主成分分析
材料科学
模式识别(心理学)
聚类分析
人工智能
小波
小波变换
组分(热力学)
成分分析
数据挖掘
计算机科学
物理
热力学
作者
Man Hyok Song,Sun Hong,G. Kim
出处
期刊:Advances in Science and Technology
日期:2024-03-27
摘要
A method to extract lithologic interfaces and identify lithofacies based on the continuous wavelet transform (CWT), principal component analysis (PCA) and K-means clustering is proposed. Well-logs which can reflect lithofacies are selected by correlation analysis of multiple well-logs and their principal components are determined by PCA of them. The CWT of the 1st principal component (PC) based on the Gaussian wavelet at a fixed scale is used to detect temporary interfaces which include lithologic interfaces as well as those reflecting intra-bed variations. Interval signal is formed by averaging the 1st PC values between adjacent interfaces. Accurate and practical lithologic interfaces are reset by considering variances of the interval signal to select interfaces using the difference moduli of the interval signal. The K-means clustering in the main PC space is effectively employed to classify and identify sedimentary lithofacies from well log data. The application to well log data indicates that the method is useful and practical in detecting lithological interfaces and identifying lithofacies.
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