Sedimentary elements,evolutions and controlling factors of the Miocene channel system:a case study of the deep-water Taranaki Basin in New Zealand

Deep-water channel systems are important petroleum reservoirs,and many have been discovered worldwide.Understanding deep-water channel sedimentary elements and evolution is helpful for deep-sea petroleum exploration and development.Based on high-resolution 3D seismic data,the Miocene channel system in the deep-water Taranaki Basin,New Zealand,was analyzed by using seismic interpretation techniques such as interlayer attribute extraction and strata slicing.The channel system was divided into five composite channels(CC-I to CC-V)according to four secondary level channel boundaries,and sedimentary elements such as channels,slump deposits,inner levees,mass transport deposits,and hemipelagic drape deposits were identified in the channel system.The morphological characteristics of several composite channels exhibited stark variances,and the overall morphology of the composite channels changed from relatively straight to highly sinuous to relatively straight.The evolution of the composite channels involved a gradual and repeated process of erosion and filling,and the composite channels could be divided into three evolutionary stages:initial erosion-filling,later erosion-filling(multistage),and channel abandonment.The middle Miocene channel system may have formed as a consequence of combined regional tectonic activity and global climatic change,and its intricate morphological alterations may have been influenced by the channel's ability to self-regulate and gravity flow properties.When studying the sedimentary evolution of a large-scale deep-water channel system in the Taranaki Basin during the Oligocene-Miocene,which transitioned from a passive margin to plate convergence,it can be understood how tectonic activity affected the channel and can also provide a theoretical reference for the evolution of the deepwater channels in areas with similar tectonic conversion environments around the world.

Sedimentary architecture models of deepwater turbidite channel systems in the Niger Delta continental slope,West Africa

This paper studied an architecture model of turbidite channel systems based on the shallow- layer high resolution 3D seismic information in the deepwater area in the Niger Delta continental slope, West Africa as a prototype model. Different types of channel systems were identified and the corresponding architecture models were established. The controlling factors, evaluation criteria and spatial distribution of different channel systems were analyzed. This study shows that turbidite channel systems of West Africa could be classified into three types; confined, semi-confined and unconfined, according to the condition of canyon and the levees on both sides. Oil one hand, along the transport direction, channel system evolves from confined to unconfined. Within channel systems, channel complexes, including two types of incised and enveloped, are the most important reservoir bodies. On the other hand, there is a channel complex evolution from incised to enveloped vertically. The geological factors exert impacts of different levels on the architecture of the turbidite channels in different sedimentary systems or even within the same system.

Formation of Nucleolar Channel System in Human Endometrium in vitro is Independent of Progesterone

Objective To determine whether formation of the nucleolar channel system (NCS) in human endometrium depends on the presence of progesteronal steroids. Materials & Methods Tissues of late proliferative endometrium were obtained from 5 normally cycling women of reproductive age. Half of each tissue was cultured in the DMEM medium containing diethylstilbesterol (25 μg/mL) plus medroxyprogesterone acetate (25 μg/mL) (E + P culture). As a control, the other half was cultured in the medium alone. After 100 h incubation, the tissues were assessed for the formation of NCS with transmission electron microscope.Results NCS was observed in the endometrial epithelium treated with E + P or the medium alone. Moreover, giant mitochondria and glycogen accumulation were both seen in epithelia derived from both types of cultures.Conclusion Progesterone would be not indispensable for the formation of NCS in human endometrium. Transition of proliferative endometrium to the secretory stage in vitro could occur even in the absence of both estrogen and progesterone.

Origin and fate of the nucleolar channel system of normal human endometrium

Human normal endometrium was examined in ultrathin sections.Nucleolar channel system(NCS) appeared in the endometrial epithelial cells during the early and mid secretory phase of menstrual cycle.The NCS was a hollow ball like structure of different sizes and was composed of 2 to 5 rows of tubules embedded in an amporphous matrix.On its surface there were numerous electron dense particles resembling ribosomes,It was usually located within or associated with the nucleolus,SOmetimes,it was close to the nuclear envelope or protruding out from the nucleus .On occasion,NCS with simplified structure was found in the perinuclear cytoplasm.Concepts concerning the genesis,involution and function(s) of the NCS were disussed.

Annual pattern of zooplankton communities and their environmental response in a subtropical maritime channel system in the northern Bay of Bengal, Bangladesh

Zooplankton plays an important role in aquatic food webs by fluxing of energy from primary producer to subsequent trophic levels in the food chain. The annual pattern of zooplankton communities and potential environmental drivers were studied in the Kohelia channel, Bangladesh from summer 2014 to spring 2015.Samples were collected using net at a depth of 1 m. A total of 32 species belonged to 18 orders, 27 families and 15 taxonomic groups were identified. Of these species, 22 distributed in all four seasons of which 8 were dominant and highly contributing to the total communities. Species number peaked in summer next to winter and fall in spring while maximum abundance was in summer and minimum in spring. Multivariate analyses showed that there was a clear annual pattern in the zooplankton communities. Species diversity and evenness peaked in spring but fall in autumn while the high value of species richness was found in winter. Biological-environmental best matching（BIO-ENV） analyses conformed that community pattern of zooplankton was mainly driven by transparency salinity, and temperature individually or combined with water nutrients. These results demonstrate that annual pattern of the zooplankton community shaped by channel environmental factors in subtropical channel ecosystems, thus might be used for community-based subtropical coastal water bioassessment.

Seismic Imaging and Reservoir Architecture of Sub-Marine Channel Systems Offshore West Nile Delta of Egypt

Offshore Nile Delta gas reservoirs are dominated by slope-channel systems of Plio-Pleistocene age. High-quality, three-dimensional seismic imaging has significantly helped in defining the geomorphology and architectures of these channels. Integrating seismic, logs and core data from four wells resulted in understanding of different stages of channel development and reservoir quality. The studied reservoirs that are largely controlled by episodes of transgressive-regressive events resulted in deposition of fine grained sediment and shale. Sienna channel complex consists of unconfined channel system with clearly defined development stages. The stages include amalgamated or stacked channels followed by channel abandonment phases and local flooding events. The depositional pattern continued through the Late Pliocene-Pleistocene. SimSat-P1 and SimSat-P2 reservoirs are characterized by isolated sand bodies, most probably relics of fan depositional setting. The depositional scenario that is largely controlled by successive transgression and flooding events resulted in deposition of interbedded, sheet-like, fine grained sediment and shale.

Seismic stratigraphy of the Qiongdongnan deep sea channel system,northwest South China Sea

Based on more than 4000 km 2D seismic data and seismic stratigraphic analysis, we discussed the extent and formation mechanism of the Qiongdongnan deep sea channel. The Qiongdongnan deep sea channel is a large incised channel which extends from the east boundary of the Yinggehai Basin, through the whole Qiongdongnan and the Xisha trough, and terminates in the western part of the northwest subbasin of South China Sea. It is more than 570 km long and 4–8 km wide. The chaotic (or continuous) middle (or high) amplitude, middle (or high) continuity seismic facies of the channel reflect the different lithological distribution of the channel. The channel formed as a complex result of global sea level drop during early Pliocene, large scale of sediment supply to the Yinggehai Basin, inversion event of the Red River strike-slip fault, and tilted direction of the Qiongdongnan Basin. The large scale of sediment supply from Red River caused the shelf break of the Yinggehai Basin to move torwards the S and SE direction and developed large scale of prograding wedge from the Miocene, and the inversion of the Red River strike-slip fault induced the sediment slump which formed the Qiongdongnan deep sea channel.

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

Channel Estimation for Reconfigurable Intelligent Surface Aided Multiuser Millimeter-Wave/THz Systems

It is assumed that reconfigurable intelligent surface(RIS)is a key technology to enable the potential of mmWave communications.The passivity of the RIS makes channel estimation difficult because the channel can only be measured at the transceiver and not at the RIS.In this paper,we propose a novel separate channel estimator via exploiting the cascaded sparsity in the continuously valued angular domain of the cascaded channel for the RIS-enabled millimeter-wave/Tera-Hz systems,i.e.,the two-stage estimation method where the cascaded channel is separated into the base station(BS)-RIS and the RIS-user(UE)ones.Specifically,we first reveal the cascaded sparsity,i.e.,the sparsity exists in the hybrid angular domains of BS-RIS and the RIS-UEs separated channels,to construct the specific sparsity structure for RIS enabled multi-user systems.Then,we formulate the channel estimation problem using atomic norm minimization(ANM)to enhance the proposed sparsity structure in the continuous angular domains,where a low-complexity channel estimator via Alternating Direction Method of Multipliers(ADMM)is proposed.Simulation findings demonstrate that the proposed channel estimator outperforms the current state-of-the-arts in terms of performance.

Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems

In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.

Joint Multi-Domain Channel Estimation Based on Sparse Bayesian Learning for OTFS System

Since orthogonal time-frequency space(OTFS)can effectively handle the problems caused by Doppler effect in high-mobility environment,it has gradually become a promising candidate for modulation scheme in the next generation of mobile communication.However,the inter-Doppler interference(IDI)problem caused by fractional Doppler poses great challenges to channel estimation.To avoid this problem,this paper proposes a joint time and delayDoppler(DD)domain based on sparse Bayesian learning(SBL)channel estimation algorithm.Firstly,we derive the original channel response(OCR)from the time domain channel impulse response(CIR),which can reflect the channel variation during one OTFS symbol.Compare with the traditional channel model,the OCR can avoid the IDI problem.After that,the dimension of OCR is reduced by using the basis expansion model(BEM)and the relationship between the time and DD domain channel model,so that we have turned the underdetermined problem into an overdetermined problem.Finally,in terms of sparsity of channel in delay domain,SBL algorithm is used to estimate the basis coefficients in the BEM without any priori information of channel.The simulation results show the effectiveness and superiority of the proposed channel estimation algorithm.

Channel Estimation for OTFS System over Doubly Spread Sparse Acoustic Channels

This paper addresses sparse channels estimation problem for the generalized linear models(GLM)in the orthogonal time frequency space(OTFS)underwater acoustic(UWA)system.OTFS works in the delay-Doppler domain,where timevarying channels are characterized as delay-Doppler impulse responses.In fact,a typical doubly spread UWA channel is associated with several resolvable paths,which exhibits a structured sparsity in the delayDoppler domain.To leverage the structured sparsity of the doubly spread UWA channel,we develop a structured sparsity-based generalized approximated message passing(GAMP)algorithm for reliable channel estimation in quantized OTFS systems.The proposed algorithm has a lower computational complexity compared to the conventional Bayesian algorithm.In addition,the expectation maximum algorithm is employed to learn the sparsity ratio and the noise variance.Simulation and experimental results show that the proposed algorithm has superior performance and low computational complexity for quantized OTFS systems.

Robust Beamforming Under Channel Prediction Errors for Time-Varying MIMO System

The accuracy of acquired channel state information(CSI)for beamforming design is essential for achievable performance in multiple-input multiple-output(MIMO)systems.However,in a high-speed moving scene with time-division duplex(TDD)mode,the acquired CSI depending on the channel reciprocity is inevitably outdated,leading to outdated beamforming design and then performance degradation.In this paper,a robust beamforming design under channel prediction errors is proposed for a time-varying MIMO system to combat the degradation further,based on the channel prediction technique.Specifically,the statistical characteristics of historical channel prediction errors are exploited and modeled.Moreover,to deal with random error terms,deterministic equivalents are adopted to further explore potential beamforming gain through the statistical information and ultimately derive the robust design aiming at maximizing weighted sum-rate performance.Simulation results show that the proposed beamforming design can maintain outperformance during the downlink transmission time even when channels vary fast,compared with the traditional beamforming design.

Machine Learning-Based Channel State Estimators for 5G Wireless Communication Systems

For a 5G wireless communication system,a convolutional deep neural network(CNN)is employed to synthesize a robust channel state estimator(CSE).The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state information.Also,it utilizes pilots to offer more helpful information about the communication channel.The proposedCNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory(BiLSTM/LSTM)NNs-based CSEs.The CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based estimators.Using three different loss function-based classification layers and the Adam optimization algorithm,a comparative study was conducted to assess the performance of the presented DNNs-based CSEs.The BiLSTM-CSE outperforms LSTM,CNN,conventional least squares(LS),and minimum mean square error(MMSE)CSEs.In addition,the computational and learning time complexities for DNN-CSEs are provided.These estimators are promising for 5G and future communication systems because they can analyze large amounts of data,discover statistical dependencies,learn correlations between features,and generalize the gotten knowledge.

QBFO-BOMP Based Channel Estimation Algorithm for mmWave Massive MIMO Systems

At present,the traditional channel estimation algorithms have the disadvantages of over-reliance on initial conditions and high complexity.The bacterial foraging optimization(BFO)-based algorithm has been applied in wireless communication and signal processing because of its simple operation and strong self-organization ability.But the BFO-based algorithm is easy to fall into local optimum.Therefore,this paper proposes the quantum bacterial foraging optimization(QBFO)-binary orthogonal matching pursuit(BOMP)channel estimation algorithm to the problem of local optimization.Firstly,the binary matrix is constructed according to whether atoms are selected or not.And the support set of the sparse signal is recovered according to the BOMP-based algorithm.Then,the QBFO-based algorithm is used to obtain the estimated channel matrix.The optimization function of the least squares method is taken as the fitness function.Based on the communication between the quantum bacteria and the fitness function value,chemotaxis,reproduction and dispersion operations are carried out to update the bacteria position.Simulation results showthat compared with other algorithms,the estimationmechanism based onQBFOBOMP algorithm can effectively improve the channel estimation performance of millimeter wave(mmWave)massive multiple input multiple output(MIMO)systems.Meanwhile,the analysis of the time ratio shows that the quantization of the bacteria does not significantly increase the complexity.

UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

Orthogonal time frequency space(OTFS)technique,which modulates data symbols in the delay-Doppler(DD)domain,presents a potential solution for supporting reliable information transmission in highmobility vehicular networks.In this paper,we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler.We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP),which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM).The empirical state evolution(SE)analysis is then leveraged to predict the performance of our proposed algorithm.To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM)algorithm.Finally,Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

Distribution of 26 Metals in the Waters of the Aquatic Ecosystems of the Cotonou Channel and Lake Nokoué, Benin

Metallic elements have various origins: natural and anthropogenic sources as geochemical, marine and atmospheric sources resulting from the fallout of pollutants emitted or dust raised and which are transported by water and air currents. Thus marine, brackish and fresh continental waters may have high metal concentrations. In addition, some essential metals can become toxic above certain concentration values in aquatic environments. The aquatic ecosystems of Cotonou channel and lake Nokoué receive the pollutants charges from the town cities of Cotonou, Abomey-Calavi and town hall of So Ava. The aim of this study is to analyze waters from Eighteen (18) stations identified in the two ecosystems (nine by ecosystem). The concentrations of magnesium (Mg), calcium (Ca), vanadium (V), chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), selenium (Se), cadmium (Cd), beryllium (Be), aluminum (Al), strontium (Sr), molybdenum (Mo), silver (Ag), tin (Sn), barium (Ba), platinum (Pt), mercury (Hg), thallium (Tl), lead (Pb), thorium (Th) and uranium (U) were measured after acid digestion of the water samples using the inductively coupled plasma source mass spectrometer (ICP-MS). The results of the analyses indicate an unequal distribution of metals in the different ecosystems. However, atypical concentrations were observed at some stations of the lake and the channel. Magnesium, calcium and manganese have very high values in Lake Nokoué respectively at Ganvié market station GAN_M (2990 ± 105 mg/L), Ganvié center, station GAN_C (4991 ± 177 mg/L) and Lake middle station MLak4 (10662 ± 17.03 μg/L). On the other hand, iron, aluminum and strontium have very high concentrations in the Cotonou Channel respectively at Agbato station AGB (5236 ± 103 and 8289 ± 519 μg/L) and at the estuary station EST (6118 ± 68 μg/L). The concentrations were compared to wells and cborehole waters in sixth neighborhood of Cotonou. We have used statistical analyzers such as MANOVA which have made it possible to classify the waters and metals in the ecosystems studied compared to groundwater and Well water waters. We use hierarchical clustering on principal components to identify similarities between stations based on metal concentration with R software packages “FactoMineR” and “factoextra”. In general, we can conclude that most of the metals have an anthropogenic source except strontium and major elements (Ca and Mg) which could respectively provide from marine waters and geochemical sources.

Statistical Channel Modeling for Indoor VLC Communications Based on Channel Measurements

Visible light communication(VLC)has attracted much attention in the research of sixthgeneration(6G)systems.Furthermore,channel modeling is the foundation for designing efficient and robust VLC systems.In this paper,we present extensive VLC channel measurement campaigns in indoor environments,i.e.,an office and a corridor.Based on the measured data,the large-scale fading characteristics and multipath-related characteristics,including omnidirectional optical path loss(OPL),K-factor,power angular spectrum(PAS),angle spread(AS),and clustering characteristics,are analyzed and modeled through a statistical method.Based on the extracted statistics of the above-mentioned channel characteristics,we propose a statistical spatial channel model(SSCM)capable of modeling multipath in the spatial domain.Furthermore,the simulated statistics of the proposed model are compared with the measured statistics.For instance,in the office,the simulated path loss exponent(PLE)and the measured PLE are 1.96and 1.97,respectively.And,the simulated medians of AS and measured medians of AS are 25.94°and 24.84°,respectively.Generally,the fact that the simulated results fit well with measured results has demonstrated the accuracy of our SSCM.

A Correlation-Based Stochastic Model for Massive MIMO Channel

In this paper,the channel impulse response matrix(CIRM)can be expressed as a sum of couplings between the steering vectors at the base station(BS)and the eigenbases at the mobile station(MS).Nakagami distribution was used to describe the fading of the coupling between the steering vectors and the eigenbases.Extensive measurements were carried out to evaluate the performance of this proposed model.Furthermore,the physical implications of this model were illustrated and the capacities are analyzed.In addition,the azimuthal power spectrum(APS)of several models was analyzed.Finally,the channel hardening effect was simulated and discussed.Results showed that the proposed model provides a better fit to the measured results than the other CBSM,i.e.,Weichselberger model.Moreover,the proposed model can provide better tradeoff between accuracy and complexity in channel synthesis.This CIRM model can be used for massive MIMO design in the future communication system design.

Artificial Intelligence Based Multi-Scenario mmWave Channel Modeling for Intelligent High-Speed Train Communications

A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a trend.This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST communications.Firstly,the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios.By setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is acquired.Then,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario classification.Finally,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.