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National Repository of Grey Literature 1 records found  Search took 0.00 seconds. 
Automatic classification of geological samples using Laser-Induced Breakdown Spectroscopy and machine learning
 Stříbrná, Klára ; Hrdlička, Aleš (referee) ; Prochazka, David (advisor)
This thesis focuses on creating a database of LIBS spectra from geological samples. These spectra are then used to train a Convolutional Neural Network (CNN)-based classification model for the automatic classification of the samples. The trained models are validated on unknown data and compared in terms of accuracy and training time. The aim of the thesis is to evaluate the potential of combining LIBS with Machine Learning for the automatic classification of geological samples. Current methods are often time-consuming and expensive. LIBS allows for fast chemical mapping and, compared to other methods of chemical analysis used in geology, is relatively inexpensive. Additionally, LIBS can detect light elements (such as Li and Be) that are undetectable by other methods.
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