Predictive deconvolution and the zero‐phase source

1982 ◽  
Author(s):  
Bruce Gibson ◽  
Ken Lamer
Keyword(s):  
Predictive Deconvolution ◽  
Zero Phase

Predictive deconvolution and the zero‐phase source

Geophysics ◽  
1984 ◽  
Vol 49 (4) ◽  
pp. 379-397 ◽  
Author(s):  
Bruce Gibson ◽  
Ken Larner
Keyword(s):  
White Noise ◽  
Field Data ◽  
Synthetic Data ◽  
Phase Correction ◽  
Phase Spectrum ◽  
Signal Spectrum ◽  
Minimum Phase ◽  
Predictive Deconvolution ◽  
Different Levels ◽  
Zero Phase

Predictive deconvolution is commonly applied to seismic data generated with a Vibroseisr® source. Unfortunately, when this process invokes a minimum‐phase assumption, the phase of the resulting trace will not be correct. Nonetheless, spiking deconvolution is an attractive process because it restores attenuated higher frequencies, thus increasing resolution. For detailed stratigraphic analyses, however, it is desirable that the phase of the data be treated properly as well. The most common solution is to apply a phase‐shifting filter that corrects for errors attributable to a zero‐phase source. The phase correction is given by the minimum‐phase spectrum of the correlated Vibroseis wavelet. Because no minimum‐phase spectrum truly exists for this bandlimited wavelet, white noise is added to its amplitude spectrum in order to design the phase‐correction filter. Different levels of white noise, however, produce markedly different results when field data sections are filtered. A simple argument suggests that the amount of white noise used should match that added in designing the (minimum‐phase) spiking deconvolution operator. This choice, however, also produces inconsistent results; field data again show that the phase treatment is sensitive to the amount of added white noise. Synthetic data tests show that the standard phase‐correction procedure breaks down when earth attenuation is severe. Deterministically reducing the earth‐filter effects before deconvolution improved the resulting phase treatment for the synthetic data. After application of the inverse attenuation filter to the field data, however, phase differences again remain for different levels of added white noise. These inconsistencies are attributable to the phase action of spiking deconvolution. This action is dependent upon the shape of the signal spectrum as well as the spectral shape and level of contaminating noise. Thus, in practice the proper treatment of phase in data-dependent processing requires extensive knowledge of the spectral characteristics of both signal and noise. With such knowledge, one could apply deterministic techniques that either eliminate the need for statistical deconvolution or condition the data so as to satisfy better the statistical model assumed in data‐dependent processing.

A critique of seismic deconvolution methods

Geophysics ◽  
1984 ◽  
Vol 49 (12) ◽  
pp. 2109-2116 ◽  
Author(s):  
Andrejs Jurkevics ◽  
Ralphe Wiggins
Keyword(s):  
Pulse Compression ◽  
Adaptive Methods ◽  
Substantial Improvement ◽  
Wiener Filtering ◽  
Minimum Entropy ◽  
Seismic Wavelet ◽  
Predictive Deconvolution ◽  
Phase Condition ◽  
Signal Distortions ◽  
Zero Phase

Different seismic pulse compression methods are evaluated. These include several algorithms for computing prediction error filters: Wiener filtering, Burg’s method, the [Formula: see text] norm criterion, Kalman filtering, and two time‐adaptive methods. Algorithms which do not assume a minimum‐phase condition for the seismic wavelet include minimum entropy, homomorphic, and zero‐phase deconvolution. The sensitivity of these algorithms is examined for various earth reflectivity functions, source waveforms, and signal distortions. The results indicate that standard Wiener predictive deconvolution is robust under a wide variety of input conditions. However, a substantial improvement in pulse compression can be obtained by the Burg algorithm under conditions of short data segments and by minimum entropy deconvolution for seismograms consisting of mixed‐phase wavelets combined with sparse reflectivity series.

On: “Predictive deconvolution and the zero‐phase source” by B. Gibson and K. Larner (GEOPHYSICS, 49, 379–397, April 1984).

Geophysics ◽  
1985 ◽  
Vol 50 (1) ◽  
pp. 172-172
Author(s):  
W. Harry Mayne
Keyword(s):  
Frequency Domain ◽  
Phase Correction ◽  
Time Function ◽  
Predictive Deconvolution ◽  
Intractable Problem ◽  
Zero Phase

The authors are to be complimented on a most courageous attempt to solve a difficult (and perhaps intractable) problem. As an active advocate of using nonlinear sweeps to combat earth attenuation, I was very interested in the results reported when inverse Q-filters were applied. Use of an appropriately selected logarithmic time function sweep (dB/Hz response in the frequency domain) can provide the necessary amplitude (but not the phase) correction of any chosen inverse Q-filter.

Study on a DC-DC Converter using a Three-Phase Transformer with Zero-Phase Current

2020 ◽  
Vol 140 (10) ◽  
pp. 760-768
Author(s):  
Yuki Itogawa ◽  
Takeshi Amimoto ◽  
Yu Kawai ◽  
Tatsuya Okuda
Keyword(s):  
Phase Current ◽  
Three Phase ◽  
Zero Phase

scholarly journals A Graphical Design Methodology Based on Ideal Gyrator and Transformer for Compensation Topology with Load-Independent Output in Inductive Power Transfer System

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 575
Author(s):  
Qian Su ◽  
Xin Liu ◽  
Yan Li ◽  
Xiaosong Wang ◽  
Zhiqiang Wang ◽  
...  
Keyword(s):  
Phase Angle ◽  
Design Methodology ◽  
Constant Current ◽  
Current Gain ◽  
Transfer System ◽  
Power Transfer ◽  
Current Output ◽  
Inductive Power Transfer ◽  
Zero Phase ◽  
Inductive Power

Compensation is crucial in the inductive power transfer system to achieve load-independent constant voltage or constant current output, near-zero reactive power, higher design freedom, and zero-voltage switching of the driver circuit. This article proposes a simple, comprehensive, and innovative graphic design methodology for compensation topology to realize load-independent output at zero-phase-angle frequencies. Four types of graphical models of the loosely coupled transformer that utilize the ideal transformer and gyrator are presented. The combination of four types of models with the source-side/load-side conversion model can realize the load-independent output from the source to load. Instead of previous design methods of solving the equations derived from the circuits, the load-independent frequency, zero-phase angle (ZPA) conditions, and source-to-load voltage/current gain of the compensation topology can be intuitively obtained using the circuit model given in this paper. In addition, not limited to only research of the existing compensation topology, based on the design methodology in this paper, 12 novel compensation topologies that are free from the constraints of transformer parameters and independent of load variations are stated and verified by simulations. In addition, a novel prototype of primary-series inductor–capacitance–capacitance (S/LCC) topology is constructed to demonstrate the proposed design approach. The simulation and experimental results are consistent with the theory, indicating the correctness of the design method.

Coherent noise in marine seismic data

Geophysics ◽  
1983 ◽  
Vol 48 (7) ◽  
pp. 854-886 ◽  
Author(s):  
Ken Larner ◽  
Ron Chambers ◽  
Mai Yang ◽  
Walt Lynn ◽  
Willon Wai
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Seismic Source ◽  
Coherent Noise ◽  
Critical Data ◽  
Predictive Deconvolution ◽  
Scattered Energy ◽  
Marine Seismic ◽  
The Many ◽  
Water Bottom

Despite significant advances in marine streamer design, seismic data are often plagued by coherent noise having approximately linear moveout across stacked sections. With an understanding of the characteristics that distinguish such noise from signal, we can decide which noise‐suppression techniques to use and at what stages to apply them in acquisition and processing. Three general mechanisms that might produce such noise patterns on stacked sections are examined: direct and trapped waves that propagate outward from the seismic source, cable motion caused by the tugging action of the boat and tail buoy, and scattered energy from irregularities in the water bottom and sub‐bottom. Depending upon the mechanism, entirely different noise patterns can be observed on shot profiles and common‐midpoint (CMP) gathers; these patterns can be diagnostic of the dominant mechanism in a given set of data. Field data from Canada and Alaska suggest that the dominant noise is from waves scattered within the shallow sub‐buttom. This type of noise, while not obvious on the shot records, is actually enhanced by CMP stacking. Moreover, this noise is not confined to marine data; it can be as strong as surface wave noise on stacked land seismic data as well. Of the many processing tools available, moveout filtering is best for suppressing the noise while preserving signal. Since the scattered noise does not exhibit a linear moveout pattern on CMP‐sorted gathers, moveout filtering must be applied either to traces within shot records and common‐receiver gathers or to stacked traces. Our data example demonstrates that although it is more costly, moveout filtering of the unstacked data is particularly effective because it conditions the data for the critical data‐dependent processing steps of predictive deconvolution and velocity analysis.

Statistical analysis of the zero-phase method for aligning noisy high-resolution radar signals

2009 ◽  
Vol 3 (1) ◽  
pp. 62
Author(s):  
R. Gil-Pita ◽  
M. Rosa-Zurera ◽  
P. Jarabo-Amores ◽  
F. López Ferreras
Keyword(s):  
Statistical Analysis ◽  
High Resolution ◽  
Phase Method ◽  
High Resolution Radar ◽  
Radar Signals ◽  
Zero Phase
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