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Analysis ofgeochemicalcharacteristics ofrocks using machinelearning methods Degtyareva KseniaKukartseva OksanaTynchenko Vadim2024 год

Analysis of geochemical characteristics of rocks using machine learning methods
статья из журнала

Дополнительные ссылки: eLibrary
База данных: Каталог библиотеки СФУ
Библиографическое описание: Analysis of geochemical characteristics of rocks using machine learning methods : научное издание / K. K. Degtyareva [и др.]. - Текст : непосредственный // E3S Web of Conferences. - 2024. - Т. 583, № . - С. 01007. - ISSN 2555-0403. - ISSN 2267-1242, DOI 10.1051/e3sconf/202458301007.
Аннотация: <jats:p>This work is devoted to the classification of rock types based on their geochemical characteristics using machine learning methods. The study used data on the content of various elements in rocks to develop classification models. Four methods were investigated and compared: decision tree, logistic regression, random forest and gradient boosting. The results showed that the random forest model demonstrates the highest classification accuracy (0.832612), which is explained by its ability to efficiently process a variety of features and their interactions. Correlation analysis has shown significant correlations between the geochemical characteristics of rocks, which underlines the importance of choosing appropriate machine learning methods for processing such data. This work highlights the importance of using ensemble methods that can take into account complex interactions between features for accurate classification of geochemical data and can be useful for specialists in the field of geology, mining and related industries.</jats:p>
Год издания: 2024
Авторы: Degtyareva Ksenia, Kukartseva Oksana, Tynchenko Vadim, Mariupolskiy Timofey, Pereverzev Denis
Издательство: EDP Sciences - Web of Conferences, Les Ulis
Источник: E3S Web of Conferences
Выпуск: Т. 583
Номера страниц: 01007
Количество экземпляров:
Твёрдая копия издания отсутствует в фонде библиотеки

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