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1.
《世界地震工程》2015,(4)
在输入激励信息未知、仅已知部分测试自由度的动力响应信息的条件下,基于广义复合反演算法,开展了结构损伤识别的研究。将衰减记忆滤波技术引入到扩展卡尔曼滤波算法,构建了衰减记忆扩展卡尔曼滤波算法以削弱滤波的发散现象。考虑了复合反演算法(须知各自由度的动力响应)和衰减记忆扩展卡尔曼滤波算法(须知输入信息)的局限性,借助子结构技术,建立了结构物理参数时域识别的广义复合反演算法。利用单元刚度变化率来判定损伤的程度和位置,建立了基于广义复合反演算法的结构损伤识别及地震动反演的三阶段法。以一个6层框架结构为例进行了结构损伤识别数值模拟研究。结果表明,在噪声存在的情况下,三阶段法能够准确确定损伤位置和损伤程度,且地震动反演时程与真实时程吻合较好,验证了三阶段法在结构损伤识别及地震动反演中的有效性。 相似文献
2.
时频峰值滤波(TFPF)算法是一种非常有效的去噪方法.但是传统的TFPF采用的单一窗长,并且仅沿时间方向进行滤波,忽略了信号的空间信息,并且TFPF近似等效成一个时不变的低通滤波器,不能追踪快速变化的信号.针对这些问题,引入空间局部加权回归自适应TFPF(SLWR-ATFPF).鉴于随机噪声在各个位置的方向随机性,以及有效信号在各个位置的方向确定性,首先利用空间局部加权回归(SLWR),对含噪信号进行空间加权,从而使加权之后的信号包含空间信息.然后,再引入凸集和Viterbi的思想,对空间加权之后的信号进行自适应滤波.从而,完成时空域二维自适应滤波.将SLWR-ATFPF应用于合成记录和实际的共炮点记录,实验结果表明,改进的方法与原算法相比,能够在压制低信噪比(SNR)随机噪声的同时更好地保留有效信号. 相似文献
3.
本文对工程结构、场地水平土层参数识别及地震动反演进行了如下6个方面研究:(1)输入已知、输出不完备条件下,基于经典最小二乘算法的工程结构物理参数时域识别;(2)输入未知、输出不完备条件下,基于复合反演算法的工程结构物理参数时域识别及地震动反演;(3)输入已知、输出不完备条件下,基于渐消记忆的扩展卡尔曼滤波算法的工程结构物理参数时域识别;(4)输入未知、有限测点条件下,基于广义复合反演算法的工程结构物理 相似文献
4.
为了恢复震动波能量在传播过程中产生的衰减损耗,提出基于褶积原理求取品质因子Q的方法与改进广义S变换相结合的反Q滤波法。通过震动波衰减补偿模型试验,对试验数据进行改进广义S变换的时频特性分析,得出了信号的能量分布情况以及时间频率对应关系;采用基于褶积原理求取品质因子的方法,得到时变Q值;对试验数据进行反Q滤波处理,使震动波能量得到了补偿。结果表明本文提出的反Q滤波法提高了对震动波能量衰减补偿的效果,拓宽了地震资料的频带,提高了地震资料分辨率,有利于进行高分辨率地震勘探、深部信号增强和油气藏预测工作的开展。 相似文献
5.
《应用地球物理》2019,(4)
为解决时间域Bregman迭代算法计算效率低、抗噪能力不强的问题,提出了一种高效、自适应的频率域Bregman稀疏脉冲反褶积算法。通过对传统时间域Brgman算法进行频率域推导,得到了一种基于频率域计算的Bregman求解算法。在频率域进行Bregman算法的求解,有效避免了高斯噪音和离群噪音对算法收敛性的影响,并且通过在主频带范围计算,算法较时间域求解效率大幅提高。在求解过程中,引入了广义交叉验证(Generalized Cross Validation,GCV)的方法优选正则化参数,使算法具有了自适应参数选择的能力。通过建立不同的模型,分别使用时间域和频率域Bregman算法进行求解,验证了改进算法在抗噪音、计算效率和自适应方面的优势。最后,将改进方法与常规时间域Bregman算法分别应用于吐哈盆地某实际资料,处理结果表明改进方法较传统算法有更优异的表现。 相似文献
6.
吸收衰减是地震波在实际地球介质中传播的固有特征.在实际应用中,通常假设表征吸收衰减特征的品质因子Q在地震频带范围内不随频率变化.高阶广义流变模型能够在时间域内精确的表征品质因子Q不随频率变化的特征,为黏弹性介质波动方程精细模拟奠定了基础.基于广义标准线性体模型理论,采用最小二乘拟合方法对Q值不随频率变化特征进行拟合,分析了不同阶次广义标准线性体模型对黏弹性介质Q值特征的拟合程度,在权衡计算精度和三维计算量的基础上,确定了五阶广义标准线性体模型并建立了相应的三维黏弹性波的速度-应力方程,结合CFS-PML边界条件开展了高精度三维黏弹性波正演模拟.通过均匀介质正演模拟,验证算法的正确性,明确了地震波的传播时的吸收衰减特征,对三维盐丘模型进行数值模拟,表明了五阶广义标准线性体可以精确的模拟黏弹性介质地震波吸收衰减特征. 相似文献
7.
由于地层的黏弹性和非均质性,地震波在传播过程中振幅衰减,相位畸变,导致地震资料分辨率和信噪比降低.反Q滤波是补偿地层衰减的一种常用方法.常规的反Q滤波方法的振幅补偿算子只与时间、频率有关,其振幅补偿频带范围、强度固定不变,不能够根据地震记录的自身特点灵活地进行振幅补偿.基于此,本文在广义稳定反Q滤波法的基础上,提出了一种改进的广义稳定反Q滤波方法.新方法提出了一种新的稳定因子,并通过参数p对振幅补偿算子进行了优化.其振幅补偿算子与地震资料信噪比、走时、频率、品质因子相适应,可以更加精确地对振幅进行补偿.理论数据测试和实际地震资料应用均表明,新方法在振幅补偿和噪声压制上优于广义稳定反Q滤波法,其处理后的地震资料振幅恢复保真度、分辨率和信噪比更高,对储层预测和精细解释工作具有重要意义. 相似文献
8.
《地震工程与工程振动》2017,(5)
结构损伤会引起结构振动信号的突变,而该突变信号会淹没于环境噪声信号中。为此,文章将复杂追踪理论(CP)引入结构损伤识别领域,将损伤识别问题转化为突变特征提取问题。提出一种复杂追踪结合集合经验模态分解(EEMD)识别结构损伤的新方法,首先采用EEMD预处理结构振动信号,接着将分解得到的本征模函数(IMF)作为混合信号输入CP模型中,提取出包含损伤特征的本征模函数,进而识别出结构损伤发生的时刻及位置。最后,通过对环境激励下六自由度质量-弹簧系统和地震激励下三层框架模型的数值分析。结果表明,该方法能够准确有效地识别结构损伤异常时刻与位置。 相似文献
9.
受窗函数的影响,短时傅里叶变换、小波变换、S变换等传统时频分析方法的分辨率不高.为了提高信号时频变换分辨率,发展了同步挤压小波变换算法;该算法将信号的时频谱值向其中心频率位置挤压,改善了信号时频分析结果,使得信号时频分辨率得到了很大的提升.但由于传统算法时窗固定,处理缺乏灵活性;因此,本文对S变换窗函数进行扩展,提出了同步挤压广义S变换.通过自适应窗函数挤压信号在S域的时频谱值,提高了算法的灵活性和时频分析聚焦能力.运用同步挤压广义S变换对南海某工区实际地震数据进行分频处理,结果显示含油气层能量随着频率的增加而逐渐衰减.因此,使用同步挤压广义S变换对地震数据进行分析处理,不仅可以对储层含油气性进行准确的检测,还可以对地层进行精确的标定. 相似文献
10.
由于地震数据中包含的噪声在不同频率或者频带数据中的分布强度存在差异,使得全频带数据上进行的噪声衰减处理改变了地震反射波信号的动力学特征,干扰后期的地震资料解释、储层预测、油气检测等问题,提出边界和振幅特性保持自适应噪声衰减方法。首先应用小波包变换对全频带地震数据进行多频段划分,然后对分频段数据进行非线性各向异性倾角导向边界保持自适应滤波处理。在该方法中,由结构张量计算的扩散张量实现自适应地确定平滑滤波方向,加入的不连续结构置信度量和不连续性算子自适应地控制不连续结构特征的保持程度,引入的去相关滤波迭代停止准则自适应地确定滤波迭代次数。这些参数的引入具有减少处理人员的干预和人为的主观性,且执行简单的特点。对合成地震记录和实际地震记录处理结果表明,提议的方法能够自适应地衰减地震数据中噪声,同时既能保持地震反射波中有效的不连续性信息,也能有效地保持有效信号的频率分布规律。能够为后期的地震资料解释和分析提供高品质的基础数据。 相似文献
11.
Tidal flow forecasting using reduced rank square root filters 总被引：1，自引：0，他引：1
M. Verlaan A. W. Heemink 《Stochastic Environmental Research and Risk Assessment (SERRA)》1997,11(5):349-368
The Kalman filter algorithm can be used for many data assimilation problems. For large systems, that arise from discretizing
partial differential equations, the standard algorithm has huge computational and storage requirements. This makes direct
use infeasible for many applications. In addition numerical difficulties may arise if due to finite precision computations
or approximations of the error covariance the requirement that the error covariance should be positive semi-definite is violated.
In this paper an approximation to the Kalman filter algorithm is suggested that solves these problems for many applications.
The algorithm is based on a reduced rank approximation of the error covariance using a square root factorization. The use
of the factorization ensures that the error covariance matrix remains positive semi-definite at all times, while the smaller
rank reduces the number of computations and storage requirements. The number of computations and storage required depend on
the problem at hand, but will typically be orders of magnitude smaller than for the full Kalman filter without significant
loss of accuracy.
The algorithm is applied to a model based on a linearized version of the two-dimensional shallow water equations for the prediction
of tides and storm surges.
For non-linear models the reduced rank square root algorithm can be extended in a similar way as the extended Kalman filter
approach. Moreover, by introducing a finite difference approximation to the Reduced Rank Square Root algorithm it is possible
to prevent the use of a tangent linear model for the propagation of the error covariance, which poses a large implementational
effort in case an extended kalman filter is used. 相似文献
12.
本文给出了一个基于Gauss-Markov卡尔曼滤波的电离层数据同化系统的初步构建和试验结果.我们选择中国及周边地区部分涉及电离层观测的台站(包括子午工程台站、中国地壳形变网和部分IGS台站)作为观测系统进行模拟试验,背景场利用IRI模式,观测值则由NeQuick模式计算得到.我们的同化结果表明,采用Kalman滤波算法,把部分斜TEC同化到背景模式当中,能够获得较好的同化结果,说明我们设计的算法可行、所选择的各种参数比较合理,采用Gauss-Markov假设进行短期预报也取得了较合理的结果.本项研究经过进一步的改进和完善,可以用来对中国地区的电离层进行现报和短期预报,一方面满足相关空间工程应用,另一方面可以提升现有观测系统的科学意义. 相似文献
13.
A. W. Heemink A. J. Segers 《Stochastic Environmental Research and Risk Assessment (SERRA)》2002,16(3):225-240
The Kalman filter is used in this paper as a framework for space time data analysis. Using Kalman filtering it is possible
to include physically based simulation models into the data analysis procedure. Attention is concentrated on the development
of fast filter algorithms to make Kalman filtering feasible for high dimensional space time models. The ensemble Kalman filter
and the reduced rank square root filter algorithm are briefly summarized. A new algorithm, the partially orthogonal ensemble
Kalman filter is introduced too. We will illustrate the performance of the Kalman filter algorithms with a real life air pollution
problem. Here ozone concentrations in a part of North West Europe are estimated and predicted. 相似文献
14.
Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation 总被引：1，自引：0，他引：1
Haishen Lü Zhongbo Yu Yonghua ZhuSam Drake Zhenchun HaoEdward A. Sudicky 《Advances in water resources》2011,34(3):395-406
With well-determined hydraulic parameters in a hydrologic model, a traditional data assimilation method (such as the Kalman filter and its extensions) can be used to retrieve root zone soil moisture under uncertain initial state variables (e.g., initial soil moisture content) and good simulated results can be achieved. However, when the key soil hydraulic parameters are incorrect, the error is non-Gaussian, as the Kalman filter will produce a persistent bias in its predictions. In this paper, we propose a method coupling optimal parameters and extended Kalman filter data assimilation (OP-EKF) by combining optimal parameter estimation, the extended Kalman filter (EKF) assimilation method, a particle swarm optimization (PSO) algorithm, and Richards’ equation. We examine the accuracy of estimating root zone soil moisture through the optimal parameters and extended Kalman filter data assimilation method by using observed in situ data at the Meiling experimental station, China. Results indicate that merely using EKF for assimilating surface soil moisture content to obtain soil moisture content in the root zone will produce a persistent bias between simulated and observed values. Using the OP-EKF assimilation method, estimates were clearly improved. If the soil profile is heterogeneous, soil moisture retrieval is accurate in the 0-50 cm soil profile and is inaccurate at 100 cm depth. Results indicate that the method is useful for retrieving root zone soil moisture over large areas and long timescales even when available soil moisture data are limited to the surface layer, and soil moisture content are uncertain and soil hydraulic parameters are incorrect. 相似文献
15.
This paper presents a novel intelligent fuzzy weighted input estimation method which effiviently and robustly estimates the unknown ground motion accelerations. The new input estimation method includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. By directly synthesizing the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable compromise between the tracking capability and the flexibility against noises. The excellent performace of this inverse method is demonstrated by solving the earthquake-excitation estimation problem, and the proposed algorithm is compared by alternating between the constant and adaptive weighting factors. The results reveal that this method has the properties of better target tracking capability and more effective noise reduction. 相似文献
16.
Bias aware Kalman filters: Comparison and improvements 总被引：1，自引：0，他引：1
This paper reviews two different approaches that have been proposed to tackle the problems of model bias with the Kalman filter: the use of a colored noise model and the implementation of a separate bias filter. Both filters are implemented with and without feedback of the bias into the model state. The colored noise filter formulation is extended to correct both time correlated and uncorrelated model error components. A more stable version of the separate filter without feedback is presented. The filters are implemented in an ensemble framework using Latin hypercube sampling. The techniques are illustrated on a simple one-dimensional groundwater problem. The results show that the presented filters outperform the standard Kalman filter and that the implementations with bias feedback work in more general conditions than the implementations without feedback. 相似文献
17.
Groundwater models are critical decision support tools for water resources management and environmental remediation. However, limitations in site characterization data and conceptual models can adversely affect the reliability of groundwater models. Therefore, there is a strong need for continuous model uncertainty reduction. Ensemble filters have recently emerged as promising high-dimensional data assimilation techniques. Two general categories of ensemble filters exist in the literature: perturbation-based and deterministic. Deterministic ensemble filters have been extensively studied for their better performance and robustness in assimilating oceanographic and atmospheric data. In hydrogeology, while a number of previous studies demonstrated the usefulness of the perturbation-based ensemble Kalman filter (EnKF) for joint parameter and state estimation, there have been few systematic studies investigating the performance of deterministic ensemble filters. This paper presents a comparative study of four commonly used deterministic ensemble filters for sequentially estimating the hydraulic conductivity parameter in low- and moderately high-dimensional groundwater models. The performance of the filters is assessed on the basis of twin experiments in which the true hydraulic conductivity field is assumed known. The test results indicate that the deterministic ensemble Kalman filter (DEnKF) is the most robust filter and achieves the best performance at relatively small ensemble sizes. Deterministic ensemble filters often make use of covariance inflation and localization to stabilize filter performance. Sensitivity studies demonstrate the effects of covariance inflation, localization, observation density, and conditioning on filter performance. 相似文献
18.
多速率Kalman滤波方法可用于低采样率的位移和高采样率的加速度数据融合,而未知的噪声协方差信息则显著制约着多速率Kalman滤波精度.本文通过将多速率Kalman滤波转换为传统的单速率Kalman滤波,建立了Kalman滤波增益的自协方差矢量与未知的加速度谱密度和观测噪声参数间的线性函数模型,并采用最小二乘估计方法对未知的噪声协方差参数进行估计,进而有效地提高了多速率Kalman滤波精度.数值仿真和震动台实验结果验证了本文方法的正确性和有效性. 相似文献
19.
20.
The generalized model of differential hysteresis contains 13 control parameters with which it can curve‐fit practically any hysteretic trace. Three identification algorithms are developed to estimate the control parameters of hysteresis for different classes of inelastic structures. These algorithms are based upon the simplex, extended Kalman filter, and generalized reduced gradient methods. Novel techniques such as global search and internal constraints are incorporated to facilitate convergence and stability. Effectiveness of the devised algorithms is demonstrated through simulations of two inelastic systems with both pinching and degradation characteristics in their hysteretic traces. Owing to very modest computing requirements, these identification algorithms may become acceptable as a design tool for mapping the hysteretic traces of inelastic structures. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献