Automated analysis of foraminifera fossil records by image classification using a convolutional neural network

Type Article
Date 2020-10
Language English
Author(s) Marchant Ross1, 2, Tetard Martin1, Pratiwi Adnya1, Adebayo Michael1, de Garidel-Thoron Thibault1
Affiliation(s) 1 : Aix-Marseille Université, CNRS, IRD, Coll. De France, INRAE, CEREGE, Technopôle de l'Arbois-Méditerranée, Aix-en-Provence, 13545, France
2 : School of Electrical Engineering & Robotics, Queensland University of Technology, Brisbane, Australia
Source Journal Of Micropalaeontology (0262-821X) (Copernicus GmbH), 2020-10 , Vol. 39 , N. 2 , P. 183-202
DOI 10.5194/jm-39-183-2020
WOS© Times Cited 3
Abstract

Manual identification of foraminiferal morphospecies or morphotypes under stereo microscopes is time consuming for micropalaeontologists and not possible for nonspecialists. Therefore, a long-term goal has been to automate this process to improve its efficiency and repeatability. Recent advances in computation hardware have seen deep convolutional neural networks emerge as the state-of-the-art technique for image-based automated classification. Here, we describe a method for classifying large foraminifera image sets using convolutional neural networks. Construction of the classifier is demonstrated on the publicly available Endless Forams image set with a best accuracy of approximately 90 %. A complete automatic analysis is performed for benthic species dated to the last deglacial period for a sediment core from the north-eastern Pacific and for planktonic species dated from the present until 180 000 years ago in a core from the western Pacific warm pool. The relative abundances from automatic counting based on more than 500 000 images compare favourably with manual counting, showing the same signal dynamics. Our workflow opens the way to automated palaeoceanographic reconstruction based on computer image analysis and is freely available for use.

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Marchant Ross, Tetard Martin, Pratiwi Adnya, Adebayo Michael, de Garidel-Thoron Thibault (2020). Automated analysis of foraminifera fossil records by image classification using a convolutional neural network. Journal Of Micropalaeontology, 39(2), 183-202. Publisher's official version : https://doi.org/10.5194/jm-39-183-2020 , Open Access version : https://archimer.ifremer.fr/doc/00655/76687/