Predicting Subsurface Lithology A Machine Learning Approach with Explainable Insights

The objective of this study is to develop and evaluate a machine learning model for lithofacies
classification and to employ explainability techniques [1] to justify the model’s predictions. By
validating the results against established geological knowledge, this approach aims to uncover
patterns in the data, generate actionable insights, and bridge the gap between domain expertise
and data-driven methods. Ultimately, this work seeks to advance the efficiency and accuracy of
reservoir characterization.