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探岩心图像自动化岩性分类》（https://doi.org/10.1016/j.petrol.2020.107933）中使用的核心图像。
该数据集包含由南澳大利亚地质调查局提供的岩心托盘图像生成的2×2厘米岩心图像，相关源数据可通过http://www.energymining.sa.gov.au/minerals/geoscience/geoscientific_data/hylogger获取。
数据集包含用于训练、验证及测试卷积神经网络（Convolutional Neural Network, CNN）模型的图像，共分为四类：砂岩、灰岩、页岩，以及包含空托盘区域和托盘图像内木质/金属物件等非岩心物体的“垃圾”类别。

搜集汇总

背景与挑战

背景概述

Lithology dataset是一个用于岩性分类的岩心图像数据集，包含砂岩、石灰岩、页岩和垃圾四个类别的图像，用于训练、验证和测试卷积神经网络模型。数据集来源于南澳大利亚地质调查局提供的岩心托盘图像。