SHRIMP Zircon U-Pb Age of the Sidingheishan Mafic-Ultramafic Intrusion in the Southern Margin of the Central Asian Orogenic Belt,NW China and its Petrogenesis implication

Objective The Sidingheishan mafic-ultramafic intrusion is located in the eastern part of the North Tianshan Mountains.This work used zircon U-Pb age data,bulk rock major and trace elements,Sr-Nd-Pb isotope data to assess mantle source characteristics and crustal assimilation of the parental magma of the Sidingheishan intrusion.We have also discussed the tectonic evolution of the southern margin of the Central Asian Orogenic belt in the Late Paleozoic.

Geochemical Characteristics and Metallogenesis of the Qingkuangshan Ni-Cu-PGE Mineralized Mafic-Ultramafic Intrusion in Huili County, Sichuan Province, SW China

The Qingkuangshan Ni-Cu-PGE deposit, located in the Xiaoguanhe region of Huili County, Sichuan Province, is one of several Ni-Cu-PGE deposits in the Emeishan Large Igneous Province (ELIP). The ore-bearing intrusion is a mafic-ultramafic body. This paper reports major elements, trace elements and platinum-group elements in different types of rocks and sulfide-mineralized samples in the intrusion. These data are used to evaluate the source mantle characteristics, the degree of mantle partial melting, the composition of parental magma and the ore-forming processes. The results show that Qingkuangshan intrusion is part of the ELIP. The rocks have trace element ratios similar to the coeval Emeishan basalts. The primitive mantle-normalized patterns of Ni-Cu-PGE have positive slopes, and the ratios of Pd/Ir are lower than 22. The PGE compositions of sulfide ores and associated rocks are characterized by Ru depletion. The PGE contents in bulk sulfides are slightly depleted relative to Ni and Cu, which is similar to the Yangliuping Ni-Cu-PGE deposit. The composition of the parental magma for the intrusion is estimated to contain about 14.65 wt% MgO, 48.66 wt% SiO2 and 15.48 wt% FeOt, and the degree of mantle partial melting is estimated to be about 20%. In comparison with other typical Ni-Cu-PGE deposits in the ELIP, the Qingkuangshan Ni-Cu-PGE deposit has lower PGE contents than the Jinbaoshan PGE deposit, but has higher PGE contents than the Limahe and Baimazhai Ni-Cu deposit, and has similar PGE contents to the Yangliuping Ni-Cu-PGE deposit. The moderate PGE depletions in the bulk sulfide of the Qingkuanghan deposit suggest that the parental magma of the host intrusion may have undergone minor sulfide segregation at depth. The mixing calculations suggests that an average of 10% crustal contamination in the magma, which may have been the main cause of sulfide saturation in the magma. We propose that sulfide segregation from a moderately PGE depleted magma took place prior to magma emplacement at Qingkuangshan, that small amounts of immiscible sulfide droplets and olivine and chromite crystals were suspended in the ascending magma, and that the suspended materials settled down when the magma passed trough the Qingkuangshan conduit. The Qingkuangshan sulfide-bearing intrusion is interpreted to a feeder of Emeishan flood basalts in the region.

Geochemical characteristics and tectonic setting of the Tuerkubantao mafic-ultramafic intrusion in West Junggar,Xinjiang,China

Mineral chemistry,whole-rock major oxide,and trace element compositions have been determined for the Tuerkubantao mafic-ultramafic intrusion,in order to understand the early Paleozoic tectonic evolution of the West Junggar orogenic belt at the southern margin of the Central Asian orogenic belt.The Tuerkubantao mafic-ultramafic intrusion is a well-differentiated complex comprising peridotite,olivine pyroxenite,gabbro,and diorite.The ultramafic rocks are mostly seen in the central part of the intrusion and surrounded by mafic rocks.The Tuerkubantao intrusive rocks are characterized by enrichment of large ion lithophile elements and depleted high field strength elements relative to N-MORB.In addition,the Tuerkubantao intrusion displays relatively low Th/U and Nb/U(1.13—2.98 and 2.53—7.02,respectively) and high La/Nb and Ba/Nb(1.15—4.19 and 37.7—79.82,respectively).These features indicate that the primary magma of the intrusion was derived from partial melting of a previously metasomatized mantle source in a subduction setting.The trace element patterns of peridotites,gabbros,and diorite in the Tuerkubantao intrusion have sub-parallel trends,suggesting that the different rock types are related to each other by differentiation of the same primary magma.The intrusive contact between peridotite and gabbro clearly suggest that the Tuerkubantao is not a fragment of an ophiolite.However,the Tuerkubantao intrusion displays many similarities with Alaskan-type mafic-ultramafic intrusions along major sutures of Phanerozoic orogenic belts.Common features include their geodynamic setting,internal lithological zoning,and geochemistry.The striking similarities indicate that the middle Devonian Tuerkubantao intrusion likely formed in a subduction-related setting similar to that of the Alaskan-type intrusions.In combination with the Devonian magmatism and porphyry mineralization,we propose that subduction of the oceanic slab has widely existed in the expansive oceans during the Devonian around the Junggar block.

Geochronology,Petrology and Geochemistry of Xingdi No.3 Mafic-Ultramafic Intrusions in the Northeastern Tarim Craton,NW China

The Xingdi mafic-ultramafic intrusions occur in the northeastern margin of the Tarim craton. The Xingdi No. 3 intrusion is the smallest of four intrusions, with an exposed area of 1.7 km^2, and the zircon U-Pb age of the intrusion is 752±4 Ma. The intrusion consists of gabbros, pyroxenites and peridotites, and exhibits a crystallization sequence of the main rock-forming minerals as olivine, orthopyroxene, clinopyroxene and plagioclase. Mineralization occurred at or near the boundaries of the intrusion between pyroxenites and peridotites, and appears as a layered or lenticular shape about 500 m long and 4–15 m wide. The primary sulfides have a relatively simple mineralogy dominated by pyrrhotite-pentlandite-chalcopyrite assemblages, which occur as droplet, star-like and graphic texture and locally sideronitic structures. Geochronological and geochemistry investigations suggest that the Xingdi mafic-ultramafic intrusions and coeval volcanic rock in the Kuluktag area of the Tarim craton formed in an intracontinental breakup environment. Based on the composition of the dominant rockforming minerals and covariant relationships of other oxides versus Mg O, the parental magma of the Xingdi No.3 intrusion belongs to high-Mg tholeiitic basaltic magmas with Mg O of 10.78 wt%. The Xingdi No.3 intrusive rocks are characterized by light REE enrichment relative to heavy REE, negative Nb-Ta anomalies, low ^(143)Nd/^(144)Nd ratios(from 0.511183 to 0.511793) and high initial ^(87)Sr/^(86)Sr ratios(from 0.7051 to 0.7113). The magma was derived from the enriched-lithospheric mantle and was contaminated during emplacement. According to rock assemblages, mineralization, olivine characteristics, geochemical characteristics and mass balance, there are better copper-nickel ore prospects in the Xingdi No.3 intrusion than in the other three intrusions in the area.

Strengthening Network Security: Deep Learning Models for Intrusion Detectionwith Optimized Feature Subset and Effective Imbalance Handling

In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security.This paper presents a novel intrusion detection system consisting of a data prepro-cessing stage and a deep learning model for accurately identifying network attacks.We have proposed four deep neural network models,which are constructed using architectures such as Convolutional Neural Networks(CNN),Bi-directional Long Short-Term Memory(BiLSTM),Bidirectional Gate Recurrent Unit(BiGRU),and Attention mechanism.These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models,we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset,resulting in an optimized feature subset.Moreover,we address class imbalance in the dataset using focal loss.Finally,we employ the BO-TPE algorithm to optimize the hyperparameters of the four models,maximizing their detection performance.The test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data effectively.In binary and multiclass experiments,it achieved accuracy rates of 0.999158 and 0.999091,respectively,surpassing other state-of-the-art methods.

ResNeSt-biGRU: An Intrusion DetectionModel Based on Internet of Things

The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasingdemands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device hascaught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. Thishas resulted in a myriad of security challenges, including information leakage, malware propagation, and financialloss, among others. Consequently, developing an intrusion detection system to identify both active and potentialintrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practicalintrusion detection model that combines the strengths of ResNeSt, a variant of Residual Neural Network, andbidirectionalGated RecurrentUnitNetwork (biGRU).Our ResNeSt-biGRUframework diverges fromconventionalintrusion detection systems (IDS) by employing this dual-layeredmechanism that exploits the temporal continuityand spatial feature within network data streams, a methodological innovation that enhances detection accuracy.In conjunction with this, we introduce the PreIoT dataset, a compilation of prevalent IoT network behaviors, totrain and evaluate IDSmodels with a focus on identifying potential intrusion traffics. The effectiveness of proposedscheme is demonstrated through testing, wherein it achieved an average accuracy of 99.90% on theN-BaIoT datasetas well as on the PreIoT dataset and 94.45% on UNSW-NB15 dataset. The outcomes of this research reveal thepotential of ResNeSt-biGRU to bolster security measures, diminish intrusion-related vulnerabilities, and preservethe overall security of IoT ecosystems.

Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

Lightweight Intrusion Detection Using Reservoir Computing

The blockchain-empowered Internet of Vehicles(IoV)enables various services and achieves data security and privacy,significantly advancing modern vehicle systems.However,the increased frequency of data transmission and complex network connections among nodes also make them more susceptible to adversarial attacks.As a result,an efficient intrusion detection system(IDS)becomes crucial for securing the IoV environment.Existing IDSs based on convolutional neural networks(CNN)often suffer from high training time and storage requirements.In this paper,we propose a lightweight IDS solution to protect IoV against both intra-vehicle and external threats.Our approach achieves superior performance,as demonstrated by key metrics such as accuracy and precision.Specifically,our method achieves accuracy rates ranging from 99.08% to 100% on the Car-Hacking dataset,with a remarkably short training time.

Network Intrusion Traffic Detection Based on Feature Extraction

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

Network intrusion detection systems(NIDS)based on deep learning have continued to make significant advances.However,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(TCNs)can lead to models that ignore the impact of network traffic features at different scales on the detection performance.On the other hand,some intrusion detection methods considermulti-scale information of traffic data,but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features.To address both of these issues,we propose a hybrid Convolutional Neural Network that supports a multi-output strategy(BONUS)for industrial internet intrusion detection.First,we create a multiscale Temporal Convolutional Network by stacking TCN of different scales to capture the multiscale information of network traffic.Meanwhile,we propose a bi-directional structure and dynamically set the weights to fuse the forward and backward contextual information of network traffic at each scale to enhance the model’s performance in capturing the multi-scale temporal features of network traffic.In addition,we introduce a gated network for each of the two branches in the proposed method to assist the model in learning the feature representation of each branch.Extensive experiments reveal the effectiveness of the proposed approach on two publicly available traffic intrusion detection datasets named UNSW-NB15 and NSL-KDD with F1 score of 85.03% and 99.31%,respectively,which also validates the effectiveness of enhancing the model’s ability to capture multi-scale temporal features of traffic data on detection performance.

Fusion of Spiral Convolution-LSTM for Intrusion DetectionModeling

Aiming at the problems of low accuracy and slow convergence speed of current intrusion detection models,SpiralConvolution is combined with Long Short-Term Memory Network to construct a new intrusion detection model.The dataset is first preprocessed using solo thermal encoding and normalization functions.Then the spiral convolution-Long Short-Term Memory Network model is constructed,which consists of spiral convolution,a two-layer long short-term memory network,and a classifier.It is shown through experiments that the model is characterized by high accuracy,small model computation,and fast convergence speed relative to previous deep learning models.The model uses a new neural network to achieve fast and accurate network traffic intrusion detection.The model in this paper achieves 0.9706 and 0.8432 accuracy rates on the NSL-KDD dataset and the UNSWNB-15 dataset under five classifications and ten classes,respectively.

A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this development has expanded the potential targets that hackers might exploit.Without adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or alteration.The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks.This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)units.The proposed model can identify various types of cyberattacks,including conventional and distinctive forms.Recurrent networks,a specific kind of feedforward neural networks,possess an intrinsic memory component.Recurrent Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended periods.Metrics such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual cyberattacks.RNNs are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection Model.This model utilises Recurrent Neural Networks,specifically exploiting LSTM techniques.The proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

Intrusion Detection Model Using Chaotic MAP for Network Coding Enabled Mobile Small Cells

Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and assaults.Complex security systems,such as Intrusion Detection Systems(IDS),are essential due to the limitations of simpler security measures,such as cryptography and firewalls.Due to their compact nature and low energy reserves,wireless networks present a significant challenge for security procedures.The features of small cells can cause threats to the network.Network Coding(NC)enabled small cells are vulnerable to various types of attacks.Avoiding attacks and performing secure“peer”to“peer”data transmission is a challenging task in small cells.Due to the low power and memory requirements of the proposed model,it is well suited to use with constrained small cells.An attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code(HHMAC)hash between transmissions since the HMAC function is generated using the shared secret.In this research,a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure“peer”to“peer”data transmission model using lightweight H-MAC(1D-LM-P2P-LHHMAC)is proposed with accurate intrusion detection.The proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels,Key Pair Generation Time Levels,Chaotic Map Accuracy Levels,Intrusion Detection Accuracy Levels,and the results represent that the proposed model performance in chaotic map accuracy level is 98%and intrusion detection is 98.2%.The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high.

A Time Series Intrusion Detection Method Based on SSAE,TCN and Bi-LSTM

In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.

Mineralogy and Chemistry Characterization of the New Basaltic Intrusion at Maasser El Chouf/Lebanon

The importance of this study is to identify the newly reordered and recognized basaltic intrusion for the first time in Maasser El Chouf in Lebanon. The recorded basaltic intrusion cut the Jurassic-Lower Cretaceous rock in this area. Necessary field inspection, geology, mineralogy and chemical tests were carried out on 8 basalt samples to determine their mineralogy, petrography and chemical composition. Representative samples have been tested with polarizing microscope, X-ray diffraction (XRD) and X-ray fluorescence (XRF). Petrographic and mineralogical studies show that the basalt is characterized by presence mainly of calcic-plagioclase feldspar, pyroxene-augite and olivine minerals. Secondary minerals of iron oxides also present (ilmenite and magnetite). The most appeared property is the alteration of olivine mineral to iddingsite that indicated highly weathered process. The composition of the basaltic samples reflects ultrabasic-basic type (Basanite-Tholeiitic basalt). The existence of volcanic activity occurred mostly with Pliocene age (< 2 Ma) as indicated by previous studies for similar basalt in Lebanon. Possibly, these boulders have been carried up from some deeper intrusive magmatic body under very active tension zones. Volcanism of Lebanon basalts belong to the alkaline olivine basalt, suite generally associated with tension, rifting and block faulting movements of the continental crust. Most of the volcanisms in Lebanon and in Harrat Ash Shaam Basalt from Syria and Palestine through Jordan to Saudi Arabia are related and connected to the opening of the Red Sea Rift System, making the area with tremendous volcanic tectonic activities.

Himalayas as a global hot spot of springtime stratospheric intrusions:Insight from isotopic signatures in sulfate aerosols

Downward transport of stratospheric air into the troposphere(identified as stratospheric intrusions)could potentially modify the radiation budget and chemical of the Earth's surface atmosphere.As the highest and largest plateau on earth,the Tibetan Plateau including the Himalayas couples to global climate,and has attracted widespread attention due to rapid warming and cryospheric shrinking.Previous studies recognized strong stratospheric intrusions in the Himalayas but are poorly understood due to limited direct evidences and the complexity of the meteorological dynamics of the third pole.Cosmogenic^(35)S is a radioactive isotope predominately produced in the lower stratosphere and has been demonstrated as a sensitive chemical tracer to detect stratospherically sourced air mass in the planetary boundary layer.Here,we report 6-month(April–September 2018)observation of^(35)S in atmospheric sulfate aerosols(^(35)SO_(4)^(2-))collected from a remote site in the Himalayas to reveal the stratospheric intrusion phenomenon as well as its potential impacts in this region.Throughout the sampling campaign,the^(35)SO_(4)^(2-)concentrations show an average of 1,070±980 atoms/m^(3).In springtime,the average is 1,620±730 atoms/m^(3),significantly higher than the global existing data measured so far.The significant enrichments of^(35)SO_(4)^(2-)measured in this study verified the hypothesis that the Himalayas is a global hot spot of stratospheric intrusions,especially during the springtime as a consequence of its unique geology and atmospheric couplings.In combined with the ancillary evidences,e.g.,oxygen-17 anomaly in sulfate and modeling results,we found that the stratospheric intrusions have a profound impact on the surface ozone concentrations over the study region,and potentially have the ability to constrain how the mechanisms of sulfate oxidation are affected by a change in plateau atmospheric properties and conditions.This study provides new observational constraints on stratospheric intrusions in the Himalayas,which would further provide additional information for a deeper understanding on the environment and climatic changes over the Tibetan Plateau.

An Assessment of Saltwater Intrusion in Coastal Regions of Lagos, Nigeria

This paper explains various factors that contribute to saltwater intrusion, including overexploitation of freshwater resources and climate change as well as the different techniques essential for effective saltwater intrusion management. The impact of saltwater intrusion along coastal regions and its impact on the environment, hydrogeology and groundwater contamination. It suggests potential solutions to mitigate the impact of saltwater intrusion, including effective water management and techniques for managing SWI. The application of A.I (assessment index) serves as a guideline to correctly identify wells with SWI ranging from no intrusion, slight intrusion and strong intrusion. The challenges of saltwater intrusion in Lagos and the salinization of wells were investigated using the hydro-chemical parameters. The study identifies four wells (“AA”, “CMS”, “OBA” and “VIL”) as having high electric conductivities, indicating saline water intrusion, while other wells (“EBM”, “IKJ, and “IKO”) with lower electric conductivities, indicate little or no salt-water intrusion, and “AJ” well shows slight intrusion. The elevation of the wells also played a vital role in the SWI across coastal regions of Lagos. The study recommends continuous monitoring of coastal wells to help sustain and reduce saline intrusion. The findings of the study are important for policymakers, researchers, and practitioners who are interested in addressing the challenges of saltwater intrusion along coastal regions. We assessed the SWI across the eight (8) wells using the Assessment Index to identify wells with SWI. Wells in “CMS” and “VIL” has strong intrusions. A proposed classification system based on specific ion ratios categorizes water quality from good (+) to highly (-) contaminated (refer to Table 4). These findings underscore the need for attention and effective management strategies to address groundwater unsuitability for various purposes.

Geochemical and Petrological Studies on the Early Carboniferous Sidingheishan Mafic-Ultramafic Iintrusion in the Southern Margin of the Central Asian Orogenic Belt,NW China

The Sidingheishan mafic-ultramafic intrusion is located in the eastern part of the Northern Tianshan Mountain, along the southern margin of the Central Asian Orogenic Belt in northern Xinjiang autonomous region of China. The Sidingheishan intrusion is mainly composed of wehrlite, olivine websterite, olivine gabbro, gabbro and hornblende gabbro. At least two pulses of magma were involved in the formation of the intrusion. The first pulse of magma produced an olivine-free unit and the second pulse produced an olivine-bearing unit. The magmas intruded the Devonian granites and granodiorites.An age of 351.4±5.8 Ma(Early Carboniferous) for the Sidingheishan intrusion has been determined by U-Pb SHRIMP analysis of zircon grains separated from the olivine gabbro unit. A U-Pb age of 359.2±6.4 Ma from the gabbro unit has been obtained by LA-ICP-MS. Olivine of the Sidingheishan intrusion reaches 82.52 mole% Fo and 1414 ppm Ni. On the basis of olivine-liquid equilibria, it has been calculated that the MgO and FeO included in the parental magma of a wehrlite sample were approximately10.43 wt% and 13.14 wt%, respectively. The Sidingheishan intrusive rocks are characterized by moderate enrichments in Th and Sm, slight enrichments in light REE, and depletions in Nb, Ta, Zr and Hf. The ε_(Nd)(t) values in the rock units vary from +6.70 to +9.64, and initial ^(87)Sr/^(86)Sr ratios range between 0.7035 and0.7042. Initial ^(206)Pb/^(204)Pb, ^(207)Pb/^(204)Pb and ^(208)Pb/^(204)Pb values fall in the ranges of 17.23-17.91,15.45-15.54 and 37.54-38.09 respectively. These characteristics are collectively similar to the Heishan intrusion and the Early Carboniferous subduction related volcanic rocks in the Santanghu Basin, North Tianshan and Beishan area. The low(La/Gd)_(PM) values between 0.26 and 1.77 indicate that the magma of the Sidingheishan intrusion was most likely derived from a depleted spinel-peridotite mantle.(Th/Nb)_(PM)ratios from 0.59 to 20.25 indicate contamination of the parental magma in the upper crust.Crystallization modeling methods suggest that the parental magma of the Sidingheishan intrusion was generated by flush melting of the asthenosphere and subsequently there was about 10 vol%contamination from a granitic melt. This was followed by about 5 vol% assimilation of upper crustal rocks. Thus, the high-Mg basaltic parental magma of Sidingheishan intrusion is interpreted to have formed from partial melting of the asthenosphere during the break-off of a subducted slab.

Sm-Nd age of syncollisional mafic-ultramafic intrusions in the Dabie Mountains

A Sm_Nd age of (228±42) Ma with initial ε Nd =-16.4 for the Renjiawan pyroxenite intrusion in the North Dabie terrane is reported. This age with another Sm_Nd age of (230±44) Ma for the Zhujiapu pyroxenite in the same terrane documents that the pyroxenite in the North Dabie terrane are formed during continental subduction time of the Yangtze craton in the Triassic.

Geochemistry of Platinum Group Elements in Mafic-Ultramafic Rocks of Xinjie Intrusion

The platinum group elements (PGE) in the mafic ultramafic suite in the Xinjie layered intrusion and associated basalts and syenites were analyzed using neutron activation techniques after fire assay preconcentration. On this basis, the geochemistry of the platinum group during the magmatic stage is discussed. With respect to PGE distribution, the Xinjie layered intrusion is similar to the Bushveld ferruginous ultramafic series and is distinct from komatiite and Alpine type peridotite. It is also similar to the Emeishan basalt in PGE characteristics, implying that the original magmas of them may be of the same type.