Automatic 1-D waveform inversion of marine seismic refraction data
Automatic 1-D waveform inversion of marine seismic refraction data
复制标题
海洋地震折射数据的自动一维波形反演
DOI：
10.1111/j.1365-246x.1988.tb03879.x
复制
发表时间：
1988
影响因子：
2.8
通讯作者：
C. Chapman
中科院分区：
文献类型：
--
作者：
P. Cary;C. Chapman
总结
全波形合成地震图现在被用作解释海洋折射数据的标准，但只有经过费力的试错拟合过程。我们展示了如何匹配观测波形与WKBJ合成地震记录可以有效地和自动地执行1-D地球模型。由于数据与合成体的不匹配具有多模态、不规则的形式，使得反演的自动化比较困难。一个完整的反演需要三个步骤的序列：失配函数的最深谷的位置，下降到全局最小值，并描述最小值的邻域进行误差分析。第一步是通过一个非常大的模型空间定义的解决方案的速度和梯度的先验知识差的Monte Carlo搜索完成。弱走时约束用于从波形计算中消除拟合不佳的模型。蒙特卡洛模型的走时和波形失配的比较清楚地表明，波形提供了比走时更多的信息。使用贝叶斯统计来构造边缘概率分布和协方差矩阵，从而给出粗略的初步误差分析。在第二步中，阻尼最小二乘线性化反演在最佳拟合蒙特卡罗模型下降到全局最小值时对最佳拟合蒙特卡罗模型进行小幅调整。最后用约束最小二乘反演方法研究了全局最小值的直接邻域。每个参数的实际误差界限是通过失配函数从所得切片定义的。这些界限比走时反演提供的要窄得多。参数之间的相关性是从协方差矩阵中获得的，协方差矩阵是根据在此误差分析期间检查的模型构建的。反演方法的说明上的FF 2折射数据集的斯克里普斯海洋研究所。蒙特卡洛搜索成功地定位了全球最低谷以及附近的次最低。误差分析提出了现实的误差范围的详细反演这一数据集的Spudich和Orcutt。
SUMMARY
Full waveform synthetic seismograms are now used as standard in the interpretation of marine refraction data, but only with a laborious trial-and-error fitting procedure. We show how the matching of observed waveforms with WKBJ synthetic seismograms can be efficiently and automatically performed for 1-D earth models. Automation of the inversion is difficult because of the multi-modal, irregular form of the misfit of data and synthetics. A sequence of three steps is required for a complete inversion: location of the deepest valley of the misfit function, descent to the global minimum, and description of the neighbourhood of the minimum for error analysis. The first step is accomplished with a Monte Carlo search through a very large model space defined by poor prior knowledge of velocities and gradients of the solution. Weak traveltime constraints are used to eliminate poorly fitting models from waveform calculations. A comparison of the traveltime and waveform misfits of the Monte Carlo models clearly illustrates that waveforms are providing more information than traveltimes alone. Bayesian statistics are used to construct marginal probability distributions and the covariance matrix, which give a rough preliminary error analysis. In the second step, damped least-squares linearized inversion makes small adjustments to the best-fitting Monte Carlo model as it descends to the global minimum. Finally the immediate neighbourhood of the global minimum is explored with constrained least-squares inversion. Realistic error bounds on each parameter are defined from the resulting slices through the misfit function. These bounds are much narrower than traveltime inversion provides. Correlations between parameters are obtained from the covariance matrix constructed from models examined during this error analysis. The inversion methods are illustrated on the FF2 refraction data set of the Scripps Institution of Oceanography. The Monte Carlo search successfully locates the valley of the global minimum as well as a nearby secondary minimum. The error analysis puts realistic error bounds on the detailed inversion of this data set by Spudich & Orcutt.