Comparison of support vector machines (SVMs) and the learning vector quantization (LVQ) techniques for geological domaining: a case study from Darehzar porphyry copper deposit, SE Iran

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F24%3A101024" target="_blank" >RIV/60460709:41210/24:101024 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s12145-024-01452-x" target="_blank" >https://doi.org/10.1007/s12145-024-01452-x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s12145-024-01452-x" target="_blank" >10.1007/s12145-024-01452-x</a>

Result language

angličtina

Original language name

Comparison of support vector machines (SVMs) and the learning vector quantization (LVQ) techniques for geological domaining: a case study from Darehzar porphyry copper deposit, SE Iran

Original language description

Geological domaining is an essential aspect of mineral resource evaluation. Various explicit and implicit modeling approaches have been developed for this purpose, but most of them are computationally expensive and complex, particularly when dealing with intricate mineralization systems and large datasets. Additionally, most of them require a time consuming process for hyperparameter tuning. In this research, the application of the Learning Vector Quantization (LVQ) classification algorithm has been proposed to address these challenges. The LVQ algorithm exhibits lower complexity and computational costs compared to other machine learning algorithms. Various versions of LVQ, including LVQ1, LVQ2, and LVQ3, have been implemented for geological domaining in the Darehzar porphyry copper deposit in southeastern Iran. Their performance in geological domaining has been thoroughly investigated and compared with the Support Vec- tor Machine (SVM), a widely accepted classification method in implicit domaining. The overall classification accuracy of LVQ1, LVQ2, LVQ3, and SVM is 90%, 90%, 91%, and 98%, respectively. Furthermore, the calculation time of these algorithms has been compared. Although the overall accuracy of the SVM method is ~ 7% higher, its calculation time is ~ 1000 times longer than LVQ methods. Therefore, LVQ emerges as a suitable alternative for geological domaining, especially when dealing with large datasets.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10505 - Geology

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Earth Science Informatics

ISSN

1865-0473

e-ISSN

1865-0473

Volume of the periodical

17

Issue of the periodical within the volume

6

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

16

Pages from-to

5273-5288

UT code for WoS article

001296598200002

EID of the result in the Scopus database

2-s2.0-85201962835