Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images
Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images
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DOI：
10.3390/en14154595
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发表时间：
2021-07
期刊：
影响因子：
3.2
通讯作者：
Parisa Asadi;L. Beckingham
中科院分区：
文献类型：
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作者：
Parisa Asadi;L. Beckingham
x射线CT成像提供了样品的三维视图，是研究多孔岩石内部特征的有力工具。在这些图像中进行可靠的相位分割是非常必要的，但与任何其他数字岩石成像技术一样，这是耗时、费力且主观的。将3D x射线CT成像与机器学习方法相结合，除了颜色衰减之外，机器学习方法还可以同时考虑提取的多个特征，是一种可靠的相位分割方法。基于机器学习的x射线CT图像相位分割能够比传统方法更快地收集和解释数据。本研究研究了三种机器学习方法和一种深度学习方法的几种滤波技术的性能，以评估可靠的x射线CT图像特征提取和像素级相位分割的潜力。首先使用众所周知的过滤器从图像中提取特征，并从预训练的VGG16架构的第二层卷积层提取特征。然后，将K-means聚类、随机森林和前馈人工神经网络方法以及改进的U-Net模型应用于提取的输入特征。然后比较和对比模型的性能，以确定机器学习方法和输入特征对可靠相位分割的影响。结果表明，考虑更多的维度是有希望的，所有分类算法的准确率都很高，在0.87 ~ 0.94之间。基于特征的随机森林在机器学习模型中表现最好，Mancos的准确率为0.88，Marcellus的准确率为0.94。对Mancos和Marcellus的精度分别为0.91和0.93，具有焦斑和色块损失线性组合的U-Net模型也表现良好。总的来说，考虑更多的特征提供了有希望和可靠的分割结果，这对于分析致密样品（如页岩）的成分有价值，页岩是采油中的重要非常规储层。
X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.