Classification of Martian pyroxenes with wide-coverage CRISM multispectral datasets
Roivainen, Matti (2024-05-17)
Roivainen, Matti
M. Roivainen
17.05.2024
© 2024 Matti Roivainen. Ellei toisin mainita, uudelleenkäyttö on sallittu Creative Commons Attribution 4.0 International (CC-BY 4.0) -lisenssillä (https://creativecommons.org/licenses/by/4.0/). Uudelleenkäyttö on sallittua edellyttäen, että lähde mainitaan asianmukaisesti ja mahdolliset muutokset merkitään. Sellaisten osien käyttö tai jäljentäminen, jotka eivät ole tekijän tai tekijöiden omaisuutta, saattaa edellyttää lupaa suoraan asianomaisilta oikeudenhaltijoilta.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405173653
https://urn.fi/URN:NBN:fi:oulu-202405173653
Tiivistelmä
A renewed interest in human presence in extraterrestrial regions is evident from the number of crewed missions planned in the near future. A continued presence will require locally sourced raw materials, making exploration critical. Pyroxene composition is a key factor in the evolution of magmatism on the Martian surface and a crucial indicator of mineralization related to ultramafic-mafic deposits. This study develops a remote classification method for pyroxenes based on the equations proposed by Gaffey et al. (2002), utilizing the connection between the centers of absorption features and the molar concentrations of ferrosilite (Fs) and wollastonite (Wo). A 72-channel multispectral surveying mode and a 489-channel targeted hyperspectral mode of the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) are used in four vast study areas spanning the southern highlands to demonstrate the capability of multispectral data-based classification on a global scale. The results indicate a dominance of Mg-rich augite and pigeonite in the study areas while demonstrating the method's sensitivity to different types of terrains. The technique can effectively identify regions of Mg-rich surfaces. It can be applied to hyperspectral data and is not sensor-specific, making it widely applicable on Mars and all planetary bodies with spectral data available. The classification results from this technique can aid future mineral exploration on the Martian surface.
Kokoelmat
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