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.

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.

Simulation of pavement roughness based on time domain model

In order to describe pavement roughness more intuitively and effectively, a method of pavement roughness simulation, i.e., the stochastic sinusoidal wave, is introduced. The method is based on the primary idea that pavement roughness is denoted as the sum of numerous sines or cosines with stochastic phases, and uses the discrete spectrum to approach the target stochastic process. It is a discrete numerical method used to simulate pavement roughness. According to a given pavement power spectral density （PSD） coefficient, under the condition that the character of displacement frequency based on the time domain model is in accordance with the given pavement surface spectrum, the pavement roughness is optimized to stochastic equivalent vibrations by computer simulation, and the curves that describe pavement roughness under each grade are obtained. The results show that the stochastic sinusoidal wave is suitable for simulation of measured pavement surface spectra based on the time domain model. The method of the stochastic sinusoidal wave is important to the research on vehicle ride comfort due to its rigorous mathematical derivation, extensive application range and intuitive simulation curve. Finally, a roughness index defined as the nominal roughness index （NRI） is introduced, and it has correlation with the PSD coefficient.

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.

Terahertz Time-Domain Spectroscopy method for optical parameter extraction of plastic materials

A method for extracting optical parameters of plastics materials based on terahertz time domain spectroscopy is presented. The transmission-type Terahertz Time-Domain Spectroscopy(THz TDS) system is adopted to detect the refractive index and extinction coefficient on different plastic materials. Then the corresponding spectral information is obtained by Fourier transform of the terahertz time domain waveform of the sampling points, including the corresponding amplitude and phase information of the waveform. The optical parameter extraction model is built. By using the simplex optimization method, the curves of the refractive index and extinction coefficient for the plastic material are obtained. The experimental samples are made of different plastic parallel plate materials. The experimental results show that the optimization of optical parameters can improve their extraction accuracy, and the error of refractive index is ±0.005. Extraction technology with the simplex optimization method of optical parameter based on THz TDS can help to extract the optical parameters of engineering plastics. It is of great significance for the research of terahertz nondestructive testing.

一类伴有部分解耦干扰的非线性系统故障诊断

针对Lipschitz非线性系统执行器故障检测和传感器故障估计问题,本文提出了一种基于H_/L_(∞)未知输入观测器的有限频域故障诊断策略.首先,将系统处理成包含传感器故障的增广系统.然后,将该系统的未知输入干扰分为可解耦与不可解耦两部分.针对可解耦部分,利用观测器匹配条件将其从估计误差中消除.针对不可解耦部分,设计L_(∞)指标抑制其对残差的影响并结合有限频域H_指标提高执行器故障检测灵敏度.接着,给出观测器存在的充分条件并将其转化为受LMIs约束的线性优化问题,实现了执行器故障的鲁棒检测及传感器故障的鲁棒估计.最后,结合仿真算例验证了所提方法的正确性与有效性.

资本市场系统性风险监测及风险跨市场溢出研究——基于金融压力指数视角

本文从股票、债券、衍生品、外汇四个市场选取指标构建资本市场压力指数,对中国资本市场系统性风险进行动态测度;在此基础上,从时域和频域视角考察风险在四个子市场间的溢出效应。研究结果表明:本文构建的资本市场压力指数能够准确识别样本区间内的重大风险事件;极端冲击将导致风险溢出水平上升,各子市场在风险传递中的作用具有差异性和时变性;根据风险溢出的大小、方向和长短期结构,能够对风险动态演化过程及驱动因素进行有效判别。本文的研究对完善资本市场风险动态监测体系具有重要价值。

聚类不确定性的三支决策方法综述

由于数据有复杂性、多样性和不确定性的特点,聚类通常存在不确定性,这种不确定性是指聚类结果的不一致性,从而影响分析的准确性,而通过三支决策方法构建不确定域,对不确定域进行延迟决策,能够带来一系列的优势。近些年来,这个研究热点涌现许多重要的工作。对聚类不确定的三支决策方法模型和研究方向进行介绍,按照三支聚类构建过程中遇到的问题进行总结。首先,给出三支聚类的表现形式。其次,分别阐述如何构建三支聚类的核心域和边界域,如何确定阈值,如何设计有效性指标,并介绍其中具有代表性的工作。最后,对聚类不确定的三支决策方法进行总结,并展望未来的研究方向。

基于图卷积网络的发明人跨领域合作伙伴识别方法

[研究目的]科学技术与社会的发展促进了不同领域理论、方法和技术的交叉融合,跨领域合作愈发成为合作创新的主流形式,如何帮助发明人定位并准确识别跨领域合作伙伴成为亟待解决的问题。[研究方法]提出一种基于图卷积网络的发明人跨领域合作伙伴识别方法,从多维特征视角下基于发明人专利信息中的合作关系特征、摘要文本特征、领域信息特征使用图卷积网络识别和预测发明人潜在合作伙伴,构建同领域指数和跨领域指数准确识别发明人跨领域合作伙伴。[研究结论]通过对比实验,证明了借助图卷积网络对合作关系特征、摘要文本特征、领域信息特征三维特征联用在进行伙伴识别时能够有效提升模型准确性。借助识别跨领域合作伙伴,有助于促进不同领域之间的交叉合作和知识转移,创造出更具创新性和前瞻性的成果。

基于防空导弹滚动通道控制时/频域指标映射关系研究

在防空导弹控制系统设计时,如何快速高效设计出满足给定指标的控制系统一直是热点问题。以滚动通道控制设计为例,从数学角度出发设计控制结构,采用解析法、根轨迹法和幅相特性Bode图法建立了时/频域指标与控制参数之间的映射关系,以及时/频域指标之间的映射关系。该映射关系可兼顾时域指标和频域指标来更快速高效设计控制参数,而且还可以得到控制参数变化时指标变化的规律。这有助于根据系统需要实现的性能指标进行控制系统的设计。最后用解析法建立系统时间常数、上升时间、截止频率和闭环带宽之间的关联关系。通过该关联关系对系统可建立更加清晰的物理认识,有利于指导在工程设计中当系统出现振荡或者发散现象时如何采取解决措施。

基于混合域注意力机制的神经网络反演大气湍流强度

本文提出了一种基于混合域注意力机制的神经网络方法反演大气湍流强度。神经网络的输入为不同大气湍流强度下的退化图像,输出为表征大气湍流强度的折射率结构常数。混合域注意力机制由空间域和通道域双重注意力机制组成,其中空间域注意力机制用于增强退化图像中受湍流影响的区域特征,通道域注意力机制用于增强由湍流引起的颜色和纹理特征。在网络训练阶段,引入的混合域注意力机制让神经网络更专注于退化图像中与大气湍流强度相关的特征,提高了模型的精度。数值实验结果表明,本文提出的方法能够较准确地实现大气湍流强度反演。

基于振动信号时域特性的传动系统故障分类与诊断

包装机长期工作在往复变速运动及高粉尘、高水汽浓度环境下,其传动系统中的滚动轴承极易出现腐蚀故障和疲劳故障。针对这一问题,基于振动信号的时域特性,对包装机传动系统的滚动轴承进行了故障诊断研究。首先,以深沟球轴承6002为研究对象,对不同封装速度下的振动信号进行了采集,结合包装机传动系统的构造与工作机理,研究了其常见故障特点及类型,并从振动信号中选取了受外部干扰最小的数据段;然后,对选取的数据段进行了幅值变化和功率谱分析,验证了其具有相同的运动特性或运动过程,从而进一步得到了任意数据段之间在同一时间节点上的转速都一致,此时这些信号之间处于“相对平稳运动状态”;最后,利用偏度、峭度、峰值等时域指标对数据段进行了分析,得到了故障特征区间,将其用于故障诊断。研究结果表明:该状态下数据段之间处于“相对稳态运动”,解决了传统变速运动时域信息不可用的问题,提升了时域指标的诊断效率与稳定性,特别是峰值指标诊断准确率达到80%以上。该研究方法满足了工业生产中实时在线诊断的要求,同时也可为其他旋转机械的故障诊断研究提供参考价值。

基于介电频谱特征参数的红壤干旱指标评价研究

为获取红壤含水量对电磁波介电频谱的响应特征,探讨介电频谱特征参数作为红壤干旱评价指标的可行性,以红壤为研究对象,开展不同体积含水量红壤的频域介电谱(FDS)试验,提取介电谱特征曲线积分值,分析介电频谱特征参数与体积含水量的相关性,结合国家《农业干旱等级》标准提出基于介电频谱特征参数的红壤干旱评价指标。结果表明:红壤介电常数实部和虚部均随着介电频谱频率的增大而逐渐减小,两者与体积含水量均呈正相关关系;介电损耗因数随频率变化具有波动性,且体积含水量越小波动性越强,总体呈现出先减小后增大的趋势;通过最小二乘法拟合得到介电频谱特征参数与体积含水量的量化关系,采用细菌觅食算法确定最优的模型系数,拟合精度均大于0.95;基于介电谱特征参数建立红壤干旱指标评价模型,预测值与标准值之间的误差均为±1%左右。该模型能有效地评价红壤干旱程度,可为红壤区域性农业干旱调查、评估及预警提供参考指标。

基于随机森林模型的合肥市主城区轨道交通站域空间活力与建成环境关联性测度

以合肥市主城区轨道交通站域为例,基于百度热力指数等多源数据分析其空间活力特征,采用随机森林模型测度地理区位、土地利用、功能设施、开发强度、交通便利度以及环境品质6个维度的建成环境要素与轨道交通站域空间活力的关联性。同时,结合K-means聚类算法,划分轨道交通站域类型并识别典型站域存在问题,从而针对不同类型轨道交通站域提出空间活力优化策略。结果表明:①合肥市主城区轨道交通站域空间活力在时序上呈现“晚高峰高于早高峰,休息日晚高峰高于工作日晚高峰”特征,在空间上呈现“内高外低”由二环线向外逐渐递减的空间分异格局;②综合工作日与休息日早/晚/非高峰时段建成环境变量对轨道交通站域空间活力的相对重要性排序,由大到小依次为:功能设施>开发强度>地理区位>土地利用>交通便利度>环境品质;③合肥市主城区轨道交通站域可划分为成熟型、成长型、初熟型、孕育型4类,聚类特征明显,在空间上呈现“圈层结构”。其中,成熟型站域集聚分布在二环线内,成长型站域沿二环线呈环状分布,初熟型站域主要呈零散状分布在二环线外滨湖区与经开区,孕育型站域主要分布在二环线外城市各区,分布范围较广。研究旨在从建成环境视角为轨道交通站域空间活力提升与低碳交通策略提供政策启示。

基于改进模糊评价法的无人艇碰撞风险计算

为了解决传统模糊评价模型在船舶风险评估中多仅适用远距离场景,以及缺乏考虑《国际海上避碰规则》(International Regulations for Collision Avoidance at Sea,COLREGs)等问题,提出一种改进的模糊评价法,用于计算水面无人艇(unmanned surface vessel,USV)的碰撞危险度。该方法采用四元船舶领域确定模型中的安全距离,并通过行动域模型明确模型中碰撞威胁距离。同时,将两船是否构成紧迫危险局面引入到碰撞危险度的计算中。实验结果表明,改进后的方法能够适用于近距离水域的USV碰撞危险度计算,同时满足COLREGs的要求和航海避碰实际情况。

Ag@SiO_(2)复合纳米颗粒薄膜的折射率传感特性研究

银纳米颗粒具有显著的表面等离激元特性,可作为折射率传感的关键材料,广泛应用于化学催化、信息传感、医用生物和新能源等领域。然而,未包覆的纯银纳米颗粒在复杂介质环境下,无法维持折射率传感的长期稳定性。文章提出了一种用于折射率传感的Ag@SiO_(2)复合纳米颗粒薄膜,其结构单元由岛状银纳米颗粒和一层依附在银颗粒表面的二氧化硅核壳构成。利用有限时域差分法(FDTD),模拟计算了不同包覆厚度的Ag@SiO_(2)复合纳米颗粒光谱特性以及折射率传感性能。结果表明:岛状Ag@SiO_(2)纳米颗粒薄膜的共振峰强烈依赖纳米颗粒的包覆材料的厚度和周围介质的折射率。Ag@SiO_(2)复合纳米颗粒薄膜折射率灵敏度优于未包覆的纯银纳米颗粒。Ag@SiO_(2)复合纳米颗粒薄膜相对于岛状纯银纳米颗粒具有更优的指标参数,有望作为折射率传感的关键材料,应用于介质环境的检测中。

外部经济政策不确定性、跨境资本流动与经济波动

近几年中国面临的外部经济政策不确定性持续攀升,国内经济也由量的发展转向质的提高,那么明确外部经济政策不确定性对国内宏观经济的影响则是当前亟须解决的重要问题。基于中国2001年1月至2022年12月的数据构建VAR模型和“BK”溢出指数实证研究外部经济政策不确定性、跨境资本流动对经济波动的影响及其周期性特征。研究表明:第一,外部经济政策不确定性升高和跨境资本流动增加都会导致中国经济波动加剧;第二,外部经济政策不确定性升高会促进跨境资本流动;第三,外部经济政策不确定性对经济波动的影响因经济金融环境的变化而存在差异,对跨境资本流动的影响主要发生在短期(半年内),而跨境资本流动在短期和长期(半年以上)均对经济波动产生不利冲击。因此,相关部门需强化不同周期下跨境资本流动的监管,针对不同周期下外部经济政策不确定性和跨境资本流动对经济波动产生的影响实施相对应的措施,并完善外部经济政策不确定性监测体系,以实现经济稳健发展。

基于领域加权稀疏的交通机电轴承故障诊断

轴承是交通机电设备中最常发生故障的核心部件之一,一旦发生故障,整个系统将被关闭,甚至引发灾难性后果,因此,开展交通机电轴承故障检测具有重要意义。针对低信噪比条件下交通机电轴承冲击特征难提取问题,提出一种基于领域加权稀疏的交通机电轴承故障诊断方法。首先基于经典稀疏模型,引入基尼指数作为权重以区分信号各分量稀疏系数的贡献;其次,考虑系数之间相关性,研究领域系数去噪替代阈值降噪方法,提高对稀疏重构分量估计的准确性;最后,对重构稀疏信号进行包络检测以识别故障。利用机电轴承故障仿真信号和地铁轴箱轴承实测信号验证了所提方法的有效性。结果表明本文方法能有效实现交通机电轴承弱故障特征提取。