Bayesian wavelet estimation from seismic and well data

@article{Buland2003BayesianWE,
  title={Bayesian wavelet estimation from seismic and well data},
  author={A. Buland and H. Omre},
  journal={Geophysics},
  year={2003},
  volume={68},
  pages={2000-2009}
}
A Bayesian method for wavelet estimation from seismic and well data is developed. The method works both on stacked data and on prestack data in form of angle gathers. The seismic forward model is based on the convolutional model, where the reflectivity is calculated from the well logs. Possible misties between the seismic traveltimes and the time axis of the well logs, errors in the log measurements, and seismic noise are included in the model. The estimated wavelets are given as probability… Expand
Parametric Wavelet Estimation
A method for parametric estimation of seismic wavelets from well logs and seismic data is developed. Parameters include amplitude, skewness, length and fluctuation order, and the link betweenExpand
Bayesian algorithm to jointly estimate wavelet, seismic noise level and correlation
This paper describes how a Bayesian framework can be modeled and applied on seismic data to estimate the wavelet. The method works on poststack and pre-stack data, in both, the convolutional forwardExpand
Comparison of deterministic wavelet estimation and statistic wavelet estimation through predictive deconvolution on the quality of well tie - application on synthetic and real data
TLDR
Two different methods to estimate the seismic wavelet for the well tie procedure are tested: one based on both the well log and seismic data, the deterministic approach, and one based only on the real seismic trace, through the predictive deconvolution. Expand
Joint AVO inversion, wavelet estimation and noise-level estimation using a spatially coupled hierarchical Bayesian model
The main objective of the AVO inversion is to obtain posterior distributions for P-wave velocity, S-wave velocity and density from specified prior distributions, seismic data and well-log data. TheExpand
Comparison between deterministic and statistical wavelet estimation methods through predictive deconvolution: Seismic to well tie example from the North Sea
TLDR
A comparative study of wavelet estimation methods for seismic-to-well tie with a deterministic and a statistical approach, based on predictive deconvolution and classical assumptions of the convolutional model, which provides a minimum-phase wavelet. Expand
Wavelet estimation by matching well-log, VSP, and surface-seismic data
In this paper, we present a method of wavelet estimation by matching well-log, VSP, and surface-seismic data. It’s based on a statistical model in which both input and output are contaminated withExpand
Bayesian Framework to Wavelet Estimation and Linearized Acoustic Inversion
In this letter, we show how a seismic inversion method based on a Bayesian framework can be applied on poststack seismic data to estimate the wavelet, the seismic noise level, and the subsurfaceExpand
Joint Bayesian wavelet and well-path estimation in the impedance domain
ABSTRACTWe addressed the problem of the well-to-seismic tie as a Bayesian inversion for the wavelet and well path in the impedance domain. The result of the joint inversion is a set of wavelets forExpand
Singular Spectrum vs. Wavelet Based Denoising Schemes in Generalized Inversion Based Seismic Wavelet Estimation
Seismic source wavelet estimation from the seismic data using borehole velocity and density information is one of the important steps in seismic data processing and is also very useful for inversionExpand
Assessment of Singular Spectrum and Wavelet based de-noising schemes in generalized inversion based seismic wavelet estimation
Seismic wavelet estimation is very important in the processing and interpretation of seismic data. The noise present in seismic data deviate the wavelet estimates and thereby mislead the processingExpand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 24 REFERENCES
Joint AVO inversion, wavelet estimation and noise-level estimation using a spatially coupled hierarchical Bayesian model
The main objective of the AVO inversion is to obtain posterior distributions for P-wave velocity, S-wave velocity and density from specified prior distributions, seismic data and well-log data. TheExpand
Wavelet Analysis Using Well Log Information
TLDR
This processing scheme provides an improved matching between well-synthetic and seismic information while ensuring wavelet reliability and robustness and the impedance model in the vicinity of the available well can be modified through the two-dimensional inversion method described in Brat et al. (1988). Expand
Bayesian Seismic AVO Inversion
Seismic analysis is a key element in successful exploration and production of natural resources. During the last decades, seismic methodology has had a significant progress with respect to bothExpand
Wavelets, well logs and Wiener filters
The deconvolution of source wavelets from seismic traces can provide useful estimates of the earth 's impulse response and thereby aid in geological interpretation. This signal analysis tool has beenExpand
Is ray theory adequate for reflection seismic modelling? (A survey of modelling methods)
The region of the Earth of interest far reflection seismology is characterised by rapid fluctuations in properties with depth superimposed on a smoother trend of general increase in seismic velocityExpand
Detailed reservoir definition by integration of well and 3-D seismic data using space adaptive wavelet processing
Temporal and lateral resolution of seismic data is principally determined by the shape and duration of the propagating wavelet. Accurate wavelet estimation and the ability to change its shape areExpand
Bayesian linearized AVO inversion
A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P‐wave velocity, S‐wave velocity, and density. Distributions forExpand
Monte Carlo Methods in Bayesian Computation
TLDR
The authors use the setting of singular perturbations, which allows them to study both weak and strong interactions among the states of the chain and give the asymptotic behavior of many controlled stochastic dynamic systems when the perturbation parameter tends to 0. Expand
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
  • S. Geman, D. Geman
  • Mathematics, Medicine
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 1984
TLDR
The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation. Expand
The Bayesian choice
TLDR
This paperback edition, a reprint of the 2001 edition, is a graduate-level textbook that introduces Bayesian statistics and decision theory and was awarded the 2004 DeGroot Prize for setting a new standard for modern textbooks dealing with Bayesian methods. Expand
...
1
2
3
...