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- All Subjects: Planetary Science
- Creators: Bell, James F.
Description
Planetary surface studies across a range of spatial scales are key to interpreting modern and ancient operative processes and to meeting strategic mission objectives for robotic planetary science exploration. At the meter-scale and below, planetary regolith conducts heat at a rate that depends on the physical properties of the regolith particles, such as particle size, sorting, composition, and shape. Radiometric temperature measurements thus provide the means to determine regolith properties and rock abundance from afar. However, heat conduction through a matrix of irregular particles is a complicated physical system that is strongly influenced by temperature and atmospheric gas pressure. A series of new regolith thermal conductivity experiments were conducted under realistic planetary surface pressure and temperature conditions. A new model is put forth to describe the radiative, solid, and gaseous conduction terms of regolith on Earth, Mars, and airless bodies. These results will be used to infer particle size distribution from temperature measurements of the primitive asteroid Bennu to aid in OSIRIS-REx sampling site selection. Moving up in scale, fluvial processes are extremely influential in shaping Earth's surface and likely played an influential role on ancient Mars. Amphitheater-headed canyons are found on both planets, but conditions necessary for their development have been debated for many years. A spatial analysis of canyon form distribution with respect to local stratigraphy at the Escalante River and on Tarantula Mesa, Utah, indicates that canyon distribution is most closely related to variations in local rock strata, rather than groundwater spring intensity or climate variations. This implies that amphitheater-headed canyons are not simple markers of groundwater seepage erosion or megaflooding. Finally, at the largest scale, volcanism has significantly altered the surface characteristics of Earth and Mars. A field campaign was conducted in Hawaii to investigate the December 1974 Kilauea lava flow, where it was found that lava coils formed in an analogous manner to those found in Athabasca Valles, Mars. The location and size of the coils may be used as indicators of local effusion rate, viscosity, and crustal thickness.
ContributorsRyan, Andrew J (Author) / Christensen, Philip R. (Thesis advisor) / Bell, James F. (Committee member) / Whipple, Kelin X (Committee member) / Ruff, Steven W (Committee member) / Asphaug, Erik I (Committee member) / Arizona State University (Publisher)
Created2018
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