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Alessandro Ghirotto

Publications and source records attributed to Alessandro Ghirotto.

2 recordsLinked to original sources

Lava Tube Exploration with LunarLeaper

Lunar pits, some of which are interpreted as collapse features into underlying lava tubes, expose otherwise inaccessible stratigraphy and may provide entry points to subsurface voids that preserve records of lunar volcanism and offer potential sites for future human exploration. We synthesize the current state of knowledge on lunar pits and lava tubes, covering their morphological characteristics, classification, proposed formation mechanisms, mechanical stability, and detection from orbit. We then review the open science questions that pit and pit-wall investigation is uniquely placed to address, spanning the volcanic stratigraphy of the lunar maria, the structure and lateral variability of the regolith, and the dimensions and accessibility of subsurface conduits. To evaluate how these questions can be tackled in situ, we assess the feasibility and expected performance of geophysical and remote-sensing investigations for subsurface voids and surface exposures, mainly focusing on gravity measurements, ground-penetrating radar, high-resolution imaging, and spectroscopy. Building on this, we present LunarLeaper, a small legged robot mission concept combining a gravimeter, ground-penetrating radar, high-resolution imager, spectrometer, and leg-based geomechanical experiments to deliver the first in situ investigation of a mare pit. The concept targets the Marius Hills Pit and its associated rille, with a mobility architecture optimized for the rugged terrain encountered at pit edges and funnel slopes.

astro-ph.EP

HMCLab: a framework for solving diverse geophysical inverse problems using the Hamiltonian Monte Carlo method

The use of the probabilistic approach to solve inverse problems is becoming more popular in the geophysical community, thanks to its ability to address nonlinear forward problems and to provide uncertainty quantification. However, such strategy is often tailored to specific applications and therefore there is a lack of a common platform for solving a range of different geophysical inverse problems and showing potential and pitfalls. We demonstrate a common framework to solve such inverse problems ranging from, e.g, earthquake source location to potential field data inversion and seismic tomography. Within this approach, we can provide probabilities related to certain properties or structures of the subsurface. Thanks to its ability to address high-dimensional problems, the Hamiltonian Monte Carlo (HMC) algorithm has emerged as the state-of-the-art tool for solving geophysical inverse problems within the probabilistic framework. HMC requires the computation of gradients, which can be obtained by adjoint methods, making the solution of tomographic problems ultimately feasible. These results can be obtained with "HMCLab", a tool for solving a range of different geophysical inverse problems using sampling methods, focusing in particular on the HMC algorithm. HMCLab consists of a set of samplers and a set of geophysical forward problems. For each problem its misfit function and gradient computation are provided and, in addition, a set of prior models can be combined to inject additional information into the inverse problem. This allows users to experiment with probabilistic inverse problems and also address real-world studies. We show how to solve a selected set of problems within this framework using variants of the HMC algorithm and analyze the results. HMCLab is provided as an open source package written both in Python and Julia, welcoming contributions from the community.

physics.geo-ph