论文标题
Swinvert：用于执行严格表面波反转的工作流程
SWinvert: A workflow for performing rigorous surface wave inversions
论文作者
论文摘要
Swinvert是在德克萨斯大学奥斯汀分校开发的工作流程，用于表面波散数据的反转。 Swinvert鼓励分析师通过提供系统的过程和开源工具来研究剪切波速度（VS）中的反转不确定性和非独立性。特别是，该工作流程可以使用多个分层参数化来解决反转的非唯一性，对每个参数化的多个全局搜索以解决逆问题的非线性，并量化结果配置文件中的不确定性。为了鼓励采用其采用，Swinvert工作流程得到了开源Python套餐的支持，Swprepost，用于表面波浪反转预处理和后处理，以及在DesignSafe-Cyberinfracture（Swbatch）上的应用，该应用程序可以通过直觉和简单的网络进行批量的表面波动逆转，以实现高表面计算，以实现批量的表面波动逆转。尽管工作流程使用流行的开源地理系统软件的餐室模块作为其反转引擎，但提出的原理可以很容易地扩展到其他反转程序。为了说明Swinvert工作流程的有效性，并开发一组基准用于未来的表面波反转研究中，倒置了12种不同复杂性的地下模型的合成实验分散数据。对于以宽带分散数据为特征的简单地下模型，反转不确定性和非唯一性的效果被证明是最小的，但在带有带限制分散数据的更复杂模型的VS配置文件中，这些效果不能忽略。 Swinvert工作流程显示出有条不紊的过程和一组强大的工具，用于执行严格的表面波反转并量化所得与配置文件中的不确定性。
SWinvert is a workflow developed at The University of Texas at Austin for the inversion of surface wave dispersion data. SWinvert encourages analysts to investigate inversion uncertainty and non-uniqueness in shear wave velocity (Vs) by providing a systematic procedure and open-source tools for surface wave inversion. In particular, the workflow enables the use of multiple layering parameterizations to address the inversion's non-uniqueness, multiple global searches for each parameterization to address the inverse problem's non-linearity, and quantification of Vs uncertainty in the resulting profiles. To encourage its adoption, the SWinvert workflow is supported by an open-source Python package, SWprepost, for surface wave inversion pre- and post-processing and an application on the DesignSafe-CyberInfracture, SWbatch, that enlists high-performance computing for performing batch-style surface wave inversion through an intuitive and easy-to-use web interface. While the workflow uses the Dinver module of the popular open-source Geopsy software as its inversion engine, the principles presented can be readily extended to other inversion programs. To illustrate the effectiveness of the SWinvert workflow and to develop a set of benchmarks for use in future surface wave inversion studies, synthetic experimental dispersion data for 12 subsurface models of varying complexity are inverted. While the effects of inversion uncertainty and non-uniqueness are shown to be minimal for simple subsurface models characterized by broadband dispersion data, these effects cannot be ignored in the Vs profiles derived for more complex models with band-limited dispersion data. The SWinvert workflow is shown to provide a methodical procedure and a powerful set of tools for performing rigorous surface wave inversions and quantifying the uncertainty in the resulting Vs profiles.
