Roundness Estimation of Sedimentary Rocks Using Eliptic Fourier and Deep Neural Networks

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.