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The southern Great Xing'an Range is located in the eastern Central Asian Orogenic Belt, where voluminous igneous rocks developed during the Late Mesozoic period. The east slope of the southern Great Xing'an Range has been the topic of numerous debates on the level of influence of the Mongol-Okhotsk and the Paleo-Pacific regimes in the Late Mesozoic period. Therefore, this area is a suitable region in which to study the temporal changes in magma sources and tectono-magmatic evolution. In this paper, whole-rock geochemical data, zircon U-Pb geochronology, and zircon Hf isotope studies were carried out on the granitoids in the east slope area of the southern Great Xing'an Range. LA-ICP-MS zircon U-Pb dating revealed the ages of four granitoid samples: 135.0 ± 0.6 Ma, 130.7 ± 1.4 Ma, 130.4 ± 1.0 Ma, and 127.6 ± 0.8 Ma, respectively. The Hf isotope values 176Hf/177Hf = 0.282751–0.283015, εHf (t) = +2.0~+11.5, and T2DM = 583~1442 Ma suggest that the magma was generated by partial melting of Meso- and Neoproterozoic accreted and thickened low crust. The whole-rock geochemical data implied that these granitoids are A-type granite and their formation is closely linked to the subduction of the Paleo-Pacific Ocean plate. These geochemical, isotopic, and geochronological data suggest that the Early Cretaceous magmatism in the east slope area of the southern Great Xing'an Range formed in an extensional back-arc tectonic setting associated with the slab roll-back of the Paleo-Pacific plate subduction. [ABSTRACT FROM AUTHOR]

In the process of deep geological structure research, gravity and aeromagnetic anomaly data are often used to invert the distribution law of concealed geological bodies. In order to reveal the boundaries of concealed geological bodies and divide structural divisions, this paper adopts fuzzy control simulated annealing genetic algorithm () to superimpose the gravity and aeromagnetic anomaly data in Benxi-Xiuyan area and classify the patterns, and identify the main geological body boundaries and fractures. Comparing the relationship with known geological maps, the two have a higher degree of matching. Experiments show that the application of SAGAFcm algorithm can quickly identify the boundaries of concealed geological bodies and provide a new means for field geological mapping.

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 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]

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]

Novel prediction methods using artificial intelligence have been developed to improve the identification, discovery, and utilization of new types of mineral resources at new depths or using new technologies. However, most artificial intelligence methods require large training data sets that are often unavailable for mineralization prediction models, leading to inaccuracies. To address this issue, we developed a semi‐supervised machine‐learning method to identify metallogenic anomalies using the density‐based spatial clustering of applications with noise method and autoencoder. The outputs of this method show irregularity in distributions inferred from geological, geochemical, and hyperspectral remote sensing data that match known mineralization locations. We focus on the Daqiao mining area of Gansu Province in China to show that the model predictions are highly consistent with known deposits of the Yinmahe and Daqiao gold mines, and two new prospecting areas have been highlighted for further field confirmation. The accuracy of this semi‐supervised learning method was verified by an interdisciplinary intelligent analysis, showing that this method could have wide‐reaching applications for improving regional geological surveys. Plain Language Summary: Mineral resources are irreplaceable and necessary for modernization. Novel prediction methods using artificial intelligence have been developed to improve the prediction of mineral resources. However, the training data sets are still a big problem for the artificial intelligence methods used in this application. To solve this issue, we developed a semi‐supervised machine‐learning method to identify metallogenic anomalies using the DBSCAN and autoencoder. The accuracy of this method was verified by an interdisciplinary intelligent analysis, showing that this method could have wide‐reaching applications for improving regional geological surveys. Key Points: A new artificial intelligence technology to analyze multivariate data for ore prospecting and predictionThe prediction results show that ore bodies in the Daqiao area are controlled by the large fault structureThe improvement and new applications of remote sensing image data to synthesize geological and geochemical data for ore‐forming anomalies [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]