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Fossil Signatures Using Elemental Abundance Distributions and Bayesian Probabilistic ClassificationElemental abundances (C6, N7, O8, Na11, Mg12, Al3, P15, S16, Cl17, K19, Ca20, Ti22, Mn25, Fe26, and Ni28) were obtained for a set of terrestrial fossils and the rock matrix surrounding them. Principal Component Analysis extracted five factors accounting for the 92.5% of the data variance, i.e. information content, of the elemental abundance data. Hierarchical Cluster Analysis provided unsupervised sample classification distinguishing fossil from matrix samples on the basis of either raw abundances or PCA input that agreed strongly with visual classification. A stochastic, non-linear Artificial Neural Network produced a Bayesian probability of correct sample classification. The results provide a quantitative probabilistic methodology for discriminating terrestrial fossils from the surrounding rock matrix using chemical information. To demonstrate the applicability of these techniques to the assessment of meteoritic samples or in situ extraterrestrial exploration, we present preliminary data on samples of the Orgueil meteorite. In both systems an elemental signature produces target classification decisions remarkably consistent with morphological classification by a human expert using only structural (visual) information. We discuss the possibility of implementing a complexity analysis metric capable of automating certain image analysis and pattern recognition abilities of the human eye using low magnification optical microscopy images and discuss the extension of this technique across multiple scales.
Document ID
Document Type
Conference Paper
Hoover, Richard B. (National Space Science and Technology Center Huntsville, AL, United States)
Storrie-Lombardi, Michael C. (California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 22, 2013
Publication Date
January 1, 2004
Subject Category
Inorganic, Organic and Physical Chemistry
Meeting Information
International Society for Optical Science and Technology 49th Annual Meeting(Denver, CO)
Distribution Limits
Work of the US Gov. Public Use Permitted.