2021
DOI: 10.1029/2020je006645
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Modeling Lunar Pyroclasts to Probe the Volatile Content of the Lunar Interior

Abstract: Several models of lunar formation that have recently gained momentum in the planetary science community involve, to an extent, the giant impact theory (e.g., the terrestrial synestia model of Lock et al. (2018)). In this giant impact theory, the Moon is thought to have been formed from the coalescence of debris from a collision between an impactor and a proto-Earth (Canup, 2004), leading to the formation of a lunar magmatic ocean and the large-scale degassing of the Moon (Lucey et al., 2006). The depletion of … Show more

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Cited by 4 publications
(2 citation statements)
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“…The Markov chain Monte Carlo is an inversion technique ideally suited for non-linear problems with fast forward solvers (here the finite volume model described in section 3). Its implementation includes computing hundreds of thousands of forward model simulations given a suite of parameters of interest (Allan et al , 2013, Florez et al , 2021. For the calculations in this study, the parameters of interest include φ m and ξ ref .…”
Section: Markov Chain Monte Carlo Inversionsmentioning
confidence: 99%
“…The Markov chain Monte Carlo is an inversion technique ideally suited for non-linear problems with fast forward solvers (here the finite volume model described in section 3). Its implementation includes computing hundreds of thousands of forward model simulations given a suite of parameters of interest (Allan et al , 2013, Florez et al , 2021. For the calculations in this study, the parameters of interest include φ m and ξ ref .…”
Section: Markov Chain Monte Carlo Inversionsmentioning
confidence: 99%
“…The MCMC is an inversion technique ideally suited for non‐linear problems with fast forward solvers (here the finite volume model described in Section 3). Its implementation includes computing hundreds of thousands of forward model simulations given a suite of parameters of interest (Allan et al., 2013; Florez et al., 2021). For the calculations in this study, the parameters of interest include ϕ m and ξ ref .…”
Section: Markov Chain Monte Carlo Inversionsmentioning
confidence: 99%