Integrating heterogeneous and multilingual geoscience texts into coherent knowledge graphs is challenged by semantic inconsistencies from terminology variations, diverse expressions, and data heterogeneity, hindering the construction of reliable mineral exploration knowledge systems. We propose a semantic-aware fusion framework that enables consistent and sustainable integration of mineral exploration knowledge. Built on a standardized geological knowledge schema defining core entities and their interrelations, the framework incorporates an incremental update paradigm via a schema-guided fusion mechanism that detects and resolves semantic conflicts while preserving provenance for traceable evolution. Evaluated on textual sources, the framework achieves an overall triple extraction F1-score of 0.82. Notably, for the critical task of entity extraction, it attains an F1-score of 0.88, outperforming BERT-BiLSTM and BERT-BiLSTM-CRF baselines by up to 11 points. Precision for key metallogenic elements exceeds 0.90. It identifies 1432 conflicts during fusion and generates a refined knowledge graph of 18,204 high-quality de-duplicated triples, retaining 87.3% of inputs. The resulting graph supports downstream applications, including case analysis, visualization, question answering, and mineral prospectivity prediction. Unlike conventional aggregation approaches, this work treats knowledge fusion as a semantically guided dynamic process, enhancing consistency, transparency, and adaptability. It provides a practical pathway toward intelligent and sustainable geoscience knowledge infrastructures.

Satellite ocean-colour data are crucial for monitoring marine ecosystems, yet data gaps caused by limitations such as cloud cover compromise the reliability of derived ecological metrics, particularly phytoplankton phenology indicators. To enable robust marine ecological monitoring, we reconstruct a gap-free global daily ocean-colour data cube at 9 km resolution for 2000–2024 using a GPU-accelerated Discrete Cosine Transform–Penalized Least Squares (DCT–PLS) framework applied to OC-CCI (Ocean Colour-Climate Change Initiative) dataset. The product provides continuous fields of chlorophyll-a (Chl-a), diffuse attenuation coefficients Kd(490), and six-band remote-sensing reflectance (Rrs). Validation against in situ Chl-a data shows high consistency (R2 = 0.75), and comparison with NOAA's DINEOF Level-4 product demonstrates good agreement. Case studies of the Hunga Tonga–Hunga Haʻapai eruption and Hurricane Lorenzo confirm that the reconstruction preserves the spatiotemporal structure of bio-optical anomalies while substantially enhancing data continuity. Using the gap-free Chl-a record, we generated a global climatology of phytoplankton phenology metrics, and quantified the impact of realistic satellite sampling patterns (simulated using MODIS-Aqua masks) on these phenology metrics. Monte Carlo reference–gap analysis reveals that data gaps cause small errors in mean bloom Chl-a (typically <5%), but substantially larger uncertainties in phenological timing: root-mean-square errors for bloom initiation, peak date, and duration often reach 10–30 days or more. Uncertainties peak in frequently clouded and dynamic regions, such as the eastern boundary upwelling systems. These findings establish gap-filling as essential for robust phenology analysis and long-term trend detection from satellite ocean-colour data. The gap-free dataset and associated uncertainty benchmarks provide a foundation for advancing studies of marine ecosystem variability and climate-driven change. The reconstructed dataset and processing code are publicly available on https://doi.org/10.5281/zenodo.18190723.

For studies utilizing methods such as water color parameter inversion and algal bloom classification, abundant spectral bands and high spectral resolution are of great significance. However, for multispectral satellite sensors that are not designed for water color studies (e.g., Sentinel-2 MSI), the number of bands in the visible–near-infrared range is limited, and lacks specific spectral bands with rich spectral information. Hyperspectral reconstruction of multispectral data based on hyperspectral remote sensing reflectance ($R_{rs}$) databases and machine learning algorithms have been proven to be a feasible solution. Based on the in situ measured $R_{rs}$ data, this study constructed a large-sample hyperspectral $R_{rs}$ database covering various optical water types using two Chinese hyperspectral satellites, and compared the spectral reconstruction accuracy of six machine learning algorithms. The results show that expanding the $R_{rs}$ database for model training by integrating hyperspectral satellite data can effectively improve the reconstruction accuracy in waters of different optical types. Comparisons with in situ measured hyperspectral $R_{rs}$ indicate that the reconstructed Sentinel-2 hyperspectral data achieve high accuracy, with the Spectral Angle Mapper (SAM) less than 5° and the correlation coefficient ($r$) higher than 0.7. Furthermore, the reconstructed data can effectively restore spectral information not captured by the original multispectral data, such as the suspended sediment $R_{rs}$ peak at 580 nm and the chlorophyll $R_{rs}$ valley at 680 nm. Through spectral reconstruction, the spectral resolution of Sentinel-2 can be maximized while retaining its advantages of fast revisit capability and high spatial resolution, thereby expanding its application potential in water color remote sensing.

Geology education, due to its deep reliance on the composition and evolution of the Earth’s material, is in dire need of practical knowledge to strengthen theoretical understanding. However, traditional field geology teaching has long been limited by space-time constraints, irreversibility of phenomena and multi-scale characteristics of complex geological processes, and the existing digital teaching tools are mostly limited to two-dimensional displays or static three-dimensional models, which lacks immersive presentations of dynamic evolution and interactive operations of geological phenomena, making it difficult for students to establish systematic geological thinking. For this reason, this study integrates the realistic 3D modelling and virtual reality technology, builds a realistic 3D field geological practice virtual simulation platform, and constructs a platform teaching system covering the whole process of “Preparation-Teaching-Examination-Evaluation”. By comparing the students’ practical scores before and after the use of the platform and collecting feedback, it was found that the students’ practical scores increased significantly after the application of the platform, and the proportion of high scores (90–100) increased from 13.8 % to 27.4 %, and more than 90 % of the students recognized the functions of the platform and expressed their willingness to use it continuously. The study confirms that the virtual-reality integration teaching mode effectively improves the cognitive depth of geological phenomena and the efficiency of practical skills transformation through realistic 3D roaming, stratigraphic dynamic evolution simulation and standardized assessment system. This study not only provides a feasible solution to solve the bottleneck of traditional geology teaching, but also lays a practical foundation for promoting the transformation of geology education to the direction of intelligence.

Geology education, due to its deep reliance on the composition and evolution of the Earth’s material, is in dire need of practical knowledge to strengthen theoretical understanding. However, traditional field geology teaching has long been limited by space-time constraints, irreversibility of phenomena and multi-scale characteristics of complex geological processes, and the existing digital teaching tools are mostly limited to two-dimensional displays or static three-dimensional models, which lacks immersive presentations of dynamic evolution and interactive operations of geological phenomena, making it difficult for students to establish systematic geological thinking. For this reason, this study integrates the realistic 3D modelling and virtual reality technology, builds a realistic 3D field geological practice virtual simulation platform, and constructs a platform teaching system covering the whole process of “Preparation-Teaching-Examination-Evaluation”. By comparing the students’ practical scores before and after the use of the platform and collecting feedback, it was found that the students’ practical scores increased significantly after the application of the platform, and the proportion of high scores (90–100) increased from 13.8 % to 27.4 %, and more than 90 % of the students recognized the functions of the platform and expressed their willingness to use it continuously. The study confirms that the virtual-reality integration teaching mode effectively improves the cognitive depth of geological phenomena and the efficiency of practical skills transformation through realistic 3D roaming, stratigraphic dynamic evolution simulation and standardized assessment system. This study not only provides a feasible solution to solve the bottleneck of traditional geology teaching, but also lays a practical foundation for promoting the transformation of geology education to the direction of intelligence.