High-resolution seismic inversion method based on joint data-driven in the time-frequency domain

Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains.Time-domain inversion has stronger stability and noise resistance compared to frequencydomain inversion.Frequency domain inversion has stronger ability to identify small-scale bodies and higher inversion resolution.Therefore,the research on the joint inversion method in the time-frequency domain is of great significance for improving the inversion resolution,stability,and noise resistance.The introduction of prior information constraints can effectively reduce ambiguity in the inversion process.However,the existing modeldriven time-frequency joint inversion assumes a specific prior distribution of the reservoir.These methods do not consider the original features of the data and are difficult to describe the relationship between time-domain features and frequency-domain features.Therefore,this paper proposes a high-resolution seismic inversion method based on joint data-driven in the time-frequency domain.The method is based on the impedance and reflectivity samples from logging,using joint dictionary learning to obtain adaptive feature information of the reservoir,and using sparse coefficients to capture the intrinsic relationship between impedance and reflectivity.The optimization result of the inversion is achieved through the regularization term of the joint dictionary sparse representation.We have finally achieved an inversion method that combines constraints on time-domain features and frequency features.By testing the model data and field data,the method has higher resolution in the inversion results and good noise resistance.

Underdetermined DOA estimation and blind separation of non-disjoint sources in time-frequency domain based on sparse representation method

This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.

Surface wave attenuation based polarization attributes in time-frequency domain for multicomponent seismic data

In the paper, we propose a surface wave suppression method in time-frequency domain based on the wavelet transform, considering the characteristic difference of polarization attributes, amplitude energy and apparent velocity between the effective signals and strong surface waves. First, we use the proposed method to obtain time-frequency spectra of seismic signals by using the wavelet transform and calculate the instantaneous polarizability at each point based on instantaneous polarization analysis. Then, we separate the surface wave area from the signal area based on the surface-wave apparent velocity and the average energy of the signal. Finally, we combine the polarizability, energy, and frequency characteristic to identify and suppress the signal noise. Model and field data are used to test the proposed filtering method.

Acoustic reflection well logging modeling using the frequency-domain finite-element method with a hybrid PML

In this paper, we propose a hybrid PML （H-PML） combining the normal absorption factor of convolutional PML （C-PML） with tangential absorption factor of Mutiaxial PML （M-PML）. The H-PML boundary conditions can better suppress the numerical instability in some extreme models, and the computational speed of finite-element method and the dynamic range are greatly increased using this HPML. We use the finite-element method with a hybrid PML to model the acoustic reflection of the interface when wireline and well logging while drilling （LWD）, in a formation with a reflector outside the borehole. The simulation results suggests that the PS- and SP- reflected waves arrive at the same time when the inclination between the well and the outer interface is zero, and the difference in arrival times increases with increasing dip angle. When there are fractures outside the well, the reflection signal is clearer in the subsequent reflection waves and may be used to identify the fractured zone. The difference between the dominant wavelength and the model scale shows that LWD reflection logging data are of higher resolution and quality than wireline acoustic reflection logging.

Comparison of delay compensation methods for real-time hybrid simulation using frequency-domain evaluation index

The delay compensation method plays an essential role in maintaining the stability and achieving accurate real-time hybrid simulation results. The effectiveness of various compensation methods in different test scenarios, however, needs to be quantitatively evaluated. In this study, four compensation methods （i.e., the polynomial extrapolation, the linear acceleration extrapolation, the inverse compensation and the adaptive inverse compensation） are selected and compared experimentally using a frequency evaluation index （FEI） method. The effectiveness of the FEI method is first verified through comparison with the discrete transfer fimction approach for compensation methods assuming constant delay. Incomparable advantage is further demonstrated for the FEI method when applied to adaptive compensation methods, where the discrete transfer function approach is difficult to implement. Both numerical simulation and laboratory tests with predefined displacements are conducted using sinusoidal signals and random signals as inputs. Findings from numerical simulation and experimental results demonstrate that the FEI method is an efficient and effective approach to compare the performance of different compensation methods, especially for those requiring adaptation of compensation parameters.

DETERMINING THE DISCRIMINATING DOMAIN FOR HYBRID LINEAR DIFFERENTIAL GAME WITH TWO PLAYERS AND TWO TARGETS

This paper studies a bounded discriminating domain for hybrid linear differential game with two players and two targets using viability theory. First of all, we prove that the convex hull of a closed set is also a discriminating domain if the set is a discriminating domain. Secondly, in order to determine that a bounded polyhedron is a discriminating domain, we give a result that it only needs to verify that the extreme points of the polyhedron meet the viability conditions. The difference between our result and the existing ones is that our result just needs to verify the finite points （extreme points） and the existing ones need to verify all points in the bounded polyhedron.

Substructure hybrid testing of reinforced concrete shear wall structure using a domain overlapping technique

An online hybrid test was carried out on a 40-story 120-m high concrete shear wall structure. The structure was divided into two substructures whereby a physical model of the bottom three stories was tested in the laboratory and the upper 37 stories were simulated numerically using ABAQUS. An overlapping domain method was employed for the bottom three stories to ensure the validity of the boundary conditions of the superstructure. Mixed control was adopted in the test. Displacement control was used to apply the horizontal displacement, while two controlled force actuators were applied to simulate the overturning moment, which is very large and cannot be ignored in the substructure hybrid test of high-rise buildings. A series of tests with earthquake sources of sequentially increasing intensities were carried out. The test results indicate that the proposed hybrid test method is a solution to reproduce the seismic response of high-rise concrete shear wall buildings. The seismic performance of the tested precast high-rise building satisfies the requirements of the Chinese seismic design code.

A ROBUST PHASE-ONLY DIRECT DATA DOMAIN ALGORITHM BASED ON GENERALIZED RAYLEIGH QUOTIENT OPTIMIZATION USING HYBRID GENETIC ALGORITHM

A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on gen- eralized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency and computational speed are improved via the hybrid GA com- posed of standard GA and Nelder-Mead simplex algorithms. First, the objective function, with a form of generalized Rayleigh quotient, is derived via the standard D3LS algorithm. It is then taken as a fitness function and the unknown phases of all adaptive weights are taken as decision variables. Then, the nonlinear optimization is performed via the hybrid GA to obtain the optimized solution of phase-only adaptive weights. As a phase-only adaptive algorithm, the proposed algorithm is sim- pler than conventional algorithms when it comes to hardware implementation. Moreover, it proc- esses only a single snapshot data as opposed to forming sample covariance matrix and operating matrix inversion. Simulation results show that the proposed algorithm has a good signal recovery and interferences nulling performance, which are superior to that of the phase-only D3LS algorithm based on standard GA.

Hybrid domain multipactor prediction algorithm and its CUDA parallel implementation

Based on the finite element method(FEM)in the frequency domain and particle-in-cell approach in the time domain,a hybrid domain multipactor threshold prediction algorithm is proposed in this paper.The proposed algorithm has the advantages of the frequency domain and the time domain algorithms at the same time in terms of high computational accuracy and considerable computational efficiency.In addition,the compute unified device architecture(CUDA)acceleration technique also can be employed to further enhance its simulation efficiency.Numerical examples are carried out to demonstrate the effectiveness of the proposed algorithm.The results indicate that the multipactor threshold can be accurately predicted and the computational efficiency can be improved.

HQNN-SFOP:Hybrid Quantum Neural Networks with Signal Feature Overlay Projection for Drone Detection Using Radar Return Signals-A Simulation

With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.

PARALLEL COMPUTATION OF 3-D HYPERSONIC FLOWS ON UNSTRUCTURED HYBRID MESHES

A parallel virtual machine （PVM） protocol based parallel computation of 3-D hypersonic flows with chemical non-equilibrium on hybrid meshes is presented. The numerical simulation for hypersonic flows with chemical non-equilibrium reactions encounters the stiffness problem, thus taking huge CPU time. Based on the domain decomposition method, a high efficient automatic domain decomposer for three-dimensional hybrid meshes is developed, and then implemented to the numerical simulation of hypersonic flows. Control equations are multicomponent N-S equations, and spatially discretized scheme is used by a cell-centered finite volume algorithm with a five-stage Runge-Kutta time step. The chemical kinetic model is a seven species model with weak ionization. A point-implicit method is used to solve the chemical source term. Numerical results on PC-Cluster are verified on a bi-ellipse model compared with references.

基于DOMAIN和NeuralEnsembles模型预测中国毛竹潜在分布

通过概形分析模型(profile technique)——DOMAIN生成物种生境适宜分布图,选取低适宜性的地区作为物种不存在区,然后应用分类判别分析模型(group discrimination technique)——NeuralEnsembles预测我国毛竹潜在分布。结果表明:通过耦合DOMAIN和NeuralEnsembles模型可以改进NeuralEnsenbles模型预测精度;AUC和敏感度对用于建模的物种不存在数据取样数量不敏感,而最大Kappa值随着不存在数据取样数量的增大逐渐减小;未来气候变化将导致毛竹向北迁移33～266km,面积增加7.4%～13.9%。

Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning

Recent developments in deep learning techniques have provided alternative and complementary approaches to the traditional matched-filtering methods for identifying gravitational wave(GW)signals.The rapid and accurate identification of GW signals is crucial to the advancement of GW physics and multi-messenger astronomy,particularly considering the upcoming fourth and fifth observing runs of LIGO-Virgo-KAGRA.In this study,we used the 2D U-Net algorithm to identify time-frequency domain GW signals from stellar-mass binary black hole(BBH)mergers.We simulated BBH mergers with component masses ranging from 7 to 50 M_(⊙)and accounted for the LIGO detector noise.We found that the GW events in the first and second observation runs could all be clearly and rapidly identified.For the third observing run,approximately 80% of the GW events could be identified.In contrast to traditional convolutional neural networks,the U-Net algorithm can output time-frequency domain signal images corresponding to probabilities,providing a more intuitive analysis.In conclusion,the U-Net algorithm can rapidly identify the time-frequency domain GW signals from BBH mergers.

ANALYSIS OF AUGMENTED THREE-FIELD MACRO-HYBRID MIXED FINITE ELEMENT SCHEMES

On the basis of composition duality principles, augmented three-field macrohybrid mixed variational problems and finite element schemes are analyzed. The compatibility condition adopted here, for compositional dualization, is the coupling operator surjectivity, property that expresses in a general operator sense the Ladysenskaja-Babulka-Brezzi inf-sup condition. Variational macro-hybridization is performed under the assumption of decomposable primal and dual spaces relative to nonoverlapping domain decompositions. Then, through compositional dualization macro-hybrid mixed problems are obtained, with internal boundary dual traces as Lagrange multipliers. Also, ＂mass＂ preconditioned aug- mentation of three-field formulations are derived, stabilizing macro-hybrid mixed finite element schemes and rendering possible speed up of rates of convergence. Dual mixed incompressible Darcy flow problems illustrate the theory throughout the paper.

基于Hybrid迭代法的多连通域数值保角变换计算法

针对模拟电荷法无界多连通区域的数值保角变换问题,文中提出了一种高精度的数值方法,即基于Hybrid迭代法的无界多连通区域的数值保角变换计算法。该方法通过模拟电荷法构造约束方程,对约束方程应用预处理构造对称正定的方程,获得新的模拟电荷和辐角,并构造近似保角变换函数。文中以多连通圆变换为径向狭缝域为例进行数值实验。使用解析函数的最大模原理作为误差的评价指标,并得出了天野法和文中算法的误差曲线。当模拟电荷数N=180,Jordan曲线为C_(2)时,文中算法E_(A2)的误差为1.6767×10^(-7),但是天野法E A2的误差为8.5977×10^(-4)。当模拟电荷数N=180,Jordan曲线为C_(3)时,文中算法E_(Θ3)的误差为5.6351×10^(-10),天野法E_(Θ3)的误差为1.1025×10^(-5)。

Hybrid lipopolymer vesicle drug delivery and release systems

Hybrid lipopolymer vesicles are membrane vesicles that can be self-assembled on both the micro-and nano-scale.On the nanoscale,they are potential novel smart materials for drug delivery systems that could combine the relative strengths of liposome and polymersome drug delivery systems without their respective weaknesses.However,little is known about their properties and how they could be tailored.Currently,most methods of investigation are limited to the microscale.Here we provide a brief review on hybrid vesicle systems with a specific focus on recent developments demonstrating that nanoscale hybrid vesicles have different properties from their macroscale counterparts.

Protein-protein Interaction Between Domains of PDZ and BAR from PICK1

Two DNA fragments encoding PDZ domain （21-110 residues） and BAR domain （ 150-360 residues） from PICK1 （1-416 residues） were amplified by PCR and then introduced into vectors, pET-32M and pMAL-e2X respectively to generate recombinant plasmids, pE-pdz and pM-bar. Having been separately transferred into the hosts E. coli BL21 and E. coli JM109, these two strains can express fusion proteins： His-tagged PDZ（PDZ domain） and maltose binding protein-BAR（ MBP-BAR domain） respectively, as confirmed by both SDS-PAGE and Wostem blotting. The interaction between these two domains is dose-dependence, as identified by a pull-down test. Moreover, it has been shown from the ELISA analysis that the actual amount of PDZ bound to MBP-BAR-amylose beads reaches （ 16 ± 0. 5）%, as calculated by the molar ratio of PDZ to MBP-BAR. In addition, the interaction between BAR（bait） and PDZ（prey） in vivo was also examined with a yeast two-hybrid system.

A High-order Coupled Compact Integrated RBF Approximation Based Domain Decomposition Algorithm for Second-order Differential Problems

This paper presents a high-order coupled compact integrated RBF(CC IRBF)approximation based domain decomposition(DD)algorithm for the discretisation of second-order differential problems.Several Schwarz DD algorithms,including one-level additive/multiplicative and two-level additive/multiplicative/hybrid,are employed.The CCIRBF based DD algorithms are analysed with different mesh sizes,numbers of subdomains and overlap sizes for Poisson problems.Our convergence analysis shows that the CCIRBF two-level multiplicative version is the most effective algorithm among various schemes employed here.Especially,the present CCIRBF two-level method converges quite rapidly even when the domain is divided into many subdomains,which shows great promise for either serial or parallel computing.For practical tests,we then incorporate the CCIRBF into serial and parallel two-level multiplicative Schwarz.Several numerical examples,including those governed by Poisson and Navier-Stokes equations are analysed to demonstrate the accuracy and efficiency of the serial and parallel algorithms implemented with the CCIRBF.Numerical results show:(i)the CCIRBF-Serial and-Parallel algorithms have the capability to reach almost the same solution accuracy level of the CCIRBF-Single domain,which is ideal in terms of computational calculations;(ii)the CCIRBF-Serial and-Parallel algorithms are highly accurate in comparison with standard finite difference,compact finite difference and some other schemes;(iii)the proposed CCIRBF-Serial and-Parallel algorithms may be used as alternatives to solve large-size problems which the CCIRBF-Single domain may not be able to deal with.The ability of producing stable and highly accurate results of the proposed serial and parallel schemes is believed to be the contribution of the coarse mesh of the two-level domain decomposition and the CCIRBF approximation.It is noted that the focus of this paper is on the derivation of highly accurate serial and parallel algorithms for second-order differential problems.The scope of this work does not cover a thorough analysis of computational time.

A New Hybrid Hierarchical Parallel Algorithm to Enhance the Performance of Large-Scale Structural Analysis Based on Heterogeneous Multicore Clusters

Heterogeneous multicore clusters are becoming more popular for high-performance computing due to their great computing power and cost-to-performance effectiveness nowadays.Nevertheless,parallel efficiency degradation is still a problem in large-scale structural analysis based on heterogeneousmulticore clusters.To solve it,a hybrid hierarchical parallel algorithm(HHPA)is proposed on the basis of the conventional domain decomposition algorithm(CDDA)and the parallel sparse solver.In this new algorithm,a three-layer parallelization of the computational procedure is introduced to enable the separation of the communication of inter-nodes,heterogeneous-core-groups(HCGs)and inside-heterogeneous-core-groups through mapping computing tasks to various hardware layers.This approach can not only achieve load balancing at different layers efficiently but can also improve the communication rate significantly through hierarchical communication.Additionally,the proposed hybrid parallel approach in this article can reduce the interface equation size and further reduce the solution time,which can make up for the shortcoming of growing communication overheads with the increase of interface equation size when employing CDDA.Moreover,the distributed sparse storage of a large amount of data is introduced to improve memory access.By solving benchmark instances on the Shenwei-Taihuzhiguang supercomputer,the results show that the proposed method can obtain higher speedup and parallel efficiency compared with CDDA and more superior extensibility of parallel partition compared with the two-level parallel computing algorithm(TPCA).

In vitro demonstration of interactions among zinc-binding domains of cellulose synthases in Arabidopsis and aspen

Plant cellulose synthases (CesAs) are the key enzymes necessary for cellulose biosynthesis. In Arabidopsis, two distinct groups of three CesAs each are necessary for cellulose synthesis during primary and secondary cell wall formation. It has also been suggested that such three CesAs interact with each other to form plasma-membrane bound rosette complexes that are functional during cellulose production. However, in vivo demonstration of such assemblies of three CesAs into rosettes has not been possible. We used yeast two-hybrid assays to demonstrate the possible interactions among several CesAs from Arabidopsis and aspen via their N-terminal zinc-binding domains (ZnBDs). While strong positive interactions were detected among ZnBDs from secondary wall associated CesAs of both Arabidopsis and aspen, the intergeneric interactions between Arabidopsis and aspen CesAs were weak. Moreover, in aspen, three primary wall associated CesA ZnBDs positively interacted with each other as well as with secondary CesAs. These results suggest that ZnBDs from either primary or secondary CesAs, and even from different plant species could interact but are perhaps insufficient for specificities of such interactions among CesAs. These observations suggest that some other more specific interacting regions might exist within CesAs. It is also possible that some hitherto unknown mechanism exists in plants for assembling the rosette complexes with different compositions of CesAs. Understanding how cellulose is synthesized will have a direct impact on utilization of lignocellulosic biomass for bioenergy production.