Wavelet extractor: A Bayesian well-tie and wavelet extraction program

Gunning, James; Glinsky, Michael E.

doi:10.1016/j.cageo.2005.10.001

Cited by 30 publications

(24 citation statements)

References 14 publications

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“…For simplicity, we formulate the seismic traces as the convolution between the reflection coefficient model with the source wavelet. The source wavelet can be estimated for the observed data (Gunning and Glinsky ). In the frequency domain, we have the angle‐dependent imaged data

m (f, θ)

m (f, θ) = R (f, θ) W false(f false),

where

R (f, θ)

and

W (f)

are the angle‐dependent reflection coefficient model and the zero‐phased source wavelet, respectively.…”

Section: Methodsmentioning

confidence: 99%

Trace‐imposed stretch correction in Kirchhoff prestack time migration

Liu

Zhang

2018

Geophysical Prospecting

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Kirchhoff prestack time migration, which works trace‐by‐trace, remains appealing to academia and industry due to its robustness and efficiency. However, like the other prestack migration methods, Kirchhoff prestack time migration also suffers from the angle‐dependent stretching effect, which narrows the amplitude spectra of seismic data. This effect gets more severe as the incident angle increases. In this paper, we propose a novel approach, which attaches a prediction shaping filter to the Kirchhoff prestack time migration, to mitigate the stretching effect. Our approach takes advantage on the trace‐by‐trace implementation of the prestack time migration algorithm, without the output of the angle‐domain common‐imaging gather. Also, our method can cascade with Q‐compensation in prestack time migration. We demonstrate our method with both a numerical example and a field data example.

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m (f, θ)

m (f, θ) = R (f, θ) W false(f false),

where

R (f, θ)

and

W (f)

are the angle‐dependent reflection coefficient model and the zero‐phased source wavelet, respectively.…”

Section: Methodsmentioning

confidence: 99%

Trace‐imposed stretch correction in Kirchhoff prestack time migration

Liu

Zhang

2018

Geophysical Prospecting

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“…Gunning and Glinsky () introduced a well‐tie algorithm formulated in the time domain as a Bayesian inverse problem. The algorithm simultaneously estimates all the wavelet coefficients, and the Bayesian formulation allows one to incorporate uncertainties associated in the time–depth mapping, positioning errors, and other useful priors.…”

Section: Wavelet Estimation Methodsmentioning

confidence: 99%

“…Similarly, the Bayesian approach provides tools for computation of full posterior uncertainties of the model parameters. Gunning and Glinsky () also discuss the problem of wavelet length, and treat this as a model dimension parameter that may be estimated via Bayesian model selection theory (Denison et al . ).…”

Section: Wavelet Estimation Methodsmentioning

confidence: 99%

“…), i.e., the preference for a simple model to a more complex one, other things being equal. We refer to Gunning and Glinsky () for more details on this technique. In what follows, we introduce two functionality enhancements in the form of priors that are specifically targeted to handle the low‐frequency character of the wavelet in this algorithm.…”

Section: Wavelet Estimation Methodsmentioning

confidence: 99%

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Well tie for broadband seismic data

Naeini¹,

Gunning

White

2016

Geophysical Prospecting

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The seismic industry is increasingly acquiring broadband data in order to reap the benefits of extra low‐ and high‐frequency contents. At the low end, as the sharp low‐cut decay gets closer to zero frequency, it becomes harder for a well tie to estimate the low‐frequency response correctly. The fundamental difficulty is that well logs are too short to allow accurate estimation of the long‐period content of the data. Three distinctive techniques, namely parametric constant phase, frequency‐domain least squares with multi‐tapering, and Bayesian time domain with broadband priors, are introduced in this paper to provide a robust solution to the wavelet estimation problem for broadband seismic data. Each of these techniques has a different mathematical foundation that would enable one to explore a wide range of solutions that could be used on a case‐by‐case basis depending on the problem at hand. A case study from the North West Shelf Australia is used to analyse the performance of the proposed techniques. Cross‐validation is proposed as a robust quality control measure for evaluating well‐tie applications. It is observed that when the seismic data are carefully processed, then the constant phase approach would likely offer a good solution. The frequency‐domain method does not assume a constant phase. This flexibility makes it prone to over‐fitting when the phase is approximately constant. Broadband priors for the time‐domain least‐squares method are found to perform well in defining low‐frequency side lobes to the wavelet.

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“…Recently, much progress has been achieved in identifying units and layers of sedimentary sequences and their internal characteristics as well. This is considerably due to the application of methods based on wavelets (Daubechies, 1992;Louis et al, 1998). Wavelets have been used successfully in the analysis of seismic (Gunninga and Glinsky, 2006;Capilla, 2006) or gravity data (Fedi et al, 2005;Cooper, 2006), and in the characterisation of porous media (Sahimi, 2000).…”

Section: Introductionmentioning

confidence: 99%

An algorithm for detecting layer boundaries in sediments

Bube

Klenke

Feudel

2006

Nonlin. Processes Geophys.

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Abstract. In this paper we present an algorithm based on wavelet multiscale decomposition, designed to detect lines of maximal gradients in horizontal direction within twodimensional data sets. The algorithm is capable of identifying layer boundaries within sediment profiles, as demonstrated for artificial as well as two field data sets. Layers are detected with a good resolution within (i) digital images of a deep sea sediment core (IODP-expedition 301, core 15H) and (ii) chemical concentration patterns of recent tidal sediments (North Sea).

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Wavelet extractor: A Bayesian well-tie and wavelet extraction program

Cited by 30 publications

References 14 publications

Trace‐imposed stretch correction in Kirchhoff prestack time migration

Trace‐imposed stretch correction in Kirchhoff prestack time migration

Well tie for broadband seismic data

An algorithm for detecting layer boundaries in sediments

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