A framework of active learning and semi-supervised learning for lithology identification based on improved naive Bayes

作者：

Highlights：

• Proposing a framework of active learning combined with semi-supervised learning.

• Introducing Gaussian Mixture Model to fit distribution of complex logging data.

• Probabilistic uncertainty measurement for lithology identification results.

摘要

•Proposing a framework of active learning combined with semi-supervised learning.•Introducing Gaussian Mixture Model to fit distribution of complex logging data.•Probabilistic uncertainty measurement for lithology identification results.

论文关键词：Lithology identification,Active learning,Semi-supervised learning,Logging data,Naive Bayes

论文评审过程：Received 21 January 2022, Revised 25 March 2022, Accepted 18 April 2022, Available online 21 April 2022, Version of Record 26 April 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117278