基于高阶统计复倒谱子波提取

WAVELET EXTRACTION BASED ON COMPLEX CEPTRUM OF HIGH-ORDER STATISTICS

子波反褶积是提高地震资料分辨率的主要方法之一,其关键在于子波的准确性。常规处理方法通常假设子波是最小相位的,但这是不符合实际的,针对这一局限性,利用高阶统计量包含系统相位信息以及抑制高斯噪声的特性,研究了基于复倒谱子波提取方法,分别给出了复倒双谱和复倒三谱提取子波的原理,并构造了平滑窗函数来提高子波估计的准确性。该方法无需对子波相位作任何假设,可估计任意相位子波。针对混合相位子波,进行了理论试算,验证了该方法的有效性,最后对实际资料处理的结果也说明了该方法的实际应用价值。

Wavelet deconvolution is one of the main methods to improve the resolution of seismic data, and the key is the accuracy of the wavelet. Conventional methods usually presume that the wavelet is the minimum phase, but this may not be correct. To solve this problem, the authors utilized the property of the higher order statistic quantity that it includes phase information of the system and sup- presses Gaussian noise to study the wavelet extraction method based on cepstrum and, as a result, gave the principles of extracted wave- let based on complex cepstrum, bispectrum and trispectra. In addition the authors constructed a smooth window function to improve the accuracy of wavelet. The method does not need to make any assumption about wavelet＇s phase, and it can extract wavelet of any phase. Tackling the mixed wavelet, the authors made theoretical experiments to verify the effectiveness of the method, and the results of actual data processing showed the effectiveness of the method.