Naslov
Correlation of Gamma Ray Spectrometry and Total Organic Carbon data using Artificial Neural Networks

Autori
Pejić, Marija ; Kapuralić, Josipa ; Brcković, Ana ; Smirčić, Duje ; Kolenković Močilac, Iva ; Cvetković, Marko

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Abstract book of the GEOMATES 2022 / Gábor Hatvani, István ; Erdélyi, Dániel ; Fedor, Ferenc - , 2022, 78-78

ISBN
978-963-7068-14-0

Skup
International Congress on Geomathematics in Earth- and Environmental Sciences (GEOMATES 2022)

Mjesto i datum
Pečuh, Mađarska, 19.05.2022. - 21.05.2022

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Gamma Ray Spectrometry, Total Organic Carbon, Artificial Neural Networks, Correlation

Sažetak
In the Croatian part of the Pannonian Basin System, near Voćin, a 45.9 m long stratigraphic section was recorded. Concentrations of naturally occurring radioelements (K, U, Th) and dose rate were measured, and 33 samples were taken to measure TOC (Total Organic Carbon). According to Lüning & Kolonic (2003), time-consuming organic geochemical analyses can be replaced by gamma-ray spectrometry in black shale systems, but shallow- marine organic-rich systems, similar to ours, are generally characterized by the absence of a stable U/TOC relationship. An attempt was made to establish a correlation between the measured values of natural radioactivity and the TOC values of the samples using artificial neural networks (ANNs). Our results show that there is a moderate correlation between uranium and dose rate (DR) concentrations with TOC, but there is insufficient data so far to train ANNs properly. The radiometric data were obtained with a gamma-ray spectrometer and the values TC (Total Carbon) and TIC (Total Inorganic Carbon) were determined with Analytik Jena multi EA 4000. TOC was calculated from TC and TIC. Radiometric data supplemented by additional analyses can be used for sequence stratigraphic analysis (Omidpour et al, 2021), which could be the next step in this research, and provide more input variables for ANNs.

Izvorni jezik
Engleski

Znanstvena područja
Geologija, Geofizika

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