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Description
Understanding the structural evolution of planetary surfaces provides key insights to their physical properties and processes. On the Moon, large-scale tectonism was thought to have ended over a billion years ago. However, new Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) high resolution images show the Moon’s surface in unprecedented detail and show many previously unidentified tectonic landforms, forcing a re-assessment of our views of lunar tectonism. I mapped lobate scarps, wrinkle ridges, and graben across Mare Frigoris – selected as a type area due to its excellent imaging conditions, abundance of tectonic landforms, and range of inferred structural controls. The distribution, morphology, and crosscutting relationships of these newly identified populations of tectonic landforms imply a more complex and longer-lasting history of deformation that continues to today. I also performed additional numerical modeling of lobate scarp structures that indicates the upper kilometer of the lunar surface has experienced 3.5-18.6 MPa of differential stress in the recent past, likely due to global compression from radial thermal contraction.
Central pit craters on Mars are another instance of intriguing structures that probe subsurface physical properties. These kilometer-scale pits are nested in the centers of many impact craters on Mars as well as on icy satellites. They are inferred to form in the presence of a water-ice rich substrate; however, the process(es) responsible for their formation is still debated. Previous models invoke origins by either explosive excavation of potentially water-bearing crustal material, or by subsurface drainage of meltwater and/or collapse. I assessed radial trends in grain size around central pits using thermal inertias calculated from Thermal Emission Imaging System (THEMIS) thermal infrared images. Average grain size decreases with radial distance from pit rims – consistent with pit-derived ejecta but not expected for collapse models. I present a melt-contact model that might enable a delayed explosion, in which a central uplift brings ice-bearing substrate into contact with impact melt to generate steam explosions and excavate central pits during the impact modification stage.
Central pit craters on Mars are another instance of intriguing structures that probe subsurface physical properties. These kilometer-scale pits are nested in the centers of many impact craters on Mars as well as on icy satellites. They are inferred to form in the presence of a water-ice rich substrate; however, the process(es) responsible for their formation is still debated. Previous models invoke origins by either explosive excavation of potentially water-bearing crustal material, or by subsurface drainage of meltwater and/or collapse. I assessed radial trends in grain size around central pits using thermal inertias calculated from Thermal Emission Imaging System (THEMIS) thermal infrared images. Average grain size decreases with radial distance from pit rims – consistent with pit-derived ejecta but not expected for collapse models. I present a melt-contact model that might enable a delayed explosion, in which a central uplift brings ice-bearing substrate into contact with impact melt to generate steam explosions and excavate central pits during the impact modification stage.
ContributorsWilliams, Nathan Robert (Author) / Bell, James (Thesis advisor) / Robinson, Mark (Committee member) / Christenen, Philip (Committee member) / Farmer, Jack (Committee member) / Shirzaei, Manoochehr (Committee member) / Arizona State University (Publisher)
Created2016
Description
There are more than 20 active missions exploring planets and small bodies beyond Earth in our solar system today. Many more have completed their journeys or will soon begin. Each spacecraft has a suite of instruments and sensors that provide a treasure trove of data that scientists use to advance our understanding of the past, present, and future of the solar system and universe. As more missions come online and the volume of data increases, it becomes more difficult for scientists to analyze these complex data at the desired pace. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and prioritize the most promising, novel, or relevant observations for scientific analysis. Machine learning methods can serve this need in a variety of ways: by uncovering patterns or features of interest in large, complex datasets that are difficult for humans to analyze; by inspiring new hypotheses based on structure and patterns revealed in data; or by automating tedious or time-consuming tasks. In this dissertation, I present machine learning solutions to enhance the tactical planning process for the Mars Science Laboratory Curiosity rover and future tactically-planned missions, as well as the science analysis process for archived and ongoing orbital imaging investigations such as the High Resolution Imaging Science Experiment (HiRISE) at Mars. These include detecting novel geology in multispectral images and active nuclear spectroscopy data, analyzing the intrinsic variability in active nuclear spectroscopy data with respect to elemental geochemistry, automating tedious image review processes, and monitoring changes in surface features such as impact craters in orbital remote sensing images. Collectively, this dissertation shows how machine learning can be a powerful tool for facilitating scientific discovery during active exploration missions and in retrospective analysis of archived data.
ContributorsKerner, Hannah Rae (Author) / Bell, James F. (Thesis advisor) / Ben Amor, Heni (Thesis advisor) / Wagstaff, Kiri L (Committee member) / Hardgrove, Craig J (Committee member) / Shirzaei, Manoochehr (Committee member) / Arizona State University (Publisher)
Created2019