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In this paper, we develop a new nearly analytic symplectic partitioned Runge–Kutta method based on locally one-dimensional technique for numerically solving two-dimensional acoustic wave equations. We first split two-dimensional acoustic wave equation into the local one-dimensional equations and transform each of the split equations into a Hamiltonian system. Then, we use both a nearly analytic discrete operator and a central difference operator to approximate the high-order spatial differential operators, which implies the symmetry of the discretized spatial differential operators, and we employ the partitioned second-order symplectic Runge–Kutta method to numerically solve the resulted semi-discrete Hamiltonian ordinary differential equations, which results in fully discretized scheme is symplectic unlike conventional nearly analytic symplectic partitioned Runge–Kutta methods. Theoretical analyses show that the nearly analytic symplectic partitioned Runge–Kutta method based on locally one-dimensional technique exhibits great higher stability limits and less numerical dispersion than the nearly analytic symplectic partitioned Runge–Kutta method. Numerical experiments are conducted to verify advantages of the nearly analytic symplectic partitioned Runge–Kutta method based on locally one-dimensional technique, such as their computational efficiency, stability, numerical dispersion and long-term calculation capability. 相似文献
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本文利用PS反射波探测了雅鲁藏布江北侧局部地区莫霍面的深度和起伏。在亚东—当雄测线的径向方向上记录到一个尚为清楚的PS反射波,视速度为8.35±0.28公里/秒,在同一莫霍面部分使用了PS和PP震相的走时,求得下地壳结构。莫霍面的倾角为-0.83°~0.84°,该面的深度为68~69公里左右,从结果表明该地区莫霍面起伏不大,地壳基本上是水平的。作者认为利用PS反射波测定莫霍面起伏状态是很有用的,值得进一步探讨。 相似文献
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基于地质统计先验信息的储层物性参数同步反演 总被引:3,自引:1,他引:3
本文提出的储层物性参数同步反演是一种高分辨率的非线性反演方法,该方法综合利用岩石物理和地质统计先验信息,在贝叶斯理论框架下,首先通过变差结构分析得到合理的变差函数,进而利用快速傅里叶滑动平均模拟算法(Fast Fourier TransformMoving Average,FFT-MA)和逐渐变形算法(Gradual Deformation Method,GDM)得到基于地质统计学的储层物性参数先验信息,然后根据统计岩石物理模型建立弹性参数与储层物性参数之间的关系,构建似然函数,最终利用Metropolis算法实现后验概率密度的抽样,得到物性参数反演结果。并将此方法处理了中国陆上探区的一块实际资料,本方法的反演结果具有较高的分辨率,与测井数据吻合度较高;由于可以直接反演储层物性参数,避免了误差的累积,大大减少了不确定性的传递,且计算效率较高。 相似文献
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In recent years, surface-wave analysis method has been developed rapidly in many fields. Multichannel analysis of surface waves can provide near-surface one-dimensional shear-wave velocity profiles. Because linearized inversion of surface-wave dispersion curves relies heavily on the choice of the initial model, setting an inappropriate initial model can lead to poor inversion results, or even failure of inversion. However, it is difficult to establish a reasonable initial model without a priori information, which is unavailable in most cases. To cope with this problem, a multiscale linearized inversion method is proposed for surface-wave dispersion curves inversion. In contrast with the traditional single-scale linearized inversion, the key idea of the proposed multiscale surface-wave inversion method is the introduction of a merging and splitting process of layers. After every scale inversion, the merging and splitting operations automatically optimize the inversion model, making it gradually approach to a reasonable subsurface stratification. Multiscale surface-wave inversion method reduces the difficulty of establishing the initial model and has high computational efficiency. In addition, it has strong ability to identify high-velocity or low-velocity interlayers and thin layers, especially suited for the geological conditions with obvious stratification. In synthetic tests, the proposed method was compared with the single-scale surface-wave inversion and particle swarm optimization algorithm to demonstrate the effectiveness and practicability of multiscale surface-wave inversion method. We also applied the multiscale surface-wave inversion method to field seismic data acquired in Guizhou, China and Texas, USA. Borehole and crosshole test data were compared with the inversion results of field data to prove the reliability of the proposed method. 相似文献
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