Lattice filtering applications to deconvolution of seismic data

Geophysics ◽  
1983 ◽  
Vol 48 (3) ◽  
pp. 295-310 ◽  
Author(s):  
John C. Robinson
Keyword(s):  
Seismic Data ◽  
Wiener Filter ◽  
Reflection Seismology ◽  
Significant Extent ◽  
Technical Literature ◽  
Lattice Filters ◽  
Effective Implementation ◽  
Deconvolution Technique ◽  
Potential Applications ◽  
Adaptive Abilities

Lattice digital filtering techniques have only recently been exposed to any significant extent through the technical literature, primarily in articles not directly related to reflection seismology. The most basic “all‐zero” lattice filter dates back only around a decade. This paper first reviews current technology in adaptive lattice filtering from the standpoint of seismic deconvolution. An “all‐pole” deconvolution technique is developed next; then a new “pole‐zero” lattice is adapted to seismic deconvolution, and operational methodologies for its effective implementation and stabilization are developed. Some advantages of deconvolution by lattice filtering and other potential applications of lattice filters to seismic data processing are suggested. The adaptive abilities and effectiveness of seismic deconvolution by the three lattice types are tested and compared with stationary Wiener deconvolution. The test data, are realistically nonstationary, thus allowing all three of the adaptive lattice filters to perform favorably in comparison to the nonadaptive or stationary Wiener filter.

THE DETERMINATION OF DIGITAL WIENER FILTERS BY MEANS OF GRADIENT METHODS

Geophysics ◽  
1973 ◽  
Vol 38 (2) ◽  
pp. 310-326 ◽  
Author(s):  
R. J. Wang ◽  
S. Treitel
Keyword(s):  
Seismic Data ◽  
High Speed ◽  
Gradient Methods ◽  
Wiener Filter ◽  
Gradient Algorithm ◽  
Rapid Convergence ◽  
True Solution ◽  
Peripheral Device ◽  
Wiener Filters

The normal equations for the discrete Wiener filter are conventionally solved with Levinson’s algorithm. The resultant solutions are exact except for numerical roundoff. In many instances, approximate rather than exact solutions satisfy seismologists’ requirements. The so‐called “gradient” or “steepest descent” iteration techniques can be used to produce approximate filters at computing speeds significantly higher than those achievable with Levinson’s method. Moreover, gradient schemes are well suited for implementation on a digital computer provided with a floating‐point array processor (i.e., a high‐speed peripheral device designed to carry out a specific set of multiply‐and‐add operations). Levinson’s method (1947) cannot be programmed efficiently for such special‐purpose hardware, and this consideration renders the use of gradient schemes even more attractive. It is, of course, advisable to utilize a gradient algorithm which generally provides rapid convergence to the true solution. The “conjugate‐gradient” method of Hestenes (1956) is one of a family of algorithms having this property. Experimental calculations performed with real seismic data indicate that adequate filter approximations are obtainable at a fraction of the computer cost required for use of Levinson’s algorithm.

scholarly journals Wiener estimation of the Green’s function

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. W31-W44 ◽  
Author(s):  
Anton Ziolkowski
Keyword(s):  
Green’S Function ◽  
Impulse Response ◽  
Green's Function ◽  
Seismic Data ◽  
Wiener Filter ◽  
Electromagnetic Data ◽  
Transient Electromagnetic ◽  
Marine Seismic ◽  
Measured Response

I consider the problem of finding the impulse response, or Green’s function, from a measured response including noise, given an estimate of the source time function. This process is usually known as signature deconvolution. Classical signature deconvolution provides no measure of the quality of the result and does not separate signal from noise. Recovery of the earth impulse response is here formulated as the calculation of a Wiener filter in which the estimated source signature is the input and the measured response is the desired output. Convolution of this filter with the estimated source signature is the part of the measured response that is correlated with the estimated signature. Subtraction of the correlated part from the measured response yields the estimated noise, or the uncorrelated part. The fraction of energy not contained in this uncorrelated component is defined as the quality of the filter. If the estimated source signature contains errors, the estimated earth impulse response is incomplete, and the estimated noise contains signal, recognizable as trace-to-trace correlation. The method can be applied to many types of geophysical data, including earthquake seismic data, exploration seismic data, and controlled source electromagnetic data; it is illustrated here with examples of marine seismic and marine transient electromagnetic data.

A KALMAN FILTER APPROACH TO THE DECONVOLUTION OF SEISMIC SIGNALS

Geophysics ◽  
1974 ◽  
Vol 39 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Norman D. Crump
Keyword(s):  
Kalman Filter ◽  
Wiener Filter ◽  
Seismic Signal ◽  
Seismic Signals ◽  
Discrete State ◽  
Seismic Wavelet ◽  
State Variable ◽  
Deconvolution Technique ◽  
Reflectivity Function ◽  
Filter Approach

It is common practice to model a reflection seismogram as a convolution of the reflectivity function of the earth and an energy waveform referred to as the seismic wavelet. The objective of the deconvolution technique described here is to extract the reflectivity function from the reflection seismogram. The most common approach to deconvolution has been the design of inverse filters based on Wiener filter theory. Some of the disadvantages of the inverse filter approach may be overcome by using a state variable representation of the earth’s reflectivity function and the seismic signal generating process. The problem is formulated in discrete state variable form to facilitate digital computer processing of digitized seismic signals. The discrete form of the Kalman filter is then used to generate an estimate of the reflectivity function. The principal advantages of this technique are its capability for handling continually time‐varying models, its adaptability to a large class of models, its suitability for either single or multi‐channel processing, and its potentially high‐resolution capabilities. Examples based on both synthetic and field seismic data illustrate the feasibility of the method.

A method of phase identification for seismic data acquired with the controlled accurate seismic source (CASS)

2020 ◽  
Vol 222 (1) ◽  
pp. 54-68
Author(s):  
Xiaolei Wang ◽  
Bing Xue ◽  
Rensheng Cui ◽  
Guoliang Gu ◽  
Chaoyong Peng ◽  
...  
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Seismic Source ◽  
Phase Identification ◽  
Time Varying ◽  
Central Frequency ◽  
Wave Velocities ◽  
Seismic Wave Velocities ◽  
Potential Applications ◽  
High Repeatability

SUMMARY With the advantages of the little destruction to the deployment site and high repeatability compared with explosive sources, the controlled accurate seismic source (CASS) has many potential applications with respect to the investigation of the crustal structure and seismic wave velocities. However, the signal generated by the CASS rapidly attenuates with the increasing distance because of its poor signal-to-noise ratio (SNR). Consequently, the difficulties in identifying specific seismic phases from the CASS data limit its application and popularization. The aim of this study is to present a new method to improve the accuracy of traveltime estimation and to identify more seismic phases travelling through the crust. We adopt the global seismic phase scanning algorithms (GSPSA) combined with an optimized narrowband time-varying filter, whose central frequency corresponds to the instantaneous frequency of the linear frequency modulation (LFM) signals produced by the CASS. Using the seismic data from the 40-ton CASS in a field experiment around Xinfengjiang reservoir in southeast China, we attain the seismic phases such as Pg, Sg, PmP and SmS at epicentral distances of more than 200 km with GSPSA. To identify and verify these seismic phases information, we also calculate synthetic waveforms. The results demonstrate that the GSPSA method is an effective tool for seismic phase identification of CASS data.

Improvement of S/N Ratio of Seismic Data by Multi-Channel Wiener Filter Algorithm

2012 ◽  
Vol 198-199 ◽  
pp. 1501-1505
Author(s):  
Xue Hao ◽  
Na Li ◽  
Lin Ren
Keyword(s):  
Noise Reduction ◽  
Seismic Data ◽  
Noise Suppression ◽  
Random Noise ◽  
Wiener Filter

Noise reduction or cancellation is important for getting clear and useful signals. This paper deals with the implementation of the multi-channel wiener filter algorithm for noise suppression of seismic data. Known the velocity of reflection event, utilizes the resemblance of reflection signal in each seismic trace, the multi-channel wiener filter algorithm is effective in enhance reflection event and suppress the random noise. This algorithm is used to CDP gathers and the simulation shows the method is effective.

Surface‐consistent phase decomposition in the log/Fourier domain

Geophysics ◽  
1993 ◽  
Vol 58 (8) ◽  
pp. 1099-1111 ◽  
Author(s):  
Guillaume Cambois ◽  
Paul Stoffa
Keyword(s):  
Seismic Data ◽  
Linear Problem ◽  
Phase Unwrapping ◽  
Fourier Domain ◽  
Minimum Phase ◽  
Phase Decomposition ◽  
Deconvolution Technique ◽  
Amplitude Spectra ◽  
Definition Of ◽  
Phase Spectra

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.

Deconvolution of the Chemical Shift Effects in Proton Nuclear Magnetic Resonance Imaging

1989 ◽  
Vol 43 (7) ◽  
pp. 1260-1264 ◽  
Author(s):  
Juwhan Liu ◽  
Antti O. K. Nieminen ◽  
Jack L. Koenig
Keyword(s):  
Chemical Shift ◽  
Imaging Technique ◽  
Spin Echo ◽  
Wiener Filter ◽  
Proton Nuclear Magnetic Resonance ◽  
Nmr Imaging ◽  
Deconvolution Method ◽  
Resonance Imaging ◽  
Nmr Spectrum ◽  
Deconvolution Technique

When the gradient strength is not strong enough to suppress the chemical shift effects, it is possible to apply a numerical deconvolution technique to remove the chemical shift artifacts. The Wiener filter and an apodization function are combined to produce an effective deconvolution method for the NMR images obtained by standard spin-echo NMR imaging technique. In the deconvolution process, an NMR spectrum is utilized which is taken at the same condition as the NMR image but with the phase encoding and the read gradients off.

Data Mining and Knowledge Discovery Based on Denoising Algorithms in Geology Exploration

2013 ◽  
Vol 310 ◽  
pp. 640-643
Author(s):  
Xue Hao ◽  
Lin Ren ◽  
Na Li ◽  
Zhi Cheng Huang
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Seismic Data ◽  
Random Noise ◽  
Wiener Filter ◽  
Wavelet Denoising ◽  
Denoising Method ◽  
Mass Data ◽  
Neighboring Coefficients

There are mass data in geology exploration, but it is vital to find useful information or knowledge from these data. This paper is concerned with the analysis of the seismic data by the multi-channel wiener filter algorithm and the wavelet denoising method using neighboring coefficients. Known the velocity of reflection event, utilizes the resemblance of reflection signals in each seismic trace, the multi-channel wiener filter algorithm is effective in enhance reflection events and suppress the random noise. But the wavelet denoising methods don’t need any assuming conditions. The computed simulations of these two kinds of algorithms are provided to prove the availability.

scholarly journals How to Approach Subsidence Evaluation for Marginal Fields: A Case History

2015 ◽  
Vol 8 (1) ◽  
pp. 214-234 ◽  
Author(s):  
Christoforos Benetatos ◽  
Vera Rocca ◽  
Quinto Sacchi ◽  
Francesca Verga
Keyword(s):  
Fluid Flow ◽  
Seismic Data ◽  
Regional Scale ◽  
Ground Surface ◽  
Deformation Analysis ◽  
Technical Literature ◽  
Area Of Interest ◽  
Deformation Properties ◽  
Flow Information ◽  
Set Up

This paper presents the evaluation of the subsidence potentially induced by underground storage of natural gas in a marginal depleted field located in Southern Italy. The critical aspect of the study was the lack of data because economic and logistic reasons had restricted data acquisition at the regional scale to perform a geomechanical study. This limitation was overcome by accurately gathering the available data from public sources so that the geometry of a largescale 3D model could be defined and the formations properly characterized for rock deformation analysis. Well logs, seismic data and subsidence surveys at the regional scale, available in open databases and in the technical literature, were integrated with the available geological and fluid-flow information at the reservoir scale. First of all, a 3D geological model, at the regional scale, incorporating the existing model of the reservoir was developed to describe the key features of a large subsurface volume while preserving the detail of the storage reservoir. Then, a regional geomechanical model was set up for coupled mechanic and fluid-flow analyses. The stress and strain evolution and the associated subsidence induced in the reservoir and surrounding formations by historical primary production as well as future gas storage activities were investigated. Eventually, the obtained results were validated against the measurements of ground surface movements available from the technical literature for the area of interest, thus corroborating the choice of the most critical geomechanical parameters and relevant deformation properties of the rocks affecting subsidence.

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