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Comprehensive mapping of lunar surface chemistry by adding Chang'e-5 samples with deep learning

Author

Listed:
  • Chen Yang

    (Jilin University
    National Astronomical Observatories, Chinese Academy of Sciences)

  • Xinmei Zhang

    (Jilin University)

  • Lorenzo Bruzzone

    (University of Trento)

  • Bin Liu

    (National Astronomical Observatories, Chinese Academy of Sciences)

  • Dawei Liu

    (National Astronomical Observatories, Chinese Academy of Sciences)

  • Xin Ren

    (National Astronomical Observatories, Chinese Academy of Sciences)

  • Jon Atli Benediktsson

    (University of Iceland, 102)

  • Yanchun Liang

    (Jilin University)

  • Bo Yang

    (Jilin University)

  • Minghao Yin

    (Northeast Normal University)

  • Haishi Zhao

    (Jilin University)

  • Renchu Guan

    (Jilin University)

  • Chunlai Li

    (National Astronomical Observatories, Chinese Academy of Sciences)

  • Ziyuan Ouyang

    (National Astronomical Observatories, Chinese Academy of Sciences
    Chinese Academy of Sciences)

Abstract

Lunar surface chemistry is essential for revealing petrological characteristics to understand the evolution of the Moon. Existing chemistry mapping from Apollo and Luna returned samples could only calibrate chemical features before 3.0 Gyr, missing the critical late period of the Moon. Here we present major oxides chemistry maps by adding distinctive 2.0 Gyr Chang’e-5 lunar soil samples in combination with a deep learning-based inversion model. The inferred chemical contents are more precise than the Lunar Prospector Gamma-Ray Spectrometer (GRS) maps and are closest to returned samples abundances compared to existing literature. The verification of in situ measurement data acquired by Chang'e 3 and Chang'e 4 lunar rover demonstrated that Chang’e-5 samples are indispensable ground truth in mapping lunar surface chemistry. From these maps, young mare basalt units are determined which can be potential sites in future sample return mission to constrain the late lunar magmatic and thermal history.

Suggested Citation

  • Chen Yang & Xinmei Zhang & Lorenzo Bruzzone & Bin Liu & Dawei Liu & Xin Ren & Jon Atli Benediktsson & Yanchun Liang & Bo Yang & Minghao Yin & Haishi Zhao & Renchu Guan & Chunlai Li & Ziyuan Ouyang, 2023. "Comprehensive mapping of lunar surface chemistry by adding Chang'e-5 samples with deep learning," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43358-0
    DOI: 10.1038/s41467-023-43358-0
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    References listed on IDEAS

    as
    1. Dawei Liu & Xing Wang & Jianjun Liu & Bin Liu & Xin Ren & Yuan Chen & Zhaopeng Chen & Hongbo Zhang & Guangliang Zhang & Qin Zhou & Zhoubin Zhang & Qiang Fu & Chunlai Li, 2022. "Spectral interpretation of late-stage mare basalt mineralogy unveiled by Chang’E-5 samples," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
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