基于VSP直达波资料的粘弹介质参数遗传算法反演

粘弹性参数变得越来越重要,其反演算法也逐渐成为众多研究者的研究热点。而遗传算法是一种随机、自适应、启发式的算法, 具有很好的鲁棒性和全局收敛性, 本文基于VSP直达波方程,引入了遗传算法来进行粘弹参数反演, 首先将频率域直达波方程表示为复速度的函数,然后通过遗传算法反演出复速度。而复速度和品质因子又是复速度的函数,从而便可很容易的得出。但若直接反演复速度, 反演参数太多, 不容易实现, 所以又将复速度表示成参数C0和C∞的函数,以减少反演参数数量。最后给出了理论模型实验,以证明该算法的有效性。     

The inversion of anelastic coefficient,source parameters and site respond using genetic algorithm

It gradually becomes a common work using large seismic wave data to obtain source parameters, such as seismic moment, break radius, stress drop, with completingof digital seismic network in China (Hough, et al, 1999; Bindi, et al, 2001). These parameters are useful on earthquake prediction and seismic hazard analysis.Although the computation methods of source parameters are simple in principle and the many research works have been done, it is not easy to obtain the parameters accurately. There are two factors affecting the stability of computation results. The first one is the effect of spread path and site respond on signal. According to the research results, there are different geometrical spreading coefficients on different epicenter distance. The better method is to introduce trilinear geometrical spreading model (Atkinson, Mereu, 1992; Atkinson, Boore, 1995; WONG, et al, 2002). In addition, traditional site respond is estimated by comparing with rock station, such as linear inversion method (Andrews, 1982), but the comparative estimation will introduce some errors when selecting different stations. Some recent research results show that site respond is not flat for rock station (Moya, et al, 2000; ZHANG,. et al, 2001; JIN, et al, 2000; Dutta, et al, 2001). The second factor is to obtain low-frequency level and corner frequency fromdisplacement spectrum. Because the source spectrum model is nonlinear function,these values are obtained by eye. The subjectivity is strong. The small change of corner frequency will affect significantly the result of stress drop.     

基于QAPM矿物模型遗传算法评价火成岩储层

在松辽盆地深层发现了含气火成岩储层。由于火成岩矿物组成复杂和含量的变化,使得选择用于测井评价的解释参数很困难。基于IUGS提出的QAPF分类方案,本文提出了采用遗传算法,利用测井数据确定火成岩矿物含量的方法。根据QAPF分类方案,将火成岩中的矿物分为五类:Q-石英;A-碱性长石;P-斜长石和方柱石;F-副长石(研究区未出现);M-铁镁矿物。本文提出用包括孔隙度在内的QAPM模型对储层进行分析。建立密度、视中子孔隙度、声波时差、自然伽玛和体积光电吸收截面指数的测井响应方程,各矿物参数从斯伦贝谢的矿物参数手册中得到。用遗传算法计算骨架中四种矿物的体积,根据四种矿物的体积含量,依据QAPF分类对火成岩命名。基于解释参数计算的孔隙度可与岩心分析的孔隙度相比,本文给出的火成岩命名与岩心化学分析的命名相一致。