【作者】 程琳；

【导师】 尹忠科；

【作者基本信息】 西南交通大学 ， 信号与信息处理， 2011， 硕士

【摘要】 石油是关系到我国国计民生的重要战略资源,而我国对外依存度占石油消费的一半以上,这严重威胁着我国的能源安全。因而,大力发展石油开采技术,不仅关系到我国经济能否持续、快速发展,而且关系到我国能源供应的战略安全。声波测井信号处理技术是目前勘探石油等能源资源的有力手段,但这种技术仍存在一定的缺陷。目前国内外仅利用首波信息,来获取地层的岩性、孔隙和油气含量等关键信息,通常与实际情况存在误差。近年来,基追踪(Basis Pursuit,BP)算法成为信号稀疏表示领域研究的热点,该算法的思想是从过完备原子库中找到信号最稀疏的表示,也就是用尽可能少的原子表示原信号,进而获取信号的内在特性。根据声波测井信号的特性将该方法应用到测井数据处理中,为获取更加全面的测井信息提供了新的方向。本文首先介绍了信号表示及信号稀疏分解的基础知识,接着以短时傅里叶变换为例介绍了常用时频分析方法的缺陷,引出了基于BP的稀疏分解方法。通过对模型信号的处理,实验结果证明利用BP算法进行信号分离是可行的。在将声波测井信号模型成功分离的基础上,把BP算法应用到实际声波测井信号中。通过对不同条件下实际声波测井信号的处理,实验证明利用BP算法仍能实现分离。根据分离结果分析不同条件下测井信号的特点,并且实验证明不同条件下测井信号的分离效果不同。最后,对含噪的实际声波测井信号进行处理,实验结果证明BP算法仍能将信号进行分离,但是由于噪声的干扰,含噪信号的分离结果比预处理后稍差。更多还原

【Abstract】 Oil is not only related to our national economy but also is an important strategic resource, and our country dependence on foreign oil consumption accounted for more than half of this severe, which threat to China’s energy security. Thus, to develop oil exploration technology is not only for the sustainability and rapid development of our economy, but also for the strategic security of the energy supply.Acoustic logging signal processing technology is an powerful means for oil and other energy resources prospecting currently, but this technology still has some flaws. At home and abroad using only the information of the first wave, to obtain the formation of lithology, porosity, oil content and other key information, Usually there are some errors with the actual situation. In recent years, the Basis Pursuit(BP) algorithm is the research focus of sparse representation of the signal field, the idea of the algorithm is to find the most sparse representation of signals based on the over-complete dictionary. In other words, using as little atoms to respect the original signal as possible, then obtaining the intrinsic properties of the signal, using this method to handle the logging data based on the characteristics of acoustic logging signal, providing a new direction to get more comprehensive logging information.The signal representation and signal sparse decomposition were introduced firstly in this thesis, and then to the basic knowledge of short-time Fourier transformation for example, the defects of common time-frequency analysis method were introduced, and based on BP the sparse decomposition method was raised. Based on model signal processing, experimental results prove BP signal separation is feasible. In will acoustic logging signal model on the basis of successful separation, the BP algorithm applied to actual acoustic logging signal. According to the different conditions actual acoustic logging signal processing, the experiment proved that BP algorithm can still achieve separation. According to the analysis of separation results under different conditions, and the characteristic of well logging signal experimental proof under various conditions, the separation effect of different logging signal. Finally, the practical with noise acoustic logging signal processing, the experimental results show the BP algorithm can still will signal, but because the separation of the noise, with noise signals than separation results after pretreatment is a bit poor.更多还原