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Towards constraining Mars’ thermal evolution using machine learning

Agarwal, Siddhant and Tosi, Nicola and Kessel, Pan and Padovan, Sebastiano and Breuer, Doris and Montavon, Grégoire (2021) Towards constraining Mars’ thermal evolution using machine learning. Earth and Space Science, 8, e2020EA001484. American Geophysical Union (AGU). doi: 10.1029/2020EA001484. ISSN 2333-5084.

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The thermal and convective evolution of terrestrial planets like Mars is governed by a number of initial conditions and parameters, which are poorly constrained. We use Mixture Density Networks (MDN) to invert various sets of synthetic present-day observables and infer five parameters: reference viscosity, activation energy and activation volume of the diffusion creep rheology, an enrichment factor for radiogenic elements in the crust, and initial mantle temperature. The data set comes from 6,130 two-dimensional simulations of the thermal evolution of Mars' interior. We quantify the possibility of constraining a parameter using the log-likelihood value from the MDN. Reference viscosity can be constrained to within 32% of its entire range (1019 − 1022 Pa s), when all the observables are available: core-mantle-boundary heat flux, surface heat flux, radial contraction, melt produced, and duration of volcanism. Furthermore, crustal enrichment factor (1–50) can be constrained, at best, to within 15%, and the activation energy (105 − 5 × 105 J mol−1) to within 80%. Initial mantle temperature can be constrained to within 39% of its range (1,600–1,800 K). Using the full present-day temperature profile or parts of it as an observable tightens the constraints further. The activation volume (4 × 10−6 − 10 × 10−6 m3 mol−1) cannot be constrained. We also tested different levels of uncertainty in the observables and found that constraints on different parameters loosen differently, with initial temperature being the most sensitive. Finally, we present how a joint probability model for all parameters can be obtained from the MDN.

Item URL in elib:https://elib.dlr.de/142379/
Document Type:Article
Title:Towards constraining Mars’ thermal evolution using machine learning
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Agarwal, SiddhantSiddhant.Agarwal (at) dlr.dehttps://orcid.org/0000-0002-0840-2114
Tosi, Nicolanicola.tosi (at) dlr.dehttps://orcid.org/0000-0002-4912-2848
Kessel, PanElectrical Engineering and Computer Science, Berlin Institute of Technology, Berlin, GermanyUNSPECIFIED
Padovan, SebastianoSebastiano.Padovan (at) dlr.dehttps://orcid.org/0000-0002-8652-3704
Breuer, DorisDoris.Breuer (at) dlr.dehttps://orcid.org/0000-0001-9019-5304
Montavon, GrégoireInstitut für Softwaretechnik und Theoretische Informatik, Technische Universität BerlinUNSPECIFIED
Journal or Publication Title:Earth and Space Science
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.1029/2020EA001484
Page Range:e2020EA001484
Publisher:American Geophysical Union (AGU)
Keywords:Mars, thermal evolution ,machine learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Exploration
DLR - Research area:Raumfahrt
DLR - Program:R EW - Space Exploration
DLR - Research theme (Project):R - Exploration of the Solar System
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Planetary Research > Planetary Physics
Deposited By: Amore, Dr. Mario
Deposited On:31 May 2021 16:06
Last Modified:31 May 2021 16:06

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