Journal ArticleDOI
Impulse response models for noisy vibroseis data
TLDR
This discussion describes the effects of noise on the application of a new method of Vibroseis deconvolution and shows that zero phase cepstral filtering is a robust wavelet estimation approach for noisy data.Abstract:
A new method of Vibroseis deconvolution has been recently proposed by the authors. This discussion describes the effects of noise on the application of this method. The initial deconvolution step involves estimating the spectrum of the Vibroseis wavelet by homomorphic filtering. It is shown that noise causes problems with phase estimation. Hence, the Vibroseis wavelet is assumed to be zero phase. Examples demonstrate that zero phase cepstral filtering is a robust wavelet estimation approach for noisy data. The second step of the deconvolution method forms an impulse response model by a spectral extension method. Although this step can improve the resolution of seismic arrivals, it must be applied with caution in view of the deleterious effects of noise.read more
Citations
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Journal ArticleDOI
Synthetics and theoretical seismology
TL;DR: In this paper, the number and spacing of wave numbers in wave number integration schemes are determined by the desired accuracy, and the vertical step size is determined by a desired maximum frequency content, which in turn determines the time step required for stability.
Journal ArticleDOI
Deconvolution by autocepstral windowing
Tim Scheuer,D. E. Wagner +1 more
TL;DR: 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.
Book ChapterDOI
Spectral Analysis and Time Series Models: A Geophysical Perspective
TL;DR: A useful contribution to this workshop might be to link maximum entropy as formulated by Burg with autoregressive (AR) modeling in time series analysis and with the ubiquitous problem of deconvolution, particularly in the framework of geophysical data analysis.
References
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Book ChapterDOI
Fitting autoregressive models for prediction
TL;DR: This is a preliminary report on a newly developed simple and practical procedure of statistical identification of predictors by using autoregressive models in a stationary time series.
Journal ArticleDOI
Maximum entropy spectral analysis and autoregressive decomposition
Tad J. Ulrych,Thomas N. Bishop +1 more
TL;DR: The duality between the maximum entropy method (MEM) and the autoregressive representation of the data allows the application of recent advances in AR analysis to MEM in an attempt to obviate some shortcomings in this method of spectral decomposition as mentioned in this paper.
Journal ArticleDOI
Nonlinear filtering of multiplied and convolved signals
TL;DR: In this article, a generalized notion of superposition has been proposed for nonlinear filtering of signals which can be expressed as products or as convolutions of components, and applications of this approach in audio dynamic range compression and expansion, image enhancement with applications to bandwidth reduction, echo removal, and speech waveform processing are presented.
Journal ArticleDOI
Source shape estimation and deconvolution of teleseismic bodywaves
TL;DR: In this article, the deconvolution of a suite of teleseismic recordings of the same event is considered, where the redundant source information contained in secondary arrivals is used to resolve the source wavelet.