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Development of efficient and robust Eikonal solver variants for first-arrival seismic modeling
Computational Geosciences ( IF 2.1 ) Pub Date : 2020-11-13 , DOI: 10.1007/s10596-020-10010-5
Zagid Abatchev , Gary Binder , Paul Davis

We have developed and tested a new Eikonal first-arrival forward model scheme by combining a fast marching method (FMM) algorithm (Sethian et al. Proc. SPIE 2726 optical microlithography IX 1996); Sethian et al. Geophys., pp. 17781781 1997); Sethian et al. SIAM Rev. 41(2), 199235 1999), an upwind Eikonal solver scheme first described by Sethian and Popovici in 1996, with a more accurate but less robust Eikonal scheme described by Vidale in 1990 (Geophysics 55(5), 521526 1990) as a finite difference method (VFD) in order to produce a robust forward model based in FMM, with application of VFD schemes for refinement of computed model times (FMM-VFD). The developed model was tested through a uniform velocity base case, and the Southern California Earthquake Center (SCEC) Harvard Velocity Model (CVM-H) (https://scec.usc.edu/scecpedia/CVM-H) of upper crust including the sedimentary basin under Los Angeles, CA. Its performance was evaluated by measuring error at different grid sizes against a set of reference times generated by an independent model. In comparison against first-order FMM (FMM-O1), second-order FMM (FMM-O2), and an improved FMM scheme known as factored FMM (FMM-F) (Fomel et al. J. Comput. Phys. 228(17), 64406455 2009; Treister et al. J. Comput. Phys. 324, 210225 2016) against a set of generated reference times, FMM-VFD error was found to be significantly lower than first- and second-order FMM, and competitive with FMM-F, outperforming it in the presented scenarios.



中文翻译:

开发用于首次到达地震建模的有效而强大的Eikonal求解器变体

我们已经通过结合快速行进方法(FMM)算法(Sethian等人,Proc。SPIE 2726光学微光刻IX,1996)开发并测试了一种新的Eikonal初到正向模型方案。Sethian等。地理物理学,Pp。17781781 1997);Sethian等。SIAM Rev. 41(2),199235 1999),是上风的Eikonal求解器方案,由Sethian和Popovici于1996年首次描述,而更精确却不那么健壮的Eikonal方案由Vidale在1990年描述(地球物理学55(5),521526 1990)为了产生基于FMM的鲁棒正向模型,使用VFD方案来完善计算的模型时间(FMM-VFD),它是一种有限差分方法(VFD)。通过均匀速度基本情况和南加州地震中心(SCEC)哈佛速度模型(CVM-H)(https://scec.usc)对开发的模型进行了测试。edu / scecpedia / CVM-H),包括加利福尼亚下洛杉矶的沉积盆地在内的上地壳。通过根据独立模型生成的一组参考时间测量不同网格大小的误差来评估其性能。与一阶FMM(FMM-O1),二阶FMM(FMM-O2)和称为因子分解FMM(FMM-F)的改进FMM方案相比(Fomel等人,J。Comput。Phys。228( 17),64406455 2009; Treister等人,J。Comput。Phys。324,210225 2016)针对一组生成的参考时间,发现FMM-VFD误差明显低于一阶和二阶FMM,并且具有竞争力与FMM-F相比,在当前方案中的表现要好。通过根据独立模型生成的一组参考时间测量不同网格大小的误差来评估其性能。与一阶FMM(FMM-O1),二阶FMM(FMM-O2)和称为因子分解FMM(FMM-F)的改进FMM方案相比(Fomel等人,J。Comput。Phys。228( 17),64406455 2009; Treister等人,J。Comput。Phys。324,210225 2016)针对一组生成的参考时间,发现FMM-VFD误差明显低于一阶和二阶FMM,并且具有竞争力与FMM-F相比,在当前方案中的表现要好。通过根据独立模型生成的一组参考时间测量不同网格大小的误差来评估其性能。与一阶FMM(FMM-O1),二阶FMM(FMM-O2)和称为因子分解FMM(FMM-F)的改进FMM方案相比(Fomel等人,J。Comput。Phys。228( 17),64406455 2009; Treister等人,J。Comput。Phys。324,210225 2016)针对一组生成的参考时间,发现FMM-VFD误差明显低于一阶和二阶FMM,并且具有竞争力与FMM-F相比,在当前方案中的表现要好。

更新日期:2020-11-13
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