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Статті в журналах з теми "Frequency domain deconvolution"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Larue, A., J. I. Mars, and C. Jutten. "Frequency-domain blind deconvolution based on mutual information rate." IEEE Transactions on Signal Processing 54, no. 5 (May 2006): 1771–81. http://dx.doi.org/10.1109/tsp.2006.872545.

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12

Frøseth, Gunnstein T., Anders Rønnquist, Daniel Cantero, and Ole Øiseth. "Influence line extraction by deconvolution in the frequency domain." Computers & Structures 189 (September 2017): 21–30. http://dx.doi.org/10.1016/j.compstruc.2017.04.014.

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13

Yanfei, Wang. "A new method of deconvolution in the frequency domain." Journal of Electronics (China) 14, no. 2 (April 1997): 112–16. http://dx.doi.org/10.1007/s11767-997-1002-8.

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14

Margrave, Gary F., Michael P. Lamoureux, and David C. Henley. "Gabor deconvolution: Estimating reflectivity by nonstationary deconvolution of seismic data." GEOPHYSICS 76, no. 3 (May 2011): W15—W30. http://dx.doi.org/10.1190/1.3560167.

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We have extended the method of stationary spiking deconvolution of seismic data to the context of nonstationary signals in which the nonstationarity is due to attenuation processes. As in the stationary case, we have assumed a statistically white reflectivity and a minimum-phase source and attenuation process. This extension is based on a nonstationary convolutional model, which we have developed and related to the stationary convolutional model. To facilitate our method, we have devised a simple numerical approach to calculate the discrete Gabor transform, or complex-valued time-frequency decomposition, of any signal. Although the Fourier transform renders stationary convolution into exact, multiplicative factors, the Gabor transform, or windowed Fourier transform, induces only an approximate factorization of the nonstationary convolutional model. This factorization serves as a guide to develop a smoothing process that, when applied to the Gabor transform of the nonstationary seismic trace, estimates the magnitude of the time-frequency attenuation function and the source wavelet. By assuming that both are minimum-phase processes, their phases can be determined. Gabor deconvolution is accomplished by spectral division in the time-frequency domain. The complex-valued Gabor transform of the seismic trace is divided by the complex-valued estimates of attenuation and source wavelet to estimate the Gabor transform of the reflectivity. An inverse Gabor transform recovers the time-domain reflectivity. The technique has applications to synthetic data and real data.

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15

Pan, Nan, Wu Xing, Yi Lin Chi, Liu Chang, and Xiao Qin Liu. "Application of Frequency-Domain Blind Deconvolution in Mechanical Fault Detection." Applied Mechanics and Materials 130-134 (October 2011): 2128–32. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2128.

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On the basis of summing up the Frequency-Domain Blind Deconvolution (FDBD), a method combine Complex-Domain FastICA algorithm and amplitude correlation was proposed to extract the typical defect signals from mechanical equipment. The application in combined failure rolling bearing acceleration signals demonstrate that, comparing with the existing Time-Domain Blind Signal Processing methods, FDBD has more advantages and better prospects in mechanical fault detection.

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16

Zazula, D. "A method for the direct frequency-domain deconvolution without division." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 40, no. 5 (May 1993): 332–37. http://dx.doi.org/10.1109/82.227373.

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17

Wang, Zhiguo, Bing Zhang, Jinghuai Gao, and Qing Huo Liu. "A frequency-domain seismic blind deconvolution based on Gini correlations." Journal of Geophysics and Engineering 15, no. 1 (January 23, 2018): 286–94. http://dx.doi.org/10.1088/1742-2140/aa8eb4.

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18

Chen, Yang, Shengyang Huang, and Emma Pickwell-MacPherson. "Frequency-wavelet domain deconvolution for terahertz reflection imaging and spectroscopy." Optics Express 18, no. 2 (January 8, 2010): 1177. http://dx.doi.org/10.1364/oe.18.001177.

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19

Mishra, M., J. Mattingly, and R. M. Kolbas. "Application of deconvolution to recover frequency-domain multiplexed detector pulses." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 929 (June 2019): 57–65. http://dx.doi.org/10.1016/j.nima.2019.03.043.

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20

Schmelzbach, C., F. Scherbaum, J. Tronicke, and P. Dietrich. "Bayesian frequency-domain blind deconvolution of ground-penetrating radar data." Journal of Applied Geophysics 75, no. 4 (December 2011): 615–30. http://dx.doi.org/10.1016/j.jappgeo.2011.08.010.

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21

Cambois, Guillaume, and Paul L. Stoffa. "Surface‐consistent deconvolution in the log/Fourier domain." GEOPHYSICS 57, no. 6 (June 1992): 823–40. http://dx.doi.org/10.1190/1.1443296.

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In the surface‐consistent hypothesis, a seismic trace is the convolution of a source operator, a receiver operator, a reflectivity operator (representing the subsurface structure) and an offset‐related operator. In the log/Fourier domain, convolutions become sums and the log of signal amplitude at a given frequency is the sum of source, receiver, structural, and offset‐related terms. Recovering the amplitude of the reflectivity for a given frequency is then a linear problem (very similar to a surface‐consistent static correction problem). However, this linear system is underconstrained. Thus, among the infinite number of possible solutions, a particular one must be selected. Studies with real data support the choice of a spatially band‐limited solution. The surface‐consistent operators can then be calculated very efficiently using an inverse Hessian method. Applications to real seismic data show improvement compared with previous techniques. Surface‐consistent deconvolution is robust and fast in the log/Fourier domain. It allows the use of long operators, improves statics estimation, and removes the amplitude variations due to source or receiver coupling.

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22

Honarvar Shakibaei, Barmak, and Peyman Jahanshahi. "Image Deconvolution by Means of Frequency Blur Invariant Concept." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/951842.

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Different blur invariant descriptors have been proposed so far, which are either in the spatial domain or based on the properties available in the moment domain. In this paper, a frequency framework is proposed to develop blur invariant features that are used to deconvolve a degraded image caused by a Gaussian blur. These descriptors are obtained by establishing an equivalent relationship between the normalized Fourier transforms of the blurred and original images, both normalized by their respective fixed frequencies set to one. Advantage of using the proposed invariant descriptors is that it is possible to estimate both the point spread function (PSF) and the original image. The performance of frequency invariants will be demonstrated through experiments. An image deconvolution is done as an additional application to verify the proposed blur invariant features.

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23

Hornbostel, Scott C. "Iterative deconvolution using generalized “positivity”." GEOPHYSICS 54, no. 10 (October 1989): 1297–305. http://dx.doi.org/10.1190/1.1442589.

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In some cases a real signal may be known a priori to be always positive. If this positive signal is later band‐limited, the knowledge of its original positivity can be used to help in recovering the lost frequencies. Specifically, the frequency‐domain values for members of this special class of signals have the interesting property that they are related to each other via the self‐convolution of Hermitian functions. This relationship is the basis for some current deconvolution approaches and can be generalized for the case of a signal of arbitrary sign. A steepest descent formulation in the frequency domain can determine these Hermitian functions while maximizing the fit to the known in‐band data and to estimated dc values. This formulation allows for the explicit calculation of the step size and is also easily modified to include finite support or penalty/reward constraints. Simulated data tests indicate good bandwidth extension for this method, while actually sometimes improving the signal‐to‐noise ratio of the in‐band values.

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24

Choi, Yunseok, and Tariq Alkhalifah. "Time-domain full-waveform inversion of exponentially damped wavefield using the deconvolution-based objective function." GEOPHYSICS 83, no. 2 (March 1, 2018): R77—R88. http://dx.doi.org/10.1190/geo2017-0057.1.

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Full-waveform inversion (FWI) suffers from the cycle-skipping problem when the available frequency-band of data is not low enough. We have applied an exponential damping to the data to generate artificial low frequencies, which helps FWI to avoid cycle skipping. In this case, the least-squares misfit function does not properly deal with the exponentially damped wavefield in FWI because the amplitude of traces decays almost exponentially with increasing offset in a damped wavefield. Thus, we use a deconvolution-based objective function for FWI of the exponentially damped wavefield. The deconvolution filter includes inherently a normalization between the modeled and observed data; thus, it can address the unbalanced amplitude of a damped wavefield. We specifically normalize the modeled data with the observed data in the frequency-domain to estimate the deconvolution filter and selectively choose a frequency-band for normalization that mainly includes the artificial low frequencies. We calculate the gradient of the objective function using the adjoint-state method. The synthetic and benchmark data examples indicate that our FWI algorithm generates a convergent long-wavelength structure without low-frequency information in the recorded data.

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25

Nakshatri, Hemanth S., and Jaya Prakash. "Model resolution matrix based deconvolution improves over non-quadratic penalization in frequency-domain photoacoustic tomography." Journal of the Acoustical Society of America 152, no. 3 (September 2022): 1345–56. http://dx.doi.org/10.1121/10.0013829.

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Frequency domain photoacoustic tomography is becoming more attractive due to low-cost and compact light-sources being used; however, frequency-domain implementation suffers from lower signal to noise compared to time-domain implementation. In this work, we have developed a non-quadratic based penalization framework for frequency-domain photoacoustic imaging, and further proposed a two-step model-resolution matrix based deconvolution approach to improve the reconstruction image quality. The model-resolution matrix was developed in the context of different penalty functions like l2-norm, l1-norm, Cauchy, and Geman-McClure. These model-resolution matrices were then used to perform the deconvolution operation using split augmented Lagrangian shrinkage thresholding algorithm in both full-view and limited-view configurations. The results indicated that the two-step approach outperformed the different penalty function (prior constraint) based reconstruction, with an improvement of about 20% in terms of peak signal to noise ratio and 30% in terms of structural similarity index measure. The improved image quality provided using these algorithms will have a direct impact on realizing practical frequency-domain implementation in both limited-view and full-view configurations.

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26

Mishra, M., and J. Mattingly. "Recovery of coincident frequency domain multiplexed detector pulses using sequential deconvolution." Journal of Instrumentation 16, no. 03 (March 4, 2021): P03011. http://dx.doi.org/10.1088/1748-0221/16/03/p03011.

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27

Zazula, D., and L. Gyergyek. "Direct frequency-domain deconvolution when the signals have no spectral inverse." IEEE Transactions on Signal Processing 41, no. 2 (1993): 977–81. http://dx.doi.org/10.1109/78.193238.

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28

Bona, Massimo. "Variance estimate in frequency-domain deconvolution for teleseismic receiver function computation." Geophysical Journal International 134, no. 2 (August 1998): 634–46. http://dx.doi.org/10.1111/j.1365-246x.1998.tb07128.x.

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29

Pan, Nan, Xing Wu, YiLin Chi, Xiaoqin Liu, and Chang Liu. "Combined failure acoustical diagnosis based on improved frequency domain blind deconvolution." Journal of Physics: Conference Series 364 (May 28, 2012): 012078. http://dx.doi.org/10.1088/1742-6596/364/1/012078.

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30

Pan, Shu-Lin, Ke Yan, Hai-Qiang Lan, and Zi-Yu Qin. "A Bregman adaptive sparse-spike deconvolution method in the frequency domain." Applied Geophysics 16, no. 4 (November 21, 2019): 463–72. http://dx.doi.org/10.1007/s11770-019-0779-9.

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31

Gao, Wei, Huai Shan Liu, and Jian Ye Sun. "Frequency Domain Seismic Blind Deconvolution Based on ICA with High Order Statistics Constraint." Applied Mechanics and Materials 321-324 (June 2013): 1827–30. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1827.

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Independent components analysis (ICA) with constraint of seismic wavelet estimated from bispectrum of seismic traces is combined with short time Fourier transforms (STFT) to improve the traditional frequency domain seismic deconvolution. Neglecting noise, the seismic record is changed from time domain to frequency domain with STFT in order to transform the common seismic model to the basic ICA model. By applying FastICA algorithm with constraint of seismic wavelet estimated from bispectrum of seismic traces, reflectivity series and the seismic wavelet can be produced in frequency domain and changed back to the time domain subsequently. The model and real seismic data numerical examples all show the algorithm valid.

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32

Sooch, Gurinderbir S., and Ashutosh Bagchi. "A New Iterative Procedure for Deconvolution of Seismic Ground Motion in Dam-Reservoir-Foundation Systems." Journal of Applied Mathematics 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/287605.

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The concrete gravity dams are designed to perform satisfactorily during an earthquake since the consequence of failure is catastrophic to the downstream communities. The foundation in a dam is usually modeled by a substructuring approach for the purpose of seismic response analysis. However, the substructuring cannot be used for solving nonlinear dynamic problems that may be encountered in dam-reservoir-foundation systems. For that reason, the time domain approach is preferred for such systems. The deconvolved earthquake input model is preferred as it can remove the seismic scattering effects due to artificial boundaries of the semi-infinite foundation domain. Deconvolution is a mathematical process that allows the adjustment of the amplitude and frequency contents of a seismic ground motion applied at the base of the foundation in order to get the desired output at the dam-foundation interface. It is observed that the existing procedures of deconvolution are not effective for all types of earthquake records. A modified procedure has been proposed here for efficient deconvolution of all types of earthquake records including high-frequency and low-frequency ground motions.

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33

Hwang, H. J., and B. J. Mitchell. "Interstation surface wave analysis by Frequency-domain Wiener deconvolution and modal isolation." Bulletin of the Seismological Society of America 76, no. 3 (June 1, 1986): 847–64. http://dx.doi.org/10.1785/bssa0760030847.

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Abstract A new technique which combines frequency-domain Wiener filtering and modal isolation is developed for determining interstation phase velocities, group velocities, and attenuation coefficients for seismic surface waves. Frequency-domain Wiener filtering is more effective than time-domain Wiener filtering for the determination because it uses a smaller window lag which produces a smoother interstation Green's function. This leads to greater accuracy and stability when noise-contaminated data are analyzed. We optimize Wiener filtering in the frequency domain by applying two trapezoidal windows of different lags to the cross-correlation function between two stations and to the autocorrelation function of the first station, respectively. The windowed correlation functions are then transformed to the frequency domain. The interstation Green's function in this technique is the ratio of the smoothed cross-spectrum to the smoothed auto-spectrum of the first station. Frequency-domain Wiener filtering is equivalent to time-domain Wiener filtering when the same rectangular window is applied to both the correlation functions. Wiener filtering, however, cannot efficiently remove higher mode interference when the higher modes are superimposed on the fundamental mode in the correlation functions. To more thoroughly eliminate the effects of such interference, phase-matched filtering or time-variable filtering can be employed to isolate one particular mode at each of two stations. Frequency-domain Wiener deconvolution is then applied to calculate the Green's function. The interstation group velocites can be obtained by applying the multiple filter technique to the Green's function, and can be refined by phase-matched filtering. The amplitude and phase spectra of the Green's function are used to calculate attenuation coefficients and phase velocities, respectively, for the interstation medium. This new technique is compared with other methods by applying them to both noise-contaminated synthetic seismograms and real data. The proposed technique is found to be superior, particularly in period ranges where the signal-to-noise is low.

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34

Amundsen, Lasse, Hongbo Zhou, Arne Reitan, and Arthur B. Weglein. "On seismic deghosting by spatial deconvolution." GEOPHYSICS 78, no. 6 (November 1, 2013): V267—V271. http://dx.doi.org/10.1190/geo2013-0198.1.

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Receiver-side deghosting can be derived and implemented in the frequency domain as spatial deterministic deconvolution of marine pressure recordings. The deghosting/deconvolution operator is found analytically as the inverse Fourier transform of the wavenumber-domain wave equation deghosting function. For a sea surface reflection coefficient of [Formula: see text], the wavenumber-domain deghosting function has well-known poles at fundamental frequencies equal to an integer multiple of a function of the receiver depth and the plane-wave dip angle relative to the depth axis. The first singularity is always at 0 Hz. The spatial deghosting operator has singularities at fundamental frequencies equal to an integer multiple of a function of the receiver depth, independent of its lateral coordinates. The first singularity is again at 0 Hz. In addition, the deghosting operator that is applied to 3D data has singularity when its lateral coordinate is zero. A simple numerical example demonstrates the method.

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35

Douglass, Alexander S., and Shima Abadi. "Investigating cepstral methods for blind deconvolution." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A295. http://dx.doi.org/10.1121/10.0016330.

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Cepstral methods are homomorphic signal processing techniques in which signals are transformed into the cepstral domain, typically to be averaged or filtered. In the cepstral domain, the convolution of the source signal and impulse response changes to a sum of the two parts by taking a logarithm in the frequency domain, followed by an inverse Fourier transform. Cepstral methods have been utilized in speech processing to separate vocal tract information from speech excitation, in marine mammal bioacoustics to classify vocalizations, and in seismic surveys to estimate source wavelets from airgun arrays, but do not appear to have been considered for blind deconvolution in other underwater acoustic applications. By assuming either the source signal or impulse response is stationary in time and/or space, averaging cepstral domain windows from a receiver array can suppress the non-stationary term. Here, we investigate the capabilities of cepstral averaging techniques for the purposes of blind deconvolution. We will exploit the spatial dependence of the impulse response across a receiver array to suppress this non-stationary term, while constructively combining the source signal term. We will consider the effects of different signal types, environmental characteristics, signal-to-noise ratios, and array design on its success. [Work supported by the ONR.]

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36

Cambois, Guillaume, and Paul Stoffa. "Surface‐consistent phase decomposition in the log/Fourier domain." GEOPHYSICS 58, no. 8 (August 1993): 1099–111. http://dx.doi.org/10.1190/1.1443494.

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In the log/Fourier domain, decomposing the amplitude spectra of seismic data into surface‐consistent terms is a linear problem that can be solved, very efficiently, one frequency at a time. However, the nonunique definition of the complex logarithm makes it much more difficult to decompose the phase spectra. The instability of phase unwrapping has previously prevented any attempt to decompose phase spectra in the log/Fourier domain. We develop a fast and robust partial unwrapping algorithm, which makes it possible to efficiently decompose the phase spectra of normal moveout‐corrected (NMO‐) data into surface‐consistent terms, in the log/Fourier domain. The dual recovery of amplitude and phase spectra yields a surface‐consistent deconvolution technique where only the average reflectivity is assumed to be white, and only the average wavelet is required to be minimum‐phase. Each individual deconvolution operator may be mixed‐phase, depending on its estimated phase spectra. For example, surface‐consistent time shifts and phase rotations, as well as any other surface‐consistent phase effects, are included in the phase spectra of the surface‐consistent deconvolution operators. Consequently, static shifts are estimated and removed without ever picking horizons or crosscorrelations.

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37

Zhang Tong, 张彤, 范研 Fan Yan, and 赵谦 Zhao Qian. "Atmospheric Laser Communication System Frequency-Domain Deconvolution Algorithm of Restraining Multiplicative Noise." Chinese Journal of Lasers 42, no. 5 (2015): 0513002. http://dx.doi.org/10.3788/cjl201542.0513002.

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38

Yi, Zeguang, Nan Pan, and Yu Guo. "Mechanical compound faults extraction based on improved frequency domain blind deconvolution algorithm." Mechanical Systems and Signal Processing 113 (December 2018): 180–88. http://dx.doi.org/10.1016/j.ymssp.2017.06.028.

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39

Colombo, Daniele, Diego Rovetta, Ernesto Sandoval-Curiel, and Apostolos Kontakis. "Transmission-based near-surface deconvolution." GEOPHYSICS 85, no. 2 (February 12, 2020): V169—V181. http://dx.doi.org/10.1190/geo2019-0443.1.

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We have developed a new framework for performing surface-consistent amplitude balancing and deconvolution of the near-surface attenuation response. Both approaches rely on the early arrival waveform of a seismic recording, which corresponds to the refracted or, more generally speaking, to the transmitted energy from a seismic source. The method adapts standard surface-consistent amplitude compensation and deconvolution to the domain of refracted/transmitted waves. A sorting domain specific for refracted energy is extended to the analysis of amplitude ratios of each trace versus a reference average trace to identify amplitude residuals that are inverted for surface consistency. The residual values are either calculated as a single scalar value for each trace or as a function of frequency to build a surface-consistent deconvolution operator. The derived operators are then applied to the data to obtain scalar amplitude balancing or amplitude balancing with spectral shaping. The derivation of the operators around the transmitted early arrival waveforms allows for deterministically decoupling the near-surface attenuation response from the remaining seismic data. The developed method is fully automatic and does not require preprocessing of the data. As such, it qualifies as a standard preprocessing tool to be applied at the early stages of seismic processing. Applications of the developed method are provided for a case in a complex, structure-controlled wadi, for a seismic time-lapse [Formula: see text] land monitoring case, and for an exploration area with high dunes and sabkhas producing large frequency-dependent anomalous amplitude responses. The new development provides an effective tool to enable better reservoir characterization and monitoring with land seismic data.

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40

Vargas, David, Ivan Vasconcelos, Matteo Ravasi, and Nick Luiken. "Time-Domain Multidimensional Deconvolution: A Physically Reliable and Stable Preconditioned Implementation." Remote Sensing 13, no. 18 (September 15, 2021): 3683. http://dx.doi.org/10.3390/rs13183683.

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Multidimensional deconvolution constitutes an essential operation in a variety of geophysical scenarios at different scales ranging from reservoir to crustal, as it appears in applications such as surface multiple elimination, target-oriented redatuming, and interferometric body-wave retrieval just to name a few. Depending on the use case, active, microseismic, or teleseismic signals are used to reconstruct the broadband response that would have been recorded between two observation points as if one were a virtual source. Reconstructing such a response relies on the the solution of an ill-conditioned linear inverse problem sensitive to noise and artifacts due to incomplete acquisition, limited sources, and band-limited data. Typically, this inversion is performed in the Fourier domain where the inverse problem is solved per frequency via direct or iterative solvers. While this inversion is in theory meant to remove spurious events from cross-correlation gathers and to correct amplitudes, difficulties arise in the estimation of optimal regularization parameters, which are worsened by the fact they must be estimated at each frequency independently. Here we show the benefits of formulating the problem in the time domain and introduce a number of physical constraints that naturally drive the inversion towards a reduced set of stable, meaningful solutions. By exploiting reciprocity, time causality, and frequency-wavenumber locality a set of preconditioners are included at minimal additional cost as a way to alleviate the dependency on an optimal damping parameter to stabilize the inversion. With an interferometric redatuming example, we demonstrate how our time domain implementation successfully reconstructs the overburden-free reflection response beneath a complex salt body from noise-contaminated up- and down-going transmission responses at the target level.

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41

Tygel, M., H. Huck, and P. Hubral. "Mixed‐delay wavelet deconvolution of the point‐source seismogram." GEOPHYSICS 56, no. 9 (September 1991): 1405–11. http://dx.doi.org/10.1190/1.1443160.

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The problem of extracting a mixed‐delay source wavelet from a point‐source seismogram for an acoustic, horizontally stratified medium (bounded by a free surface above and a half‐space below or between two half‐spaces) can be completely solved without any further assumptions about the source pulse or the model parameters. The solution relies on information contained in the so‐called evanescent part of the point‐source seismogram, which can be extracted via a plane‐wave decomposition, i.e., by a transformation of the point‐source seismogram from the time‐space domain into the frequency‐rayparameter domain.

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Koya, Takeshi, Takaaki Ishibashi, Hiroshi Shiratsuchi, and Hiromu Gotanda. "Blind Source Deconvolution Based on Frequency Domain Convolution Model Under Highly Reverberant Environments." Transactions of the Institute of Systems, Control and Information Engineers 22, no. 8 (2009): 287–94. http://dx.doi.org/10.5687/iscie.22.287.

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43

Dong, Junliang, J. Bianca Jackson, Marcello Melis, David Giovanacci, Gillian C. Walker, Alexandre Locquet, John W. Bowen, and D. S. Citrin. "Terahertz frequency-wavelet domain deconvolution for stratigraphic and subsurface investigation of art painting." Optics Express 24, no. 23 (November 11, 2016): 26972. http://dx.doi.org/10.1364/oe.24.026972.

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44

Zhang, Qianqian, Haochi Pan, Qiuxia Fan, Fujing Xu, and Yulong Wu. "Research on Fault Extraction Method of CYCBD Based on Seagull Optimization Algorithm." Shock and Vibration 2021 (July 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/8552024.

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Maximum cyclostationarity blind deconvolution (CYCBD) can recover the periodic impulses from mixed fault signals comprised by noise and periodic impulses. In recent years, blind deconvolution has been widely used in fault diagnosis. However, it requires a preset of filter length, and inappropriate filter length may cause the inaccurate extraction of fault signal. Therefore, in order to determine filter length adaptively, a method to optimize CYCBD by using the seagull optimization algorithm (SOA) is proposed in this paper. In this method, the ratio of SNR to kurtosis is used as the objective function; firstly, SOA is used to search the optimal filter length in CYCBD by iteration, and then it uses the optimal filter length to perform CYCBD; finally, the frequency-domain waveform is determined through Fourier transformation. The method proposed is applied to the fault extraction of a simulated signal and a test vibration signal of the closed power flow gearbox test bed, and the fault frequency is successfully extracted, in addition, using maximum correlation kurtosis deconvolution (MCKD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) to compare with CYCBD-SOA, which validated availability of the proposed method.

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45

Morozov, Igor, Mohamed Haiba, and Wubing Deng. "Inverse attenuation filtering." GEOPHYSICS 83, no. 2 (March 1, 2018): V135—V147. http://dx.doi.org/10.1190/geo2016-0211.1.

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Inverse-[Formula: see text] filtering is an important seismic-processing operation often used to correct for attenuation and dispersion effects and increase the resolution of reflection records. However, it is important to realize that the [Formula: see text] is an apparent (phenomenological) attribute of the propagating wavefield and not guaranteed to be a material property. By recognizing the apparent character of the [Formula: see text], the attenuation-correction procedure can be significantly extended and generalized. Our approach consists of forward modeling the propagating source waveform by using multiple physical laws followed by multiple types of inverse filtering. The modeling and inverse-filtering algorithms are selectable according to the geology of the study area, data, and goals of processing, which may include reduction of attenuation effects or more general enhancements of reflectivity images. Apparent [Formula: see text] models are inherently smooth in space, which facilitates efficient use of time-variant deconvolution implemented by using overlapping tapered time windows. When using conventional [Formula: see text] models and frequency-domain deconvolution, this procedure contains all existing types of inverse-[Formula: see text] filtering. However, many more realistic forward modeling approaches can (and should) be used depending on the specific subsurface environments, such as wavefront focusing and defocusing, scattering, solid viscosity, or internal friction caused by pore-fluid flows. In general, velocity-dispersion relations cannot be inferred from the frequency-dependent [Formula: see text] and need to be considered separately. It is more precise to view frequency-dependent velocity dispersion and [Formula: see text] as concomitant and arising from a common physical mechanism of wave propagation. Time-domain deconvolution, such as an iterative method well-known in earthquake seismology, offers significant improvements in attenuation-corrected images. The approaches are illustrated on a real reflection data set by using several attenuation laws and types of deconvolution.

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46

Connolly, Francis T., and Giorgio Rizzoni. "Real Time Estimation of Engine Torque for the Detection of Engine Misfires." Journal of Dynamic Systems, Measurement, and Control 116, no. 4 (December 1, 1994): 675–86. http://dx.doi.org/10.1115/1.2899267.

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The need for improvements in the on-line estimation of engine performance variables is greater nowadays as a result of more stringent emission control legislation. There is also a concurrent requirement for improved on-board diagnostics to detect different types of malfunctions. For example, recent California Air Resources Board (CARB) regulations mandate continuous monitoring of misfires, a problem which, short of an expensive measurement of combustion pressure in each cylinder, is most directly approached by estimating individual cylinder torque. This paper describes the theory and experimental results of a method for the estimation of individual cylinder torque in automative engines, with the intent of satisfying the CARB misfire detection requirements. Estimation, control, and diagnostic functions associated with automotive engines involve near periodic processes, due to the nature of multi-cylinder engines. The model of the engine dynamics used in this study fully exploits the inherent periodicity of the combustion process in the crank angle domain in order to obtain a simple deconvolution method for the estimation of the mean torque produced by each cylinder during each stroke from a measurement of crankshaft angular velocity. The deconvolution is actually performed in the spatial frequency domain, recognizing that the combustion energy is concentrated at discrete spatial frequencies, which are harmonics of the frequency of rotation of the crankshaft. Thus, the resulting deconvolution algorithm is independent of engine speed, and reduces to an algebraic operation in the frequency domain. It is necessary to perform a Discrete Fourier Transform (DFT) on the measured angular velocity signal, sampled at fixed uniform crank angle intervals. The paper discusses the model used in the study, and the experimental validation of the algorithm, which has been implemented in real time using a portable computer and has been tested extensively on different production vehicles on a chassis dynamometer and on the road.

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Zhang, Beibei, Ning Bi, Chao Zhang, Xiangping Gao, and Zhao Lv. "Robust EOG-based saccade recognition using multi-channel blind source deconvolution." Biomedical Engineering / Biomedizinische Technik 64, no. 3 (May 27, 2019): 309–24. http://dx.doi.org/10.1515/bmt-2018-0018.

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Abstract Human activity recognition (HAR) is a research hotspot in the field of artificial intelligence and pattern recognition. The electrooculography (EOG)-based HAR system has attracted much attention due to its good realizability and great application potential. Focusing on the signal processing method of the EOG-HAR system, we propose a robust EOG-based saccade recognition using the multi-channel convolutional independent component analysis (ICA) method. To establish frequency-domain observation vectors, short-time Fourier transform (STFT) is used to process time-domain EOG signals by applying the sliding window technique. Subsequently, we apply the joint approximative diagonalization of eigenmatrix (JADE) algorithm to separate the mixed signals and choose the “clean” saccadic source to extract features. To address the problem of permutation ambiguity in a case with a six-channel condition, we developed a constraint direction of arrival (DOA) algorithm that can automatically adjust the order of eye movement sources according to the constraint angle. Recognition experiments of four different saccadic EOG signals (i.e. up, down, left and right) were conducted in a laboratory environment. The average recognition ratios over 13 subjects were 95.66% and 97.33% under the between-subjects test and the within-subjects test, respectively. Compared with “bandpass filtering”, “wavelet denoising”, “extended infomax algorithm”, “frequency-domain JADE algorithm” and “time-domain JADE algorithm, the recognition ratios obtained relative increments of 4.6%, 3.49%, 2.85%, 2.81% and 2.91% (within-subjects test) and 4.91%, 3.43%, 2.21%, 2.24% and 2.28% (between-subjects test), respectively. The experimental results revealed that the proposed algorithm presents robust classification performance in saccadic EOG signal recognition.

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Zhou, Huailai, Yuanjun Wang, Tengfei Lin, Fangyu Li, and Kurt J. Marfurt. "Value of nonstationary wavelet spectral balancing in mapping a faulted fluvial system, Bohai Gulf, China." Interpretation 3, no. 3 (August 1, 2015): SS1—SS13. http://dx.doi.org/10.1190/int-2014-0128.1.

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Seismic data with enhanced resolution allow interpreters to effectively delineate and interpret architectural components of stratigraphically thin geologic features. We used a recently developed time-frequency domain deconvolution method to spectrally balance nonstationary seismic data. The method was based on polynomial fitting of seismic wavelet magnitude spectra. The deconvolution increased the spectral bandwidth but did not amplify random noise. We compared our new spectral modeling algorithm with existing time-variant spectral-whitening and inverse [Formula: see text]-filtering algorithms using a 3D offshore survey acquired over Bohai Gulf, China. We mapped these improvements spatially using a suite of 3D volumetric coherence, energy, curvature, and frequency attributes. The resulting images displayed improved lateral resolution of channel edges and fault edges with few, if any artifacts associated with amplification of random noise.

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49

Woo, Wai Lok, Bin Gao, Ahmed Bouridane, Bingo Wing-Kuen Ling, and Cheng Siong Chin. "Unsupervised Learning for Monaural Source Separation Using Maximization–Minimization Algorithm with Time–Frequency Deconvolution." Sensors 18, no. 5 (April 27, 2018): 1371. http://dx.doi.org/10.3390/s18051371.

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This paper presents an unsupervised learning algorithm for sparse nonnegative matrix factor time–frequency deconvolution with optimized fractional β-divergence. The β-divergence is a group of cost functions parametrized by a single parameter β. The Itakura–Saito divergence, Kullback–Leibler divergence and Least Square distance are special cases that correspond to β=0, 1, 2, respectively. This paper presents a generalized algorithm that uses a flexible range of β that includes fractional values. It describes a maximization–minimization (MM) algorithm leading to the development of a fast convergence multiplicative update algorithm with guaranteed convergence. The proposed model operates in the time–frequency domain and decomposes an information-bearing matrix into two-dimensional deconvolution of factor matrices that represent the spectral dictionary and temporal codes. The deconvolution process has been optimized to yield sparse temporal codes through maximizing the likelihood of the observations. The paper also presents a method to estimate the fractional β value. The method is demonstrated on separating audio mixtures recorded from a single channel. The paper shows that the extraction of the spectral dictionary and temporal codes is significantly more efficient by using the proposed algorithm and subsequently leads to better source separation performance. Experimental tests and comparisons with other factorization methods have been conducted to verify its efficacy.

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ZHOU, Jun. "Blind Deconvolution and Frequency Domain Compressive Sensing Application in Bearing Composite Acoustic Fault Diagnosis." Journal of Mechanical Engineering 52, no. 3 (2016): 63. http://dx.doi.org/10.3901/jme.2016.03.063.

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