{"title":"Feature Engineering for estimating the maturity of lunar soils from spectroscopic data","authors":"Sandeepan Dhoundiyal , Shivam Kumar , Debosmita Paul , Malcolm Aranha , Guneshwar Thangjam , Alok Porwal","doi":"10.1016/j.oreoa.2024.100064","DOIUrl":null,"url":null,"abstract":"<div><p>Existing algorithms for estimating the maturity of lunar soils are not optimized for data from any of the orbital sensors which are currently active. This paper addresses this issue by proposing an algorithm for estimating soil maturity (I<sub>S</sub>/FeO) using spectroscopic data at the spectral resolution of the Moon Mineralogy Mapper (M<sup>3</sup>). As part of this method, four key spectral parameters for estimating I<sub>S</sub>/FeO are identified and used to train a Support Vector Regression (SVR) model. The physical significance of each parameter is discussed, and the equation of the predictive hyperplane is provided for increased transparency. The proposed method outperforms state-of-the-art algorithms and returns a coefficient of determination (<em>R</em><sup><em>2</em></sup>) of 0.92 over the Lunar Soil Characterization Consortium (LSCC) dataset.</p></div>","PeriodicalId":100993,"journal":{"name":"Ore and Energy Resource Geology","volume":"17 ","pages":"Article 100064"},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ore and Energy Resource Geology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666261224000269","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Existing algorithms for estimating the maturity of lunar soils are not optimized for data from any of the orbital sensors which are currently active. This paper addresses this issue by proposing an algorithm for estimating soil maturity (IS/FeO) using spectroscopic data at the spectral resolution of the Moon Mineralogy Mapper (M3). As part of this method, four key spectral parameters for estimating IS/FeO are identified and used to train a Support Vector Regression (SVR) model. The physical significance of each parameter is discussed, and the equation of the predictive hyperplane is provided for increased transparency. The proposed method outperforms state-of-the-art algorithms and returns a coefficient of determination (R2) of 0.92 over the Lunar Soil Characterization Consortium (LSCC) dataset.
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