FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3189333437> ?p ?o ?g. }

• W3189333437 endingPage "102423" @default.
• W3189333437 startingPage "102423" @default.
• W3189333437 abstract "Rare earth elements (REE) became a strategic raw material in the 21 st century and carbonatite-related deposits frequently carry potentially economic levels of these critical metals. To determine concentrations of major and trace elements (including REE) conventional analysis of bulk rock geochemical samples by means of inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS) is mostly applied. However, these analytical methods are labor-intensive, costly and time-consuming. In contrast, modern Remote Sensing (RS) – e.g. proximal remote sensing or imaging spectroscopy – has become a novel tool for detecting and quantifying geological materials as they offer an efficient, non-destructive and cost-effective way not only to identify different minerals and/or chemical elements but also model their abundances. In this study, we investigated whether Partial Least Squares Regression (PLSR) models linking the laboratory sample reflectance to mineral/geochemical property could be employed for quantitative predictions of bulk LREE and HREE concentrations. The suite included samples from four different geographic regions in India, East Africa and the USA. Our results showed that for such samples the quantitative approaches have some limitations. First, our models required to exclude samples with high modal abundance of hematite and samples with too low REE contents (LREE < 500 μg/g, HREE < 100 μg/g). Furthermore, it was revealed that the sample mineralogy has a significant impact on the PLSR predictions. To achieve reliable models the sample suite had to be divided into two datasets following their mineralogy and geochemistry. The first dataset comprised rocks with increased amounts of strontium (Sr, Dataset Sr ), where REE were predominantly bound in Sr-carbonates. On the other hand, REE mainly bound in Ca–REE-carbonates characterized the second dataset (Dataset Other ). Following this division, we were able to construct valid prediction models for bulk LREE and HREE concentrations using PLSR. However, the detected spectral assignments associated with the REE presence indicated that the predictions were mainly indirect, based on the present mineral phases rather than direct absorptions related to LREE and HREE. This illustrates the difficulty and limitations for further model generalization and the ability to be further transferred to other lithologically diverse carbonatite sites. We posit that this topic requires future systematic investigations using the extended carbonatite datasets – samples having a wide range of REE abundances – collected from lithologically diverse regions. This would enable further validation and re-calibration of the constructed prediction models." @default.
• W3189333437 created "2021-08-16" @default.
• W3189333437 creator A5018650877 @default.
• W3189333437 creator A5023248651 @default.
• W3189333437 creator A5027302861 @default.
• W3189333437 creator A5066089927 @default.
• W3189333437 creator A5091114253 @default.
• W3189333437 date "2021-12-01" @default.
• W3189333437 modified "2023-10-16" @default.
• W3189333437 title "Quantitative estimation of rare earth element abundances in compositionally distinct carbonatites: Implications for proximal remote-sensing prospection of critical elements" @default.
• W3189333437 cites W1542392815 @default.
• W3189333437 cites W1581126802 @default.
• W3189333437 cites W1597087730 @default.
• W3189333437 cites W1964058583 @default.
• W3189333437 cites W1968537264 @default.
• W3189333437 cites W1972878094 @default.
• W3189333437 cites W1976354610 @default.
• W3189333437 cites W1976664405 @default.
• W3189333437 cites W1977225481 @default.
• W3189333437 cites W1977457396 @default.
• W3189333437 cites W1984432256 @default.
• W3189333437 cites W1987760411 @default.
• W3189333437 cites W1987881644 @default.
• W3189333437 cites W2000577221 @default.
• W3189333437 cites W2001671350 @default.
• W3189333437 cites W2007808016 @default.
• W3189333437 cites W2010838483 @default.
• W3189333437 cites W2025014611 @default.
• W3189333437 cites W2039768055 @default.
• W3189333437 cites W2040350101 @default.
• W3189333437 cites W2045746353 @default.
• W3189333437 cites W2045858843 @default.
• W3189333437 cites W2054672918 @default.
• W3189333437 cites W2073503722 @default.
• W3189333437 cites W2083307257 @default.
• W3189333437 cites W2100411277 @default.
• W3189333437 cites W2129904226 @default.
• W3189333437 cites W2156737155 @default.
• W3189333437 cites W2158863190 @default.
• W3189333437 cites W2192020007 @default.
• W3189333437 cites W2264040787 @default.
• W3189333437 cites W2327661402 @default.
• W3189333437 cites W2335344567 @default.
• W3189333437 cites W2462223115 @default.
• W3189333437 cites W2505983381 @default.
• W3189333437 cites W2528685991 @default.
• W3189333437 cites W2586813976 @default.
• W3189333437 cites W2604177637 @default.
• W3189333437 cites W2606412288 @default.
• W3189333437 cites W2738193193 @default.
• W3189333437 cites W2757781625 @default.
• W3189333437 cites W2789229955 @default.
• W3189333437 cites W2804146571 @default.
• W3189333437 cites W2885617059 @default.
• W3189333437 cites W2905956993 @default.
• W3189333437 cites W2920797488 @default.
• W3189333437 cites W2946246059 @default.
• W3189333437 cites W2979812152 @default.
• W3189333437 cites W3093258085 @default.
• W3189333437 cites W3093335132 @default.
• W3189333437 cites W3142254115 @default.
• W3189333437 cites W3184975442 @default.
• W3189333437 cites W4247537791 @default.
• W3189333437 cites W4293842439 @default.
• W3189333437 doi "https://doi.org/10.1016/j.jag.2021.102423" @default.
• W3189333437 hasPublicationYear "2021" @default.
• W3189333437 type Work @default.
• W3189333437 sameAs 3189333437 @default.
• W3189333437 citedByCount "3" @default.
• W3189333437 countsByYear W31893334372022 @default.
• W3189333437 crossrefType "journal-article" @default.
• W3189333437 hasAuthorship W3189333437A5018650877 @default.
• W3189333437 hasAuthorship W3189333437A5023248651 @default.
• W3189333437 hasAuthorship W3189333437A5027302861 @default.
• W3189333437 hasAuthorship W3189333437A5066089927 @default.
• W3189333437 hasAuthorship W3189333437A5091114253 @default.
• W3189333437 hasBestOaLocation W31893334371 @default.
• W3189333437 hasConcept C119857082 @default.
• W3189333437 hasConcept C121332964 @default.
• W3189333437 hasConcept C127313418 @default.
• W3189333437 hasConcept C140441402 @default.
• W3189333437 hasConcept C162356407 @default.
• W3189333437 hasConcept C165697059 @default.
• W3189333437 hasConcept C17409809 @default.
• W3189333437 hasConcept C178790620 @default.
• W3189333437 hasConcept C185592680 @default.
• W3189333437 hasConcept C199289684 @default.
• W3189333437 hasConcept C22354355 @default.
• W3189333437 hasConcept C2776432453 @default.
• W3189333437 hasConcept C2777615417 @default.
• W3189333437 hasConcept C2778576202 @default.
• W3189333437 hasConcept C2780191791 @default.
• W3189333437 hasConcept C2983155866 @default.
• W3189333437 hasConcept C34682378 @default.
• W3189333437 hasConcept C39432304 @default.
• W3189333437 hasConcept C41008148 @default.
• W3189333437 hasConcept C43617362 @default.
• W3189333437 hasConcept C62520636 @default.

• next