Visible and near-InfraRed (VNIR) reflectance of silicate glasses: Characterization of a featureless spectrum and implications for planetary geology

Pisello, Alessandro; Simone, De; Ferrari, M.; Massimiliano, Porreca; Vetere, Francesco; Behrens, Harald; Sanctis, M. C. De; Perugini, Diego

doi:10.1016/j.icarus.2021.114801

Cited by 14 publications

(17 citation statements)

References 52 publications

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“…Instead for higher iron content (>7-8 wt%) it is possible to see that the more oxidized samples have systematically lower average reflectance with respect to samples with the same amount of iron in the reduced state. Data from Carli et al (2016) as well as the data from Pisello et al (2022) with FeO + TiO 2 > 5 wt% tend to have a lower reflectance with respect to the main trend defined by the data of Stockstill-Cahill et al (2014) and Cannon et al (2017). Both data sets (Carli et al, 2016;Pisello et al, 2022) have the highest Fe 3+ /Fe TOT among the data presented in the DB being, respectively, 0.58 and 0.66 on average.…”

Section: Average Reflectance Versus (Feo + Tio 2 ) Relationshipmentioning

confidence: 72%

“…(2017). Both data sets (Carli et al., 2016; Pisello et al., 2022) have the highest Fe 3+ /Fe TOT among the data presented in the DB being, respectively, 0.58 and 0.66 on average.…”

Section: Discussionmentioning

confidence: 91%

“…The optimal bin number was optimized by testing each algorithm and evaluating the performance of the model while keeping the other hyperparameters fixed. The spectra from the works of Moroz et al (2009), andPisello et al (2022) were digitized.…”

Section: Computational Strategy For Machine Learning Algorithmsmentioning

confidence: 99%

“…We decided to build a database merging all the relevant information found in the literature about glasses and glass-bearing materials to better comprehend and contextualize the data acquired in this work. The DB built so far is composed of the data retrieved from the works of Minitti et al (2002), Moroz et al (2009), Stockstill-Cahill et al (2014, Carli et al (2016), Farrand et al (2016, Cannon et al (2017), andPisello et al (2022), in addition to the data presented in this work. These studies were chosen for the availability of reflectance data, chemical data, Fe 3+ /Fe TOT information and grain size characterization.…”

Section: Database Buildingmentioning

confidence: 99%

“…However, this is not a trivial task since (very often) natural glasses are accompanied by crystalline phases and as discussed by Horgan et al (2014), Fe-bearing glasses behave as weak absorbers. Several studies explored the spectral (visible-near-infrared, hereafter VNIR reflectance) features of glasses and glass-bearing rocks (e.g., Cannon et al, 2017;Carli et al, 2016;Farrand et al, 2016;Minitti et al, 2002;Moroz et al, 2009;Pisello et al, 2022;Rader et al, 2022;Stockstill-Cahill et al, 2014). These studies addressed several issues, such as (a) What are the band features of a typical glass?…”

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confidence: 99%

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Spectroscopic Characterization of Impactites and a Machine Learning Approach to Determine the Oxidation State of Iron in Glass‐Bearing Materials

et al. 2023

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We investigated a suite of impact glass‐bearing rocks using a multi‐analytical approach including visible‐near‐infrared diffuse reflectance spectroscopy, Mössbauer spectroscopy, and powder X‐ray diffraction. In order to better understand and interpret the obtained results, we built a database containing physical, chemical, and spectroscopic information on glasses and glass‐bearing materials using new results from this study and published works. We used the database to explore systematic relationships between parameters of interest and finally we applied several machine learning algorithms (support vector machine, random forests, and gradient boosting) to test the possibility to regress the oxidation state of iron from chemical and spectroscopic information. Our results show that even small amounts of mafic crystalline phases have a big influence on the spectral features of glass‐bearing rocks. Samples without mafic crystalline inclusions show the typical spectrum of glasses (two broad and shallow bands roughly centered around 1,100 and 1,900 nm) with minor variations due to bulk chemistry. We described a non‐linear relationship between average reflectance (average reflectance value between 500 and 1,000 nm), FeO + TiO2 content, grain size, and Fe3+/FeTOT. We tested the relation for the finer grain size (0–25 μm), and we qualitatively assessed how it is affected by grain size, Fe3+/FeTOT, and crystal content. Finally, we developed a machine learning pipeline to regress the Fe3+/FeTOT of glass‐bearing materials using the proposed database. Our machine learning calculations give satisfactory results (MAE: 0.0321) and additional data will enable the application of our computational strategy to remotely acquired data to extract chemical and mineralogical information of planetary surfaces.

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Section: Average Reflectance Versus (Feo + Tio 2 ) Relationshipmentioning

confidence: 72%

“…(2017). Both data sets (Carli et al., 2016; Pisello et al., 2022) have the highest Fe 3+ /Fe TOT among the data presented in the DB being, respectively, 0.58 and 0.66 on average.…”

Section: Discussionmentioning

confidence: 91%

Section: Computational Strategy For Machine Learning Algorithmsmentioning

confidence: 99%