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Title: Global Analysis of Experimental Data on the Rheology of Olivine Aggregates
Abstract

Following the reanalysis of individual experimental runs of some widely cited studies (Jain et al., 2018,https://doi.org/10.1002/2017JB014847), we revisit the global data analysis of Korenaga and Karato (2008,https://doi.org/10.1029/2007JB005100) with a significantly improved version of their Markov chain Monte Carlo inversion. Their algorithm, previously corrected by Mullet et al. () to minimize potential parameter bias, is further modified here to estimate more efficiently interrun biases in global data sets. Using the refined Markov chain Monte Carlo inversion technique, we simultaneously analyze experimental data on the deformation of olivine aggregates compiled from different studies. Realistic composite rheological models, including both diffusion and dislocation creep, are adopted, and the role of dislocation‐accommodated grain boundary sliding is also investigated. Furthermore, the influence of interrun biases on inversion results is studied using experimental and synthetic data. Our analysis shows that existing data can tightly constrain the grain‐size exponent for diffusion creep at ∼2, which is different from the value commonly assumed (p= 3). Different data sets and model assumptions, however, yield nonoverlapping estimates on other flow‐law parameters, and the flow‐law parameters for grain boundary sliding are poorly resolved in most cases. We thus provide a few plausible candidate flow‐law models for olivine rheology to facilitate future geodynamic modeling. The availability of more data that explore a wider range of experimental conditions, especially higher pressures, is essential to improve our understanding of upper mantle rheology.

 
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Award ID(s):
1736563
NSF-PAR ID:
10453287
Author(s) / Creator(s):
 ;  ;  
Publisher / Repository:
DOI PREFIX: 10.1029
Date Published:
Journal Name:
Journal of Geophysical Research: Solid Earth
Volume:
124
Issue:
1
ISSN:
2169-9313
Page Range / eLocation ID:
p. 310-334
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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