Publication Date:
2022-10-26
Description:
Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research-Planets 124, (2019): 3095-3118, doi: 10.1029/2019JE005937.
Description:
We applied localized gravity/topography admittance and correlation analysis, as well as the Markov chain Monte Carlo method, to invert for loading and flexural parameters of 21 subregions on Mars with five distinct tectonic types. The loading styles of the five tectonic types are distinct: The surface and subsurface loading in the polar and plain regions can be assumed to be largely uncorrelated, in contrast to the correlated loading associated with the volcanic montes and Valles Marineris. For the impact basins, we consider the initial topographic depression and mantle plug before postimpact surface loading. Our analyses yield four main results: (1) The inverted effective lithospheric thickness (Te) is highly dependent on assumptions of loading type. (2) There is a trend of increasing Te from the Noachian southern highlands (20–60 km) to the Hesperian northern lowlands (〉90 km) and from the Hesperian Elysium Mons (〈55 km) to the Hesperian/Amazonian Olympus Mons (〉105 km). These Te estimates are consistent with the thermal states at the time of loading, corresponding to a global secular cooling history with decreasing heat flux. (3) Our analyses suggest high‐density basaltic surface loading at the volcanic montes and Isidis basin, in contrast to the low‐density sedimentary surface loading at the Utopia and Argyre basins. (4) We find some degree of correlation between the surface and subsurface loading for the northern polar cap and the northern plains, likely due to earlier, larger polar deposits and ancient buried features, respectively.
Description:
The gravity model JGMRO120d and topography model MarsTopo719 used in this paper were retrieved from the Geosciences Node of NASA's Planetary Data System (http://pds‐geosciences.wustl.edu/mro/mro‐m‐rss‐5‐sdp‐v1/mrors_1xxx/data/shadr/) and from the SHTOOLS package (http://sourceforge.net/projects/shtools/), respectively. The MATLAB codes to reproduce the data analysis, parameter estimation, and key figures are available in a github repository (https://github.com/MinaDing/marslithosphere/tree/v1.0.0, DOI: 10.5281/zenodo.3530057). We are grateful to Mark Wieczorek and Frederik Simons for sharing relevant software online. We thank Ken Tanaka for providing a digital map of Mars chronographic ages. We thank Brandon Johnson for consultation on the loading processes of impact basins. We also thank Editor Laurent Montesi and Steven A. Hauck, as well as Patrick McGovern and anonymous reviewers for their invaluable feedbacks. This work was supported by National Natural Science Foundation of China (41806067, 41890813, 91628301 and U1606401), Key Laboratory of Ocean and Marginal Sea Geology, Chinese Academy of Sciences (OMG18‐02), Chinese Academy of Sciences (Y4SL021001, QYZDY‐SSW‐DQC005 and 133244KYSB20180029), Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (GML2019ZD0205), Radio Science Gravity investigation of the NASA Mars Reconnaissance Orbiter mission (M.T.Z.), and National Science Foundation (EAR 1220280) and Henry Bigelow Chair for Excellence in Oceanography (J.L.).
Description:
2020-05-20
Keywords:
Mars
;
Lithospheric flexure
;
Tectonic loading styles
;
Lithospheric strength
;
Markov chain Monte Carlo method
;
Inverse spectral method
Repository Name:
Woods Hole Open Access Server
Type:
Article
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