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A Bayesian approach to infer radial models of temperature and anisotropy in the transition zone from surface wave dispersion curves

Research areas:

• Environnement, Paléo-Environnement et Bio-Indicateurs (2011 - 2016)

Year:

2013

Authors:

• Éric Beucler
• Antoine Mocquet
• Olivier Verhoeven
• G. Moebs
• G. Burgos
• J. -P. Montagner
• Pierre Vacher

Journal:

GEOPHYSICAL JOURNAL INTERNATIONAL

Volume:

195

Number:

2

Pages:

1165-1183

Month:

November

ISSN:

0956-540X

BibTex:

@article{WOS:000325770900031, author = "{\'E}ric Beucler and Antoine Mocquet and Olivier Verhoeven and G. Moebs and G. Burgos and J. -P. Montagner and Pierre Vacher", abstract = "Mineralogical transformations and material transfers within the Earth{\&}#039;s mantle make the 350-1000 km depth range (referred here as the mantle transition zone) highly heterogeneous and anisotropic. Most of the 3-D global tomographic models are anchored on small perturbations from 1-D models such as PREM, and are secondly interpreted in terms of temperature and composition distributions. However, the degree of heterogeneity in the transition zone can be strong enough so that the concept of a 1-D reference seismic model must be addressed. To avoid the use of any seismic reference model, we present in this paper a Markov chain Monte Carlo algorithm to directly interpret surface wave dispersion curves in terms of temperature and radial anisotropy distributions, here considering a given composition of the mantle. These interpretations are based on laboratory measurements of elastic moduli and Birch-Murnaghan equation of state. An originality of the algorithm is its ability to explore both smoothly varying models and first-order discontinuities, using C1-Bezier curves, which interpolate the randomly chosen values for depth, temperature and radial anisotropy. This parametrization is able to generate a self-adapting parameter space exploration while reducing the computing time. Thanks to a Bayesian exploration, the probability distributions on temperature and anisotropy are governed by uncertainties on the data set. The method is applied to both synthetic data and real dispersion curves. Though surface wave data are weakly sensitive to the sharpness of the of the mid-mantle seismic discontinuities, the interpretation of the temperature distribution is highly related to the chosen composition and to the modelling of mineralogical phase transformations. Surface wave measurements along the Vanuatu-California path suggest a strong anisotropy above 400 km depth, which decreases below, and a monotonous temperature distribution between 350 and 1000 km depth.", doi = "10.1093/gji/ggt284", extra = "PICL", issn = "0956-540X", journal = "GEOPHYSICAL JOURNAL INTERNATIONAL", month = "NOV", number = "2", pages = "1165-1183", title = "{A} {B}ayesian approach to infer radial models of temperature and anisotropy in the transition zone from surface wave dispersion curves", volume = "195", year = "2013", }

Abstract:

Mineralogical transformations and material transfers within the Earth's
mantle make the 350-1000 km depth range (referred here as the mantle
transition zone) highly heterogeneous and anisotropic. Most of the 3-D
global tomographic models are anchored on small perturbations from 1-D
models such as PREM, and are secondly interpreted in terms of
temperature and composition distributions. However, the degree of
heterogeneity in the transition zone can be strong enough so that the
concept of a 1-D reference seismic model must be addressed. To avoid the
use of any seismic reference model, we present in this paper a Markov
chain Monte Carlo algorithm to directly interpret surface wave
dispersion curves in terms of temperature and radial anisotropy
distributions, here considering a given composition of the mantle. These
interpretations are based on laboratory measurements of elastic moduli
and Birch-Murnaghan equation of state. An originality of the algorithm
is its ability to explore both smoothly varying models and first-order
discontinuities, using C1-Bezier curves, which interpolate the randomly
chosen values for depth, temperature and radial anisotropy. This
parametrization is able to generate a self-adapting parameter space
exploration while reducing the computing time. Thanks to a Bayesian
exploration, the probability distributions on temperature and anisotropy
are governed by uncertainties on the data set. The method is applied to
both synthetic data and real dispersion curves. Though surface wave data
are weakly sensitive to the sharpness of the of the mid-mantle seismic
discontinuities, the interpretation of the temperature distribution is
highly related to the chosen composition and to the modelling of
mineralogical phase transformations. Surface wave measurements along the
Vanuatu-California path suggest a strong anisotropy above 400 km depth,
which decreases below, and a monotonous temperature distribution between
350 and 1000 km depth.