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    2019, Volume 26 Issue 4
    25 July 2019
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    2019, 26(4): 0-0. 
    Abstract ( 157 )   PDF (8094KB) ( 281 )  
     
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    2019, 26(4): 0-0. 
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    Characteristics and rational utilization of geological big data
    ZHAO Pengda
    2019, 26(4): 1-5. 
    DOI: 10.13745/j.esf.sf.2018.9.8

    Abstract ( 743 )   PDF (787KB) ( 809 )  
    Rational development and utilization of geological big data may open up new recognition space, create new approaches to problem solving, as well as promote a new form of digital economy and construct a new way of life. Integration of mathematical geology and information technology has emerged as a new scientific discipline known as the “Digital Geology”, i.e. the “Digital Science” of geological sciences. Geological digital science uses digital methodology to study geological sciences as well as develops and utilizes geological big data according to the characteristics of geological data and the demands of geological problems. Here we first elaborate on the main characteristics of geological data including mixed population, population sampling, causality, spatial-temporal property, multiple-states and multivariate variables with diverse sources. Further, we propose to construct a “knowledge base” prior to building “data base”, “model base” and “method base” in order to facilitate big data acquisition and analysis for geological research and application.
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    Big data pioneers new ways of geoscience research: identifying relevant relationships to enhance research feasibility
    LUO Jianmin,ZHANG Qi
    2019, 26(4): 6-12. 
    DOI: 10.13745/j.esf.sf.2019.4.28

    Abstract ( 254 )   PDF (1082KB) ( 594 )  
    Humans have entered the era of big data. Research ideas and methods based on big data have gained much attention and start to apply widely in the field of geoscience. In our view, the subject of big data research is data, the tool is the computer, the method and means are to find out the correlation between data, and the characteristics is to make decisions based on probability criteria. To reiterate: big data is the idea and method of finding out the correlation between data; it researches problems and make correct decisions by mining large amounts of data. In this paper, we suggest that the inductive method is the way to carry out big data research, specially as its research power has been greatly enhanced by high performance computer and big data technology. Through an in-depth analyses of statistics and machine learning algorithm, we came to the conclusion that big data shall change the ways people learn and understand nature and scientific studies are designed and performed. And it shall subvert the long-standing habit of conducting scientific research by finding causal relationships. Big data shall create a new approach to conducting geoscience research across complex causal relationships and obtaining research results directly. We concluded in this study that with the explosive growth of data, and with popularization of high-performance computers and rapid development of computing technology, the statistical analysis method has largely broken through the limitation of data volume. This shall enable statistical analysis and prediction models to generate truer thus more reliable results. Ultimately, the ability to explain conditions and outcomes, combining with the advantages of machine learning algorithms for semi-structured and unstructured data, will make quantitative geoscience research truly feasible.
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    Characteristics of the temporal-spatial distribution of global Cenozoic picrite and their significance
    YUAN Fanglin,ZHANG Qi2,ZHANG Chengli
    2019, 26(4): 13-21. 
    DOI: 10.13745/j.esf.sf.2019.5.12

    Abstract ( 267 )   PDF (2371KB) ( 498 )  
    Picrite is a kind of ultral-mafic rock formed at temperatures about 300400 ℃ higher than the melting temperature of the mantle-derived basalt, thus it can indicate the presence of hotspots. In this paper, based on two geological databases, GEOROC and PetDB, we aim to find out the temporal-spatial distribution characteristics of Cenozoic picrite and figure out the possible locations of modern high-temperature hotspots by analyzing the aging and tectonic environmental information in the two databases. The study shows that Cenozoic picrite volcanism became active in the Miocene and peaked in the Pleistocene and Holocene. Meanwhile, the tectonic environment of picrite is diverse and regularly changeable. Its tectonic characteristics changed from CFB and ocean island to volcanic arc and ocean island before and after the Miocene and then turned to ocean island in the Pleistocene and Holocene. In general, Cenozoic picrite is mainly produced in oceanic environment, especially ocean island. We conclude that, depending on the picrite exposure characteristics, modern hotspots are occurred in the following 10 locations: Hawaii islands, Colon islands (Ecuador), Iceland, Canary Islands, Effer mountain (Europe), Red Ocean Ridge, East African Rift, Reunion island, Guangdong Province(China) and Kamchatka arc.
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    Tectonic discrimination based on convolution neural network and big data of volcanic rocks
    GE Can,WANG Fangyue,GU Hai'ou,GUAN Huaifeng,LI Xiuyu,YUAN Feng
    2019, 26(4): 22-32. 
    DOI: 10.13745/j.esf.sf.2019.7.6

    Abstract ( 288 )   PDF (3373KB) ( 472 )  
    The traditional tectonic discrimination graphs have some shortcomings due to the limitation of available analytical methods and techniques of times. This has led to some confusions and contradictions for scholars using the graphs in their researches. Under the impact of big data, the reliability of some traditional tectonic discrimination graphs is being tested. In this paper, we proposed a method for the two-dimensional visualization of geochemical data. Using this method, we converted the geochemical compositions of volcanic rocks from 11 tectonic environments registered in the GEOROC database into 34468 two-dimensional coded images. Relying on deep learning method, 75% of the images were used to learn and train automatically to construct the convolution neural network (CNN) model, which can be employed to classify volcanic rocks into tectonic groups at an overall accuracy of 95%. This model has good generalization capability and can be routinely used to distinguish the tectonic source regions of volcano rock samples.
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    Data analysis of major and trace element of gabbro clinopyroxene from different tectonic setting
    ZHANG Baoyue,SUN Jiankun,LUO Xiong,JIN Weijun,WANG Long,DU Xueliang,CHEN Wanfeng,DU Jun,ZHANG Qi,ZHU Yueqin
    2019, 26(4): 33-44. 
    DOI: 10.13745/j.esf.sf.2019.7.8

    Abstract ( 370 )   PDF (7022KB) ( 420 )  
    Recently, it has become an important part of petrological and geochemical research including the field of geodynamics to identify tectonic discrimination in magmatic rocks formation. Traditionally, basalt is considered the best member of rocks to identify tectonic settings. However, the use of clinopyxene has not been effective in this aspect due to limitations in data usage; and in theory gabbro is not consistent in its characterization under different tectonic environments. For this reason, it is imperative to explore the use of machine learning algorithms in studying global clinopyroxene gabbro. Here, we mainly focus on feature screening and data classification of clinopyxene gabbros from three structural backgrounds: island arc (IAB), oceanic island (OIB) and mid-ocean ridge (MORB). From the GEOROC database, after data collection and processing, we identified 385 entries of island arc, 756 ocean island and 5500 mid-ocean clinopyxene gabbros. Most data were of main elements and the rest trace elements. During feature extraction, we used chi-square test to judge feature independence, F-test to estimate the linear dependence between two random variables, and mutual information method to capture other kinds of statistical correlations. Statistically reliable data features were obtained using these three methods. During data classification, we compared the performances of three mainstream machine learning classification algorithms, namely K-Nearest Neighbor, Decision Tree and Support Vector Machine on gabbro data. The results show that for clinopyxene gabbros in above three tectonic settings, Al2O3 and TiO2 were the most distinctive main elemental compositions in clinopyroxene gabbro for differentiation, while Sr was the most discriminating trace element. The backgrounds classification accuracy of the machine learning model on data of main and trace element both reached 94%.
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    Prediction of REEs in OIB by major elements based on machine learning
    HONG Jin,GAN Chengshi,LIU Jie
    2019, 26(4): 45-54. 
    DOI: 10.13745/j.esf.sf.2019.7.3

    Abstract ( 353 )   PDF (2194KB) ( 419 )  
    Geoscience shared databases (GEOROC, PetDB, etc.) provide important basic data for geoscience research. However, there is an obvious defect in these databases, i.e., in database samples, the nine major elements (SiO2, TiO2, Al2O3, CaO, MgO, MnO, K2O, Na2O and P2O5) are mostly present, but rare earth element (REE) data are often missing. In view of the important role of REE in geochemistry, here we attempt to provide a solution for supplementing the missing REE data by using random forest method of machine learning to predict REE values by major elements. Taking Ocean Island Basalt (OIB) as an example, 1283 OIB samples collected from the GEOROC database were divided into two groups: 80% of the data were used as training data for modeling and the remaining 20% were test data for model validation. Comparing the modeling and prediction results using random forest and multivariable linear regression methods on the same data, we found that the random forest method was superior in both aspects with clear advantage; however, the relationship between input and output parameters was not simple. The random forest method predicted the test data very well for light REEs, but prediction power decreased gradually with increasing atomic number, possibly due to a weaker or more complex relationship between heavy rare earth and major elements. The predicted REE distribution pattern by the random forest method matched the actual REE distribution pattern, with good distinguishing power to reflect the relative difference between the actual distribution patterns, which is particularly important to infer the geochemical process. With increasing training data, the model established by the random forest method will be more stable thus to provide more accurate prediction results. Ultimately, REE value prediction will be more reliable and feasible with continuous improvement of databases.
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    Quantitative analysis of 3D microtomographic data of rocks and its applications in geosciences
    WANG Wei,CAI Yuna,LIU Jie
    2019, 26(4): 55-66. 
    DOI: 10.13745/j.esf.sf.2018.4.47

    Abstract ( 207 )   PDF (5119KB) ( 374 )  
    Microtomography provides digital images at microns or sub-micron resolution and enables the study of rock microstructures. Quantitative analyses of microtomographic data mainly include three parts: (1) characterizing rock microstructures; (2) determining representative volume element (RVE) size and pore-scale fluid simulation for extracting fluid transport properties; and (3) determining mechanical RVE size and extracting mechanical properties. Here, we briefly introduced the implementing schemes of quantitative analyses of microtomography, using visualization methods to assistant data analyses. We analyzed three typical samples—a mylonite, a synthetic sandstone and a carbonate sample-to illustrate the procedure and validity of our method. Microtomographic data are voluminous, vary at different processing stages, and contain plentiful yet specific information, thus they are important components of geoscience big-data.
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    Exploration geochemical data mining and weak geochemical anomalies identification
    ZUO Renguang
    2019, 26(4): 67-75. 
    DOI: 10.13745/j.esf.sf.2018.6.25

    Abstract ( 372 )   PDF (1780KB) ( 717 )  
    We have collected in China many high-quality, multi-element and multi-scale geochemical exploration datasets, which provide an effective data support for mineral exploration and environmental studies. It is of great interest to further explore these datasets to identify geochemical anomalies associated with mineralization, in supporting breakthroughs in the next round of mineral exploration to alleviate current mineral shortage distress. However, deep buried or covered mineral deposits pose significant challenge to mineral exploration, what we are currently facing is how to identify and evaluate weak geochemical anomalies in the field of exploration geochemical data processing. In this paper, we reviewed the state-of-the-art methods and models for exploration geochemical data processing and geochemical anomaly identification. We also discussed the data closure problem and its solutions, as well as application of big data and machine learning methods to geochemical data processing. It is shown that the hybrid method, which combines big data thinking and machine learning methods under the constraints of geological settings, is a powerful tool to explore geochemical patterns and identify geochemical anomalies. The hybrid method takes into account the correlations between geochemical patterns and locations of mineral deposit, involving all geochemical variables, and can reveal non-linear characteristics of geochemical patterns.
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    Application of geological big data to quantitative target area optimization for regional mineral prospecting in China
    LUO Jianmin,WANG Xiaowei,ZHANG Qi,SONG Bingtian,YANG Zhongming,ZHAO Yanqing
    2019, 26(4): 76-83. 
    DOI: 10.13745/j.esf.sf.2019.5.11

    Abstract ( 315 )   PDF (2290KB) ( 517 )  
    Today, with rapid development of computer science and technology, ever more geologists are learning and applying big data based research methods, as it has become evident that many geological problems can be solved or clarified by analyzing correlativity of geological data. Here, we used multivariant statistical analysis method to mine the 1∶200000 scale geochemical survey data of stream sediments from western Qinling district, and established a series of quantitative optimization models of target areas for regional Au prospecting. These series of optimization models increased the accuracy for Au metallogenetic prediction in the study area (up to 30% Au prediction accuracy in randomly selected areas); meanwhile, they predicted the elemental composition of Au ore with high agreement with the theoretical value based on Au metallogenic geochemistry. We quantitatively evaluated each element according to its predicting power in all predication models to provide quantitative basis for further research on the genesis and controlling factors of Au ore deposit. Our results demonstrate that massive geological data possess great research potential which can only be exploited by applying big data and quantitative analytical methodologies. At the same time, it fully proved that the feasibility and necessity of quantitative optimization selection of geological research and exploration target area is realized by “identifying the relationship between data and replacing the causal relationship between things”.
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    Quantitative analysis of ore-controlling factors based on exploration data of the Dayingezhuang gold deposit in the Jiaodong Peninsula
    MAO Xiancheng,WANG Mijun,LIU Zhankun,CHEN Jin,DENG Hao
    2019, 26(4): 84-93. 
    DOI: 10.13745/j.esf.2019.04.010

    Abstract ( 191 )   PDF (6649KB) ( 415 )  
    The Jiaodong gold deposit typically occurs in an extensional fault system and often exhibits obvious structural ore-control characteristics. However, the step metallogenic model of the Jiaodong gold deposit is inadequate to delineate the structural features that essentially control the formation of the deposit. The big data technology has provided a new approach to mine exploration data and explore minerogenetic regularities. In this paper, we studied the Dayingezhuang gold deposit in the Jiaodong peninsula. Based on the collected exploration data and using the spatial analysis method, we performed quantitative assessment of different features of ore-controlling factors to determine their association with gold mineralization. The results show a bimodal distribution between either gold grade or quantity and the fault distance factor. The bimodal distribution is consistent with the occurrence of two ore types in the gold deposit and directly indicates the relative spatial positions between the two ore types. Plot of gold grade vs. fault slope showed a near Gaussian distribution peaking at a slope range for large gold accumulation, suggesting that ore-forming fluids prefer to converge and remain at certain sloped sections. We observed that slope change and undulation of fault have significant effects on ore distribution and can clearly distinguish the locations of orebodies in terms of ore quality. We also observed sharp increase of gold accumulation in the intense alteration zone, where continuous hydrothermal process or multiple mineralization most likely had occurred. We conclude that the morphological features of the Zhaoping fault played key roles in the formation of the Dayingezhuang gold deposit. This conclusion may be applied to other Jiaodong gold deposits.
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    Quantitative remote sensing modeling and inversion of laterite type bauxite based on sample data
    CHENG Gong,ZHONG Chaoling,YUAN Haiming,REN Ming,XU Wenwen,WANG Dongjun
    2019, 26(4): 109-116. 
    DOI: 10.13745/j.esf.sf.2019.7.5

    Abstract ( 196 )   PDF (2976KB) ( 284 )  
    Quantitative remote sensing technique is at the forefront of using remote sensing big data for ore prospecting. To explore the effects of number of samples on quantitative remote sensing modeling, we used multiple regression analysis method to perform remote sensing modeling and inversion experiments with varying number of surface bauxite samples. In this study, surface samples were first analyzed to obtain Al2O3 and SiO2 content information. Then, from the Landsat 8 remote sensing data, spectral reflectance of 1 to 7 spectral bands were read according to sample position. Next, multiple regression analysis on Al2O3 and SiO2 contents and corresponding reflectance of the 17 spectral bands were carried out using the SPSS software to establish a remote sensing quantitative inversion model according to the Al2O3 and SiO2 contents. In order to obtain the best inversion model, we randomly selected 6 batches of different number of samples for the modeling experiments, using about 2/3 of samples for modeling and the rest for model testing. The experimental results showed that as number of samples increased, the coefficient of determination (R2) first rose rapidly and then slowly declined; whereas the root mean square error (RMSE) behaved oppositely. At number of samples 50, maximum R2 and minimum RMSE were reached, showing an overall skewed data distribution. Finally, the remote sensing image was used to invert the established model at number of samples 50. The inversion results were in good agreement with experimentally measured Al2O3 and SiO2 contents in the study area, confirming that the modeling method is reliable and its application can be further expanded.
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    Discrimination and comparison experiments of basalt tectonic setting based on improved genetic algorithm-optimized neural network
    REN Qiubing,LI Mingchao,HAN Shuai
    2019, 26(4): 117-124. 
    DOI: 10.13745/j.esf.2019.04.013

    Abstract ( 305 )   PDF (2197KB) ( 355 )  
    One of the most important applications of geochemical whole-rock analysis is to discriminate the tectonic settings for magma formation and properties of magmatic source areas through geochemical characteristics of magmatic rocks. This approach allows discrimination of tectonic setting of a given suite of magmatic rocks (basalt, granite, etc.) using whole-rock geochemical data including major and trace elemental and isotopic compositions. As a new application of artificial intelligence technique in the field of geochemistry, machine learning discrimination algorithm has gradually become a research tool supplementary to the classical discrimination diagram approach. However, the algorithms classification accuracy is affected by two main factors: high-dimensional data feature screening and multiple unknown parameter determination. To this end, we propose here a coupling discrimination method involving improved genetic algorithm and optimized neural network (GA-NNDM), based on genetic algorithm (GA) and neural network discrimination method (NNDM). The proposed method uses the feedback links between feature selection, parameter determination and classification performance. It treats classification accuracy as a fitness function and seeks the best feature subset and unknown parameters through iterative evolution. As a result, data features are reduced, unknown parameters are optimized and classification performance is improved. In addition, according to the published geochemical data of basalt samples, vertical and horizontal comparison experiments are set up through K-fold cross-validation method to verify accuracy, stability and extensibility of GA-NNDM in the application of basalt tectonic setting discrimination. Simulation results show that GA-NNDM has an excellent discrimination effect and generalization ability, with the overall classification accuracy near 90%. We conclude that, as a whole, GA-NNDM can be applied widely in geochemistry.
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    Application of association rule algorithm in studying abnormal elemental associations in the Pangxidong area in western Guangdong Province, China
    LIU Xinyi,ZHOU Yongzhang
    2019, 26(4): 125-130. 
    DOI: 10.13745/j.esf.sf.2019.5.19

    Abstract ( 226 )   PDF (1751KB) ( 255 )  
    We conducted a case study on the application of association rule algorithm (programmed in Python) using the original 1∶50000 geochemical survey and anomaly verification data of stream sediments in the Pangxidong deposit prospect district in the southern section of the Qinzhou Bay-Hangzhou Bay orogenic belt. The results showed that the Apriori algorithm can effectively mine the association rule itemsets of elemental combinations. For example, we found that As had a 95.5% probability being abnormal when Au, Cu and Sb in the itemset were in abnormal range. The association rules selected by Apriori algorithm were in line with survey results; and the strong rules of combination anomalies had high agreement with the anomaly combinations of known mineral deposits in the study area. Facing with massive geochemical survey data, it is often time-consuming to understand elements one by one, and in many cases it is impossible to observe the relationship among them. Therefore, it is advantageous to expose the abnormal combination rules of elements using association rule algorithm. By doing so, related information among various elements can be stored to a great extent and used to find the hidden combinations of elements and potential correlations among them. And compared to traditional methods, it can be more convenient and effective to establish data bases of metallogenic association rules and to carry out mineral deposit prediction.
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    Recommendation system algorithm and its application in ore deposits forecast at Wendi district of the southern Qinzhou-Hangzhou metallogenic belt, South China
    WANG Kunyi,ZHOU Yongzhang,WANG Jun,ZHANG Aoduo, YU Xiaotong,JIAO Shoutao,LIU Xinyi
    2019, 26(4): 131-137. 
    DOI: 10.13745/j.esf.2019.04.015

    Abstract ( 208 )   PDF (4623KB) ( 295 )  
    Recommendation system algorithm is one of the important technologies of big data mining. In this study, we applied the content-based recommendation system algorithm to construct an utility matrix of the active and factor items, based on data obtained from the 1∶50000 geological and mineral resource survey at the Wendi district of southern Qinzhou-Hangzhou metallogenic belt, South China. The predicted active item included the middle- and small-sized (ore spot) electrum and verified non-electrum deposits; it also included the unevaluated electrum deposits, medium- and small-sized (ore spot) lead-zinc and verified non-lead-zinc (ore spot) deposits, and the unevaluated lead-zinc deposits. Caledonian migmatite, early Yanshanian and late Yanshanian intrusions, NE- and NW-trending faults, and Au, Ag, Pb and Zn elements were considered the factor item. The Euclidean distance similarity between known deposit (or ore spot) and other unevaluated areas was calculated and then used to predict the prospecting area of silver-gold and lead-zinc deposit. The results show that the recommendation system algorithm can effectively mine mineralization related information, and quickly extract the potential deposit areas based on its similarity to certain types of deposits (ore spot). For electrum deposit within the Wendi district, high similarity areas were mainly distribute around known ore deposits and on both sides of NE-trending faults, with a small portion distributed near the overlapped fracture. In comparison, for lead-zinc deposits, medium-sized deposit showed a high degree of discrimination. The high-value areas covered almost all known lead-zinc deposits while more concentrated distribution were found for small-sized deposits. In addition to known deposits, several high value areas can also be used as key prospecting targets.
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    Prediction and analysis of gold deposit sizes based on coupled PCA-SVM algorithm
    LIU Chengzhao,HAN Shuai,LI Mingchao,ZHU Yueqin
    2019, 26(4): 138-145. 
    DOI: 10.13745/j.esf.sf.2019.7.7

    Abstract ( 213 )   PDF (1941KB) ( 240 )  
    Identification of ore deposit types is an important part of mineral exploration. Traditional methods for predicting deposit size are time-consuming, laborious and costly. In order to improve prospecting efficiency and accuracy and reveal potential relation between chemical composition and the size of gold mineralization, we propose here an integrated approach using the Principal Component Analysis (PCA) and Support Vector Machine (SVM) algorithms. In this approach, we first extract the major features of samples using PCA, and we then train a set of SVM classifiers by these features to predict deposit sizes. In this study, we collected and analyzed 3812 gold mine samples from Beishan, Gansu region to establish a PCA-SVM model with the training accuracy of 92.3% and the test accuracy of 88.7%, which were 14.3% and 17.1% higher, respectively, than using SVM. We demonstrated that the PCA-SVM method not only can eliminate subjective factors, but also can improve the accuracy of identifying ore deposits as well as prospecting efficiency, thus to provide reliable support for decision making.
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    Method development of 3D immersive visualization and interaction of multi-scale geological data based on VR technology
    GUO Yanjun,ZHANG Jinjiang,CHEN Bin,CUI Ying,XIONG Wentao,LI Mei, ZHANG Zhicheng,QIN Shan
    2019, 26(4): 146-158. 
    DOI: 10.13745/j.esf.sf.2018-5-29

    Abstract ( 350 )   PDF (9752KB) ( 368 )  
    Rapid technology development leads to new direction of mineralogy exploration and teaching involving visualization and interaction of geological big data. Further exploration is needed on how to visualize and interact with the voluminous and complex geological big data which have no apparent logical structures. We have studied the 3D immersive visualization and interaction method for multi-scale geological data based on virtual reality (VR) and recognize the method is consistent with computational thinking and geological theory. In this paper, we first propose a theoretic framework for the multi-scale geological data visualization method. Next, we apply this method to visualize the multi-scale/multi-resolution geological data based on VR technology. Finally, we construct the 3D immersive VR platform and validate the method using actual data. We present here a new approach in visualization and interaction of geological data, as well as in scientific quantitative evaluation, analysis and teaching, illustrating an inevitable future development trend.
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    Applicability of quantum entanglement technology in geology
    ZHANG Qi,JIAO Shoutao,LI Mingchao,ZHU Yueqin,HAN Shuai,LIU Xuelong, JIN Weijun,CHEN Wanfeng,LIU Xinyu
    2019, 26(4): 159-169. 
    DOI: 10.13745/j.esf.sf.2019.5.10

    Abstract ( 894 )   PDF (2761KB) ( 736 )  
    Newtonian mechanics is a theory for studying the macroscopic world and quantum mechanics is for the microscopic world. Many phenomena described by quantum mechanics are beyond the macroscopic world; some of them are mysterious and even subvert our understanding of science. The most typical one is quantum entanglement. It describes two mutually entangled quanta not independent of each other regardless of the distance between them, i.e., when one of the entangled quanta is measured, the other-although far apart-can also be sensed and measured by correlation. Quantum entanglement is the most incredible feature of quantum systems distinguished from classical systems, and it has caused academic debate precisely because it is magical and difficult to verify. However, due to its powerful functionality, quantum entanglement has become the focus of world powers. In this paper, we discussed the current status of quantum entanglement research and its characteristics and explored a few examples of its applications including the teleportation function of quantum entanglement, possible relationship between quantum entanglement and telepathy, advancing quantum computing through quantum entanglement, combining quantum entanglement and big data to create quantum machine learning, and so on. Presently, there is no precedent of applying quantum entanglement technology in geology, but we believe it is not impossible theoretically. Studies have shown that two related particles are prone to entanglement; and entanglement is particularly prone to occur in groups whose members are related by kinship or causality. Coincidentally, in studying geology, our most favored and concerned subjects are the causes of geological phenomena such as rock formation, mineral deposits, metamorphosis, sedimentation, and so on. The causal relationship is causality. In fact, most geological causal relationships resemble a genetic or kinship relationship. Therefore, it seems to us that research on causality could be the entry point to geological applications of quantum entanglement technology. Here we discussed several attentive issues in applying quantum entanglement to geology. From the perspective of China's economic development, its current resources status and its survival in a modern world, we need to pay attention to quantum entanglement technology, whose introduction into the field of geology is something that has never been seen before but will be for sure.
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    Study of fault activity risk in typical strong seismic regions in northern China by in-situ stress measurements and the influence on the Xiongan New Area
    FENG Chengjun,QI Bangshen,WANG Xiaoshan,ZHANG Peng,SUN Mingqian,MENG Jing,TAN Chengxuan,CHEN Qunce
    2019, 26(4): 170-190. 
    DOI: 10.13745/j.esf.sf.2019.4.17

    Abstract ( 176 )   PDF (10997KB) ( 265 )  
    Three typical strong seismic regions, Tangshan, Xingtai and Zhangbei, are located within 300 km radius around the Xiong'an New Area in northern China. The Xingtai M 7.2 earthquake in 1966, Tangshan M 7.8 earthquake in 1976, and Zhangbei M 6.2 earthquake in 1998 had occurred in the past 50 years and future risks of earthquake destruction in the region still exist. Under the present tectonic stress environment, what are the fault activity hazards in these three regions and how do moderate-strong earthquakes affect ground stability of the Xiong'an New Area? To answer these questions, we first calculated the critical failure state of the main potential seismogenic faults based on the hydraulic fracturing in-situ stress measurements in deep wells in the three areas, using Byerlee's fault slip friction criterion, and then investigated the fault activity hazards. Next, we determined the potential main earthquake source regions within 300 km radius of the Xiong'an New Area and the maximum earthquake magnitude for each source region, according to the fifth generation seismic ground motion parameters zonation map as well as potential source regions zonation map of China. Finally, we used the appropriate seismic intensity attenuation model to quantitatively calculate the seismic intensity attenuation in the Xiong'an New Area from the three strong seismic areas, providing some scientific guidelines for seismic fortification of major projects in the Xiong'an New Area. The results show that (1) if potential maximum magnitude earthquakes occur in the three strong earthquake regions, the attenuation of seismic intensity in the Xiong'an New Area will vary between Ⅳ and Ⅶ degrees; (2) the potential M 8.0 earthquake in the Tongzhou district of Beijing, or M 6.5 earthquake along the Laishui-Gaobeidian of Baoding, will generate Ⅶ degrees of intensity attenuation in the Xiong'an New Area, which may result in minor earthquake damage; (3) the attenuation of seismic intensity produced by other potential seismic source areas will be less than V degrees, causing no significant seismic damage in the Xiong'an New Area. Therefore, the seismic fortification intensity of the Xiong'an New Area is recommended to be adjusted from the original Ⅶ degrees to Ⅷ degrees.
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    Lithospheric extension of the post-collision stage of the Paleo-Tethys oceanic system in the East Kunlun Orogenic Belt: insights from Late Triassic plutons
    CHEN Guochao,PEI Xianzhi,LI Ruibao,LI Zuochen,PEI Lei,LIU Chengjun,CHEN Youxin,WANG Meng,GAO Feng,LI Xiaobing
    2019, 26(4): 191-208. 
    DOI: 10.13745/j.esf.sf.2019.1.18

    Abstract ( 331 )   PDF (6705KB) ( 428 )  
    We studied in detail the rock assembly, distribution and petrogenesis of Late Triassic plutons in the East Kunlun Orogenic Belt (EKOB) by performing a comprehensive analysis of rock types, intrusion ages, petrogeochemistry and isotopic geochemistry characteristics of the plutons. The Late Triassic is a tectonically pivotal transitional period during the evolution of the Paleo-Tethys oceanic system in EKOB when various magmatic rocks, including gabbro, granodiorite, monzonite and syenite, crystallized extensively while adakitic magmatic rock and A-type granite were extensively exposed. Compared to plutons intruded during subduction stage, the Late Triassic plutons possess smaller scale outcrop, such as small intrusions, stocks and dykes intruding into earlier magmatite and strata. Geochemically, the plutons mainly belong to metaluminous to weakly peraluminous high-K to shoshonite series. They are enriched in light rare earth elements (LREE) and large-ion lithophile elements (LILE), Rb, Th, Ba and Cs, and depleted in heavy rare earth elements (HREE) and high field strength elements (HFSE), Nb, Ta and Ti, with varying degrees of differentiation, enrichment and depletion from rock to rock. Most of the plutons have similar isotopic characteristics to that of Late PermianTriassic mafic magmatic rocks; furthermore, some rocks have higher εNd(t) and εHf(t) values. Mafic magmatite, ordinary granite and adakitic magmatite are ubiquitous in EKOB. However, A-type granites are mainly developed in the Qimantagh tectonic zone, near the Ayakekumulake-Xiangride fault. The mafic plutons are most likely derived from partial melting of metasomatic mantle wedge with subduction fluid based on their arc magmatite features. Most of the ordinary granites and adakitic magmatites are partial-melting products of juvenile lower crust, except for some mantle-derived adakitic magmatites with mantle magma mixing, as well as some A-type granites from relict of partial-melting of lower crust. All studies indicate that EKOB stepped into the post-collision stage of the Paleo-Tethys oceanic system in the Late Triassic period. Crust thickening and density increasing, triggered by continuous collision between the Bayanhar block and EKOB, led to lithospheric delamination due to gravitational instability, which resulted in lithospheric mantle decompressional melting to yield much of mafic magma. Mafic magma later on intruded into different crust melts and partial-melts of delaminated crust to form metasomatic mantle magma, which, through magmatic mixing and later stage evolution, produced the rich and diverse plutons of the Late Triassic in EKOB.
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    Characteristics and petrogenesis of feldspathoid syenite in Gejiu, Yunnan
    WANG Yufei,DONG Guochen,CHEN Wei,SU Lin,YIN Guodong,ZHU Hongyun,LI Huawei
    2019, 26(4): 209-220. 
    DOI: 10.13745/j.esf.2019.04.021

    Abstract ( 301 )   PDF (4113KB) ( 281 )  
    The main types of alkaline rocks in Gejiu, Yunnan are alkaline and feldspathoid syenites. The feldspathoid syenite contains many feldspathoid minerals including nepheline, sodalite and alkaline dark minerals. In this paper, we further divide the feldspathoid syenite into four categories according to its mineral composition and characteristics: melanite nepheline sodalite, nepheline sodalite, nepheline and sodalite nepheline syenites. Geochemical analysis revealed peralkaline characteristics with very high K2O+Na2O content. The rocks also show potassium characteristics. The high rock differentiation index (DI) indicates high degree evolutionary differentiation of magma at its origin. The rare earth element (REE) patterns show enrichment in light rare earth elements (LREE) and depletion in heavy rare earth elements (HREE). The rocks are generally enriched in high field strength elements (HFSE) Zr and Hf but depleted in Ta and Ti; they are enriched in large ion lithophile elements (LILE) Th and U but depleted in Ba and Sr, and P. They have low Cr, Co and Ni contents and moderate negative Eu and weak negative Ce anomalies. These petrographic and geochemical features are obvious indication that feldspathoid syenite underwent fractional crystallization during the magmatic evolution. The temperature of the magma origin was about 835 ℃ and the origin laid relatively deep. The rock subtype is A1 of the A type suites. We suggest that the Gejiu feldspathoid syenite may be the product of high fractional crystallization from enriched mantle of amphibole or phlogopite facies at high temperatures and that it was formed in the late Yanshanian extensional tectonic setting with the mixing of continental crust components.
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    Abnormalities of stable carbon and hydrogen isotopes of methane in the Mohe Basin, Northeast China and mechanisms of isotopic change
    ZHAO Xingmin,DENG Jian,RAO Zhu,YI Li,LU Cheng,LI Song
    2019, 26(4): 231-237. 
    DOI: 10.13745/j.esf.sf.2019.3.4

    Abstract ( 236 )   PDF (2174KB) ( 196 )  
    The Mohe Basin in northeastern China possesses the potential for gas hydrate accumulation and exploration in the permafrost zone of China. During a recent investigation of wells used for gas hydrate testing and gas source exploration, we discovered significant negative shifts in stable carbon and hydrogen isotopes of methane in the Mohe Basin. The measured δ13CCH4 and δDCH4 values commonly ranged from -60‰ to -82.9‰ and from -350‰ to -450‰, respectively. We also observed that at burial depth of approximately less than 1500 m, δ13CCH4 value generally increased with depth, whereas δDCH4 value decreased; however, both values increased synchronously with burial depth at greater than 1500 m. We believe that the negative shifts of stable carbon isotope are associated with the microbial origin of methane; whilst the negative shifts of stable hydrogen isotope are the results of a combined effect of Rayleigh distillation during the evaporation of surface water or atmospheric condensation process driven by basin‘s unique climate at higher latitude, and methane production through acetate fermentation.
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    Sedimentary sequence and its controlling effect on hydrocarbon enrichment in Qie12 block of Qaidam Basin
    LIU Zhanguo,GONG Qingshun,ZHU Chao,WANG Peng,LI Jiyong,WU Jin, DING Xuecheng,PANG Xu
    2019, 26(4): 238-249. 
    DOI: 10.13745/j.esf.sf.2019.6.2

    Abstract ( 179 )   PDF (6520KB) ( 213 )  
    The thick sandy conglomerate reservoir is developed in Qie12 block of the Kunbei oilfield of Qaidam Basin. The key geological factors restricting reservoir development are the genetic type and distribution law of sandy conglomerate. By comprehensive utilization of geological information, wireline logging data, seismic information and production dynamic data, the genetic type, distribution law of sandy conglomerate and its controlling effect on hydrocarbon enrichment are systematically studied under the control of the isochronal stratigraphic framework. The results shows that alluvial fan facies is developed in the lower part of the Ganchaigou Group in Qie12 block. Five microfacies are identified including braided channel, debris flow, sheet flood, runoff channel and flood plain. Four sedimentary structure units are identified including “pano-connective sand-gravel body” sandwiched intermittent channel, stable channel forming overlapping compound channel, braided channel and lateral sheet flood, and runoff channel inlaid flood plain mudstone. Alluvial fan shows a sedimentary sequence of grain size upward-fining and retrograding sequence vertically and six alluvial fan bodies from north to south are developed on the plane. The sedimentary facies belt has obvious control effect on oil and gas distribution, the sedimentary structure units control the vertical enrichment of oil and gas, and the fan body evolution controls hydrocarbon plane aggregation.
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    New discovery of hyperpycnal flow deposits in the Elx1 section of the steep slope belt in the Weixinan sag
    JIN Jiehua,CAO Yingchang,WANG Jian,YANG Tian,LIU Juan,WANG Xinyi,WANG Shuping
    2019, 26(4): 250-258. 
    DOI: 10.13745/j.esf.sf.2019.5.16

    Abstract ( 182 )   PDF (3043KB) ( 232 )  
    In a case study in the lower part of the first member of the Liushagang Formation (Elx1) in the middle of the steep slope zone in the Weixinan sag, we examined the sedimentary characteristics, controlling factors and distribution features of deep-water fan formed by hyperpycnal flow through analyzes of 3D seismic information, well logging and core drilling data and laboratory measurements. The results show that hyperpycnites are distributed from the steep slope to the bottom of lacustrine basin, where hyperpycnal flow developed sequentially straight and meandering channels and lobes which expand 15 km long and 50100 m wide. The seismic reflections of hyperpycnites are obviously stronger than that of the surrounding deep-water mudstones. Hyperpycnites are mainly composed of fine-grained sandstone, siltstone and small amounts of pebbly sandstone; terrigenous biolastics (plant material) are also common. The grain size accumulation curve is mainly overarching and the sample point distribution is roughly parallel to the baseline of C=M, showing the characteristics of gravity flow deposits. A bed of sandstone is usually composed of paired inverse and normal grading sequences, with the coarsest grain depositing in the middle of the bed. Rip-up mud clasts are typical with imbrication. The intrasequence weak erosional contacts are also obvious. In addition, hyperpycnal flow developed sedimentary structures of massive and parallel bedding, Hummocky cross-stratification and climbing sand ripples by bed- and suspended-load transportation. Since sedimentary characteristics and distribution of hyperpycnites are determined by source-to-sink system, magmatic rocks originated from eastern Wanshan uplift provided the material basis of hyperpycnites, while strong tectonic movements, steep slope, wet-half humid climate and relatively low density of water are the main controlling factors for the development of hyperpycnites. Our discovery of hyperpycnal flow deposits in the study area not only enriches our knowledge of sedimentary environment in the steep slope belt in lacustrine basin, but also provides theoretical guidance for further hydrocarbon exploration in steep slope regions.
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    Stratigraphic division of the Upper Pleistocene, environmental change and formation of the Yellow River in the Hetao Basin, Inner Mongolia
    NIE Zongsheng
    2019, 26(4): 259-272. 
    DOI: 10.13745/j.esf.sf.2019.5.7

    Abstract ( 315 )   PDF (4557KB) ( 398 )  
    The Hetao Basin in Inner Mongolia is a Cenozoic faulting basin. It is also called Paleo-Hetao Lake as an inherited and closed sag pond in the Late Pleistocene. The lake sedimentary system was mainly formed in the Late Pleistocene, encompassing the Donghecun, Wanshuiquan and Dalate Formations in its upper, middle and lower sections, respectively. As the lake basin continued to subside, the Dalate Formation was formed under semi-deep and deep lake sedimentary conditions in an alternating sulphate and carbonate water environment. In contrast, equilibrium between lake basin subsidence and sedimentation led to the formation of the Wanshuiquan Formation in shallow to semi-deep brackish waters. As the rate of sedimentation surpassed rate of subsidence, the Donghecun Formation was formed in brackish-water and at times in carbonate beach and shallow lake environment. At the end of Late Pleistocene, the Paleo-Hetao Lake experienced a high water period. River capture at the boundary between Inner Mongolia, Shanxi and Shaanxi, and strong fault activity and extremely powerful earthquake in the northern Hetao Basin, caused the outflow of the Paleo-Hetal Lake linking the Yingchuan and Hetao Basins and the gorge between Shanxi and Shaanxi to form the modern day Yellow River.
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    Anti-seepage performance for oily pollutants in compacted clay layer of Yunnan Province, China
    LIU Yulong,WU Weiyang,FAN Junxin,CHEN Honghan
    2019, 26(4): 273-278. 
    DOI: 10.13745/j.esf.sf.2019.5.26

    Abstract ( 219 )   PDF (1048KB) ( 128 )  
    In the current design of anti-seepage projects using compacted clay layers (CCL) against oily pollutants, the criterion that the hydraulic conductivity of water in CCLs should be less than 10-7 cm/s may be abused. In this paper, we performed oil (0# diesel and 93# gasoline) and water alternate seepage through CCLs of Yunnan Province, China. The results show that (1) under different seepage pressure, the hydraulic conductivity of water ranged (0.412.52)×10-8 cm/s, indicating the Yunnan clay can be used as natural anti-seepage lining; (2) critical hydraulic gradients for 0# diesel and 93# gasoline seepage through water-saturated CCLs were 0.05 and 0.02 MPa, respectively; once the critical hydraulic gradient was broken, the permeability of CCL increased rapidly and hydraulic conductivity of oily pollutants was 13 orders of magnitude higher than that of water; (3) in the process of water seepage through 0# diesel or 93# gasoline filled CCLs, the hydraulic conductivity of water was (10-710-6) cm/s; oil-soaked CCLs could not serve as anti-seepage lining again; and (4) the current standard for anti-seepage design against oily pollutant requires that hydraulic conductivity of water in CCL to be less than 1×10-7 cm/s, which is not applicable; the level of CCLs anti-seepage performance should be improved.
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    Sorption characteristics of different forms of lomefloxacin on kaolinite
    BIAN Xinyi,BI Erping
    2019, 26(4): 279-286. 
    DOI: 10.13745/j.esf.sf.2019.5.31

    Abstract ( 154 )   PDF (4993KB) ( 148 )  
    We carried out batch experiments to investigate the sorption characteristics of different forms of lomefloxacin (LOM) on kaolinite. Sorption kinetics were pseudo-second-order, and all equilibrium data could be well described by the Langmuir equation. As solution pH increased, the amount of LOM sorption increased first and then decreased. Maximum sorption was achieved at pH values between pKa1 and pKa2 of LOM. The sorption capacity of different forms of LOM on kaolinite ranked as LOM±>LOM+>LOM-. The ionic strength of LOM solution and inorganic cations had little effect on LOM+ sorption, but both factors significantly inhibited LOM± sorption. The sorption capacity of LOM± decreased with increasing ionic strength, and the inhibition potency of different inorganic cations was in the order of Mg2+>Ca2+>K+>Na+. The sorption mechanisms were mainly inner-sphere complexation and cation exchange for LOM+ or cation exchange, hydrogen bonding and electrostatic attraction for LOM± on kaolinite. LOM- sorption on kaolinite is very small due to strong repulsive electrostatic effect; the small sorption may resulted from outer-sphere surface complexation.
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    Experimental study on the photocatalytic activity of natural wolframite under natural light
    LI Linghui,LI Yan,LI Yanzhang,LU Anhuai,DING Hongrui
    2019, 26(4): 287-294. 
    DOI: 10.13745/j.esf.sf.2019.5.29

    Abstract ( 217 )   PDF (4088KB) ( 200 )  
    Natural semiconducting minerals have excellent solar photocatalytic properties. In this study, we selected natural wolframite as the research object. We performed mineralogical and photocatalytic experiments using natural wolframite from three mining areas: Wuming (WM), Limu (LM) and Chongyi (CY). We used X-ray diffraction, Raman and infrared spectroscopy, and electron probe microanalysis to analyze the structure and composition of natural samples. The main mineral phases were identified as natural wolframite in the form of (Fe, Mn)WO4 with decreasing Fe/Mn molar ratios at 7.1, 0.9 and 0.3 for WM, LM and CY mines, respectively. The forbidden band widths for the three mines were 1.5, 1.6 and 1.7 eV, respectively, indicating a good visible light response. Photocatalytic experiments were performed at pH 7. The concentrations of wolframite and methylene blue (MB) were 1 g/L and 5 mg/L, respectively, with 0.01 mol/L H2O2 in the degradation solution. The results showed that the degradation efficiency of WM wolframite was the highest, equaling to 1.1 and 1.6 times that of LM and CY wolframite, respectively. Free radical ·OH was detected in all solutions during the reaction by electron paramagnetic resonance (EPR), with the stronger signal coming from WM wolframite. ·OH was demonstrated as the major reactive oxygen species by using different scavenger in the photocatalytic reaction. The experimental results further showed that the rate of MB decolorization degradation was up to 99% (after 3 hours) in the experimental group where both wolframite and H2O2 were used under light. In the control group, wolframite or H2O2 was used and the MB degradation rates were only 7% and 31% after 3 hours, respectively. Under darkroom condition, the MB removal rate was 34% with additions of wolframite and H2O2. Degradation of MB in wolframite under different H2O2 concentrations was analyzed and found to conform to quasi-first-order kinetics, indicating the degradation process was independent of catalyst content, and H2O2 was more likely to act as an electron acceptor. According to our analysis, under sunlight, the catalytic efficiency of wolframite from all producing areas was positively correlated with Fe content and negatively correlated with band gap width, for which the suggested reaction mechanism involves MB oxidative degradation by ·OH generated by photocatalytic and Fenton reactions. Our study presented a new method of utilizing natural minerals for environmental pollution remediation.
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    Groundwater quality assessment based on optimization of fuzzy synthetic evaluation
    FANG Yunhai,ZHENG Xilai,PENG Hui,WANG Huan,XIN Jia,ZHANG Bo
    2019, 26(4): 301-306. 
    DOI: 10.13745/j.esf.sf.2019.5.27

    Abstract ( 220 )   PDF (1363KB) ( 234 )  
    Fuzzy synthetic evaluation method can be problematic when it is applied to groundwater quality assessment due to absolutization of membership degree or mismatch between national groundwater quality standard and classification criteria required by fuzzy synthetic evaluation. Here, we created an optimized fuzzy synthetic evaluation model to solve these problems. We introduced the concept of relative membership degree for establishing a dynamic fuzzy relationship between evaluation index and groundwater quality standard. Furthermore, we used the optimized model to analyze and assess the groundwater quality of the Dagu River Basin in Qingdao for validation. The results show that the national groundwater quality standard satisfied the requirements of evaluation criteria type in the optimized model. The optimized model solved the incompatibility problem in the traditional model. When the measure index fell in a mid-grade interval, the relative membership degrees for this and two adjacent grades were greater than 0. Compared with the absolute distribution of membership degree, the optimized model expanded the distribution over different grades and reflected the relative and dynamic characteristics of the distribution. The overall hardness and total concentration of dissolved solids and other characteristic pollutants exceeded the threshold value for grade IV groundwater quality standard in the southern area (monitoring wells S1 and S3), in agreement with the optimized model assessment. Thus we have verified the reliability of the optimized model.
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    Quick analysis of sixteen PFAAs in groundwater and aquifer by ultra-performance liquid chromatographytriple quadrupole mass spectrometry
    LIU Mingrui,WANG Lingli,CHEN Liang
    2019, 26(4): 307-314. 
    DOI: 10.13745/j.esf.sf.2019.5.32

    Abstract ( 207 )   PDF (1298KB) ( 120 )  
    Perfluorinated alkyl acids (PFAAs) are a group of emerging persistent organic pollutant that have received increasing attention due to their global occurrence, accumulation and toxicity. At present, PFAA detection mainly uses high-performance liquid chromatographytandem mass spectrometry (HPLC-MS/MS), ultra-performance liquid chromatographytandem mass spectrometry (UPLC-MS/MS), gas chromatography (GC) and spectroscopy. However, these detection methods often involve complicated sample pretreatment, longer work cycle and higher cost, therefore, they are not suitable for quick PFAA detection. In this study, we developed a method for quick analysis of 16 PFAAs in groundwater and aquifer based on UPLC-MS/MS. In the experiment, the groundwater sample was filtered through 0.22 μm membrane and injected directly into the detection instrument; the aquifer sample was prepared by liquid solvent extraction. A good linear calibration using 16 PFAAs standards was achieved (R2>0.99) in the low and high concentration ranges of 550 and 100500 μg/L, respectively. The method’s detection limits (MDLs) of 16 PFAAs in groundwater were 0.061.07 μg/L, and the recovery rates were 75%120% with relative standard deviations (RSD) at 2.61%9.19%. In aquifer media, MDLs of 14 PFAAs were 2.6912.33 ng/L, and the recovery rates were 65%103% with RSDs at 1.96%7.22%.
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