Graptolites,化石对进化研究和页岩气勘探具有重要意义,由于其复杂的形态和保存挑战,传统上由分类学家在视觉上识别。人工智能(AI)有望改变这种细致的任务。在本文中,我们证明了使用深度学习模型可以以分类学家的准确性识别石墨石。我们构建了迄今为止最复杂和最大的专业单一生物体图像数据集,它由以像素级分辨率注释的113个石墨石物种的>34,000张图像组成,以训练模型,发展,并评估深度学习网络对石墨石进行分类。该模型的性能在准确性上超过了分类学家,时间,和概括,在识别石墨石属和物种方面达到86%和81%的准确率,分别。这种基于人工智能的方法,能够比分类学家更好地识别微小的形态细节,可以集成到网络和移动应用程序中,将石墨石识别扩展到研究机构之外,提高页岩气勘探效率。
Graptolites, fossils significant for evolutionary studies and shale gas exploration, are traditionally identified visually by taxonomists due to their intricate morphologies and preservation challenges. Artificial intelligence (AI) holds great promise for transforming such meticulous tasks. In this paper, we demonstrate that graptolites can be identified with taxonomist accuracy using a deep learning model. We construct the most sophisticated and largest professional single organisms image dataset to date, which is composed of >34,000 images of 113 graptolite species annotated at pixel-level resolution to train the model, develop, and evaluate deep learning networks to classify graptolites. The model\'s performance surpassed taxonomists in accuracy, time, and generalization, achieving 86% and 81% accuracy in identifying graptolite genus and species, respectively. This AI-based method, capable of recognizing minute morphological details better than taxonomists, can be integrated into web and mobile apps, extending graptolite identification beyond research institutes and enhancing shale gas exploration efficiency.