AUTHOR=Lv Wenmin , Zhang Jinhai TITLE=High-resolution permittivity estimation of ground penetrating radar data by migration with isolated hyperbolic diffractions and local focusing analyses JOURNAL=Frontiers in Astronomy and Space Sciences VOLUME=10 YEAR=2023 URL=https://www.frontiersin.org/journals/astronomy-and-space-sciences/articles/10.3389/fspas.2023.1188232 DOI=10.3389/fspas.2023.1188232 ISSN=2296-987X ABSTRACT=

Ground penetrating radar (GPR) is important for detecting shallow subsurface structures, which has been successfully used on the Earth, Moon, and Mars. It is difficult to analyze the underground permittivity from GPR data because its observation system is almost zero-offset. Traditional velocity analysis methods can work well with separable diffractions but fail with strong-interfered diffractions. However, in most situations, especially for lunar or Martian exploration, the diffractions are highly interfered, or even buried in reflections. Here, we proposed a new method to estimate the underground permittivity and apply it to lunar penetrating radar data. First, we isolate a group of diffractions with a hyperbolic time window determined by a given velocity. Then, we perform migration using the given velocity and evaluate the focusing effects of migration results. Next, we find the most focused results after scanning a series of velocities and regard the corresponding velocity as the best estimation. Finally, we assemble all locally focused points and derive the best velocity model. Tests show that our method has high spatial resolution and can handle strong noises, thus can achieve velocity analyses with high accuracy, especially for complex materials. The permittivity of lunar regolith at Chang’E-4 landing area is estimated to be ∼4 within 12 m, ranging from 3.5 to 4.2 with a local perturbation of ∼2.3%, consistent with ∼3% obtained by numerical simulations using self-organization random models. This suggests that the lunar regolith at Chang’E-4 landing area is mature and can be well described by self-organization random models.