Articles | Volume 35, issue 1
https://doi.org/10.5194/ejm-35-45-2023
https://doi.org/10.5194/ejm-35-45-2023
Research article
 | 
17 Jan 2023
Research article |  | 17 Jan 2023

Shear properties of MgO inferred using neural networks

Ashim Rijal, Laura Cobden, Jeannot Trampert, Hauke Marquardt, and Jennifer M. Jackson

Viewed

Total article views: 1,908 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,518 326 64 1,908 73 120
  • HTML: 1,518
  • PDF: 326
  • XML: 64
  • Total: 1,908
  • BibTeX: 73
  • EndNote: 120
Views and downloads (calculated since 17 Jan 2023)
Cumulative views and downloads (calculated since 17 Jan 2023)

Viewed (geographical distribution)

Total article views: 1,843 (including HTML, PDF, and XML) Thereof 1,843 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 21 Jul 2026
Download
Short summary
Using neural networks with experimental data, we infer the relationship between pressure, temperature and shear properties of MgO. Fixing the form of the relationship, which is a common practice, provides the properties that are largely constrained by the form and not the data. Our approach provides realistic uncertainties in shear properties, which should improve uncertainty quantification in interpretations of observed shear wave speed to infer the structure and dynamics of the Earth’s mantle.
Share