1. Signatures of Life Detected in Images of Rocks Using Neural Network Analysis Demonstrate New Potential for Searching for Biosignatures on the Surface of Mars
- Author
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Corenblit, Dov, Decaux, Olivier, Delmotte, Sébastien, Toumazet, Jean-Pierre, Arrignon, Florent, André, Marie-Françoise, Darrozes, José, Davies, Neil S, Julien, Frédéric, Otto, Thierry, Ramillien, Guillaume, Roussel, Erwan, Steiger, Johannes, Viles, Heather, Corenblit, Dov [0000-0002-2431-7913], Apollo - University of Cambridge Repository, Laboratoire de Géographie Physique et Environnementale (GEOLAB), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut Sciences de l'Homme et de la Société (IR SHS UNILIM), Université de Limoges (UNILIM)-Université de Limoges (UNILIM)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Service de radiologie et imagerie médicale [Rennes] = Radiology [Rennes], CHU Pontchaillou [Rennes], MAD-Environnement, Géosciences Environnement Toulouse (GET), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Department of Earth Sciences [University of Cambridge], University of Cambridge [UK] (CAM), Laboratoire Ecologie Fonctionnelle et Environnement (LEFE), Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT), Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), University of Oxford, and Observatoire Midi-Pyrénées (OMP), Toulouse, France
- Subjects
Geologic Sediments ,Extraterrestrial Environment ,Fossils ,Biogeomorphology ,[SDU.STU]Sciences of the Universe [physics]/Earth Sciences ,Mars ,Astrobiology ,Agricultural and Biological Sciences (miscellaneous) ,Neural network ,Space and Planetary Science ,[SDE]Environmental Sciences ,Exobiology ,Biosignatures ,Humans ,Microbially induced sediment structures ,Neural Networks, Computer - Abstract
Microorganisms play a role in the construction or modulation of various types of landforms. They are especially notable for forming microbially induced sedimentary structures (MISS). Such microbial structures have been considered to be among the most likely biosignatures that might be encountered on the martian surface. Twenty-nine algorithms have been tested with images taken during a laboratory experiment for testing their performance in discriminating mat cracks (MISS) from abiotic mud cracks. Among the algorithms, neural network types produced excellent predictions with similar precision of 0.99. Following that step, a convolutional neural network (CNN) approach has been tested to see whether it can conclusively detect MISS in images of rocks and sediment surfaces taken at different natural sites where present and ancient (fossil) microbial mat cracks and abiotic desiccation cracks were observed. The CNN approach showed excellent prediction of biotic and abiotic structures from the images (global precision, sensitivity, and specificity, respectively, 0.99, 0.99, and 0.97). The key areas of interest of the machine matched well with human expertise for distinguishing biotic and abiotic forms (in their geomorphological meaning). The images indicated clear differences between the abiotic and biotic situations expressed at three embedded scales: texture (size, shape, and arrangement of the grains constituting the surface of one form), form (outer shape of one form), and pattern of form arrangement (arrangement of the forms over a few square meters). The most discriminative components for biogenicity were the border of the mat cracks with their tortuous enlarged and blistered morphology more or less curved upward, sometimes with thin laminations. To apply this innovative biogeomorphological approach to the images obtained by rovers on Mars, the main physical and biological sources of variation in abiotic and biotic outcomes must now be further considered.
- Published
- 2023
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