Modeling and Parameterization of Basin Effects for Engineering Design Applications

Citation

Ayoubi, Peyman (2022) Modeling and Parameterization of Basin Effects for Engineering Design Applications. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/4e61-q346. https://resolver.caltech.edu/CaltechTHESIS:03312022-021127047

Abstract

The term "Basin effects" refers to trapped and reverberating earthquake waves in soft sedimentary deposits overlying convex depressions of the basement bedrock, which significantly alter the frequency content, amplitude, and duration of seismic waves. This has played an important role in shaking duration and intensity in past earthquakes such as the M_w 8.0 1985 Michoácan, Mexico, M_w 6.9 1995 Kobe, Japan, and M_w 7.8 2015 Gorkha, Nepal earthquakes. While the standard practice is to perform a 1D analysis of a soil column, edge-effect and surface waves are among the key contributors to the surface ground motion within a basin. This thesis studies basin effects in a 2D medium to help better understand the phenomena, better parameterize them, and suggest a path to appropriately incorporate them in ground motion prediction equations and building design codes. After the introduction in Chapter 1, I present the results in three main parts as follows:

In Chapter 2, we perform an extensive parametric study on the characteristics of surface ground motion associated with basin effects. We use an elastic idealized-shaped medium subjected to vertically propagating SV plane waves and examine the effects of basin geometry and material properties. We specifically study the effects of four dimensionless parameters, the width-to-depth (aspect) ratio, the rock-to-soil material contrast, a dimensionless frequency that quantifies the depth of the basin relative to the dominant incident wavelength, and a dimensionless distance that quantifies the distance of the basin edges relative to the dominant wavelength. Our results show that basin effects can be reasonably characterized using at least three independent parameters, each of which can significantly alter the resultant ground motion. To demonstrate the application of dimensional analysis applied here, we investigate the response of the Kathmandu Valley during the 2015 M_w 7.8 Gorkha Earthquake in Nepal using an idealized basin geometry and soil properties. Our results show that a simplified model can capture notable ground motion characteristics associated with basin effects.

Chapter 3 uses the identified parameters from the previous chapter to estimate surface acceleration time-series given earthquake frequency content, basin geometry and material properties, and location inside a basin. This is of practical use when the amount of available data is limited or the fast estimation of time-series is desirable. For that, we train a neural network to estimate surface ground acceleration time-series across a basin. Three input parameters are needed for the estimation: basin-to-bedrock shear wave velocity ratio, aspect ratio of the basin, and dimensionless location. These parameters define an idealized-shaped basin and the location at which the time-series are to be computed. It will be shown that the model performs with high accuracy in comparison to the result of a full-fidelity Finite Element (FE) simulation (ground truth) and generalizes reasonably well for input parameters outside of the training set. Moreover, we will also use the model for the case of Kathmandu Valley, Nepal during the 2015 M_w 7.8 Gorkha earthquake and compare the results of NN versus recordings of the mainshock, similarly to Chapter 2.

Once we have studied basin behavior in a homogeneous case in previous chapters, we focus on material representation inside a basin in Chapter 4. Here, we study basin effects for the cases where high-frequency response and realistic material representation are desirable. However, the lack of sufficient information about the material properties and stratigraphy of a basin prevents accurate simulation of the phenomena. To do that, we perform a stochastic analysis using the Monte Carlo technique, where a random field represents basin material. Similarly to the previous chapters, we use a 2D FE model with an idealized basin subjected to vertically propagating SV plane waves and investigate the spatial variation of surface ground motion (SGM) associated with basin effects by assuming different realizations of the correlated random field. We then study various correlation lengths, coefficients of variations, and autocorrelation functions to evaluate their contribution to SGM. We show that the coefficient of variation is the most influential parameter on SGM, followed by correlation lengths and type of autocorrelation function. Increasing the coefficient of variation not only affects the mean surface amplification, but also results in a dramatic change in the standard deviation. Correlation lengths and autocorrelation functions, on the other hand, are of less importance for the cases we examine in this study.

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