三维叠前深度偏移速度分析及蒙特卡洛自动层速度拾取
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摘要
是否能够正确地建立深度域三维速度模型是三维叠前深度偏移成败的关键 .本文根据Deregowski循环 ,利用叠前深度域地震成像对速度模型变化的敏感性 ,采用偏移迭代逐次逼近最佳成像速度 ,研究开发了一套快捷有效的三维叠前深度偏移深度域速度模型建立技术 .借鉴时间域CDP(共深度点 )道集上常规叠加速度分析的策略 ,在深度域CRP(共反射点 )道集上 ,提出剩余慢度平方谱的概念并建立相应的实现技术 .导出深度域中均方根速度与层速度之间的关系 ;按照串级偏移原理确定偏移循环过程中初始速度、剩余速度及修改后速度之间的关系 ;采用蒙特卡洛非线性优化算法实现从剩余慢度平方谱中自动拾取层速度 ,讨论了其地质速度约束条件和蒙特卡洛非线性优化的收敛准则 ,使得所拾取的层速度模型具有合理的地质意义并获得最佳偏移成像效果 .SEG EAGE理论模型数值试算验证了方法的有效性 ,在海拉尔盆地霍多莫尔工区 ,5 8km2 三维资料的速度模型建立并获得满意的三维叠前深度偏移成像 .
The key issue for the success of 3 D pre stack depth migration is how to make a reasonable interval velocity model in depth. In this paper, a robust method has been developed to effectively set up velocity depth models. According to Deregowski loop, the best migration velocity can be approached by cascade migration based on the sensibility of seismic imaging to velocity variation. Within CRP gather, the concept of squared residual slowness semblance was first proposed that is similar with the routine stack velocity semblance of CDP gather in time, and it had been put into practice. The relationship between stack velocity and interval velocity was deduced in depth. On cascade migration, the initial velocity, residual velocity and modified velocity are mathematically united during the migration iterations. Within squared residual slowness semblance, the velocity picking is automatically achieved by Monte Carlo nonlinear optimizing. In order to make the picked velocity reasonable in geology, velocity constraints are given in velocity picking, and convergence criteria are forced during the Monte Carlo nonlinear optimizing. Data tests with SEG EAGE data and 58km 2 Hailaer 3 D seismic data have shown good prospects of this method, some improved seismic imaging and accurate velocity models were achieved. This algorithm can be used to quickly establish a velocity model in depth, and it will also reduce the processing costs, that makes the method suitable for velocity analysis of large area 3 D survey. [
引文
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