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

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.

深度卷积神经网络在碳酸盐微相分析过程中自动识别化石和非生物谷物

基于薄相微相识别的岩相分析被广泛用于沉积环境解释和古生态重建。微相的化石识别是岩相学家完成这项任务的必要程序。.. 区分骨骼片段的形态和微结构多样性需要在微相中具有广泛的化石形态类型的先验知识，并且需要在显微镜下进行长时间的训练。这一要求给沉积学家和古生物学家，特别是新手带来了某些挑战。但是，机器分类器可以帮助解决这一难题。在这项研究中，我们收集了一个微相图像数据集，该数据集包含来自1149个参考文献的公开数据和我们自己的材料（包括22个化石和非生物谷物类的30815张图像）。我们使用了高性能工作站来实现四个经典的深度卷积神经网络（DCNN），在过去的几年中，事实证明它们在计算机视觉方面非常高效。我们的框架使用转移学习技术，它重用在较大ImageNet数据集上训练的预训练参数作为网络的初始化，从而以较低的计算成本实现高精度。在Inception ResNet v2架构中，我们获得了最高95％的顶级测试准确性和最高99％的前三项测试准确性。机器分类器对白云石和黄铁矿等矿物表现出0.99的精度。尽管它对具有类似形态的样品（例如双壳类，腕足类和成骨类）有一定的困难，但仍获得0.88的精度。我们的机器学习框架具有可重复性和避免偏倚的高度准确性，可与人类分类器相媲美。因此，它的应用可以消除进行常规识别的人类专家的许多繁琐的手动工作。 （阅读更多）