author_facet Scalzo, Richard
Kohn, David
Olierook, Hugo
Houseman, Gregory
Chandra, Rohitash
Girolami, Mark
Cripps, Sally
Scalzo, Richard
Kohn, David
Olierook, Hugo
Houseman, Gregory
Chandra, Rohitash
Girolami, Mark
Cripps, Sally
author Scalzo, Richard
Kohn, David
Olierook, Hugo
Houseman, Gregory
Chandra, Rohitash
Girolami, Mark
Cripps, Sally
spellingShingle Scalzo, Richard
Kohn, David
Olierook, Hugo
Houseman, Gregory
Chandra, Rohitash
Girolami, Mark
Cripps, Sally
Geoscientific Model Development
Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
Polymers and Plastics
General Environmental Science
author_sort scalzo, richard
spelling Scalzo, Richard Kohn, David Olierook, Hugo Houseman, Gregory Chandra, Rohitash Girolami, Mark Cripps, Sally 1991-9603 Copernicus GmbH Polymers and Plastics General Environmental Science http://dx.doi.org/10.5194/gmd-12-2941-2019 <jats:p>Abstract. 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. </jats:p> Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success Geoscientific Model Development
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title Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
title_unstemmed Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
title_full Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
title_fullStr Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
title_full_unstemmed Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
title_short Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
title_sort efficiency and robustness in monte carlo sampling for 3-d geophysical inversions with obsidian v0.1.2: setting up for success
topic Polymers and Plastics
General Environmental Science
url http://dx.doi.org/10.5194/gmd-12-2941-2019
publishDate 2019
physical 2941-2960
description <jats:p>Abstract. 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. </jats:p>
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author Scalzo, Richard, Kohn, David, Olierook, Hugo, Houseman, Gregory, Chandra, Rohitash, Girolami, Mark, Cripps, Sally
author_facet Scalzo, Richard, Kohn, David, Olierook, Hugo, Houseman, Gregory, Chandra, Rohitash, Girolami, Mark, Cripps, Sally, Scalzo, Richard, Kohn, David, Olierook, Hugo, Houseman, Gregory, Chandra, Rohitash, Girolami, Mark, Cripps, Sally
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description <jats:p>Abstract. 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. </jats:p>
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spelling Scalzo, Richard Kohn, David Olierook, Hugo Houseman, Gregory Chandra, Rohitash Girolami, Mark Cripps, Sally 1991-9603 Copernicus GmbH Polymers and Plastics General Environmental Science http://dx.doi.org/10.5194/gmd-12-2941-2019 <jats:p>Abstract. 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. </jats:p> Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success Geoscientific Model Development
spellingShingle Scalzo, Richard, Kohn, David, Olierook, Hugo, Houseman, Gregory, Chandra, Rohitash, Girolami, Mark, Cripps, Sally, Geoscientific Model Development, Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success, Polymers and Plastics, General Environmental Science
title Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
title_full Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
title_fullStr Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
title_full_unstemmed Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
title_short Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
title_sort efficiency and robustness in monte carlo sampling for 3-d geophysical inversions with obsidian v0.1.2: setting up for success
title_unstemmed Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
topic Polymers and Plastics, General Environmental Science
url http://dx.doi.org/10.5194/gmd-12-2941-2019