Stochastic hyperbola fitting, probabilistic inversion, reverse-time migration and clustering: A novel interpretation toolbox for in-situ planetary radar

• DOI: 10.1016/j.icarus.2023.115555
• Publication Date: 2023-04-01T14:54:02Z
• Authors: Iraklis Giannakis; Javier Martin-Torres; Maria-Paz Zorzano; Yan Su; Craig Warren; Antonios Giannopo...
• Keywords: Ground-Penetrating Radar; Hyperbola

Ground-penetrating radar (GPR) is becoming a mainstream tool in planetary exploration, and one of the few in-situ geophysical methods. There are currently three missions (Perseverance, Tianwen-1, Chang'E-4) with GPR-equipped rovers, two future (Chang'E-7, ExoMars) that will include GPR their scientific payload. The large number data, combined novel setup measurements, creates need for new data processing interpretation techniques to address unique challenges radar. current paper proposes an pipeline starts stochastic hyperbola fitting estimates probability kernel density bulk permittivity at different depths. Subsequently, distribution transformed via probabilistic inversion 1-dimensional (1D) profile. inverted 1D profile then used as input bespoke reverse-time migration (RTM) using finite-difference time-domain (FDTD) method. RTM FDTD does not assume clinical homogeneous half-space; instead, it accounts expected layered structure investigated medium. Lastly, migrated radargram clustered order identify subsurface targets distinguish them from background Each steps has never been reported radar; together act complete toolbox tuned science. suggested validated numerically case study complex multiple targets. proposed scheme applied Chang'E-4 mission Von Kármán crater, revealing previously unseen rocks/boulders.