Refine
Has Fulltext
- yes (3) (remove)
Document Type
- Doctoral Thesis (3) (remove)
Language
- English (3)
Is part of the Bibliography
- yes (3)
Keywords
- seismic hazard (3) (remove)
Institute
Rapidly growing seismic and macroseismic databases and simplified access to advanced machine learning methods have in recent years opened up vast opportunities to address challenges in engineering and strong motion seismology from novel, datacentric perspectives. In this thesis, I explore the opportunities of such perspectives for the tasks of ground motion modeling and rapid earthquake impact assessment, tasks with major implications for long-term earthquake disaster mitigation.
In my first study, I utilize the rich strong motion database from the Kanto basin, Japan, and apply the U-Net artificial neural network architecture to develop a deep learning based ground motion model. The operational prototype provides statistical estimates of expected ground shaking, given descriptions of a specific earthquake source, wave propagation paths, and geophysical site conditions. The U-Net interprets ground motion data in its spatial context, potentially taking into account, for example, the geological properties in the vicinity of observation sites. Predictions of ground motion intensity are thereby calibrated to individual observation sites and earthquake locations.
The second study addresses the explicit incorporation of rupture forward directivity into ground motion modeling. Incorporation of this phenomenon, causing strong, pulse like ground shaking in the vicinity of earthquake sources, is usually associated with an intolerable increase in computational demand during probabilistic seismic hazard analysis (PSHA) calculations. I suggest an approach in which I utilize an artificial neural network to efficiently approximate the average, directivity-related adjustment to ground motion predictions for earthquake ruptures from the 2022 New Zealand National Seismic Hazard Model. The practical implementation in an actual PSHA calculation demonstrates the efficiency and operational readiness of my model. In a follow-up study, I present a proof of concept for an alternative strategy in which I target the generalizing applicability to ruptures other than those from the New Zealand National Seismic Hazard Model.
In the third study, I address the usability of pseudo-intensity reports obtained from macroseismic observations by non-expert citizens for rapid impact assessment. I demonstrate that the statistical properties of pseudo-intensity collections describing the intensity of shaking are correlated with the societal impact of earthquakes. In a second step, I develop a probabilistic model that, within minutes of an event, quantifies the probability of an earthquake to cause considerable societal impact. Under certain conditions, such a quick and preliminary method might be useful to support decision makers in their efforts to organize auxiliary measures for earthquake disaster response while results from more elaborate impact assessment frameworks are not yet available.
The application of machine learning methods to datasets that only partially reveal characteristics of Big Data, qualify the majority of results obtained in this thesis as explorative insights rather than ready-to-use solutions to real world problems. The practical usefulness of this work will be better assessed in the future by applying the approaches developed to growing and increasingly complex data sets.
The seismicity of the Dead Sea fault zone (DSFZ) during the last two millennia is characterized by a number of damaging and partly devastating earthquakes. These events pose a considerable seismic hazard and seismic risk to Syria, Lebanon, Palestine, Jordan, and Israel. The occurrence rates for large earthquakes along the DSFZ show indications to temporal changes in the long-term view. The aim of this thesis is to find out, if the occurrence rates of large earthquakes (Mw ≥ 6) in different parts of the DSFZ are time-dependent and how. The results are applied to probabilistic seismic hazard assessments (PSHA) in the DSFZ and neighboring areas. Therefore, four time-dependent statistical models (distributions), including Weibull, Gamma, Lognormal and Brownian Passage Time (BPT), are applied beside the exponential distribution (Poisson process) as the classical time-independent model. In order to make sure, if the earthquake occurrence rate follows a unimodal or a multimodal form, a nonparametric bootstrap test of multimodality has been done. A modified method of weighted Maximum Likelihood Estimation (MLE) is applied to estimate the parameters of the models. For the multimodal cases, an Expectation Maximization (EM) method is used in addition to the MLE method. The selection of the best model is done by two methods; the Bayesian Information Criterion (BIC) as well as a modified Kolmogorov-Smirnov goodness-of-fit test. Finally, the confidence intervals of the estimated parameters corresponding to the candidate models are calculated, using the bootstrap confidence sets. In this thesis, earthquakes with Mw ≥ 6 along the DSFZ, with a width of about 20 km and inside 29.5° ≤ latitude ≤ 37° are considered as the dataset. The completeness of this dataset is calculated since 300 A.D. The DSFZ has been divided into three sub zones; the southern, the central and the northern sub zone respectively. The central and the northern sub zones have been investigated but not the southern sub zone, because of the lack of sufficient data. The results of the thesis for the central part of the DSFZ show that the earthquake occurrence rate does not significantly pursue a multimodal form. There is also no considerable difference between the time-dependent and time-independent models. Since the time-independent model is easier to interpret, the earthquake occurrence rate in this sub zone has been estimated under the exponential distribution assumption (Poisson process) and will be considered as time-independent with the amount of 9.72 * 10-3 events/year. The northern part of the DSFZ is a special case, where the last earthquake has occurred in 1872 (about 137 years ago). However, the mean recurrence time of Mw ≥ 6 events in this area is about 51 years. Moreover, about 96 percent of the observed earthquake inter-event times (the time between two successive earthquakes) in the dataset regarding to this sub zone are smaller than 137 years. Therefore, it is a zone with an overdue earthquake. The results for this sub zone verify that the earthquake occurrence rate is strongly time-dependent, especially shortly after an earthquake occurrence. A bimodal Weibull-Weibull model has been selected as the best fit for this sub zone. The earthquake occurrence rate, corresponding to the selected model, is a smooth function of time and reveals two clusters within the time after an earthquake occurrence. The first cluster begins right after an earthquake occurrence, lasts about 80 years, and is explicitly time-dependent. The occurrence rate, regarding to this cluster, is considerably lower right after an earthquake occurrence, increases strongly during the following ten years and reaches its maximum about 0.024 events/year, then decreases over the next 70 years to its minimum about 0.0145 events/year. The second cluster begins 80 years after an earthquake occurrence and lasts until the next earthquake occurs. The earthquake occurrence rate, corresponding to this cluster, increases extremely slowly, such as it can be considered as an almost constant rate about 0.015 events/year. The results are applied to calculate the time-dependent PSHA in the northern part of the DSFZ and neighbouring areas.
Adjustment of empirically derived ground motion prediction equations (GMPEs), from a data- rich region/site where they have been derived to a data-poor region/site, is one of the major challenges associated with the current practice of seismic hazard analysis. Due to the fre- quent use in engineering design practices the GMPEs are often derived for response spectral ordinates (e.g., spectral acceleration) of a single degree of freedom (SDOF) oscillator. The functional forms of such GMPEs are based upon the concepts borrowed from the Fourier spectral representation of ground motion. This assumption regarding the validity of Fourier spectral concepts in the response spectral domain can lead to consequences which cannot be explained physically.
In this thesis, firstly results from an investigation that explores the relationship between Fourier and response spectra, and implications of this relationship on the adjustment issues of GMPEs, are presented. The relationship between the Fourier and response spectra is explored by using random vibration theory (RVT), a framework that has been extensively used in earthquake engineering, for instance within the stochastic simulation framework and in the site response analysis. For a 5% damped SDOF oscillator the RVT perspective of response spectra reveals that no one-to-one correspondence exists between Fourier and response spectral ordinates except in a limited range (i.e., below the peak of the response spectra) of oscillator frequencies. The high oscillator frequency response spectral ordinates are dominated by the contributions from the Fourier spectral ordinates that correspond to the frequencies well below a selected oscillator frequency. The peak ground acceleration (PGA) is found to be related with the integral over the entire Fourier spectrum of ground motion which is in contrast to the popularly held perception that PGA is a high-frequency phenomenon of ground motion.
This thesis presents a new perspective for developing a response spectral GMPE that takes the relationship between Fourier and response spectra into account. Essentially, this frame- work involves a two-step method for deriving a response spectral GMPE: in the first step two empirical models for the FAS and for a predetermined estimate of duration of ground motion are derived, in the next step, predictions from the two models are combined within the same RVT framework to obtain the response spectral ordinates. In addition to that, a stochastic model based scheme for extrapolating the individual acceleration spectra beyond the useable frequency limits is also presented. To that end, recorded acceleration traces were inverted to obtain the stochastic model parameters that allow making consistent extrapola- tion in individual (acceleration) Fourier spectra. Moreover an empirical model, for a dura- tion measure that is consistent within the RVT framework, is derived. As a next step, an oscillator-frequency-dependent empirical duration model is derived that allows obtaining the most reliable estimates of response spectral ordinates. The framework of deriving the response spectral GMPE presented herein becomes a self-adjusting model with the inclusion of stress parameter (∆σ) and kappa (κ0) as the predictor variables in the two empirical models. The entire analysis of developing the response spectral GMPE is performed on recently compiled RESORCE-2012 database that contains recordings made from Europe, the Mediterranean and the Middle East. The presented GMPE for response spectral ordinates should be considered valid in the magnitude range of 4 ≤ MW ≤ 7.6 at distances ≤ 200 km.