基于带状混合矩阵ICA实现地震盲反褶积

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.     

随机稀疏脉冲非线性反褶积

常规的反褶积方法通过线性褶积压缩子波提高地震记录的分辨率,其能力受到有效信号频带的限制.随机稀疏脉冲非线性反褶积方法将传统的以子波压缩为核心理念的反褶积方法转移到反射系数位置和大小的检测上来,它直接从地震记录中通过非线性反演方法得到反射系数的位置和大小,突破了地震资料有效频带的限制,能够较大幅度提高地震记录的分辨率.同时通过对反射系数统计特征的有效约束,减小了反褶积结果的多解性.模型实验表明,随机稀疏脉冲反褶积对噪声和子波的敏感性较小,能够较好的保护弱反射信号.在模型实验的基础上,利用随机稀疏脉冲反褶积对实际地震资料进行了实验处理,有效的改善了地震资料的分辨率.     

时频域时变反褶积方法

常规反褶积方法具有很多局限性,往往要求地震子波是最小相位并且是平稳的,反射系数序列为白噪等等.本文将常规褶积模型扩展成时变褶积模型,通过S变换对地震记录进行谱分解,估计出震源子波和衰减因子,求出时变子波,在S域进行时变反褶积,再反S变换到时间域输出结果.此方法完全打破了常规反褶积方法的局限性,数值试验和实际资料处理均证明了此方法的有效性.     

基于Gabor变换反褶积技术在渤海某工区的应用研究

使用常规的Wiener反褶积必须假设震源子波在地层旅行过程中是平稳的即一成不变的,这个前提条件与实际野外地震资料采集差别较大,而基于Gabor变换反褶积技术考虑到地震能量的衰减、子波的形变等非平稳性特征.地震道在Gabor域可因式分解成三项即震源子波、衰减函数和反射系数,该技术设计POU窗函数,并利用此函数在Gabor域对地震信号进行局部时频分解.Gabor域反褶积算法在Gabor域通过除以衰减函数和震源子波的乘积来估算地层反射系数,然后再做Gabor反变换可求得时间域的地层反射系数.理论模型的测试和实际地震资料的应用均表明,与Wiener反褶积相比较,基于Gabor变换反褶积可补偿中深层的能量衰减并因此拓宽有效频带和提高时间分辨率.     

基于广义高斯分布的地震盲反褶积方法研究

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.     

稀疏约束反褶积方法实验分析与应用研究(英文)

稀疏条件约束下的反褶积方法突破了地震资料有效频带的限制,能够获得较常规反褶积方法更高的分辨率。但这类反褶积方法存在较强的多解性,且对于弱反射有压制作用。柯西约束、修正柯西约束和Huber约束是稀疏反褶积方法常用的约束准则,本文利用模型数据对不同约束准则条件下稀疏约束反褶积恢复反射系数和保护弱反射的能力进行了实验分析。实验结果表明,稀疏约束反褶积的效果取决于约束准则与反射系数概率分布特征的一致程度,修正柯西约束较其它约束准则能够更好地保护弱反射信号。在模型实验的基础上,利用测井数据对碎屑岩地层和碳酸盐岩地层的反射系数概率分布特征进行了统计分析,采用修正柯西约束反褶积方法对实际地震资料进行了实验处理,较大幅度地提高了地震数据分辨率。     

分形脉冲反褶积方法

解地震反演问题的脉冲反褶积方法是基于反射系数白噪和子波为最小相位的假设下提出的．近几年的研究证明反射系数并不都是白噪,而是某种分形噪声,如果用一类分形反褶积方法,则将地震反演问题化为难以求解的非线性方程组．本文用反射系数的分形性质,推导出一个更为简单易解的线性方程组,称为分形脉冲反褶积．数值计算表明,本文的方法是有效的．     

基于PSO的地震盲反褶积方法

本文基于地层反射系数非高斯的统计特性,在反褶积输出单位方差约束下,将反褶积输出的负熵表示为非多项式函数,作为盲反褶积的目标函数,然后采用粒子群算法优化目标函数寻找最佳反褶积算子,实现地震信号的盲反褶积.数值模拟和实际资料处理结果表明,与传统反褶积方法相比,本文方法同时适应于最小相位子波及混合相位子波的反褶积,能够更好地从地震数据中估计反射系数,有效拓宽地震资料的频谱,得到高分辨率的地震资料.     

基于S域谱模拟技术的时变子波提取方法研究

反褶积是叠前地震数据处理中的重要环节,反褶积效果的好坏很大程度上依赖于地震子波的准确性.早期的反褶积处理大多数都是基于Robinson提出的平稳褶积模型,即地震子波是时不变的,但实际上由于地下介质的吸收衰减作用,地震子波是随时间不断变化的,这说明要进一步改善反褶积,使用时变的地震子波是必要的,因此本文提出了一种从地震资料中直接提取时变子波的方法.具体地,首先对单道地震数据做S变换求出其时频谱,进而得到其时变振幅谱,然后利用谱模拟技术从求得的地震记录振幅谱中拟合出每一时刻的子波振幅谱,在子波是零相位假设的前提下,拟合出的时变子波振幅谱即是所求频率域的时变子波,本文最后利用正演的单道地震记录和实际资料分别验证了所求频率域时变子波的准确性.     

非稳态地震记录时变子波估计

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.     

Improved Gabor Deconvolution and Its Extended Applications

In log time-frequency spectra, the nonstationary convolution model is a linear equation and thus we improved the Gabor deconvolution by employing a log hyperbolic smoothing scheme which can be implemented as an iteration process. Numerical tests and practical applications demonstrate that improved Gabor deconvolution can further broaden frequency bandwidth with less computational expenses than the ordinary method. Moreover, we attempt to enlarge this method’s application value by addressing nonstationary and evaluating Q values. In fact, energy relationship of each hyperbolic bin (i.e., attenuation curve) can be taken as a quantitative indicator in balancing nonstationarity and conditioning seismic traces to the assumption of unchanging wavelet, which resultantly reveals more useful information for constrained reflectivity inversion. Meanwhile, a statistical method on Q-value estimation is also proposed by utilizing this linear model’s gradient. In practice, not only estimations well agree with geologic settings, but also applications on Q-compensation migration are favorable in characterizing deep geologic structures, such as the pinch-out boundary and water channel.     

An Approximation to Sparse-Spike Reflectivity Using the Gold Deconvolution Method

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.     

Estimation of the seismic wavelet through homomorphic deconvolution and well log data: application on well-to-seismic tie procedure

Wavelet estimation and well-tie procedures are important tasks in seismic processing and interpretation. Deconvolutional statistical methods to estimate the proper wavelet, in general, are based on the assumptions of the classical convolutional model, which implies a random process reflectivity and a minimum-phase wavelet. The homomorphic deconvolution, however, does not take these premises into account. In this work, we propose an approach to estimate the seismic wavelet using the advantages of the homomorphic deconvolution and the deterministic estimation of the wavelet, which uses both seismic and well log data. The feasibility of this approach is verified on well-to-seismic tie from a real data set from Viking Graben Field, North Sea, Norway. The results show that the wavelet estimated through this methodology produced a higher quality well tie when compared to methods of estimation of the wavelet that consider the classical assumptions of the convolutional model.     

Nonstationary deconvolution using maximum kurtosis optimization

Deconvolution is an essential step for high-resolution imaging in seismic data processing. The frequency and phase of the seismic wavelet change through time during wave propagation as a consequence of seismic absorption. Therefore, wavelet estimation is the most vital step of deconvolution, which plays the main role in seismic processing and inversion. Gabor deconvolution is an effective method to eliminate attenuation effects. Since Gabor transform does not prepare the information about the phase, minimum-phase assumption is usually supposed to estimate the phase of the wavelet. This manner does not return the optimum response where the source wavelet would be dominantly a mixed phase. We used the kurtosis maximization algorithm to estimate the phase of the wavelet. First, we removed the attenuation effect in the Gabor domain and computed the amplitude spectrum of the source wavelet; then, we rotated the seismic trace with a constant phase to reach the maximum kurtosis. This procedure was repeated in moving windows to obtain the time-varying phase changes. After that, the propagating wavelet was generated to solve the inversion problem of the convolutional model. We showed that the assumption of minimum phase does not reflect a suitable response in the case of mixed-phase wavelets. Application of this algorithm on synthetic and real data shows that subtle reflectivity information could be recovered and vertical seismic resolution is significantly improved.     

用遗传算法实现地震信号反褶积

遗传算法作为寻优手段具有全局优化和很好的稳定性．本文将遗传算法用于地震信号反褶积处理，与已往方法相比它具有更好的分辨率和稳定性我们采用Bernoulli－Gaussian模型和ARMA模型分别描述地震反射系数序列和地震子波，用最大似然和最小预测误差准则分别构造用于估计反射系数序列和地震子波的目标函数，用遗传算法优化目标函数，以实现地震信号反褶积．     

Prestack nonstationary deconvolution based on variable-step sampling in the radial trace domain

The conventional nonstationary convolutional model assumes that the seismic signal is recorded at normal incidence. Raw shot gathers are far from this assumption because of the effects of offsets. Because of such problems, we propose a novel prestack nonstationary deconvolution approach. We introduce the radial trace （RT） transform to the nonstationary deconvolution, we estimate the nonstationary deconvolution factor with hyperbolic smoothing based on variable-step sampling （VSS） in the RT domain, and we obtain the high-resolution prestack nonstationary deconvolution data. The RT transform maps the shot record from the offset and traveltime coordinates to those of apparent velocity and traveltime. The ray paths of the traces in the RT better satisfy the assumptions of the convolutional model. The proposed method combines the advantages of stationary deconvolution and inverse Q filtering, without prior information for Q. The nonstationary deconvolution in the RT domain is more suitable than that in the space-time （XT） domain for prestack data because it is the generalized extension of normal incidence. Tests with synthetic and real data demonstrate that the proposed method is more effective in compensating for large-offset and deep data.     

多分辨率地震信号反褶积

基于二进小波变换提出了一种新的反褶积方法─-多分辨率地震信号反褶积．在地震信号二进小波变换域中的各尺度上分别进行其分辨率随小波尺度变化的反褶积,利用不同分辨率反褶积结果之间的相关性,以及测量噪声随尺度的衰减特性,从低分辨率反褶积结果逼近高分辨率反褶积结果．理论分析和实验表明,该方法有较高的精度,并且在较低信噪比情况下有好的效果．