Adaptive Multiexpert Learning for Lithology Recognition

Abstract

Summary Lithology recognition based on well logging plays an important role in drilling optimization and minimizing investment costs. Many existing lithology recognition methods resolve this problem by assuming that they have a balanced lithology data set (i.e., the different classes of lithology data samples have an even distribution). However, this assumption is usually not feasible in the real world, where the data sample size varies enormously between different categories of lithology and thereby leads to significantly degrading recognition performance. To address this problem, we propose adaptive multiexpert learning (AMEL) in this paper. Reweighting methods can be used to solve imbalanced distributions. However, blindly increasing the loss weights of the minority class does not create a balanced model. However, AMEL incorporates reweighting into knowledge distillation (KD) to further optimize the learning process. Our process involves dividing the training set into several groups and training various expert models. Then, we trained student models using a combination of weighted instance losses and distillation losses. Furthermore, we carried out extensive experiments on real-life log data to prove the efficiency of AMEL.