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Remote sensing offers distinct advantages for lithological mapping, but its ability to detect underlying bedrock is limited in covered areas, whereas geochemical data are constrained by sparse sampling and low spatial resolution. To address these challenges, this study proposes a texture-guided adaptive data fusion framework combined with a Multi-scale Convolutional Neural Network (MCNN) for lithological mapping, using the Guyang area in Inner Mongolia as a case study. First, the non-linear relationships between geochemical components and remote sensing spatial textures are modeled to achieve complementary integration of heterogeneous multi-source data. Second, an MCNN model is constructed to extract multi-scale geological features, enabling improved discrimination of lithological units and more effective inference of concealed bedrock beneath Quaternary cover. Experimental results show that the proposed method overcomes the limitations of single data sources and achieves an overall accuracy (OA) of 0.95 on the fused dataset. Ablation experiments further demonstrate that the texture-guided fusion strategy significantly improves lithological identification performance. This study provides an effective framework for intelligent geological mapping and confirms the feasibility of inferring underlying bedrock in covered areas using multi-source surface information.

Exploration geochemical data are typically collected and analyzed according to standard map sheets. Different map sheets may be tested by different companies, at different times, or using different instruments. As a result, “systematic errors” inevitably arise, which are directly reflected in the “shift” effect between different map sheets. This study compares various leveling methods, including contrast return (CR), boundary leveling (BL), and multi-scale correction (MSC), based on compositional data transformation of six map sheets from the Baiyinchagan–Maodeng area. The results show the following features: the raw data from multiple map sheets contain certain errors, and these raw data cannot be directly used to create geochemical maps; the CR method is unsuitable for leveling in areas with small scales or significant geological background differences; the BL method yields more reasonable results when there is a similar geochemical background (geological background) between map sheets, and it is better suited for leveling between map sheets in areas with small scales or significant regional differences in geochemical backgrounds; finally, the MSC method yields results similar to the BL method, but it does not require boundary consistency, thus having fewer limitations, although it requires multi-scale geochemical data. In practical terms, this study provides valuable guidance for selecting appropriate geochemical data leveling methods in exploration projects, thereby improving the accuracy and reliability of geochemical mapping and enhancing the effectiveness of mineral exploration.

With the decrease in surface and shallow ore deposits, mineral exploration has focused on deeply buried ore bodies, and large-scale metallogenic prediction presents new opportunities and challenges. This paper adopts the predictive thinking method in this era of big data combined with specific research on the special exploration and exploitation of deep-earth resources. Four basic theoretical models of large-scale deep mineralization prediction and evaluation are explored: mineral prediction geological model theory, multidisciplinary information correlation theory, mineral regional trend analysis theory, and mineral prediction geological differentiation theory. The main workflow of large-scale deep resource prediction in the digital and information age is summarized, including construction of ore prospecting models of metallogenic systems, multiscale 3D geological modeling, and 3D quantitative prediction of deep resources. Taking the Lala copper mine in Sichuan Province as an example, this paper carries out deep 3D quantitative prediction of mineral resources and makes a positive contribution to the future prediction and evaluation of mineral resources.

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.

With the development of geological data analysis and big data technology, intelligent mineral prospectivity mapping (MPM) has become a key direction in the integration of geoscience and artificial intelligence, showing promising applications in the identification and evaluation of strategic mineral resources such as gold. To address the limitations of conventional methods—including insufficient training samples, complex model structures, and weak capability in recognizing anomalous zones—this study proposes an improved convolutional neural network (CNN) approach for mineral prediction. A lightweight, modular CNN structure with repeatable stacking is designed to reduce computational cost while enhancing model robustness and generalization. In addition, a dynamic learning rate scheduling strategy is adopted to optimize the training process, significantly improving convergence speed and training stability. Furthermore, high-probability prediction samples and low-probability background samples are combined to form a new training dataset for regional prospectivity evaluation, yielding a high area under the curve (AUC) score. The method is applied and validated in the Xiong’ershan region, and the predicted high-potential zones account for 30% of the study area and contain 81.4% of the known gold deposits. These results demonstrate the method’s effectiveness in mineral information extraction and blind-area targeting, offering a new approach for mineral prospectivity mapping.

The characterization of lithology within Quaternary aquifers holds significant geological importance for the protection, management, and utilization of groundwater resources, yet it continues to present considerable challenges. Indicator Kriging (IK) is a non-parametric, probability-based method of spatial interpolation. It considers the correlation and variability between data points, and its popularity stems from its alignment with geological experts’ principles. However, it still encounters issues in complex geological conditions. To address the limited capacity of conventional IK in reproducing geological variables within heterogeneous geological settings, this study develops an ordered IK method incorporating field variogram function parameters. This framework dynamically extends IK applications by integrating stratigraphic extension trends, requiring experts to formalize spatial variation trends into geological knowledge data, subsequently transformed into constraint parameters for interpolation. Estimation paths are determined via Euclidean distances between points-to-be-estimated and valid data, executing ordered IK following near-to-far and bottom-to-top principles. Results directly depict QLS formation spatial distributions or undergo expert modification for quantitative analysis, demonstrating superior integration of geological knowledge compared to empirical variogram fitting and partitioned IK estimation. The method reduces deviation from expert-interpreted spatial distributions while maintaining computational efficiency and multi-factor integration, with three case analyses confirming enhanced accuracy in lithology distribution reproduction and improved geostructural congruence in complex geological reconstruction. This approach revitalizes Kriging applications in complex geological research, synergizing domain cognition with computational efficacy to advance precision in geological characterization and support government decision-making.

A large-scale sandstone-type uranium deposit, recently discovered within the petroleum field of the Jingchuan area on the southwestern margin of the Ordos Basin, exemplifies a classic case of uranium exploration success achieved through the analysis of petroleum geological data including borehole logs. By synthesizing borehole radioactive logs and seismic surveys, we delineated target sandstone geometry, connectivity, and ore-controlling structures (e.g., paleochannels, redox interfaces). This study establishes a novel methodology for sandstone-type uranium exploration in petroliferous basins, unifying geophysical and geochemical datasets to define drill-validated targets. We integrated detailed core logging, petrography, and assay data to delineate the deposit’s geology. This included the host strata composition, ore-body morphology, mineralogy, and alteration assemblages. Our analysis identified the critical controls on mineralization: sandbody architecture, structural framework, and redox zonation. Based on these constraints, we constructed a genetic metallogenic model. Furthermore, we elucidated the mechanistic role of hydrocarbons in uranium mineralization and demonstrated the strategic potential of repurposing legacy oilfield data for synergistic uranium targeting. The Jingchuan uranium deposit provides both an exploration blueprint and theoretical foundations for uranium targeting in analogous sedimentary basins.

Although Qionghai Lake is one of the 11 large and medium-sized lakes (lake area > 25 km2) in the Yunnan–Guizhou Plateau (YGP), there has been little research on its water quality, especially over the long term. Herein, meteorological, hydrologic, trophic, and biochemical indices were investigated over the 2011–2020 period to explore the spatiotemporal variations in water quality in Qionghai Lake. The results showed that the CCME-WQI value for Qionghai Lake ranked between marginal and fair during 2011–2020, that the water quality of Qionghai Lake before 2017 was worse than after 2017, and that the water quality of the western part of Qionghai Lake was worse than that of the eastern part. Total nitrogen and total phosphorus were 0.39–0.51 and 0.019–0.027 mg/L during 2011–2020, respectively, and were the main pollution factors in Qionghai Lake. In addition, Qionghai Lake was at the mesotrophic level, but the chlorophyll and trophic state levels (TLI) increased year by year, and the levels in the western area were higher than in the eastern area. Increased anthropogenic activities (industrialization, urbanization, agricultural intensification, etc.) were the main reasons for the poor water quality of Qionghai Lake before 2017, while, after 2017, effective government environmental restoration and management measures improved the water quality. Moreover, the difference in land-use types within the watershed was the main reason for the spatial heterogeneity of water quality in Qionghai Lake. Potassium permanganate index (CODMn) and ammonia nitrogen content index (NH3-N) were not very high, but both showed seasonal variations. Water transparency (SD) in Qionghai Lake was reduced by sediment input and increased algal biomass, while dissolved oxygen (DO) decreased due to thermal stratification. This study is expected to provide a theoretical reference for understanding changes in the water quality and water environmental protection of Qionghai Lake and the YGP.

Climate change is a crucial factor impacting the geographical distribution of plants and potentially increases the risk of invasion for certain species, especially for aquatic plants dispersed by water flow. Here, we combined six algorithms provided by the biomod2 platform to predict the changes in global climate-suitable areas for five species of Hydrocharis (Hydrocharitaceae) (H. chevalieri, H. dubia, H. laevigata, H. morsus-ranae, and H. spongia) under two current and future carbon emission scenarios. Our results show that H. dubia, H. morsus-ranae, and H. laevigata had a wide range of suitable areas and a high risk of invasion, while H. chevalieri and H. spongia had relatively narrow suitable areas. In the future climate scenario, the species of Hydrocharis may gain a wider habitat area, with Northern Hemisphere species showing a trend of migration to higher latitudes and the change in tropical species being more complex. The high-carbon-emission scenario led to greater changes in the habitat area of Hydrocharis. Therefore, we recommend strengthening the monitoring and reporting of high-risk species and taking effective measures to control the invasion of Hydrocharis species.

The close spatial and genetic coexistence of Skarn-type and Hydrothermal Vein-type Pb-Zn deposits in the Shanghulin area, Inner Mongolia, poses a significant challenge to conventional “ undifferentiated” prediction models. This study aims to decouple these distinct metallogenic patterns using a data-driven, “type-specific modeling” strategy, establishing separate prediction models for Skarn-type and Hydrothermal Vein-type mineralization. Our workflow first employs Lasso–RFECV for rigorous pre-screening of over 60 geoscience features to identify the optimal predictive subset. Subsequently, an XGBoost model is trained on these selected features, and the SHAP framework is applied to interpret the geological significance of its decision logic. The results confirm two distinct indicator systems. (1) The Skarn-type model is controlled by spatial proximity to a heat source, heavily relying on Distance_to_Volcano and high-temperature indicators (CLR_Mo, CLR_W, CLR_Mn). (2) The Hydrothermal Vein-type model is “chemical fingerprint-driven”, prioritizing CLR_Y and identifying a complex “leaching-enrichment” pattern: mineralization requires simultaneous wall-rock leaching (low CLR_Al2O3, low CLR_Y) and specific metal enrichment (high CLR_Co, high CLR_Zn). This study confirms the controlling factors: Skarn-type deposits are governed by magmatic proximity, whereas Hydrothermal Vein-type deposits are defined by specific alteration geochemical signatures. The proposed “Lasso–RFECV → XGBoost → SHAP” workflow successfully decouples these independent, geologically meaningful prospectivity models from complex data, offering a new paradigm for precise exploration.