共查询到20条相似文献，搜索用时 861 毫秒
1.
One of the main objectives of seismic digital processing is the improvement of the signal-to-noise ratio in the recorded data. Wiener filters have been successfully applied in this capacity, but alternate filtering devices also merit our attention. Two such systems are the matched filter and the output energy filter. The former is better known to geophysicists as the crosscorrelation filter, and has seen widespread use for the processing of vibratory source data, while the latter is. much less familiar in seismic work. The matched filter is designed such that ideally the presence of a given signal is indicated by a single large deflection in the output. The output energy filter ideally reveals the presence of such a signal by producing a longer burst of energy in the time interval where the signal occurs. The received seismic trace is assumed to be an additive mixture of signal and noise. The shape of the signal must be known in order to design the matched filter, but only the autocorrelation function of this signal need be known to obtain the output energy filter. The derivation of these filters differs according to whether the noise is white or colored. In the former case the noise autocorrelation function consists of only a single spike at lag zero, while in the latter the shape of this noise autocorrelation function is arbitrary. We propose a novel version of the matched filter. Its memory function is given by the minimum-delay wavelet whose autocorrelation function is computed from selected gates of an actual seismic trace. For this reason explicit knowledge of the signal shape is not required for its design; nevertheless, its performance level is not much below that achievable with ordinary matched filters. We call this new filter the “mini-matched” filter. With digital computation in mind, the design criteria are formulated and optimized with time as a discrete variable. We illustrate the techniques with simple numerical examples, and discuss many of the interesting properties that these filters exhibit. 相似文献
2.
A. N. BOWEN 《Geophysical Prospecting》1986,34(3):366-382
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. 相似文献
3.
以地震勘探记录去噪为目标,本文提出了一种隐马尔可夫模型平滑估计方法.它是在基本隐马尔可夫模型滤波基础之上,运用信号检测环节将带噪信号段和无信号段加以区分,构建带噪地震记录的状态转移模型,在贝叶斯框架下,利用平滑密度函数进行状态估计,从而达到压制噪声的目的.数值模拟表明,无论对信噪比还是均方误差,隐马尔可夫模型平滑估计处理后的重构信号优于常规的维纳滤波所恢复信号.我们可以期待这种方法会成为实际地震记录噪声压制的有效手段. 相似文献
4.
N. DE VOOGD 《Geophysical Prospecting》1974,22(2):354-369
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. 相似文献
5.
S. M. DEREGOWSKI 《Geophysical Prospecting》1971,19(4):729-768
Two distinct filters are developed in the frequency domain which represent an attempt to increase the resolution of fine structure contained in the signal whilst keeping the expected filtered noise energy within reasonable bounds. A parameter termed the White Noise Amplification is defined and used together with a measure of the deconvolved pulse width in order to provide a more complete characterisation of the filters. Each of the two main types of frequency domain filters discussed varies in properties with respect to a single adjustable parameter. This may be contrasted with a time domain Wiener filter which in general has three variables: length, delay and an adjustable noise parameter or weight. The direct frequency domain analogue of the Wiener filter is termed a gamma-Fourier filter, and is shown to have properties which span the range from those of a spiking filter with zero least square error at one extreme, to those of a matched filter at the other extreme of its variable parameter's range. The second type of filter considered—termed the modulated Gaussian filter—is similarly shown to be a perfect spiking filter at one extreme of its parameter range, but adopts the properties of an output energy filter at the other extreme. 相似文献
6.
7.
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. 相似文献
8.
Several types of multichannel filters have been introduced in the past with the purpose of rejecting, in a seismic section, coherent noise having a slope different from that of the signal. These filters, generally, tend to introduce a certain amount of mixing and therefore the output trace shows increased horizontal coherence. This is due to the model on which these filters are based, since the hypothesis is posed that the reflectors are continuous. This may be dangerous since it could lead to mistaken interpretations, for example when small faults or breaks are made to disappear in the output section. Other problems that could arise in the application of multichannel filters after-stack are space-aliasing and high-pass filtering. The former occurs when coherent noise is rejected with apparent Velocity V and frequency fa=V/X, where X is the distance between traces. In this case, the signal also is distorted since it is rejected in the same frequency range. The high pass filtering effect occurs when the multichannel filter is designed to remove low coherent noise with high apparent velocity. In the paper a family of multichannel filters is presented based on a model of the seismic section such that minimum mixing effects appear. The filters are designed to give good results even in the case of low frequency and high velocity coherent noise. Some practical examples are shown. 相似文献
9.
R. E. WHITE 《Geophysical Prospecting》1977,25(1):165-178
Optimum stacking filters based on estimates of trace signal-to-uncorrelated noise ratios are assessed and compared in performance with conventional straight stacking. It is shown that for the trace durations and signal bandwidths normally encountered in seismic reflection data the errors in estimating signal/noise ratios largely counteract the theoretical advantages of the optimum filter. The more specific the filter (e.g. the more frequency components included in its design) the more this is true. Even for a simple weighted stack independent of frequency, the performance is likely to be better than a straight (equal weights) stack only for relatively high signal/noise ratios, when the performance is not critical anyway. 相似文献
10.
Seismic directional random noise suppression by radial‐trace time–frequency peak filtering using the Hurst exponent statistic 
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下载免费PDF全文 Radial‐trace time–frequency peak filtering filters a seismic record along the radial‐trace direction rather than the conventional channel direction. It takes the spatial correlation of the reflected events between adjacent channels into account. Thus, radial‐trace time–frequency peak filtering performs well in denoising and enhancing the continuity of reflected events. However, in the seismic record there is often random noise whose energy is concentrated in certain directions; the noise in these directions is correlative. We refer to this kind of random noise (that is distributed randomly in time but correlative in the space) as directional random noise. Under radial‐trace time–frequency peak filtering, the directional random noise will be treated as signal and enhanced when this noise has same direction as the signal. Therefore, we need to identify the directional random noise before the filtering. In this paper, we test the linearity of signal and directional random noise in time using the Hurst exponent. The time series of signals with high linearity lead to large Hurst exponent value; however, directional random noise is a random series in time without a fixed waveform and thus its linearity is low; therefore, we can differentiate the signal and directional random noise by the Hurst exponent values. The directional random noise can then be suppressed by using a long filtering window length during the radial‐trace time–frequency peak filtering. Synthetic and real data examples show that the proposed method can remove most directional random noise and can effectively recover the reflected events. 相似文献
11.
Wiener optimal filtering of GRACE data 总被引：4，自引：0，他引：4
We present a spatial averaging method for Gravity Recovery and Climate Experiment (GRACE) gravity-field solutions based on
the Wiener optimal filtering.
The optimal filter is designed from the least-square minimization of the difference between the desired and filtered signals.
It requires information about the power spectra of the desired gravitational signal and the contaminating noise, which is
inferred from the average GRACE degree-power spectrum. We show that the signal decreases with increasing spherical harmonic
degree j with approximately j−b, where b = 1.5 for GRACE data investigations. This is termed the Second Kaula rule of thumb for temporal variations of the
Earth’s gravity field. The degree power of the noise increases, in the logarithmic scale, linearly with increasing j.
The Wiener optimal filter obtained for the signal model with b = 1.5 closely corresponds to a Gaussian filter with a spatial
half width of 4° (∼440 km). We find that the filtered GRACE gravity signal is relatively insensitive to the exponent b of
the signal model, which indicates the robustness of Wiener optimal filtering. This is demonstrated using the GFZ-GRACE gravity-field
solution for April 2004. 相似文献
12.
In mathematical statistical filtering the deconvolution problem can be solved by two different methods:
- 1 by inverse filtering
- 2 by calculating the prediction error.
13.
E. J. DOUZE 《Geophysical Prospecting》1971,19(2):253-264
Optimum filters can be computed using orthogonal coordinates obtained from the eigenvalues and eigenvectors of the autocorrelation matrix. The method is used to obtain unit distance prediction error filters. The output of a unit distance prediction error filter when applied to the input wavelet is an impulse at zero time. The effect on the output of added white noise is easily obtained using the approach through the orthogonal coordinates. The added white noise results in output wavelets which are no longer impulses at zero time. The decrease in time resolution gives a filter that does not increase undesirable high frequency noise as much as filters computed without white noise. Orthogonal coordinates with little signal energy can be omitted from the filter computation resulting in output wavelets resembling those computed using added white noise. 相似文献
14.
G. P. ANGELERI 《Geophysical Prospecting》1983,31(5):726-747
A model of the seismic trace is generally given as a convolution between the propagating wavelet and the reflectivity series of the earth and normally it is assumed that a white noise is added to the trace. The knowledge of the propagating wavelet is the basic point to estimate the reflectivity series from the seismic trace. In this paper a statistical method of wavelet extraction from several seismic traces, assuming the wavelet to be unique, is discussed. This method allows one to obtain the propagating wavelet without any classical limitative assumptions on the phase spectrum. Furthermore, a phase unwrapping method is suggested and some statistical properties of the phase spectrum of the reflectivity traces are examined. 相似文献
15.
M. R. Thapar 《Pure and Applied Geophysics》1971,92(1):5-18
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. 相似文献
16.
支持向量机(Support Vector Machine: SVM)一直作为机器学习方法在统计学习理论基础上被研究和发展,本文从信号与系统的角度出发,证明了平移不变核最小二乘支持向量机(Least Squares SVM: LS-SVM)是一个线性时不变系统.以Ricker子波核为例,探讨了不同参数对最小二乘支持向量回归(Least Squares Support Vector Regression: LS-SVR)滤波器频率响应特性的影响,这些参数的不同选择相应地控制着滤波器通带上升沿的陡峭性、通带的中心频率、通带带宽以及信号能量的衰减,即滤波器长度越长通带的上升沿越陡,核参数值越大通带的中心频率越高,且通带带宽越宽,正则化参数值越小,通带带宽越窄(但通带中心频率基本保持恒定),有效信号幅度衰减越严重.合成地震记录的仿真实验结果表明,Ricker子波核LS-SVR滤波器在处理地震勘探信号的应用中,滤波性能优于径向基函数(Radial Basic Function: RBF)核LS-SVR滤波器以及小波变换滤波和Wiener滤波方法. 相似文献
17.
—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. 相似文献
18.
介绍了自适应对消技术的基本原理,分析了自适应对消技术的优缺点,针对一些缺点提出了相应的改进方法。由于常规自适应对消技术是通过迭代技术实现的,需要迭代一定次数后才能确定最佳参数,所以在信号起始位置和有效信号尾部收敛效果不好,从而影响滤波效果。引入hausdorff维数来约束地震信号,首先求取原始地震信号的hausdorff维数谱,利用有效信号与随机噪音维数的不同,将有效信号以外的随机噪音滤除,并将自适应对消中的参考噪声对应位置的随机噪音去除;然后将更新后的地震信号和参考噪声进行自适应滤波,从而得到一个优化的自适应对消滤波器。 相似文献
19.
Noise has traditionally been suppressed or eliminated in seismic data sets by the use of Fourier filters and, to a lesser degree, nonlinear statistical filters. Although these methods are quite useful under specific conditions, they may produce undesirable effects for the low signal to noise ratio data. In this paper, a new method, multi-scale ridgelet transform, is used in the light of the theory of ridgelet transform. We employ wavelet transform to do sub-band decomposition for the signals and then use non-linear thresholding in ridgelet domain for every block. In other words, it is based on the idea of partition, at sufficiently fine scale, a curving singularity looks straight, and so ridgelet transform can work well in such cases. Applications on both synthetic data and actual seismic data from Sichuan basin, South China, show that the new method eliminates the noise portion of the signal more efficiently and retains a greater amount of geologic data than other methods, the quality and consecutiveness of seismic event are improved obviously as well as the quality of section is improved. 相似文献
20.
The least squares estimation procedures used in different disciplines can be classified in four categories:
- a. Wiener filtering,
- b. b. Autoregressive estimation,
- c. c. Kalman filtering,
- d. d. Recursive least squares estimation.