岩性
多元统计
线性判别分析
基质(化学分析)
统计
地质学
主成分分析
模式识别(心理学)
矿物学
计算机科学
人工智能
数学
地球化学
材料科学
复合材料
作者
Guili Lui,Juan Zhai,Wei Zhang,Wanchang Lai,Ziqi Zhao,Wen Li,Guangxi Wang,Qiang Yang,Ran Chen
摘要
X-ray fluorescence data are not only an important indicator in petroleum exploration, but they are also an advantage in lithology discrimination. From the perspective of the application of nuclear technology in drilling and exploration, in this paper, the X-ray fluorescence count rate of the core is used as the basic data for core logging, and the geology and multivariate statistical data are analyzed to analyze the lithology of the core. X-ray fluorescence analysis can provide the fluorescence count rates of 35 elements in a core sample. If there are n core samples, an n*35 matrix can be created. If 35 types of fluorescence count rates are used as the independent variables to directly identify the lithology, the dataset is very large, and some of the independent variables are not strongly correlated. Therefore, the discrimination effect is insufficient for achieving the purpose. Therefore, based on the diagenetic characteristics of sedimentary rocks and the main rock-forming elements and using multivariate statistical methods, in this paper, the count rates of five elements (i.e., Si, Fe, Al, K, and Ca) are selected as independent variables for the lithology discrimination. It is reasonably transformed to obtain the matrix coefficients before dimensionality reduction. The original five-dimensional dataset is reduced to two dimensions using matrix coefficients. Then, Fisher's discriminant method is applied to the dimensionally reduced data to discriminate and classify the lithology. Based on the discrimination results, the X-ray fluorescence count rate and mathematical statistics can be used to obtain a good effect in lithology discrimination
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