共查询到18条相似文献，搜索用时 171 毫秒
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《地球物理学进展》2017,(2)
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. 相似文献
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探地雷达具有高效、快速、无损、抗干扰能力强等优点,成为浅层勘探的有力工具.雷达探测的准确性与资料处理有重要关系,处理技术将直接影响探测结果的准确性和可信度,本文主要对探地雷达信号直耦波的滤波处理技术进行了研究.典型的规则噪声直耦波具有强相关、能量强、呈水平状、出现早等特点,传统的滤波方法是采用KL变换.KL变换是对记录道在整个时间轴上的分析,不具备局部时间范围内的分析能力,KL变换滤波在提高剖面横向分辨率的同时,在垂向上会出现干扰,降低垂向分辨率.如果在KL变换基础上结合小波分时分频的特点,可以很好地解决上述问题.因此针对滤除直耦波及水平波,本文提出了小波域KL变换法,经模型实验与实际应用验证了改进后的滤波不仅横向分辨率较KL变换有明显的提高,而且垂向上也克服了KL变换的缺点,没有产生干扰波. 相似文献
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提出一种自适应协方差的时频域极化滤波方法。该方法在广义S变换时频方法的基础上,构造时频域自适应协方差矩阵,通过特征分析计算时频域瞬时极化参数,设计极化滤波器,实现多分量地震极化分析和滤波。其优势在于协方差矩阵的分析时窗的长度由多分量地震数据的瞬时频率确定,可以自适应于有效信号的周期,在每个时频点计算极化参数不需要进行插值处理;结合时间频率信息,解决在时间域或频率域波形或频率重叠的信号具有明显的直观性。模型数据及实际三分量台站地震数据处理结果表明,该极化滤波方法在台站地震资料分析和处理方面具有很好的直观性和较高的分辨率。 相似文献
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《地球物理学进展》2015,(5)
低频阴影现象为油气识别的一个重要标志.然而对于薄储层,低频阴影现象仍然较弱,因此有必要采用更高时频分辨率的时频分析方法进行低频阴影的识别.将信号的短时傅立叶变换谱与窗函数Wigner-Ville分布进行二维反褶积可得到信号Wigner-Ville分布,该二维反褶积方法即为反褶积短时傅立叶变换.该方法不仅提高了时间和频率分辨率而且减少了交叉项.文中对两种理论信号进行多种时频分析方法的计算机仿真效果对比,结果证明,反褶积短时傅立叶变换与传统的时频分析方法相比更具优势.文中采用反褶积短时傅立叶变换的方法对信号进行时频分析,首次将该方法用于单频剖面的提取.由低频阴影现象的数值模拟结果可知,该方法比广义S变换在薄储层预测中取得了更好的效果.在实际资料的应用中证实了此方法检测含油气储层的可行性. 相似文献
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本文给出了地震记录变子波模型的一种近似数学表达式．基于该表达式研究了反射系数序列不满足白噪假设和子波在地下传播时发生变化这两种情况下地震道谱的组成及结构,讨论了谱白化及反褶积方法在这两种情况下效果不佳的原因．然后基于变子波模型,提出了一种新的提高地震记录分辨率的方法：第一步,用自适应于地震记录的Gabor分子窗把地震记录恰当地划分成若干片断,每段内信号近似平稳,然后将地震记录变换到时间-频率域;第二步,在变换域对每个分子窗内信号的振幅谱进行处理以拓宽频带;最后把处理后的时间-频率域函数反变换回时间域得到提高分辨率后的结果．本文提出的方法具有能较好地适用于反射系数不满足白噪假设的情况及提高分辨率后的地震记录能较好地保持原地震记录的相对能量关系等优点,模型和实际资料算例结果均表明,本文方法在拓宽地震资料频带及保持地震记录局部能量相对关系方面均明显优于谱白化方法． 相似文献
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基于二阶谱及多阶微分融合的频谱拓展方法 总被引：1，自引：0，他引：1
《地球物理学进展》2016,(5)
受地下介质吸收衰减及环境噪声的影响,地震反射波的频率主要集中在中低频,且频带较窄,信噪比较低.反褶积方法是解决此类问题的重要手段,而谱模拟反褶积方法因克服了传统反褶积的假设条件"反射系数为白噪声"而备受推崇,但子波振幅谱无法准确获取的缺陷限制了其应用.为此,在传统的谱模拟反褶积方法基础上进行改进,提出了一种基于二阶谱及多阶微分融合的频谱拓展方法.微分算子在频率域具有单调递增的线性特征,可提高信号的高频成分并压制低频成分,即具有分频属性,且反映频率的高低与微分阶数成正比.以期望子波振幅谱为约束条件,对不同阶微分信号的振幅谱进行融合,可获得精确的宽频地震资料,避免传统谱模拟反褶积方法在求取子波振幅谱过程中存在的误差.经过对薄互层模型及实际地震记录的试算,获得了比传统方法更好的效果,证明本文方法对提高地震资料分辨率比传统谱模拟反褶积方法更加有效. 相似文献
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基于第二代小波变换的提升方案构造了插值小波,将雷达波场函数进行了二维小波变换,得到所有尺度上与计算网格相联系的小波系数和尺度系数.对所有尺度上的小波系数进行分析,根据解的局部性与小波系数阈值的控制,实现网格压缩和配点的自适应调节.保留大于给定阈值的小波系数及对应网格点,令小于给定阈值的小波系数为零,并舍弃其对应网格点.达到光滑区域采用较少的计算网格点,在奇异性较大的区域采用较多的计算网格点的目的.通过对自适应网格进行邻域校正、重构检查等附加修正,推导了场值更新的显式时间迭代方案.最后,以均匀、阶梯与复杂三个典型GPR模型为例,与常规数值计算结果对比表明：自适应小波配点法（AWCM）利用第二代小波的多尺度分解和快速变换的特点,可以使计算网格随着时间步适应解的移动和变化,允许计算资源更有效地使用,具有高压缩率,达到跟踪奇异性的目的,特别适合于探地雷达正演中波传问题的模拟. 相似文献
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目前对多次波的有效利用仅围绕多次波成像技术展开,通过成像多次波试图获取更丰富的地下构造信息.不同于该思路,本文另辟蹊径,从利用多次波提高地震数据分辨率角度出发,对多次波的有效利用进行了深入挖掘.首先基于聚焦变换思想在聚焦域内实现多次波的降阶,通过理论推导得出聚焦域内多次波表现为原始数据的多维子波反褶积这一重要结论,从理论上证明了本文方法提高地震数据分辨率的可行性;然后采用引入整形正则化的非稳态回归自适应匹配滤波方法将聚焦域内由多次波构建的高分辨率数据分离出来,实现原始数据的高分辨率转换.与常规反褶积模型不同,该方法基于波动理论推导得出,可以适用于任意复杂情况;每一道输出结果中所有炮记录都参与了运算,从空间上加以约束,在提高纵向分辨率的同时可以改善数据的横向分辨率.最后通过模型试算和实际资料处理对本文方法的有效性、适应性和实用性进行了验证. 相似文献
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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
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Statistical deconvolution, as it is usually applied on a routine basis, designs an operator from the trace autocorrelation to compress the wavelet which is convolved with the reflectivity sequence. Under the assumption of a white reflectivity sequence (and a minimum-delay wavelet) this simple approach is valid. However, if the reflectivity is distinctly non-white, then the deconvolution will confuse the contributions to the trace spectral shape of the wavelet and reflectivity. Given logs from a nearby well, a simple two-parameter model may be used to describe the power spectral shape of the reflection coefficients derived from the broadband synthetic. This modelling is attractive in that structure in the smoothed spectrum which is consistent with random effects is not built into the model. The two parameters are used to compute simple inverse- and forward-correcting filters, which can be applied before and after the design and implementation of the standard predictive deconvolution operators. For whitening deconvolution, application of the inverse filter prior to deconvolution is unnecessary, provided the minimum-delay version of the forward filter is used. Application of the technique to seismic data shows the correction procedure to be fast and cheap and case histories display subtle, but important, differences between the conventionally deconvolved sections and those produced by incorporating the correction procedure into the processing sequence. It is concluded that, even with a moderate amount of non-whiteness, the corrected section can show appreciably better resolution than the conventionally processed section. 相似文献
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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
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A method is proposed to obviate the shortcomings of conventional deconvolution approaches applied to vibroseis data. The vibroseis wavelet reduces the time domain resolution of the earth's impulse response by restricting its passband. The spectrum of the wavelet is assumed to be a “low quefrency”phenomenon, and hence it can be estimated by low cut cepstral filtering. The wavelet's amplitude spectrum can then be removed by spectral division. By using an approach which is consistent with the principle of maximum entropy, the undetermined portions of the seismogram's Fourier transform can be filled in by autoregressive prediction. The process of initially deconvolving in a restricted passband reduces the enhancement of noise contaminated parts of the spectrum, and the spectral extension scheme increases the time domain resolution of the process. 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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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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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