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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]

Intelligent prospecting and prediction are important research foci in the field of mineral resource exploration. To solve the problem of the performance degradation of deep convolutional neural networks, enhancing the attention to target information and suppressing unnecessary feature information, this paper proposes a new prospecting prediction method based on a two-dimensional convolutional neural network (CNN2D). This method mainly uses known Cu deposits as the positive sample labels, adopts the sliding window method for data enhancement, and uses the window area as a unit to extract spatial variation features. It is important to supplement squeeze-and-excitation networks (SENets) to add an attention mechanism to the channel dimension, assign a weight value to each feature layer, and finally make prospecting predictions by matching the features of the known deposit window area and the features of the unknown window area. This method allows the neural network to focus on certain characteristic channels and realizes prospecting prediction in the case where there are few known deposits so that the deep learning method can be more effectively used for the prospecting prediction of mineralization. Based on geological data, geochemical exploration data of water system sediments, and aeromagnetic data, and via this method, this study carried out prospecting prediction of Cu deposits in the Zhunuo area of Tibet and predicted 12 favorable Cu prospecting prediction areas. Combined with previous research results and field exploration, the predicted result is consistent with the established mineralization and prospecting pattern and has good prospects for Cu deposit prospecting. [ABSTRACT FROM AUTHOR]

The Central Asian Orogenic Belt (CAOB) is the world's largest accretionary orogenic belt, and its formation is related to the closure of the Paleo-Asian Ocean (PAO). However, the closure time and style of the PAO remain controversial. To address these issues, this paper presents zircon U-Pb dating, whole-rock geochemistry and zircon Lu-Hf isotope analyses of the volcanic rocks in the Faku-Kaiyuan area on the northern margin of the North China Craton. The results show that the Bachagou andesites formed in the Early Permian (287 ± 2 Ma), while the Chaijialing andesites and dacites formed in the Late Permian (253.3 ± 3.7 Ma) and Middle Triassic (244.3 ± 1.3 Ma), respectively. The Bachagou andesites and Chaijialing andesites are enriched in LILEs and LREEs and depleted in HFSEs and HREEs, indicating that they formed in the active continental margins. The Chaijialing dacites show similar geochemical signatures to adakite and formed in a syn-collisional setting. Geochemistry and isotopic analysis indicates that the Bachagou andesites were derived from a partial melting of the mantle wedge that was metasomatized by subduction fluids. The Chaijialing andesites were generated from a metasomatized mantle by slab-derived and sediment fluids. The Chaijialing dacites formed by a partial melting of thickened lower crust. Combined with previous research results, we can conclude that the Eastern PAO closed by a scissor-like movement from west to east during the Late Permian–Middle Triassic. [ABSTRACT FROM AUTHOR]

The Trans-North China Orogen is a major Neoarchean Paleoproterozoic collisional orogenic belt above the North China Craton, formed due to prolonged and complex processes. Even though many Neoarchean-Paleoproterozoic magmatic and metamorphic activities have been reported, due to the Huozhou Complex's small outcropping range, little attention has been paid to the origin of various igneous rocks of the Huozhou Complex in the center of the Trans-North China Orogen. The Huozhou Complex, located south of the Lüliang, Wutai, and Hengshan complexes, is an important window into the Early Precambrian structure and evolution of the North China Craton. Its magma and metamorphism are crucial to understanding the development of the structural evolution of the Trans-North China Orogen. The Huozhou metamorphic complex area exposes a range of Precambrian metamorphic rocks, among which the most extensively dispersed is felsic biotite plagioclase gneiss. In this study comprehensive geological field survey, micropetrology, chronology, geochemistry, and Hf isotope analysis were carried out for the Qinggangping and Anziping gneiss in the north of the Huozhou Complex. The results show that the magmatic zircon age of the Qinggangping gneiss is 2196 ± 14 Ma, and its protolith is I-type granite, formed by partial melting of igneous rocks in the absence of weathering. Its source is mainly the juvenile crust from depleted mantle dating 2431–2719 Ma, with a small amount of mantle-derived material. The Anziping gneiss has a metamorphic zircon age of 1931 ± 13 Ma with an S-type granite protolith belonging to peraluminous granite. The Anziping gneiss is formed by recycling pre-existing crustal components at 2613–2848 Ma. A minor quantity of mantle-derived magma is also introduced to the crust simultaneously. The samples of Qinggangping gneiss and Anziping gneiss show the characteristics of obvious negative Nb, Ti, and P elements in the spider diagram of primitive mantle standardization. This implies that the rocks have the characteristics of magmatic rocks in an island arc or subduction environment, which could have formed in the tectonic environment of the continental margin arc. [ABSTRACT FROM AUTHOR]

The cross section is the basic data for building 3D geological models. It is inefficient to draw a large number of cross sections to build an accurate model. This paper reports the use of multi-source and heterogeneous geological data, such as geological maps, gravity and aeromagnetic data, by a conditional generative adversarial network (CGAN) and implements an intelligent generation method of cross sections to overcome the problem of inefficient modeling data based on CGAN. Intelligent generation of cross sections and 3D geological modeling are carried out in three different areas in Liaoning Province. The results show that: (a) the accuracy of the proposed method is higher than the GAN and Variational AutoEncoder (VAE) models, achieving 87%, 45% and 68%, respectively; (b) the 3D geological model constructed by the generated cross sections in our study is consistent with manual creation in terms of stratum continuity and thickness. This study suggests that the proposed method is significant for surmounting the difficulty in data processing involved in regional 3D geological modeling. [ABSTRACT FROM AUTHOR]

Substantial research has been undertaken on the role of specimen resources in the teaching and learning of geology and related fields. But the contributions of virtual 3D libraries have received little attention within geological heritage. We built a Virtual 3D Geological Library (V3GL) that utilizes photographic modeling, and the V3GL is based on the Cesium engine; its data-oriented distributed architecture provides specimen resources from many universities. The study utilized a comparison control group design with three groups in testing. The principal findings of this research are that the V3GL is a suitable solution for displaying and sharing geological specimens. The questionnaire in this article also verified the importance of the V3GL in improving the perception of field geological specimens and establishing the spatial relationship between the specimens and heritage. V3GL uses a unified virtual 3D space to carry three-dimensional models of specimens and heritage, photo POIs, and layer data, and also it is vital for studying the spatial relationship between specimens and the environment or between specimens and specimens. This study should, therefore, be of value to librarians, geological heritage departments, those with mobility issues, geo-practitioners wishing to examine specimens from other areas, and students who cannot be taught in the field due to underfunding of their colleges or the epidemic. The V3GL is of great significance for the protection of geoscience teaching resources through cooperation or sharing, the maintenance of the right to a fair education, and the construction of future virtual simulation solutions. In addition, photographic modeling lays the foundation for a more immersive virtual laboratory and brings opportunities to reform and improve teaching methods in the field of the natural sciences, especially in the field of the geosciences. [ABSTRACT FROM AUTHOR]

The energy consumption and carbon footprint of cryptocurrencies have always been a popular topic. However, most of the existing studies only focus on one cryptocurrency, Bitcoin, and there is a lack of long-term monitoring studies that summarize all cryptocurrencies. By constructing a time series hash rate/power model, this research obtained the 10-year time series data on energy consumption dataset of global top-25 cryptocurrencies for the first time. Both the temporal coverage and the spatiotemporal resolution of the data exceed previous studies. The results show that Bitcoin's power consumption only accounts for 58% of the top-25 cryptocurrencies. After China bans cryptocurrencies, the conservative change in global CO2 emissions from 2020 will be between −0.4% and 4.4%, and Central Asian countries such as Kazakhstan are likely to become areas of rapid growth in carbon emissions from cryptocurrencies. [ABSTRACT FROM AUTHOR]

Gas-bearing volcanic reservoirs have been found in the deep Songliao Basin, China. Choosing proper interpretation parameters for log evaluation is difficult due to complicated mineral compositions and variable mineral contents. Based on the QAPF classification scheme given by IUGS, we propose a method to determine the mineral contents of volcanic rocks using log data and a genetic algorithm. According to the QAPF scheme, minerals in volcanic rocks are divided into five groups: Q(quartz), A (Alkaline feldspar), P (plagioclase), M (mafic) and F (feldspathoid). We propose a model called QAPM including porosity for the volumetric analysis of reservoirs. The log response equations for density, apparent neutron porosity, transit time, gamma ray and volume photoelectrical cross section index were first established with the mineral parameters obtained from the Schlumberger handbook of log mineral parameters. Then the volumes of the four minerals in the matrix were calculated using the genetic algorithm (GA). The calculated porosity, based on the interpretation parameters, can be compared with core porosity, and the rock names given in the paper based on QAPF classification according to the four mineral contents are compatible with those from the chemical analysis of the core samples. [ABSTRACT FROM AUTHOR]

High-resolution geological mapping is an important supporting condition for mineral and energy exploration. However, high-resolution geological mapping work still faces many problems. At present, high-resolution geological mapping is still generated by expert interpretation of survey lines, compasses, and field data. The work in the field is constrained by the weather, terrain, and personnel, and the working methods need to be improved. This paper proposes a new method for high-resolution mapping using Unmanned Aerial Vehicle (UAV) and deep learning algorithms. This method uses the UAV to collect high-resolution remote sensing images, cooperates with some groundwork to anchor the lithology, and then completes most of the mapping work on high-resolution remote sensing images. This method transfers a large amount of field work into the room and provides an automatic mapping process based on the Simple Linear Iterative Clustering-Convolutional Neural Network (SLIC-CNN) algorithm. It uses the convolutional neural network (CNN) to identify the image content and confirms the lithologic distribution, the simple linear iterative cluster (SLIC) algorithm can be used to outline the boundary of the rock mass and determine the contact interface of the rock mass, and the mode and expert decision method is used to clarify the results of the fusion and mapping. The mapping method was applied to the Taili waterfront in Xingcheng City, Liaoning Province, China. In this study, the Area Under the Curve (AUC) of the mapping method was 0.937. The Kappa test result was k = 0.8523, and a high-resolution geological map was obtained. [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]

The Liaodong area which lies in the Liaoji Belt of northeastern China is rich in geothermal resources, but locating the resources is challenging. Here non-seismic geophysical data, existing geological data and the physical properties of rocks are examined to locate and characterise potential geothermal resources. High-precision gravity, aeromagnetic and magnetotelluric data are analysed to determine the characteristics of the hidden faults in the region and map the spatial distribution of the subsurface rock mass and its properties. The distribution of hidden faults and the basic characteristics of the rock mass were determined via Euler deconvolution of the gravity and aeromagnetic data. The subsurface extension of concealed faults, the spatial distribution of intrusive rocks, and the specific locations of underground thermal storage structures were determined from magnetotelluric data analysis, thereby enabling geothermal energy targets to be identified. The results highlight four potential sites of geothermal energy development in the study area, which indicate that non-seismic geophysical data can provide reliable clues to the geothermal energy potential in a given region. [ABSTRACT FROM AUTHOR]