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Understanding the temporal and spatial evolution of the North China Craton (NCC) basement, formed by amalgamation, is a crucial issue in global geosciences. The Huozhou complex is situated at the core of the Trans-North China Orogen (TNCO) in the NCC and comprises a considerable number of Palaeoproterozoic granitic gneisses, providing valuable insights into the tectonic evolution of the TNCO. In this study, comprehensive field geological surveys, petrology, chronology, geochemistry, and Hf isotope analysis were conducted to investigate the genesis and tectonic context of the Xingtangsi and Zhengnangou granitic gneisses and elucidate the TNCO's tectonic evolution. The Xingtangsi granite gneiss yielded a magmatic zircon age of 2495±34 Ma, implying its Palaeoproterozoic or Archean origin, as previously suggested. Its protolith was I-type peraluminous granite, primarily generated through the partial melting of pre-existing continental crust materials with a small quantity of mantle-derived magma. The Zhengnangou granitic gneiss's protolith was A-type granite, and its magmatic zircon age was 2,190±11 Ma, indicating its Palaeoproterozoic origin rather than Archean. TDM1(Ma) for the Zhengnangou granitic gneiss ranged from 2,424 to 2,498 Ma, TDM2(Ma) varied from 2563 to 2684 Ma, and the εHf(t) value ranged from 1.3 to 3.3. These results suggest that it was primarily derived from newly formed crustal materials without any mantle-derived addition. Integrating our data with the literature, the ∼2.5 Ga magmatic activity in the Huozhou area may have formed in the tectonic setting of the continental arc, and ∼2.2 Ga A-type granite may have formed in a post-collisional extensional environment. [ABSTRACT FROM AUTHOR]

[Display omitted] • Expression of geological map information in the form of knowledge graphs to shown regional geological characteristics and connections. • Using the knowledge embedding method to embed the geological map knowledge in order to show the mineralization characteristics. • Knowledge and geochemical data was aligned in space by knowledge embedding gridding to realize the forward integration of knowledge and data, and to realize bi-directional knowledge and data-driven mineral prospectivity. The knowledge graph in geological applications is highly regarded, and its usefulness requires the support of geological survey data. Therefore, this paper proposes a mineral prospectivity mapping (MPM) method that integrates geological map knowledge graphs with geochemical data. The geological map knowledge graph is used in the vectorized form as knowledge features in the training of the mineral prediction model. It is also used for knowledge-based ore-forming interpretation of prospective areas, achieving bidirectional application of knowledge in prediction and interpretation of prediction results. Unlike most knowledge graphs, the geological map knowledge graph represents spatial correlations and geological features in the form of "entity-relation-entity" triplets, with geological map units as entities and assigned geological attributes. Based on this, an MPM process driven by knowledge graph and data is proposed: (1) Convert the geological map knowledge graph into low-dimensional vectors using node2VEC. (2) Align geological knowledge with geochemical data spatially through knowledge embedding gridization, achieving a forward fusion of knowledge and data. (3) Collect the integrated data using window sampling as the training dataset for DenseNet to train the mineral prospection model. A case study was conducted to demonstrate the advantages of the method for producing a potential map linked to gold mineralization in the Rao Feng area, Shaanxi Province, China. The results show that the prospective areas with knowledge graph embedding and geochemical data are better and delineate six unmineralized prospective areas. Ore-forming interpretations based on knowledge graph information were conducted on the prospective areas, demonstrating strong consistency between the ore-forming geological features of the prediction areas and known mineral deposits. [ABSTRACT FROM AUTHOR]

The automatic identification of rock type in the field would aid geological surveying, education, and automatic mapping. Deep learning is receiving significant research attention for pattern recognition and machine learning. Its application here has effectively identified rock types from images captured in the field. This paper proposes an accurate approach for identifying rock types in the field based on image analysis using deep convolutional neural networks. The proposed approach can identify six common rock types with an overall classification accuracy of 97.96%, thus outperforming other established deep-learning models and a linear model. The results show that the proposed approach based on deep learning represents an improvement in intelligent rock-type identification and solves several difficulties facing the automated identification of rock types in the field. [ABSTRACT FROM AUTHOR]