KAUST Grant NumberKUS-C1-016-04
Online Publication Date2014-04-22
Print Publication Date2014-04
Permanent link to this recordhttp://hdl.handle.net/10754/597701
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AbstractWe present BurnMan, an open-source mineral physics toolbox to determine elastic properties for specified compositions in the lower mantle by solving an Equation of State (EoS). The toolbox, written in Python, can be used to evaluate seismic velocities of new mineral physics data or geodynamic models, and as the forward model in inversions for mantle composition. The user can define the composition from a list of minerals provided for the lower mantle or easily include their own. BurnMan provides choices in methodology, both for the EoS and for the multiphase averaging scheme. The results can be visually or quantitatively compared to observed seismic models. Example user scripts show how to go through these steps. This paper includes several examples realized with BurnMan: First, we benchmark the computations to check for correctness. Second, we exemplify two pitfalls in EoS modeling: using a different EoS than the one used to derive the mineral physical parameters or using an incorrect averaging scheme. Both pitfalls have led to incorrect conclusions on lower mantle composition and temperature in the literature. We further illustrate that fitting elastic velocities separately or jointly leads to different Mg/Si ratios for the lower mantle. However, we find that, within mineral physical uncertainties, a pyrolitic composition can match PREM very well. Finally, we find that uncertainties on specific input parameters result in a considerable amount of variation in both magnitude and gradient of the seismic velocities. © 2014. American Geophysical Union. All Rights Reserved.
CitationCottaar S, Heister T, Rose I, Unterborn C (2014) BurnMan: A lower mantle mineral physics toolkit. Geochem Geophys Geosyst 15: 1164–1179. Available: http://dx.doi.org/10.1002/2013GC005122.
SponsorsThe authors are ordered alphabetically to represent their roughly equal contributions to the code and this manuscript. SC, TH, IR, and CU are grateful for the possibility to participate in CIDER 2012, where this work was initiated. CIDER 2012 is funded through NSF FESD grant 1135452. CIDER also funded a follow-up meeting for this project. We thank all the fellow member of the Cider Mg/Si team for their input: Valentina Magni, Yu Huang, JiaChao Liu, Marc Hirschmann, and Barbara Romanowicz. We thank Lars Stixrude for providing benchmarking calculations and Motohiko Murakami for providing various parameters. We also welcomed helpful discussions with Zack Geballe, Bill McDonough, Quentin Williams, Wendy Panero, and Wolfgang Bangerth. SC is supported through NSF/CSEDI grant 1067513 and Draper's Company Research Fellowship from Pembroke College, Cambridge. TH is supported in part through the Computational Infrastructure in Geodynamics initiative (CIG), through the NSF EAR-0949446 and The University of California-Davis and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). IR is supported through NSF grant EAR-1246670. CU is supported by NSF CAREER grant EAR-60023026 to Wendy R. Panero.
PublisherAmerican Geophysical Union (AGU)