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Machine learning application to automatically classify heavy minerals in river sand by using SEM/EDS data
کاربرد یادگیری ماشین برای طبقه بندی خودکار مواد معدنی سنگین در ماسه رودخانه با استفاده از داده های SEM / EDS-2019 Heavy minerals are generally trace components of sand or sandstone. Fast and accurate heavy mineral classification
has become a necessity. Energy Dispersive X-ray Spectrometers (EDS) integrated with Scanning Electron
Microscopy (SEM) were used to obtain rapid heavy mineral elemental compositions. However, mineral identification
is challenging since there are wide ranges of spectral datasets for natural minerals. This study aimed to
find a reliable, machine learning classifier for identifying various heavy minerals based on EDS data. After
selecting 22 distinct heavy minerals from modern river sands, we obtained their elemental data by SEM/EDS.
The elemental data from a total of 3067 mineral grains were collected under various instrumental conditions. We
compared the classification performance of four classifiers (Decision Tree, Random Forest, Support Vector
Machine, Bayesian Network). Our results indicated that machine learning methods, especially Random Forest,
can be used as the most effective classifier for heavy mineral classification. Keywords: Heavy mineral | Machine learning | Energy dispersive X-ray spectrometers | Sand | Classification | Sedimentology | Geology |
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