Predictability And Repeatability Of Non Ergodic Site Response For Diverse Geological Conditions

Ground motion models (GMMs) are used to predict ground motion intensity measures given parameters descriptive of source, path, and site conditions. These GMMs incorporate source, path, and site response models that represent approximately the average conditions in the database from which the GMMs were derived. In the case of NGA-type models, global data is used, potentially with path and site adjustments for large regions with ample data (e.g., California), so the predictions represent either global or regional averages. In contrast, when GMMs are applied for a specific engineering project, the source, path, and site response attributes of interest are those local to the site, which may depart from the global or regional averages represented by the GMM. In this context, I refer to the source, path, and site models in the GMM as ergodic. Alternative models that consider local, or site-specific features, are considered non-ergodic, and have the potential to significantly reduce the ground motion variability that is considered in probabilistic seismic hazard analysis. My thesis work is concerned principally with the site response component of GMMs, and in particular, with evaluating the effectiveness of predictive models available for non-ergodic site response analysis. The ergodic site amplification within a GMM represents the global or regional average for the site's value of time-averaged upper 30 meters shear wave velocity and basin depth. Many local effects may introduce departures in site response from the ergodic model, including strong impedance contrasts within the shear wave velocity profile, an unfavorable location relative to a basin edge, complexity of local terrain, and perhaps other factors. Therefore, the ergodic site response model has two drawbacks: (1) potential for a biased estimate of mean site response and (2) because the ergodic model averages over a diverse array of conditions having many different site responses, the model carries a relatively large standard deviation. The alternative of non-ergodic site response takes into account the particular geologic conditions at a site that control site response. If applied properly, non-ergodic site response can produce unbiased estimates of site response and remove site-to-site variability from the total standard deviation, which is a significant contributor. One method of evaluating non- ergodic site response in practice is to utilize recordings at the site to evaluate misfits from a GMM, and then use this information to construct a median site response model. However, when on-site recordings are not available, site-specific analysis requires the application of various predictive models. The questions addressed in this research relate to the effectiveness of different predictive models for estimation of site response. The general approach followed in this research was to develop a database of available recordings for sites in a study region, analyze the data to develop non-ergodic site responses, and then either 1) apply existing predictive models to the sites with "measured" (i.e., non- ergodic) site responses and then evaluate their effectiveness over the population of sites or; 2) develop a new predictive model where existing models cannot be reasonably applied. The first approach of evaluating existing tools is applied to a population of 159 sites in California. The second approach of developing a new model is applied to 7 sites in Obihiro (Japan), where soft soil conditions (VS30 = 102 to 211 m/s) require the development of a novel modeling framework. For the California sites, the predictive models considered are ground response analysis (GRA; one-dimensional shear wave propagation through a soil column), square-root impedance method (SRI), and models conditioned on horizontal-to-vertical spectral ratio (HVSR) vs frequency plots. The GRA and SRI methods require a shear wave velocity (VS) profile for the site and models for material damping for each soil horizon in the profile. Among the 159 sites, the profile depth range is 30 to 255 m (profile period range is 0.06 to 1.02 sec). The HVSR model requires HVSR data, which can be derived from microtremors or earthquake recordings. A challenge that was encountered in the application of GRA and SRI methods was the lack of soil profiles to accompany VS profiles. I developed protocols for estimation of soil type parameters that allow geotechnical damping models to be applied. Additional damping models were also considered, including one that is informed by high-frequency spectral decay of site ground motions ( 0). Despite the depth of the profiles considered in this work being relatively modest, ground response analyses (or square-root-impedance analyses) are able to improve site response predictions relative to ergodic models for approximately 36% of sites (for periods less than or equal to the site period). The inability of site-specific methods to improve prediction accuracy for the 64% sites could stem from three potential sources: (1) simulations of one-dimension wave propagation do not accurately characterize the physics of site response; (2) the measured VS profile from the site does not accurately represent site conditions, either because of strong site heterogeneity or inaccurate measurements; (3) portions of the site profile beneath the profile depth significantly impact the site response in the frequency range of the measured profile. These problems are common to some extent in virtually all site response simulations, so understanding their collective impact is of practical importance. The unknown influence of these factors introduces epistemic uncertainties, which we quantify. Lacking any knowledge of whether a given site is well represented with one-dimensional simulations, this epistemic uncertainty is only slightly reduced from that of the site-to-site variability in ergodic models within soil column period range. For the subset of sites where this modeling is effective, the epistemic uncertainty is more substantially reduced by amounts ranging from 0.05-0.10 in natural log units. The HVSR model considered in this work (adapted from a model in literature) uses the frequency and amplitude of peaks in HVSR spectra. I identify three populations of sites based on microtremor data - those for which a clear HVSR peak is evident (40%), those for which no peak occurs (40%), and intermediate/ambiguous cases (20%). When the ergodic model is used, sites with a peak are observed to have higher bias and site-to-site variability than sites without peaks; as a result, commonly used models for site-to-site variability represent a blending of these condition because the occurrence of peaks is not accounted for. Use of the HVSR model for sites with peaks does not appreciably change the bias but reduces dispersion at long periods (> 1 sec) relative to what is obtained with an ergodic model. The lack of improvement at short period could be caused by false positives (peaks in HVSR that do not appear in site response) and not well-aligned peak positions between HVSR and site response, and may also be influenced by the model used in our analyses having been derived for conditions in Japan. I recommend a California-specific bias correction for sites without a peak. For the Obihiro (Japan) sites, I developed a region-specific site amplification model applicable to the peaty organic soils in this region. The analysis of site response from regional data required removal of source-specific biases and careful consideration of source-to-site path effects. These considerations were essential to avoid mapping source- or path-related model misfits into estimates of site response. I considered two subduction ground motion models as reference models. By paying special attention to the conditions for which the path models are effective, and making adjustments for between-island path misfits (Hokkaido to Honshu and vice-versa), I found the proposed approach effectively identifies site effects, and that the results are insensitive to the selected ground motion model. Observed site responses are characterized by strong resonances at first-mode site frequencies as derived from HVSR measurements.

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