Indicators for identification of rare-earth type of granitoids of Ukrainian Shield and their connection with paleogeodynamics using ANN

Earth Surface Processes & Geodynamics

Authors

First and Last Name Academic degree E-mail Affiliation
Serhiy Vyzhva Sc.D. s.vyzhva [at] knu.ua Taras Shevchenko National University of Kyiv
Kyiv, Ukraine
Andriy Gozhyk Ph.D. vice-rector.education [at] knu.ua Taras Shevchenko National University of Kyiv
Kyiv, Ukraine
Oleksandr Shabatura Sc.D. oshabatura [at] knu.ua Taras Shevchenko National University of Kyiv
Kyiv, Ukraine

I and my co-authors (if any) authorize the use of the Paper in accordance with the Creative Commons CC BY license

First published on this website: 25.08.2024 - 08:52
Abstract 

We propose the use of Automated Neuron Network (ANN) to identify the type of rare-earth elements and determine their relationship with paleogeodynamics are index of accumulated concentrations, mineralogical profile and metallogenic specialization (type rare earth mineralization) for a number of petrotypes of the Ukrainian Shield. Neural networks effectively perform the task of classifying metallogenic groups of granitoids and confirms the leading role of silicic acidity and alkalinity in the differentiation of RE, and also reveals a high role of FeO, mineral density and natural residual magnetization as a probable marker of influence of the substrate of S-type granitoids.

Promising of hypabyssal (shallow-depth) intrusions, which are formed under conditions of extension tectonics, for the search for TR-REE objects of the yttrium type, where gravimagnetic geophysical methods can be useful.

Keywords 
paleogeodynamics
granitoids
Ukrainian shield
ANN
Deep Learning
classification
References 

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