This paper focuses on the scientific problem of quantitative mineralization prediction at large depth in the Zaozigou gold deposit, west Qinling, China. Five geological and geochemical indicators are used to establish geological and geochemical quantitative prediction model. Machine learning and Deep learning algorithms are employed for 3D Mineral Prospectivity Mapping (MPM). Especially, the Student Teacher Ore-induced Anomaly Detection (STOAD) model is proposed based on the knowledge distillation (KD) idea combined with Deep Auto-encoder (DAE) network model. Compared to DAE, STOAD uses three outputs for anomaly detection and can make full use of information from multiple levels of data for greater overall robustness. The results show that the quantitative mineral resources prediction by applying the STOAD model has a good performance, where the value of Area Under Curve (AUC) is 0.97. Finally, three main mineral exploration targets are delineated for further investigation.

This paper focuses on researching the scientific problem of deep extraction and inference of favorable geological and geochemical information about mineralization at depth, based on which a deep mineral resources prediction model is established and machine learning approaches are used to carry out deep quantitative mineral resources prediction. The main contents include: (i) discussing the method of 3D geochemical anomaly extraction under the multi-fractal content-volume (C-V) models, extracting the 12 element anomalies and constructing a 3D geochemical anomaly data volume model for laying the data foundation for researching geochemical element distribution and association; (ii) extracting the element association characteristics of primary geochemical halos and inferring deep metallogenic factors based on compositional data analysis (CoDA), including quantitatively extracting the geochemical element associations corresponding to ore-bearing structures (Sb-Hg) based on a data-driven CoDA framework, quantitatively identifying the front halo element association (As-Sb-Hg), near-ore halo element association (Au-Ag-Cu-Pb-Zn) and tail halo element association (W-Mo-Co-Bi), which provide quantitative indicators for the primary haloes’ structural analysis at depth; (iii) establishing a deep geological and geochemical mineral resources prediction model, which is constructed by five quantitative mineralization indicators as input variables: fracture buffer zone, element association (Sb-Hg) of ore-bearing structures, metallogenic element Au anomaly, near-ore halo element association Au-Ag-Cu-Pb-Zn and the ratio of front halo to tail halo (As-Sb-Hg)/(W-Mo-Bi); and (iv) three-dimensional MPM based on the maximum entropy model (MaxEnt) and Gaussian mixture model (GMM), and delineating exploration targets at depth. The results show that the C-V model can identify the geological element distribution and the CoDA method can extract geochemical element associations in 3D space reliably, and the machine learning methods of MaxEnt and GMM have high performance in 3D MPM.

The Xiahe-Hezuo area in Gansu Province, China, located in the West Qinling Metallogenic Belt, is characterized by complex regional geological structures and abundant mineral resources. A number of gold-polymetallic deposits have been identified in this region, demonstrating significant potential for gold-polymetallic mineral prospecting within the metallogenic belt. This study focuses on regional Mineral Prospectivity Mapping (MPM) in the Xiahe-Hezuo area. To address the common challenge of small-sample data limitations in geological prediction, we introduce a Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) to generate high-fidelity geological feature samples, effectively expanding the training dataset. A Convolutional Neural Network (CNN) was used to train and predict on both pre- and post-augmentation data. The experimental results show that, before augmentation, the CNN model’s Receiver Operating Characteristic (ROC) value was 0.9648. After data augmentation with the WGAN-GP, the CNN model’s ROC value improved to 0.9792. Additionally, the CNN model’s classification performance was significantly enhanced, with the training set accuracy increasing by 5% and the test set accuracy improving by 2%, successfully overcoming the issue of insufficient model generalization caused by small sample sizes. The mineralization prediction results based on data augmentation delineate five prospective mineralization targets, whose spatial distribution exhibits strong correlations with known deposits and fault structural belts, confirming the reliability of the predictions. This study validates the effectiveness of data augmentation techniques in MPM and provides a transferable technical framework for MPM in data-scarce regions.

As a critical ecological security barrier in the Indo-China Peninsula, the Lao People’s Democratic Republic (Lao PDR) is increasingly threatened by forest degradation, frequent geological hazards, and intensified anthropogenic disturbances. To address the urgent need for a scientific evaluation of eco-geological environmental quality, this study develops a comprehensive assessment framework integrating multi-source remote sensing imagery, geological maps, and socio-economic datasets. A total of ten indicators were selected across four dimensions—geology, topography, ecology, and human activity. A stacking ensemble learning model was constructed by combining seven heterogeneous base classifiers—AdaBoost, KNN, Gradient Boosting, Random Forest, SVC, MLP, and XGBoost—with a logistic regression meta-learner. Model interpretability was enhanced using SHAP values to quantify the contribution of each input variable. The stacking model outperformed all individual models, achieving an accuracy of 91.14%, an F1 score of 93.62%, and an AUC of 95.05%. NDVI, GDP, and slope were identified as the most influential factors: vegetation coverage showed a strong positive relationship with environmental quality, while economic development intensity and steep terrain were associated with degradation. Spatial zoning results indicate that high-quality eco-geological zones are concentrated in the low-disturbance plains of the northeast and southeast, whereas vulnerable areas are primarily distributed around the Vientiane metropolitan region and tectonically active mountainous zones. This study offers a robust and interpretable methodological approach to support ecological diagnosis, zonal management, and sustainable development in tropical mountainous regions.

In order to achieve a breakthrough in the exploration of uranium orebodies in the Changjiang area of Guangdong province in China, the geo-electrochemical exploration method is used for mineral resource prediction. The logarithmically and isometric log-ratio (ilr) transformations are applied to the geo-electrochemical data in this study area to extract geochemical anomalies. The relationship between element associations and mineralization is revealed through descriptive statistical analysis and further biplot analysis. Then, the energy spectrum density–area fractal model (S-A model) is used to identify geochemical backgrounds and anomalies. The results show that: (i) the logarithmically and ilr-transformed data are more uniform, and they more or less obey the rules of normal distribution; (ii) the biplot shows that the ilr-transformed data eliminates the closure effect, and the robust principal component analysis (RPCA) has a better indicative significance for element associations—PC1 reveals the mineralization element association dominated by U and the multiple periods of hydrothermal activity; (iii) the S-A method could extract the local anomalies from different geochemical backgrounds, which indicates mineralization is more reliable. Finally, four favorable prospecting targets are delineated based on the geological and geochemical indicators.