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1.
Bussgang算法是针对褶积盲源分离问题提出的,本文将其用于地震盲反褶积处理.由于广义高斯概率密度函数具有逼近任意概率密度函数的能力,从反射系数序列的统计特征出发,引入广义高斯分布来体现反射系数序列超高斯分布特征.依据反射系数序列的统计特征和Bussgang算法原理,建立以Kullback-Leibler距离为非高斯性度量的目标函数,并导出算法中涉及到的无记忆非线性函数,最终实现了地震盲反褶积.模型试算和实际资料处理结果表明,该方法能较好地适应非最小相位系统,能够同时实现地震子波和反射系数估计,有效地提高地震资料分辨率. 相似文献
2.
预条件共轭梯度反褶积方法是结合盲反褶积的实现,运用基于Krylov子空间上优化的预条件共轭梯度法,完成反射系数的反演.用该方法处理地震资料时可提高资料频率,展宽有效频率宽度.但由于地震数据对不同频带的信噪比有差异,若直接运用该反褶积处理常伴随分辨率提高的同时出现信噪比显著降低的现象.对于此,本文采取如下方法的改进措施：①在时间域上,当地震数据的振幅较大时,对应的反褶积数据的振幅取值与原地震数据的振幅相等;②在频率域上,当地震数据的频谱幅值大于一定阀值时,对应的反褶积数据的频谱取做原地震数据的频谱.由本文所给的数值算例可以看出,此两项改进方法可取得较好的实用效果. 相似文献
3.
For years, reflection coefficients have been the main aim of traditional deconvolution methods for their significant informational content. A method to estimate seismic reflection coefficients has been derived by searching for their amplitude and their time positions without any other limitating assumption. The input data have to satisfy certain quality constraints like amplitude and almost zero phase noise—ghosts, reverberations, long period multiples, and diffracted waves should be rejected by traditional processing. The proposed algorithm minimizes a functional of the difference between the spectra of trace and reflectivity in the frequency domain. The estimation of reflection coefficients together with the consistent “wavelet’ is reached iteratively with a multidimensional Newton-Raphson technique. The residual error trace shows the behavior of the process. Several advantages are then obtainable from these reflection coefficients, like conversion to interval velocities with an optimum calibration either to the well logs or to the velocity analysis curves. The procedure can be applied for detailed stratigraphic interpretations or to improve the resolution of a conventional velocity analysis. 相似文献
4.
本文首先分析了地震波在黏弹介质的传播规律,基于黏弹介质地震波动方程总结了时变子波振幅谱和相位谱的关系,从而得出结论,准确估计子波相位谱初值和不同时刻的子波振幅谱是实现时变子波准确提取的必要条件.在此基础上,针对传统方法限制子波振幅谱形态且受限于分段平稳假设的问题,提出了一种利用EMD(Empirical Mode Decomposition)和子波振幅谱与相位谱关系的时变子波提取方法,根据子波对数振幅谱光滑连续而反射系数对数振幅谱振荡剧烈的特点,采用EMD方法将不同时刻地震记录的对数振幅谱分解为一组具有不同振荡尺度的模态分量,通过滤除振荡剧烈分量、重构光滑连续分量提取时变子波振幅谱;再应用子波振幅谱和相位谱的关系提取时变子波相位谱,将分别提取的振幅谱和相位谱逐点进行合成,最终实现时变子波的准确提取.本文方法不需要求取Q值,适用于变Q值的情况,具有良好的抗噪性能.数值仿真和叠后实际资料处理结果表明,相比传统的分段提取方法,利用本文方法提取的时变子波准确度更高,研究成果对提高地震资料分辨率具有重要意义. 相似文献
5.
The amplitude spectrum of ground penetrating radar (GPR) reflection data acquired with a particular antenna set is normally concentrated over a spectral bandwidth of a single octave, limiting the resolving power of the GPR wavelet. Where variously-sized GPR targets are located at numerous depths in the ground, it is often necessary to acquire several profiles of GPR data using antennas of different nominal frequencies. The most complete understanding of the subsurface is obtained when those frequency-limited radargrams are jointly interpreted, since each frequency yields a particular response to subsurface reflectivity. The application of deconvolution to GPR data could improve image quality, but is often hindered by limited spectral bandwidth.We present multiple-frequency compositing as a means of combining data from several frequency-limited datasets and improving the spectral bandwidth of the GPR profile. A multiple-frequency composite is built by summing together a number of spatially-coincident radargrams, each acquired with antennae of different centre frequency. The goal of the compositing process is therefore to produce a composite radargram with balanced contributions from frequency-limited radargrams and obtain a composite wavelet that has properties approximating a delta function (i.e. short in duration and having a broad, uniform spectral bandwidth).A synthetic investigation of the compositing process was performed using Berlage wavelets as proxies for GPR source pulses. This investigation suggests that a balanced, broad bandwidth, effective source pulse is obtained by a compositing process that equalises the spectral maxima of frequency-limited wavelets prior to summation into the composite. The compositing of real GPR data was examined using a set of 225, 450 and 900 MHz GPR common offset profiles acquired at a site on the Waterloo Moraine in Ontario, Canada. The most successful compositing strategy involved derivation of scaling factors from a time-variant least squares analysis of the amplitude spectra of each frequency-limited dataset. Contributions to the composite from each nominal acquisition frequency are clear, and the trace averaged amplitude spectrum of the corresponding composite is broadened uniformly over a bandwidth approaching two-octaves. Improvements to wavelet resolution are clear when a composite radargram is treated with a spiking deconvolution algorithm. Such improvement suggests that multiple-frequency compositing is a useful imaging tool, and a promising foundation for improving deconvolution of GPR data. 相似文献
6.
基于保角变换的双谱域相位谱估计方法及其在地震子波估计中的应用(英文) 总被引：2，自引：0，他引：2
地震子波估计是地震资料处理与解释中的重要环节,它的准确与否直接关系到反褶积及反演等结果的好坏。高阶谱(双谱和三谱)地震子波估计方法是一类重要的、新兴的子波估计方法,然而基于高阶谱的地震子波估计往往因为高阶相位谱卷绕的原因,导致子波相位谱求解产生偏差,进而影响了混合相位子波估计的效果。针对这一问题,本文在双谱域提出了一种基于保角变换的相位谱求解方法。通过缩小傅里叶相位谱的取值范围,有效避免了双谱相位发生卷绕的情况,从而消除了原相位谱估计中双谱相位卷绕的影响。该方法与最小二乘法相位谱估计相结合,构成了基于保角变换的最小二乘地震子波相位谱估计方法,并与最小二乘地震子波振幅谱估计方法一起,应用到了地震资料混合相位子波估计中。理论模型和实际资料验证了该方法的有效性。同时本文将双谱域地震子波相位谱估计中保角变换的思想推广到三谱域地震子波相位谱估计中。 相似文献
7.
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. 相似文献
8.
预条件共轭梯度反褶积方法是结合稀疏反褶积的实现,运用优化的预条件共轭梯度法,完成反射系数的反演。用该方法处理地震资料时可提高资料频率,展宽有效频率宽度。但由于地震信号具有时变性,因此本文将该反褶积过程中的子波用多尺度时变子波代替。由数值算例可以看出,该方法可取得较好的实用效果。 相似文献
9.
C. Schmelzbach F. ScherbaumJ. Tronicke P. Dietrich 《Journal of Applied Geophysics》2011,75(4):615-630
Enhancing the resolution and accuracy of surface ground-penetrating radar (GPR) reflection data by inverse filtering to recover a zero-phased band-limited reflectivity image requires a deconvolution technique that takes the mixed-phase character of the embedded wavelet into account. In contrast, standard stochastic deconvolution techniques assume that the wavelet is minimum phase and, hence, often meet with limited success when applied to GPR data. We present a new general-purpose blind deconvolution algorithm for mixed-phase wavelet estimation and deconvolution that (1) uses the parametrization of a mixed-phase wavelet as the convolution of the wavelet's minimum-phase equivalent with a dispersive all-pass filter, (2) includes prior information about the wavelet to be estimated in a Bayesian framework, and (3) relies on the assumption of a sparse reflectivity. Solving the normal equations using the data autocorrelation function provides an inverse filter that optimally removes the minimum-phase equivalent of the wavelet from the data, which leaves traces with a balanced amplitude spectrum but distorted phase. To compensate for the remaining phase errors, we invert in the frequency domain for an all-pass filter thereby taking advantage of the fact that the action of the all-pass filter is exclusively contained in its phase spectrum. A key element of our algorithm and a novelty in blind deconvolution is the inclusion of prior information that allows resolving ambiguities in polarity and timing that cannot be resolved using the sparseness measure alone. We employ a global inversion approach for non-linear optimization to find the all-pass filter phase values for each signal frequency. We tested the robustness and reliability of our algorithm on synthetic data with different wavelets, 1-D reflectivity models of different complexity, varying levels of added noise, and different types of prior information. When applied to realistic synthetic 2-D data and 2-D field data, we obtain images with increased temporal resolution compared to the results of standard processing. 相似文献
10.
Klauder wavelet removal before vibroseis deconvolution 总被引：1，自引：0，他引：1
The spiking deconvolution of a field seismic trace requires that the seismic wavelet on the trace be minimum phase. On a dynamite trace, the component wavelets due to the effects of recording instruments, coupling, attenuation, ghosts, reverberations and other types of multiple reflection are minimum phase. The seismic wavelet is the convolution of the component wavelets. As a result, the seismic wavelet on a dynamite trace is minimum phase and thus can be removed by spiking deconvolution. However, on a correlated vibroseis trace, the seismic wavelet is the convolution of the zero-phase Klauder wavelet with the component minimum-phase wavelets. Thus the seismic wavelet occurring on a correlated vibroseis trace does not meet the minimum-phase requirement necessary for spiking deconvolution, and the final result of deconvolution is less than optimal. Over the years, this problem has been investigated and various methods of correction have been introduced. In essence, the existing methods of vibroseis deconvolution make use of a correction that converts (on the correlated trace) the Klauder wavelet into its minimum-phase counterpart. The seismic wavelet, which is the convolution of the minimum-phase counterpart with the component minimum-phase wavelets, is then removed by spiking deconvolution. This means that spiking deconvolution removes both the constructed minimum-phase Klauder counterpart and the component minimum-phase wavelets. Here, a new method is proposed: instead of being converted to minimum phase, the Klauder wavelet is removed directly. The spiking deconvolution can then proceed unimpeded as in the case of a dynamite record. These results also hold for gap predictive deconvolution because gap deconvolution is a special case of spiking deconvolution in which the deconvolved trace is smoothed by the front part of the minimum-phase wavelet that was removed. 相似文献
11.
地震子波估计是地震资料处理和解释中的一个关键问题,子波估计的可靠性会直接影响反褶积和反演的准确度.现有的子波估计方法分为确定型和统计型两种类型,本文通过结合这两类方法,利用确定型的谱分析法和统计型的偏度最大化方法,分别提取时变子波的振幅和相位信息,得到估计的时变子波.这种方法不需要对子波进行任何时不变或相位等的假设,具有对时变相位的估计能力.进而利用估计时变子波进行非稳态反褶积,提高地震记录的保真度,为精细储层预测和描述提供高质量的剖面.理论模型试算验证了方法的可行性,通过实际地震资料的处理应用,表明该方法能有效地提取出子波时变信息. 相似文献
12.
基于正演模拟的薄层时频特征响应分析是薄层定性识别与厚度定量预测的基础与关键.本文基于波场延拓理论,利用深度域相移法对不同厚度薄层进行了正演模拟.采用广义S变换对零偏移距地震道的反射复合波进行瞬时频谱分析,发现当薄层顶底反射系数极性相反时,复合波的干涉作用具有升频降幅作用,而极性相同时具有升幅降频作用;理论推导和实验分析均证明主峰值频率与薄层双程旅行时间厚度存在定量解析关系,这为薄层厚度定量预测提供了重要的技术手段;由于峰值频率对反射系数大小与极性变化不敏感,因此,峰值频率方法与时域振幅方法相比更具稳定性. 相似文献
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传统地震资料处理将多次波视为干扰波进行压制,而多次波是有效波在地层中来回传播的反馈,因此它携带了地层反射系数信息。本文从多次波成因出发,推导出反射系数公式(反射系数等于一阶多次波与一次反射波的反褶积)。理论模型实验证明了该方法的正确性。对实际海洋单道地震资料进行处理,求取的反射系数与有效波基本吻合,但由于资料信噪比、子波及反褶积算法等局限性,该技术有待进一步研究。 相似文献
14.
The rough‐sea reflection‐response varies (1) along the streamer (2) from shot to shot and (3) with time along the seismic trace. The resulting error in seismic data can be important for time‐lapse imaging. One potential way of reducing the rough‐sea receiver error is to use conventional statistical deconvolution, but special care is needed in the choice of the design and application windows. The well‐known deconvolution problem associated with the non‐whiteness of the reflection series is exacerbated by the requirement of an unusually short design window – a requirement that is imposed by the non‐stationary nature of the rough‐sea receiver wavelet. For a synthetic rough‐sea data set, with a white 1D reflection series, the design window needs to be about 1000 ms long, with an application window about 400 ms long, centred within the design window. Although such a short design window allows the deconvolution operator to follow the time‐variation of the rough‐sea wavelet, it is likely to be too short to prevent the non‐whiteness of the geology from corrupting the operator when it is used on real data. If finely spatial‐sampled traces are available from the streamer, the design window can be extended to neighbouring traces, making use of the spatial correlations of the rough‐sea wavelet. For this ‘wave‐following’ approach to be fruitful, the wind (and hence the dominant wave direction) needs to be roughly along the line of the streamer. 相似文献
15.
Derman Dondurur 《Pure and Applied Geophysics》2010,167(10):1233-1245
Wiener deconvolution is generally used to improve resolution of the seismic sections, although it has several important assumptions. I propose a new method named Gold deconvolution to obtain Earth’s sparse-spike reflectivity series. The method uses a recursive approach and requires the source waveform to be known, which is termed as Deterministic Gold deconvolution. In the case of the unknown wavelet, it is estimated from seismic data and the process is then termed as Statistical Gold deconvolution. In addition to the minimum phase, Gold deconvolution method also works for zero and mixed phase wavelets even on the noisy seismic data. The proposed method makes no assumption on the phase of the input wavelet, however, it needs the following assumptions to produce satisfactory results: (1) source waveform is known, if not, it should be estimated from seismic data, (2) source wavelet is stationary at least within a specified time gate, (3) input seismic data is zero offset and does not contain multiples, and (4) Earth consists of sparse spike reflectivity series. When applied in small time and space windows, the Gold deconvolution algorithm overcomes nonstationarity of the input wavelet. The algorithm uses several thousands of iterations, and generally a higher number of iterations produces better results. Since the wavelet is extracted from the seismogram itself for the Statistical Gold deconvolution case, the Gold deconvolution algorithm should be applied via constant-length windows both in time and space directions to overcome the nonstationarity of the wavelet in the input seismograms. The method can be extended into a two-dimensional case to obtain time-and-space dependent reflectivity, although I use one-dimensional Gold deconvolution in a trace-by-trace basis. The method is effective in areas where small-scale bright spots exist and it can also be used to locate thin reservoirs. Since the method produces better results for the Deterministic Gold deconvolution case, it can be used for the deterministic deconvolution of the data sets with known source waveforms such as land Vibroseis records and marine CHIRP systems. 相似文献
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The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and the noise level not changes with space, whereas the actual situation does not satisfy this assumption, so that results after resolution improvement processing is not up to the expected effect. To solve these problems, we propose a seismic resolution improvement method based on the secondary time–frequency spectrum. First, we propose the secondary time-frequency spectrum based on S transform (ST) and discuss the reflection coefficient sequence and time-dependent wavelet in the secondary time–frequency spectrum. Second, using the secondary time–frequency spectrum, we design a twodimensional filter to extract the amplitude spectrum of the time-dependent wavelet. Then, we discuss the improvement of the resolution operator in noisy environments and propose a novel approach for determining the broad frequency range of the resolution operator in the time–frequency–space domain. Finally, we apply the proposed method to synthetic and real data and compare the results of the traditional spectrum-modeling deconvolution and Q compensation method. The results suggest that the proposed method does not need to estimate the Q value and the resolution is not limited by the bandwidth of the source. Thus, the resolution of the seismic data is improved sufficiently based on the signal-to-noise ratio (SNR). 相似文献
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True-amplitude (TA) migration, which is a Kirchhoff-type modified weighted diffraction stack, recovers (possibly) complex angle-dependent reflection coefficients which are important for amplitude-versus-offset (AVO) inversion. The method can be implemented using existing prestack or post-stack Kirchhoff migration and fast Green's function computation programs. Here, it is applied to synthetic single-shot and constant-offset seismic data that include post-critical reflections (complex reflection coefficients) and caustics. Comparisons of the amplitudes of the TA migration image with theoretical reflection coefficients show that the (possibly complex) angle-dependent reflection coefficients are correctly estimated. 相似文献
20.
叠前地震资料中,高频分量和低频分量随传播距离的衰减特性不同.本文给出了一种在小波域定性估计叠前地震资料衰减参数的方法.该方法利用连续小波变换提取共反射点道集的高、低频分量,以低频分量和高频分量之差定性反映地震波的衰减.通过累加不同偏移距的衰减,提高了估计的稳定性;采用幅度归一化方法,降低了信号幅值对衰减参数估计的影响.将本文提出方法与常用的基于叠后地震资料衰减估计方法用于某油田的地震资料处理,结果表明,本文方法得到的衰减估计结果能够更好地反映油气的空间展布. 相似文献