US 12,142,024 B2
Automated bioturbation image classification using deep learning
Korhan Ayranci, Dhahran (SA); and Umair Bin Waheed, Dhahran (SA)
Assigned to KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS, Dhahran (SA)
Filed by KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS, Dhahran (SA)
Filed on Dec. 28, 2021, as Appl. No. 17/563,741.
Prior Publication US 2023/0206590 A1, Jun. 29, 2023
Int. Cl. G06V 10/44 (2022.01); E21B 25/00 (2006.01); G06V 10/764 (2022.01)
CPC G06V 10/454 (2022.01) [E21B 25/00 (2013.01); G06V 10/764 (2022.01)] 14 Claims
OG exemplary drawing
 
1. A method of ichnological classification of geological images, comprising:
receiving, by a computing device having circuitry including a memory storing program instructions and one or more processors configured to perform the program instructions, a geological image;
formatting, by the computing device, the geological image to generate a formatted geological image;
applying the formatted geological image to a deep convolutional neural network (DCNN) trained to classify bioturbation indices; and
matching, by a classifier of the DCNN, the formatted geological image to a bioturbation index class,
wherein the DCNN is trained on a training set of geological images pre-labeled with bioturbation indices and classifies the training set into bioturbation index classes,
wherein each geological image of the training set is a 224×224 pixel, three channel image, wherein the three channels comprise a red channel, a blue channel and a green channel. and each geological image of the training set has low-level features including lines, edges and dots and high level features including objects,
applying the formatted geological image to a series of 3×3 convolution filters, wherein each convolution filter generates a set of weights;
freezing the weights of a first portion of the series of 3×3 convolution filters;
training, using the training set, the weights of a second portion of the series of 3×3 convolution filters; and
recognizing, by the DCNN, the objects.