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The identification of shale lithology is of great importance for the exploration and development of shale reservoirs. The lithology and mineralogical composition of shale are closely related, but a small number of laboratory core analysis samples are insufficient to evaluate the lithology of the entire formation. In this study, a lithology identification method using conventional logging curves is proposed for the shale stratigraphy of the Qingshankou Formation in the Gulong Depression of the Songliao Basin, northeastern China. First, a mineral pre-training model is constructed using discrete petrophysical experimental data with logging data, and features are generated for the logging data. Second, an adaptive multi-objective swarm crossover optimization method is employed to address the imbalance of logging data. Finally, the model is combined with a Bayesian gradient boosting algorithm for lithology identification. The proposed method demonstrates superior performance to eXtreme Gradient Boosting, Support Vector Machines, Multilayer Perceptron, and Random Forest in terms of accuracy, weight perspective, and macro perspective evaluation indexes. The method has been successfully applied in actual wells, with excellent results. The results indicate that the workflow is a reliable means of shale lithology identification.

The geothermal gradient in the eastern area of Liaoning Province is very low, but hot springs resources are variable. The reason is not clear till now but leads to the fact that a few strong influence factors can cause imbalances in the results of many prediction algorithms. It can be found as a black-box algorithm, deep learning will obtain a more unbalanced result with the fault influence factors. To tackle this issue, the role of preprocessing during the process of profound learning was enhanced and four comparative experiments were carried out. The results show that compared with the unprocessed experiment, the accuracy rate of the experiment with fully processed data increased by 11.9 p.p., and the area under the curve increased by 0.086 (0.796–0.882). This inspires us that even though the deep learning method can achieve high accuracy in the prediction of geological resources, we still need to pay attention to the analysis and pretreatment of data with expertise according to local conditions.

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 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 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]

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 characterization of the concentration, chemical speciation and source of heavy metals in soils is an imperative for pollution monitoring and the potential risk assessment of the metals to animal and human health. A total of 154 surface horizons and 53 underlying horizons of grassland soil were collected from the Baicheng–Songyuan area in Jilin Province, Northeast China, in which the concentrations and chemical fractionations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were investigated. The mean concentrations of heavy metals in grassland topsoil were 7.2, 0.072, 35, 16.7, 0.014, 15.2, 18.3 and 35 mg kg − 1 for As, Cd, Cr, Cu, Hg, Ni, Pb and Zn, respectively, and those averaged contents were lower than their China Environmental Quality Standard values for the Soils, implying that heavy metal concentrations in the studied soils were of the safety levels. The mobility sequence of the heavy metals based on the sum of the soluble, exchangeable, carbonate-bound and humic acid-bound fractions among the seven fractions decreased in the order of Cd 50.4%) > Hg (39.8%) > Cu (26.5%) > As (19.9%) > Zn (19.1%) > Ni (15.9%) > Pb (14.1%) > Cr (4.3%), suggesting Cd and Hg may pose more potential risk of soil contamination than other metals. Multivariate statistical analysis suggested that As, Cr, Cu, Ni, Pb, Zn, Cd and Hg had the similar lithogenic sources, however, Cd and Hg were more relevant to organic matter than other heavy metals, which was confirmed by the chemical speciation analysis of the metals. The study provides a base for local authority in the studied area to monitor the long term accession of heavy metals into grassland soil. [ABSTRACT FROM AUTHOR]