Probabilistic lowermost mantle P-wave tomography from hierarchical Hamiltonian Monte Carlo and model parametrization cross-validation
Bayesian methods, powered by Markov Chain Monte Carlo estimates of posterior densities, have become a cornerstone of geophysical inverse theory. These methods have special relevance to the deep Earth, where data are sparse and uncertainties are large. We present a strategy for efficiently solving hierarchical Bayesian geophysical inverse problems for fixed parametrizations using Hamiltonian Monte Carlo sampling, and highlight an effective methodology for determining optimal parametrizations...[Show more]
|Collections||ANU Research Publications|
|Source:||Geophysical Journal International|
|Access Rights:||Open Access|
|01_Muir_Probabilistic_lowermost_mantle_2020.pdf||3.86 MB||Adobe PDF|
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