共查询到20条相似文献，搜索用时 15 毫秒
1.
Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter (PEF), is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data’s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance. 相似文献
2.
地震数据本质上是时变的,不仅有效同相轴表现出确定性信号的时变特征,而且复杂地表和构造条件以及深部探测环境总是引入时变的非平稳随机噪声.标准的频率-空间域预测滤波只适合压制平面波信号假设下的平稳随机噪声,而处理非平稳地震随机噪声时,需要将数据体分割为小窗口进行分析,但效果不够理想,而传统非预测类随机噪声压制方法往往适应性不高,因此开发能够保护地震信号时变特征的随机噪声压制方法具有重要的工业价值.压缩感知是近年出现的一个新的采样理论,通过开发信号的稀疏特性,已经在地震数据处理中的数据插值以及噪声压制中得到了应用.本文系统地分析了压缩感知理论框架下的地震随机噪声压制问题,建立了阈值消噪的数学反演目标函数;针对时变有效信息具有的可压缩性,利用有限差分算法求解炮检距连续方程,构建有限差分炮检距连续预测算子(FDOC),在seislet变换框架下,提出一种新的快速稀疏变换域———FDOC-seislet变换,实现地震数据的高度稀疏表征;结合非平稳随机噪声不可压缩的特征,提出了一种整形迭代消噪方法,该方法是一种广义的迭代收缩阈值(IST)算法,在无法计算稀疏变换伴随算子的条件下,仍然能够对强噪声环境中的时变有效信息进行有效恢复.通过对模型数据和实际数据的处理,验证了FDOC-seislet稀疏变换域随机噪声迭代压制方法能够在保护复杂构造地震波信息的前提下,有效地衰减原始数据中的强振幅随机噪声干扰. 相似文献
3.
Multicomponent seismic data are acquired by orthogonal geophones that record a vectorial wavefield. Since the single components are not independent, the processing should be performed jointly for all the components. In this contribution, we use hypercomplex numbers, specifically quaternions, to implement the Wiener deconvolution for multicomponent seismic data. This new approach directly derives from the complex Wiener filter theory, but special care must be taken in the algorithm implementation due to the peculiar properties of quaternion algebra. Synthetic and real data examples show that quaternion deconvolution, either spiking or predictive, generally performs superiorly to the standard (scalar) deconvolution because it properly takes into account the vectorial nature of the wavefields. This provides a better wavelet estimation and thus an improved deconvolution performance, especially when noise affects differently the various components. 相似文献
4.
In mathematical statistical filtering the deconvolution problem can be solved by two different methods:
- 1 by inverse filtering
- 2 by calculating the prediction error.
5.
N. DE VOOGD 《Geophysical Prospecting》1978,26(3):509-524
A finite realization of a discrete random noise process may be considered as a one-sided energy signal. Its phase property can then be described by means of the center position. The samples of such a realization are the components of a random signal vector and the center position is therefore a random variable. A statistical analysis shows that the expected value of the center position equals half the time duration of the realization. This implies that the Z-transform of the realization may be expected to have an equal number of poles and zeros inside and outside the unit circle. The standard deviation from the expected value of the center position is shown to depend on the time duration of the realization and on the autocorrelation of the process. It follows that, for processes that can be described by the convolution of a white series and a disturbance wavelet, the center position is independent of the phase property of the wavelet. A conclusion based on these results is that the homomorphic technique of wavelet estimation through cepstrum stacking must give questionable outcomes. Another conclusion is that the super-position of a realization of random noise on a minimum phase wavelet will in general give a mixed phase resulting signal. It is pointed out that schemes for the derivation of deconvolution filters do not take account of this phenomenon. 相似文献
6.
Hakan Karslı 《Geophysical Prospecting》2011,59(1):56-65
Interpreting a post‐stack seismic section is difficult due to the band‐limited nature of the seismic data even post deconvolution. Deconvolution is a process that is universally applied to extend the bandwidth of seismic data. However, deconvolution falls short of this task as low and high frequencies of the deconvolved data are either still missing or contaminated by noise. In this paper we use the autoregressive extrapolation technique to recover these missing frequencies, using the high signal‐to‐noise ratio (S/N) portions of the spectrum of deconvolved data. I introduce here an algorithm to extend the bandwidth of deconvolved data. This is achieved via an autoregressive extrapolation technique, which has been widely used to replace missing or corrupted samples of data in signal processing. This method is performed in the spectral domain. The spectral band to be extrapolated using autoregressive prediction filters is first selected from the part of the spectrum that has a high signal‐to‐noise ratio (S/N) and is then extended. As there can be more than one zone of good S/N in the spectrum, the results of prediction filter design and extrapolation from three different bands are averaged. When the spectrum of deconvolved data is extended in this way, the results show higher vertical resolution to a degree that the final seismic data closely resemble what is considered to be a reflectivity sequence of the layered medium. This helps to obtain acoustic impedance with inversion by stable integration. The results show that autoregressive spectral extrapolation highly increases vertical resolution and improves horizon tracking to determine continuities and faults. This increase in coherence ultimately yields a more interpretable seismic section. 相似文献
7.
基于ARMA模型非因果空间预测滤波(英文) 总被引：3，自引：1，他引：2
常规频域预测滤波方法是建立在自回归(autoregressive,AR)模型基础上的,这导致滤波过程中前后假设的不一致,即首先利用源噪声的假设计算误差剖面,却又将其作为可加噪声而从原始剖面中减去来得到有效信号。本文通过建立自回归-滑动平均(autoregres sive/moving-average,ARMA)模型,首先求解非因果预测误差滤波算子,然后利用自反褶积形式投影滤波过程估计可加噪声,进而达到去除随机噪声目的。此过程有效避免了基于AR模型产生的不一致性。在此基础上,将一维ARMA模型扩展到二维空间域,实现了基于二维ARMA模型频域非因果空间预测滤波在三维地震资料随机噪声衰减中的应用。模型试验与实际资料处理表明该方法在很好保留反射信息同时,压制随机噪声更加彻底,明显优于常规频域预测去噪方法。 相似文献
8.
随机噪声的影响在地震勘探中是不可避免的,常规的随机噪声压制方法在处理中往往会破坏具有时空变化特征的非平稳有效地震信号,影响地震数据的准确成像.当前油气勘探的目标已经转变为“两宽一高”,随着数据量的增大,对去噪方法的处理效率也提出了更高的要求.因此,开发高效的非平稳地震数据随机噪声压制方法具有重要意义.预测滤波技术广泛用于地震随机噪声的衰减,本文基于流式处理框架提出一种新的f-x域流式预测滤波方法,通过在频率域建立预测自回归方程,运用直接复数矩阵逆运算代替迭代算法求解非平稳滤波器系数,实现时空变地震同相轴预测,提高自适应预测滤波的计算效率.通过与工业标准的FXDECON方法和f-x域正则化非平稳自回归(RNA)方法进行对比,理论模型和实际数据的测试结果表明,提出的f-x域流式预测滤波方法能更好地平衡时空变有效信号保护、随机噪声压制和高效计算三者之间的关系,获得合理的处理效果. 相似文献
9.
电磁法数据处理的奇异值分解法 总被引：1，自引：0，他引：1
本文提出用奇异值分解方法处理电磁响应数据.根据电磁响应数据的奇异值分布特征,可以指出有用信号和随机误差所对应的奇异值范围.截断随机误差所对应的奇异值,利用与有用信号对应的特征象重构数据,可以消除随机误差.对加入随机噪声的理论数据处理的结果表明,这种方法的效果明显,随机噪声的标准差可降低60％多.另外,利用不同区段的特征象重构数据,可区分不同级次的电磁响应特征.最后,给出对实测数据处理的结果. 相似文献
10.
基于对地震反褶积本质上是一个盲过程的认识,引入高阶统计学盲源分离技术——独立分量分析（ICA）实现地震盲反褶积.在无噪声假设条件下,利用地震记录时间延迟矩阵和地震子波带状褶积矩阵,将地震褶积模型转化为一般线性混合ICA模型,采用FastICA算法,将带状性质作为先验信息,实现所谓带状ICA算法（B\|ICA）,得到个数与子波算子长度相等的多个估计反射系数序列和估计子波序列,最后利用褶积模型提供的附加信息从中优选出最佳的反射系数序列及相应的地震子波.模型数据和实际二维地震道数值算例表明：对于统计性反褶积,在不对反射系数作高斯白噪假设,不对子波作最小相位假设的所谓“全盲”条件下,基于ICA方法（反射系数非高斯分布,地震子波非最小相位）可以较好解决地震盲反褶积问题,是基于二阶统计特性的地震信号统计性反褶积方法的提升,具有可行性和应用前景. 相似文献
11.
Multiridge Euler deconvolution 总被引：1，自引：0，他引：1
Potential field interpretation can be carried out using multiscale methods. This class of methods analyses a multiscale data set, which is built by upward continuation of the original data to a number of altitudes conveniently chosen. Euler deconvolution can be cast into this multiscale environment by analysing data along ridges of potential fields, e.g., at those points along lines across scales where the field or its horizontal or vertical derivative respectively is zero. Previous work has shown that Euler equations are notably simplified along any of these ridges. Since a given anomaly may generate one or more ridges we describe in this paper how Euler deconvolution may be used to jointly invert data along all of them, so performing a multiridge Euler deconvolution. The method enjoys the stable and high‐resolution properties of multiscale methods, due to the composite upward continuation/vertical differentiation filter used. Such a physically‐based field transformation can have a positive effect on reducing both high‐wavenumber noise and interference or regional field effects. Multiridge Euler deconvolution can also be applied to the modulus of an analytic signal, gravity/magnetic gradient tensor components or Hilbert transform components. The advantages of using multiridge Euler deconvolution compared to single ridge Euler deconvolution include improved solution clustering, increased number of solutions, improvement of accuracy of the results obtainable from some types of ridges and greater ease in the selection of ridges to invert. The multiscale approach is particularly well suited to deal with non‐ideal sources. In these cases, our strategy is to find the optimal combination of upward continuation altitude range and data differentiation order, such that the field could be sensed as approximately homogeneous and then characterized by a structural index close to an integer value. This allows us to estimate depths related to the top or the centre of the structure. 相似文献
12.
A new approach to deconvolution has been developed to improve the attenuation of multiple energy. This approach to deconvolution is unique in that it not only eliminates the usual assumptions of a minimum phase lag wavelet and a random distribution of impulses, but also overcomes the noise limitation of the homomorphic deconvolution and its inherent instability to phase computation. We attempt to analyse the continuous alteration of the acoustic waveform during the propagation through a linear system. Based on the results of this analysis, the surface-related measurements are described as a convolution of the impulse response of the system with the non-stationary forward wavelet which includes all multiple terms generated within the system. The amplitude spectrum of the forward wavelet is recovered from the amplitude spectrum of the recorded signal, using the difference between the rate of decay of the source wavelet and the duration of the measurement. The phase spectrum of the forward wavelet is estimated using the Hilbert transform and the fact that the mixed phase lag wavelet can be presented as a convolution of the minimum and maximum phase lag wavelets. The multiples are discriminated from primaries by comparison of the phase spectrum of the seismic signal and the inverse of the forward wavelet. Therefore, the technique is called phase inversion deconvolution (PID). This approach requires no velocity information in order to recognize and attenuate multiple energy. Therefore, primary energy is recovered in the near-offset region where the velocity differential between primary and multiple energies is very small. 相似文献
13.
本文将时频峰值滤波(TFPF)去噪技术应用于共炮点地震资料的随机噪声压制.时频峰值滤波技术是通过频率调制将信号调制成解析信号的瞬时频率,利用解析信号的Wigner-Ville分布的峰值进行瞬时频率估计,恢复有效信号,与其它去噪方法相比,TFPF具有在较少的约束条件下压制强随机噪声的优点.本文针对实际地震资料的非线性特性,利用加窗的Wigner-Ville分布实现TFPF,使得地震信号在一个窗长内近似满足线性瞬时频率条件,减小由地震信号非线性引起的偏差.本文对共炮点地震记录做时频峰值滤波处理,滤波结果表明在地震勘探资料中存在强随机噪声的情况下,利用局部线性化处理的时频峰值滤波技术可以有效地压制地震资料中的随机噪声,恢复出湮没在随机噪声中的地震反射信号.信噪比提高3～6 dB. 相似文献
14.
首先通过对加入随机噪声的合成信号进行Morlet小波变换并进行显著性检验, 分析所得信号的周期成分的显著性和非显著性, 然后研究信号周期长短、 信号观测时间段长短与精度之间的关系．在此基础上,以活动周期比较稳定的太阳黑子活动作为实例分析,结果显示本文的精度分析和显著性检验方法对于周期谱的精度和显著性研究是可行的．最后将该方法应用于全球地震活动周期谱的分析,求出全球地震活动周期谱及其显著性与精度．研究结果表明,在利用Morlet小波分析地磁场和地震活动性周期时引入显著性检验,并结合本文给出的精度, 可以从数据中提取出周期谱及其显著性和精度． 相似文献
15.
应用正则化反演理论研究了提高自然伽马测井曲线纵向分辨率的正则反褶积处理技术，通过正则化因子对噪音和信号影响的分析和研究，给出了估计反褶积结果与地层真值偏差大小的新公式，提出了直接根据信噪比大小选择正则化因子的新方法．由于正则化因子保证使反褶积结果与地层真值偏差为最小，使计算结果的稳定性和精度均得到明显提高．此外，大部分测井记录的信噪比均可以很容易估计，从而大大提高了正则反褶积的适用性．最后通过理论模型实验和井场数据处理证明了正则反褶积技术是十分有效的． 相似文献
16.
强随机噪声干扰是导致地震勘探资料低信噪比的主要原因,如何在强随机噪声干扰下获取有效的信息是值得关注的问题.Duffing振子混沌系统是一个非线性的动力学系统,其对强随机噪声具有免疫能力,而对特定的周期性信号具有敏感性.本文提出一种基于Duffing振子混沌系统的速度分析方法.对CMP道集按照时距曲线关系进行移动窗口截取,将所截取的信号构建为待测信号加入Duffing振子混沌系统,通过相图网格分割方法(GPM)判断系统状态的改变,从而在强随机噪声背景下获得高分辨率的速度谱.理论模型和实际资料的处理结果表明,与传统的水平叠加速度分析方法相比,本方法能够在强随机噪声背景下获得更准确的速度分析结果. 相似文献
17.
经验模态分解算法(EMD)是一种基于有效波和噪声尺度差异进行波场分离的随机噪声压制方法,但由于实际地震数据波场复杂,导致模态混叠较严重,仅凭该方法进行去噪很难达到理想效果.本文基于EMD算法对信号多尺度的分解特性,结合Hausdorff维数约束条件,提出一种用于地震随机噪声衰减的新方法.首先对地震数据进行EMD自适应分解,得到一系列具有不同尺度的、分形自相似性的固有模态分量(IMF);在此基础上,基于有效信号和随机噪声的Hausdorff维数差异,识别混有随机噪声的IMF分量,对该分量进行相关的阈值滤波处理,从而实现有效信号和随机噪声的有效分离.文中从仿真信号试验出发,到模型地震数据和实际地震数据的测试处理,同时与传统的EMD处理结果相对比.结果表明,本文方法对地震随机噪声的衰减有更佳的压制效果. 相似文献
18.
在地震勘探领域,随机噪声一直是影响地震信号信噪比的主要因素之一,如何从被干扰的地震信号中有效去除随机噪声并保护有用信号具有重要的意义.针对经典小波变换在计算效率方面的缺陷,本文推荐应用提升算法实现第二代小波变换的构建,分析和对比了提升算法(Lifting Scheme)下不同小波变换方法的特性,选取更加符合小波域去噪原理的CDF 9/7双正交小波变换作为基本算法,同时应用了简单、有效的百分位数(Percentiles)软阈值进行信噪分离.通过理论模型处理,本方法可以在去噪能力和保护有用信号之间找到很好的平衡点.实际剖面的处理效果表明,此方法不仅能有效的滤除随机噪声,而且很好地保护有用信号,提高地震数据分析的精确性. 相似文献
19.
Further improvement of temporal resolution of seismic data by autoregressive (AR) spectral extrapolation 总被引：1，自引：0，他引：1
Hakan Karsl&#x; 《Journal of Applied Geophysics》2006,59(4):324-336
Seismic data have still no enough temporal resolution because of band-limited nature of available data even if it is deconvolved. However, lower and higher frequency information belonging to seismic data is missing and it is not directly recovered from seismic data. In this paper, a method originally applied by Honarvar et al. [Honarvar, F., Sheikhzadeh, H., Moles, M., Sinclair, A.N., 2004. Improving the time-resolution and signal–noise ratio of ultrasonic NDE signals. Ultrasonics 41, 755–763.] which is the combination of the most widely used Wiener deconvolution and AR spectral extrapolation in frequency domain is briefly reviewed and is applied to seismic data to improve temporal resolution further. The missing frequency information is optimally recovered by forward and backward extrapolation based on the selection of a high signal–noise ratio (SNR) of signal spectrum deconvolved in signal processing technique. The combination of the two methods is firstly tested on a variety of synthetic examples and then applied to a stacked real trace. The selection of necessary parameters in Wiener filtering and in extrapolation are discussed in detail. It is used an optimum frequency windows between 3 and 10 dB drops by comparing results from these drops, while frequency windows are used as standard between 2.8 and 3.2 dB drops in study of Honarvar et al. [Honarvar, F., Sheikhzadeh, H., Moles, M., Sinclair, A.N., 2004. Improving the time-resolution and signal–noise ratio of ultrasonic NDE signals. Ultrasonics 41, 755–763.]. The results obtained show that the application of the purposed signal processing technique considerably improves temporal resolution of seismic data when compared with the original seismic data. Furthermore, AR based spectral extrapolated data can be almost considered as reflectivity sequence of layered medium. Consequently, the combination of Wiener deconvolution and AR spectral extrapolation can reveal some details of seismic data that cannot be observed in raw signal or which lost during the previous processing. 相似文献
20.
Vibroseis is a source used commonly for inland seismic exploration. This non-destructive source is often used in urban areas with strong environmental noise. The main goal of seismic data processing is to increase the signal/noise ratio where a determinant step is deconvolution. Vibroseis seismic data do not meet the basic minimum-phase assumption for the application of spiking and predictive deconvolution, therefore various techniques, such as phase shift, are applied to the data, to be able to successfully perform deconvolution of vibroseis data.This work analyzes the application of deconvolution techniques before and after cross-correlation on a real data set acquired for high resolution prospection of deep aquifers. In particular, we compare pre-correlation spiking and predictive deconvolution with Wiener filtering and with post-correlation time variant spectral whitening deconvolution. The main result is that at small offsets, post cross-correlation spectral whitening deconvolution and pre-correlation spiking deconvolution yield comparable results, while for large offsets the best result is obtained by applying a pre-cross-correlation predictive deconvolution. 相似文献