可变网格与局部时间步长的交错网格高阶差分弹性波模拟

交错网格高阶差分方法是一种在保持效率的前提下提高弹性波模拟精度的有效方法.本文将可变空间网格与变化的时间步长技术引入到交错网格高阶差分弹性波模拟中,提出一种空间网格可任意奇数倍变化与时间步长任意变化的交错网格高阶差分弹性波模拟方法.一系列数值试验表明,该方法能够在保证模拟精度的同时,通过有效降低空间与时间维度上的过采样来显著提高弹性波模拟的效率.同时,该方法还能够精细刻画含孔缝洞介质以及横向变化剧烈介质的局部细微结构,减小弹性波模拟误差,提高介质细微结构处的弹性波传播模拟精度.     

一种时空域优化的高精度交错网格差分算子与正演模拟

如何有效压制数值频散是有限差分正演模拟研究中的关键问题之一.近年来,许多学者对二阶声波方程的差分算子开展了大量的优化工作,在压制频散方面取得不错的效果.一阶压强-速度方程广泛用于研究地震波在地下变密度模型中传播规律,目前针对一阶方程的优化工作大多只是在空间差分算子上展开.本文在前人研究的基础上,推导出一阶声波方程中压强场与偏振速度场之间的解析关系,据此在传统交错网格基础上给出一种高精度的显式时间递推格式,该递推格式将时间差分与空间差分算子结合在一起,并采用共轭梯度法得到精确时间递推匹配系数,实现时空差分算子的同时优化.在编程实现算法的基础上,通过频散分析与三个典型模型测试表明：本文方法能够较为有效地压制时间频散与空间频散,提高数值计算精度;同时对复杂模型也有很好适用性.     

求解弹性波动方程的频率域近似解析离散化波场模拟方法

为提高频率域弹性波动方程数值求解的计算效率,本文引入近似解析离散化(NAD)方法将其进行数值离散并得到大型线性代数方程组.在详细分析了相应系数矩阵的稀疏分块结构与数学性质之后,本文提出采用不精确旋转分块三角预处理子加速Krylov子空间迭代方法来快速求解该线性方程组,并利用数值试验证实这种方法在弹性波场模拟方面的数值效...     

Accelerating the discontinuous Galerkin method for seismic wave propagation simulations using multiple GPUs with CUDA and MPI

We have successfully ported an arbitrary high-order discontinuous Galerkin method for solving the three-dimensional isotropic elastic wave equation on unstructured tetrahedral meshes to multiple Graphic Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) of NVIDIA and Message Passing Interface (MPI) and obtained a speedup factor of about 28.3 for the single-precision version of our codes and a speedup factor of about 14.9 for the double-precision version. The GPU used in the comparisons is NVIDIA Tesla C2070 Fermi, and the CPU used is Intel Xeon W5660. To effectively overlap inter-process communication with computation, we separate the elements on each subdomain into inner and outer elements and complete the computation on outer elements and fill the MPI buffer first. While the MPI messages travel across the network, the GPU performs computation on inner elements, and all other calculations that do not use information of outer elements from neighboring subdomains. A significant portion of the speedup also comes from a customized matrix–matrix multiplication kernel, which is used extensively throughout our program. Preliminary performance analysis on our parallel GPU codes shows favorable strong and weak scalabilities.