A mission control architecture for robotic lunar sample return as field tested in an analogue deployment to the Sudbury impact structure

A Mission Control Architecture is presented for a Robotic Lunar Sample Return Mission which builds upon the experience of the landed missions of the NASA Mars Exploration Program. This architecture consists of four separate processes working in parallel at Mission Control and achieving buy-in for plans sequentially instead of simultaneously from all members of the team. These four processes were: Science Processing, Science Interpretation, Planning and Mission Evaluation. Science Processing was responsible for creating products from data downlinked from the field and is organized by instrument. Science Interpretation was responsible for determining whether or not science goals are being met and what measurements need to be taken to satisfy these goals. The Planning process, responsible for scheduling and sequencing observations, and the Evaluation process that fostered inter-process communications, reporting and documentation assisted these processes. This organization is advantageous for its flexibility as shown by the ability of the structure to produce plans for the rover every two hours, for the rapidity with which Mission Control team members may be trained and for the relatively small size of each individual team. This architecture was tested in an analogue mission to the Sudbury impact structure from June 6-17, 2011. A rover was used which was capable of developing a network of locations that could be revisited using a teach and repeat method. This allowed the science team to process several different outcrops in parallel, downselecting at each stage to ensure that the samples selected for caching were the most representative of the site. Over the course of 10 days, 18 rock samples were collected from 5 different outcrops, 182 individual field activities - such as roving or acquiring an image mosaic or other data product - were completed within 43 command cycles, and the rover travelled over 2,200 m. Data transfer from communications passes were filled to 74%. Sample triage was simulated to allow down-selection to 1kg of material for return to Earth

Compositional variations of Titan's impact craters indicates active surface erosion

International audienceTitan, the only moon in the solar system with a considerable atmosphere, is host to a variety of exogenic processes that shape its surface. These processes form features that are quite similar to features on Earth, including sand dunes, rivers, and lakes. The combination of a thick atmosphere and active surface processes also leads to a scarcity of impact craters on the surface of Titan. The compositions of these craters vary and may relate to the type and extent of erosion occurring at their location. In this work, we examined the composition of 12 impact features on Titan using Cassini's Visible and Infrared Mapping Spectrometer (VIMS) and 2.18-cm emissivity data from the RADAR radiometer on board the probe. Comparisons were made between crater composition as inferred from VIMS and composition as inferred from emissivity data with corresponding crater characteristics such as latitude, longitude and erosional state. We see a correlation between crater subsurface composition as inferred from the emissivity data and its erosional state and location. Well-preserved craters typically are more enriched in water-ice than degraded, organic-rich craters, suggesting that variations in composition are partially controlled by erosion and infilling. Moreover, craters located in dune fields show more subsurface organic enrichment than craters within the plains, which indicates that the efficiency of erosion and infilling varies with location and geologic context. VIMS data provide complementary information about crater surficial composition. We note that VIMS spectra do not change with erosional state, but rather appear to depend on the crater location on the moon. We suggest that there are active surface processes occurring on Titan, such as wind or rain, which are actively clearing off its surface and filling in subsurface fractures with organic materials. These processes would act to change the emissivity of the craters over time but leave the surface sensed by VIMS unchanged

Differentiating Fissure-Fed Lava Flow Types and Facies Using RADAR and LiDAR: An Example From the 2014–2015 Holuhraun Lava Flow-Field

Distinguishing between lava types and facies using remote sensing data is important for interpreting the emplacement history of lava flow-fields on Earth and other planetary bodies. Lava facies typically include a mixture of lava types and record the collective emplacement history of material preserved at a particular location. We seek to determine if lava facies in the 2014–2015 Holuhraun lava flow-field are discernible using radar roughness analysis. Furthermore, we also seek to distinguish between lava types using high resolution Light Detection and Ranging (LiDAR) data. We extracted circular polarization ratios (CPR) from the Uninhabited Aerial Vehicle Synthetic Aperture Radar and cross-polarization (VH/VV) data from the Sentinel-1 satellite to analyze the surface roughness of three previously mapped lava facies: rubbly, spiny, and undifferentiated rubbly–spiny. Using the Kruskal-Wallis test, we reveal that all but one pair of the facies are statistically separable. However, the populations overlap by 88%–89% for CPR and 64%–67% for VH/VV. Therefore, owing to large sample populations (n > 2 × 105), slight differences in radar data may be used to probabilistically infer the presence of a particular facies, but not directly map them. We also calculated the root-mean-square slope and Hurst exponents of five different lava types using LiDAR topography (5 cm/pixel). Our results show minute differences between most of the lava types, with the exception of the rubbly pāhoehoe, which is discernible at 1σ. In brief, the presence of “transitional” lava types (e.g., rubbly pāhoehoe) within fissure-fed lava flow-fields complicates remote sensing-based mapping. © 2022. American Geophysical Union. All Rights Reserved.6 month embargo; first published: 20 June 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Reexamining the Potential to Classify Lava Flows From the Fractality of Their Margins

Can fractal analysis of a lava flow's margin enable classification of the lava's morphologic type (e.g., pāhoehoe)? Such classifications would provide insights into the rheology and dynamics of the flow when it was emplaced. The potential to classify lava flows from remotely-sensed data would particularly benefit the analysis of flows that are inaccessible, including flows on other planetary bodies. The technique's current interpretive framework depends on three assumptions: (1) measured margin fractality is scale-invariant; (2) morphologic types can be uniquely distinguished based on measured margin fractality; and (3) modification of margin fractality by topography, including substrate slope and confinement, would be minimal or independently recognizable. We critically evaluate these assumptions at meter scales (1–10 m) using 15 field-collected flow margin intervals from a wide variety of morphologic types in Hawaiʻi, Iceland, and Idaho. Among the 12 margin intervals that satisfy the current framework's suitability criteria (e.g., geomorphic freshness, shallowly-sloped substrates), we show that five exhibit notably scale-dependent fractality and all five from lava types other than ‘a‘ā or pāhoehoe would be classified as one or both of those types at some scales. Additionally, an ‘a‘ā flow on a 15° slope (Mauna Ulu, Hawaiʻi) and a spiny pāhoehoe flow confined by a stream bank (Holuhraun, Iceland) exhibit significantly depressed fractalities but lack diagnostic signatures for these modifications. We therefore conclude that all three assumptions of the current framework are invalid at meter scales and propose a new framework to leverage the potential of the underlying fractal technique while acknowledging these complexities. © 2021. American Geophysical Union. All Rights Reserved.6 month embargo; first published: 25 March 2021This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Titan’s surface properties inferred from the seasonal brightness variation at 2-cm wavelength

International audienceA comprehensive calibration and mapping of the thermal microwave emission from Titan’s surface at 2.2-cm wavelength has been completed by the passive radiometer included in the Cassini RADAR instrument. A seasonal brightness temperature variation has been determined that is comparable to but slightly smaller than that obtained by Cassini's Composite Infrared Spectrometer (CIRS). This difference has implications for the composition and structure of Titan’s surface; namely, that most of Titan’s surface is covered by the deposition and possible redistribution of tholin-like atmospheric photochemical products to a depth of at least a meter

Cryovolcanic Features on Titan

International audienceWe present evidence to support the cryovolcanic origin of some features, which includes the deepest pit known on Titan (Sotra Patera) and some of the highest mountains (Doom and Erebor Montes). We interpret this region to be a cryovolcanic complex of multiple cones, craters, and flows. Elsewhere, a circular feature, approximately 100 km across, is morphologically similar to a laccolith, showing a cross pattern interpreted to be extensional fractures. However, we find that some other previously supposed cryovolcanic features were likely formed by other processes. We discuss implications for eruption style and composition of cryovolcanism on Titan. Our analysis shows the great value of combining data sets when interpreting Titan's geology and in particular stresses the value of topographic dat