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In recent years, with the development of geological prospecting from shallow ore to deep and hidden ore, the difficulty of prospecting is increasing day by day, so the application of computer technology and new methods of geological and mineral exploration is paid more and more attention. The mining and prediction of geological prospecting information based on deep learning have become the frontier field of earth science. However, as a deep artificial intelligence algorithm, deep learning still has many problems to be solved in the big data mining and prediction of geological prospecting, such as the small number of training samples of geological and mineral images, the difficulty of building deep learning network models, and the universal applicability of deep learning models. In this paper, the training samples and convolutional neural network models suitable for geochemical element data mining are constructed to solve the above problems, and the model is successfully applied to the prediction research of gold, silver, lead and zinc polymetallic metallogenic areas in South China. Taking the Pangxidong research area in the west of Guangdong Province as an example, this paper carries out prospecting target prediction research based on a 1:50000 stream sediment survey original data. Firstly, the support vector machine (SVM) model and statistical method were used to determine the ore-related geochemical element assemblage. Secondly, the experimental data of geochemical elements were augmented and a dataset was established. Finally, ResNet-50 neural network model is used for data training and prediction research. The experimental results show that the areas numbered 9, 29, 38, 40, 95, 111, 114, 124, 144 have great metallogenic potential, and this method would be a promising tool for metallogenic prediction. By applying the ResNet-50 neural network in metallogenic prediction, it can provide a new idea for the future exploration of mineral resources. In order to verify the generality of the research method in this paper, we conducted experimental tests on the geochemical dataset of B area, another deposit research area in South China. The results show that 100% of the prediction area obtained by using the proposed method covers the known ore deposit area. This model also provides method support for further delineating the prospecting target area in study area B. [ABSTRACT FROM AUTHOR]

Seasonal variations in the vertical Global Positioning System (GPS) time series are mainly caused by environmental loading, e.g., hydrological loading (HYDL), atmospheric loading (ATML), and nontidal oceanic loading (NTOL), which can be synthesized based on models developed by various institutions. A comprehensive comparison among these models is essential to extract reliable vertical deformation data, especially on a regional scale. In this study, we selected 4 HYDL, 5 ATML, 2 NTOL, and their 40 combined products to investigate their effects on seasonal variations in vertical GPS time series at 27 GPS stations in Yunnan, southwest China. These products were provided by the German Research Center for Geosciences (GFZ), School and Observatory of Earth Sciences (EOST), and International Mass Loading Service (IMLS). Furthermore, we used the Cross Wavelet Transform (XWT) method to analyze the relative phase relationship between the GPS and the environmental loading time series. Our result showed that the largest average Root-Mean-Square (RMS) reduction value was 1.32 mm after removing the deformation associated with 4 HYDL from the vertical GPS time series, whereas the RMS reductions after 5 ATML and 2 NTOL model corrections were negative at most stations in Yunnan. The average RMS reduction value of the optimal combination of environmental loading products was 1.24 mm, which was worse than the HYDL (IMLS_GEOSFPIT)-only correction, indicating that HYDL was the main factor responding for seasonal variations at most stations in Yunnan. The XWT result showed that HYDL also explained the annual variations reasonably. Our finding implies that HYDL (IMLS_GEOSFPIT) contributes the most to the environmental loading in Yunnan, and that the ATML and NTOL models used in this paper cannot be effective to correct seasonal variations. [ABSTRACT FROM AUTHOR]

Through the Earth's ∼ 4.54 billion year history, icehouse climates have been relatively short-lived but have exerted a significant impact on the evolution of the habitable environment. These icehouse episodes, such as those that occurred in the Cryogenian, mid-Ediacaran, Late Ordovician, and Late Paleozoic, developed through the complex interaction of Earth System processes; however, there is still ongoing debate regarding the causes, processes, and consequences of these glaciations. To improve our understanding of the past history of icehouse climates and the responses of the Earth System, we present this new special issue entitled The Earth System during Icehouse Climate Modes: Contributions from Glacial Records in China. This special issue includes eighteen papers that focus on four major icehouse events: the Cryogenian Snowball Earth glaciations (Sturtian and Marinoan global glaciations), the Ediacaran glaciation, the Ordovician glaciation, and the late Paleozoic Ice Age. This collection of papers provide novel insights into glacial processes, marine environments, and chemical weathering through key intervals of Earth History. We anticipate that this special issue will attract wide interest across the geosciences field. [ABSTRACT FROM AUTHOR]

Since its official establishment in 2010, Google Earth Engine (GEE) has developed rapidly and has played a significant role in the global remote sensing community. A bibliometric analysis was conducted on 1995 peer-reviewed articles related to GEE, indexed in the Scopus database up to December 2022 to investigate its trends and main applications. Our main findings are as follows: (1) The number of GEE-related articles has increased rapidly, with nearly 85% of them published in the last three years; (2) The top three domains where GEE has been extensively applied are earth and planetary sciences, environmental science, and agricultural and biological sciences. The majority of GEE-related articles were authored by scholars from China and the US, accounting for 58% of the total, with US scholars having the largest impact on the community, contributing to over 50% of the total citations; (3) Remote Sensing published the highest number of articles (26.82%), whereas Remote Sensing of Environment received the highest number of citations (30.40%); (4) The applications of GEE covered a broad range of topics, with a focus on land applications, water resource applications, climate change, and crop mapping; (5) Landsat imagery were the most popular and widely used dataset; and (6) Random forest, decision trees, support vector machines were the most commonly used machine learning algorithms in GEE. Although having a few limitations, this type of analysis should be conducted regularly to observe the development of this field on a regular basis, as the number of publications related to GEE is expected to continue to increase strongly in the coming years. [ABSTRACT FROM AUTHOR]

Silver is a highly toxic element for marine organisms. However, its controlling factor in marine sediments remains largely unknown, limiting our understanding of its biogeochemical cycling. Based on a sediment core from the Vietnam upwelling area in the South China Sea, it is found that Ag is significantly enriched in sediments of this area (as high as 0.39 μg/g), and it shows a very similar geochemical behavior to Ca and Sr. Our study supports the theory that Ag could be a marine paleo‐productivity indicator. Burial of Ag over the past 3,200 years shows an abrupt increase at around 1850 CE, in concordance with the global atmospheric CO2 record. It is hypothesized that elevated CO2 and global warming enhance marine productivity in the Vietnam coastal upwelling area, favoring the burial of Ag. Human‐induced global warming thus significantly impacts its biogeochemical cycling. Plain Language Summary: Cycling of elements in Earth systems is fundamental to Earth vitality. Examining biogeochemical cycling of elements is thus important in geoscience research. Global warming in recent time has exerted significant impact on the cycles of some elements, including carbon (C), nitrogen (N), and phosphorus (P). This imposes challenges to the integrity and security of Earth ecosystems. However, little is known about some other bio‐active elements. For example, silver (Ag) is highly toxic to marine organisms, but its occurrence, distribution, and fate under a warming background is largely unknown. We analyzed a marine core from the South China Sea, Western Pacific. It was found that concentrations of Ag in the sediments were pretty high, and its burial rate showed an abrupt increase at around 1850 CE. This is probably associated with increased marine productivity as Earth warmed. This study advances our understanding of the Ag cycle. Key Points: High concentrations of and significant variations in Ag were found in marine sedimentsAg in marine sediments is a marker for ocean paleoproductivityGlobal warming favors the burial of Ag in oceans [ABSTRACT FROM AUTHOR]

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[Display omitted] • The magnetotelluric imaging of the southernmost segment of the Tanlu fault zone reveals the occurrence and extension direction of this zone. • The resistivity model shows that the Tanlu fault zone terminates at the southeast end of the Dabie orogenic belt and does not extend further to the south. • The Tanlu fault zone cannot extend further to the south due to the high-resistivity rigid crust of the South China Block. • A new model is proposed for the origin of the Tanlu fault zone. The Tanlu Fault Zone (TLFZ) is an important tectonic boundary in eastern China , and its deep structure, formation and evolution have always been the focus of earth science research. However, there is still much debate about its southward extension and formation pattern. The research on the deep structure at the southernmost end of the TLFZ is of great significance to the interpretation of these problems. This paper analyzes the magnetotelluric (MT) data at the southeast end of the Dabie orogenic belt (DOB), providing evidence for the deep structure in this area. Three MT profiles extend southwards from the south of the DOB, pass through the TLFZ, Xiangfan-Guangji fault (XGF) and Yangxin-Changzhou fault (YCF), and then reach the Jiangnan uplift (JNU). The two dimensional and three dimensional inversion of the MT data were conducted using a nonlinear conjugate gradient method, and the electrical structure images of the profiles above -60 km were obtained. The results showed that the depth of the TLFZ was about 30 km, reaching the lower crust. Its scale reduced significantly from the north to the south, not extending southwards, but westwards connecting with the XGF. It was also found that the lithospheric thickness at the northern margin of the Yangtze Block (YZB) increased significantly due to the Indosinian collisional orogeny. The connection between the TLFZ and XGF as well as the thick and high resistivity rigid lithosphere at the northern margin of the YZB effectively limited the southward extension of the TLFZ. The TLFZ stopped extending southwards at the southeast end of the DOB. With it as a fulcrum, the blocks on the eastern side of the TLFZ rapidly moved northwards during the Early Cretaceous, resulting in counterclockwise rotation. [ABSTRACT FROM AUTHOR]

Deep learning algorithms are increasingly being used in mineral prospectivity mapping (MPM). As mineralization is a rare event, there are significantly fewer ore-bearing locations than non-ore-bearing locations. Therefore, a geoscience dataset used for training in MPM is typically imbalanced. However, few studies have addressed the training data imbalance issue in deep learning modeling for MPM. In addition to the imbalance in the number of samples in the training classes (i.e., ore-bearing and non-ore-bearing locations), the information-overlapping training samples, which cause an imbalance of information in feature space, also affect the performance of deep learning modeling for MPM. This study provides a novel class-balanced focal loss function (CBFL) to address the abovementioned data imbalance issue. Here, the long short-term memory network and the CBFL were utilized to produce a three-dimensional prospectivity model in the Wulong Au district, China. The hyperparameters of the deep learning model were tuned automatically using Bayesian hyperparameter optimization. The performance of the CBFL was compared with that of the widely used cross-entropy loss and focal loss functions in deep learning modeling for MPM, and the results demonstrated that the CBFL outperformed the other loss functions. The three-dimensional targets delineated in this study can benefit subsurface mineral exploration in the future. [ABSTRACT FROM AUTHOR]

The multispectral imager (MII), onboard the Sustainable Development Science Satellite 1 (SDGSAT-1), performs detailed terrestrial change detection and coastal monitoring. SDGSAT-1 was launched at 2:19 UTC on 5 November 2021, as the world's first Earth science satellite to serve the United Nations 2030 Sustainable Development Agenda. A vicarious radiometric calibration experiment was conducted at the Dunhuang calibration site (Gobi Desert, China) on 14 December 2021. In-situ measurements of ground reflectance, aerosol optical depth (AOD), total columnar water vapor, radiosonde data, and diffuse-to-global irradiance (DG) ratio were performed to predict the top-of-atmosphere radiance by the reflectance-, irradiance-, and improved irradiance-based methods using the moderate resolution atmospheric transmission model. The MII calibration coefficients were calculated by dividing the top-of-atmosphere radiance by the average digital number value of the image. The radiometric calibration coefficients calculated by the three calibration methods were reliable (average relative differences: 2.20% (reflectance-based vs. irradiance-based method) and 1.43% (reflectance-based vs. improved irradiance-based method)). The total calibration uncertainties of the reflectance-, irradiance-, and improved irradiance-based methods were 2.77–5.23%, 3.62–5.79%, and 3.50–5.23%, respectively. The extra DG ratio measurements in the latter two methods did not improve the calibration accuracy for AODs ≤ 0.1. The calibrated MII images were verified using Landsat-8 Operational Land Imager (OLI) and Sentinel-2A MultiSpectral Instrument (MSI) images. The retrieved ground reflectances of the MII over different surface types were cross-compared with those of OLI and MSI using the FAST Line-of-sight Atmospheric Analysis of Hypercubes software. The MII retrievals differed by <0.0075 (7.13%) from OLI retrievals and <0.0084 (7.47%) from MSI retrievals for calibration coefficients from the reflectance-based method; <0.0089 (7.57%) from OLI retrievals and <0.0111 (8.65%) from MSI retrievals for the irradiance-based method; and <0.0082 (7.33%) from OLI retrievals and <0.0101 (8.59%) from MSI retrievals for the improved irradiance-based method. Thus, our findings support the application of SDGSAT-1 data. [ABSTRACT FROM AUTHOR]

This letter introduces the founding of a new Deep Underground Science and Engineering Laboratory-DUSEL in China, the distinguishing feature of which is focusing on the key scientific issue on the law of fluid matter migration within the Earth's Critical Zone in geoscience. Various technical, economic, and social challenges were elucidated. The achievements of this facility and thereby ambitious research may provide essential solutions to both energy transition and climate security, and then bolster support for decarbonization of China's energy sector, finally helping attain its double carbon' goal. [ABSTRACT FROM AUTHOR]

Accurate estimation of precipitation is critically important for a variety of fields, such as climatology, meteorology, and water resources. However, the availability of precipitation measurements has proved to be spatially inadequate for many applications. In this study, to acquire high-quality precipitation fields with enhanced accuracy and a fine-scale spatial resolution of 1 km × 1 km, we developed a new data fusion method by establishing an energy function model using the downscaled Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) precipitation product and high-density station observation in mainland China. Our merging approach was inspired by the interdisciplinary research framework integrating the methods in the fields of image processing, earth science, and machine learning. Cross-validation analyses were performed for the monthly precipitation over the period 2009–2018. It was found that the results of the newly developed method were more accurate than the original IMERG products in terms of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (CC), and Kling–Gupta efficiency (KGE). The merging precipitation results exhibit consistent spatial patterns with the original IMERG products, yet have good agreement with station observations. The gauge observations were the major source of the prediction skill of precipitation for the proposed method, and the downscaled-IMERG precipitation products added additional spatial details in the final merging results. Results indicate that the proposed merging method can reproduce the spatial details of the precipitation fields as well as enhance their accuracy. In addition, the time evolution of the error index indicates that the improvement in the merged result was stable over time, with KGE improving by 14% on average. The developed approach provides a promising way of estimating precipitation with high spatial resolution and high accuracy, which will benefit hydrological and climatological studies. [ABSTRACT FROM AUTHOR]

The occurrence of plate tectonic processes on Earth during the Paleoproterozoic is supported by ca. 2.2–1.8 Ga subduction‐collision orogens associated with the assembly of the Columbia‐Nuna supercontinent. Subsequent supercontinent breakup is evidence by global ca. 1.8–1.6 Ga large igneous provinces. The North China craton is notable for containing Paleoproterozoic orogens along its margins, herein named the Northern Margin orogen, yet the nature and timing of orogenic and extensional processes of these orogens and their role in the supercontinent cycle remain unclear. In this contribution, we present new field observations, U‐Pb zircon and baddeleyite geochronology dates, and major/trace‐element and isotope geochemical analyses from the northern margin of the North China craton that detail its Paleoproterozoic tectonic and magmatic history. Specifically, we record the occurrence of ca. 2.2–2.0 Ga magmatic arc rocks, ca. 1.9–1.88 Ga tectonic mélange and mylonitic shear zones, and folded lower Paleoproterozoic strata. These rocks were affected by ca. 1.9–1.8 Ga granulite‐facies metamorphism and ca. 1.87–1.78 Ga post‐collisional, extension‐related magmatism along the cratonal northern margin. We interpret that the generation and emplacement of these rocks, and the coupled metamorphic and magmatic processes, were related to oceanic subduction and subsequent continent‐continent collision during the Paleoproterozoic. The occurrence of ca. 1.77–1.73 Ga mafic dykes and ca. 1.75 Ga mylonitic shear zones along the northern margin of the North China craton may have been related to a regional mantle plume event. Our results are consistent with modern style plate tectonics, including oceanic subduction‐related plate convergence and continent‐continent collision, operating in the Paleoproterozoic. Plain Language Summary: Earth likely went through an early magma ocean stage, which rapidly solidified and evolved though about 4.5 billion years to the modern plate‐tectonic lithosphere. Whether plate‐tectonics operate in early Earth's history, is one of the hotly debated questions in Earth Sciences. The North China craton of central Asia is a natural laboratory for investigating early Earth tectonic processes including subduction‐collision and large‐scale horizontal crustal motions. Paleoproterozoic subduction and continent‐continent collisional between the North China craton and outboard craton were associated with the assembly of the oldest‐known Columbia‐Nuna supercontinent. The configuration of cratons forming the supercontinent remains debated, which is partially due to an inadequate understanding of the Paleoproterozoic rocks that form the margins of the cratons and their tectonic histories. Our observations and analyses indicate the development of Paleoproterozoic Northern Margin orogen, the collisions are not just narrow belts in time and space, but rather broad, long‐lived orogenic cycles that exist well within the intraplate continental interior. Key Points: Northern margin of the North China craton experienced two periods of orogenesis in the Neoarchean and Paleoproterozoic, respectivelyNorthern Margin orogen of the North China craton terminated by at least ca. 1.78 GaCa. 1.77–1.73 Ga mafic dykes and ca. 1.75 Ga mylonitic shear zone are associated with a regional mantle plume event [ABSTRACT FROM AUTHOR]

Mining-induced ground movement is a complicated nonlinear process and a regional geological hazard. Time series in Earth sciences are often characterized as self-affine, long-range persistent, where the power spectra exhibit a power-law dependence on frequency. Whether there exists a periodic signal and a fundamental frequency in the time series is significant in analyzing ground-movement patterns. To evaluate whether a power law describes the power spectra of a ground-movement time series and whether a fundamental frequency exists, GPS monitoring records taken over 14.5 years describing ground movement in the Jinchuan Nickel Mine, China, were analyzed. The data sets consisted of 500 randomly selected GPS monitoring points, spanning the April 2001–October 2015 time period. Whether a periodic signal in the ground movements existed was determined through the autocorrelation function. The power spectra of the ground-movement time series were found to display power-law behavior over vastly different timescales. The spectral exponents of the horizontal and vertical displacements ranged from 0.47 to 3.58 and from 0.43 to 3.37, with mean values of 2.05 and 1.79, respectively. The ground movements of minefields No.1 and No.2 had 1.1-month and 1.4-month fundamental periods, respectively. Together with a discussion of the underlying mechanisms of power-law behavior and relevant influencing factors, these results indicate that ground-movement time series are a type of self-affine time series that exhibit long-range persistence and scale invariance and show a complex periodicity. These conclusions provide a basis for predicting land subsidence in the study area over a timescale. [ABSTRACT FROM AUTHOR]

The prolonged complex geological evolutionary history of the South China Block is documented in a well-preserved rock record that comprises an Archean to Neoproterozoic crystalline basement (metamorphosed igneous and sedimentary rocks) and Phanerozoic supracrustal rocks. Some global Precambrian tectonic events, such as the formation and breakup of the Columbia and Rodinia supercontinents are also represented in the region. Here we present an outline of potential contributions to the proposed special issue of the Journal of Asian Earth Sciences on "Precambrian tectonothermal events and crustal evolution of South China". A total of 13 papers primarily focus on the Precambrian geological events in the South China Block encompassing magmatism, sedimentation, and geodynamics. The new data included in the special issue would provide an update on the crustal evolution of the South China Block, and its implication on an improved understanding of the global events. [ABSTRACT FROM AUTHOR]

The article discusses research which reported the first occurrence of an ultra-high pressure (UHP) mineral inclusion in an Archean ophiolitic podiform chromitite block in an ophiolitic subduction related mélange, from the Neoarchean Central Orogenic Belt (COB), North China. Topics covered include the textures exhibited by the chromitites, and processes such as subduction, melting, volcanism, and the release of volatiles have regulated climate and life on Earth billions of years ago.

Quantification of uncertainties associated with satellite precipitation products is a prior requirement for their better applications in earth science studies. An improved scheme is developed in this study to decompose mean bias error (MBE) and mean square error (MSE) into three components, i.e., MBE and MSE associated hits, missed precipitation, and false alarms, respectively, which are weighted by their relative frequencies of occurrence (RFO). The trend of total MBE or MSE is then naturally decomposed into six components according to the chain rule for derivatives. Quantitative estimation of individual contributions to total MBE and MSE is finally derived. The method is applied to validation of Integrated MultisatellitE Retrievals for GPM (IMERG) in Mainland China. MBE associated with false alarms is an important driver for total MBE, while MSE associated with hits accounts for more than 85% of MSE, except in inland semi-arid area. The RFO of false alarms increases, whereas the RFO of missed precipitation decreases. Both factors lead in part to a growing trend for total MBE. Detection of precipitation should be improved in the IMERG algorithm. More specifically, the priority should be to reduce false alarms. [ABSTRACT FROM AUTHOR]

Air temperature is one of the most essential variables in understanding global warming as well as variations of climate, hydrology, and eco-systems. However, current products and assimilation approaches alone can provide temperature data with high resolution, high spatio-temporal continuity, and high accuracy simultaneously (refer to 3H data). To explore this kind of potential, we proposed an integrated temperature downscaling framework by fusing multiple remotely sent, model-based, and in-situ datasets, which was inspired by point-surface data fusion and deep learning. First, all of the predictor variables were processed to maintain spatial seamlessness and temporal continuity. Then, a deep belief neural network was applied to downscale temperature with a spatial resolution of 1 km. To further enhance the model performance, calibration techniques were adopted by integrating station-based data. The results of the validation over the Yangtze River Basin indicated that the average Pearson correlation coefficient, RMSE, and MAE of downscaled temperature achieved 0.983, 1.96 °C, and 1.57 °C, respectively. After calibration, the RMSE and MAE were further decreased by ~20%. In general, the results and comparative analysis confirmed the effectiveness of the framework for generating 3H temperature datasets, which would be valuable for earth science studies. [ABSTRACT FROM AUTHOR]