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Zoeppritz-based AVO inversion using an improved Markov chain Monte Carlo method.

Xin-Peng Pan¹, Guang-Zhi Zhang^1,2, Jia-Jia Zhang^1,2

¹School of Geosciences, China University of Petroleum (Huadong), Qingdao, 266580 Shandong China.

Petroleum Science

|February 28, 2017

Summary

This summary is machine-generated.

This study combines adaptive Metropolis (AM) and delayed rejection (DR) algorithms to improve Markov chain Monte Carlo (MCMC) methods for Bayesian amplitude versus offset (AVO) inversion. The enhanced method accurately retrieves P- and S-wave velocities and density, offering a more stable and anti-noise solution.

Keywords:

Adaptive Metropolis (AM) algorithm Bayesian AVO inversion Delayed rejection (DR) algorithm Exact Zoeppritz Nonlinear inversion

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Area of Science:

Geophysics
Computational Seismology
Bayesian Inference

Background:

Conventional Markov chain Monte Carlo (MCMC) methods face limitations with proposal distribution selection and initial conditions.
These limitations hinder efficient exploration of target distributions in complex geophysical inversions.

Purpose of the Study:

To develop an improved Bayesian amplitude versus offset (AVO) inversion method by combining adaptive Metropolis (AM) and delayed rejection (DR) algorithms.
To enhance the stability, accuracy, and noise resistance of AVO inversion for seismic data analysis.

Main Methods:

Integration of the adaptive Metropolis (AM) algorithm to dynamically adjust proposal distributions.
Incorporation of the delayed rejection (DR) algorithm to improve sampling efficiency.
Application of the combined AM-DR approach within a Bayesian framework using the exact Zoeppritz equation for AVO inversion.

Main Results:

Direct retrieval of P-wave velocity, S-wave velocity, and density from seismic and logging data.
Successful estimation of uncertainty in AVO inversion results.
Demonstrated feasibility and robustness of the method using real-world data.

Conclusions:

The developed exact Zoeppritz-based nonlinear inversion method using improved MCMC is effective for reservoirs with strong-contrast interfaces and long-offset ranges.
The enhanced MCMC approach provides a more stable, accurate, and anti-noise solution compared to conventional methods.
This method offers a reliable tool for quantitative seismic interpretation and reservoir characterization.