivity is not a fundamental assumption in the convolutional models. However the deconvolution algorithms should be modified slightly to honour color in the reflectivity series. This color correction is just possible when enough well-log information is available to build a mathematical model for the regional reflectivity. A method for correcting reflectivity color effects in ...

Characterization of Time-varying Human Operator Dynamics

Varying the Time-frequency Lattice of Gabor Frames

A Gabor or Weyl-Heisenberg frame for L2(Rd) is generated by time-frequency shifts of a square-integrable function, the Gabor atom, along a time-frequency lattice. The dual frame is again a Gabor frame, generated by the dual atom. In general, Gabor frames are not stable under a perturbation of the lattice constants; that is, even for arbitrarily small changes of the parameters the frame property...

Gabor Deconvolution: Surface-Consistent and Iterative

Nonstationary deconvolution has emerged as a tool for extracting the most realistic earth reflectivity from seismic traces, because of its adaptability to the characteristics of a given data set. Recently, deconvolution using the Gabor transform has been extended to yield deconvolution operators that are not only nonstationary in time, but also surface-consistent. We describe here a further ext...