利用小波参数检测砂体分布
  符志国,尹成,张白林,赵伟,刘志斌,张金淼
  　　由于砂体储层对地震反射信号产生时间和频率的影响,而小波变换正好能反映信号的时间及频率成分,所以本文利用小波变换分析了地震记录的时频特征。鉴于小波变换对信号具有局部分析能力,并能提供多尺度的小波系数,因而小波变换能更精细地用于储层预测。通过理论模型试验进一步论述了小波系数可以作为信号时频特征的量度,提出了将地震信号的小波参数作为一类新的地震属性即小波属性。在实践中可得到目的层小波属性图,了解目的层反射特征的横向变化,检测储层分布。本文认为,小波属性可以像其他地震属性参数一样,用于多属性分析。
【作者单位】：北京昌平石油大学;西南石油学院;西南石油学院;中海石油研究中心;中海石油研究中心;中海石油研究中心 102249
【关键词】：时频特征;小波变换;Mallat算法;小波系数;地震属性
【基金】：国家高技术发展计划(863计划)经费资助(编号:2001AA602011-5)。

【正文快照】：
　　1引言 2方法原理 现今小波变换的应用已渗人到工程和研究的许 多领域,如地震信号处理中的数据压缩[lj、时频分 析川、信噪分离阁、地震道奇性检测[4j、提高分辨率 处理闹、瞬时参数提取困、同相轴及断层检测、薄层 信号奇异点及薄层厚度检测、小波参数的模式识别 油气预测等方面巨7一‘4〕。随着小波变换研究的深人, 它在石油勘探与开发中的应用得到了进一步的发 展,例如利用其对信号局部分析的能力来提取更精 确的一类信号局部特征参数,进而提供更精细的解 释数据与地质信息,为地震勘探精细解释和储层预 测提供了又一种参考因素。 本文从小…

 
  Using wavelet parameters for detection of distribution of sand bodv.
  Fu Zhi-guo;Yi Cheng;Zhang Bai-lin;Zhao Wei;Liu Zhi-bin and Zhang Jin-miao. Fu Zhi-guo;University of Petroleum;Changping District;Beijing City;102249;China
  　Since the sand body reservoir have influence on seismic reflected signals in time and frequency and wavelet transform can just reflect the time and frequency components of signals ,therefore,the pa per uses wavelet transform to analyze the time-frequency feature of seismic records. In view of the capability of locally analyzing the signals and providing multi-scale wavelet coefficients that the wavelet transform has, so the wavelet transform can finely be used for reservoir prediction. The wavelet coefficients can be taken as measuring the time-frequency feature of signals that could be further demonstrated by tests of the theoretical models, The paper presented the wavelet parameters of seismic signals being taken as a new kind of seismic attributes,i, e. wavelet attributes. The map of seismic attributes of the targets can be acquired in practice where the lateral variation of reflected feature of targets is known and distribution of reservoirs is detected. The paper considered that like other seismic attributes parameters ,the wavelet attributes belong to multi-attributes analysis.
【Keyword】：time-frequency feature ,wavelet transform,Mallat algorithm, wavelet coefficient ,seismic attributes