ADAPTIVE PREDICTIVE DECONVOLUTION OF SEISMIC DATA*

The Wiener prediction filter has been an effective tool for accomplishing dereverberation when the input data are stationary. For non-stationary data, however, the performance of the Wiener filter is often unsatisfactory. This is not surprising since it is derived under the stationarity assumption. Dereverberation of nonstationary seismic data is here accomplished with a difference equation model having time-varying coefficients. These time-varying coefficients are in turn expanded in terms of orthogonal functions. The kernels of these orthogonal functions are then determined according to the adaptive algorithm of Nagumo and Noda. It is demonstrated that the present adaptive predictive deconvolution method, which combines the time-varying difference equation model with the adaptive method of Nagumo and Noda, is a powerful tool for removing both the long- and short-period reverberations. Several examples using both synthetic and field data illustrate the application of adaptive predictive deconvolution. The results of applying the Wiener prediction filter and the adaptive predictive deconvolution on nonstationary data indicate that the adaptive method is much more effective in removing multiples. Furthermore, the criteria for selecting various input parameters are discussed. It has been found that the output trace from the adaptive predictive deconvolution is rather sensitive to some input parameters, and that the prediction distance is by far the most influential parameter.     

SEQUENTIAL WIENER DECONVOLUTION TO IMPROVE SEISMIC RESOLUTION1

Two different techniques for performing time-variable Wiener deconvolution are compared using stacked seismic data. The conventional technique involves the empirical division of the data into a number of gates and the determination of time-invariant deconvolution filters for each gate. In the second technique, the deconvolution filter is recomputed after each time increment from a fixed-length data gate sliding along the trace. This scheme has the advantage that no a priori segmentation of the data is needed.     

An experimental study of potential distribution in different geological structures

Summary This paper deals with the problem of finding a Wiener filter when the length of the filter output in not larger than the length of the filter input. Measure for the efficiency of a filter is defined in terms of the relation between the desired filter output and the actual filter output. This measure, called the filter efficiency, is used to find the optimum length of the filter memory function. The relation between the signal-to-noise-ratio (SNR) of the filter input and the SNR of the filter output is discussed. It is shown that there is always some improvement in the SNR through the use of a Wiener filter.     

用Wiener滤波方法提取台站接收函数

本文提出了一种在时间域用Wiener滤波方法提取台站接收函数的方法，用远震P波波形的垂直分量为输入，接收函数作为滤波因子，远震P波波形的径向和切向分量作为期望输出，通过期望输出与实际输出的均方误差达极小，来提取接收函数。接收函数的计算可归结为Toeplitz方程的求解，可以采用Levinson递推算法。Toeplitz方程的非奇异性保证了Wiener滤波反褶积方法的稳定性。合成地震图与观测地震图的检验表明，用Wiener滤波方法测定台站接收函数是一种有效的时间域反褶积方法。     

Receiver Function Estimated by Wiener Filtering

Wiener filtering is used to estimate receiver function in a time-domain. With the vertical component of 3-component teleseismic P waveform as the input of a Wiener filter, receiver function as the filter response, and radial and tangential components as the expected output, receiver function is estimated by minimizing the error between expected and actual outputs. Receiver function can be obtained by solving the Toeplitz equation using the Leviuson algorithm. The non-singularity of the Toeplitz equation ensures the stability of Wiener Deconvolution. Both synthetic and observational seismogram checks show that Wiener Deconvolution is an effective time-domain method to estimate receiver function from teleseismic P waveform.     

Sparsity‐enhanced wavelet deconvolution

We propose a three‐step bandwidth enhancing wavelet deconvolution process, combining linear inverse filtering and non‐linear reflectivity construction based on a sparseness assumption. The first step is conventional Wiener deconvolution. The second step consists of further spectral whitening outside the spectral bandwidth of the residual wavelet after Wiener deconvolution, i.e., the wavelet resulting from application of the Wiener deconvolution filter to the original wavelet, which usually is not a perfect spike due to band limitations of the original wavelet. We specifically propose a zero‐phase filtered sparse‐spike deconvolution as the second step to recover the reflectivity dominantly outside of the bandwidth of the residual wavelet after Wiener deconvolution. The filter applied to the sparse‐spike deconvolution result is proportional to the deviation of the amplitude spectrum of the residual wavelet from unity, i.e., it is of higher amplitude; the closer the amplitude spectrum of the residual wavelet is to zero, but of very low amplitude, the closer it is to unity. The third step consists of summation of the data from the two first steps, basically adding gradually the contribution from the sparse‐spike deconvolution result at those frequencies at which the residual wavelet after Wiener deconvolution has small amplitudes. We propose to call this technique “sparsity‐enhanced wavelet deconvolution”. We demonstrate the technique on real data with the deconvolution of the (normal‐incidence) source side sea‐surface ghost of marine towed streamer data. We also present the extension of the proposed technique to time‐varying wavelet deconvolution.     

位场向下延拓的改进迭代维纳滤波法

根据维纳滤波理论导出的位场向下延拓滤波器为最佳下延滤波器,但因其实现需要已知待求位场和噪声的功率谱而在实际应用中受到限制.针对该问题,本文首先提出一种基于位场径向平均功率谱的位场噪声水平估计方法,进而利用偏差准则求取正则化参数,实现位场正则化向下延拓;然后将位场正则化下延结果的功率谱作为待求位场功率谱的估计初值,采用带修正项的迭代维纳滤波方法来更新对待求位场功率谱的估计,最后提出本文的位场向下延拓改进迭代维纳滤波方法.基于理论重力模型数据及航磁实测数据进行了向下延拓对比试验,结果表明,改进迭代法具有较好的收敛性,且下延精度优于Tikhonov正则化法和递增型维纳滤波法.     

PRINCIPLES OF DIGITAL WIENER FILTERING*

The theory of statistical communication provides an invaluable framework within which it is possible to formulate design criteria and actually obtain solutions for digital filters. These are then applicable in a wide range of geophysical problems. The basic model for the filtering process considered here consists of an input signal, a desired output signal, and an actual output signal. If one minimizes the energy or power existing in the difference between desired and actual filter outputs, it becomes possible to solve for the so-called optimum, or least squares filter, commonly known as the “Wiener” filter. In this paper we derive from basic principles the theory leading to such filters. The analysis is carried out in the time domain in discrete form. We propose a model of a seismic trace in terms of a statistical communication system. This model trace is the sum of a signal time series plus a noise time series. If we assume that estimates of the signal shape and of the noise autocorrelation are available, we may calculate Wiener filters which will attenuate the noise and sharpen the signal. The net result of these operations can then in general be expected to increase seismic resolution. We show a few numerical examples to illustrate the model's applicability to situations one might find in practice.     

Enhancement of Deep Seismic Reflections in Pre-stack Data by Adaptive Filtering

—Adaptive filters offer advantages over Wiener filters for time-varying processes. They are used for deconvolution of seismic data which exhibit non-stationary behavior, and seldom for noise reduction. Different algorithms for adaptive filtering exist. The least-mean-squares (LMS) algorithm, because of its simplicity, has been widely applied to data from different fields that fall outside geophysics. The application of the LMS algorithm to improve the signal-to-noise ratio in deep reflection seismic pre-stack data is studied in this paper. Synthetic data models and field data from the DEKORP project are used to this end.¶Three adaptive filter techniques, one-trace technique, two-trace technique and time-slice technique, are examined closely to establish the merits and demerits of each technique. The one-trace technique does not improve the signal-to-noise ratio in deep reflection seismic data where signal and noise cover the same frequency range. With the two-trace technique, the strongest noise reduction is achieved for small noise on the data. The filter efficiency decreases rapidly with increasing noise. Furthermore, the filter performance is poor upon application to common-midpoint (CMP) gathers with no normal-moveout (NMO) corrections. Application of the two-trace method to seismic traces before dynamic correction results in gaps in the signal along the reflection hyperbolas. The time-slice technique, introduced in this paper, offers the best answer. In this case, the one-trace technique is applied to the NMO-corrected gathers across all traces in each gather at each time to separate the low-wavenumber component of the signal in offset direction from the high-wavenumber noise component. The stacking velocities used for the dynamic correction do not need to be known very accurately because in deep reflection seismics, residual moveouts are small and have only a minor influence on the results of the adaptive time-slice technique. Noise reduction is more significant with the time-slice technique than with the two-trace technique. The superiority of the adaptive time-slice technique is demonstrated with the DEKORP data.     

Identification of model structure parameters via combination of AFMM and ARX from seismic response data

To identify the model structure parameters in shaking table tests from seismic response, especially from timevarying response records, this paper presents a new methodology by combining the online recursive Adaptive Forgetting through Multiple Models(AFMM) and offline Auto-Regression with eXogenous variables(ARX) model. First, the AFMM is employed to detect whether the response of model structure is time-invariant or time-varying when subjected to strong motions. Second, if the response is time-invariant, the modal parameters are identifi ed from the entire response record, such as the acceleration time-history using the ARX model. If the response is time-varying, the acceleration record is divided into three segments according to the accurate time-varying points detected by AFMM, and parameters are identifi ed by only using the tail segment data, which is time-invariant and suited for analysis by the ARX model. Finally, the changes in dynamic properties due to various strong motions are obtained using the presented methodology. The feasibility and advantages of the method are demonstrated by identifying the modal parameters of a 12-story reinforced concrete(RC) frame structure in a shaking table test.     

A regularized Wiener–Hopf filter for inverting models with magnetic susceptibility

For a magnetic target, the spatial magnetic signal can be expressed as a convolutional integral over Green's function of an assumed model with susceptibility as its parameter. A filter can be used to obtain the susceptibility by minimizing the mismatch between observed and the computed magnetic anomalies. In this perspective, we report the development of an advanced digital filter, which is efficient and can be used to map rock susceptibility from the acquired magnetic data. To design the new filter, we modified the space‐domain standard Wiener–Hopf filter by imposing two different constraints: (i) the filter energy constraint; and (ii) normalization of the filter coefficients. These constraints make it capable to characterize source bodies from their produced magnetic anomalies. We assume that the magnetic data are produced by induced magnetization only and interpretation can be as good as the subsurface model. Our technique is less sensitive to the data noise, which makes it efficient in enhancing the interpretation model. The modified filter demonstrates its applicability over the synthetic data with additive white Gaussian noise. In order to check the efficacy and adaptivity of this tool in a more realistic perspective, it is also tested on the real magnetic data acquired over a kimberlitic district adjoining to the western margin of the Cuddapah Basin in India to identify the source bodies from the anomalies. Our result shows that the modified Wiener–Hopf filter with the constraint for the magnetic data is more stable and efficient than the standard Wiener–Hopf filter.     

分离重磁区域场与局部场的维纳滤波器

本文从最佳线性滤波理论出发,对目前重磁资料数据处理中分离区域场与局部场的两种滤波器--匹配滤波和维纳滤波的频率响应特性作了分析比较,指出了匹配滤波只是一般维纳滤波的一个特例。将该两种滤波器与一般情况的维纳滤波器的误差作了对比,并通过简单的理论试例,说明它们的局限性和应用范围。     

The response of saltation to wind speed fluctuations

The response time of saltation to spatial or temporal wind speed fluctuations constitutes an important control parameter for aeolian sediment transport and deposition. In this paper, we present direct measurements of the response time obtained from several field experiments. The sand transport was studied using six small microphones arranged in a vertical profile and collocated with a sonic anemometer, a webcam and a cup anemometer tower. The webcam was coupled with the sonic anemometer via a personal computer and provides information on creeping and saltating grains with a sampling rate of 10 Hz. Sediment transport measurements were obtained over four periods. The Wiener filter, a signal processing technique, is used to obtain a discrete transfer function that relates the horizontal wind speed and the non‐intermittent sand transport. The transfer function can be established using an exponential function with a time constant or characteristic response time τ without time shift. The response time fluctuated between zero and 1·5 seconds depending on the turbulence intensity, the saltation activity, the measuring height and sampling rates. The Wiener filter coefficients suggest that the response of saltation to wind speed alterations is determined by more than one process. Copyright © 2012 John Wiley & Sons, Ltd.     

二维Wiener滤波器及在物探中应用的一些问题

本文结合二维随机场函数z（x,y）及其统计特征、平稳性和各态遍历性的概念,导出了二维Wiener滤波器的两个波数响应H_opt（u,v）和 _opt（u,v）,并详细地讨论了导出它们的条件。为了正确地应用Wiener滤波技术,我们提供了检验物探观测数据的平稳性和各态遍历性的简单而适用的方法。最后还指出,当应用Wiener滤波的不相关技术时,将在区域异常的谱函数中引入一个非线性畸变因子,这个区域异常是从具有局部干扰的观测数据中提取的。于是,所提取的区域异常必然会产生某种程度的失真。     

TRANSFORMATION OF RESISTIVITY SOUNDING MEASUREMENTS OBTAINED IN ONE ELECTRODE CONFIGURATION TO ANOTHER CONFIGURATION BY MEANS OF DIGITAL LINEAR FILTERING*

The technique of digital linear filtering is used for transformation of apparent resistivity data from one electrode configuration into another. Usually filter spectra are determined via the discrete Fourier transforms of input and output functions: the filter characteristic is the quotient of the spectra of the output function and input function. In this paper, the transformation of the apparent resistivities is presented for four electrode configurations (Wenner, the two-electrode, Schlumberger, and dipole configurations). In our method, there is no need to use the discrete Fourier transform of the input and output functions in order to determine the filter spectrum for converting apparent resistivity in one electrode configuration to any other configuration. Sine responses for determination of the derivative of apparent resistivities are given in analytical form. If the filter spectrum for converting the apparent resistivity to the resistivity transform for one electrode configuration is known, the filter spectra for transforming the apparent resistivity to the resistivity transform for any electrode configurations can be calculated by using newly derived expressions.     

GOCE卫星重力测量中有色噪声滤波器设计

本文根据卫星重力梯度测量的有色噪声特性,设计了Wiener、AR、FIR三种滤波器,并利用模拟的有色噪声数据对其滤波效果进行了测试,结果表明:对于文中采用的有色噪声数据,AR的滤波效果最好,其次为Wiener滤波器,FIR的滤波效果最差;三种滤波器均可用于GOCE卫星重力测量中有色噪声数据滤波,但其实用性尚需利用实测数据进行检验;可以利用不同的滤波器对含有色噪声的卫星重力梯度数据进行多次滤波,以进一步减弱有色噪声对卫星重力梯度测量精度的影响.     

Applications of Savitzky-Golay Filter for Seismic Random Noise Reduction

This article utilizes Savitzky–Golay (SG) filter to eliminate seismic random noise. This is a novel method for seismic random noise reduction in which SG filter adopts piecewise weighted polynomial via leastsquares estimation. Therefore, effective smoothing is achieved in extracting the original signal from noise environment while retaining the shape of the signal as close as possible to the original one. Although there are lots of classical methods such as Wiener filtering and wavelet denoising applied to eliminate seismic random noise, the SG filter outperforms them in approximating the true signal. SG filter will obtain a good tradeoff in waveform smoothing and valid signal preservation under suitable conditions. These are the appropriate window size and the polynomial degree. Through examples from synthetic seismic signals and field seismic data, we demonstrate the good performance of SG filter by comparing it with the Wiener filtering and wavelet denoising methods.     

Depth Estimation from the Scaling Power Spectral Density of Nonstationary Gravity Profile

-- A technique to estimate the depth to anomalous sources from the scaling power spectra of long nonstationary gravity profiles is presented. The nonstationary profile is divided into piecewise stationary segments based on the criterion of optimum gate length in which the time-varying and time-invariant autocorrelation functions are similar. The division of a nonstationary into piecewise stationary allows identification of the portion of the crust with different geological histories, and using the stationary portion of the gravity profiles, more consistent depths to the anomalous sources have been obtained. The technique is tested with the synthetic gravity profile and applied along the Jaipur-Raipur geotransect in western and central India. The geotransect has been divided into four stationary parts: Vindhyan low, Bundelkhand low, Narmada rift and Chhattisgarh basin; each section corresponding to a different geological formation. Forward modeling of gravity data using results of each stationary section is carried out to propose the subsurface structure along the Jaipur-Raipur transect.     

A COMPARISON OF STATISTICAL AND DETERMINISTIC WIENER DECONVOLUTION OF DEEP-TOW SEISMIC DATA*

Wiener ‘spiking’ deconvolution of seismic traces in the absence of a known source wavelet relies upon the use of digital filters, which are optimum in a least-squares error sense only if the wavelet to be deconvolved is minimum phase. In the marine environment in particular this condition is frequently violated, since bubble pulse oscillations result in source signatures which deviate significantly from minimum phase. The degree to which the deconvolution is impaired by such violation is generally difficult to assess, since without a measured source signature there is no optimally deconvolved trace with which the spiked trace may be compared. A recently developed near-bottom seismic profiler used in conjunction with a surface air gun source produces traces which contain the far-field source signature as the first arrival. Knowledge of this characteristic wavelet permits the design of two-sided Wiener spiking and shaping filters which can be used to accurately deconvolve the remainder of the trace. In this paper the performance of such optimum-lag filters is compared with that of the zero-lag (one-sided) operators which can be evaluated from the reflected arrival sequence alone by assuming a minimum phase source wavelet. Results indicate that the use of zero-lag operators on traces containing non-minimum phase wavelets introduces significant quantities of noise energy into the seismic record. Signal to noise ratios may however be preserved or even increased during deconvolution by the use of optimum-lag spiking or shaping filters. A debubbling technique involving matched filtering of the trace with the source wavelet followed by optimum-lag Wiener deconvolution did not give a higher quality result than can be obtained simply by the application of a suitably chosen Wiener shaping filter. However, cross correlation of an optimum-lag spike filtered trace with the known ‘actual output’ of the filter when presented with the source signature is found to enhance signal-to-noise ratio whilst maintaining improved resolution.     

WAVELET SHAPING AND NOISE REDUCTION *

Approximate deconvolution by means of Wiener filters has become standard practice in seismic data-processing. It is well-known that addition of a certain percentage of noise energy to the autocorrelation of the signal wavelet leads to a filter that does not increase, or even reduces, the noise level on the seismogram. This noise addition will, in general, cause a minimum phase signal to become mixed phase. A technique is presented for the calculation of the optimum-lag shaping filter for a contaminated signal wavelet. The advantages of this method over the more conventional approach are that it needs less arithmetic operations and that it automatically gives the filter with the optimum combination of shaping performance and noise reduction.