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.

中文翻译：

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使用深度卷积神经网络在碳酸盐微相分析中自动识别化石和非生物颗粒

基于薄片微相识别的岩相分析广泛应用于沉积环境解释和古生态重建。从微相中识别化石是岩石学家完成这项任务的必要程序。区分骨骼碎片的形态和微观结构多样性需要对微相中的化石形态类型有广泛的先验知识，并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，尤其是新手带来了一定的挑战。但是，机器分类器可以帮助解决这一挑战。在这项研究中，我们收集了一个微相图像数据集，其中包括来自 1,149 篇参考文献的公共数据和我们自己的材料（包括 22 个化石和非生物谷物组的 30,815 张图像）。我们采用了一个高性能工作站来实现四个经典的深度卷积神经网络 (DCNN)，这些网络在过去几年中已被证明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，该技术重用在更大的 ImageNet 数据集上训练的预训练参数作为网络的初始化，以实现高精度和低计算成本。我们在 Inception ResNet v2 架构中获得了高达 95% 的前一和前三的测试准确率的 99%。机器分级机对白云石和黄铁矿等矿物的精度为 0.99。虽然对双壳类、腕足类、介形类等形态相似的样本有一定的难度，但仍获得了 0.88 的精度。我们的机器学习框架展示了与人类分类器相当的可重复性和避免偏差的高精度。因此，它的应用可以消除人类专家进行常规识别的许多繁琐、人工密集的工作。