SWprocess: a workflow for developing robust estimates of surface wave dispersion uncertainty
SWprocess: a workflow for developing robust estimates of surface wave dispersion uncertainty
复制标题
DOI：
10.1007/s10950-021-10035-y
复制
发表时间：
2022-04
影响因子：
1.6
通讯作者：
J. Vantassel;B. Cox
中科院分区：
文献类型：
--
作者：
J. Vantassel;B. Cox
非侵入性面波方法越来越多地被用作估计场地小应变横波速度(Vs)的主要技术。然而，与有创方法相比，非有创表面波方法的实践标准变化很大，每个公司/集团/分析师都估计面波频散数据，量化其不确定性(或在许多情况下忽略它)，并以自己独特的方式进行反演以获得VS剖面。作为回应，这项工作提出了一种记录良好、经过生产测试和易于采用的工作流程，用于开发具有稳健不确定性度量的实验表面波频散数据估计。这是将色散不确定性向前传播到由反演得到的V的估计值所需的关键步骤。本文主要讨论面波测试的两种最常见的应用：第一种是只进行有源测试，第二种是既进行有源测试又进行被动波场测试。在这两种情况下，都提供了关于将实验获得的波形转换为对场地面波频散数据的估计并量化其不确定性的步骤的明确指导。特别是，面波数据采集和处理的变化会影响由此产生的实验色散数据，从而突出了它们在量化不确定性时的重要性。此外，这项工作还伴随着一个开放源码的Python包、swprocess和相关的Jupyter工作流，使读者能够轻松采用本文提出的建议。希望这些建议将导致关于制定面波数据采集、处理和反演的实践标准的进一步讨论。
Non-invasive surface wave methods are increasingly being used as the primary technique for estimating a site’s small-strain shear wave velocity (Vs). Yet, in comparison to invasive methods, non-invasive surface wave methods suffer from highly variable standards of practice, with each company/group/analyst estimating surface wave dispersion data, quantifying its uncertainty (or ignoring it in many cases), and performing inversions to obtain Vs profiles in their own unique manner. In response, this work presents a well-documented, production-tested, and easy-to-adopt workflow for developing estimates of experimental surface wave dispersion data with robust measures of uncertainty. This is a key step required for propagating dispersion uncertainty forward into the estimates of Vs derived from inversion. The paper focuses on the two most common applications of surface wave testing: the first, where only active-source testing has been performed, and the second, where both active-source and passive-wavefield testing has been performed. In both cases, clear guidance is provided on the steps to transform experimentally acquired waveforms into estimates of the site’s surface wave dispersion data and quantify its uncertainty. In particular, changes to surface wave data acquisition and processing are shown to affect the resulting experimental dispersion data, thereby highlighting their importance when quantifying uncertainty. In addition, this work is accompanied by an open-source Python package,swprocess, and associated Jupyter workflows to enable the reader to easily adopt the recommendations presented herein. It is hoped that these recommendations will lead to further discussions about developing standards of practice for surface wave data acquisition, processing, and inversion.