Deep learning lunar penetrating radar inversion: An example from Chang'e-3

Zi Xian Leong, Tieyuan Zhu

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


The Moon's deeper subsurface layers beyond the regolith are not well-studied. Using data from Change'E-3 Yutu rover's lunar penetrating radar (LPR), we invert for its subsurface dielectric permittivity (εr) model. We use convolutional neural network based deep learning architecture to train numerous εr profiles and their corresponding synthetic radargrams. The dielectric permittivity training dataset is designed to encapsulate all possible εr realizations that the lunar subsurface materials may have. We test our trained model on synthetic data, and on the Change'E-3 LPR data. We validate our predicted εr by comparing its forward data and the field data. Our interpretation suggests multiple layers in the upper 200 meters in the order of regolith, ejectas, Eratosthenian basaltic lava flows, paleoregolith, and lava flows from Imbrium period.

Original languageEnglish (US)
Pages (from-to)1379-1383
Number of pages5
JournalSEG Technical Program Expanded Abstracts
StatePublished - 2021
Event1st International Meeting for Applied Geoscience and Energy - Denver, United States
Duration: Sep 26 2021Oct 1 2021

All Science Journal Classification (ASJC) codes

  • Geotechnical Engineering and Engineering Geology
  • Geophysics


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