Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/2207
Title: Roundness Estimation of Sedimentary Rocks Using Eliptic Fourier and Deep Neural Networks
Authors: Mejía Hernández, Erik
Moreno Chávez, Gamaliel
Villa Hernández, José de Jesús
Issue Date: 26-Nov-2020
Publisher: IEEE
Abstract: Sedimentary rocks analysis is useful in geological science, economic sector, and risk evaluation. Roundness is a morphological parameter that provide information to characterize and classify sedimentary material. Roundness degrees is estimated from the contour of the particle. Waddell (1932) proposed a remarkable method based on the measurement of particle’s curvature. This method is accurate; evertheless, it is not invariant to scale and rotation. This problem can be solved by mapping the contour to the frequencydomain, however, spectral analysis is a difficult task. Based on these two approaches, we propose to use a deep neural network whose input is the elliptical Fourier spectrum and target is roundness proposed by Wadell. The training database consists of 623 realrocks images from some geological phenomena. We have found the neural networks perform very well on the 88.8% of rocks.
URI: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/2207
https://doi.org/10.48779/jghd-s210
ISBN: 978-1-7281-9953-5
ISSN: 2573-0770
Other Identifiers: info:eu-repo/semantics/publishedVersion
Appears in Collections:*Documentos Académicos*-- M. en Ciencias del Proc. de la Info.

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