Shark Bay, Stromatolites and the Origins of Life on Earth
References
Storrie-Lombardi, M.C. and Awramik, S.M. 2006. A sideways view of stromatolites: complexity metrics for stromatolite laminae in Instruments, Methods, and Missions for Astrobiology IX. Eds. R. B. Hoover, G. V. Levin, A. Y. Rozanov, Proc. of SPIE Vol. 6309, 63090P, 1-12.Abs_Strom_2006.pdf
Storrie-Lombardi, M.C., Brown, A.J., and Walter, M.R. 2004. Remote and In Situ Detection of Aqueous and Biotic Alteration: Cyanobacteria in Archean and Modern Australia. Instruments, Methods and Missions for Astrobiology VII, (R. B. Hoover, Ed.), Proc. SPIE, 5555: p. 270-280.Abs_Hyperspectral_2004.pdf
Storrie-Lombardi, M.C. and Brown, A.J. 2004. Using complexity analysis to distinguish field images of stromatoloids from surrounding rock matrix in 3.45 Ga Stanley Pool Chert, Western Australia. LPSC XXXV, #1414: p. www.lpi.usra.edu/meetings/lpsc2004/pdf/1414.pdf.
Storrie-Lombardi, M.C., Corsetti, F.A., Grigolini, P., Ignaccolo, M., Allegrini, P., Galatolo, S., and Tinetti, G. 2003. Complexity analysis to explore the structure of ancient stromatolites. Chaos, Solitons & Fractals, 20(1): p. 138-145. Abs_Strom_2003b.pdf
Corsetti, F.A. and Storrie-Lombardi, M.C. 2003. Lossless Compression of Stromatolite Images: A Biogenicity Index? Astrobiology, 3(4): p. 649-655.Abs_Strom_2003c.pdf
Storrie-Lombardi, M.C., Grigolini, P., Galatolo, S., Tinetti, G., Ignaccolo, M., Allegrini, P., and Corsetti, F.A. 2003. Advanced techniques in complexity analysis for the detection of biosignatures in ancient and modern stromatolites. Astrobiology, 2(4): p. 630-631.Abs_Strom_2003a.pdf
Inputs to these algorithms include image complexity indices, UV-Vis-IR spectra, and elemental abundances. Spatial resolution ranges from several meters for orbital hyperspectral imaging, to nanometers for visible microscopy.
Stromatolite communities in Shark Bay, NW Australia, are the modern representatives of microbial life that appeared within the first billion years of Earth's history. Remote and in situ automated identification of such living rocks is a core requirement for robotic exploration of water-rich planets orbiting neighboring stars.
We are developing automated classification algorithms to predict the biotic or abiotic origin of geobiological formations.
