Interior structure of terrestrial planets: Modeling Mars' mantle and its electromagnetic, geodetic, and seismic properties

Details Interior structure of terrestrial planets: Modeling Mars' mantle and its electromagnetic, geodetic, and seismic properties Interior structure of terrestrial planets: Modeling Mars' mantle and its electromagnetic, geodetic, and seismic properties

Apr 2005

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005jgre..11004009v&link_type=abstract

Journal of Geophysical Research, Volume 110, Issue E4, CiteID E04009

Computer Science

Sound

16

Planetary Sciences: Solid Surface Planets: Interiors (8147), Planetary Sciences: Solar System Objects: Mars, Mineralogy And Petrology: Planetary Mineralogy And Petrology (5410)

Scientific paper

We present a new procedure to describe the one-dimensional thermodynamical state and mineralogy of any Earth-like planetary mantle, with Mars as an example. The model parameters are directly related to expected results from a geophysical network mission, in this case electromagnetic, geodetic, and seismological processed observations supplemented with laboratory measurements. We describe the internal structure of the planet in terms of a one-dimensional model depending on a set of eight parameters: for the crust, the thickness and the mean density, for the mantle, the bulk volume fraction of iron, the olivine volume fraction, the pressure gradient, and the temperature profile, and for the core, its mass and radius. Currently, available geophysical and geochemical knowledge constrains the range of the parameter values. In the present paper, we develop the forward problem and present the governing equations from which synthetic data are computed using a set of parameter values. Among all Martian models fitting the currently available knowledge, we select eight candidate models for which we compute synthetic network science data sets. The synergy between the three geophysical experiments of electromagnetic sounding, geodesy, and seismology is emphasized. The stochastic inversion of the synthetic data sets will be presented in a companion paper.

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