Exploring carbon mineralogy and mineral evolution through deep time with advanced analytics and visualization
Authors
Morrison, SM
Eleish, Ahmed
Prabhu, Anirudh
Narkar, Shweta
Pan, Feifei
Huang, Fang
Fox, Peter
Hystad, G.
Liu, C.
Buongiorno, J.
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2019-12-12
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Morrison SM, Eleish A, Prabhu A, Narkar S, Pan F, Huang F, Fox PA, Hystad G, Liu C, Buongiorno J, Zhang S. Exploring carbon mineralogy and mineral evolution through deep time with advanced analytics and visualization. In AGU Fall Meeting 2019 Dec 12. AGU.
Abstract
Recent years have seen a dramatic increase in the volume of mineralogical data available for study, including that of carbon. These large and expanding data resources have created an opportunity to characterize changes in near-surface mineralogy through deep time and to relate these findings to the geologic and biologic evolution of our planet over the past 4.5 billion years [1]. Using databases, such as the Mineral Evolution Database (MED), we explore the spatial and temporal distribution of carbon minerals on Earth’s surface while considering the multidimensional relationships between composition, oxidation state, structural complexity [2], and paragenetic mode.
These studies, driven by advanced analytical and visualization techniques such as mineral ecology [3], network analysis [4], affinity analysis [5], and natural kind clustering [6] allow us to begin tackling big questions in Earth, planetary, and biosciences. These questions relate to understanding the relationships of mineral formation and preservation with large-scale geologic processes, such as Wilson cycles, the oxidation of Earth’s atmosphere, and changes in ocean chemistry. We can also estimate the probability of finding a mineral or mineral assemblage at any locality on Earth or another planetary body. Given the spatial and temporal distribution of minerals on Earth, which was heavily influenced by life, we can explore the possibility that Earth’s mineral diversity and distribution is a biosignature that can be used for future planetary evaluation and exploration. With natural kind clustering, we can predict the formational environment of mineral samples. These geologic resources also facilitate cross-disciplinary integration, such as how the geochemical makeup of our planet affected the emergence and evolution of life, and, likewise, how life influenced chemical composition and geological processes throughout Earth history [10-11].
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AGU