What is it about?
We involve the sparsity in the nolinear seismic AVO inversion to improve the inversion result resolution. We use the adaptive Markov Chain Monte Carlo(MCMC) algorithm. This method increases the acceptance rate of sampling, thus improving the computational efficiency of MCMC methods.
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Why is it important?
The sparsity was introduced to the nolinear seismic inversion. We use the exact zoeppritz equation to forward. The adaptive delayed acceptance MCMC algorithm improves the computational efficiency of the inversion process.
Perspectives
This paper is my first paper about geophysical inverse problem. I am very happy and thank my advisor. I will try my best to do more research work in the geophysical inverse problem.
Chuan Luo
Chengdu University of Technology
Read the Original
This page is a summary of: Adaptive Markov Chain Monte Carlo with sparse constraints for nonlinear seismic inversion, Geophysics, July 2025, Society of Exploration Geophysicists,
DOI: 10.1190/geo2024-0923.1.
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