Seismic source wavelet estimation and the random reflectivity assumption
نویسندگان
چکیده
In the February 1991 issue of GEOPHYSICS, Anton Ziolkowski gives a scathing criticism of statistical wavelet estimation methods. Among other points, Ziolkowski questions the validity of the randomness assumption. This assumption allows statistical methods to estimate the seismic source wavelet autocorrelation from the seismic trace autocorrelation. In this study, we examine this traditional assumption of a random reflectivity sequence. The validity of this assumption is examined by using well-log synthetic seismograms and by using a procedure for evaluating the resulting deconvolutions. With real data, we compare our wavelet estimations with the in-situ recording of the wavelet from a vertical seismic profile (VSP). As a result of our examination of the randomness assumption, we present a fairly simple test that can be used to evaluate the validity of a randomness assumption. INTRODUCTION Ziolkowski’s work criticizes the conventional assumptions of wavelet deconvolution as being generally invalid. These assumptions included wavelet stationarity, minimum phase and zero phase, and the assumption of random reflectivity. Ziolkowski advocates that seismic source signatures be measured rather than estimated when performing deconvolution. Although it is difficult to ever argue against using physical measurements, the expense and difficulty of the measurements may lead one to use statistical wavelet estimation. It is then worthwhile to consider how accurate these statistical measurements might be. In particular, we seek to evaluate the common assumption of reflectivity randomness by use of model synthetics and real data. Since one would expect this assumption to be dependent on geology, we have examined data from various oil fields. Statistical wavelet estimation methods usually assume that the reflectivity is a random uncorrelated signal (that is, the reflectivity has an autocorrelation which approximates a delta function) and this effectively means that the seismic trace autocorrelation is approximately equal to the seismic wavelet autocorrelation, ) ( ) ( ) ( ) ( * * z W z W z X z X ≅ . Consider the following figure from Russell (1994).
منابع مشابه
Seismic source wavelet estimation and sparse-spike deconvolution
In this paper, we present an algorithm for seismic source wavelet estimation that is based on seismic time frequency spectral decomposition with matching pursuit technique. The main assumption of this algorithm is that the source wavelet is stationary for single wavelet estimation in a selected time window and that the source wavelet has normalized energy to avoid scale ambiguity between reflec...
متن کاملReflectivity 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 ...
متن کاملSimultaneous wavelet estimation and deconvolution of reflection seismic signals
In this paper, the problem of simultaneous wavelet estimation and deconvolution is investigated with a Bayesian approach under the assumption that the reflectivity obeys a Bernoulli-Gaussian distribution. Unknown quantities, including the seismic wavelet, the reflection sequence, and the statistical parameters of reflection sequence and noise are all treated as realizations of random variables ...
متن کاملMonte Carlo Markov Chain methods in seismic deconvolution
One prevailing assumption in reflection seismology is that the observed trace can be described as a convolution of a source wavelet with the Earth’s reflectivity plus some noise. In a conventional deconvolution approach one thus estimates a linear deconvolution filter to retrieve the reflectivity series from the observed data. This amounts to taking linear combinations of noisy observations and...
متن کاملMultichannel Blind Deconvolution of Seismic Signals
A new algorithm for simultaneous wavelet estimation and deconvolution of seismic reflection signals is given. To remove the inherent ambiguity in this blind deconvolution problem, we introduce relevant a priori information. Our major assumption is sparseness of the reflectivity, which corresponds to a layered earth model. This allows non-minimum phase wavelets to be recovered reliably and close...
متن کامل