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Published December 2020 | Supplemental Material + Published
Journal Article Open

Probabilistic lowermost mantle P-wave tomography from hierarchical Hamiltonian Monte Carlo and model parametrization cross-validation

Abstract

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 from a set of candidates by using efficient approximations to leave-one-out cross-validation for model complexity. To illustrate these methods, we use a case study of differential traveltime tomography of the lowermost mantle, using short period P-wave data carefully selected to minimize the contributions of the upper mantle and inner core. The resulting tomographic image of the lowermost mantle has a relatively weak degree 2—instead there is substantial heterogeneity at all low spherical harmonic degrees less than 15. This result further reinforces the dichotomy in the lowermost mantle between relatively simple degree 2 dominated long-period S-wave tomographic models, and more complex short-period P-wave tomographic models.

Additional Information

© The Author(s) 2020. Published by Oxford University Press on behalf of The Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model). Accepted 2020 August 19. Received 2020 January 2; in original form 2020 August 10. Published: 15 September 2020. The authors would like to thank associate editor Frederik Simons and editorial assistants Fern Storey and Anna Evripidou for handling the publication process, and two anonymous reviewers for substantially improving the manuscript. JBM would like to thank the General Sir John Monash Foundation and the Origin Energy Foundation for financial support. In addition, JBM would like to thank Michael Betancourt for providing an intensive STAN workshop gratis at Caltech, that helped to reinvigorate this study. The newly analysed PKPab-PKPbc dataset can be constructed from the raw PKPab-PKPdf and PKPbc-PKPdf data found at http://rses.anu.edu.au/~hrvoje/+INNER_CORE_SUPPLEMENTS/IC_suppl.html; the PcP-P data were previously reported.

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Supplemental Material - ggaa397_supplemental_file.pdf

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Additional details

Created:
August 20, 2023
Modified:
October 20, 2023