A method for identifying lithology based on a feature-weighted KNN model
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.How-ever,the physical property data of different rocks frequently overlap to some extent,posing challenges to accurate lithology identifica-tion 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.
万方数据
维普
