Since Chinas reform and opening up, the social economy has achieved rapid development, followed by a sharp increase in carbon dioxide (CO₂) emissions. Therefore, at the 75th United Nations General Assembly, China proposed to achieve carbon peaking by 2030 and carbon neutrality by 2060. The research work on advance forecasting of CO₂ emissions is essential to achieve the above-mentioned carbon peaking and carbon neutrality goals in China. In order to achieve accurate prediction of CO₂ emissions, this study establishes a hybrid intelligent algorithm model suitable for CO₂ emissions prediction based on Chinas CO₂ emissions and related socioeconomic indicator data from 1971 to 2017. The hyperparameters of Least Squares Support Vector Regression (LSSVR) are optimized by the Adaptive Artificial Bee Colony (AABC) algorithm to build a high-performance hybrid intelligence model. The research results show that the hybrid intelligent algorithm model designed in this paper has stronger robustness and accuracy with relative error almost within ±5 in the advance prediction of CO₂ emissions. The modeling scheme proposed in this study can not only provide strong support for the Chinese government and industry departments to formulate policies related to the carbon peaking and carbon neutrality goals, but also can be extended to the research of other socioeconomic-related issues.

Lithology identification supports reservoir characterization and well-log interpretation. Random sample splits can overestimate performance because adjacent samples from the same well are correlated, while logging-channel differences may be underused. We propose a Residual Feature-Gated BiGRU framework (RFG-BiGRU) for blind-well lithology identification. The framework combines singular spectrum analysis for signal conditioning, a learnable feature gate, residual feature fusion, and bidirectional GRU sequence modeling with temporal attention. RFG-BiGRU was evaluated on the six-class North Sea subset of the FORCE 2020 well-log dataset. The two designated blind wells, 31/2-19 S and 31/2-8, were excluded from model development, while the remaining 13 wells were used for development. RFG-BiGRU achieved a mean accuracy of 89.85%, a mean Macro-F1 of 55.69%, and a mean weighted F1-score of 89.16%. Supplementary five-fold evaluation across the 13 development wells yielded a mean Macro-F1 of 37.69% for RFG-BiGRU under a fixed configuration, with differences in preprocessing and weighting between model pipelines. In a separate matched five-seed comparison on the two blind wells, Full RFG-BiGRU, Plain BiGRU, and BiGRU + Temporal Attention achieved mean Macro-F1 values of 55.78%, 55.35%, and 55.71%, respectively. The Full model’s mean difference from Plain BiGRU was 0.43 percentage points with a sample standard deviation of 2.50 points, so the single-run 3.32-point difference is not treated as a stable repeated-run advantage.

Apatite rare-earth-element (REE) compositions can discriminate igneous host-rock types, but systematic comparisons of deep-learning models and alternative REE representations remain limited, while analyses from shared publications or geological bodies complicate estimates of generalization. We evaluated four-class classification in a cohort of 4148 apatite analyses from 76 connected source groups. Six primary models (SVM, RF, XGBoost, MLP, ResNet, and FT-Transformer) were compared across seven representations derived from the same 14 REEs. Logistic regression and linear discriminant analysis served as baselines, and ILR features were examined separately. Source-group-aware nested cross-validation used five outer and three inner folds, with candidate selection and tuning confined to outer training groups. The selected procedure achieved a five-seed pooled out-of-fold Macro-F1 of 0.556 (95% source-group bootstrap CI, 0.398–0.652). With Raw inputs, logistic regression achieved 0.573 (0.436–0.672). Confidence intervals overlap, and paired comparisons after multiplicity adjustment do not support a reliable ranking of the candidates or a consistent advantage for deeper architectures or combined representations. Raw, Geo, CLR, and ILR are alternative representations of the same REE measurements rather than independent chemical information. Logistic regression using Raw inputs remained competitive, while transfer to unseen geological sources remains uncertain, especially for classes represented by few independent sources.

Total organic carbon (TOC) is a key parameter for source-rock evaluation, but laboratory measurements are sparse and do not resolve continuous vertical variation. Conventional well logs provide continuous physical responses, whereas X-ray fluorescence (XRF) data add geochemical information. We evaluated the incremental value of combining these data sources using 58 depth-matched samples from the Yiwan-1 well in the Wangjiawan area, South China. Four well-log variables and six XRF variables selected within each training fold were evaluated with nine models under repeated 5-fold cross-validation (20 repeats). The fused SVR model performed best (RMSE = 0.789 ± 0.267; R = 0.912 ± 0.081). At the repetition level, the absolute RMSE reduction relative to logs alone was 0.430 (95% CI: 0.386–0.478), equivalent to 35.3% (Holm-adjusted p = 1.34 × 10⁻⁵). A 500-run permutation test that shuffled complete XRF sample rows gave an empirical p-value of 0.002. In a separate contiguous depth-block analysis with a 0.16 m exclusion buffer, the SVR RMSE decreased from 2.566 to 1.837, corresponding to an improvement of 28.4%, although the benefit was not retained by every linear model. Mo, V, Cr, Cd and Zn were selected in all Pearson-selection folds and had positive mean held-out permutation importance. The results support a complementary contribution from XRF data within this well while also showing that estimated accuracy depends on the validation design.