Study of mineralization in geological samples by means of LIBS and neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F16%3A00093802" target="_blank" >RIV/00216224:14310/16:00093802 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26620/16:PU120344

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Study of mineralization in geological samples by means of LIBS and neural networks

Popis výsledku v původním jazyce

This work aims on the description of possible element association within a sample of sandstone-hosted uranium ore by means of Laser-Induced Breakdown Spectroscopy (LIBS). As an element association in the interaction region and in terms of LIBS we refer to the simultaneous presence of spectral lines within a respective single spectrum. Presented results show element associations within a sandstone ore sample carrying high abundance of zirconium, uranium, niobium and hafnium. To manage this task a multivariate method was utilized, namely the self-organized maps (SOM). SOM is a type of artificial neural network, which provides dimensionality reduction based on the similarity of input data. Responses of SOM weights associated with certain elemental lines were easily discriminated as either simultaneous or isolated. Deduced association of U-Zr and isolation of Ti, Fe and Si responses is in good correlation with geological studies made on ores from the same place of origin.

Název v anglickém jazyce

Study of mineralization in geological samples by means of LIBS and neural networks

Popis výsledku anglicky

This work aims on the description of possible element association within a sample of sandstone-hosted uranium ore by means of Laser-Induced Breakdown Spectroscopy (LIBS). As an element association in the interaction region and in terms of LIBS we refer to the simultaneous presence of spectral lines within a respective single spectrum. Presented results show element associations within a sandstone ore sample carrying high abundance of zirconium, uranium, niobium and hafnium. To manage this task a multivariate method was utilized, namely the self-organized maps (SOM). SOM is a type of artificial neural network, which provides dimensionality reduction based on the similarity of input data. Responses of SOM weights associated with certain elemental lines were easily discriminated as either simultaneous or isolated. Deduced association of U-Zr and isolation of Ti, Fe and Si responses is in good correlation with geological studies made on ores from the same place of origin.

Druh

O - Ostatní výsledky

CEP obor

CB - Analytická chemie, separace

OECD FORD obor

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Projekt

<a href="/cs/project/LQ1601" target="_blank" >LQ1601: CEITEC 2020</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů