Deep Learning Applications in Geosciences: Insights into Ichnological Analysis

• Published in: Applied sciences 2021-08, Vol.11 (16), p.7736
• Main Authors: Ayranci, Korhan, Yildirim, Isa E., Waheed, Umair bin, MacEachern, James A.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Bioturbation; Classification; Classifiers (sedimentation); Datasets; Deep learning; Earth science; Human error; ichnology; Image classification; Machine learning; Neural networks; Oil reservoirs; Outcrops; Reservoirs; trace fossils; transfer learning
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11167736

Ichnological analysis, particularly assessing bioturbation index, provides critical parameters for characterizing many oil and gas reservoirs. It provides information on reservoir quality, paleodepositional conditions, redox conditions, and more. However, accurately characterizing ichnological characteristics requires long hours of training and practice, and many marine or marginal marine reservoirs require these specialized expertise. This adds more load to geoscientists and may cause distraction, errors, and bias, particularly when continuously logging long sedimentary successions. In order to alleviate this issue, we propose an automated technique to determine the bioturbation index in cores and outcrops by harnessing the capabilities of deep convolutional neural networks (DCNNs) as image classifiers. In order to find a fast and robust solution, we utilize ideas from deep learning. We compiled and labeled a large data set (1303 images) composed of images spanning the full range (BI 0–6) of bioturbation indices. We divided these images into groups based on their bioturbation indices in order to prepare training data for the DCNN. Finally, we analyzed the trained DCNN model on images and obtained high classification accuracies. This is a pioneering work in the field of ichnological analysis, as the current practice is to perform classification tasks manually by experts in the field.