基于特征加权的KNN模型岩性识别方法

doi:10.11720/wtyht.2024.1260

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Abstract

Lithology identification, as a major geological task, strongly underpins the exploration of solid minerals, oil, and gas. Since the physical properties of rocks bridge lithologies and geophysical fields, their differences can be used for lithology identification. However, the physical property data of different rocks frequently overlap to some extent, posing challenges to accurate lithology identification using cross plots alone. The K-nearest neighbor (KNN) model is suitable for multi-class classification since it is a simple and direct machine learning method with high accuracy and sensitivity. This study introduced a feature-weighted KNN model for lithology identification. In this model, different weights were assigned to different features by combining the conventional KNN model with the information gain of attribute features. This allowed for intuitive reflection of the importance of attribute features to classification. Experiments show that compared to the conventional KNN model, the feature-weighted KNN model can more significantly identify lithologic boundaries, thus improving the overall accuracy and stability of lithology identification.

Key words： KNN lithology identification information gain feature weight

Received: 15 June 2023 Published: 16 April 2024

ZTFLH:

P631

Corresponding Authors: WANG Wan-Yin E-mail: gys103319@163.com;wwy7902@chd.edu.cn

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	Yu-Shan GUO
	Wan-Yin WANG

Cite this article:

Yu-Shan GUO,Wan-Yin WANG. A method for identifying lithology based on a feature-weighted KNN model[J]. Geophysical and Geochemical Exploration, 2024, 48(2): 428-436.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2024.1260 OR https://www.wutanyuhuatan.com/EN/Y2024/V48/I2/428

Rock distribution of the model

Physical property parameter of modal

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

Interaction diagram of physical property parameters of 250 training samples

Interaction diagram of physical property parameters of 435 training samples

Interaction diagram of physical property parameters of 870 training samples

Accuracy rate and error statistical table of modal

Optimal K value and accuracy of the model

Statistics table of feature information gain and weight

Accuracy rate and error statistical table of feature weighted modal

Accuracy line graph of two models under different K value

Comparison of model error rates

The recognition result of the model under the optimal K value
Figures(a) ~ (c) is the recognition results of training samples 250, 435 and 870 under the traditional KNN model. Figures (d) ~ (f) is the recognition junction of 250, 435 and 870 training samples based on the feature-weighted KNN model

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