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Shear properties of MgO inferred using neural networks

Rijal, Ashim and Cobden, Laura and Trampert, Jeannot and Marquardt, Hauke and Jackson, Jennifer M. (2023) Shear properties of MgO inferred using neural networks. European Journal of Mineralogy, 35 (1). pp. 45-58. ISSN 1617-4011. doi:10.5194/ejm-35-45-2023.

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Shear properties of mantle minerals are vital for interpreting seismic shear wave speeds and therefore inferring the composition and dynamics of a planetary interior. Shear wave speed and elastic tensor components, from which the shear modulus can be computed, are usually measured in the laboratory mimicking the Earth's (or a planet's) internal pressure and temperature conditions. A functional form that relates the shear modulus to pressure (and temperature) is fitted to the measurements and used to interpolate within and extrapolate beyond the range covered by the data. Assuming a functional form provides prior information, and the constraints on the predicted shear modulus and its uncertainties might depend largely on the assumed prior rather than the data. In the present study, we propose a data-driven approach in which we train a neural network to learn the relationship between the pressure, temperature and shear modulus from the experimental data without prescribing a functional form a priori. We present an application to MgO, but the same approach works for any other mineral if there are sufficient data to train a neural network. At low pressures, the shear modulus of MgO is well-constrained by the data. However, our results show that different experimental results are inconsistent even at room temperature, seen as multiple peaks and diverging trends in probability density functions predicted by the network. Furthermore, although an explicit finite-strain equation mostly agrees with the likelihood predicted by the neural network, there are regions where it diverges from the range given by the networks. In those regions, it is the prior assumption of the form of the equation that provides constraints on the shear modulus regardless of how the Earth behaves (or data behave). In situations where realistic uncertainties are not reported, one can become overconfident when interpreting seismic models based on those defined equations of state. In contrast, the trained neural network provides a reasonable approximation to experimental data and quantifies the uncertainty from experimental errors, interpolation uncertainty, data sparsity and inconsistencies from different experiments.

Item Type:Article
Related URLs:
URLURL TypeDescription
Rijal, Ashim0000-0002-6826-0541
Cobden, Laura0000-0002-7641-9671
Trampert, Jeannot0000-0002-5868-9491
Marquardt, Hauke0000-0003-1784-6515
Jackson, Jennifer M.0000-0002-8256-6336
Additional Information:© Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License. We would like to thank the two anonymous reviewers for constructive comments which improved the manuscript. Ashim Rijal and Laura Cobden received funding from the Dutch Research Council (NWO) under grant number 016.Vidi.171.022. Hauke Marquardt acknowledges the support provided through the European Union's Horizon 2020 research and innovation programme (ERC grant 864877). Jennifer M. Jackson is thankful for support of this research by the National Science Foundation's Collaborative Studies of the Earth's Deep Interior (EAR-2009735). This research has been supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (grant no. 016.Vidi.171.022), Horizon 2020 (DEEP-MAPS (grant no. 864877)) and the National Science Foundation (grant no. EAR-2009735).
Group:Division of Geological and Planetary Sciences, Seismological Laboratory
Funding AgencyGrant Number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO)016.Vidi.171.022
European Research Council (ERC)864877
Issue or Number:1
Record Number:CaltechAUTHORS:20230213-466022900.5
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:119237
Deposited By: Research Services Depository
Deposited On:24 Mar 2023 03:43
Last Modified:24 Mar 2023 16:13

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