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An improved ONAD algorithm based on a modified nearly-analytic discrete operator for solving 2D scalar wave equation

机译:一种改进的基于改进的近天分析离散算子来解决2D标量波动方程的算法

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High resolution seismic imaging requires high accuracy forward numerical modeling method. However, most of existing methods suffer from strong numerical dispersion when a coarse grid is used and energy shifts after long-time calculations. The optimal nearly analytic discrete method (ONADM) can effectively suppress the numerical dispersion, but still leads to serious energy distortion after long time integration. In this paper, to further reduce the numerical dispersion, we suggest an improved spatial approximate operator which is called modified nearly-analytic discrete (MNAD) operator, by minimizing the energy error. The temporal operator uses the traditional central difference. Promising numerical experiments show that: (1) the MNAD can significantly reduce the computational costs because it can provide clear wave fields on a coarse grid like the traditional NAD operator; (2) the MNAD is stable and only has a less than 1.4% energy error after 300-second seismic wave field simulation (150000 temporal iterative steps) on a coarse grid with a spatial step of 30m; (3) For complex models, like the SEG/Salt model, MNAD can also provide clearer wave fields without any visible numerical dispersion.
机译:高分辨率地震成像需要高精度前向数值建模方法。然而,当使用粗略网格并且在长时间计算后的能量移位时,大多数现有方法遭受强大的数值分散体。最佳的几乎分析离散方法(onadm)可以有效地抑制数值分散,但在长时间集成后仍然导致严重的能量失真。在本文中,为了进一步降低数值分散,我们建议一种改进的空间近似算子,通过最小化能量误差来称为改进的几乎分析离散(Mnad)操作员。时间运算符使用传统的中心区别。有前途的数值实验表明:(1)MNAD可以显着降低计算成本,因为它可以在像传统的NAD操作员这样的粗网上提供清晰的波浪场; (2)MNAD稳定,在粗网上在300秒的地震波场仿真(150000次迭代步骤)上仅具有30米的空间步长,仅具有小于1.4%的能量误差; (3)对于复杂的模型,如SEG /盐模型,MNAD还可以提供更清晰的波场而没有任何可见的数值分散体。

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