基于特征加权的KNN模型岩性识别方法

A method for identifying lithology based on a feature-weighted KNN model

岩性识别是一项重要的地质工作,为固体矿产勘探与油气勘探奠定了坚实的地质基础。岩石物性是连接岩性和地球物理场的桥梁,可以通过物性之间的差异进行岩性识别,但不同岩石的物性数据往往存在一定重合,仅靠交会图无法准确地识别岩性。KNN(K近邻)模型是一种简单、直接的机器学习方法,准确度和灵敏度都很高,适用于多分类问题。基于此,本文将基于特征加权的KNN模型引入岩性识别中,该方法将传统KNN模型与属性特征的信息增益相结合,对不同特征赋予不同权重,可以直观地反映属性特征对分类的重要程度。实验证明,相比于传统KNN方法,基于特征加权的KNN模型对岩性交界处的识别能力有大幅提升,整体提高了岩性识别的准确性和稳定性。

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 di- rect machine learning method with high accuracy and sensitivity. This study introduced a feature-weighted KNN model for lithology i-dentification. In this model, different weights were assigned to different features by combining the conventional KNN model with the in-formation gain of attribute features. This allowed for intuitive reflection of the importance of attribute features to classification. Experi-ments 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.