Facies Classification with Copula Entropy

TL;DR

This paper introduces a novel method for facies classification using copula entropy to select relevant geological variables, improving interpretability and efficiency without compromising accuracy.

Contribution

The paper applies copula entropy to select geological variables for facies classification, enhancing interpretability and reducing variable count compared to existing methods.

Findings

Selected variables are fewer and interpretable.

Classification performance is maintained with fewer variables.

Method verified on a typical facies dataset.

Abstract

In this paper we propose to apply copula entropy (CE) to facies classification. In our method, the correlations between geological variables and facies classes are measured with CE and then the variables associated with large negative CEs are selected for classification. We verified the proposed method on a typical facies dataset for facies classification and the experimental results show that the proposed method can select less geological variables for facies classification without sacrificing classification performance. The geological variables such selected are also interpretable to geologists with geological meanings due to the rigorous definition of CE.