Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success

The rigorous quantification of uncertainty in geophysical inversions is a challenging problem. Inversions are often ill-posed and the likelihood surface may be multi-modal; properties of any single mode become inadequate uncertainty measures, and sampling methods become inefficient for irregular pos...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Geoscientific Model Development Ročník 12; číslo 7; s. 2941 - 2960
Hlavní autoři:	Scalzo, Richard, Kohn, David, Olierook, Hugo, Houseman, Gregory, Chandra, Rohitash, Girolami, Mark, Cripps, Sally
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Katlenburg-Lindau Copernicus GmbH 15.07.2019 Copernicus Publications
Témata:	Adaptive sampling Bayesian analysis Covariance Efficiency Geochemical cycles Geological structures Geophysical methods Geophysics Ill posed problems Inversions Markov analysis Markov chains Markov processes Measurement methods Monte Carlo methods Monte Carlo simulation Noise levels Obsidian Probability theory Random walk Sampling Sampling methods Sensors Statistical methods Uncertainty Australia
ISSN:	1991-9603, 1991-959X, 1991-962X, 1991-9603, 1991-962X
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	The rigorous quantification of uncertainty in geophysical inversions is a challenging problem. Inversions are often ill-posed and the likelihood surface may be multi-modal; properties of any single mode become inadequate uncertainty measures, and sampling methods become inefficient for irregular posteriors or high-dimensional parameter spaces. We explore the influences of different choices made by the practitioner on the efficiency and accuracy of Bayesian geophysical inversion methods that rely on Markov chain Monte Carlo sampling to assess uncertainty using a multi-sensor inversion of the three-dimensional structure and composition of a region in the Cooper Basin of South Australia as a case study. The inversion is performed using an updated version of the Obsidian distributed inversion software. We find that the posterior for this inversion has a complex local covariance structure, hindering the efficiency of adaptive sampling methods that adjust the proposal based on the chain history. Within the context of a parallel-tempered Markov chain Monte Carlo scheme for exploring high-dimensional multi-modal posteriors, a preconditioned Crank–Nicolson proposal outperforms more conventional forms of random walk. Aspects of the problem setup, such as priors on petrophysics and on 3-D geological structure, affect the shape and separation of posterior modes, influencing sampling performance as well as the inversion results. The use of uninformative priors on sensor noise enables optimal weighting among multiple sensors even if noise levels are uncertain.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1991-9603 1991-959X 1991-962X 1991-9603 1991-962X
DOI:	10.5194/gmd-12-2941-2019