Bayesian inference and model selection to investigate seabed shear-wave velocity profiles via interface-wave dispersion inversion

2017 ◽  
Vol 142 (4) ◽  
pp. 2591-2591
Author(s):  
Stan E. Dosso ◽  
Hefeng Dong
Keyword(s):  
Bayesian Inference ◽  
Model Selection ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Wave Dispersion ◽  
Velocity Profiles ◽  
Interface Wave

scholarly journals Shear-wave velocity structure at Mt. Etna from inversion of Rayleigh-wave dispersion patterns (2 s < T < 20 s)

2010 ◽  
Vol 53 (2) ◽  
Author(s):  
Luigia Cristiano ◽  
Simona Petrosino ◽  
Gilberto Saccorotti ◽  
Matthias Ohrnberger ◽  
Roberto Scarpa
Keyword(s):  
Rayleigh Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Velocity Structure ◽  
Wave Dispersion ◽  
Dispersion Patterns ◽  
Mt Etna ◽  
Rayleigh Wave Dispersion ◽  
Shear Wave Velocity Structure

scholarly journals Evaluation of liquefaction potentials based on shear wave velocities in Pohang City, South Korea

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yumin Ji ◽  
Byungmin Kim ◽  
Kiseog Kim
Keyword(s):  
South Korea ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Ground Surface ◽  
Velocity Profiles ◽  
Liquefaction Potential ◽  
Attenuation Relationships ◽  
Cyclic Resistance ◽  
Sand Boils

AbstractThis study evaluates the potentials of liquefaction caused by the 2017 moment magnitude 5.4 earthquake in Pohang City, South Korea. We obtain shear wave velocity profiles measured by suspension PS logging tests at the five sites near the epicenter. We also perform downhole tests at three of the five sites. Among the five sites, the surface manifestations (i.e., sand boils) were observed at the three sites, and not at the other two sites. The maximum accelerations on the ground surface at the five sites are estimated using the Next Generation Attenuation relationships for Western United State ground motion prediction equations. The shear wave velocity profiles from the two tests are slightly different, resulting in varying cyclic resistance ratios, factors of safety against liquefaction, and liquefaction potential indices. Nevertheless, we found that both test approaches can be used to evaluate liquefaction potentials. The liquefaction potential indices at the liquefied sites are approximately 1.5–13.9, whereas those at the non-liquefied sites are approximately 0–0.3.

scholarly journals Evaluating simplified methods for liquefaction assessment for loss estimation

2017 ◽  
Vol 17 (5) ◽  
pp. 781-800 ◽  
Author(s):  
Indranil Kongar ◽  
Tiziana Rossetto ◽  
Sonia Giovinazzi
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Ground Deformation ◽  
Insurance Industry ◽  
Binary Classification ◽  
Remote Sensing Data ◽  
Velocity Profiles ◽  
Loss Estimation ◽  
Liquefaction Assessment

Abstract. Currently, some catastrophe models used by the insurance industry account for liquefaction by applying a simple factor to shaking-induced losses. The factor is based only on local liquefaction susceptibility and this highlights the need for a more sophisticated approach to incorporating the effects of liquefaction in loss models. This study compares 11 unique models, each based on one of three principal simplified liquefaction assessment methods: liquefaction potential index (LPI) calculated from shear-wave velocity, the HAZUS software method and a method created specifically to make use of USGS remote sensing data. Data from the September 2010 Darfield and February 2011 Christchurch earthquakes in New Zealand are used to compare observed liquefaction occurrences to forecasts from these models using binary classification performance measures. The analysis shows that the best-performing model is the LPI calculated using known shear-wave velocity profiles, which correctly forecasts 78 % of sites where liquefaction occurred and 80 % of sites where liquefaction did not occur, when the threshold is set at 7. However, these data may not always be available to insurers. The next best model is also based on LPI but uses shear-wave velocity profiles simulated from the combination of USGS VS30 data and empirical functions that relate VS30 to average shear-wave velocities at shallower depths. This model correctly forecasts 58 % of sites where liquefaction occurred and 84 % of sites where liquefaction did not occur, when the threshold is set at 4. These scores increase to 78 and 86 %, respectively, when forecasts are based on liquefaction probabilities that are empirically related to the same values of LPI. This model is potentially more useful for insurance since the input data are publicly available. HAZUS models, which are commonly used in studies where no local model is available, perform poorly and incorrectly forecast 87 % of sites where liquefaction occurred, even at optimal thresholds. This paper also considers two models (HAZUS and EPOLLS) for estimation of the scale of liquefaction in terms of permanent ground deformation but finds that both models perform poorly, with correlations between observations and forecasts lower than 0.4 in all cases. Therefore these models potentially provide negligible additional value to loss estimation analysis outside of the regions for which they have been developed.

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