变参考慢度Born近似傅氏偏移

针对常规Born近似傅氏偏移方法对于剧烈横向变速介质不能精确成像的状况而提出了变参考慢度Born近似傅氏偏移，理论上解决了任意速度变化地质模型的偏移成像问题。此外，为进一步提高复杂地层的成像精度和波场延拓算子的稳定性，对散射波场的计算公式作了改进。将改进的方法运用于盐丘模型的正演和偏移试验，并与常规Born近似偏移方法相比较，可明显看出变参考慢度Born近似傅氏偏移方法在效果上要优于后者，其处理速度横向变化的能力大大增强。     

基于纵横波保幅分离的粘弹介质弹性波正演模拟

地下介质往往表现为粘弹特性,研究基于粘弹假设的数值模拟方法对于正确认识地震波的传播规律和提高地震勘探精度具有重要意义。波动方程正演过程中纵横波的保幅解耦是研究准确的粘弹介质中地震波传播机理的前提,基于散度和旋度算子的纵横波解耦方法会使波场的相位和振幅产生畸变,且解耦后的波场在极性反转位置上无法与分离前混合波场各分量对应。在散度和旋度算子上再做一次梯度和旋度运算的波场分离方法虽然能够克服上述缺陷,但存在保幅性差等问题。本文从粘弹介质中的一阶速度-应力方程出发,推导了矢量纵横波分离的波数域表达式,结合有限差分思路给出了其在空间域的求解方法。本文方法利用纵横波的传播速度对现有的矢量波场分离方法进行振幅校正,并将校正结果分别作为纵波与横波对时间的二阶偏导,实现了粘弹介质中的纵横波分离。模型试算结果表明,本文方法能够克服现有方法的缺陷,获得更具保幅性的波场分离结果。     

线性粘弹介质中地震波场数值模拟

本文将以往两种粘弹介质中地震波模拟方法的优点结合起来，以模型理论和积分本构方程为基础，从理论上分析了模型对地震波场的影响；采用交错网格有限差分法对粘弹介质中的地震波进行数值模拟.数值计算结果表明该方法不仅便于计算，同时也便于从力学的角度来分析地震波的传播.数值计算结果与理论分析一致，说明这种方法可以更为有效地模拟粘弹介质中地震波的传播.     

Wavefield continuation datuming using a near surface model

When topography and low velocity zone differences vary greatly, conventional vertical static time shifts will cause wavefield distortion and influence wave equation seismic imaging for seismic data acquired on a complex near surface. In this paper, we propose an approach to datum correction that combines a joint tomography inversion with wavefield continuation to solve the static problem for seismic data on rugged acquisition topography. First, the near surface model is obtained by refracted wave tomography inversion. Second, the wavefield of sources and receivers are continued downward and upward to accomplish datum correction starting from a flat surface and locating the datum above topography. Based on the reciprocal theorem, Huygens＇ and Fresnel principles, the location of sources and receivers, and regarding the recorded data on the surface as a secondary emission, the sources and receivers are upward-continued to the datum above topography respectively. Thus, the datum correction using joint tomography inversion and wavefield continuation with the condition of a complex near surface is accomplished.     

A sequential sparse polynomial chaos expansion using Bayesian regression for geotechnical reliability estimations

Polynomial chaos expansions (PCEs) have been widely employed to estimate failure probabilities in geotechnical engineering. However, PCEs suffer from two deficiencies: (a) PCE coefficients are solved by the least-square minimization method which easily causes overfitting issues; (b) building a high order PCE is often computationally expensive. In order to overcome the aforementioned drawbacks, the Bayesian regression technique is employed to evaluate PCE coefficients, which not only provides a sparse solution but also avoids overfitting. With the aid of the predictive means and variances given by Bayesian analysis, a learning function is proposed to sequentially select the most informative samples that are critical to build a PCE. This sequential learning scheme can highly enhance the computational efficiency of PCEs. Besides, importance sampling (IS) is incorporated into the sequential learning (SL)-PCEs to deal with geotechnical problems with small failure probabilities. The proposed method of SL-PCE-IS is applied to three illustrative examples, which shows that the improved PCE method is more effective and efficient than the common PCEs method, leading to accurate estimations of small failure probabilities using fewer training samples.