Erratum: Layering ratios: a systematic approach to the inversion of surface wave data in the absence of a-priori information

Cox, Brady R.; Teague, David P.

doi:10.1093/gji/ggx319

Cited by 15 publications

(37 citation statements)

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“…The present study expands on the results presented in Cox and Teague (2016) [15]. Specifically, linear-elastic and equivalent-linear site response analyses using both low-and highintensity input ground motions have been performed on Vs profiles from the semi-synthetic blind-study site inverted in Cox and Teague (2016) [15]. Vs profiles considered include those that were developed from under-, over-, and adequately-constrained inversion parameterizations.…”

Section: Introductionmentioning

confidence: 93%

“…While it is commonly assumed that there is less uncertainty associated with invasive/borehole methods, a recent, comprehensive, blind-analysis study at three geologically-distinct sites in Europe revealed that Vs profiles derived from surface wave testing had coefficients of variation that were similar to, and at times lower than, those derived from a combination of crosshole, downhole and suspension logging (Garafalo et al 2016a [9], Garafalo et al 2016b [10]). Nonetheless, when Vs profiles are derived from an inversion of surface wave data, one must acknowledge that the solution is non-unique (Lai et al 2005 [11], Cornou et al 2009 [12], Cox et al 2014 [13], Garafalo et al 2016a [9]), and the uncertainties may be significant, particularly if results are reported without performing a systematic, rigorous investigation of different trial subsurface layering models (DiGiulio et al 2012 [14], Cox and Teague 2016 [15]).…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the model solution for deeper layers is dependent on the model solution for shallow layers, resulting in a mix-determined problem. As a result, a number of significantly different layered earth models may possess theoretical dispersion curves that fit the experimental data equally well (Foti et al 2009 [16], Cox and Teague 2016 [15]).…”

Section: Introductionmentioning

confidence: 99%

“…In each case, the inversion algorithm was restricted to a single, predefined number of layers and ranges in their respective properties. As mentioned above, the inversion parameterization has been shown to significantly impact the results of a surface wave inversion (DiGiulio et al 2012 [14], Cox and Teague 2016 [15]). This is because the number of unknowns, which is controlled by the number of layers in the parameterization, is itself an unknown.…”

Section: Introductionmentioning

confidence: 99%

“…Cox and Teague (2016) [15] outlined a systematic procedure to investigate potential inversion parameterizations, each with a unique number of layers defined by a specific layering ratio. This procedure aids in avoiding blatantly under-and over-constrained parameterizations.…”

Section: Introductionmentioning

confidence: 99%

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Site response implications associated with using non-unique Vs profiles from surface wave inversion in comparison with other commonly used methods of accounting for Vs uncertainty

Teague

Cox

2016

Soil Dynamics and Earthquake Engineering

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This paper discusses variability and accuracy of site response predictions performed using shear wave velocity (Vs) profiles derived from non-unique surface wave inversions and other commonly used statistical methods of accounting for epistemic uncertainty and aleatory variability in Vs. Specifically, linear and equivalent linear site response analyses were performed on the following three classes of Vs profiles: (1) 350 Vs profiles developed by performing multiple surface wave inversions, each with a unique set of layering parameters, on a common dispersion dataset, (2) two upper/lower range base-case Vs profiles developed by systematically increasing or decreasing the solution Vs profile by 20%, and (3) 100 Vs profiles developed using the Vs randomization procedure proposed by Toro (1995). Vs profiles derived from surface wave inversions generally yielded accurate site response estimates with minimal variability, so long as their theoretical dispersion data fit the experimental dispersion data well. On the other hand, the upper/lower range and randomized Vs profiles generally produced inaccurate and highly variable site response predictions, although the inclusion of site-specific parameters in the randomization model improved the results. At real sites where substantial aleatory variability is anticipated and/or the epistemic uncertainty is quite high, the site response estimates associated with the randomized and/or upper/lower range Vs profiles may be deemed acceptable. However, if the experimental dispersion data and horizontal-to-vertical spectral ratios are shown to be consistent over the footprint of a site, it may be possible to significantly reduce the uncertainty associated with the input Vs profile and the resulting uncertainty in the site response.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Site response implications associated with using non-unique Vs profiles from surface wave inversion in comparison with other commonly used methods of accounting for Vs uncertainty

Teague

Cox

2016

Soil Dynamics and Earthquake Engineering

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Forward modeling of Rayleigh surface waves for analytical characterization of dominant dispersion trends

Lin

Ashlock

et al. 2021

Earthq Engng Struct Dyn

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Forward modeling is of critical importance for inversion analysis of surface wave methods to obtain shear‐wave velocity (VS) profiles of soil sites. The dynamic stiffness matrix (DSM) method can provide forward modeling of Rayleigh surface waves to simulate complex wave propagation in layered soil sites. However, contamination from body waves and interference of multiple Rayleigh wave modes can reduce the accuracy of theoretical dispersion curves, especially at irregular soil sites with embedded low‐velocity or high‐velocity layers. An analytical method is developed herein to combine the techniques of the multichannel analysis of surface waves method with the DSM method to improve the accuracy of the theoretical dispersion analysis for soil sites. The proposed method implements multichannel analysis of the analytical displacement responses to capture dominant dispersion trends. Comparison of the results obtained with the new method against those from the transfer matrix method and the literature indicates that the new method can (1) effectively minimize the effects of contamination caused by body waves and interference from several Rayleigh wave modes, and (2) generate accurate dominant dispersion trends for soil sites with various stiffness profiles, especially for the high‐frequency dispersion characteristics of the profiles with embedded low‐velocity layers.

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A hybrid residual neural network–Monte Carlo approach to invert surface wave dispersion data

Aleardi

Stucchi

2021

Near Surface Geophysics

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Surface-wave inversion is a non-linear and ill-conditioned problem usually solved through deterministic or global optimization approaches. Here, we present an alternative method based on machine learning. Under the assumption of a local onedimensional model, we train a residual neural network to predict the non-linear mapping between the full dispersion image and the model space, parameterized in terms of shear wave velocity and layer thicknesses. On the one hand, compared to standard convolutional neural networks, the residual network prevents the vanishing gradient problem when training a deep network. On the other hand, the use of the full dispersion image avoids the time-consuming and often ambiguous picking procedure and allows considering higher modes in the inversion framework. One key aspect of any machine learning inversion strategy is the definition of an appropriate training set. In this case, the models forming the training and validation examples are uniformly drawn from previously defined ranges that cover a wide range of possible near-surface layered Vs models. The reflectivity method constitutes the forward modelling operator that converts the model parameters into the observed shot gathers. The inversion also includes a Monte Carlo simulation strategy that propagates onto the model space the uncertainties related to noise in the data and the modelling error introduced by the network approximation. We first discuss synthetic inversions to assess the applicability of the proposed method and to analyse the effect of erroneous model parameterizations. The inversion results are also benchmarked with those provided by a more standard approach in which the particle swarm optimization algorithm inverts the fundamental mode only. Then, we discuss a field data application. Our tests confirm that the residual neural network inversion provides accurate model estimations and reliable uncertainty appraisals. One of the main benefits of the proposed approach is that once the network is trained it provides the near-surface shear wave velocity profile in near real-time.

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Erratum: Layering ratios: a systematic approach to the inversion of surface wave data in the absence of a-priori information

Cited by 15 publications

References 0 publications

Site response implications associated with using non-unique Vs profiles from surface wave inversion in comparison with other commonly used methods of accounting for Vs uncertainty

Site response implications associated with using non-unique Vs profiles from surface wave inversion in comparison with other commonly used methods of accounting for Vs uncertainty

Forward modeling of Rayleigh surface waves for analytical characterization of dominant dispersion trends

A hybrid residual neural network–Monte Carlo approach to invert surface wave dispersion data

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