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一个数据集和碳酸盐薄截面图像的基准,用于深度学习
Keran Li1,2, Jinmin Song3, Zhaoyi Zhang1
1State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, 610059, China.
Scientific data
|January 28, 2026
概括
研究人员开发了DeepCarbonate,这是一个标准化的数据集,用于碳酸盐岩石学中的深度学习. 这一基准数据集提高了可重现性,并使机器学习模型在地质图像分析中的公平比较成为可能.
科学领域:
- 地质科学是地球科学.
- 计算机科学 计算机科学
- 石油地质学 石油地质学
背景情况:
- 深度学习模型越来越多地用于分析碳酸盐薄截面图像.
- 这一领域的一个重大局限是缺乏大型,公开可用的数据集,这阻碍了可复制性和模型比较.
研究的目的:
- 介绍DeepCarbonate,一个清理和标准化的碳酸盐薄截面图像分析的基准数据集.
- 促进可复制的研究和对碳酸盐岩石学中的深度学习模型的公平评估.
主要方法:
- 从中国和阿联的各种地质构造 (埃迪亚卡朗,坎布里亚,三叠纪,白纪) 收集了碳酸盐样本.
- 与石油地质学专家一起策划数据集,通过专家投票和2σ过来删除无效图像.
- 在ImageNet格式中组织图像,创建了22个按光学模式 (PPL,XPL,R) 分类为列车,验证和测试子集的层次结构的石材类别.
主要成果:
- 通过使用PyTorch与CUDA加速,评估了多个深度学习模型 (ResNet,VGG,DenseNet,MobileNet,EfficientNet).
- 在各种条件下进行实验,包括基线,切除,长尾分布和平衡的Top 9子集.
- 证明了数据集作为碳酸盐岩石学强大的基准的有效性.
结论:
- 深碳酸盐解决了对于碳酸盐图像分析的深度学习中标准化数据集的关键需求.
- 该数据集支持可重现的研究,并促进在现场开发和比较先进的机器学习模型.
- 这项工作为推进碳酸盐岩石学研究及其实际应用提供了宝贵的资源.
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