R. Herrera, and M. Van der Baan. (2012)cite arxiv:1209.0196Comment: 13 pages, 5 figures, submitted to JGE.
Abstract
Successful wavelet estimation is an essential step for seismic methods like
impedance inversion, analysis of amplitude variations with offset and full
waveform inversion. Homomorphic deconvolution has long intrigued as a
potentially elegant solution to the wavelet estimation problem. Yet a
successful implementation has proven difficult. Associated disadvantages like
phase unwrapping and restrictions of sparsity in the reflectivity function
limit its application. We explore short-time homomorphic wavelet estimation as
a combination of the classical homomorphic analysis and log-spectral averaging.
The introduced method of log-spectral averaging using a short-term Fourier
transform increases the number of sample points, thus reducing estimation
variances. We apply the developed method on synthetic and real data examples
and demonstrate good performance.
%0 Generic
%1 herrera2012shorttime
%A Herrera, Roberto H.
%A Van der Baan, Mirko
%D 2012
%K cepstrum cepstrum, extraction, short-time wavelet
%T Short-time homomorphic wavelet estimation
%U http://arxiv.org/abs/1209.0196
%X Successful wavelet estimation is an essential step for seismic methods like
impedance inversion, analysis of amplitude variations with offset and full
waveform inversion. Homomorphic deconvolution has long intrigued as a
potentially elegant solution to the wavelet estimation problem. Yet a
successful implementation has proven difficult. Associated disadvantages like
phase unwrapping and restrictions of sparsity in the reflectivity function
limit its application. We explore short-time homomorphic wavelet estimation as
a combination of the classical homomorphic analysis and log-spectral averaging.
The introduced method of log-spectral averaging using a short-term Fourier
transform increases the number of sample points, thus reducing estimation
variances. We apply the developed method on synthetic and real data examples
and demonstrate good performance.
@misc{herrera2012shorttime,
abstract = {Successful wavelet estimation is an essential step for seismic methods like
impedance inversion, analysis of amplitude variations with offset and full
waveform inversion. Homomorphic deconvolution has long intrigued as a
potentially elegant solution to the wavelet estimation problem. Yet a
successful implementation has proven difficult. Associated disadvantages like
phase unwrapping and restrictions of sparsity in the reflectivity function
limit its application. We explore short-time homomorphic wavelet estimation as
a combination of the classical homomorphic analysis and log-spectral averaging.
The introduced method of log-spectral averaging using a short-term Fourier
transform increases the number of sample points, thus reducing estimation
variances. We apply the developed method on synthetic and real data examples
and demonstrate good performance.},
added-at = {2012-10-15T04:30:32.000+0200},
author = {Herrera, Roberto H. and Van der Baan, Mirko},
biburl = {https://www.bibsonomy.org/bibtex/23876c07dbd54508e55168401cc3e58ba/hentronix},
description = {Short-time homomorphic wavelet estimation},
interhash = {c85146a3594db634974b2cbcf965c14c},
intrahash = {3876c07dbd54508e55168401cc3e58ba},
keywords = {cepstrum cepstrum, extraction, short-time wavelet},
note = {cite arxiv:1209.0196Comment: 13 pages, 5 figures, submitted to JGE},
timestamp = {2012-10-26T08:07:15.000+0200},
title = {Short-time homomorphic wavelet estimation},
url = {http://arxiv.org/abs/1209.0196},
year = 2012
}