Clustering Supported Classification of ChemCam Data From Gale Crater, Mars

Abstract The Chemistry and Camera (ChemCam) instrument on board the Mars Science Laboratory (MSL) rover Curiosity has collected a very large and unique data set of in‐situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot laser‐induced breakdown spectroscopy (LIB...
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Autor*in:	Kristin Rammelkamp [verfasserIn] Olivier Gasnault [verfasserIn] Olivier Forni [verfasserIn] Candice C. Bedford [verfasserIn] Erwin Dehouck [verfasserIn] Agnès Cousin [verfasserIn] Jeremie Lasue [verfasserIn] Gaël David [verfasserIn] Travis S. J. Gabriel [verfasserIn] Sylvestre Maurice [verfasserIn] Roger C. Wiens [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	ChemCam Mars LIBS clustering classification

Übergeordnetes Werk:	In: Earth and Space Science - American Geophysical Union (AGU), 2015, 8(2021), 12, Seite n/a-n/a
Übergeordnetes Werk:	volume:8 ; year:2021 ; number:12 ; pages:n/a-n/a

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1029/2021EA001903

Katalog-ID:	DOAJ002943409

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520			\|a Abstract The Chemistry and Camera (ChemCam) instrument on board the Mars Science Laboratory (MSL) rover Curiosity has collected a very large and unique data set of in‐situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot laser‐induced breakdown spectroscopy (LIBS) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a data set is ideally suited for the application of statistical methods for the recognition of patterns that are difficult to observe by humans. In this work, we develop an approach relying on the feature extraction method Non‐Negative Matrix Factorization (NMF) and the repetition of k‐means clustering to classify ChemCam spectra. A strong consistency of the clustering results among the repetitions were found, which allowed us to identify six clusters representing the dominant compositions measured by ChemCam in Gale crater so far. By tracking clusters across the rover traverse from landing to sol 2756, we are able to provide a chemostratigraphic overview of the Gale crater from the ChemCam perspective. Transitions between major geologic groups (such as the Bradbury and the Mt. Sharp groups) are identifiable demonstrating that they are compositionally distinct, consistent with previous work. Compositional differences between their members also appear in the results. Furthermore, a first approach in which a random forest classifier was trained and validated with the obtained cluster assignments, reveals promising results for predicting cluster memberships of new ChemCam LIBS data acquired after sol 2756.
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700	0		\|a Sylvestre Maurice \|e verfasserin \|4 aut
700	0		\|a Roger C. Wiens \|e verfasserin \|4 aut
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spelling	10.1029/2021EA001903 doi (DE-627)DOAJ002943409 (DE-599)DOAJ452cad07553e4c54b318a90414ac2dcc DE-627 ger DE-627 rakwb eng QB1-991 QE1-996.5 Kristin Rammelkamp verfasserin aut Clustering Supported Classification of ChemCam Data From Gale Crater, Mars 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The Chemistry and Camera (ChemCam) instrument on board the Mars Science Laboratory (MSL) rover Curiosity has collected a very large and unique data set of in‐situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot laser‐induced breakdown spectroscopy (LIBS) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a data set is ideally suited for the application of statistical methods for the recognition of patterns that are difficult to observe by humans. In this work, we develop an approach relying on the feature extraction method Non‐Negative Matrix Factorization (NMF) and the repetition of k‐means clustering to classify ChemCam spectra. A strong consistency of the clustering results among the repetitions were found, which allowed us to identify six clusters representing the dominant compositions measured by ChemCam in Gale crater so far. By tracking clusters across the rover traverse from landing to sol 2756, we are able to provide a chemostratigraphic overview of the Gale crater from the ChemCam perspective. Transitions between major geologic groups (such as the Bradbury and the Mt. Sharp groups) are identifiable demonstrating that they are compositionally distinct, consistent with previous work. Compositional differences between their members also appear in the results. Furthermore, a first approach in which a random forest classifier was trained and validated with the obtained cluster assignments, reveals promising results for predicting cluster memberships of new ChemCam LIBS data acquired after sol 2756. ChemCam Mars LIBS clustering classification Astronomy Geology Olivier Gasnault verfasserin aut Olivier Forni verfasserin aut Candice C. Bedford verfasserin aut Erwin Dehouck verfasserin aut Agnès Cousin verfasserin aut Jeremie Lasue verfasserin aut Gaël David verfasserin aut Travis S. J. Gabriel verfasserin aut Sylvestre Maurice verfasserin aut Roger C. Wiens verfasserin aut In Earth and Space Science American Geophysical Union (AGU), 2015 8(2021), 12, Seite n/a-n/a (DE-627)816694206 (DE-600)2807271-6 23335084 nnns volume:8 year:2021 number:12 pages:n/a-n/a https://doi.org/10.1029/2021EA001903 kostenfrei https://doaj.org/article/452cad07553e4c54b318a90414ac2dcc kostenfrei https://doi.org/10.1029/2021EA001903 kostenfrei https://doaj.org/toc/2333-5084 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 12 n/a-n/a
allfields_unstemmed	10.1029/2021EA001903 doi (DE-627)DOAJ002943409 (DE-599)DOAJ452cad07553e4c54b318a90414ac2dcc DE-627 ger DE-627 rakwb eng QB1-991 QE1-996.5 Kristin Rammelkamp verfasserin aut Clustering Supported Classification of ChemCam Data From Gale Crater, Mars 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The Chemistry and Camera (ChemCam) instrument on board the Mars Science Laboratory (MSL) rover Curiosity has collected a very large and unique data set of in‐situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot laser‐induced breakdown spectroscopy (LIBS) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a data set is ideally suited for the application of statistical methods for the recognition of patterns that are difficult to observe by humans. In this work, we develop an approach relying on the feature extraction method Non‐Negative Matrix Factorization (NMF) and the repetition of k‐means clustering to classify ChemCam spectra. A strong consistency of the clustering results among the repetitions were found, which allowed us to identify six clusters representing the dominant compositions measured by ChemCam in Gale crater so far. By tracking clusters across the rover traverse from landing to sol 2756, we are able to provide a chemostratigraphic overview of the Gale crater from the ChemCam perspective. Transitions between major geologic groups (such as the Bradbury and the Mt. Sharp groups) are identifiable demonstrating that they are compositionally distinct, consistent with previous work. Compositional differences between their members also appear in the results. Furthermore, a first approach in which a random forest classifier was trained and validated with the obtained cluster assignments, reveals promising results for predicting cluster memberships of new ChemCam LIBS data acquired after sol 2756. ChemCam Mars LIBS clustering classification Astronomy Geology Olivier Gasnault verfasserin aut Olivier Forni verfasserin aut Candice C. Bedford verfasserin aut Erwin Dehouck verfasserin aut Agnès Cousin verfasserin aut Jeremie Lasue verfasserin aut Gaël David verfasserin aut Travis S. J. Gabriel verfasserin aut Sylvestre Maurice verfasserin aut Roger C. Wiens verfasserin aut In Earth and Space Science American Geophysical Union (AGU), 2015 8(2021), 12, Seite n/a-n/a (DE-627)816694206 (DE-600)2807271-6 23335084 nnns volume:8 year:2021 number:12 pages:n/a-n/a https://doi.org/10.1029/2021EA001903 kostenfrei https://doaj.org/article/452cad07553e4c54b318a90414ac2dcc kostenfrei https://doi.org/10.1029/2021EA001903 kostenfrei https://doaj.org/toc/2333-5084 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 12 n/a-n/a
allfieldsGer	10.1029/2021EA001903 doi (DE-627)DOAJ002943409 (DE-599)DOAJ452cad07553e4c54b318a90414ac2dcc DE-627 ger DE-627 rakwb eng QB1-991 QE1-996.5 Kristin Rammelkamp verfasserin aut Clustering Supported Classification of ChemCam Data From Gale Crater, Mars 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The Chemistry and Camera (ChemCam) instrument on board the Mars Science Laboratory (MSL) rover Curiosity has collected a very large and unique data set of in‐situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot laser‐induced breakdown spectroscopy (LIBS) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a data set is ideally suited for the application of statistical methods for the recognition of patterns that are difficult to observe by humans. In this work, we develop an approach relying on the feature extraction method Non‐Negative Matrix Factorization (NMF) and the repetition of k‐means clustering to classify ChemCam spectra. A strong consistency of the clustering results among the repetitions were found, which allowed us to identify six clusters representing the dominant compositions measured by ChemCam in Gale crater so far. By tracking clusters across the rover traverse from landing to sol 2756, we are able to provide a chemostratigraphic overview of the Gale crater from the ChemCam perspective. Transitions between major geologic groups (such as the Bradbury and the Mt. Sharp groups) are identifiable demonstrating that they are compositionally distinct, consistent with previous work. Compositional differences between their members also appear in the results. Furthermore, a first approach in which a random forest classifier was trained and validated with the obtained cluster assignments, reveals promising results for predicting cluster memberships of new ChemCam LIBS data acquired after sol 2756. ChemCam Mars LIBS clustering classification Astronomy Geology Olivier Gasnault verfasserin aut Olivier Forni verfasserin aut Candice C. Bedford verfasserin aut Erwin Dehouck verfasserin aut Agnès Cousin verfasserin aut Jeremie Lasue verfasserin aut Gaël David verfasserin aut Travis S. J. Gabriel verfasserin aut Sylvestre Maurice verfasserin aut Roger C. Wiens verfasserin aut In Earth and Space Science American Geophysical Union (AGU), 2015 8(2021), 12, Seite n/a-n/a (DE-627)816694206 (DE-600)2807271-6 23335084 nnns volume:8 year:2021 number:12 pages:n/a-n/a https://doi.org/10.1029/2021EA001903 kostenfrei https://doaj.org/article/452cad07553e4c54b318a90414ac2dcc kostenfrei https://doi.org/10.1029/2021EA001903 kostenfrei https://doaj.org/toc/2333-5084 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 12 n/a-n/a
allfieldsSound	10.1029/2021EA001903 doi (DE-627)DOAJ002943409 (DE-599)DOAJ452cad07553e4c54b318a90414ac2dcc DE-627 ger DE-627 rakwb eng QB1-991 QE1-996.5 Kristin Rammelkamp verfasserin aut Clustering Supported Classification of ChemCam Data From Gale Crater, Mars 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The Chemistry and Camera (ChemCam) instrument on board the Mars Science Laboratory (MSL) rover Curiosity has collected a very large and unique data set of in‐situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot laser‐induced breakdown spectroscopy (LIBS) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a data set is ideally suited for the application of statistical methods for the recognition of patterns that are difficult to observe by humans. In this work, we develop an approach relying on the feature extraction method Non‐Negative Matrix Factorization (NMF) and the repetition of k‐means clustering to classify ChemCam spectra. A strong consistency of the clustering results among the repetitions were found, which allowed us to identify six clusters representing the dominant compositions measured by ChemCam in Gale crater so far. By tracking clusters across the rover traverse from landing to sol 2756, we are able to provide a chemostratigraphic overview of the Gale crater from the ChemCam perspective. Transitions between major geologic groups (such as the Bradbury and the Mt. Sharp groups) are identifiable demonstrating that they are compositionally distinct, consistent with previous work. Compositional differences between their members also appear in the results. Furthermore, a first approach in which a random forest classifier was trained and validated with the obtained cluster assignments, reveals promising results for predicting cluster memberships of new ChemCam LIBS data acquired after sol 2756. ChemCam Mars LIBS clustering classification Astronomy Geology Olivier Gasnault verfasserin aut Olivier Forni verfasserin aut Candice C. Bedford verfasserin aut Erwin Dehouck verfasserin aut Agnès Cousin verfasserin aut Jeremie Lasue verfasserin aut Gaël David verfasserin aut Travis S. J. Gabriel verfasserin aut Sylvestre Maurice verfasserin aut Roger C. Wiens verfasserin aut In Earth and Space Science American Geophysical Union (AGU), 2015 8(2021), 12, Seite n/a-n/a (DE-627)816694206 (DE-600)2807271-6 23335084 nnns volume:8 year:2021 number:12 pages:n/a-n/a https://doi.org/10.1029/2021EA001903 kostenfrei https://doaj.org/article/452cad07553e4c54b318a90414ac2dcc kostenfrei https://doi.org/10.1029/2021EA001903 kostenfrei https://doaj.org/toc/2333-5084 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 12 n/a-n/a
language	English
source	In Earth and Space Science 8(2021), 12, Seite n/a-n/a volume:8 year:2021 number:12 pages:n/a-n/a
sourceStr	In Earth and Space Science 8(2021), 12, Seite n/a-n/a volume:8 year:2021 number:12 pages:n/a-n/a
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	ChemCam Mars LIBS clustering classification Astronomy Geology
isfreeaccess_bool	true
container_title	Earth and Space Science
authorswithroles_txt_mv	Kristin Rammelkamp @@aut@@ Olivier Gasnault @@aut@@ Olivier Forni @@aut@@ Candice C. Bedford @@aut@@ Erwin Dehouck @@aut@@ Agnès Cousin @@aut@@ Jeremie Lasue @@aut@@ Gaël David @@aut@@ Travis S. J. Gabriel @@aut@@ Sylvestre Maurice @@aut@@ Roger C. Wiens @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	816694206
id	DOAJ002943409
language_de	englisch
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callnumber-first	Q - Science
author	Kristin Rammelkamp
spellingShingle	Kristin Rammelkamp misc QB1-991 misc QE1-996.5 misc ChemCam misc Mars misc LIBS misc clustering misc classification misc Astronomy misc Geology Clustering Supported Classification of ChemCam Data From Gale Crater, Mars
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topic_title	QB1-991 QE1-996.5 Clustering Supported Classification of ChemCam Data From Gale Crater, Mars ChemCam Mars LIBS clustering classification
topic	misc QB1-991 misc QE1-996.5 misc ChemCam misc Mars misc LIBS misc clustering misc classification misc Astronomy misc Geology
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title	Clustering Supported Classification of ChemCam Data From Gale Crater, Mars
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title_full	Clustering Supported Classification of ChemCam Data From Gale Crater, Mars
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author_browse	Kristin Rammelkamp Olivier Gasnault Olivier Forni Candice C. Bedford Erwin Dehouck Agnès Cousin Jeremie Lasue Gaël David Travis S. J. Gabriel Sylvestre Maurice Roger C. Wiens
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title_sort	clustering supported classification of chemcam data from gale crater, mars
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title_auth	Clustering Supported Classification of ChemCam Data From Gale Crater, Mars
abstract	Abstract The Chemistry and Camera (ChemCam) instrument on board the Mars Science Laboratory (MSL) rover Curiosity has collected a very large and unique data set of in‐situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot laser‐induced breakdown spectroscopy (LIBS) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a data set is ideally suited for the application of statistical methods for the recognition of patterns that are difficult to observe by humans. In this work, we develop an approach relying on the feature extraction method Non‐Negative Matrix Factorization (NMF) and the repetition of k‐means clustering to classify ChemCam spectra. A strong consistency of the clustering results among the repetitions were found, which allowed us to identify six clusters representing the dominant compositions measured by ChemCam in Gale crater so far. By tracking clusters across the rover traverse from landing to sol 2756, we are able to provide a chemostratigraphic overview of the Gale crater from the ChemCam perspective. Transitions between major geologic groups (such as the Bradbury and the Mt. Sharp groups) are identifiable demonstrating that they are compositionally distinct, consistent with previous work. Compositional differences between their members also appear in the results. Furthermore, a first approach in which a random forest classifier was trained and validated with the obtained cluster assignments, reveals promising results for predicting cluster memberships of new ChemCam LIBS data acquired after sol 2756.
abstractGer	Abstract The Chemistry and Camera (ChemCam) instrument on board the Mars Science Laboratory (MSL) rover Curiosity has collected a very large and unique data set of in‐situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot laser‐induced breakdown spectroscopy (LIBS) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a data set is ideally suited for the application of statistical methods for the recognition of patterns that are difficult to observe by humans. In this work, we develop an approach relying on the feature extraction method Non‐Negative Matrix Factorization (NMF) and the repetition of k‐means clustering to classify ChemCam spectra. A strong consistency of the clustering results among the repetitions were found, which allowed us to identify six clusters representing the dominant compositions measured by ChemCam in Gale crater so far. By tracking clusters across the rover traverse from landing to sol 2756, we are able to provide a chemostratigraphic overview of the Gale crater from the ChemCam perspective. Transitions between major geologic groups (such as the Bradbury and the Mt. Sharp groups) are identifiable demonstrating that they are compositionally distinct, consistent with previous work. Compositional differences between their members also appear in the results. Furthermore, a first approach in which a random forest classifier was trained and validated with the obtained cluster assignments, reveals promising results for predicting cluster memberships of new ChemCam LIBS data acquired after sol 2756.
abstract_unstemmed	Abstract The Chemistry and Camera (ChemCam) instrument on board the Mars Science Laboratory (MSL) rover Curiosity has collected a very large and unique data set of in‐situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot laser‐induced breakdown spectroscopy (LIBS) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a data set is ideally suited for the application of statistical methods for the recognition of patterns that are difficult to observe by humans. In this work, we develop an approach relying on the feature extraction method Non‐Negative Matrix Factorization (NMF) and the repetition of k‐means clustering to classify ChemCam spectra. A strong consistency of the clustering results among the repetitions were found, which allowed us to identify six clusters representing the dominant compositions measured by ChemCam in Gale crater so far. By tracking clusters across the rover traverse from landing to sol 2756, we are able to provide a chemostratigraphic overview of the Gale crater from the ChemCam perspective. Transitions between major geologic groups (such as the Bradbury and the Mt. Sharp groups) are identifiable demonstrating that they are compositionally distinct, consistent with previous work. Compositional differences between their members also appear in the results. Furthermore, a first approach in which a random forest classifier was trained and validated with the obtained cluster assignments, reveals promising results for predicting cluster memberships of new ChemCam LIBS data acquired after sol 2756.
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container_issue	12
title_short	Clustering Supported Classification of ChemCam Data From Gale Crater, Mars
url	https://doi.org/10.1029/2021EA001903 https://doaj.org/article/452cad07553e4c54b318a90414ac2dcc https://doaj.org/toc/2333-5084
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up_date	2024-07-03T15:01:40.830Z
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Clustering Supported Classification of ChemCam Data From Gale Crater, Mars

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