| S变换谱分解技术在深反射地震弱信号提取中的应用 |
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| 引用本文： | 邓攻,梁锋,李晓婷,赵俊猛,刘红兵,王洵.S变换谱分解技术在深反射地震弱信号提取中的应用[J].地球物理学报,2015,58(12):4594-4604. |
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| 作者姓名： | 邓攻 梁锋 李晓婷 赵俊猛 刘红兵 王洵 |
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| 作者单位： | 1. 中国科学院青藏高原研究所, 大陆碰撞与高原隆升重点实验室, 北京 100101;2. 中国地质科学院矿产资源研究所, 国土资源部成矿作用与资源评价重点实验室, 北京 100037;3. 中国矿业大学(北京), 煤炭资源与安全开采国家重点实验室, 北京 100083;4. 中国科学院青藏高原地球科学卓越创新中心, 北京 100101 |
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| 基金项目： | 国家"深部探测技术与实验研究"专项课题(SinoProbe-03-02)和国家自然科学基金重点项目(40930418),中国科学院战略性先导科技专项(B类)(XDB03010702)联合资助. |
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| 摘 要： | 在深反射地震资料处理中,当来自深部的有效弱信号和噪声干扰频带差异较小且难以区分时,传统滤波方法的应用会受到限制.谱分解方法是一种使用离散傅里叶变换,基于信号的频率-振幅谱等信息生成高分辨率地震图像的方法,通常用来识别介质物性横向分布特征,处理复杂介质内频谱变化和局部相位的不稳定性等问题,包括定位复杂断层和小尺度断裂等.S变换作为一种新的时频分析方法,具有自动调节分辨率的能力,近些年来被广泛应用到勘探地震、大地电磁等数据处理中,逐渐成为地球物理方法中噪声压制的有效方法之一.与常规石油反射地震资料相比,深反射主动源地震为了探测深部结构信息,常采用大药量激发方式、长排列观测系统等,导致深部有效信号基本湮灭在噪声干扰之中.针对深反射数据特点,本文结合谱分解和S变换技术,首先设计了简单的脉冲函数实验数据,证实S变换方法的有效性,同时说明谱分解方法的效果受所用时频分析方法影响较大,而其中决定分辨能力的变换窗函数的选取尤为重要.在此基础上,分别应用到深反射地震资料的单道和叠加剖面实际数据上,对比分析了传统变换谱分解和S变换谱分解的应用效果,单道资料对比结果表明:相比传统谱分解,S变换谱分解方法具有自动调节分辨率的能力,能够精确的标定深反射地震资料中弱信号不同时刻的频率分量;叠加剖面资料应用结果表明:由S变换谱分解得到的剖面结果与其他谱分解方法结果整体上具有较高的一致性,同时清晰地刻画出原叠加剖面上被噪声湮灭的低频细节特征,提高了剖面的分辨率及同相轴连续性;对比结果明显看出,Gabor变换谱分解方法得到的结果同相轴较为破碎,分析原因认为这是由Gabor变换的时频分解方法的定长窗函数所致,窗口大小不会随着信号频率的变化来调节长度,只能在处理的过程中根据一定的记录长度范围选取窗函数参数,而S变换谱分解方法在窗函数的选取时,通过时变信号的局部频率特征自动调节窗口长度,能够更好的刻画各个频段的细节特征,在深反射剖面成像应用中效果尤为明显.本文结果表明S变换谱分解技术在深地震叠加剖面上的应用有效地提高了来自深部弱反射信号的信噪比和分辨率,并刻画出了叠加剖面上所不具有的低频细节特征,在实际深反射地震资料处理中能有效保护低频弱信号获得更好的成像效果.本文为深地震反射资料中弱信号的保护处理找到一种有效的方法.
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| 关 键 词： | Gabor变换 S变换 谱分解 深反射地震 时频分析 |
| 收稿时间： | 2015-05-22 |
S-transform spectrum decomposition technique in the application of the extraction of weak seismic signals |
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| DENG Gong,LIANG Feng,LI Xiao-Ting,ZHAO Jun-Meng,LIU Hong-Bing,WANG Xun.S-transform spectrum decomposition technique in the application of the extraction of weak seismic signals[J].Chinese Journal of Geophysics,2015,58(12):4594-4604. |
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| Authors: | DENG Gong LIANG Feng LI Xiao-Ting ZHAO Jun-Meng LIU Hong-Bing WANG Xun |
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| Abstract: | In processing of deep seismic reflection data, when the frequency band difference between the weak useful signal and noise both from the deep subsurface is very small and hard to distinguish, the traditional method of filtering will be limited. To solve this problem, we apply different spectral decomposition methods respectively to experimental data and real data and compare the results from these methods. Our purpose is to find an effective way to protect weak signals during processing deep seismic reflection data.#br#The spectral decomposition method is based on the discrete Fourier transform, which uses the signal frequency-amplitude spectrum and other information to generate a high-resolution seismic image. Typically, it is used to identify the lateral distribution of media properties, solve spectrum changes within complex media and local phase instability and other issues, such as locating faults and small-scale complex fractures. S transform as a new time-frequency analysis method, which is a generalization of STFT developed by Stockwell in 1994, has the ability to automatically adjust the resolution. This method has been widely applied to exploration seismic, MT and other geophysical datasets in recent years. It has become one of the effective methods in noise suppressing during geophysical data processing. Comparing deep seismic reflection data with conventional oil reflection seismic data, in order to probe deep structure, this approach employs a large number of explosives, long observing systems, leading to a phenomenon that valid signals from the deep and noise are mixed together both in the time domain and frequency domain. Considering these characteristics of deep reflection data, this paper combines spectral decomposition with S transform technology. First we design a simple pulse function experimental data to confirm the validity of the S transform method. Then we illustrate the effect of spectral decomposition which is influenced by choosing frequency analysis methods and the transform window function which determines the strength of the resolving power of the method. On this basis, S transform spectrum decomposition is applied to a single channel of deep reflection seismic data and the stacked profile, then the application results of traditional transform spectral decomposition and S transform spectral decomposition are compared.#br#Comparison of single channel data shows that compared with traditional spectral decomposition, the S transform spectral decomposition method is able to automatically adjust the resolution, accurately calibrate frequency component of weak signals at different times in deep reflection seismic data. Application to stacked profile data shows that the stacked profile results obtained by the S transform spectral decomposition and those from other spectral decomposition method are largely consistent, while the results of S transform spectral decomposition clearly depict the characteristics of low-frequency details which are superimposed by noise in original stacked profile. At the same time, it improves the resolution and enhances the phase axis continuity on the stacked profile. Comparison also clearly indicates that the phase axis on the resultant profile obtained by Gabor transform spectral decomposition is more broken, which is caused by fixed-length window function used by Gabor transform decomposition, in which the window length does not change with the signal frequency. In Gabor transform decomposition, the length of the window function parameters can only be selected from the start of processing and is set to a certain value, while the S transform spectral decomposition method chooses the variable length of the window function according to signal change. It can automatically adjust the frequency characteristics of the signal by the local window length to better characterize the details of each frequency range. Such an effect is very obvious in deep reflection seismic imaging.#br#Our results show that the key of the spectral decomposition technique is to select the transform window function. The S transform spectral decomposition technology used in real deep reflection seismic data processing can effectively protect the weak low-frequency signals. It can effectively improve the signal to noise ratio and resolution of weak reflection signals from the deep subsurface, while depicting the characteristics of low-frequency details on the stacked section and ultimately obtaining better imaging results. |
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| Keywords: | Gabor transform Stockwell transform Spectral decomposition Deep seismic reflectio Time-frequency analysis |
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