Published 2025-04-01
“…This study introduces three key innovations to address these challenges: (1) A Dynamic Weighting–Calibrated Random Forest Regression (DW-RFR) model integrating high-resolution Gamma-Ray-guided dynamic time warping (±0.06 m depth alignment precision derived from 237 core-log calibration points using cross-validation), Principal Component Analysis-Deyang–Anyue Rift Trough Shapley Additive Explanations (PCA-SHAP) hybrid feature engineering (89.3% cumulative variance, VIF < 4), and Bayesian-
optimized ensemble learning; (2) systematic benchmarking against conventional ΔlogR (R<sup>2</sup> = 0.700, RMSE = 0.264) and multi-attribute joint inversion (R<sup>2</sup> = 0.734, RMSE = 0.213) methods, demonstrating superior accuracy (R<sup>2</sup> = 0.917, RMSE = 0.171); (3) identification of Gamma Ray (r = 0.82) and bulk density (r = −0.76) as principal TOC predictors, contrasted with resistivity’s thermal maturity-dependent signal
attenuation (r = 0.32 at Ro > 3.0%). …”
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