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

a 2016

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

KLUS, Jakub, Pavel POŘÍZKA, David PROCHAZKA, Petr MIKYSEK, Jan NOVOTNÝ et. al.

Základní údaje

Originální název

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

Autoři

KLUS, Jakub (203 Česká republika), Pavel POŘÍZKA (203 Česká republika), David PROCHAZKA (203 Česká republika), Petr MIKYSEK (203 Česká republika, domácí), Jan NOVOTNÝ (203 Česká republika), Karel NOVOTNÝ (203 Česká republika, garant, domácí) a Jozef KAISER (203 Česká republika)

Vydání

9-th International Conference on Laser-Induced Breakdown Spectroscopy - LIBS2016, 2016

Další údaje

Jazyk

angličtina

Typ výsledku

Konferenční abstrakt

Obor

10406 Analytical chemistry

Stát vydavatele

Francie

Utajení

není předmětem státního či obchodního tajemství

Kód RIV

RIV/00216224:14310/16:00093802

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

Geology; mineralization; neural networks

Změněno: 6. 3. 2017 10:20, doc. Mgr. Karel Novotný, Ph.D.

Anotace

V originále

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. Finally a mineralization visualization performed in unconventional manners is shown. Instead of the creation of chemical map by rearranging the line intensities to the rectangular grid of points, a correlation of spectra with neuron responses was calculated. It is shown that map of correlation coefficients provides better insight into the problem of element associations or possible co-mineralization in general.

Návaznosti

LQ1601, projekt VaV

Název: CEITEC 2020 (Akronym: CEITEC2020)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, CEITEC 2020

Citovat

KLUS, Jakub, Pavel POŘÍZKA, David PROCHAZKA, Petr MIKYSEK, Jan NOVOTNÝ, Karel NOVOTNÝ a Jozef KAISER. Study of mineralization in geological samples by means of LIBS and neural networks. In 9-th International Conference on Laser-Induced Breakdown Spectroscopy - LIBS2016. 2016.

@proceedings{1374761,
   author = {Klus, Jakub and Pořízka, Pavel and Prochazka, David and Mikysek, Petr and Novotný, Jan and Novotný, Karel and Kaiser, Jozef},
   booktitle = {9-th International Conference on Laser-Induced Breakdown Spectroscopy - LIBS2016},
   keywords = {Geology; mineralization; neural networks},
   language = {eng},
   title = {Study of mineralization in geological samples by means of LIBS and neural networks},
   year = {2016}
}

TY  - CONF
ID  - 1374761
AU  - Klus, Jakub - Pořízka, Pavel - Prochazka, David - Mikysek, Petr - Novotný, Jan - Novotný, Karel - Kaiser, Jozef
PY  - 2016
TI  - Study of mineralization in geological samples by means of LIBS and neural networks
KW  - Geology
KW  - mineralization
KW  - neural networks
N2  - 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. Finally a mineralization visualization performed in unconventional manners is shown. Instead of the creation of chemical map by rearranging the line intensities to the rectangular grid of points, a correlation of spectra with neuron responses was calculated. It is shown that map of correlation coefficients provides better insight into the problem of element associations or possible co-mineralization in general.
ER  -

KLUS, Jakub, Pavel POŘÍZKA, David PROCHAZKA, Petr MIKYSEK, Jan NOVOTNÝ, Karel NOVOTNÝ a Jozef KAISER. Study of mineralization in geological samples by means of LIBS and neural networks. In \textit{9-th International Conference on Laser-Induced Breakdown Spectroscopy - LIBS2016}. 2016.

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