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Frontiers of Earth Science

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RESEARCH ARTICLE
Spatial-temporal variations of natural suitability of human settlement environment in the Three Gorges Reservoir Area—A case study in Fengjie County, China
Jieqiong LUO, Tinggang ZHOU, Peijun DU, Zhigang XU
Front. Earth Sci.. 2019, 13 (1): 1-17.  https://doi.org/10.1007/s11707-018-0683-2
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With rapid environmental degeneration and socio-economic development, the human settlement environment (HSE) has experienced dramatic changes and attracted attention from different communities. Consequently, the spatial-temporal evaluation of natural suitability of the human settlement environment (NSHSE) has become essential for understanding the patterns and dynamics of HSE, and for coordinating sustainable development among regional populations, resources, and environments. This study aims to explore the spatial-temporal evolution of NSHSE patterns in 1997, 2005, and 2009 in Fengjie County near the Three Gorges Reservoir Area (TGRA). A spatially weighted NSHSE model was established by integrating multi-source data (e.g., census data, meteorological data, remote sensing images, DEM data, and GIS data) into one framework, where the Ordinary Least Squares (OLS) linear regression model was applied to calculate the weights of indices in the NSHSE model. Results show that the trend of natural suitability has been first downward and then upward, which is evidenced by the disparity of NSHSE existing in the south, north, and central areas of Fengjie County. Results also reveal clustered NSHSE patterns for all 30 townships. Meanwhile, NSHSE has significant influence on population distribution, and 71.49% of the total population is living in moderate and high suitable districts.

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Parameter transferability across spatial resolutions in urban hydrological modelling: a case study in Beijing, China
Xiaoshu HOU, Lei CHEN, Xiang LIU, Miao LI, Zhenyao SHEN
Front. Earth Sci.. 2019, 13 (1): 18-32.  https://doi.org/10.1007/s11707-018-0710-3
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This study examined the influence of spatial resolution on model parameterization, output, and the parameter transferability between different resolutions using the Storm Water Management Model. High-resolution models, in which most subcatchments were homogeneous, and high-resolution-based low-resolution models (in 3 scenarios) were constructed for a highly urbanized catchment in Beijing. The results indicated that the parameterization and simulation results were affected by both spatial resolution and rainfall characteristics. The simulated peak inflow and total runoff volume were sensitive to the spatial resolution, but did not show a consistent tendency. High-resolution models performed very well for both calibration and validation events in terms of three indexes: 1) the Nash-Sutcliffe efficiency, 2) the peak flow error, and 3) the volume error; indication of the advantage of using these models. The parameters obtained from high-resolution models could be directly used in the low-resolution models and performed well in the simulation of heavy rain and torrential rain and in the study area where sub-area routing is insignificant. Alternatively, sub-area routing should be considered and estimated approximately. The successful scale conversion from high spatial resolution to low spatial resolution is of great significance for the hydrological simulation of ungauged large areas.

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The distribution and sources of polycyclic aromatic hydrocarbons in shallow groundwater from an alluvial-diluvial fan of the Hutuo River in North China
Jincui WANG, Yongsheng ZHAO, Jichao SUN, Ying ZHANG, Chunyan LIU
Front. Earth Sci.. 2019, 13 (1): 33-42.  https://doi.org/10.1007/s11707-018-0701-4
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This paper has investigated the concentration and distribution of polycyclic aromatic hydrocarbons in shallow groundwater from an alluvial-diluvial fan of the Hutuo River in North China. Results show that the concentration levels of 16 priority polycyclic aromatic hydrocarbons range from 0 to 92.06 ng/L, do not conform to drinking water quality standards in China (GB 5749-2006). However, the concentration figures of priority polycyclic aromatic hydrocarbons are much lower than that of other studies conducted elsewhere in China. In addition, highly-concentrated polycyclic aromatic hydrocarbons (50–92 ng/L) are fragmentarily distributed. The composition of polycyclic aromatic hydrocarbons from this study indicates that low molecular polycyclic aromatic hydrocarbons are predominant in groundwater samples, medium molecular compounds occur at low concentrations, and high molecular hydrocarbons are not detected. The polycyclic aromatic hydrocarbon composition in groundwater samples is basically the same as that of gaseous samples in the atmosphere in this study. Therefore, the atmospheric input is assumed to be an important source of polycyclic aromatic hydrocarbons, no less than wastewater discharge, adhesion on suspended solids, and surface water leakage. Ratios of specific polycyclic aromatic hydrocarbons demonstrate that they mainly originate from wood or coal combustion as well as natural gas and partially from petroleum according to the result of principal component analysis. On the whole, conclusions are drawn that the contamination sources of these polycyclic aromatic hydrocarbons are likely petrogenic and pyrolytic inputs. Future investigations by sampling topsoil, vadose soil, and the atmosphere can further verify aforementioned conclusions.

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Assessing the relative role of climate change and human activities in desertification of North China from 1981 to 2010
Duanyang XU, Alin SONG, Dajing LI, Xue DING, Ziyu WANG
Front. Earth Sci.. 2019, 13 (1): 43-54.  https://doi.org/10.1007/s11707-018-0706-z
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Desertification is a severe environmental problem induced by both climate change and human activities. This study assessed the relative contribution of climate change, human activities, and different climatic and anthropogenic factors in desertification reversion and expansion of North China from 1981 to 2010. The results showed that the desertification of North China had changed significantly over the past 30 years; desertification reversion and expansion covered an area of 750,464 km2, and the spatial distribution of these regions exhibited considerable heterogeneity. For desertification reversion, climate change and human activity accounted for 22.6% and 26%, respectively of total reverted land. Wind speed reduction and the improvement of hydrothermal conditions were the most important climatic factors for desertification reversion in the arid region of Northwest China (ARNC) and the Three-River Headwaters region (TRHR), and the reduction in grassland use intensity was the most important anthropogenic factor related to desertification reversion in Inner Mongolia and regions along the Great Wall (IMGW). For desertification expansion, the relative role of climate change was more obvious, which was mainly attributed to the continuous reduction in precipitation in eastern IMGW, and the increase in grassland use intensity was the main factor underlying regional human-induced desertification expansion.

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Quantitative analysis of planation surfaces of the upper Yangtze River in the Sichuan-Yunnan Region, Southwest China
Fenliang LIU, Hongshan GAO, Baotian PAN, Zongmeng LI, Huai SU
Front. Earth Sci.. 2019, 13 (1): 55-74.  https://doi.org/10.1007/s11707-018-0707-y
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Identification of the planation surfaces (PSs) is key for utilizing them as a reference in studying the long-term geomorphological evolution of the Upper Yangtze River Basin in the Sichuan-Yunnan region, Southwest China. Using a combined method of DEM-based fuzzy logic and topographic and river profiles analysis and based on a comprehensive analysis of four morphometric parameters: slope, curvature, terrain ruggedness index, and relative height, we established the relevant fuzzy membership functions, and then calculated the membership degree (MD) of the study area. Results show that patches with a MD>80% and an area>0.4 km2 correspond well to the results of Google Earth and field investigation, representing the PS remnants. They consist of 1764 patches with an altitude, area, mean slope, and relief of mostly 2000–2500 m above sea level (asl), 0–10 km2, 4°–9°, 0–500 m, respectively, covering 9.2% of the study area’s landscape, dipping to southeast, decreasing progressively from northwest to southeast in altitude, and with no clear relation between each patch’s altitude and slope, or relief. All these results indicate that they are remnants of once regionally continuous PSs which were deformed by both the lower crust flow and the faults in upper crust, and dissected by the network of Upper Yangtze River. Additionally, topographic and river profiles analysis show that three PSs (PS1–PS3) well developed along the main valleys in the Yongren-Huili region, indicating several phases of uplift then planation during the Late Cenozoic era. Based on the incision amount deduced from projection of relict river profiles on PSs, together with erosion rates, breakup times of the PS1, PS2, and PS3 were estimated to be 3.47 Ma, 2.19 Ma, and 1.45 Ma, respectively, indicating appearance of modern Upper Yangtze River valley started between the Pliocene to early Pleistocene.

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Effects of nano-pore system characteristics on CH4 adsorption capacity in anthracite
Chang’an SHAN, Tingshan ZHANG, Xing LIANG, Dongchu SHU, Zhao ZHANG, Xiangfeng WEI, Kun ZHANG, Xuliang FENG, Haihua ZHU, Shengtao WANG, Yue CHEN
Front. Earth Sci.. 2019, 13 (1): 75-91.  https://doi.org/10.1007/s11707-018-0712-1
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This study aims to determine the effects of nanoscale pores system characteristics on CH4 adsorption capacity in anthracite. A total of 24 coal samples from the southern Sichuan Basin, China, were examined systemically using coal maceral analysis, vitrinite reflectance tests, proximate analysis, ultimate analysis, low-temperature N2 adsorption–desorption experiments, nuclear magnetic resonance (NMR) analysis, and CH4 isotherm adsorption experiments. Results show that nano-pores are divided into four types on the basis of pore size ranges: super micropores (<4 nm), micropores (4–10 nm), mesopores (10–100 nm), and macropores (>100 nm). Super micropores, micropores, and mesopores make up the bulk of coal porosity, providing extremely large adsorption space with large internal surface area. This leads us to the conclusion that the threshold of pore diameter between adsorption pores and seepage pores is 100 nm. The “ink bottle” pores have the largest CH4 adsorption capacity, followed by semi-opened pores, whereas opened pores have the smallest CH4 adsorption capacity which indicates that anthracite pores with more irregular shapes possess higher CH4 adsorption capacity. CH4 adsorption capacity increased with the increase in NMR porosity and the bound water saturation. Moreover, CH4 adsorption capacity is positively correlated with NMR permeability when NMR permeability is less than 8×103 md. By contrast, the two factors are negatively correlated when NMR permeability is greater than 8×103 md.

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Spatio-temporal analysis of phenology in Yangtze River Delta based on MODIS NDVI time series from 2001 to 2015
Yongfeng WANG, Zhaohui XUE, Jun CHEN, Guangzhou CHEN
Front. Earth Sci.. 2019, 13 (1): 92-110.  https://doi.org/10.1007/s11707-018-0713-0
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Phenology has become a good indicator for illustrating the long-term changes in the natural resources of the Yangtze River Delta. However, two issues can be observed from previous studies. On the one hand, existing time-series classification methods mainly using a single classifier, the discrimination power, can become deteriorated due to fluctuations characterizing the time series. On the other hand, previous work on the Yangtze River Delta was limited in the spatial domain (usually to 16 cities) and in the temporal domain (usually 2000–2010). To address these issues, this study attempts to analyze the spatio-temporal variation in phenology in the Yangtze River Delta (with 26 cities, enlarged by the state council in June 2016), facilitated by classifying the land cover types and extracting the phenological metrics based on Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series collected from 2001 to 2015. First, ensemble learning (EL)-based classifiers are used for land cover classification, where the training samples (a total of 201,597) derived from visual interpretation based on GlobelLand30 are further screened using vertex component analysis (VCA), resulting in 600 samples for training and the remainder for validating. Then, eleven phenological metrics are extracted by TIMESAT (a package name) based on the time series, where a seasonal-trend decomposition procedure based on loess (STL-decomposition) is used to remove spikes and a Savitzky-Golay filter is used for filtering. Finally, the spatio-temporal phenology variation is analyzed by considering the classification maps and the phenological metrics. The experimental results indicate that: 1) random forest (RF) obtains the most accurate classification map (with an overall accuracy higher than 96%); 2) different land cover types illustrate the various seasonalities; 3) the Yangtze River Delta has two obvious regions, i.e., the north and the south parts, resulting from different rainfall, temperature, and ecosystem conditions; 4) the phenology variation over time is not significant in the study area; 5) the correlation between gross spring greenness (GSG) and gross primary productivity (GPP) is very high, indicating the potential use of GSG for assessing the carbon flux.

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Mapping paddy rice in Jiangsu Province, China, based on phenological parameters and a decision tree model
Jianhong LIU, Le LI, Xin HUANG, Yongmei LIU, Tongsheng LI
Front. Earth Sci.. 2019, 13 (1): 111-123.  https://doi.org/10.1007/s11707-018-0723-y
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Timely and accurate mapping of rice planting areas is crucial under China’s current cropping structure. This study proposes a new paddy rice mapping method by combining phenological parameters and a decision tree model. Six phenological parameters were developed to identify paddy rice areas based on the analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series and the Land Surface Water Index (LSWI) time series. The six phenological parameters considered the performance of different land cover types during specific phenological phases (EVI1 and EVI2), one-half of or the entire rice growing cycle (LSWI1 and LSWI2), and the shape of the LSWI time series (KurtosisLSWI and SkewnessLSWI). A hierarchical decision tree model was designed to classify paddy rice areas according to the potential separability of different land cover types in paired phenological parameter spaces. Results showed that the decision tree model was more sensitive to LSWI1, LSWI2, and SkewnessLSWI than the other phenological parameters. A paddy rice map of Jiangsu Province for 2015 was generated with an optimal threshold set of (0.4, 0.42, 9, 19, 1.5, –1.7, 0.0) with a total accuracy of 93.9%. The MODIS-derived paddy rice map generally agreed with the paddy land fraction map from the National Land Cover Dataset project, but there were regional discrepancies because of their different definitions of land use and the inability of MODIS to map paddy rice at a fragmental level. The MODIS-derived paddy rice map showed high correlation (R2=0.85) with county-level agricultural statistics. The results of this study indicate that the phenological parameter-based paddy rice mapping algorithm could be applied at larger spatial scales.

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Estimation of wind speeds inside Super Typhoon Nepartak from AMSR2 low-frequency brightness temperatures
Lei ZHANG, Xiaobin YIN, Hanqing SHI, Zhenzhan WANG, Qing XU
Front. Earth Sci.. 2019, 13 (1): 124-131.  https://doi.org/10.1007/s11707-018-0698-8
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Accurate estimations of typhoon-level winds are highly desired over the western Pacific Ocean. A wind speed retrieval algorithm is used to retrieve the wind speeds within Super Typhoon Nepartak (2016) using 6.9- and 10.7-GHz brightness temperatures from the Japanese Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor on board the Global Change Observation Mission-Water 1 (GCOM-W1) satellite. The results show that the retrieved wind speeds clearly represent the intensification process of Super Typhoon Nepartak. A good agreement is found between the retrieved wind speeds and the Soil Moisture Active Passive wind speed product. The mean bias is 0.51 m/s, and the root-mean-square difference is 1.93 m/s between them. The retrieved maximum wind speeds are 59.6 m/s at 04:45 UTC on July 6 and 71.3 m/s at 16:58 UTC on July 6. The two results demonstrate good agreement with the results reported by the China Meteorological Administration and the Joint Typhoon Warning Center. In addition, Feng-Yun 2G (FY-2G) satellite infrared images, Feng-Yun 3C (FY-3C) microwave atmospheric sounder data, and AMSR2 brightness temperature images are also used to describe the development and structure of Super Typhoon Nepartak.

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Onshore-offshore wind energy resource evaluation based on synergetic use of multiple satellite data and meteorological stations in Jiangsu Province, China
Xianglin WEI, Yuewei DUAN, Yongxue LIU, Song JIN, Chao SUN
Front. Earth Sci.. 2019, 13 (1): 132-150.  https://doi.org/10.1007/s11707-018-0699-7
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The demand for efficient and cost-effective renewable energy is increasing as traditional sources of energy such as oil, coal, and natural gas, can no longer satisfy growing global energy demands. Among renewable energies, wind energy is the most prominent due to its low, manageable impacts on the local environment. Based on meteorological data from 2006 to 2014 and multi-source satellite data (i.e., Advanced Scatterometer, Quick Scatterometer, and Windsat) from 1999 to 2015, an assessment of the onshore and offshore wind energy potential in Jiangsu Province was performed by calculating the average wind speed, average wind direction, wind power density, and annual energy production (AEP). Results show that Jiangsu has abundant wind energy resources, which increase from inland to coastal areas. In onshore areas, wind power density is predominantly less than 200 W/m2, while in offshore areas, wind power density is concentrates in the range of 328–500 W/m2. Onshore areas comprise more than 13,573.24 km2, mainly located in eastern coastal regions with good wind farm potential. The total wind power capacity in onshore areas could be as much as 2.06 × 105 GWh. Meanwhile, offshore wind power generation in Jiangsu Province is calculated to reach 2 × 106 GWh, which is approximately four times the electricity demand of the entire Jiangsu Province. This study validates the effective application of Advanced Scatterometer, Quick Scatterometer, and Windsat data to coastal wind energy monitoring in Jiangsu. Moreover, the methodology used in this study can be effectively applied to other similar coastal zones.

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Bank gully extraction from DEMs utilizing the geomorphologic features of a loess hilly area in China
Xin YANG, Jiaming NA, Guoan TANG, Tingting WANG, Axing ZHU
Front. Earth Sci.. 2019, 13 (1): 151-168.  https://doi.org/10.1007/s11707-018-0700-5
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As one of most active gully types in the Chinese Loess Plateau, bank gullies generally indicate soil loss and land degradation. This study addressed the lack of detailed, large scale monitoring of bank gullies and proposed a semi-automatic method for extracting bank gullies, given typical topographic features based on 5 m resolution DEMs. First, channel networks, including bank gullies, are extracted through an iterative channel burn-in algorithm. Second, gully heads are correctly positioned based on the spatial relationship between gully heads and their corresponding gully shoulder lines. Third, bank gullies are distinguished from other gullies using the newly proposed topographic measurement of “relative gully depth (RGD).” The experimental results from the loess hilly area of the Linjiajian watershed in the Chinese Loess Plateau show that the producer accuracy reaches 87.5%. The accuracy is affected by the DEM resolution and RGD parameters, as well as the accuracy of the gully shoulder line. The application in the Madigou watershed with a high DEM resolution validated the duplicability of this method in other areas. The overall performance shows that bank gullies can be extracted with acceptable accuracy over a large area, which provides essential information for research on soil erosion, geomorphology, and environmental ecology.

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Monitoring and analysis of mining 3D deformation by multi-platform SAR images with the probability integral method
Meinan ZHENG, Kazhong DENG, Hongdong FAN, Jilei HUANG
Front. Earth Sci.. 2019, 13 (1): 169-179.  https://doi.org/10.1007/s11707-018-0703-2
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Only one-dimensional (1D) deformation along the radar line of sight (LOS) can be obtained using differential interferometry synthetic aperture radar (D-InSAR), and D-InSAR observation is insensitive to deformation in the north direction. This study inferred three-dimensional (3D) deformation of a mining subsidence basin by combining the north-south deformation predicted by a probability integral method with the LOS deformation obtained by D-InSAR. The 15235 working face in Fengfeng mining area (Hebei Province, China) was used as the object of study. The north-south horizontal movement was predicted by the probability integral method according to the site’s geological and mining conditions. Then, the vertical and east-west deformation fields were solved by merging ascend-orbit RadarSAT-2, descend-orbit TerraSAR, and predicted north-south deformation based on a least squares method. Comparing with the leveling data, the results show that the vertical deformation accuracy of the experimental method is better than the inversed vertical deformation neglecting the horizontal deformation. Finally, the impact of the relationship between the azimuth of the working face and the SAR imaging geometry on the monitoring of the mining subsidence basin was analyzed. The results can be utilized in monitoring mining subsidence basins by single SAR image sources.

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Unsupervised learning on scientific ocean drilling datasets from the South China Sea
Kevin C. TSE, Hon-Chim CHIU, Man-Yin TSANG, Yiliang LI, Edmund Y. LAM
Front. Earth Sci.. 2019, 13 (1): 180-190.  https://doi.org/10.1007/s11707-018-0704-1
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Unsupervised learning methods were applied to explore data patterns in multivariate geophysical datasets collected from ocean floor sediment core samples coming from scientific ocean drilling in the South China Sea. Compared to studies on similar datasets, but using supervised learning methods which are designed to make predictions based on sample training data, unsupervised learning methods require no a priori information and focus only on the input data. In this study, popular unsupervised learning methods including K-means, self-organizing maps, hierarchical clustering and random forest were coupled with different distance metrics to form exploratory data clusters. The resulting data clusters were externally validated with lithologic units and geologic time scales assigned to the datasets by conventional methods. Compact and connected data clusters displayed varying degrees of correspondence with existing classification by lithologic units and geologic time scales. K-means and self-organizing maps were observed to perform better with lithologic units while random forest corresponded best with geologic time scales. This study sets a pioneering example of how unsupervised machine learning methods can be used as an automatic processing tool for the increasingly high volume of scientific ocean drilling data.

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Geological significance of the former Xiong’er Volcanic Belt on the southwestern margin of the North China Craton
Guanxu CHEN, Jinhai LUO, Huan XU, Jia YOU, Yifei LI, Zichen CHE
Front. Earth Sci.. 2019, 13 (1): 191-208.  https://doi.org/10.1007/s11707-018-0694-z
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The rock association of low-grade metasedimentary rocks and greenschists located within the Meso-Cenozoic Liupanshan Fault system on the southwestern margin of the North China Craton (NCC) is regarded as part of the Paleoproterozoic Xiong’er Group. These low-grade rocks are separated by normal faults, with the greenschist located in the hanging walls. Zircon LA–ICP–MS U–Pb ages of the greenschists range from 2455 to 423 Ma, suggesting that they are not Paleoproterozoic in age. The protolith ages (206–194 Ma) of the greenschists were determined by LA–ICP–MS U–Pb dating of zircons from two siltstone interlayers. The petrology and geochemistry of the greenschists reveal that their protolith was continental tholeiitic basalt that formed in an extensional environment such as a continental rift. Thus, it is proposed that the protolith of the greenschists was a mafic volcanic rock of Late Triassic–Early Jurassic age and was metamorphosed during the Jurassic due to tectonism within the Liupanshan tectonic belt. These results show that the greenschists should be reclassified and removed from the Xiong’er Group, and explains why they differ so much from those of typical Xiong’er Group successions in other areas. The formation of the mafic volcanic rocks under conditions of continental rifting differs from that of coeval granitic rocks in the western Qinling Orogen, where the extension occurred during a post-collisional stage in the Late Triassic, which further suggests that the southwestern margin of the NCC became an extensional setting after the Late Triassic.

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Lithotectonic elements of Archean basement on the Liaodong Peninsula and its vicinity, North China Craton, China
Zhuang LI, Bin CHEN
Front. Earth Sci.. 2019, 13 (1): 209-228.  https://doi.org/10.1007/s11707-018-0708-x
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The Liaodong Peninsula, in the northeastern part of the Eastern Block in the North China Craton, China, consists of lithologic units from Archean to Cenozoic in age. The basement rocks consist of widespread amphibolite- to granulite-facies Archean supracrustal assemblages and granitoid gneisses, as well as Paleoproterozoic volcano-sedimentary successions that were intruded by granitic–mafic complexes, and then metamorphosed under greenschist- to amphibolite-facies conditions. The basement rocks are overlain by thick Mesoproterozoic–Cenozoic sedimentary sequences. A synthesis of the available petrological and geochronological data allowed us to establish a geological framework for the Precambrian basement on the Liaodong Peninsula and its vicinity. The basement can be subdivided into three tectonic units: the Neoarchean Liaonan Block, the Eo–Neoarchean Longgang Block, and the intervening Paleoproterozoic Jiao–Liao–Ji Belt. In this paper we delineate the characteristics of an Archean tectonothermal event, and in a companion paper we examine the Paleoproterozoic lithotectonic assemblages. Rock samples of the Hadean eon are rare worldwide, but Hadean zircons have been identified in rocks of the Liaodong Peninsula, and they provide one of the oldest known mineralogical records on Earth. The Archean gneisses in the Liaonan Block are dominated by quartz dioritic–granodioritic gneisses that were emplaced between 2.55 and 2.44 Ga, and these rocks later underwent a lower-amphibolite-facies metamorphism. On the other hand, the Archean basement in the Longgang Block is dominated by TTG (tonalitic–trondhjemitic–granodioritic) and granitic gneisses, charnockites, and small amounts of supracrustal sequences with much older protolith ages of up to 3.85 Ga, and these rocks have undergone amphibolite- to granulite-facies metamorphism. Post-tectonic magmatism (ca. 2.5 Ga) marked the end of the Archean tectonothermal event in the Eastern Block of the North China Craton.

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Applied statistical functions and multivariate analysis of geochemical compositional data to evaluate mineralization in Glojeh polymetallic deposit, NW Iran
F DARABI-GOLESTAN, A HEZARKHANI
Front. Earth Sci.. 2019, 13 (1): 229-246.  https://doi.org/10.1007/s11707-018-0705-0
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Various genesis of epithermal veins as well as host rock cause complication in the modeling process. Thus LINEST and controlling function were applied to improve the accuracy and the quality of the model. The LINEST is a model which is based on multiple linear regression and refers to a branch of applied statistics. This method concerns directly to the application of t-test (TINV and TDIST to analyses of variables in the model) and F-test (FDIST, F-statistic to compare different models) analysis. Backward elimination technique is applied to reduce the number of variables in the model through all the borehole data. After 18 steps, an optimized reduced model (ORM) was constructed and ranked in order of importance as Pb>Ag>P>Hg>Mn>Nb>U>Sr>Sn>As>Cu, with the lowest confidence level (CL) of 92% for Cu. According to the epigenetic vein genesis of Glojeh polymetallic deposit, determination of spatial patterns and elemental associations accompanied by anomaly separation were conducted by K-means cluster and robust factor analysis method based on centered log-ratio (clr) transformed data. Therefore, 12 samples (cluster 2) with the maximum distance from centroid, indicates the intensity of vein polymetallic mineralization in the deposit. In addition, an ORM for vein population was extracted for Sb>Al>As>Mg>Pb>Cu>Ag elements with the R2 up to 0.99. On the other hand, after 23 steps of optimization process at the host rock population, an ORM was conducted by Ag>Te>Hg>Pb>Mg>Al>Sb>As represented in descending order of t-values. It revealed that Te and Hg can be considered as pathfinder elements for Au at the host rock. Based on the ORMs at each population Ag, Pb, and As were often associated with Au mineralization. The concentration ratio of (tSb×tAl)vein/(tSb×tAl)background as an enrichment index can intensify the mineralization detection. Finally, Glojeh deposit was evaluated to be classified as a vein-style Au (Ag, Pb, As)-polymetallic mineralization.

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