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\begin{document}

\title{Deep Learning driven interpretation of Chang'E4 Lunar Penetrating Radar}



\author[1]{Giacomo RONCORONI}%
\author[1]{Emanuele Forte}%
\author[1]{Ilaria Santin}%
\author[1]{Ana Černok}%
\author[2]{Andrea Rajšić}%
\author[3]{Alessandro Frigeri}%
\author[4]{wenke zhao}%
\author[1]{Michele Pipan}%
\affil[1]{University of Trieste}%
\affil[2]{Purdue University}%
\affil[3]{Istituto Nazionale di Astrofisica}%
\affil[4]{Zhejiang University}%


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  \date{June 18, 2024}


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\begin{abstract}
We reprocessed and interpreted Chang'E-4 Lunar Penetrating Radar (LPR)
data collected until 14th February 2023, exploiting a new Deep
Learning-based algorithm to automatically extract reflectors from a
processed radar dataset. The results are in terms of horizon probability
and have been interpreted by integrating signal attribute analysis with
orbital imagery. The approach provides more objective results by
minimizing the subjectivity of data interpretation allowing to link
radar reflectors to their geological context and surface structures. For
the first time, we imaged dipping layers and at least 20 shallow buried
crateriform structures within the regolith using LPR data. We further
recognized four deeper structures similar to craters, locating ejecta
deposits related to a crater rim crossed by the rover path and visible
in satellite image data.%
\end{abstract}%



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