Stromatolites [Nature 383 (1996) 423] are macroscopic rock formations resembling layered microbial communities and historically considered the fossil remains of microbiological activity [Stromatolites, Elsevier, 1976; Calcareous Algae and Stromatolites, Springer-Verlag, New York, 1990; Microbial Sediments, Springer-Verlag, 2000]. They have been interpreted as evidence for ancient life on Earth and, if discovered in an extraterrestrial locale (e.g., Mars), would be interpreted similarly. However, new evidence indicates stromatolites can be produced abiotically [Nature 383 (1996) 423; Geology 24 (1996) 119; N.P. James (Ed.), Carbonate Sedimentation and Diagenesis in the Evolving Precambrian World, SEPM Special Publication, vol. 67, SEPM, Tulsa, 2000, p. 123]. These findings compromise the utility of stromatolites as a biosignature unless it is possible to estimate their biogenicity. Since abiotic stromatolites arise from diffusion-limited, random processes and biotic layered communities develop from repetitive cellular processes, the possibility existed that this disparity at the microscopic level would manifest at courser granularity. In this first of two contributions we explore using a widely available lossless compression algorithm [IEEE Trans. Inform. Theory 23 (1977) 337; The Data Compression Book, M&T Books, New York, 1995] as a complexity metric for unmagnified reflectance multi-band stromatolite images. Abiotic stromatolite and surrounding rock matrix image compression characteristics are indistinguishable, while the biotic stromatolite is significantly more compressible than its rock matrix. Understanding the origins of these differences will require a variety of complexity tools including several currently under development in our laboratories including algorithms detecting true periodicity. The over-arching goal of this work is to identify both the fossil signatures of self-organizing biogeochemical activity and the environmental control mechanisms responsible for modulating these phenomena. (C) 2003 Elsevier Ltd. All rights reserved.
Complexity Analysis to explore the structure of ancient stromatolites
GALATOLO, STEFANO;
2004-01-01
Abstract
Stromatolites [Nature 383 (1996) 423] are macroscopic rock formations resembling layered microbial communities and historically considered the fossil remains of microbiological activity [Stromatolites, Elsevier, 1976; Calcareous Algae and Stromatolites, Springer-Verlag, New York, 1990; Microbial Sediments, Springer-Verlag, 2000]. They have been interpreted as evidence for ancient life on Earth and, if discovered in an extraterrestrial locale (e.g., Mars), would be interpreted similarly. However, new evidence indicates stromatolites can be produced abiotically [Nature 383 (1996) 423; Geology 24 (1996) 119; N.P. James (Ed.), Carbonate Sedimentation and Diagenesis in the Evolving Precambrian World, SEPM Special Publication, vol. 67, SEPM, Tulsa, 2000, p. 123]. These findings compromise the utility of stromatolites as a biosignature unless it is possible to estimate their biogenicity. Since abiotic stromatolites arise from diffusion-limited, random processes and biotic layered communities develop from repetitive cellular processes, the possibility existed that this disparity at the microscopic level would manifest at courser granularity. In this first of two contributions we explore using a widely available lossless compression algorithm [IEEE Trans. Inform. Theory 23 (1977) 337; The Data Compression Book, M&T Books, New York, 1995] as a complexity metric for unmagnified reflectance multi-band stromatolite images. Abiotic stromatolite and surrounding rock matrix image compression characteristics are indistinguishable, while the biotic stromatolite is significantly more compressible than its rock matrix. Understanding the origins of these differences will require a variety of complexity tools including several currently under development in our laboratories including algorithms detecting true periodicity. The over-arching goal of this work is to identify both the fossil signatures of self-organizing biogeochemical activity and the environmental control mechanisms responsible for modulating these phenomena. (C) 2003 Elsevier Ltd. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
