Non-invasive surface wave methods have become a popular alternative to traditional invasive forms of site-characterization for inferring a site's subsurface shear wave velocity (Vs) structure. The advantage of surface wave methods over traditional forms of site characterization is that measurements made solely at the ground surface can be used routinely and economically to infer the subsurface structure of a site to depths of engineering interest (20–50 m), and much greater depths (>1 km) in some special cases. However, the quantification and propagation of uncertainties from surface wave measurements into the Vs profiles used in subsequent engineering analyses remains challenging. While this has been the focus of much work in recent years, and while considerable progress has been made, no approach for doing so has been widely accepted, leading analysts to either address the propagation of uncertainties in their own specialized manner or, worse, to ignore these uncertainties entirely. In response, this paper presents a new, effective, and verifiable method for developing uncertainty-consistent Vs profiles from inversion of surface wave dispersion data. We begin by examining four approaches presented in the literature for developing suites of Vs profiles meant to account for uncertainty present in the measured dispersion data. These methods are shown to be deficient in three specific ways. First, all approaches are shown to be highly sensitive to their many user-defined inversion input parameters, making it difficult/impossible for them to be performed repeatedly by different analysts. Second, the suites of inverted Vs profiles, when viewed in terms of their implied theoretical dispersion curves, are shown to significantly underestimate the uncertainty present in the experimental dispersion data, though some may appear satisfactory when viewed purely qualitatively. Third, if the uncertainties in the implied theoretical dispersion curves were to be examined quantitatively, which has not been done previously, there is no obvious remedy available for the analyst to resolve any inconsistency between the measured and inverted dispersion uncertainty. Therefore, a new approach is proposed that seeks to remedy these shortcomings. First, beyond appropriate considerations that must be given to all inversions, the new approach is governed by only one user-defined input parameter, to which it is not overly sensitive. Second, the new approach is shown to produce suites of Vs profiles whose theoretical dispersion curves quantitatively reproduce the uncertainties in the experimental dispersion data. Third, the final step of the procedure requires the analyst to compare the measured and inverted dispersion uncertainties quantitatively, and should the analyst find the results of the new approach to be lacking, clear guidance is provided on the additional actions necessary to produce Vs profiles whose theoretical dispersion curves better account for the experimental uncertainty. Using two synthetic tests and a real-world example, the procedure is shown to produce suites of Vs profiles that accurately capture the site's Vs structure, while rigorously propagating the dispersion data's uncertainty through the inversion process.

非侵入性表面波方法已经成为代替传统侵入性形式的场所特征的一种流行替代方法，用于推断场所的地下剪切波速度（Vs）结构。相对于传统形式的站点表征，表面波方法的优势在于，可以常规且经济地使用仅在地面上进行的测量将站点的地下结构推断为工程感兴趣的深度（20–50 m），甚至更大。某些特殊情况下的深度（> 1 km）。但是，从表面波测量到随后的工程分析中使用的Vs轮廓的不确定性的量化和传播仍然具有挑战性。尽管近年来这已成为许多工作的重点，并且虽然已取得了相当大的进展，但尚未广泛接受这样做的方法，导致分析人员要么以自己的专业方式解决不确定性的蔓延，要么更糟的是完全忽略这些不确定性。作为回应，本文提出了一种新的，有效的，可验证的方法，该方法可通过反演表面波频散数据来开发不确定性一致的Vs剖面。我们首先研究文献中提出的用于开发Vs曲线套件的四种方法，以解决测量的色散数据中存在的不确定性。这些方法显示出在三种特定方式上的缺陷。首先，所有方法对许多用户定义的反演输入参数都非常敏感，因此很难/不可能由不同分析人员重复执行。第二，倒置的Vs轮廓套件，从隐含的理论弥散曲线的角度来看，虽然明显地低估了实验弥散数据中存在的不确定性，但从纯定性的角度来看可能有些令人满意。第三，如果要定量检查隐含的理论色散曲线中的不确定性（以前没有做过），则分析师没有明显的补救方法可用来解决所测得的色散不确定度和倒置色散不确定度之间的任何不一致之处。因此，提出了一种新的方法，试图弥补这些缺点。首先，除了必须对所有反演给出适当的考虑之外，新方法仅由一个用户定义的输入参数控制，该参数对它不太敏感。第二，该新方法显示出可以产生一组Vs曲线，其理论弥散曲线定量地再现了实验弥散数据中的不确定性。第三，程序的最后一步要求分析人员定量比较测得的和倒置的色散不确定度，如果分析人员发现缺乏新方法的结果，则应提供明确的指导，以提供产生Vs轮廓所需的其他操作理论色散曲线可以更好地说明实验的不确定性。使用两个综合测试和一个实际示例，该程序显示出可生成一组Vs轮廓，这些轮廓可精确捕获站点的Vs结构，同时通过反演过程严格传播色散数据的不确定性。