Vibroseis Deconvolution with Maximum Likelihood Approach
نویسنده
چکیده
The most commonly used deconvolution (Decon) method is based on minimum-phase assumption for the wavelet which is often not valid. Explosive energy source is close to minimum-phase which makes seismic data with explosive somewhat amenable to predictive Decon though the result is imperfect in phase. Decon of Vibroseis data is more problematic because the wavelet in a correlated Vibroseis trace is mixed-phase in nature. In the commonly used method for Vibroseis Decon, the Klauder wavelet is replaced with its minimum-phase equivalent which is a difficult proposition for band limited Vibroseis sweep. As an alternative, we have used Maximum Likelihood Deconvolution (MLD) for Vibroseis data because it has no restrictive assumption for the phase of the wavelet.
منابع مشابه
Examining the phase property of the nonstationary vibroseis wavelet
We have observed that Vibroseis wavelets behave very much as if they are minimum phase. This was discovered by applying minimum-phase Wiener deconvolution to the separated vibroseis VSP downgoing waves and observing that the result is effectively a band-limited spike. Motivated by this finding, we simulated a synthetic nonstationary vibroseis wavelet in a constant-Q medium by nonstationary conv...
متن کاملAn alternative to standard maximum likelihood for Gaussian mixtures
Because true Maximum Likelihood (ML) is too expensive, the dominant approach in Bernoulli-Gaussian (BG) myopic deconvolution consists in the joint maximization of a single Generalized Likelihood with respect to the input signal and the hyperparameters. This communication assesses the theoretical properties of a related Maximum Generalized Marginal Likelihood (MGML) estimator in a simplified fra...
متن کاملGeneralized Marginal Likelihood for Gaussian mixtures
The dominant approach in Bernoulli-Gaussian myopic deconvolution consists in the joint maximization of a single Generalized Likelihood with respect to the input signal and the hyperparameters. The aim of this correspondence is to assess the theoretical properties of a related Generalized Marginal Likelihood criterion in a simpliied framework where the lter is reduced to identity. Then the outpu...
متن کاملProbabilistic Formulation of Independent Vector Analysis Using Complex Gaussian Scale Mixtures
We propose a probabilistic model for the Independent Vector Analysis approach to blind deconvolution and derive an asymptotic Newton method to estimate the model by Maximum Likelihood.
متن کاملParametric Blind Deconvolution of Microscopic Images: Further Results
Blind deconvolution microscopy, the simultaneous estimation of the specimen function and the point spread function (PSF) of the microscope is an under-determined problem with non-unique solutions. The non-uniqueness is commonly avoided by enforcing constraints on both the specimen function and the PSF, such as non-negativity and band limitation. These constraints are some times enforced in ad h...
متن کامل