Artificial neural network-based method to classify sedimentary rocks

In a geological study, an important step is to determine the type of sedimentary rock or its grain size. Such a determination requires accurate analysis in the field or in a laboratory. As the size of the study area grows, this activity can be time consuming and error prone because the number of spe...

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Bibliographic Details
Published in2018 12th International Conference on Sensing Technology (ICST) pp. 282 - 286
Main Authors Figueiredo, Rodrigo Marques, Veronez, Mauricio Roberto, Wohnrath, Francisco Manoel, da Silva, Marcio Rosa, Gonzaga, Luiz, Kupssinsku, Lucas Silveira, Bordin, Fabiane, Brum, Diego, Cazarin, Caroline Lessio
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2018
Subjects
Grain size
neural networks
Reflectivity
Rocks
Rough surfaces
sedimentary rocks
Surface roughness
Training
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Summary:In a geological study, an important step is to determine the type of sedimentary rock or its grain size. Such a determination requires accurate analysis in the field or in a laboratory. As the size of the study area grows, this activity can be time consuming and error prone because the number of specialists working under rigid criteria also increases. This paper proposes a novel methodology to classify grain size using unique wavelength reflectance data and artificial neural networks. The results indicate that the proposed method can be reliably used in the field.
ISSN:2156-8073
DOI:10.1109/ICSensT.2018.8603576