CONVOLUTIONAL NEURAL NETWORKS AS AN AID TO BIOSTRATIGRAPHY AND MICROPALEONTOLOGY: A TEST ON LATE PALEOZOIC MICROFOSSILS

• Published in: Palaios Vol. 35; no. 9; pp. 391 - 402
• Main Authors: de Lima, Rafael Pires, Welch, Katie F, Barrick, James E, Marfurt, Kurt J, Burkhalter, Roger, Cassel, Murphy, Soreghan, Gerilyn S
• Format: Journal Article
• Language: English
• Published: Society for Sedimentary Geology 01-09-2020
• Subjects: accuracy; Beedeina; biostratigraphy; Carboniferous; Foraminifera; Fusulinella; Fusulinidae; Fusulinina; identification; invertebrate; machine learning; methods; microfossils; morphology; neural networks; Paleontology; Paleozoic; Parafusulina; Pennsylvanian; Pseudofusulina; Pseudoschwagerina; Schwagerina; Stratigraphy; taxonomy; Triticites; Wedekindellina
• ISSN: 0883-1351; 1938-5323
• DOI: 10.2110/palo.2019.102

Accurate taxonomic classification of microfossils in thin-sections is an important biostratigraphic procedure. As paleontological expertise is typically restricted to specific taxonomic groups and experts are not present in all institutions, geoscience researchers often suffer from lack of quick access to critical taxonomic knowledge for biostratigraphic analyses. Moreover, diminishing emphasis on education and training in systematics poses a major challenge for the future of biostratigraphy, and on associated endeavors reliant on systematics. Here we present a machine learning approach to classify and organize fusulinids—microscopic index fossils for the late Paleozoic. The technique we employ has the potential to use such important taxonomic knowledge in models that can be applied to recognize and categorize fossil specimens. Our results demonstrate that, given adequate images and training, convolutional neural network models can correctly identify fusulinids with high levels of accuracy. Continued efforts in digitization of biological and paleontological collections at numerous museums and adoption of machine learning by paleontologists can enable the development of highly accurate and easy-to-use classification tools and, thus, facilitate biostratigraphic analyses by non-experts as well as allow for cross-validation of disparate collections around the world. Automation of classification work would also enable expert paleontologists and others to focus efforts on exploration of more complex interpretations and concepts.