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September 10, 2025Geophysics

Adaptive Markov Chain Monte Carlo with sparse constraints for nonlinear seismic inversion

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This method improves estimation of elastic properties in subsurface rocks, highlighting Bayesian inversion advancements and sampling efficiency.

The method estimates elastic properties using a nonlinear seismic inversion technique that integrates probabilistic frameworks.
A key result shows improved accuracy by adopting adaptive delayed acceptance and non-Gaussian posterior distributions.
Comparative tests using one-dimensional models reveal this approach outperforms traditional Bayesian inversion methods.
The methodology demonstrates its applicability to two-dimensional datasets, indicating potential for broader geological applications.

Luo et al. (2025) studied this question.

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This method improves estimation of elastic properties in subsurface rocks, highlighting Bayesian inversion advancements and sampling efficiency.