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

Muir, Jack B. and Tkalčić, Hrvoje (2020) Probabilistic lowermost mantle P-wave tomography from hierarchical Hamiltonian Monte Carlo and model parametrization cross-validation. Geophysical Journal International, 223 (3). pp. 1630-1643. ISSN 0956-540X. https://resolver.caltech.edu/CaltechAUTHORS:20201103-145828880

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1093/gji/ggaa397DOIArticle
http://rses.anu.edu.au/~hrvoje/+INNER_CORE_SUPPLEMENTS/IC_suppl.htmlRelated ItemData
ORCID:
AuthorORCID
Muir, Jack B.0000-0003-2617-3420
Tkalčić, Hrvoje0000-0001-7072-490X
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.
Group:Seismological Laboratory
Funders:
Funding AgencyGrant Number
General Sir John Monash FoundationUNSPECIFIED
Origin Energy FoundationUNSPECIFIED
Subject Keywords:Structure of the Earth, Inverse theory, Tomography, Body waves
Issue or Number:3
Record Number:CaltechAUTHORS:20201103-145828880
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201103-145828880
Official Citation:Jack B Muir, Hrvoje Tkalčić, Probabilistic lowermost mantle P-wave tomography from hierarchical Hamiltonian Monte Carlo and model parametrization cross-validation, Geophysical Journal International, Volume 223, Issue 3, December 2020, Pages 1630–1643, https://doi.org/10.1093/gji/ggaa397
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106408
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:04 Nov 2020 17:09
Last Modified:04 Nov 2020 17:09

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