Journal ArticleDOI
Deconvolution by autocepstral windowing
Tim Scheuer,D. E. Wagner +1 more
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TLDR
In this article, the authors used the autocorrelation function of a reflection seismogram to estimate the wavelet autocorerelation function and then used this estimate to remove a minimum phase source wavelet to unmask subsurface reflectivity.Abstract:
The autocepstrum of a reflection seismogram is defined by the cepstrum of its autocorrelation function. Using the autocepstrum extends the basic deconvolution method for removing a minimum‐phase source wavelet to unmask subsurface reflectivity. When we record only the seismic trace and assume a minimumphase source wavelet, deconvolution reduces to estimating the wavelet autocorrelation. In practice, a portion of the seismic trace autocorrelation is used as an estimate of the wavelet autocorrelation. This can be justified by assuming a random reflectivity series with a white power spectrum. However, in cases where the reflectivity spectrum is not white, a preferred wavelet autocorrelation may be obtained by low‐pass windowing the trace autocepstrum. This approach liberates the selection of various deconvolution parameters such as filter length and design window length that are typically chosen to reinforce the assumption of a white reflectivity spectrum. For problems that require short, deconvolution‐filte...read more
Citations
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Journal ArticleDOI
Why don't we measure seismic signatures?
TL;DR: In this article, the authors proposed a convolutional model of the reflection seismogram, which is the convolution of the source signature with the impulse response of the earth, of Green's function, which contains all possible arrivals, including reflections, refractions, multiples and diffractions.
Journal ArticleDOI
Wavelet‐based cepstrum decomposition of seismic data and its application in hydrocarbon detection
TL;DR: In this article, wavelet-based cepstrum decomposition is proposed as a valid technology for enhancing geophysical responses in specific frequency bands, in the same way as traditional spectrum decomposition methods do.
Journal ArticleDOI
A Wideband Spectrum Sensing Approach for Cognitive Radios Based on Cepstral Analysis
TL;DR: The improved passband autocepstrum detector is introduced to tackle the misdetection problem of noise-like signals and it outperforms different state-of-the-art techniques and the uncertainty problem of the subbands center frequencies is addressed.
Proceedings ArticleDOI
Autocepstrum Approach for Spectrum Sensing in Cognitive Radio
TL;DR: The main scope of this work is to mitigate the problem of weak signal detection so as to allow for interference-free spectrum sharing, and indicates lower misdetection probability in low signal-to-noise (SNR) ratio environment.
References
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Journal ArticleDOI
Predictive deconvolution: theory and practice
TL;DR: The least-squares prediction filter with unit prediction distance is equivalent within a scale factor to the zero-lag inverse filter as mentioned in this paper for deconvolution of seismograms, and it has been widely used in the last few decades.
Journal ArticleDOI
Predictive decomposition of time series with application to seismic exploration
TL;DR: In exploration seismology, a charge of dynamite is exploded under controlled conditions, and the resulting vibrations at various points on the surface of the ground are detected by geophones and are recorded as seismic traces on the seismogram as discussed by the authors.
Journal ArticleDOI
Application of homomorphic deconvolution to seismology
TL;DR: In this paper, the authors deal with the application of homomorphic deconvolution to the recovery of the seismic wavelet from a time series formed by the convolution of this wavelet with an impulse train.
Journal ArticleDOI
The application of homomorphic deconvolution to shallow‐water marine seismology—part i: models
TL;DR: The complex cepstrum is investigated mathematically and through models for functions of interest in shallowwater marine seismology in this paper, where the slowly varying components of the phase spectrum with the source replaces the usual minimum-phase assumption.