A novel user-friendly Jupyter-based tool for analysing orbital subsurface sounding radar data.

Abstract

Orbital radars, such as the SHAllow RADar (SHARAD) [1] or the Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) [2] instruments on board Mars Reconnaissance Orbiter (MRO) and Mars Express (MEX) respectively, provide valuable data about the Martian subsurface [3,4].Common analysis methodologies comprise a direct comparison between the radargram (RDR) and the corresponding Surface Clutter Simulation (SCS) to visually spot any subsurface reflector. The surface time delays converted in the space domain are then compared with the corresponding topographic profile to check if any discrepancy occurred. and thus be mistaken for subsurface reflections. Once confirmed that the subsurface reflector is valid, the proper picking can be performed by looking at the radargram and both the radargram and the simulation power intensities. Finally, it is possible to estimate the real dielectric constant ε', which is the real component of the complex permittivity ε' - iε'' using Equation Eq1 [3]:where Δt is the two-way travel time between the surface and the subsurface reflector, c is the speed of light in a vacuum and h is the reflector’s depth. Assuming different values for ε' and inverting Eq1, is possible to estimate the depth, thus the thickness of the reflector’s unit. In this work, we present the first pre-release of a user-friendly interface, with which is possible to easily perform the above analysis while granting robustness and reproducibility. Besides, it is possible to implement further custom processing functions to increase the accuracy of the results and/or expand the tool capabilities. We started the development using SHARAD US RDR and SCS, while MARSIS compatibility is under implementation. We provided also additional Jupyter notebooks for data download. This tool is based on the Jupyter lab environment and open-source python packages served as a docker container.Open Research: The tool presented here is available on GitHub [5]Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No 101004214 and No 871149.