Fossil signatures using elemental abundance distributions and Bayesian probabilistic classification

Michael C. Storrie-Lombardi M.D.; Richard B. Hoover

doi:10.1117/12.563573

1 November 2004 Fossil signatures using elemental abundance distributions and Bayesian probabilistic classification

Michael C. Storrie-Lombardi M.D., Richard B. Hoover

Proceedings Volume 5555, Instruments, Methods, and Missions for Astrobiology VIII; (2004) https://doi.org/10.1117/12.563573
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

Abstract

Elemental abundances (C⁶, N⁷, O⁸, Na¹¹, Mg¹², Al¹³, Si¹⁴, 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.

Citation Download Citation

Michael C. Storrie-Lombardi M.D. and Richard B. Hoover "Fossil signatures using elemental abundance distributions and Bayesian probabilistic classification", Proc. SPIE 5555, Instruments, Methods, and Missions for Astrobiology VIII, (1 November 2004); https://doi.org/10.1117/12.563573

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