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Inversion on S-Wave Velocity Structure of Shallow Soil Layer Site using Parallel Genetic Algorithm

机译:使用并行遗传算法在浅层土段现场的S波速度结构中的反演

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摘要

Microtremors was developed to inverse S-wave velocity structure of sites because it costs little and easy to monitor, can be performed at any place even in a densely populated city with non-destructive measurements, the genetic algorithm is widely used in inversion and there are many disadvantages in using genetic algorithms to solve practical problems, so the authors did a lot of efforts to overcome these disadvantages. In order to solve these disadvantages, a coarse-grained parallel genetic algorithm(PGA) based on personal computer cluster was proposed to inverse S-wave velocity structure of shallow soil layer of actual engineering sites, the simulated annealing algorithm and parallel technique message passing interface(MPI) were adopted to implement the coarse-grained parallel compute. The subpopulations were collaboratively optimized through individual migration strategy and the dynamic populations were adopted to balance the computing load. The shallow S-wave velocity structures of two examples and the actual engineering sites were inversed through a 4-node PC cluster test system, the results showed that the algorithm has a good parallel efficiency and can be used in engineering site.
机译:MicroTremors开发出逆S波速度结构,因为它几乎且易于监测,即使在具有非破坏性测量的密集城市中,可以在任何一个地方进行,遗传算法广泛用于反转和存在使用遗传算法解决实际问题的许多缺点,因此作者对克服这些缺点进行了很多努力。为了解决这些缺点,基于个人计算机集群的粗粒并行遗传算法(PGA)被提出为实际工程站点的浅层土层的S波速度结构,模拟退火算法和并行技术消息传递接口(MPI)被采用以实施粗粒粒度并行计算。通过各个迁移策略协同优化亚步骤,并采用动态群体来平衡计算负荷。两个示例和实际工程站点的浅S波速度结构通过4节点PC集群测试系统逆转,结果表明该算法具有良好的平行效率,可用于工程部位。

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