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LOGICAL INFORMATION MODELS FOR PREDICTION AND EXPRESS-EVALUATION OF NEW GOLD ORE DEPOSITS IN THE ARCTIC ZONE OF RUSSIA

Irina Chizhova

First published: 2019-06-20https://doi.org/10.5593/sgem2019/2.1/s08.121View metrics

Abstract

Mathematical data processing has allowed us to build logical information models based on machine learning (select a lot of informative features (elements), indicating their separating weights and ranges of changes in their values (intervals-indicators), typical for each of the groups of deposits of various formation types). The constructed logical-information models are based on a representative analytical database of 95 gold deposits and gold occurrences in the North-East of Russia. Samples were studied by modern analysis methods (AAS, ICP-MS and RFA) to identify the geochemical features of gold ore deposits of different formation types from database. Ores were analyzed for 52 elements. The models are constructed for five formation types of deposits: Au-Ag, epithermal; Au-quartz; Au-sulfide (disseminated ores); Cu-Mo-Au-porphyry; pyrite-polymetallic, enriched with Au and Ag. Developed rules reliably identify the formation type of new objects (recognition quality = 0.85). It is shown that the created models can be used for the rapid assessment of new gold ore occurrences in the Arctic zone of Russia. In order to determine the formation type of ore occurrence by peat samples, it is necessary to calculate the total weight of the indicator data of the samples for each model using the elements for which the value in the sample falls within the interval indicator of a certain formation type. The estimated new object refers to the formation type for which the total weight of indicator data will have a maximum value.

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Publication details

Title
LOGICAL INFORMATION MODELS FOR PREDICTION AND EXPRESS-EVALUATION OF NEW GOLD ORE DEPOSITS IN THE ARCTIC ZONE OF RUSSIA
Authors
Irina Chizhova
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 19th International Multidisciplinary Scientific GeoConference SGEM2019, Informatics, Geoinformatics and Remote Sensing
Publisher
STEF92 Technology
Year
2019
Pages
935-942
SWS Citekey
Chizhova20198935942
ISSN
1314-2704
ISBN
978-619-7408-79-9
Language
en
Publication type
Conference Paper
Keywords
Arctic zonegold depositslogical information modelore formation typeexpress-evaluation.
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