Geologic Mapping Methods for a Mission-Driven Mapping Scenario: The Dawn at Vesta Example

Kurzfassung

We report on methods used to create a global geologic map of Vesta based on data from the Dawn spacecraft’s High-Altitude Mapping Orbit (HAMO) data. Each of Dawn’s several orbital phases at Vesta provided increasingly higher spatial resolution data that fed into three main iterations of the global map. The first iteration was created during the approach phase and was based on clear filter data from the Framing Camera (FC), which at this stage covered the surface at 3-9 km/pixel resolution. The second iteration used FC clear filter data at ~200 m/pixel resolution and a Digital Terrain Model derived from image data acquired during Survey orbit. The third iteration was based on data from the High-Altitude Mapping Orbit (HAMO; spatial resolution ~61 m/pixel). We mapped three broad terrain types: heavily-cratered, ridge-and-trough (equatorial and northern fossae), and terrain associated with the Rheasilvia and Veneneia impact structures. Local features include bright and dark material and ejecta, lobate deposits, and mass-wasting materials. Stratigraphy of Vesta’s geologic units suggests a history in which primary crust formation was followed by first the Veneneia and then the Rheasilvia impact events, along with associated structural deformation that shaped the Saturnalia and Divalia Fossae Formations respectively. Subsequent impacts and mass wasting events subdued impact craters and parts of ridge-and-trough sets, and formed slumps and landslides. Discontinuous low-albedo deposits also formed or were emplaced; these lie stratigraphically above the equatorial fossae. The youngest features are bright-rayed craters and other surface mantling deposits. Lessons learned in this mapping effort include: (1) iterative mapping provides teams with a robust way to organize knowns and unknowns, feeding forward into subsequent science decisions; (2) the process must include enough people working concurrently as well as collaboratively and individually, to be efficient enough to serve tactical needs; and (3) generic descriptors of features should be retained for as long as possible — during the iterative mapping process, ideally until features can be observed with the highest resolution. This lessens bias and potentially reduces confusion (among the team and the community) as feature interpretations evolve.