基于自适应核密度的贝叶斯概率模型岩性识别方法研究

doi:10.11720/wtyht.2020.0069

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Abstract

Accurately characterizing rock types and their structural relationships can provide important information for researches on energy and mineral exploration, deep structure and tectonic, and so on. At present, geophysical data can be used to identify lithology through the differences between the corresponding physical property parameters of different rocks (such as density, susceptibility, resistivity, speed,etc.). However, different rock physical properties often coincide at a certain degree, on the other hand the result of lithological identification is not accurate enough when a single physical property is used. Therefore, it is of great significance to use multi-source data for lithological identification. Bayesian method belongs to the statistical classification methods, which relies on probability for classification, and the calculation of probability density depends on the correlation between sample attributes. On such a basis, the authors introduce the Bayesian probability model based on adaptive kernel density estimation to lithology identification. This method has good adaptability for many different types of physical property parameters and such the predicted lithology classification results with probability parameters, fuzzy intervals, and a variety of lithology classification results. This method has a strong scalability that can process both parametric and non-parametric information at the same time to maximally the known geological information and physical parameters. Synthetic models prove that this method has the ability to provide more stable and accurate results of lithology recognition compared with the methods of traditional Gaussian algorithm and fixed bandwidth kernel density estimation.

Key words： Bayesian lithology identification physical property parameter adaptive kernel density

Received: 17 February 2020 Published: 28 August 2020

P631

Corresponding Authors: Bao-Liang LU E-mail: 847330992@qq.com;lulb@chd.edu.cn

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	Articles by authors
	Ze-Yuan CAI
	Bao-Liang LU
	Sheng-Qing XIONG
	Wan-Yin WANG

Cite this article:

Ze-Yuan CAI,Bao-Liang LU,Sheng-Qing XIONG, et al. Lithology identification based on Bayesian probability using adaptive kernel density[J]. Geophysical and Geochemical Exploration, 2020, 44(4): 919-927.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2020.0069 OR https://www.wutanyuhuatan.com/EN/Y2020/V44/I4/919

Probability density function curve under different bandwidth

Statistical table of model parameters

Physical properties of the model
a— density of the model;b—magnetic susceptibility of the model;c—resistivity of the model

rock distribution of the model

Interaction diagram of physical parameters of 250 training samples

Classification results of 250 training samples
Figures a~f respectively represent the Bayesian classification results, corresponding probability distribution map of the traditional Gaussian classification, fixed bandwidth kernel density estimation, adaptive bandwidth kernel density estimation for 250 training sample points

Interaction diagram of physical parameters of 435 training samples

Classification results of 435 training samples
Figures a~f respectively represent the Bayesian classification results, corresponding probability distribution map of the traditional Gaussian classification, fixed bandwidth kernel density estimation, adaptive bandwidth kernel density estimation for 435 training sample points

Interaction diagram of physical parameters of 869 training samples

Classification results of 435 training samples
Figures a~frespectively represent the Bayesian classification results, corresponding probability distribution map of the traditional Gaussian classification, fixed bandwidth kernel density estimation, adaptive bandwidth kernel density estimation for 869 training sample points

Statistical table of model error rate

Forecast classification results
a~f are the classification results when the probability difference is 10%, 20%, 30%, 40%, 50% and 60% respectively

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