Citation:
Li, X. Z., Rong, Z. J., Gao, J. W., Wei, Y., Shi, Z., Yu, T., and Wan, W. X. (2020). A local Martian crustal field model: Targeting the candidate landing site of the 2020 Chinese Mars Rover. Earth Planet. Phys., 4(4), 420–428doi: 10.26464/epp2020045
2020, 4(4): 420-428. doi: 10.26464/epp2020045
A local Martian crustal field model: Targeting the candidate landing site of the 2020 Chinese Mars Rover
1. | Key Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China |
2. | College of Earth Science, University of Chinese Academy of Sciences, Beijing 100049, China |
3. | Beijing National Observatory of Space Environment, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China |
4. | China University of Geosciences, Wuhan 430074, China |
Unlike Earth, Mars lacks a global dipolar magnetic field but is dominated by patches of a remnant crustal magnetic field. In 2021, the Chinese Mars Rover will land on the surface of Mars and measure the surface magnetic field along a moving path within the possible landing region of 20°W–50°W, 20°N–30°N. One scientific target of the Rover is to monitor the variation in surface remnant magnetic fields and reveal the source of the ionospheric current. An accurate local crustal field model is thus considered necessary as a field reference. Here we establish a local crust field model for the candidate landing site based on the joint magnetic field data set from Mars Global Explorer (MGS) and Mars Atmosphere and Volatile Evolution (MAVEN) data combined. The model is composed of 1,296 dipoles, which are set on three layers but at different buried depths. The application of the dipole model to the joint data set allowed us to calculate the optimal parameters of their dipoles. The calculated results demonstrate that our model has less fitting error than two other state-of-the art global crustal field models, which would indicate a more reasonable assessment of the surface crustal field from our model.
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