共查询到20条相似文献,搜索用时 31 毫秒
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Hui Luo Yanda Li 《Proceedings of the IEEE. Institute of Electrical and Electronics Engineers》1998,86(10):2082-2089
One objective of seismic signal processing is to identify the layered subsurface structure by sending seismic wavelets into the ground. This is a blind deconvolution process since the seismic wavelets are usually not measurable and therefore, the subsurface face layers are identified only by the reflected seismic signals. Conventional methods often approach this problem by making assumptions about the subsurface structures and/or the seismic wavelets. In this paper an alternative technique is presented. It applies blind channel identification methods to prestack seismic deconvolution. A unique feature of this proposed method is that no such assumptions are needed. In addition, it fits into the structure of current seismic data acquisition techniques, thus no extra cost is involved. Simulations on both synthetic and field seismic data demonstrate that it is a promising new method for seismic signal processing 相似文献
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A convolution may be represented as x(.)=r(.)* w(.). The goal of deconvolution is to extract r(.) and w(.) from a knowledge of x(.) and it finds numerous applications in digital signal processing. Of practical interest in oil exploration is the case where w(.) is a seismic pressure wavelet, x(.) is the observed seismic response, and r(.) is the reflectivity of the Earth. A number of procedures have been proposed, including predictive, deterministic, and homomorphic deconvolution. Homomorphic deconvolution has been found to be particularly efficient for those cases where x(.) is known to be fullband. This paper presents a robust constructive procedure for efficient homomorphic deconvolution for those cases where x(.) is a bandpass signal. Extensive comparisons with other methods for deconvolving bandpass signals on measured seismic data traces (including the Novaya Zemlya event) illustrate the improvement in the deconvolution 相似文献
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The purpose of this paper is to describe a broad spectrum of seismic deconvolution problems and solutions which we refer to collectively as maximum-likelihood (seismic) deconvolution (MLD). Our objective is to perform deconvolution and wavelet estimation for the case of nonminimum phase wavelets. Our approach is to exploit state-variable technology, maximum-likelihood estimation, and a sparse spike train (Bernoulli-Gaussian) model for the reflection signal. Our solution requires detection of significant reflectors, wavelet and variance identification (nonlinear optimization), and estimation of the spike density parameter. 相似文献
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This correspondence presents a solution to a multiscale deconvolution problem using higher order spectra where the data to be deconvolved consist of noise-corrupted sensor array measurements. We assume that the data are generated as a convolution of an unknown wavelet with reflectivity sequences that are linearly time-scaled versions of an unknown reference reflectivity sequence. This type of data occurs in many signal processing applications, including sonar and seismic processing. Our approach relies on exploiting the redundancy in the measurements due to time scaling and does not require knowledge of the wavelet or the reflectivity sequences. We formulate and solve the deconvolution problem as a quadratic minimization subject to a quadratic constraint in the sum-of-cumulants (SOC) domain. The formulation using the SOC approach reduces the effect of additive Gaussian noise on the accuracy of the results when compared with the standard time-domain formulation. We demonstrate this improvement using a simulation example 相似文献
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实现弱回波信号检测和高信噪比(SNR)浅剖图像获取是浅剖精细探测的首要任务。该文在分析分数阶傅里叶变换(FrFT)解卷积原理,推导时间量纲化变换公式的基础上,提出一种基于FrFT的浅剖精细探测新方法。该方法通过FrFT解卷积实现分数阶傅里叶域(u域)沉积层冲激响应求解,采用u域加窗滤波技术对带内噪声进行有效抑制,经时间量纲化变换实现高信噪比u域沉积层冲激响应包络信号至时域浅剖包迹的直接变换,得到高质量的浅剖图像。仿真实验和实测数据处理验证了算法的精细探测能力,算法性能优于脉冲压缩和自回归(AR)预测滤波方法。 相似文献
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Multichannel seismic deconvolution 总被引:1,自引:0,他引:1
Deals with Bayesian estimation of 2D stratified structures from echosounding signals. This problem is of interest in seismic exploration, but also for nondestructive testing or medical imaging. The proposed approach consists of a multichannel Bayesian deconvolution method of the 2D reflectivity based upon a theoretically sound prior stochastic model. The Markov-Bernoulli random field representation introduced by Idier et al. (1993) is used to model the geometric properties of the reflectivity, and emphasis is placed on representation of the amplitudes and on deconvolution algorithms. It is shown that the algorithmic structure and computational complexity of the proposed multichannel methods are similar to those of single-channel B-G deconvolution procedures, but that explicit modeling of the stratified structure results in significantly better performances. Simulation results and examples of real-data processing illustrate the performances and the practicality of the multichannel approach 相似文献
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The diverse environments emerging for wireless communication applications could render the centralized prediction-based channel assignment methodology, conventionally employed in cellular radio networks, impractical. The distributed measurement-based approach seems to be a more practical solution. We evaluate and compare several distributed measurement-based algorithms for dynamic channel assignment (DCA). Their performance is also compared with a centralized prediction-based algorithm. It is found that a simple aggressive algorithm with the use of a threshold, known as the least interference algorithm (LIA), performs the best 相似文献
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Blind image deconvolution 总被引:7,自引:0,他引:7
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Parker L.E. Tang F. 《Proceedings of the IEEE. Institute of Electrical and Electronics Engineers》2006,94(7):1289-1305
This paper presents a reasoning system that enables a group of heterogeneous robots to form coalitions to accomplish a multirobot task using tightly coupled sensor sharing. Our approach, which we call ASyMTRe, maps environmental sensors and perceptual and motor control schemas to the required flow of information through the multirobot system, automatically reconfiguring the connections of schemas within and across robots to synthesize valid and efficient multirobot behaviors for accomplishing a multirobot task. We present the centralized anytime ASyMTRe configuration algorithm, proving that the algorithm is correct, and formally addressing issues of completeness and optimality. We then present a distributed version of ASyMTRe, called ASyMTRe-D, which uses communication to enable distributed coalition formation. We validate the centralized approach by applying the ASyMTRe methodology to two application scenarios: multirobot transportation and multirobot box pushing. We then validate the ASyMTRe-D implementation in the multirobot transportation task, illustrating its fault-tolerance capabilities. The advantages of this new approach are that it: 1) enables robots to synthesize new task solutions using fundamentally different combinations of sensors and effectors for different coalition compositions and 2) provides a general mechanism for sharing sensory information across networked robots. 相似文献
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本文提出了基于二进小波变换自适应Kalman滤波反褶积(AKFD)新方法.它抛弃了传统预测反褶积对信号平稳性的假设,克服了提高分辨率反而明显降低信噪比的矛盾,其较好地压缩反射波形,但噪声并没有明显提高,所以具有很好的抗噪性能.在小波域进行的AKFD压制假反射比在时间域AKFD好,此外,该方法具有对信号分频进行AKFD的特性,增强了Kalman滤波的自适应性,所以在小波域下的分辨率明显比在时域内高.同时,该方法克服了在时域内进行的AKFD抬升低频成份的缺陷.经大量的模型及实际资料处理表明该方法具有明显的效果. 相似文献
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We present an approach to determine sufficient conditions for the global convergence of iterative blind deconvolution algorithms using finite impulse response (FIR) deconvolution filters. The novel technique, which incorporates Lyapunov's direct method, is general, flexible, and can be easily adapted to analyze the behavior of many types of nonlinear iterative signal processing algorithms. Specifically, we find sufficient conditions to guarantee a unique solution for the NAS-RIF algorithm used for blind image restoration. We determine that in many cases, there exists a tradeoff between the quality of the deconvolution result and the uniqueness of the solution. A procedure to determine the length of the deconvolution filter to guarantee a unique solution is established 相似文献
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《Proceedings of the IEEE. Institute of Electrical and Electronics Engineers》1984,72(10):1276-1289
The seismic method in petroleum exploration is an echo-location technique to detect interfaces between the subsurface sedimentary layers of the earth. The received seismic reflection record (field trace), in general, may be modeled as a linear time-varying (LTV) system. However, in order to make the problem tractable, we do not deal with the entire field trace as a single unit, but instead subdivide it into time gates. For any time gate on the trace, there is a corresponding vertical section of rock layers within the earth, such that the primary (direct) reflections from these layers all arrive within the gate. Each interface between layers is characterized by a local (or Fresnel) reflection coefficient, which physically must be less than unity in magnitude. Under the hypothesis that the vertical earth section has small reflection coefficients, then within the corresponding time gate the LTV model of the seismic field trace reduces to a linear time-invariant (LTI) system. This LTI system, known as the convolutional model of the seismic trace, says that the field trace is the convolution of a seismic wavelet with the reflection coefficient series. If, in addition, the reflection coefficient series is white, then all the spectral shape of the trace within the gate can be attributed to the seismic wavelet. Thus the inverse wavelet can be computed as the prediction error operator (for unit prediction distance) by the method of least squares. The convolution of this inverse wavelet with the field trace yields the desired reflection coefficients. This statistical pulse compression method, known as predictive deconvolution with unit prediction distance, is also called spike deconvolution. Alternatively, predictive deconvolution with greater prediction distance can be used, and it is known as gapped deconvolution. Other pulse compression methods used in seismic processing are signature deconvolution, wavelet processing, and minimum entropy deconvolution. 相似文献
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L. V. Novikov 《Journal of Communications Technology and Electronics》2006,51(11):1261-1270
Wavelets obtained from known orthonormal wavelets modified by the impulse response of a stationary linear system are proposed. It is shown that the new wavelets offer additional possibilities for signal processing in the presence of noise. In particular, these wavelets provide for estimation of linearly transformed signals and simultaneous suppression of the noise effect. Filter banks that realize fast computational algorithms are synthesized. The wavelet approach is exemplified by solution of deconvolution, decorrelation, and differentiation problems. 相似文献
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《Proceedings of the IEEE. Institute of Electrical and Electronics Engineers》2008,96(10):1576-1587
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Erdogmus D. Hild K.E. Principe J.C. Lazaro M. Santamaria I. 《Signal Processing, IEEE Transactions on》2004,52(6):1489-1498
Blind deconvolution of linear channels is a fundamental signal processing problem that has immediate extensions to multiple-channel applications. In this paper, we investigate the suitability of a class of Parzen-window-based entropy estimates, namely Renyi's entropy, as a criterion for blind deconvolution of linear channels. Comparisons between maximum and minimum entropy approaches, as well as the effect of entropy order, equalizer length, sample size, and measurement noise on performance, will be investigated through Monte Carlo simulations. The results indicate that this nonparametric entropy estimation approach outperforms the standard Bell-Sejnowski and normalized kurtosis algorithms in blind deconvolution. In addition, the solutions using Shannon's entropy were not optimal either for super- or sub-Gaussian source densities. 相似文献
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SCA电台一般基于CORBA组件模型来设计业务、控制、信号处理、硬件抽象以及人机相关的各种组件,其分布式处理架构使得电台内部的业务数据难以集中管控,导致业务极不安全[S1],为解决该问题进行了深入的探索,研究了SCA软件架构及其安全架构,提出了电台内部业务消息的SCA组件化处理流程,给出了消息内容的安全集中管理机制以及消息存放位置单独传送的设计方法,并在SCA电台中对组件设计和安全集中管理机制进行了实现与测试验证。 相似文献
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White noise deconvolution or input white noise estimation problem has important application backgrounds in oil seismic exploration, communication and signal processing. By the modern time series analysis method, based on the Auto-Regressive Moving Average (ARMA) innovation model, under the linear minimum variance optimal fusion rules, three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and colored measurement noises. They can handle the input white noise fused filtering, prediction and smoothing problems. The accuracy of the fusers is higher than that of each local white noise estimator. In order to compute the optimal weights, the formula of computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances. 相似文献