论文标题
使用深卷积神经网络在碳酸盐小路分析过程中自动鉴定化石和非生物晶粒
Automatic identification of fossils and abiotic grains during carbonate microfacies analysis using deep convolutional neural networks
论文作者
论文摘要
基于薄部分中的小比例鉴定的岩石学分析被广泛用于沉积环境解释和古生态重建中。小幅面位的化石识别是岩石学家完成此任务的重要程序。区分骨骼碎片的形态和微观结构多样性需要广泛的先验知识，以了解显微镜下的化石形态和长期训练。这项要求对沉积学家和古生物学家，尤其是新手面临某些挑战。但是，机器分类器可以帮助应对这一挑战。在这项研究中，我们收集了一个小相图像数据集，其中包括来自1,149个参考文献和我们自己的材料的公共数据（包括22个化石和非生物谷物组的30,815张图像）。我们采用了高性能的工作站来实施四个经典的深卷卷积神经网络（DCNNS），在过去的几年中，它们在计算机视觉上已被证明非常有效。我们的框架使用了转移学习技术，该技术重复了在较大的成像网数据集上训练的预训练参数作为网络的初始化，以通过低计算成本实现高精度。在Inception Resnet V2体系结构中，我们获得了最高三个测试精度的最多95％，而99％的占99％。机器分类器在矿物质（例如白云岩和黄铁矿）上表现出0.99的精度。尽管它在具有类似形态的样品（例如双壳，腕足动物和奥斯特拉科德）方面存在一些困难，但它仍然获得了0.88的精度。我们的机器学习框架证明了与人类分类器相当的可重复性和避免偏见的高精度。因此，它的应用可以消除进行常规识别的人类专家的许多乏味，手动密集的努力。
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.
