Interpretable Semisupervised Classification Method Under Multiple Smoothness Assumptions With Application to Lithology Identification

In this letter, considering the lack of core and drilling cuttings, an interpretable semisupervised classification method (ISSCM) under multiple smoothness assumptions is proposed and applied to lithology identification. The contribution is threefold. First, the novel semisupervised learning algorithm is developed based on the decision tree, the interpretability of which is highly beneficial to solve risk-aware problems. Second, both smoothness in the feature space and depth is utilized to generate pseudo-labels for the unlabelled data by using label propagation. Third, an algorithm to approximate the optimal affinity matrix is added to avoid degradation rendered by inappropriate manual settings under multiple smoothness assumptions. All these contributions could yield a classification model that is interpretable, accurate, and insusceptible to imprecise empirical settings. In the experiment, the proposed method is applied to lithology identification and verified by real-world data.