Signatures of life detected in images of rocks using neural network analysis demonstrate new potential for searching for biosignatures on the surface of Mars
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...
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
Mary Ann Liebert
2023
|
| _version_ | 1797111253453242368 |
|---|---|
| author | Corenblit, D Decaux, O Delmotte, S Toumazet, J-P Arrignon, F André, M-F Darrozes, J Davies, NS Julien, F Otto, T Ramillien, G Roussel, E Steiger, J Viles, H |
| author_facet | Corenblit, D Decaux, O Delmotte, S Toumazet, J-P Arrignon, F André, M-F Darrozes, J Davies, NS Julien, F Otto, T Ramillien, G Roussel, E Steiger, J Viles, H |
| author_sort | Corenblit, D |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T08:06:09Z |
| format | Journal article |
| id | oxford-uuid:22146dec-0487-4aa4-b248-4a7e2847cdd9 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:06:09Z |
| publishDate | 2023 |
| publisher | Mary Ann Liebert |
| record_format | dspace |
| spelling | oxford-uuid:22146dec-0487-4aa4-b248-4a7e2847cdd92023-11-03T08:19:30ZSignatures of life detected in images of rocks using neural network analysis demonstrate new potential for searching for biosignatures on the surface of MarsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:22146dec-0487-4aa4-b248-4a7e2847cdd9EnglishSymplectic ElementsMary Ann Liebert2023Corenblit, DDecaux, ODelmotte, SToumazet, J-PArrignon, FAndré, M-FDarrozes, JDavies, NSJulien, FOtto, TRamillien, GRoussel, ESteiger, JViles, HMicroorganisms 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. |
| spellingShingle | Corenblit, D Decaux, O Delmotte, S Toumazet, J-P Arrignon, F André, M-F Darrozes, J Davies, NS Julien, F Otto, T Ramillien, G Roussel, E Steiger, J Viles, H Signatures of life detected in images of rocks using neural network analysis demonstrate new potential for searching for biosignatures on the surface of Mars |
| title | Signatures of life detected in images of rocks using neural network analysis demonstrate new potential for searching for biosignatures on the surface of Mars |
| title_full | Signatures of life detected in images of rocks using neural network analysis demonstrate new potential for searching for biosignatures on the surface of Mars |
| title_fullStr | Signatures of life detected in images of rocks using neural network analysis demonstrate new potential for searching for biosignatures on the surface of Mars |
| title_full_unstemmed | Signatures of life detected in images of rocks using neural network analysis demonstrate new potential for searching for biosignatures on the surface of Mars |
| title_short | Signatures of life detected in images of rocks using neural network analysis demonstrate new potential for searching for biosignatures on the surface of Mars |
| title_sort | signatures of life detected in images of rocks using neural network analysis demonstrate new potential for searching for biosignatures on the surface of mars |
| work_keys_str_mv | AT corenblitd signaturesoflifedetectedinimagesofrocksusingneuralnetworkanalysisdemonstratenewpotentialforsearchingforbiosignaturesonthesurfaceofmars AT decauxo signaturesoflifedetectedinimagesofrocksusingneuralnetworkanalysisdemonstratenewpotentialforsearchingforbiosignaturesonthesurfaceofmars AT delmottes signaturesoflifedetectedinimagesofrocksusingneuralnetworkanalysisdemonstratenewpotentialforsearchingforbiosignaturesonthesurfaceofmars AT toumazetjp signaturesoflifedetectedinimagesofrocksusingneuralnetworkanalysisdemonstratenewpotentialforsearchingforbiosignaturesonthesurfaceofmars AT arrignonf signaturesoflifedetectedinimagesofrocksusingneuralnetworkanalysisdemonstratenewpotentialforsearchingforbiosignaturesonthesurfaceofmars AT andremf signaturesoflifedetectedinimagesofrocksusingneuralnetworkanalysisdemonstratenewpotentialforsearchingforbiosignaturesonthesurfaceofmars AT darrozesj signaturesoflifedetectedinimagesofrocksusingneuralnetworkanalysisdemonstratenewpotentialforsearchingforbiosignaturesonthesurfaceofmars AT daviesns signaturesoflifedetectedinimagesofrocksusingneuralnetworkanalysisdemonstratenewpotentialforsearchingforbiosignaturesonthesurfaceofmars AT julienf signaturesoflifedetectedinimagesofrocksusingneuralnetworkanalysisdemonstratenewpotentialforsearchingforbiosignaturesonthesurfaceofmars AT ottot signaturesoflifedetectedinimagesofrocksusingneuralnetworkanalysisdemonstratenewpotentialforsearchingforbiosignaturesonthesurfaceofmars AT ramillieng signaturesoflifedetectedinimagesofrocksusingneuralnetworkanalysisdemonstratenewpotentialforsearchingforbiosignaturesonthesurfaceofmars AT roussele signaturesoflifedetectedinimagesofrocksusingneuralnetworkanalysisdemonstratenewpotentialforsearchingforbiosignaturesonthesurfaceofmars AT steigerj signaturesoflifedetectedinimagesofrocksusingneuralnetworkanalysisdemonstratenewpotentialforsearchingforbiosignaturesonthesurfaceofmars AT vilesh signaturesoflifedetectedinimagesofrocksusingneuralnetworkanalysisdemonstratenewpotentialforsearchingforbiosignaturesonthesurfaceofmars |