Automatic identification of fossils and abiotic grains during carbonate microfacies analysis using deep convolutional neural networks

• URL: http://arxiv.org/abs/2009.11429v2
• Date: Wed, 4 Nov 2020 02:04:02 GMT
• Title: Automatic identification of fossils and abiotic grains during carbonate microfacies analysis using deep convolutional neural networks
• Authors: Xiaokang Liu, Haijun Song
• Abstract summary: Petrographic analysis based on microfacies identification in thin sections is widely used in sedimentary environment interpretation and paleoecological reconstruction. Distinguishing the morphological and microstructural diversity of skeletal fragments requires extensive prior knowledge of fossil morphotypes in microfacies.
• Score: 1.520387509697271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Petrographic analysis based on microfacies identification in thin sections is widely used in sedimentary environment interpretation and paleoecological reconstruction. Fossil recognition from microfacies is an essential procedure for petrographers to complete this task. Distinguishing the morphological and microstructural diversity of skeletal fragments requires extensive prior knowledge of fossil morphotypes in microfacies and long training sessions under the microscope. This requirement engenders certain challenges for sedimentologists and paleontologists, especially novices. However, a machine classifier can help address this challenge. In this study, we collected a microfacies image dataset comprising both public data from 1,149 references and our own materials (including 30,815 images of 22 fossil and abiotic grain groups). We employed a high-performance workstation to implement four classic deep convolutional neural networks (DCNNs), which have proven to be highly efficient in computer vision over the last several years. Our framework uses a transfer learning technique, which reuses the pre-trained parameters that are trained on a larger ImageNet dataset as initialization for the network to achieve high accuracy with low computing costs. We obtained up to 95% of the top one and 99% of the top three test accuracies in the Inception ResNet v2 architecture. The machine classifier exhibited 0.99 precision on minerals, such as dolomite and pyrite. Although it had some difficulty on samples having similar morphologies, such as the bivalve, brachiopod, and ostracod, it nevertheless obtained 0.88 precision. Our machine learning framework demonstrated high accuracy with reproducibility and bias avoidance that was comparable to those of human classifiers. Its application can thus eliminate much of the tedious, manually intensive efforts by human experts conducting routine identification.

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