用耦合熵选地质变量，少而准地做岩相分类。
Facies Classification with Copula Entropy
- 用耦合熵衡量地质变量与岩相的关联，筛选关键变量。
- 仅用更少变量实现同等分类性能，效率更高。
- 选出的变量具地质意义，便于地质学家理解。
本文提出将耦合熵（Copula Entropy, CE）应用于岩相分类。方法通过CE度量地质变量与岩相类别之间的相关性，选取具有大负值CE的变量用于分类。在典型的岩相分类数据集上验证，结果表明该方法可在不降低分类性能的前提下，选用更少的地质变量。所选变量因耦合熵的严格定义，具备明确的地质含义，对地质学家具有可解释性。
原文摘要 · Abstract (English)
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.
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