Low-P granulite facies metapelitic migmatites in the Wuluma Hills, Strangways Metamorphic Complex, Arunta Block, preserve evidence of polyphase deformation and migmatite formation which is of the same age of the c. 1730 Ma Wuluma granite. Mineral equilibria modelling of garnet-orthoproxenecordierite-bearing assemblages using THERMOCALC THERMOCALC is consistent with peak S3 conditions of 6.0-6.5 kbar and 850-900°C. The growth of orthopyroxene and garnet was primarily controlled by biotite breakdown during partial melting reactions. Whereas orthopyroxene in the cordierite-biotite mesosome shows enrichment of heavy-REE (HREE) relative to medium-REE (MREE), orthopyroxene in adjacent garnet-bearing leucosome shows depletion of HREE relative to MREE. There is no appreciable difference in major element contents of minerals common to both the mesosome and leucosome. The REE variations can be satisfactorily explained by decoupling of major element and REE partitioning, in the context of appropriate phase-equilibria modelling of a prograde path at 6 kbar. Sparse garnet nucleii formed at 760°C, along with concentrated leucosome development and preferentially partitioned HREE. Further heating to 800°C at constant or subtly increasing pressure conditions additionally stabilized orthopyroxene and decreased the garnet mode. Orthopyroxene in the leucosome inherited an REE pattern consequent to the partial consumption of garnet, it being distinct from the REE pattern in mesosome orthoproxene that was mostly controlled by biotite breakdown. Such withinsample variability in the enrichment of heavy REE indicates that caution needs to be exercised in the application of common elemental partitioning coefficients in spatially complex metamorphic rocks.
Outdoor robots such as planetary rovers must be able to navigate safely and reliably in order to successfully perform missions in remote or hostile environments. Mobility prediction is critical to achieving this goal due to the inherent control uncertainty faced by robots traversing natural terrain. We propose a novel algorithm for stochastic mobility prediction based on multi-output Gaussian process regression. Our algorithm considers the correlation between heading and distance uncertainty and provides a predictive model that can easily be exploited by motion planning algorithms. We evaluate our method experimentally and report results from over 30 trials in a Mars-analogue environment that demonstrate the effectiveness of our method and illustrate the importance of mobility prediction in navigating challenging terrain.
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