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Multiscale modeling of acoustic wave propagation in 2D heterogeneous media using local spectral basis functions

机译:使用本地谱基函数的2D异构介质声波传播的多尺度建模

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Seismic wave equation modeling is an essential part of all full wavefield based seismic imaging, inversion and data analysis methods. Solving seismic wave equation on highly heterogeneous media is computationally expensive and reduced-order models can be used to speed-up the computations by reducing the degrees of freedom. In this abstract we present a reduced order model that consists of an application of the multiscale finite element method to solving the acoustic wave equation using spectral multiscale basis functions (Enriched MsFEM). Generally, the continuous multiscale basis functions are constructed by solving local spectral problems first and a careful selection of partition of unity functions. We then choose the eigenvectors that correspond to small, asymptotically vanishing eigenvalues to form our approximation spaces. This method efficiently captures the effects of fine scale features of the domain on wave propagation without solving the problem on the fine mesh. The computation of basis functions on different coarse grid blocks is totally independent, which makes this method easy to parallelize. Compared to discontinuous multi-scale methods, the proposed continuous methods have advantages in capturing subgrid effects more accurately; however, the mass matrix resulting in continuous multiscale methods is no longer block-diagonal. Our numerical experiments demonstrate that the method shows higher accuracy compared to traditional MsFEM and can reduce computation time and memory costs.
机译:地震波方程建模是基于全波场的地震成像,反演和数据分析方法的重要组成部分。在高度异构介质上求解地震波方程是计算昂贵的,并且通过减少自由度来加速计算来加速计算。在该摘要中,我们提出了一种减少的订单模型,包括使用光谱多尺度基函数(丰富的MSFEM)来求解声波方程的多尺度有限元方法。通常,通过求解局部光谱问题并仔细选择单位函数的仔细选择来构建连续的多尺度基函数。然后,我们选择对应于小型渐近消失的特征值的特征向量,以形成我们的近似空间。该方法有效地捕获域对波传播上的细尺特征的影响而不解决细网上的问题。不同粗略电网块上的基函数的计算完全独立,这使得该方法易于并行化。与不连续的多尺度方法相比,所提出的连续方法在更准确地捕获底耕效应方面具有优势;然而,产生连续多尺度方法的质量矩阵不再是对角线。我们的数值实验表明,与传统的MSFEM相比,该方法的准确性更高,并且可以降低计算时间和内存成本。

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