Lithology dataset

The dataset includes core images used in the paper “Automated lithology classification from drill core images using convolutional neural networks” https://doi.org/10.1016/j.petrol.2020.107933.
It includes 2 x 2 cm core images created from core tray images provided by the Geological Survey of South Australia, available on http://www.energymining.sa.gov.au/minerals/geoscience/geoscientific_data/hylogger.
It includes images used for training, validating, and testing the CNN models for four categories: sandstone, limestone, shale, and a ‘garbage’ category that contains empty tray areas and non-core objects such as and wooden/metallic objects in the tray images.

本数据集包含论文《基于卷积神经网络的钻孔岩芯图像自动化岩性分类》（DOI: 10.1016/j.petrol.2020.107933）中使用的核心岩芯图像。本数据集包含从南澳大利亚地质调查局（Geological Survey of South Australia）提供的岩芯托盘图像中裁剪得到的2×2厘米规格岩芯图像，相关源图像可通过链接http://www.energymining.sa.gov.au/minerals/geoscience/geoscientific_data/hylogger获取。本数据集包含用于训练、验证及测试卷积神经网络（Convolutional Neural Networks, CNN）模型的图像，共分为四类：砂岩、石灰岩、页岩，以及“杂类”——该类别包含空托盘区域以及托盘图像中的非岩芯物体，如木质、金属物件等。