应用遗传算法估计非稳态地震数据的混合相位子波及Q值  

Estimation of mixed⁃phase wavelet and Q value of nonstationary seismic data using genetic algorithm

作　　者：朱耀旭 包乾宗[1,2,3] ZHU Yaoxu;BAO Qianzong(Faculty of Geophysics,College of Geological Engineering and Geomatics,Chang’an University,Xi’an,Shaanxi 710054,China;National Engineering Research Center of Offshore Oil and Gas Exploration,Beijing 100028,China;Key Labora-tory of Geological Disaster Mechanism and Prevention,Ministry of Natural Resources,Xi’an,Shaanxi 710054,China)

机构地区：[1]长安大学地质工程与测绘学院地球物理系,陕西西安710054 [2]海洋油气勘探国家工程研究中心,北京100028 [3]自然资源部矿山地质灾害成灾机理与防控重点实验室,陕西西安710054

出　　处：《石油地球物理勘探》2024年第4期763-770,共8页Oil Geophysical Prospecting

基　　金：国家重点研发计划项目“黄土地质体/灾害体结构和物性参数大范围快速探测技术装备”(2022YFC3003402)资助。

摘　　要：地震高分辨率处理或反演的目的是获得精确的反射系数或弹性参数模型。然而地层滤波效应模糊了地震记录中的地层反射信息,因此有必要消除这种滤波效应。现有的大部分提高分辨率方法并不能完全摒弃关于地震子波和Q模型的一些假设。为了获得更为切合实际的地震子波,同时自适应获取Q模型,文中将地震子波相位估计与Q模型估计相结合,提出了基于遗传算法的非稳态地震数据混合相位子波及Q值估计方法。首先通过井旁道记录拟合得到初始地震子波的振幅信息,然后依据子波Z变换的根关于单位圆移动与否构建用于遗传算法的编码链条。另一方面,十进制Q模型对应的二进制表示形式同样能够利用编码链条表征,因此利用全局优化算法能够同时估计地震混合相位子波以及Q模型。结合根变换和遗传算法不断改变子波相位的同时自适应生成Q模型,利用子波和Q模型构造的时变子波矩阵与测井反射系数得到合成地震记录,并与井旁道记录进行匹配,最终得到合理的混合相位子波和地层Q模型,进而构造时变子波矩阵进行时变反褶积。通过井旁道记录与测井数据拟合得到的混合相位子波,与实际地震子波相位更为接近,理论数据和实际数据处理结果证实了该方法的有效性。Seismic high‑resolution processing or inversion is aimed to obtain an accurate reflection coefficient or elastic parameter model.However,the formation filtering effect blurs the formation reflection information in seismic records,so it is necessary to eliminate this filtering effect.Most of the existing resolution enhancement methods can not completely discard some assumptions about seismic wavelet and Q model.In order to obtain more realistic seismic wavelets,while simultaneously adaptively acquiring the Q model,this paper combines the seismic wavelet phase estimation with the Q model estimation,proposing an estimation method of mixed‑phase wavelet and Q value for nonstationary seismic data based on genetic algorithm.First,the amplitude information of the initial seismic wavelet is obtained by fitting the well‑side seismic record.Then the coding chain for genetic algorithm is constructed based on whether the root of the wavelet Z transform moves with the unit circle or not.On the other hand,the binary representation corresponding to the decimal Q model can also be characterized by coded chain,so the global optimization algorithm can be used to simultaneously estimate the seismic mixed‑phase wavelet and the Q model.Combining root transformation with genetic algorithms can continuously adjust the phase of the wavelet while adaptively generating the Q model.The time‑varying wavelet matrix,along with well logging reflection coefficients,are used to obtain the synthetic seismic record,which is matched with the well‑side seismic record.Finally,a reasonable mixed phase wavelet and formation Q model are obtained,and then the time‑varying wavelet matrix is constructed for time‑varying deconvolution.The phase of mixed‑phase wavelet obtained by fitting the well‑side seismic records and logging data is closer to the actual seismic wavelet.The theoretical data and practical data processing results confirm the effectiveness of this method.

关 键 词：高分辨率处理 品质因子 非稳态地震数据 混合相位子波 遗传算法 

分 类 号：P631[天文地球—地质矿产勘探]