Orogenic magmatic Cu-Ni sulfide deposits represent an important source of copper, nickel, and other critical metals, and their formation is closely related to mantle-derived magma evolution and crust–mantle interaction. The Beishan Orogenic Belt, located in a key tectonic position within the Central Asian metallogenic domain, hosts numerous mafic–ultramafic intrusions formed during the post-collisional extensional stage, many of which are associated with varying degrees of Cu-Ni mineralization. However, uncertainties remain in distinguishing ore-bearing from barren intrusions and in evaluating their mineralization potential. In this study, whole-rock geochemical data of mafic-ultramafic intrusions from the Beishan Orogenic Belt were systematically compiled from published literature to construct a dataset comprising 360 samples. A total of 27 geochemical variables were selected as input features, including major elements (e.g., SiO₂, Al₂O₃, Fe₂O₃, MgO), rare earth elements (La-Lu), and trace elements (e.g., Rb, Nb, Sr, Y). Four machine learning modelswere employed to classify mineralized and non-mineralized intrusions and to evaluate their mineralization potential, such as multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). The results indicate that all models exhibit strong classification performance, with the MLP model achieving the highest accuracy of 97%, followed by XGBoost (94%), RF (91%), and SVM (90%). The modeling results demonstrate that whole-rock geochemical characteristics can effectively capture key metallogenic processes such as magmatic differentiation and crustal contamination, serving as reliable indicators for identifying ore-bearing intrusions. The discrimination method established based on the optimal model enables effective evaluation of the mineralization potential of mafic-ultramafic intrusions in the Beishan Orogenic Belt. This study not only provides a new technical approach for Cu-Ni sulfide exploration in the Beishan region but also offers methodological insights for assessing the mineralization potential of similar orogenic magmatic deposits.

Pyrite is the most common gold bearing mineral in gold deposits, and its geochemical composition can effectively indicate mineralization processes. This study systematically compiles trace element data of pyrite from epithermal, porphyry, and orogenic gold deposits worldwide. Nine commonly used geochemical features are selected, and three machine learning models including the K nearest neighbors algorithm, random forest, and support vector machine are employed for model training, with the aim of establishing an efficient and accurate method for identifying gold deposit types based on pyrite composition. The evaluation results of model classification performance show that the classification accuracies of the trained K nearest neighbors, random forest, and support vector machine models are 0.89, 0.96, and 0.95, respectively. In addition, pyrite compositional data from the East Kunlun orogenic gold deposits that are not involved in model training are used for independent validation to assess the applicability and accuracy of the models in practical scenarios. The results indicate that all three models correctly identify these pyrites as mainly derived from orogenic gold deposits, among which the support vector machine model achieves the best performance with an accuracy of 0.88. To address the black box issue in machine learning, an interpretable machine learning method is further introduced to quantify the contributions of different pyrite trace element features to the classification results. The SHAP analysis reveals that As, Co, and Cu are the three most influential trace element features for distinguishing porphyry and orogenic gold deposits, whereas Cu, As, and Ni exert the greatest influence on the identification of epithermal gold deposits. The results demonstrate that trace elements in pyrite can serve as effective indicators for discriminating genetic types of gold deposits. In the future, combined with machine learning approaches, pyrite composition has the potential to be further extended to the identification and classification of other types of mineral deposits, providing new technical approaches for studies on ore genesis and mineral exploration prediction.

Pyrite is the most common metallic sulfide in gold deposits, and its trace element composition effectively records the physicochemical properties of ore-forming fluids and mineralization environments, thus serving as an important indicator for discriminating genetic types of gold deposits. With the rapid development of information technology, geoscience research has entered a data-intensive era, and the accumulation of multi-source, multi-scale geochemical data provides new opportunities for quantitative classification of deposit types. However, existing studies are still limited by small sample sizes, insufficient coverage of deposit types, and relatively low classification accuracy. To address these issues, this study systematically compiles LA-ICP-MS trace element data of pyrite from seven types of gold deposits, including iron oxide–copper–gold (IOCG), volcanogenic massive sulfide (VMS), porphyry, low-sulfidation and high-sulfidation epithermal, Carlin-type, and orogenic deposits, and constructs a large geochemical dataset. After missing value control and centered log-ratio (CLR) transformation, Support Vector Machine (SVM) and Random Forest (RF) models are applied to classify deposit types, and their performance is systematically evaluated using cross-validation and multiple metrics. The results show that the SVM model based on the original CLR feature space achieves the best performance in terms of classification accuracy and stability, whereas the RF model, while maintaining strong predictive capability, reveals the key trace element combinations controlling classification through SHAP analysis. Overall, this study demonstrates that machine learning methods can effectively extract geologically meaningful mineralization signatures, and that the integration of high-performance classification models with interpretability analysis provides an effective approach for the identification and genetic study of gold deposit types in complex mineralization systems.

Accurate identification of ore-forming intrusions is critical for porphyry copper exploration. Previous studies have extensively used trace-element compositions of magmatic accessory minerals, such as zircon and apatite, to evaluate the metallogenic potential of magmas. However, the trace-element compositions of accessory minerals are controlled by multiple factors, which may limit their effectiveness in recording ore-forming magma signatures. Biotite is a common rock-forming mineral in intermediate to felsic igneous rocks, and its major-element compositions are mainly controlled by the parental magma, making it a potential indicator mineral for magmatic metallogenic potential. Based on a systematic compilation of biotite data from porphyry copper ore-forming intrusions and barren intermediate to felsic intrusions worldwide, this study selected nine major elements of biotite to construct three machine-learning models, namely support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB), for identifying porphyry copper ore-forming intrusions. The results show that all three models perform well, with accuracies of 0.9561, 0.9614, and 0.9587, respectively, indicating that this approach can effectively identify ore-forming intrusions related to porphyry copper mineralization. SHAP analysis further reveals that FeO^T, Na₂O, and MnO are the three most important features controlling model outputs, and that biotite from ore-forming intrusions is generally characterized by low FeO^T, low MnO, and high Na₂O. These results demonstrate that the major-element compositions of biotite can effectively indicate the metallogenic potential of intrusions, providing a new approach for identifying ore-forming intrusions in porphyry copper systems and for guiding exploration of similar deposit types.

The orthoamphibole rocks exposed in the Hengshan-Wutai area are characterized by a distinctive mineral assemblages (anthophyllite + garnet) and unique geochemical signatures (enriched in Fe, Mg and Al, depleted in Ca and alkalis). However, their protolith and petrogenesis remain a subject of long-standing debate due to the perturbed effects of subsequent metamorphism and tectonic deformation. In this study, we conducted a systematic investigation of these rocks by integrating detailed field surveys and petrographic observations with machine learning algorithms and geochemical fingerprints. For the machine learning techniques, we constructed a geochemical database comprising Archean basalts and pelitic rocks and designed a Random Forest classification model by training classifers with the geochemical compositional database. Combined with Isocon mass balance analysis, which should be possible to formulate the relationships between immobile and potentially mobile elements, the trained classifers enable to classify the orthoamphibole rocks exhibiting a strong genetic affinity with the Archean basalts. Moreover, their rare earth element (REE) patterns and elevated Co and Sc concentrations are consistent with those of Archean basalts and distinct from typical pelitic rocks. Isocon mass balance analysis indicates that the protoliths underwent a net mass loss of approximately 26%, accompanied by intense leaching of mobile components such as Ca and Na. Notably, the decoupling of Zr–Hf, the anomalous enrichment of Th, and the scattered distribution of Zn suggest that the precursors underwent mechanical sorting during sedimentation. Additionally, zircon U–Pb dating yielded a primary detrital age peak at ～2520 Ma, suggesting a uniform provenance derived mainly from local ～2.52 Ga magmatic rocks. Metamorphic zircon ages cluster around 1.92 Ga, recording a Paleoproterozoic collision event in the North China Craton. Consequently, we propose that the protoliths of the Hengshan anthophyllite rocks was a mafic-rich sedimentary rock derived from Archean basaltic sources. These rocks formed through chemical weathering of the basaltic source, followed by depositional processes and subsequent amphibolite-facies metamorphism at ~1.92 Ga.

To address insufficient multi-source data fusion, strong subjectivity, low quantification, and difficulty in uncertainty quantification in traditional mineral exploration target optimization methods, this study constructs a probabilistic decision-making model integrating Hybrid Improved Fuzzy Analytic Hierarchy Process (HI-FAHP), Monte Carlo simulation, and machine learning. The model aims to achieve systematic fusion of multi-source information and probabilistic expression of results, improving the scientific reliability of exploration risk assessment in the early exploration stage. Taking the Zushimiao polymetallic mining area in Luanchuan County, Henan Province, as a case study, a HI-FAHP system is established by combining improved AHP, principal component analysis, and fuzzy evaluation. Weights are objectively determined for geological indicators (ore-bearing strata, fault structures, intrusive rocks, geophysical anomalies) and geochemical indicators (four groups: Ag-Pb-Sb, Au-Mo-As-Bi, Cu-V-Zn, W). Monte Carlo simulation is introduced to randomly perturb membership degrees (perturbation coefficient 0.08, triangular distribution) and grade boundary values (uniform distribution), generating probability distributions and ranking probabilities for comprehensive membership degrees of each target area. Three machine learning models—Decision Tree, Random Forest, and XGBoost—are trained on 5,000 randomly generated score datasets based on HI-FAHP weights. XGBoost is selected via error analysis to invert geological and geochemical weights and calculate the mineralization potential index. Five prospecting target areas (Ⅰ–Ⅴ) are delineated. Monte Carlo simulation yields comprehensive membership rankings: Target Ⅰ (79.55)>Target Ⅳ (76.70)>Target Ⅱ (74.50)>Target Ⅲ (71.30)>Target Ⅴ (67.20). XGBoost shows the best fit (R²=0.90), with inverted weights of 0.25 for geological and 0.75 for geochemical features, and consistent mineralization potential index ranking. Engineering verification confirms that Target I exposes galena ore bodies, Target IV delineates copper ore bodies, while the other targets show weak mineralization, highly consistent with predictions. The HI-FAHP-Monte Carlo-machine learning integrated model achieves objective multi-source data fusion and uncertainty quantification. XGBoost effectively inverts physically meaningful feature weights. Verified by engineering results, the model is reliable and suitable for multi-source data integration and exploration risk assessment in the early exploration stage.

With the advancement of large language models and knowledge graph technologies, the construction of geoscience knowledge systems based on semantic modeling and logical reasoning has gradually become an important technical approach for intelligent mineral exploration. To address the long-standing issue of single-level ontological semantics in geoscience knowledge representation, this paper proposes a dual-axis ontology design centered on the "concept axis-function axis." This design integrates key geological elements such as stratigraphy, lithology, structure, and magmatic rocks, combined with exploration indicators including ore-bearing lithology, ore-controlling structures, and geochemistry, to construct a concept-function dual-axis ontology structure. Leveraging large language models, entity recognition and relation extraction from geological texts are achieved, and the Neo4j platform is utilized for graph-structured storage and reasoning applications of mineral deposit knowledge. Taking the Qukulekedong Gold-Antimony Ore Concentration Area as a case study, the dual-axis ontology is embedded into prompt engineering to enable knowledge extraction in the study area using large language models. Through semantic association and structural analysis of the knowledge graph, logical reasoning and pattern induction for exploration models are conducted. The results indicate that the dual-axis ontology model, by collaboratively representing geoscience entities through the dual semantic dimensions of the concept axis and function axis, demonstrates significant advantages in semantic retrieval, relationship organization, and knowledge reasoning. This model overcomes the limitations of traditional geological knowledge, which primarily relies on static attribute descriptions, and expands from structural representation to functional semantics. It provides a solid theoretical foundation and methodological support for constructing geoscience knowledge systems with intelligent representation and logical reasoning capabilities.

Accurate prediction of deep tight sandstone reservoir property parameters is essential for reservoir evaluation and sweet spot prediction. However, existing models struggle to fully characterize the complex nonlinear relationships between parameters and cannot achieve simultaneous prediction of multiple parameters. Taking the Yangxia Formation tight sandstone reservoir in the Kuqa Depression of the Tarim Basin's Jurassic System as a case study, this paper conducted high-pressure mercury intrusion and nuclear magnetic resonance experiments on cores to extract reservoir micro-parameters. Average pore throat radius, displacement pressure, fractal dimension, and sorting coefficient are selected as feature parameters. A multi-task learning (MTL) framework was introduced based on a single-task XGBoost model, with an attention mechanism employed to enhance feature selection capabilities. This study proposes a joint prediction model combining Extreme Gradient Boosting (XGBoost) and MTL, suitable for small-sample datasets, to simultaneously predict permeability and porosity. Multiple comparative experiments were conducted to evaluate model performance from multiple perspectives, including prediction accuracy and error metrics. The research findings indicate: ① Both high-pressure mercury intrusion porosimetry and NMR fractal dimension curves exhibit a "three-segment" characteristic, indicating that deep tight sandstone reservoirs possess typical multifractal features; ② The XGBoost-MTL model achieved high correlation coefficients of 0.91 and 0.95 for predicted and actual values in pore-permeability prediction, respectively. Its root mean square error was reduced by approximately 70% compared to the single-task XGBoost model, demonstrating excellent predictive performance; ③ The pore throat scale in the study area is dominated by medium-sized pores. Using the attention score method, it was found that the fractal dimension has the most significant impact on reservoir permeability, while the average pore throat radius has the most significant impact on porosity. This study presents a reliable data-driven approach for predicting the physical properties of deep tight sandstone reservoirs. The findings have been successfully applied in reservoir evaluation of the Yangxia Formation in the Kuqa Depression, with prediction errors controlled within 8.3%. This approach holds significant implications for the efficient development of deep tight sandstone gas reservoirs.

As oilfield development enters the middle and late stages, formation energy deficit becomes increasingly severe and injection-production conflicts become more prominent. Conventional empirical injection allocation and numerical simulation methods often face challenges such as high computational cost, difficult history matching, and insufficient prediction accuracy when applied to complex reservoirs with strong heterogeneity and rapid dynamic changes. To address these problems, an injection-production optimization method integrating production dynamic data, physical constraints, and adaptive machine learning is proposed. In this method, LSTM and XGBoost are used to construct a parallel prediction framework, and the prediction model is selected through multi-index competition. Kalman filtering and nonlinear diffusion filtering are adopted to reconstruct injection-production response signals, and a physics-constrained ANN combined with gradient sensitivity analysis is used to invert inter-well connectivity coefficients. On this basis, a dual-objective injection allocation optimization model considering both oil production enhancement and water-cut control is established and solved using the differential evolution algorithm. A case study from B Oil Production Plant shows that the physics-constrained LSTM achieves an R² of 0.9521 and reduces the MSE to 0.6513, outperforming the standard LSTM and XGBoost in both prediction accuracy and physical consistency. The inter-well connectivity inversion results identify dominant high-water-cut flow channels and agree well with geological understanding. After optimization, block oil production increases from 175.00 m³/d to 176.10 m³/d, with an incremental oil production of 1.10 m³/d. The results indicate that coupling data-driven models with seepage-physics constraints can reduce reliance on repeated history matching, improve the evaluation efficiency of injection-production schemes, and provide technical support for refined injection allocation and stable oil production with water control in high-water-cut oilfields.

Existing rock mass stability analysis methods are mostly based on simplified structural models, making it difficult to accur ately reflect the complex spatial network characteristics of structural planes. To address the lack of intelligent construction methods for three-dimensional structural plane networks and stability analysis using UAV-acquired data, this paper is based on UAV oblique photogrammetry to obtain multi-view images of rock masses. A deep learning model is constructed by fusing geometric and texture features to achieve automatic identification of rock mass structural planes and intelligent extraction of attitude parameters, thereby constructing a three-dimensional structural plane network model. This network is introduced into rock mechanics analysis and combined with block theory to automatically identify potentially unstable blocks and quantitatively calculate their stability. Finally, method validation is carried out from four dimensions: structural plane identification accuracy, block identification integrity, network topology rationality, and stability analysis consistency, while 3D visualization is used to reveal rock mass failure modes and stability characteristics. Experimental results show that the proposed method achieves an accuracy of 94.2%, a recall rate of 92.5%, and an intersection over union (IoU) of 87.6% for structural plane recognition. Block identification results indicate that when the volume increases from 10 m³ to 1000 m³, the safety factor decreases from 1.3 to 0.6, revealing the scaling effect law. By fusing geometric and texture features, this method optimizes the structural plane identification model, enhances the topological integrity of three-dimensional network construction, and elevates the dimension of rock mass stability analysis from individual structural planes to the network level, significantly improving the accuracy and engineering applicability of stability assessment.

Landslide susceptibility assessment is crucial for the disaster mitigation in mountainous regions; however, traditional models often have limited generalization on small-sample datasets, and linear approaches struggle to capture the complex interactions among influencing factors. This study introduces the TabPFN machine learning model, which, with its hyperparameter-free nature and strong generalization advantage under small-sample conditions, provides a novel approach for overcoming the modeling bottlenecks. Taking Huangshan City as the study area, based on 736 landslide points, an equal number of non-landslide samples were selected from areas beyond 1 km of these points to construct a balanced dataset with a 1:1 ratio of positive to negative samples,this research establishes a landslide susceptibility indicator system by systematically selecting 14 evaluation factors across four categories: topography, geology, hydrology, and human activity. Building upon this foundation,The study introduces the TabPFN model for landslide susceptibility evaluation, and employs SHAP to analyze the underlying driving mechanisms. The results show that, compared with baseline models, TabPFN achieves the best overall performance in terms of accuracy (0.903), precision (0.965), recall (0.841), F1-score (0.899), and AUC (0.964), demonstrating stronger generalization capability and advantages in modeling complex relationships. Furthermore, SHAP analysis reveals that distance to roads, NDVI, and lithology are the primary driving factors influencing the spatial distribution of landslides, reflecting the fundamental pattern wherein human disturbance, vegetation cover, and geological conditions collectively govern landslide development, Specifically, landslide susceptibility increases significantly in areas proximal to roads, reflecting the prominent disturbance of human engineering activities on slope stability. Higher vegetation coverage exerts a notable inhibitory effect on landslide occurrence, while landslide sensitivity varies markedly across different lithological conditions, underscoring the fundamental controlling role of the geological setting. This study integrates the TabPFN machine learning model with SHAP-based interpretability to achieve both high-accuracy prediction and interpretability analysis of landslide susceptibility, It overcomes the shortcomings of traditional "black box" models in revealing the mechanisms of disaster formation, providing a technical pathway for landslide risk assessment in mountainous cities that ensures both precision and explanatory insight.

In the field of modeling the susceptibility of county-level earthquake-induced landslides, comparative analysis research on ensemble learning models is still insufficient. This study takes Luding County as an example, where detailed information on 502 landslide events was collected based on historical records, remote sensing data, and field surveys to construct a sample database. Twelve key influencing factors were selected, including digital elevation model (DEM), slope, aspect, profile curvature, plan curvature, topographic position index, lithology, and the distances to roads, faults, rivers, and seismic sources, as well as land use. After conducting collinearity tests on these factors, the Bayesian optimization method was used to optimize ensemble learning models such as AdaBoost, GBDT, XGBoost, LightGBM, and CatBoost, thereby constructing a landslide susceptibility assessment system. The accuracy and predictive precision of each model were evaluated through ROC curves and frequency ratio comparison. The results show that after Bayesian optimization, the performance of all models was significantly improved, with the BO-GBDT model's AUC value increasing by 7.4%. Frequency ratio analysis further indicated that the BO-XGBoost model had the strongest identification capability at high susceptibility levels, with a frequency ratio of 13.73, demonstrating its superior risk area identification capability. Although the BO-GBDT model had a slight advantage in AUC value (0.974), its frequency ratio at high susceptibility levels was only 10.14. Therefore, the selected BO-XGBoost model reveals that high-susceptibility areas are mainly concentrated along the Dadu River and its nearby towns and transportation routes, while the landslide susceptibility in the eastern mountainous area is relatively low, providing important guidance for future research and disaster prevention and mitigation efforts.

Surface waves in seismic exploration data provide crucial information about the near surface overburden structure. However, the surface wave energy is distributed in each mode, it is not accurate to discriminate the surface wave mode based on dispersive energy, so the conventional multi-mode surface wave inversion has the problem of mode misidentification, which affects the accuracy of near-surface structure inversion. In this paper, the modes of dispersion curves involved in inversion are determined on the based Kuhn-Munkres pattern searches algorithm, the algorithm is also applied to process and inverse actual seismic surface waves, and the underground velocity structure is established by the inversion based on accurately determining the order of higher-mode dispersion curves. Firstly, the traditional algorithm is utilized to invert the single fundamental mode dispersion curve to obtain a preliminary model. Then, the preliminary model and a weighted bipartite graph of phase velocities with no-explicitly-specified-mode at each frequency is established. The phase velocities of different modes are optimally matched by the Kuhn-Munkres algorithm, and then the mode discrimination and joint inversion with higher-order surface wave dispersion curves are implemented. The effectiveness of this algorithm is verified by three typical theoretical stratigraphic model examples, finally, two sets of actual data are processed to obtain the near-surface one-dimensional shear-wave velocity structure and two-dimensional profile of the relevant study area. The results demonstrate that the algorithm effectively solves the problem of mode misidentification in surface wave multi-mode dispersion curve inversion.

In the field of geophysical oil and gas exploration, the velocity of seismic wave is regarded as one of the key parameters to distinguish underground media. Full Waveform Inversion (FWI) is a popular geophysical imaging technique in which a high-resolution subsurface velocity model is learned by first giving an initial velocity model and then minimizing the deviation between the simulated seismic record generated by the velocity model and the actual observed seismic record through an iterative process. The initialization of accurate velocity model is a prerequisite for the realization of full waveform inversion imaging, which is strongly dependent on the initial velocity model. When there is a big difference between the initial velocity model and the real model, the matching between the theoretical wave field and the observed wave field is easy to produce the phenomenon of period hopping. This paper proposes a full-waveform inversion model (FWI with initial velocity learnable by neural network, FWI_IVLNN)based on generative adversarial networks that can learn initial velocity parameters. It learns initial velocity parameters through neural networks and integrates stochastic velocity to generate velocity models. Based on generative adversarial idea, the velocity model is inversion, the simulated seismic record is generated by physics generator, the simulated record and the observation record are discriminated by discriminator, and the initial velocity model and network parameters are learned by unsupervised mode. The gradient returned by the network is transmitted to both the neural network and the physical generator, and the data-driven and model-driven velocity model inversion is realized with the help of the gradient. Quantitative and qualitative experiments show that FWI_IVLNN is less dependent on the initial velocity model, and the accuracy of velocity model inversion is higher.

To achieve refined inversion outcomes of TEM and enhance the precision of geological problem interpretation, conducting research on inversions under different initial models holds substantial significance. This study selected two distinct initial models: the homogeneous half-space model and the model derived from the inversion result of the least-squares method with a smoothing factor. Initially, the forward-response data of a three-layer geoelectric model (H-type and K-type) were utilized to conduct inversion computations via the least-squares method with a smoothing factor and the regularization-damped least squares method under different initial models. The inversion performances under various initial models were then compared and analyzed. Subsequently, field - measured data were employed to perform inversion calculations identical to those of the forward - fitting data. The normalized potential of the secondary field at a single point with different decay times, resistivity, the result model, and their corresponding errors were selected for comparative analysis. Additionally, the inversion effect of a complete apparent-resistivity profile was analyzed in conjunction with geological data. Finally, the research findings were validated through engineering cases. The results show that: The inversion of the least - squares method with a smoothing factor is poorly fitted with transient electromagnetic data. Under equivalent conditions, although the regularization-damped least squares method can more realistically reflect the stratal characteristics, its depiction of deep - stratum information is less accurate compared to when the initial model is the inversion - result model of the least - squares method with a smoothing factor. The inversion result of the regularization-damped least-squares method, with the inversion result of the least-squares method with a smoothing factor serving as the initial model, provides a more precise reflection of the underground geological conditions. The choice of different initial models exerts a remarkable influence on the inversion effect of TEM data. Through comparative analysis, it is further verified that the inversion of the regularization-damped least-squares method, with the inversion result of the least-squares method with a smoothing factor as the initial model, enjoys significant advantages in terms of accuracy and reliability. This conclusion offers crucial references and practical guidance for the application of the TEM in detecting complex geoelectric structure.

Located in the Augaro-Adobha metallogenic belt in the Nubian terrane, the Kerkebet exploration area shows superior metallogenic conditions in Eritrea. Recent years, major breakthroughs have been made in this exploration area, and the Mier medium-sized gold deposit has been found and evaluated. It is also found by field investigation that many target areas (e.g., Shukula, Locotat, Yasiney, Koken et al.) in this area show great gold polymetallic prospecting potential, and the mineralization is obviously controlled by geological-tectonic evolution. However, due to the limitation of previous basic geological survey and comprehensive researches, this region has not yet carried out systematic studies on geological-tectonic evolution, which seriously restricts the prospecting exploration and theoretical guidance. Based on this, after systematically sorting out the existing data of this region, this paper adopts the "multi-island-arc-basin system" tectonic theory to re-divide the previous Nakfa terrane into three new tectonic units (Barka magmatic arc, Kerkebet subduction accretionary complex, and Nakfa magmatic arc), among which the Kerkebet subduction accretionary complex was further divided into three second tectonic units, and reveals the relationship between tectonic evolution stages and different gold deposit types of the Kerkebet subduction accretion complex belt, combined with field investigations and researches. We also construct the tectonic-stratigraphic framework of this region, based on the fact that different tectonic units correspond to similar or different rock groups/segments. According to the fact that specific deposits have the characteristics of reflecting the temporal-spatial specificity of the tectonic environment, we also constructed the tectonic-mineralization model based on regional deposit types and distribution. These researches will not only provide the basis for deepening the theory of regional tectonics-metallogenesis, but also point out the directions for the next prospecting and exploration practice at Kerkebet.

The Jussiape Complex developed in Gaviao Block of Sao Francisco Craton is composed by Jussiape Ⅰ granite and Jussiape Ⅱ syenogranite with intrusion ages of 2 121 Ma and 2 052 Ma respectively. In order to determination the intrusive age and petrological characteristics of the Jussiape Complex, and discuss the significance to regional magmatic activity, tectonic setting, and mineralization,petrology,zircon U−Pb isotope chronology, and Lu−Hf isotope studies of the jussiape granite are carried out. Finally, a group of mesoarchean monzogranite intrusions with zircon U−Pb isotopic age of (3108±22) Ma were identified in the jussiape Ⅰ granite. Petrogeochemical date show that, the samples SiO₂ content is 72.23%–72.50%, Al₂O₃ content is 14.68%–15.09%, TiO₂ content is 0.24%, Na₂O content ranges from 5.29%–5.26%,K₂O content is less than 2% with K₂O/Na₂O value is 0.29, the average value of TFeO+MgO is 2.42%. The samples exhibit depletion in Nb, Ta, P, Ti, Sr, and compatible elements such as Ni and Cr, the Hf−Rb−Ta diagram shows a collision tectonic granite feature; The rare earth element distribution curve shows a relatively gentle right tilt type and the La/Yb ratio is greater than 15, with enrichment of LREE and negative Ce andEu anomalies. Lu−Hf isotope analysis show that the ε_Hf (t) value range from −18.31 to −0.53 and the value of T_DM2 is 3 523–4 063 Ma,indicating that the Jussiape monzogranite originated from the recycling of ancient crustal materials and controlled by the Barra do Mendes−Joao Correa lineament which is responding to the magmatic arc activity in the west of the southern part of the Gaviao Block.It is consistent with the subduction and collisional orogeny tectonic background that briefly existed in the southern part of the Gaviao Block during the Mesoarchean. The Barra do Mendes−Joao Correa lineament reactivated during the period of 2.1–1.9 Ga, forming a series of granite bodies such as Jussiape Ⅰ granite,Jussiape Ⅱ syenogranite and Ibitiara granodiorite that spread in a NNW direction.It is the most important structral−magmatic−metallogenetic belt in the region and has potential for IOCG-type deposits.

Bolivia is located at the junction of the Antarctic Plate and the American Plate, situated at the intersection of two major tectonic units, the Andes and the Amazonia craton. It has experienced a long and complex geological evolution process and has been significantly influenced by the Andean orogeny. The stratigraphy of Bolivia is well-developed from the Archean to the Cenozoic, with Cenozoic magmatic activity being closely related to the formation of metal mineral deposits in Bolivia. Bolivia is rich in mineral resources, with iron, lead, zinc, tungsten, tin, bismuth, antimony, silver, lithium, and other minerals being the dominant ones. It holds an important position globally and is a world-renowned mineral-rich country and traditional exporter of mineral products. Historically, it was known as the "Republic of Mining." Due to natural conditions, political situations, and mining policies, the overall geological work level in Bolivia is relatively low, with significant differences in work levels among different regions. The degree of mineral resource development is far behind that of neighboring countries such as Brazil, Peru, and Chile. Therefore, Bolivia is considered one of the most prospective countries for exploration in the world today. In recent years, the Bolivian government has issued new decrees, implemented tax reforms, and abolished the value-added tax on the import of capital goods, industrial plants, machinery, and equipment to attract foreign mining companies to invest and cooperate in Bolivia, thereby increasing the added value of Bolivian mineral exports. This paper introduces the geological and mineral resources overview of Bolivia, with a focus on interpreting the mining regulations and other documents issued since 2014. Four mining investment suggestions are proposed: First, it is necessary to study the geological background and mineralization laws of Bolivia to prepare for entering the Bolivian mining market. Second, systematic exploration preparations should be made to conduct regional or small-scale mineral rights resource exploration work together with Bolivia and to refine and correct existing data. Third, respect for the indigenous people and environmental organizations of Bolivia is essential. While exploiting resources, efforts should be made to promote the development of surrounding communities and gain public recognition. Fourth, professional legal teams should be hired to conduct due diligence and establish flexible exit mechanisms.

Peru is extremely rich in copper resources, with both its reserves and production ranking second globally. It is one of the world’s most significant regions for copper exploration and development and serves as a major source of copper imports for China. The porphyry copper deposits are the dominant deposit type in Peru and the primary target of exploration and development activities. Peru has discovered 12 large copper mines, and its copper reserves and resource increments are among the highest in the world. Copper exploration and development in Peru are predominantly led by European, American, and local Peruvian mining companies. Peru's copper production has steadily increased, and its production costs are globally competitive. China is the largest importer of Peruvian copper, with Chinese companies actively participating in the exploration and development of peru’s copper resources. This paper systematically sorts out the copper resource endowment, exploration and development status, and potential in Peru. It also analyzes the opportunities and risks associated with copper-related cooperation. There are significant risks and challenges, such as strikes and community issues, that are commonly encountered in the process of copper investment cooperation in Peru especially for Chinese companies. This paper concludes by offering policy and strategic recommendations for Chinese companies seeking to participate in copper resource exploration and development activities in Peru.

The magmatic-hydrothermal evolution during the differentiation of granitic magma is a key stage to the formation of magmatic pegmatites and related rare-metal mineralization. However, the controlling factors during this process are still unclear. Numerous Paleozoic felsic plutons and rare-metal-mineralized granitic pegmatites occur in the East Qinling tectonic belt, and their genetic relationship has attracted much attention. In order to examine the magmatic-hydrothermal evolution at the late stage of the felsic plutons and shed light on the formation of rare-metal mineralization in East Qinling, we carried out comprehensive analyses on in situ major and trace elements for tourmaline from the Piaochi pluton, the largest S-type granite in East Qinling. Tourmaline from the Piaochi pluton can be divided into four types, i.e., disseminated tourmaline (Tur-D), quartz-tourmaline nodules (Tur-N), tourmaline-quartz veins (Tur-V), and tourmaline along schistosity (Tur-d). The substitution of major elements of Tur-N and Tur-V tourmalines is mainly controlled by (Na, Mg) (X_vac, Al)_-1 and (Mg, OH) (Al,O)_-1, instead of Fe³⁺Al_–1 substitution. This indicates that the hydrothermal fluids of the Piaochi pluton are in a reducing environment with medium-high salinity content. Based on the occurrence and composition of Tur-D tourmaline, it can be further divided into Tur-D1 and Tur-D2. Tur-D1 is schorlite with a considerable amount of Al in the Y-site, indicating that they crystallize at an early stage of magma cooling. In contrast, Tur-D2 is dravite and replaces Tur-D1 in local places with Tur-D1 tourmaline inclusions, possibly due to the interaction with external fluids at a late stage. Tur-N tourmaline can be further divided into Tur-N1 and Tur-N2. Tur-N1 is schorlite with high Fe, Sn, Nb, Ta, and Zn and low Mg, V, and Sr, and crystallizes from immiscible aqueous boron-rich fluids at the late stage of magma differentiation. Tur-N2 and Tur-V tourmaline have similar compositions, varying from schorlite to dravite, with higher Mg, V, Sr, and Pd compared to Tur-N1. This suggests the influence of the external fluids from country rocks on the composition of magmatic-hydrothermal evolution of the Piaochi pluton. Tur-d tourmaline (dravite) is closely related to biotite, with magnetite occurring along its rims and fractures, indicating that the metasomatism of biotite by boron-rich fluids. Additionally, the high Nb and Ta concentrations of Tur-N1 tourmaline suggest a transient Nb-Ta enrichment on cooling of the Piaochi pluton. Nevertheless, the reduced condition and intensive interaction with country rocks may impede the Nb-Ta mineralization at a late stage of cooling. Therefore, rare metals would be enriched by the felsic melt differentiation and the exsolution of immiscible fluid during the magmatic-hydrothermal stage. However, the potential of rare-metal mineralization is mainly controlled by the redox condition of the fluids and the interaction with wall-rocks.

A series of quartz granodiorite-granodiorite rock assemblage has been identified in Heiwan area of the Guanzi Town ophiolite mélange. In order to determine the formation mechanism of them, the petrology and whole rock geochemistry of gneissic granodiorite and metamorphic tonalite were conducted, and the zircon U-Pb dating, Lu-Hf isotope, Rb-Sr and Sm-Nd isotope of gneissic granodiorite were conducted. The results show that gneissic granodiorite and metamorphic tonalite have low contents of rare earth and trace elements, and metamorphic tonalite has obvious Eu positive anomaly, and both of them have the characteristics of oceanic plagiogranite. The age of granodiorite obtained is 512.6±2.0 Ma, and the ε_Hf(t) value varies from +8.9 to +13.8, indicating that its magma source area is depleted mantle. The I_Sr value of granodiorite ranges from 0.7046 to 0.7053, and the ε_Nd(t) value ranges from +2.55 to +3.08. Combined with the regional tectonic evolution, this set of ocean plagiogranite was formed by the emplacement of acidic magma formed by partial melting of basaltic rocks in the mantle at low temperature (602.9～707.4 ℃) and low pressure (＜ 0.2 GPa), and was formed in the island arc environment related to the subduction of Shangdan Ocean in the early Middle Cambrian.

This study focuses on the tonalite in the Huashigou in the western section of the Quanji massif, northwestern margin of the Qaidam basin. Based on detailed field investigations, LA-ICP-MS in-situ isotopic dating and elemental analyses were carried out on the primary zircons to explore the evolutionary history of this area during the important tectonic transition period of the Devonian. The CL images of zircons in Huashigou tonalite show that zircon crystals are mostly columnar or irregularly shaped, euhedral to subhedral grains with clear oscillatory zonations. Zircon U-Pb dating yields a weighted average age of 371.1±3.5 Ma, a concordant age of 370.9±1 Ma, and a Th/U ratio of 0.42–0.83. LA-ICP-MS in-situ elemental analysis shows obvious Ce positive anomalies, strong Eu negative anomalies, and relative enrichment in light rare earth elements (LREE) compared to heavy rare earth elements (HREE). Zircon Ti thermometer calculation reveals zircon crystallization temperatures (t_Zr-Ti) ranging from 628 to 722 ℃, indicating a typical magmatic origin. In the Pb-Th diagram, all zircon spots fall within the I-type granite zircon field, representing that the original magma is crust-mantle mixed. Based on the above information, it is inferred that Huashigou tonalite was formed in an extensional environment after the collision, representing a magmatic event caused by mantle upwelling and crustal reworking.

Deep conglomerate reservoirs are developed in the Jiamuhe Formation of the Zhongguai Uplift, Junggar Basin, and are generally characterized by low mud content, abundant zeolite cement, and strong heterogeneity. Based on core observations, thin-section petrography, reservoir physical property analyses, and fluid inclusion tests, a comprehensive investigation was conducted on pore types, diagenetic modifications, and hydrocarbon charging characteristics of the Jiamuhe Formation conglomerates in the Zhongguai Uplift. The results indicate that the formation and evolution of these conglomerate reservoirs are jointly controlled by sedimentary architecture, diagenetic alteration, and hydrocarbon charging processes, which are manifested in three aspects. First, the large clast size and rapid sedimentation rates significantly weakened mechanical compaction during early burial, providing a favorable material basis for the preservation of primary intergranular pores. Second, variations in source-rock lithology and depositional microfacies are key factors controlling reservoir heterogeneity; abundant supply of basic andesitic rocks from the provenance area facilitated extensive precipitation of zeolite cements. Third, the combined contribution from multiple source rocks and the sustained alteration of conglomerates by multi-stage organic acids created favorable reservoir conditions for hydrocarbon accumulation in the full-sag conglomerates of the Jiamuhe Formation. Overall, the formation of conglomerate hydrocarbon reservoirs in the Jiamuhe Formation of the Zhongguai Uplift is interpreted as the result of coupled evolution between near-source, high-energy depositional processes under a fault–sag transition setting and subsequent diagenetic–accumulation processes. The results provide valuable insights for the study and development of deep conglomerate reservoirs in the Junggar Basin.

The undersea gold deposit off northern Sanshandao is a newly discovered super-large gold deposit in the northern Jiaodong Peninsula, located at the northern end of the Sanshandao- Cangshang Fault Zone. A large amount of research work has been carried out on the shallow part of this deposit, but the research on the deep mineralization laws and resource potential is relatively weak. The systematic mineralogical study on pyrite of the deep part of this deposit (at an elevation of –1253 to –1 455 m) is conducted in this paper, to reveal its characteristics of geochemical compositions, explore the genesis of the deposit, evaluate the potential for deep mineral exploration, and provide a scientific basis for regional deep mineral exploration prediction. Based on the theories of genetic mineralogy, this paper conducts an in-depth study on the morphology and compositional characteristics of pyrites from three metallogenic stagesfrom drill coresin this deposit. We obeserved the morphological, particle size, and self-shaping degree characteristics of pyrite and analyzed in-situ trace element composition of pyrites at each stage, using electron probe technology, with a focus on determining the contents of elements such As Fe, S, As, Co, Ni, and Au, and calculating standard parameters such as Co/Ni value and Fe/(S+As) value. The results indicate that the pyrites in this deposit can be divided into three mineralization stages: pyrite sericilization stage (Py1), quartz-pyrite-arsenopyrite stage (Py2), and quartz-polymetallic sulfide stage (Py3). Among them, the pyrites of stage Py2 and Py3 have smaller particle size and poorer hedral degree which have better gold content. he Co/Ni ratio of pyrites in this deposit ranges from 1 to 4, with an average of 3. The pyrites are rich in As and Co, which further indicate that this deposit is of magmatic hydrothermal origin. Collectively, Fe/(S+As) ratios of pyrites, alongside the vertical zonation characteristics of trace element, indicate high prospectivity at depth in the undersea gold deposit off northern Sanshandao.

The water resources of the Ordovician oilfield in the Tarim Basin's Tahe Oilfield are characterized by a high concentration of potassium, lithium, and other trace elements with significant economic value. These resources represent an important strategic asset in the Tarim Basin, alongside oil and gas resources. In this paper, Ordovician oilfield water samples from the Tahe oilfield were collected and analyzed to study its geochemical characteristics and its associated resource distribution pattern. The results demonstrate that the primary cations present in the Ordovician oilfield water of the Tahe Oilfield are Na⁺, Ca²⁺, K⁺, and Mg²⁺, while the primary anions are Cl⁻, SO₄²⁻, HCO³⁻, and NO₃⁻. The mineralization of the oilfield water ranges from 140 to 260 g/L, with an average value of 216.56 g/L, indicating a medium level of mineralization. The oilfield water can be classified as a CaCl₂ type. The characteristic coefficients indicate that the oilfield water undergoes alternating cation adsorption and robust water-rock interactions. It is a solution-filtered oilfield water, exhibiting complete reduction, optimal reservoir confinement, and a notable degree of metamorphism. The ions present in the oilfield water are predominantly derived from rock salt, gypsum, and crumbly clay minerals. The useful components, including K⁺, Li⁺, Br⁻, and Sr²⁺, exist in oilfield water can meet the industrial indicators of comprehensive utilization: the K⁺ content ranging from 556 to 2740 mg/L with an average value of 1781.19 mg/L; the Li⁺ content varying from 2.9 to 18.5 mg/L with an average concentration of 11.16 mg/L; the concentration of Br⁻ ranging from 45 to 323 mg/L with an average concentration of 230.77 mg/L; The concentration of Sr²⁺ ranging from 341 to 605 mg/L with an average of 473.31 mg/L. Conducting research on oilfield water that coexists with oil and gas resources, and comprehensively developing and utilizing the associated resources, is of significant importance for enhancing the resource value of oil and gas fields.

The protection of groundwater environment is a critical foundation for the sustainable utilization of regional water resources. To address the issues of groundwater quality characteristics and human health risks in industrial and agricultural area of Guanzhong plain, this study systematically collected 38 groundwater samples and comprehensively applied mathematical statistics, Piper trilinear diagram, ion ratio analysis, and entropy-weighted water quality index (EWQI) methods to analyze hydrochemical characteristics and assess health risks. The results indicate that: ① The concentration order of cations is Na⁺＞Mg²⁺＞Ca²⁺＞K⁺, while the concentration order of anions is HCO₃⁻＞SO₄²⁻＞Cl⁻＞NO₃⁻, with the main hydrochemical types being Na-Cl-SO₄ and Na-Ca-HCO₃; ② The hydrochemical evolution is dominated by dolomite dissolution and cation exchange, and the distribution of NO₃⁻ concentrations is closely related to industrial activities; ③ EWQI evaluation shows that 92.1% of the samples are suitable for drinking, with poor-quality water mainly concentrated in the industrial agglomeration area in the central-southern part of the region; ④ Human health risk assessment of F⁻ and NO₃⁻ indicates that the non-carcinogenic health risks for different populations follow the order: children＞adult females＞adult males. It is recommended to reduce health risks through targeted measures such as optimizing industrial layout and controlling agricultural non-point source pollution. The research results provide a scientific basis for groundwater pollution prevention and sustainable management in the Loess Tableland region.

Loess is widely distributed in the seasonally frozen regions of China, and its particle composition, pore structure, and engineering properties exhibit significant regional zonation. Under freeze–thaw cycles, the internal pore structure of loess is continuously restructured, thereby affecting its water-holding capacity and the evolution of the soil-water characteristic curve (SWCC). To investigate the regional differences in the water-holding behavior of collapsible loess under freeze–thaw cycles, undisturbed sandy loess from Ningxia, silty loess from Shanxi, and clayey loess from Jingyang were selected. Considering different initial water contents and freeze–thaw cycle numbers, SWCCs were measured using a pressure plate apparatus and fitted with the Fredlund–Xing model. The results show that all SWCCs exhibit a typical S-shaped pattern, and the differences induced by freeze–thaw cycles are mainly concentrated in the main desaturation stage and the high-suction tail. Jingyang samples are the most sensitive to freeze–thaw action and show obvious staged restructuring; Ningxia samples exhibit pronounced moisture-content-dependent divergence; Shanxi samples change more gently overall. The fitted parameters a, b, and c all vary with freeze–thaw conditions, indicating that freeze–thaw cycles not only alter the air-entry-related feature, but also change the slope of the main desaturation section and the residual water-retention behavior in the high-suction range. Regional differences therefore control not only the initial water-holding capacity of loess, but also the restructuring path of its pore system under freeze–thaw disturbance. These findings provide an experimental basis for unsaturated seepage analysis, freeze–thaw degradation assessment, and water-induced hazard prevention in loess engineering in seasonally frozen regions.

Against the background of global climate change and regional water scarcity, in-depth research on the synergistic driving mechanisms of multiple factors controlling vegetation water use in arid and semi-arid regions is urgently required. Existing studies lack sufficient analysis on the synergistic regulation of meteorology-soil water-groundwater in the sap flow process of Salix matsudana Koidz in the Mu Us Sandy Land. Therefore, taking 40-year-old Salix matsudana Koidz in the Mu Us Sandy Land as the research object, this study systematically analyzed the water use strategy of Salix matsudana Koidz under the synergistic effect of meteorological factors and groundwater in the rainy season, and clarified the key paths of meteorological factors regulating the sap flow process of Salix matsudana Koidz, through in-situ continuous monitoring of meteorological elements, groundwater depth, soil moisture content and stem sap flow density, combined with Structural Equation Model and Boosted Regression Tree model. The results show that Salix matsudana Koidz is a deep water-dependent tree species, forming a water use strategy dynamically adjusted with hydro-meteorological conditions in the growing season. It preferentially utilizes deep soil water and groundwater at 250–450 cm, with extremely low utilization efficiency of surface soil water recharged by rainfall. Meteorological factors regulate the sap flow of Salix matsudana Koidz through dual paths of direct driving and indirect synergy. Solar radiation (β=0.71, P<0.001) is the core direct driving factor, relative humidity (β=−0.27, P<0.05) is the key atmospheric constraint factor, and groundwater level exerts a significant positive indirect effect (total effect 0.39) through the hydrological transmission chain of groundwater-deep soil water-vegetation sap flow. The sap flow density of Salix matsudana Koidz peaks when the air temperature is 9.97–21.20℃ and the relative humidity is moderate. This study clarifies the multi-factor coupled driving mechanism of the sap flow process of Salix matsudana Koidz and provides theoretical support for the management of Salix matsudana Koidz forests and ecological restoration projects in the Mu Us Sandy Land.

The disintegration, erosion, and collapse of red-bed mudstone slopes in watershed gullies is prominent in the extreme climate, heightening the risk of disaster chains related to debris flows in these channels. Developing hazard chain-cutting technology in watershed gullies is of great significance for controlling the sediment supply of debris flows. This study selects Tertiary red-bed mudstone from the Xining Basin, and utilizes a self-developed testing apparatus for measuring the disintegration indexes of untreated red-bed mudstone and MICP-treated red-bed mudstone. A list of disintegration tests were conducted to explore the disintegration characteristics and protective effects of MICP-solidified red-bed mudstones under the influence of factors such as temperature, pressure, initial moisture content and curing time. The results indicate that MICP mitigates the disintegration process of red-bed mudstone, which is significantly influenced by initial water content. At low water content, the disintegration process of red-bed mudstone is considerable, which is also accelerated by higher temperatures and pressures. The pressure plays a dual role of mechanical constraint and hydraulic driving in the disintegration process of red bed mudstone. The prolongation of curing time exerts a significant inhibitory effect on the disintegration process of MICP-solidified red-bed mudstones. Specifically, for the specimens cured for 3, 7, and 14 days, the disintegration ratio at initial-stage decreases by an average of 13%, 33%, and 39%, respectively. The disintegration rate drops by an average of 13%, 27%, and 36%, respectively. This is because MICP converts particle contacts from point contact to surface contact, and a calcareous cementation crust forms on the surface of the red-bed mudstone, resulting in a notably lower disintegration ratio and rate during the intense disintegration stage. The findings offer support for ecological protection of red-bed mudstone slopes in weathered gullies of Northwest China.