Relevant bibliographies by topics / Frequency domain deconvolution

Academic literature on the topic 'Frequency domain deconvolution'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Frequency domain deconvolution"

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Friesen, W. I., and K. H. Michaelian. "Deconvolution in the Frequency Domain." Applied Spectroscopy 39, no. 3 (May 1985): 484–90. http://dx.doi.org/10.1366/0003702854248647.

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The Finite Impulse Response Operator (FIRO) method for improving the resolution of bands in a frequency-domain spectrum, developed recently by Jones and Shimokoshi (Applied Spectroscopy 37, 59 (1983)), is discussed in detail and its relationship to the analogous method of Kauppinen et al. (Applied Spectroscopy 35, 271 (1981), is shown. Under-, self-, and over-deconvolution are discussed for simulated and experimental spectra. Deconvolution of instrumentally broadened bands is discussed and implemented for the v1, Raman band of CC14; use of a Gauss-Lorentz lineshape gives the best results for this band. General guidelines for application of the FIRO method are also given.
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Goez, Martin, and Rainer Heun. "Reference Deconvolution in the Frequency Domain." Journal of Magnetic Resonance 136, no. 1 (January 1999): 69–75. http://dx.doi.org/10.1006/jmre.1998.1617.

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Sacchi, Mauricio D., Danilo R. Velis, and Alberto H. Comínguez. "Minimum entropy deconvolution with frequency‐domain constraints." GEOPHYSICS 59, no. 6 (June 1994): 938–45. http://dx.doi.org/10.1190/1.1443653.

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A method for reconstructing the reflectivity spectrum using the minimum entropy criterion is presented. The algorithm (FMED) described is compared with the classical minimum entropy deconvolution (MED) as well as with the linear programming (LP) and autoregressive (AR) approaches. The MED is performed by maximizing an entropy norm with respect to the coefficients of a linear operator that deconvolves the seismic trace. By comparison, the approach presented here maximizes the norm with respect to the missing frequencies of the reflectivity series spectrum. This procedure reduces to a nonlinear algorithm that is able to carry out the deconvolution of band‐limited data, avoiding the inherent limitations of linear operators. The proposed method is illustrated under a variety of synthetic examples. Field data are also used to test the algorithm. The results show that the proposed method is an effective way to process band‐limited data. The FMED and the LP arise from similar conceptions. Both methods seek an extremum of a particular norm subjected to frequency constraints. In the LP approach, the linear programming problem is solved using an adaptation of the simplex method, which is a very expensive procedure. The FMED uses only two fast Fourier transforms (FFTs) per iteration; hence, the computational cost of the inversion is reduced.
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Ilahi, A. K., M. F. R. Auly, D. A. Zaky, A. Abdullah, R. P. Nugroho, S. K. Suhardja, A. D. Nugraha, et al. "Early Results of Time Domain Receiver Function Data Processing in Mt Merapi and Mt Merbabu." IOP Conference Series: Earth and Environmental Science 873, no. 1 (October 1, 2021): 012055. http://dx.doi.org/10.1088/1755-1315/873/1/012055.

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Abstract The receiver function method is a method to image the earth subsurface by utilizing Ps conversion waves that are formed due to the velocity boundary. In general, the receiver function method estimates depth of structures such as the mantle-crust boundary by deconvoluting the vertical component from the horizontal component. Typical receiver function data processing is done in the frequency domain where the deconvolution process can be seen as a division between two components. In this study, we tried to reprocess the data using a deconvolution technique in time domain, popularly known as iterative time-domain deconvolution. The principle of iterative time domain deconvolution consists of iterative cross-correlation between the horizontal and vertical component. We use data from the DOMERAPI seismic station network located in the vicinity of Mt Merapi and Mt Merbabu. Mt Merapi is one of the most active volcanoes in the world with frequent eruptions and located at the ring of fire chain volcano in Indonesia. Note that the previous receiver function study in this region showed complex signals at some stations that may be related to sediment at shallow sediment and possible layers of low velocity zone that interfering main signal for a crust-mantle boundary. Our current results show iterative time domain RFs have clearer and smoother signal than the frequency domain that help interpreting the waveform signals. We estimate a range of crust thickness between 26-31 km near Mt Merapi. However, we noticed that iterative time domain calculation requires longer computation time and input signal.
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Brittle, K. F., L. R. Lines, and A. K. Dey. "Vibroseis deconvolution: a comparison of cross-correlation and frequency-domain sweep deconvolution." Geophysical Prospecting 49, no. 6 (November 2001): 675–86. http://dx.doi.org/10.1046/j.1365-2478.2001.00291.x.

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Bennia, A., and S. M. Riad. "An optimization technique for iterative frequency-domain deconvolution." IEEE Transactions on Instrumentation and Measurement 39, no. 2 (April 1990): 358–62. http://dx.doi.org/10.1109/19.52515.

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Gramann, Mark R., Josh G. Erling, and Michael J. Roan. "A frequency domain blind deconvolution algorithm in acoustics." Journal of the Acoustical Society of America 114, no. 4 (October 2003): 2406. http://dx.doi.org/10.1121/1.4778384.

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Zhang, Ya Nan, Yong Shou Dai, Jin Jie Ding, Man Man Zhang, and Rong Rong Wang. "An Application of the Frequency-Domain Experience Mode Decomposition to Enhance Deconvolution Results." Applied Mechanics and Materials 397-400 (September 2013): 2120–23. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2120.

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To improve the resolution of the seismic section after deconvolution, a method based on frequency-domain experience mode decomposition was proposed. Empirical mode decomposition (EMD) method is usually used to analyze the time domain non-stationary signal, in order to better recover original reflection coefficient sequence, empirical mode decomposition was implemented for frequency-domain amplitude spectrum. Through the different characteristics between the equivalent filter amplitude after deconvolution and reflection coefficient sequence amplitude in frequency-domain, the real reflection coefficient sequence was recovered. Simulation results indicate that the method is effective and feasible.
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Osorio, Luana Nobre, Bruno Pereira-Dias, André Bulcão, and Luiz Landau. "Migration deconvolution using domain decomposition." GEOPHYSICS 86, no. 3 (April 21, 2021): S247—S256. http://dx.doi.org/10.1190/geo2020-0352.1.

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Least-squares migration (LSM) is an effective technique for mitigating blurring effects and migration artifacts generated by limited data frequency bandwidth, incomplete coverage of geometry, source signature, and unbalanced amplitudes caused by complex wavefield propagation in the subsurface. Migration deconvolution (MD) is an image-domain approach for LSM that approximates the Hessian operator using a set of precomputed point spread functions. We have developed a new workflow by integrating the MD and domain decomposition (DD) methods. DD techniques aim to solve large and complex linear systems by splitting problems into smaller parts, facilitating parallel computing, and providing a higher convergence in iterative algorithms. We suggest that instead of solving the problem in a unique domain, as conventionally performed, splitting the problem into subdomains that overlap and solve each of them independently. We accelerate the convergence rate of the conjugate-gradient solver by applying the DD methods to retrieve better reflectivity, which is mainly visible in regions with low amplitudes. Moreover, using the pseudo-Hessian operator, the convergence of the algorithm is accelerated, suggesting that the inverse problem becomes better conditioned. Experiments using the synthetic Pluto model demonstrate that our algorithm dramatically reduces the required number of iterations while providing a considerable enhancement in image resolution and better continuity of poorly illuminated events.
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Dhaene, T., Z. Martens, and D. De Zutter. "Extended Bennia-Riad criterion for iterative frequency-domain deconvolution." IEEE Transactions on Instrumentation and Measurement 43, no. 2 (April 1994): 176–80. http://dx.doi.org/10.1109/19.293416.

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Dissertations / Theses on the topic "Frequency domain deconvolution"

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LOPES, LAURENT. "Etude methodologique du traitement sismique : deconvolution directionnelle dans le domaine frequence - nombre d'onde ; traitement de donnees sismiques acquises avec un dispositif tracte pres du fond de la mer. application a des donnees reelles." Paris 6, 1997. http://www.theses.fr/1997PA066685.

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Deconvolution directionnelle dans le domaine frequence - nombre d'onde de par leur dimension, les reseaux de sources sismiques marines sont directionnels. Cette directivite est a eliminer avant tout traitement base sur l'hypothese d'une source unique. Une methode allant dans ce sens, ainsi que le design de l'operateur correspondant, est explicitee. Cette methode est testee sur des donnees du delft air gun experiment (1983). Cette methode considere des sections en recepteur commun et utilise une approche exacte. Elle s'oppose en cela a la methode proposee par ziolkowski et spoelstra (1987), qui considere des sections en point milieu commun et qui n'est qu'une approximation. Considerer des sections en recepteur commun permet en outre de doubler l'intervalle des nombres d'onde consideres. Par suite, une amelioration de la resolution etait attendue. Elle n'a pas ete obtenue. Cette etude montre la necessite de transformer le champ d'onde emis par la source, lie a un milieu a trois dimensions, en un champ d'onde lie a un milieu a deux dimensions. Un algorithme oeuvrant dans ce sens est propose et relie a la deconvolution. Traitement de donnees acquises avec un dispositif tracte pres du fond de la mer. Un dispositif sismique comprenant une source situee en surface et une flute monotrace tractee pres du fond de la mer a ete developpe par l'ifremer. Cette geometrie permet de reduire la taille de la zone de fresnel, et donc d'ameliorer la resolution spatiale. Un algorithme de correction geometrique et un algorithme de migration profondeur dedies a ce dispositif ont ete developpes. Les variations de la geometrie d'acquisition se traduisent sur la section sismique par des variations des temps d'arrivee. Comme il est primordial de comparer des donnees acquises avec ce systeme a des donnees de sismique classique, un algorithme visant a simuler une section a zero-offset a ete developpe. Cet algorithme est robuste et converge en peu d'iterations. De par la dissymetrie du dispositif, des evenements situes sur une meme trace ne correspondent pas a des points de reflexion situes a la meme abscisse le long du profil. Cela peut conduire a des interpretations erronees. Deux methodes de migration profondeur, celle proposee par bowen (1984) et une de type kirchhoff ont ete testees. La superiorite de l'algorithme de type kirchhoff est montree. Les ameliorations et tests ulterieurs sont definis.
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Samarasinghe, Devanarayanage Pradeepa. "Efficient methodologies for real-time image restoration." Phd thesis, 2011. http://hdl.handle.net/1885/9859.

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In this thesis we investigate the problem of image restoration. The main focus of our research is to come up with novel algorithms and enhance existing techniques in order to deliver efficient and effective methodologies, applicable in real-time image restoration scenarios. Our research starts with a literature review, which identifies the gaps in existing techniques and helps us to come up with a novel classification on image restoration, which integrates and discusses more recent developments in the area of image restoration. With this novel classification, we identified three major areas which need our attention. The first developments relate to non-blind image restoration. The two mostly used techniques, namely deterministic linear algorithms and stochastic nonlinear algorithms are compared and contrasted. Under deterministic linear algorithms, we develop a class of more effective novel quadratic linear regularization models, which outperform the existing linear regularization models. In addition, by looking in a new perspective, we evaluate and compare the performance of deterministic and stochastic restoration algorithms and explore the validity of the performance claims made so far on those algorithms. Further, we critically challenge the ne- cessity of some complex mechanisms in Maximum A Posteriori (MAP) technique under stochastic image deconvolution algorithms. The next developments are focussed in blind image restoration, which is claimed to be more challenging. Constant Modulus Algorithm (CMA) is one of the most popular, computationally simple, tested and best performing blind equalization algorithms in the signal processing domain. In our research, we extend the use of CMA in image restoration and develop a broad class of blind image deconvolution algorithms, in particular algorithms for blurring kernels with a separable property. These algorithms show significantly faster convergence than conventional algorithms. Although CMA method has a proven record in signal processing applications related to data communications systems, no research has been carried out to the investigation of the applicability of CMA for image restoration in practice. In filling this gap and taking into account the differences of signal processing in im- age processing and data communications contexts, we extend our research on the applicability of CMA deconvolution under the assumptions on the ground truth image properties. Through analyzing the main assumptions of ground truth image properties being zero-mean, independent and uniformly distributed, which char- acterize the convergence of CMA deconvolution, we develop a novel technique to overcome the effects of image source correlation based on segmentation and higher order moments of the source. Multichannel image restoration techniques recently gained much attention over the single channel image restoration due to the benefits of diversity and redundancy of the information between the channels. Exploiting these benefits in real time applications is often restricted due to the unavailability of multiple copies of the same image. In order to overcome this limitation, as the last area of our research, we develop a novel multichannel blind restoration model with a single image, which eliminates the constraint of the necessity of multiple copies of the blurred image. We consider this as a major contribution which could be extended to wider areas of research integrated with multiple disciplines such as demosaicing.
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Books on the topic "Frequency domain deconvolution"

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Gamage, Gayani N. K. A survey of optimal frequency domain deconvolution methods. 1986.

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Book chapters on the topic "Frequency domain deconvolution"

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Nam, Seung H., and Seungkwon Beack. "A Frequency-Domain Normalized Multichannel Blind Deconvolution Algorithm for Acoustical Signals." In Independent Component Analysis and Blind Signal Separation, 524–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30110-3_67.

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Conference papers on the topic "Frequency domain deconvolution"

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Ulrych, Tad J., Milton Porsani, Jacob T. Fokkema, W. Scott, P. Leaney, and E. P. Schlumberger. "Predictive deconvolution in the frequency domain." In SEG Technical Program Expanded Abstracts 1988. Society of Exploration Geophysicists, 1988. http://dx.doi.org/10.1190/1.1892436.

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Du, Xin, Guofa Li, Hao Li, Wenbo Zhang, and Bowen Tang. "Frequency-domain multitrace band-limited sparsity deconvolution." In SEG Technical Program Expanded Abstracts 2017. Society of Exploration Geophysicists, 2017. http://dx.doi.org/10.1190/segam2017-17660344.1.

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Grauer, Jared A., and Matthew J. Boucher. "Frequency-Domain Deconvolution for Flight Dynamics Applications." In 2018 Atmospheric Flight Mechanics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-3157.

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Broadhead, Michael K., Christopher L. Liner, and Tadeusz J. Ulrych. "Predictive deconvolution by frequency domain Wiener filtering." In SEG Technical Program Expanded Abstracts 2007. Society of Exploration Geophysicists, 2007. http://dx.doi.org/10.1190/1.2792997.

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J. Day, A., S. Hegna, G. Parkes, and N. Turnbull. "Air–Leak Detection Using Frequency Domain Deconvolution." In 69th EAGE Conference and Exhibition incorporating SPE EUROPEC 2007. European Association of Geoscientists & Engineers, 2007. http://dx.doi.org/10.3997/2214-4609.201401464.

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Wei, Gao, and Liu Huaishan. "Frequency Domain Seismic Blind Deconvolution Based on ICA." In 2010 International Forum on Information Technology and Applications (IFITA). IEEE, 2010. http://dx.doi.org/10.1109/ifita.2010.121.

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Wang, Zuoguan, and Yutian Fu. "Frequency-domain Regularized Deconvolution for Images with Stripe Noise." In Fourth International Conference on Image and Graphics (ICIG 2007). IEEE, 2007. http://dx.doi.org/10.1109/icig.2007.115.

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Nan Pan, Xing Wu, Yilin Chi, Xiaoqin Liu, and Chang Liu. "Machine fault diagnosis based on Frequency-Domain Blind Deconvolution." In 2011 International Conference on Modelling, Identification and Control. IEEE, 2011. http://dx.doi.org/10.1109/icmic.2011.5973677.

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Schmelzbach, C., and F. Scherbaum. "Bayesian Frequency-domain Mixed-phase Wavelet Estimation and Deconvolution." In 73rd EAGE Conference and Exhibition incorporating SPE EUROPEC 2011. Netherlands: EAGE Publications BV, 2011. http://dx.doi.org/10.3997/2214-4609.20149193.

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Wei, S., O. Y. Yonglin, Z. QingCai, H. JiaQiang, and S. Yaying. "Generalized S-transform Based Time-frequency Domain Spectral Modeling Deconvolution." In 77th EAGE Conference and Exhibition 2015. Netherlands: EAGE Publications BV, 2015. http://dx.doi.org/10.3997/2214-4609.201413415.

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