Volcanic Lithology Identification via Improved Random Forest with Conventional-Elemental-Logging Feature Interpolation: A Case of Block KL16-1

Conventional logging and elemental logging-based lithology identification for volcanic buried-hill reservoirs is hampered by overlapping logging responses, low vertical resolution of elemental measurements, and inter-sample class imbalance, which lead to unsatisfactory classification accuracy. Existing cross-plot empirical methods cannot reliably distinguish lithologies with similar geophysical signatures (e.g., volcanic breccia versus andesite), especially for complex Mesozoic volcanic successions in offshore Bohai Bay Basin. To fill this technical gap, this work proposes an improved random-forest workflow integrating multi-source log-data fusion, depth-aligned linear interpolation, mutual-information feature screening, and SMOTE oversampling. The core methodological innovations lie in: (1) depth matching between conventional continuous logs and sparsely sampled elemental logging by linear interpolation to construct complete multi-feature datasets; (2) eliminating redundant input variables via mutual-information-based feature selection; and (3) mitigating lithology-sample imbalance with SMOTE synthetic-sample generation prior to random-forest training, rather than directly applying off-the-shelf random-forest classifiers. Six dominant lithologies are recognized within the Mesozoic buried-hill of Block KL16-1: basalt, andesite, rhyolite, volcanic breccia, tuff, and tuffaceous conglomerate. The proposed improved random-forest model yields an overall lithology-identification accuracy of 87% and average recall of 85.9%, substantially outperforming traditional cross-plot approaches. Confusion-matrix error analysis demonstrates that the workflow greatly reduces misclassification between easily confused lithological pairs (volcanic breccia andesite, tuff andesite). This study not only delivers a practical tool for fine reservoir evaluation and reservoir-facies prediction in Bohai Mesozoic buried-hill plays but also provides a reproducible reference for machine-learning-driven lithology interpretation in analogous offshore volcanic-reservoir settings.