Utilizing both borehole and Cone Penetration Testing (CPT) data in soil stratification helps to get more convincing soil stratification results. However, the soil classification results revealed by borehole (Unified Soil Classification System, USCS) and CPT tests (soil behavior type, SBT) are commonly not consistent. This study proposes a feasible solution to integrate the borehole and CPT data with the tree-based method. The tree-based method is naturally suitable for soil stratification tasks as it aims to divide the subsurface space into several clusters based on the similarities of the soil types. A novel boundary dictionary method is proposed to enhance the model performance on complex soil layer conditions. A probabilistic mapping matrix between the USCS-SBT system is built based on a collected municipal database with collocated borehole and CPT data. The optimal soil stratification results can be selected based on considering multiple borehole information and pruning the structure of trees. The structure of the trees can be optimized in a back analysis perspective with the Sequential Model-Based Global Optimization (SMBO) algorithm which aims to maximize the possibility of observing the borehole information based on the USCS-SBT probabilistic mapping matrix. The uncertainties of the optimal soil stratification results can be estimated based on a weighted Gini index method. The performance of the proposed method is validated based on a real case in New Zealand with a cross-validation method. The results indicate that the proposed method is robust and effective.
As most air quality monitoring sites are in urban areas worldwide, machine learning models may produce substantial estimation bias in rural areas when deriving spatiotemporal distributions of air pollutants. The bias stems from the issue of dataset shift, as the density distributions of predictor variables differ greatly between urban and rural areas. We propose a data-augmentation approach based on the multiple imputation by chained equations (MICE-DA) to remedy the dataset shift problem. Compared with the benchmark models, MICE-DA exhibits superior predictive performance in deriving the spatiotemporal distributions of hourly PM2.5 in the megacity (Chengdu) at the foot of the Tibetan Plateau, especially for correcting the estimation bias, with the mean bias decreasing from -3.4 µg/m3 to -1.6 µg/m3. As a complement to the holdout validation, the semi-variance results show that MICE-DA decently preserves the spatial autocorrelation pattern of PM2.5 over the study area. The essence of MICE-DA is strengthening the correlation between PM2.5 and aerosol optical depth (AOD) during the data augmentation. Consequently, the importance of AOD is largely enhanced for predicting PM2.5, and the summed relative importance value of the two satellite-retrieved AOD variables increases from 5.5% to 18.4%. This study resolved the puzzle that AOD exhibited relatively lower importance in local or regional studies. The results of this study can advance the utilization of satellite remote sensing in modeling air quality while drawing more attention to the common dataset shift problem in data-driven environmental research.
The accurate prediction of displacement is crucial for landslide deformation monitoring and early warning. This study focuses on a landslide in Wenzhou Belt Highway and proposes a novel multivariate landslide displacement prediction method that relies on graph deep learning and Global Navigation Satellite System (GNSS) positioning. First model the graph structure of the monitoring system based on the engineering positions of the GNSS monitoring points and build the adjacent matrix of graph nodes. Then construct the historical and predicted time series feature matrixes using the processed temporal data including GNSS displacement, rainfall, groundwater table and soil moisture content and the graph structure. Last introduce the state-of-the-art graph deep learning GTS (Graph for Time Series) model to improve the accuracy and reliability of landslide displacement prediction which utilizes the temporal-spatial dependency of the monitoring system. This approach outperforms previous studies that only learned temporal features from a single monitoring point and maximally weighs the prediction performance and the priori graph of the monitoring system. The proposed method performs better than SVM, XGBoost, LSTM and DCRNN models in terms of RMSE (1.35 mm), MAE (1.14 mm) and MAPE (0.25) evaluation metrics, which is provided to be effective in future landslide failure early warning.
Stimulating renewable energy consumption is a major focus of the Sustainable Development Goals in combating climate change and global warming. The International Energy Agency estimates that renewable energy consumption should be doubled to achieve the COP21 targets. In this context, the question is whether renewable energy types promote the improvement of ecological quality and economic growth. Most studies have investigated the influence of renewable energy on ecological pollution using carbon dioxide emissions or ecological footprint indicators, which only represent the pollution caused by human consumption patterns, and these indicators neglect the supply side. Motivated by this point, this study uses the LCF (Load Capacity Factor) as an environmental indicator and examines the causality relationship among different types of renewable energy, income, and environmental quality in the USA, while also incorporating employment and capital stock into the analysis. Through using the Fourier causality test with the wavelet-decomposed series, the study explores for the validity of the renewable energy-based growth hypothesis and answers to the question of whether there is a causal effect of renewable energy types on environmental quality. The results demonstrate that there is a bidirectional causality between total renewable energy, wood, biomass, and economic growth as well as between these renewable energy types and the LCF.
The trace element compositions of melts and minerals from high-pressure experiments on hydrous pyroxenites containing K-richterite are presented. The experiments used mixtures of a third each of the natural minerals clinopyroxene, phlogopite and K-richterite, some with the addition of 5% of an accessory phase ilmenite, rutile or apatite. Although the major element compositions of melts resemble natural lamproites, the trace element contents of most trace elements from the three-mineral mixture are much lower than in lamproites. Apatite is required in the source to provide high abundances of the rare earth elements, and either rutile and/or ilmenite is required to provide the high field strength elements Ti, Nb, Ta, Zr and Hf. Phlogopite controls the high levels of Rb, Cs and Ba.Since abundances of trace elements in the various starting mixtures vary strongly because of the use of natural minerals, we calculated mineral/melt partition coefficients (DMin/melt) using mineral modes and melting reactions and present trace element patterns for different degrees of partial melting of hydrous pyroxenites. Rb, Cs and Ba are compatible in phlogopite and the partition coefficient ratio phlogopite/K-richterite is high for Ba (136) and Rb (12). All melts have low contents of most of the first row transition elements, particularly Ni and Cu ((0.1-0.01)×primitive mantle). Nickel has high DMin/melt for all the major minerals (12 for K-richterite, 9.2 for phlogopite and 5.6 for Cpx) and so behaves at least as compatibly as in melting of peridotites. Fluorine/chlorine ratios in melts are high and DMin/melt for fluorine decreases in the order apatite (2.2) > phlogopite (1.5) > K-richterite (0.87). The requirement for apatite and at least one Ti-oxide in the source of natural lamproites holds for mica pyroxenites that lack K-richterite. The results are used to model isotopic ageing in hydrous pyroxenite source rocks: phlogopite controls Sr isotopes, so that lamproites with relatively low 87Sr/86Sr must come from phlogopite-poor source rocks, probably dominated by Cpx and K-richterite. At high pressures (>4 GPa), peritectic Cpx holds back Na, explaining the high K2O/Na2O of lamproites.
The end-Permian mass extinction was one of the major global crises spanning the entire Early Triassic or longer. Eruptions of volcanos were one of the factors that delayed the biotic recovery after this event. Supervolcano eruptions can cause catastrophic effects on global environment, climate, and life. Here we investigate the tuff layers from Early-Middle Triassic boundary in the Yangtze Block and identify a supervolcano eruption event. The zircon U-Pb ages of the section-Langdai, section-Daijiagou and section-Longmendong tuff samples are 247.1 ±1.9 Ma, 247.6 ±2.0 Ma and 247.7 ±1.7 Ma, respectively. These ages mark the Olenekian-Anisian boundary. The zircon grains from the tuff layers have negative εHf(t) (-15.3 to -0.8), two-stage Hf model (TDM2) ages (1.7 to 2.2 Ga) and display high-δ18O values (mostly > 10‰). Clay minerals and quartz dominate the rock composition. The whole rock compositions show that the tuff layers were derived from magma of intermediate to felsic composition, which formed by the remelting of Paleoproterozoic materials of continental crust. The volcanic eruption site is located in the Jinshajiang-Ailaoshan-Song Ma suture zone in the southwestern margin of the Yangtze Block. A combination of the closure of the Paleo-Tethys Ocean Basin and the collision of the Indochina Block and South China contributed to the eruption, which was a supervolcano eruption under the active continental margin arc settings. We speculate that this supervolcano eruption might have contributed to the delayed biotic recovery after the end-Permian mass extinction.
Environmental goods and low-carbon technologies have long been identified as having the potential to drive long-term economic progress without compromising environmental quality. However, their exact role in mitigating environmental degradation are yet to be unravelled. In addressing this shortfall, the extant literature relied on research funding and patent application as proxies for green technologies. Having established the weaknesses in the use of these variables as proxies for green technologies, this study explored the role of trade in environmental goods and low-carbon technologies in boosting environmental quality among G20 nation using a panel dataset from 1994 to 2018. The study employed the Method of Moment quantile regression for the model estimation and the Ridge regression, Discroll-Kraay standard error, and the Newey-West standard error estimators to test the robustness of our findings. Our findings indicate that whereas environmental goods promote environmental quality, low-carbon technologies decrease same. Also, the study found economic growth to exert an aggravating effect on environmental quality, while foreign direct investments, natural resource rents, human capital development, and renewable energy consumption exert positive influence on environmental quality. Based on the findings of the study, G20 nations are encouraged to improve green market structures to improve the trade in environmental goods and low-carbon technologies. Also the share of renewable energy sources in the overall energy basket must be improved to help improve environmental quality.
Detrital zircon geochronology and Hf isotope analysis can be used for inferring provenance characteristics, and to evaluate the tectonic evolution of sedimentary basins and their link with regional orogenesis. The Paleozoic sequences of the Okcheon Belt consist of the Lower Paleozoic Joseon and the Upper Paleozoic Pyeongan supergroups with Middle Paleozoic hiatus locally on top of the Neoproterozoic bimodal volcanic rocks, reflecting an intracontinental rift setting between the two basements (viz. Gyeonggi and Yeongnam massifs) at southern part of the Korean Peninsula. Our detrital zircon U-Pb ages and Lu-Hf isotope results show that all these Paleozoic strata commonly have Paleoproterozoic and Paleozoic zircon ages with rare Meso- to Neoproterozoic ages. The individual zircon populations display following features, allowing estimation of their sedimentary provenances: (i) The Paleoproterozoic zircons (ca. 1.85 Ga and 2.50 Ga) with similar ranges of εHf(t) values are most common in the basement rocks of the Korean Peninsula, and were sourced from both the Gyeonggi and Yeongnam massifs. (ii) The Meso- to Neoproterozoic zircons, preserved only in the Middle to Late Cambrian clastic sedimentary rocks within the carbonate sequences probably reflect proximal provenance. (iii) The youngest Paleozoic zircons of each formation, almost coincident with their deposition ages, suggest presence of syndepositional magmatism, indicating proximal magmatic sources during their deposition. (iv) The Cambrian-Ordovician zircons, from the Lower Paleozoic sequences, but rare in the successive Upper Paleozoic sequences, suggest a provenance change after the hiatus between the two sedimentary successions. (v) The Permian zircons showing different εHf(t) values indicate that detrital sources were varied at that time. The integrated results in our study suggest provenance variability linked to diverse tectonic environments, reflecting prolonged subduction-related crustal evolution of the proto-Korean Peninsula during the Paleozoic.
Precise determination of cation diffusivity in garnet can provide critical information for quantitatively understanding the timescales and thermodynamics of various geological processes, but very few studies have been performed for Fe-Mn interdiffusion. In this study, Fe-Mn interdiffusion rates in natural single crystals of Mn-bearing garnet with 750 ppm H2O are determined at 6 GPa and 1273-1573 K in a Kawai-type multi-anvil apparatus. Diffusion profiles were acquired by electron microprobe and fitted using Boltzmann-Matano equation. The experimental results show that the Fe-Mn interdiffusion coefficient (DFe-Mn) slightly decreases with increasing XFe. The experimentally determined DFe-Mn in Mn-bearing garnet can be fitted by the Arrhenius equation: DFe-Mn(m2/s)=D0XFenexp(-E*/RT), where E*=(1-XFe)E*Mn+XFeE*Fe, D0 = 8.06-6.04+9.87×10-9 m2/s,E*Mn = 248 ±27 KJ/mol,E*Fe = 226 ±59 KJ/mol, n = -1.36 ±0.51. The comparing the present results with previous experimental data suggest that water can greatly enhance the DFe-Mn in garnet. Our results indicate that the time required for homogenization of the compositional zoning of a garnet is much shorter than previously thought.
The early Archean oceans were marked by significant redox changes which have subsequently shaped the Earth’s biosphere. Archean chemical sediments of banded Iron and Manganese formations provide important geochemical proxies for these historical shifts in the redox conditions and to trace the ancient sedimentation patterns and protoliths. In this study, we investigate the proto-ore of the Archean Mn-formations of the Sandur, Chitradurga and Shimoga greenstone belts of Dharwar Craton of southern Peninsular India, which is geochemically characterised as quartz arenites, Mn-arenites, Fe-arenites, Mn-argillites and Fe-argillites. The geochemical systematics suggest their deposition in shallow to deeper shelf in the Archean proto-ocean. The detrital zircon U-Pb systematics of Mn arenites and argillites indicate their maximum depositional age of 3230 ±52 Ma representing the oldest onset of sedimentation during the Paleo-Mesoarchean timeframe in the Chitradurga Group of Dharwar Supergroup. The detrital influx proxies suggest variations in sedimentation rates associated with the Archean transgressive-regressive cycles and fluctuating hydrodynamic conditions, together reflecting an increasing trend in the contributions of recycled sediment from Sandur to Chitradurga and Shimoga greenstone belts. The available detrital zircon ages of the Mn arenites and argillites from these greenstone belts indicate a ~ 600 Ma prolonged period of Mn deposition for which high-T hydrothermal fluids from Archean mid-oceanic ridges supplied the manganese. The trace element compositions of the concordant detrital zircons suggest 3.3-3.1 Ga Dharwar basement TTG/granitoid source which is corroborated by the zircon crystallization temperatures of 690-820 ℃. The source-normalised α-dose rates of the detrital zircons signify greater degrees of sediment transport and multi-cycle nature which correspond to the earliest episode of crustal growth in the Indian sub-continent associated with the Mesoarchean Ur supercontinent. The clastic-chemogenic sedimentation attained through concomitant detrital sediment-seawater-metalliferous hydrothermal fluid mixing at an epicontinental passive margin resulted in the deposition of Mn-arenites and argillites closer to the higher Eh shore, while the Fe-rich sediments formed at a relatively deeper shelf characterised by comparatively lower Eh and more alkaline conditions. The comprehensive geochemical and geochronological data of the Archean Mn arenite-argillite sequences reveal the significance of regional episodes of ocean oxygenation at the shallow shelves of Archean oceans prior to great oxygenation event (GOE) that was mediated by the prolific growth of ancient microbiota which transformed the Earth to a more habitable planet.
The chemical evolution and pressure-temperature conditions of subduction zone magmatism along ancient suture zones in orogenic belts can provide important information regarding plate convergence processes in paleo-oceans. Carboniferous magmatism in West Junggar is key to understanding the tectonothermal and subduction history of the Junggar Ocean, which was a branch of the Paleo-Asian Ocean, as well as the accretionary processes in the southwestern Central Asian Orogenic Belt (CAOB). We undertook a geochronological, mineralogical, geochemical, and Sr-Nd-Hf-Pb isotopic study of volcanic rocks from the Baikouquan area of West Junggar. We used these data to determine the petrogenesis, mantle source, and pressure-temperature conditions of these magmas, and further constrain the subduction and tectonic history of the Junggar Ocean. The studied volcanic rocks yielded zircon U-Pb ages of 342-337 Ma and are characterized by enrichments of large-ion lithophile elements (LILEs), and depletions in high-field-strength elements (HFSEs), indicative of an island arc affinity. The volcanic rocks have positive ƐNd(t) (5.83-7.04) and ƐHf(t) (13.47-15.74) values, 87Sr/86Sr(t) ratios of 0.704023-0.705658, and radiogenic 207Pb/204Pb(t) and 208Pb/204Pb(t) ratios at a given 206Pb/204Pb(t) ratio, indicative of a depleted mantle source contaminated by subduction-related materials. Geochemical modeling calculations indicate that ≤1% of a subduction component comprising fluid and sediment melt could have generated the source of the parental melts of the Baikouquan volcanic rocks. Clinopyroxene phenocrysts in the volcanic rocks are classified as high- and low-Ti clinopyroxene, and pressure-temperature calculations suggest the host rocks formed at high temperatures (~1300 ℃) and shallow to moderate depths (<2 GPa). The magma was probably generated by hot and hydrous melting in a mantle wedge in response to subduction of young, hot oceanic lithosphere. The present results, combined with published data, suggest that the Baikouquan volcanic rocks record a transition in tectonic setting from normal cold to anomalous hot subduction of young oceanic lithosphere close to a mid-ocean ridge. This indicates ridge subduction began shortly after 337 Ma. Our results provide new insights into the tectonomagmatic evolution during intra-oceanic subduction prior to ridge subduction.
Geochemical survey data analysis is recognized as an implemented and feasible way for lithological mapping to assist mineral exploration. With respect to available approaches, recent methodological advances have focused on deep learning algorithms which provide access to learn and extract information directly from geochemical survey data through multi-level networks and outputting end-to-end classification. Accordingly, this study developed a lithological mapping framework with the joint application of a convolutional neural network (CNN) and a long short-term memory (LSTM). The CNN-LSTM model is dominant in correlation extraction from CNN layers and coupling interaction learning from LSTM layers. This hybrid approach was demonstrated by mapping leucogranites in the Himalayan orogen based on stream sediment geochemical survey data, where the targeted leucogranite was expected to be potential resources of rare metals such as Li, Be, and W mineralization. Three comparative case studies were carried out from both visual and quantitative perspectives to illustrate the superiority of the proposed model. A guided spatial distribution map of leucogranites in the Himalayan orogen, divided into high-, moderate-, and low-potential areas, was delineated by the success rate curve, which further improves the efficiency for identifying unmapped leucogranites through geological mapping. In light of these results, this study provides an alternative solution for lithologic mapping using geochemical survey data at a regional scale and reduces the risk for decision making associated with mineral exploration.
Major Sn deposits are commonly linked to crust-derived and highly evolved granites, with magma generation aided by mantle heating. However, whether and how the mantle components contribute to Sn polymetallic mineralization remains unclear. In this study, in combination with a compilation of equivalent data in the region, we provide new constraints on this issue based on detailed investigations on the petrogenesis and metallogenic significance of granitoids including the causative batholith and later granodiorite porphyry dike in the giant Dachang Sn deposit from South China. The former has zircon U-Pb ages of 93-91 Ma and belongs to highly evolved S-type biotite granite, which experienced fractionation of massive feldspar. The latter shows zircon U-Pb ages of 90 Ma and displays I-type granite features. The batholith was mainly derived from the dehydration melting of biotite in the metasedimentary sources, as revealed by the relatively low whole-rock Pb contents (<30 ppm) and high Ba/Pb ratios (2.71-17.1) and initial T(ti-zr) of 790 ℃. Compared with the adjacent crust-derived S-type granites (-24.8 - -5.1) and I-type granites (-11.0 to -5.2), the Dachang S-type biotite granites present higher zircon εHf(t) values (-9.1 to -2.1). Furthermore, the low magmatic zircon δ18O values (6.2 ‰) and higher apatite LREE (3277-4114 ppm) and Sr (1137-1357 ppm) contents than of arc-type basic rocks were discerned. These characteristics jointly hint the contributions of mantle components. The higher initial T(ti-zr) (>850 ℃), whole-rock Mg# (52 to 58), apatite εNd(t) (-9.2 to -6.5) and zircon εHf(t) (-7.6 to 2.5) values but lower zircon δ18O values (6.33 to 8.30 ‰) of the later granodiorite porphyry dike than those of the batholith also suggest that mantle material was involved in the generation of the dikes, which is evident by the variational features of zircon and apatite trace elements. In addition, at the zircon Hf <12000 ppm and Eu/Eu* > 0.05, the higher zircon ΔFMQ values (mostly from -1.8 to 2.0) and H2O contents (100-1100 ppm) of the Dachang granitoids than the pure crust-derived S-type granites (ΔFMQ = mostly from -3.7 to -1.5; H2O < 100 ppm) imply that mantle materials involved are relatively rich in water and oxidized. These suggest that the addition of mantle components is conducive to the extraction of Sn from metasedimentary sources, and moderately facilitates the increase of oxygen fugacity which still maintains the incompatibility of Sn in magmas with ΔFMQ < 2. Also, the involvement of mantle components upgrades the H2O contents in S-type magmas, favoring the migration of ore-forming elements from magmas to hydrothermal fluids. The sediment-derived causative granites displayed higher εHf(t) and εNd(t) values with greater Sn tonnages of their associated world-class Sn polymetallic deposits, supporting the opinion that the contributions of mantle components play an important role in the generation of giant Sn deposits.
The representation of spatial variation of soil properties in the form of random fields permits advanced probabilistic assessment of slope stability. In many studies, the safety margin of the system is typically characterized by the term “probability of failure (Pfailure)”. As the intensity and spatial distribution of soil properties vary in different random field realizations, the failure mechanism and deformation field of a slope can vary as well. Not only can the location of the failure surfaces vary, but the mode of failure also changes. Such information is equally valuable to engineering practitioners. In this paper, two slope examples that are modified from a real case study are presented. The first example pertains to the stability analysis of a multi-layer -slope while the second example deals with the serviceability analysis of a multi-layer c-φ slope. In addition, due to the large number of simulations needed to reveal the full picture of the failure mechanism, Convolutional Neural Networks (CNNs) that adopt a U-Net architecture is proposed to offer a soft computing strategy to facilitate the investigation. The spatial distribution of the failure surfaces, the statistics of the sliding volume, and the statistics of the deformation field are presented. The results also show that the proposed deep-learning model is effective in predicting the failure mechanism and deformation field of slopes in spatially variable soils; therefore encouraging probabilistic study of slopes in practical scenarios.
Vanadium mineralization at Los Chihuidos deposit of the Neuquén Basin is linked to the development of a redox front system related to the inflow of hydrocarbons into the red sandstone of the Huincul Formation. Interaction of hydrocarbons with oxidized red beds and connate water generated redox reactions where hematite was dissolved due to iron reduction resulting in the discoloration of the red strata. At the contact between oxidized red sandstone and reduced white sandstone, precipitation of specific mineral phases resulted in the V ore with minor amounts of Cu. With the implementation of the redox interface, abundant V-montmorillonite and V-hematite precipitated at the more oxidizing conditions and Cu-V-corrensite-type at the more reducing conditions of the redox front. As the redox front advanced with fluids constantly migrating into the reservoir, more reducing conditions were stablished, promoting chloritization and minor illitization with V-Cu incorporation and continuous upgrading of the ore. Main ore mineralogy consists of clay minerals including V-bearing montmorillonite, Cu-V-corrensite-type, V-di-trioctahedral chlorite and Cu-tri-trioctahedral chlorite with minor V-illite-smectite mixed-layer minerals and associated secondary V-hematite. Chloritization over illitization was favored due to high amounts of Fe and Mg in detrital clasts and in connate fluids and by low K availability related to low amounts of detrital K-feldspar. The spatial transition of V and /or Cu bearing clay minerals observed through the mineralized redox front at Los Chihuidos deposit (kaolinite → smectite → illite/smectite → corrensite-type → di- trioctahedral -chlorite → tri- trioctahedral -chlorite) and the related variation of V-Cu concentrations in bulk rock are indicative of increasing pH and decreasing Eh of resident solutions from red to white sandstones during the hypogene mineralization process. Late influx of Cu-rich oxidized basinal brines precipitated main copper ore with Cu-sulfides in the white sandstone up to the contact with the redox front in contact with hydrocarbons. During uplift and exhumation, percolation of meteoric water promoted remobilization of V and Cu and the precipitation of oxidized V-Cu ore.
Recent studies indicate dwindling groundwater quantity and quality of the largest regional aquifer system in North West India, raising concern over freshwater availability to about 182 million population residing in this region. Widespread agricultural activities have resulted severe groundwater pollution in this area, demanding a systematic vulnerability assessment for proactive measures. Conventional vulnerability assessment models encounter drawbacks due to subjectivity, complexity, data-prerequisites, and spatial-temporal constraints. This study incorporates isotopic information into a weighted-overlay framework to overcome the above-mentioned limitations and proposes a novel vulnerability assessment model. The isotope methodology provides crucial insights on groundwater recharge mechanisms (18O and 2H) and dynamics (3H) - often ignored in vulnerability assessment. Isotopic characterisation of precipitation helped in establishing Local Meteoric Water Line (LMWL) as well as inferring contrasting recharge mechanisms operating in different aquifers. Shallow aquifer (depth < 60 m) showed significant evaporative signature with evaporation loss accounting up to 18.04% based on Rayleigh distillation equations. Inter-aquifer connections were apparent from Kernel Density Estimate (KDE) and isotope correlations. A weighted overlay isotope-geospatial model was developed combining 18O, 3H, aquifer permeability, and water level data. The central and northern parts of study area fall under least (0.29%) and extremely (1.79%) vulnerable zones respectively, while majority of the study area fall under moderate (42.71%) and highly vulnerable zones (55.20%). Model validation was performed using groundwater NO3- concentration, which showed an overall accuracy up to 82%. Monte Carlo Simulation (MCS) was performed for sensitivity analysis and permeability was found to be the most sensitive input parameter, followed by 3H, 18O, and water level. Comparing the vulnerability map with Land Use Land Cover (LULC) and population density maps helped in precisely identifying the high-risk sites, warranting a prompt attention. The model developed in this study integrates isotopic information with vulnerability assessment and resulted in model output with good accuracy, scientific basis, and widespread relevance, which highlights its crucial role in formulating proactive water resource management plans, especially in less explored data-scarce locations.
The Mesoproterozoic (1.11 Ga) Umkondo large igneous province (LIP) in southern Africa and Antarctica was emplaced in < 5 Myr and is dominated by low-Ti tholeiitic doleritic-gabbroic sills. It is of particular interest because it is the least studied LIP in southern Africa with both sublithospheric and lithospheric mantle sources proposed and it coincides with the early assembly of Rodinia, so it has importance in understanding the nature of magmatism and tectonics in and around the Kalahari craton during the Mesoproterozoic. In this study, we compiled a large database of existing (~750) and new (~100) major and trace element data for the Umkondo province, as well as 42 new Sr-Nd isotopic measurements, to provide constraints on its magma sources and geochemical evolution. Major element compositional variations in the low-Ti tholeiites are explained by low-pressure (1 kbar) three-phase fractional crystallisation (olivine, clinopyroxene and plagioclase) of a parent magma with ~ 10 wt.% MgO in oxidising conditions (QFM + 1). Inverse models show that the low-Ti tholeiitic magmas were derived as residual melts after the crystallization of 12%-33% olivine from primary komatiitic-basaltic magmas (up to ~ 20 wt.% MgO) in equilibrium with mantle olivine (Fo90). Low Sm/Yb and TiO2/Yb-Nb/Yb indicate that the primary magmas were derived by 2%-20% shallow (40-50 km) partial melting of spinel lherzolite. High Sm/Yb is restricted to dyke swarms and may imply limited magma production from deeper (up to ~ 70 km) garnet lherzolite-like sources. The low-Ti tholeiites of the Umkondo province are enriched in large ion lithophile elements (Rb-Sr-Cs-K) and depleted in high-field strength elements (Zr-Hf-Nb-Ta), indicating the involvement of crustal material and/or the subcontinental lithospheric mantle. This is supported by covariations in Th/Nb, Nb/Yb, Nb/La and Ce/Sm with generally negative ΔNb. Sr-Nd isotopes lend support to the notion that the Umkondo magmas were derived from depleted and/or enriched sublithospheric mantle sources and subsequently contaminated by enriched lithospheric material during emplacement (initial (at 1.11 Ga) 87Sr/86Sr between 0.704820 and 0.737464 and εNd between -8.9 and +5.3). The Vredefort sills are significant as they display the most depleted Sr-Nd isotopic signatures (average initial 87Sr/86Sr of 0.705342 and average εNd of 0.4) and are the least contaminated magma suite in the Umkondo province. Because of (i) the large volume of low-Ti magmas, (ii) evidence of a primary hot and MgO-rich (komatiitic) magma, and (iii) the short duration of magmatism, we suggest that the Umkondo province was formed by plume-induced melting of the sublithospheric mantle beneath the Kalahari craton in an extensional setting. This contrasts with previous suggestions that the heat source developed in response to the “thermal insulation” of the mantle beneath a thickened Kalahari craton in the absence of a mantle plume. There is further evidence from the elevated Zn/Fe that the sublithospheric mantle was lithologically heterogeneous and consisted of mixed peridotite and pyroxenite domains. There is a general lack of ultramafic cumulates in the low-Ti magma suite that may imply there was deeper ponding and storage of the primary magmas that fractionated large quantities of ultramafic rocks. There is also a paucity of high-Ti rocks in the Umkondo province that may reflect limited direct melting of the lithospheric mantle or that they are simply not as well-preserved in this province compared to the Karoo province. The similar trace element and Sr-Nd isotopic compositions of the Umkondo sills in southern Africa with the Borgmassivet sills in Antarctica support the concept that the Kalahari craton and Grunehogna terrane were adjoined at 1.11 Ga. The timing of the Umkondo province indicates there was localised lithospheric extension and upwelling asthenospheric mantle during a time of dominantly compressional tectonics on Earth at the end of the ‘boring billion’.
Globally, shallow aquifer groundwater (GW) has been severely affected in recent decades for both geogenic and anthropogenic reasons. The hydro-geochemical characteristics of the GW change inconsistently with the addition of unwanted inorganic trace elements into the GW aquifer of the Indo-Bangladesh delta region (IBDR), such as arsenic (As) along with fluoride (F-) contamination. Contaminated GW can have a negative impact on drinking water supplies and agricultural output. GW pollution can have serious adverse effects on the environment and human health. Thus, the GW quality of this region is deteriorating progressively, and human health threatening by various life-threatening disorders. Hence, the current study concentrated on the GW quality evaluation and prediction of possible health issues in the IBDR due to elevated contamination of As along with F- within GW aquifers by considering sixteen causative. Field survey-based statistical methods such as entropy quality index (EWQI) combined with health risk index (HRI) was implemented for evaluating the As and F- sensitivity with the help of correlation testing and principal component analysis. The study's outcome explains that a substantial portion of the IBDR has been vastly experiencing inferior GW quality, environmental issues, and health-related problems in dry and wet seasons, correspondingly for As and F- exposure. Piper diagram verified the suitability of water that almost 55% of GW across the study area’s aquifers are unfit for drinking as well as cultivation of crops. Sensitivity analysis and the Monte Carlo simulation method were also applied to assess the contaminant's concentration level and probable health risk appraisal. The present study concludes that the elevated exposure of As and F- pollution has to be monitored regularly and prevent unwanted GW contamination through implementing sustainable approaches and policies to fulfil the sustainable development goal 6 (SDG-6) till 2030, ensuring the most basic human right of clean, safe, and hygienic water.
Seafloor and buried reliefs occur along continental margin of the Ross Sea (Antarctica). These features are several kilometres wide and tens of metres high, exhibiting cone or flat-top dome shapes. Previous studies have proposed a volcanic or glacial origin for these formations, but these hypotheses do not account for all the available evidence.In this study, we use morpho-bathymetric data, intermediate resolution multichannel seismic and high resolution chirp profiles, as well as magnetic lines to investigate these clusters of mounds. By employing targeted processing techniques to enhance the geophysical characterization of the seafloor and buried reliefs, and to understand the underlying geological features, we propose that the reliefs are mud volcanoes. Some of these formations appear to be associated with a plumbing system, as indicated by acoustic anomalies linked to sediment containing gas. These formations are likely fed by clayey source rocks of Miocene age. Additionally, other reliefs might be the result of mud mobilisation caused by gravity instability and fluid overpressure.
The formation and growth mechanisms of Mid-Ocean Ridges (MOR) are relatively well known, whereas those of back-arc spreading ridges are comparatively less known because geophysical, geochemical, and morphological data are scarce and of low density. Here we present a high-resolution bathymetry of the Marsili Seamount (MS; 1 Ma - 3 ka), which represents the inflated spreading ridge of the 2 Ma old Marsili back-arc basin associated to the subduction of the Ionian Sea below the Calabrian Arc and Tyrrhenian Sea. MS is 70 km long, 30 km wide, and its height reaches about 3000 m from surrounding seafloor. Our new digital bathymetric model has a 5 m grid cell size resolution and covers the MS bathymetry from -1670 mbsl to the top at -491 mbsl. We conduct morphometric and morphological analyses of the bathymetry and recognize landforms due to volcanic, tectonic, hydrothermal and gravity processes. MS consists of volcanoes related to fissural and central-type activity, this latter located at the northern and southern tips of the main dike swarms. Dike swarms represent the surface expression of different ridge segments whose strikes are controlled by the larger scale back-arc spreading processes and by the local occurrence of an active hydrothermal field. This latter develops in a flat area between two partly overlapping ridge segments where historical volcanism and extensional processes concentrate. Such ridges represent the embryonic stage of the formation of transform-like faults. Central volcanoes, the northern of which is characterized by a caldera, form at the tips of MS because the decrease in width of the major volcanic fissures promotes vent localization associated with the formation of sill-like reservoirs from which central-type vents may develop. Gravity processes affecting the MS flanks are due to shallow seafloor sliding. Caldera collapses affecting the northernmost central-type polygenic volcano must be included in the evaluation of the hazard related to potential tsunami. Inward dipping faults characterize the MS eastern flank suggesting a moderately asymmetric growth of the spreading ridge possibly associated with the eastward opening of the Marsili back-arc.The Marsili back-arc spreading rate is similar to those of MOR slow spreading ridges. However, the MS morphology resembles that of fast spreading ridges. These two features also characterize more extended back-arc spreading ridges (e.g. the Mariana in Western Pacific). We conclude that, independently from the spatial scale, the increase in the ridge accretion rate is related to the progressive addition of a subduction-related component to a pure spreading mantle source.
Technological progress and the rapid increase in geochemical data often create bottlenecks in many studies, because current methods are designed using limited number of data and cannot handle large datasets. In geoscience, tectonic discrimination illustrates this issue, using geochemical analyses to define tectonic settings when most of the geological record is missing, which is the case for most of the older portion of the Earth’s crust. Basalts are the primary target for tectonic discrimination because they are volcanic rocks found within all tectonic settings, and their chemical compositions can be an effective way to understand tectonics-related mantle processes. However, the classical geochemical discriminant methods have limitations as they are based on a limited number of 2 or 3-dimensional diagrams and need successive and subjective steps that often offers non-unique solutions. Also, weathering, erosion, and orogenic processes can modify the chemical composition of basalts and eliminate or obscure other complementary geotectonic records. To address those limitations, supervised machine learning techniques (a part of artificial intelligence) are being utilized more often as a tool to analyze multidimensional datasets and statistically process data to tackle big data challenges. This contribution starts by reviewing the current state of tectonic discrimination methods using supervised machine learning. Deep learning, especially Convolutional Neural Network (CNN) is the most accurate approach. However, it requires a large dataset and considerable processing time, and the gain of accuracy can be at the expense of interpretability. Therefore, this study designed guidelines for data pre-processing, tectonic setting classification and objectively evaluating the model performance. We also identify research gaps and propose potential directions for the application of supervised machine learning to tectonic discrimination research, aimed at closing the divide between earth scientists and data scientists.
One of the ophiolites that record the Proto-Tethys Ocean’s episodic closure is the Munabulake ophiolitic mélange, which is situated in the middle of the Kunlun-Qaidam and Altun-Qilian blocks. Detailed field mapping revealed that the Munabulake ophiolitic mélange comprises local (ultramafic rocks, basalts, andesites, gabbros, diorites, and plagiogranites) and exotic (marble, gneiss, schist, and amphibolite) blocks, many of which are in the schist matrix (Qimantage Group). Based on geochronological, geochemical, and petrological observations, the mafic rocks in the Munabulake ophiolitic mélange can be categorized into three categories: 498-Ma OIB-like gabbros, 468-Ma Hawaiian alkaline basalt-like dolerite and pillow basaltic slices, and 428 Ma massive SSZ-like ultramafic rocks. The 501-452 Ma I-type granites exhibit arc affinities due to the oceanic crust subduction. These findings, along with spatial relationships, suggest that the Early Paleozoic ophiolite complex, island arc rocks, and accretionary complex generated as an intra-oceanic arc system as a result of obduction of the south Altun Ocean’s onto the Central Altun block within a north-directed subduction event. A dextral strike-slip was evident throughout the Early Paleozoic oceanic crust subduction based on the whole set of planar and linear structural data, and the subduction polarity was likely to the north. According to the ophiolitic mélange’s youngest rocks and the existence of 413 Ma granite dykes that are widely exposed in the Munabulake ophiolitic mélange, the Munabulake ophiolitic mélange was most likely emplaced during the Middle Silurian. This Munabulake ophiolitic mélange is similar in age and petrochemical characteristics to the other ophiolites in the South Altun subduction-collision belt, indicating that the Manabulak ophiolite mélange is a westward extension of the Apa-Mangya subduction-collision belt, which formed during the northward subduction of the South Altun Ocean slab during the Early Paleozoic. Thus, the final closing time of the South Altun Ocean is between 413 and 428 Ma.
Heatwaves (HWs) present a major hazard to our society and more extreme heatwaves are expected with future climatic changes. Hence, it is important to improve our understanding of the underlying processes that drive HWs, in order to boost our socioeconomic-ecological resilience. In this study, we quantified the influences of key driving factors (large-scale atmospheric circulation, soil moisture, and sea surface temperature) and their synergies on recent heatwaves in East Asia. We conducted a factor separation analysis for three recent HW events by constraining the key factors in the regional Weather Research and Forecasting model with their climatologies or pseudo-observations in different combinations. Our study showed distinct spatial variations in the HW-controlling factors in East Asia. The synergistic interaction of large-scale circulation and soil moisture was the most important factors in the 2013 Chinese HW. During the 2018 HWs in Korea and Japan, the same stagnant large-scale atmospheric circulation played a dominant role in driving the HW events. The land-atmosphere coupling via soil moisture, its interaction with circulation, and SST exhibited stronger influences during the Korean HW than the Japanese HW. Our analysis also revealed temporal variations in the factors driving Korean and Chinese HWs due to typhoon passage and other multiple processes over heterogeneous surfaces (i.e., topographically induced Foehn winds, large-scale warm advection from the warm ocean, spatial differences in soil moisture). Our findings suggest that future heatwave-related studies should consider interactive contributions of key factors, their interplay with surface heterogeneities of complex terrain.
Himalayan leucogranites are important for understanding the tectonic evolution of collision zones in general and the causes of crustal melting in the Himalayan orogen in particular. This paper aims to understand the melt source and emplacement age of the leucogranites from Sikkim in order to decipher the deep geodynamic processes of the eastern Himalayas. Zircon U-Pb analysis of the Higher Himalayan Sequence (HHS) metamorphic core reveals a prolonged period of crustal melting between > 33 Ma and ca. 14 Ma. Major and trace element abundances are presented for 27 leucogranites from North Sikkim that are classified into two-mica and tourmaline leucogranite types. They are peraluminous in composition, characterized by high SiO2 (70.91-74.9 wt.%), Al2O3 (13.69-15.82 wt.%), and low MgO (0.13-0.74 wt.%). Elemental abundances suggest that Sikkim Himalayan leucogranites are derived from crustal melts. The two-mica leucogranites are derived from a metagreywacke source, whereas the tourmaline leucogranites are sourced from metapelitic sources, with inherited zircons indicating an HHS origin for both types. U-Pb zircon geochronology of the two mica leucogranites indicates ages of ca. 19-15 Ma, consistent with crustal melting recorded in HHS gneisses from Darjeeling. Monazites from both the two-mica and tourmaline leucogranites yield a crystallization age of ca. 15-14 Ma, coeval with movement on the Main Central Thrust and South Tibetan Detachment System which further provides constraints on the timing and mechanism of petrogenesis of leucogranites in the Sikkim Himalayas.
Groundwater contamination source identification (GCSI) is a prerequisite for contamination risk evaluation and efficient groundwater contamination remediation programs. The boundary condition generally is set as known variables in previous GCSI studies. However, in many practical cases, the boundary condition is complicated and cannot be estimated accurately in advance. Setting the boundary condition as known variables may seriously deviate from the actual situation and lead to distorted identification results. And the results of GCSI are affected by multiple factors, including contaminant source information, model parameters, boundary condition, etc. Therefore, if the boundary condition is not estimated accurately, other factors will also be estimated inaccurately. This study focuses on the unknown boundary condition and proposed to identify three types of unknown variables (contaminant source information, model parameters and boundary condition) innovatively. When simulation-optimization (S-O) method is applied to GCSI, the huge computational load is usually reduced by building surrogate models. However, when building surrogate models, the researchers need to select the models and optimize the hyperparameters to make the model powerful, which can be a lengthy process. The automated machine learning (AutoML) method was used to build surrogate model, which automates the model selection and hyperparameter optimization in machine learning engineering, largely reducing human operations and saving time. The accuracy of AutoML surrogate model is compared with the surrogate model used in eXtreme Gradient Boosting method (XGBoost), random forest method (RF), extra trees regressor method (ETR) and elasticnet method (EN) respectively, which are automatically selected in AutoML engineering. The results show that the surrogate model constructed by AutoML method has the best accuracy compared with the other four methods. This study provides reliable and strong support for GCSI.
