An Igneous Rock Lithology Identification Method Based on Data-Algorithm Bidirectional-Driven Framework

• Published in: Ce jing ji shu Vol. 49; no. 2; pp. 218 - 225
• Main Authors: HAN Ruiyi, SONG Xiaoni, WANG Xinru, GUO Yuhang
• Format: Journal Article
• Language: Chinese
• Published: Editorial Office of Well Logging Technology 01.04.2025
• Subjects: class imbalance handling; deep forest algorithm; igneous; lithology identification; log interpretation; logging data fusion
• ISSN: 1004-1338
• DOI: 10.16489/j.issn.1004-1338.2025.02.009

Current igneous rock lithology identification technology faces compounded technical bottlenecks arising from significant disparities between conventional logging responses and lithological sensitivity, challenges in constructing continuous stratigraphic models using discrete mineral experimental data, and class imbalance in core-logging calibration samples. To address these challenges, this study proposes a Data-Algorithm Bidirectional-Driven Framework (DABDF) for igneous lithology identification. First, a feature space optimization algorithm constrained by discrete minerals is developed, integrating mineral chemical components with log data to establish a petrophysically meaningful feature characterization framework. Subsequently, a hierarchical resampling mechanism based on Mahalanobis distance is designed to mitigate recognition bias in minority sample classes. Finally, a probabilistically interpretable Bayesian deep forest model is constructed to achieve high-precision identification of complex lithologie