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Reservoir-triggered seismicity has been observed near dams during construction, impoundment, and cyclic filling in many parts of the earth. In Turkey, the number of dams has increased substantially over the last decade, with Ataturk Dam being the largest dam in Turkey with a total water capacity of 48.7 billion m(3). After the construction of the dam, the monitoring network has improved. Considering earthquakes above the long-term completeness magnitude of M-C = 3.5, the local seismicity rate has substantially increased after the filling of the reservoir. Recently, two damaging earthquakes of M-w 5.5 and M-w 5.1 occurred in the town of Samsat near the Ataturk Reservoir in 2017 and 2018, respectively. In this study, we analyze the spatio-temporal evolution of seismicity and its source properties in relation to the temporal water-level variations and the stresses resulting from surface loading and pore-pressure diffusion. We find that water-level and seismicity rate are anti-correlated, which is explained by the stabilization effect of the gravitational induced stress imposed by water loading on the local faults. On the other hand, we find that the overall effective stress in the seismogenic zone increased over decades due to pore-pressure diffusion, explaining the enhanced background seismicity during recent years. Additionally, we observe a progressive decrease of the Gutenberg-Richter b-value. Our results indicate that the stressing rate finally focused on the region where the two damaging earthquakes occurred in 2017 and 2018.
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.
Paleoearthquakes and historic earthquakes are the most important source of information for the estimation of long-term earthquake recurrence intervals in fault zones, because corresponding sequences cover more than one seismic cycle. However, these events are often rare, dating uncertainties are enormous, and missing or misinterpreted events lead to additional problems. In the present study, I assume that the time to the next major earthquake depends on the rate of small and intermediate events between the large ones in terms of a clock change model. Mathematically, this leads to a Brownian passage time distribution for recurrence intervals. I take advantage of an earlier finding that under certain assumptions the aperiodicity of this distribution can be related to the Gutenberg-Richter b value, which can be estimated easily from instrumental seismicity in the region under consideration. In this way, both parameters of the Brownian passage time distribution can be attributed with accessible seismological quantities. This allows to reduce the uncertainties in the estimation of the mean recurrence interval, especially for short paleoearthquake sequences and high dating errors. Using a Bayesian framework for parameter estimation results in a statistical model for earthquake recurrence intervals that assimilates in a simple way paleoearthquake sequences and instrumental data. I present illustrative case studies from Southern California and compare the method with the commonly used approach of exponentially distributed recurrence times based on a stationary Poisson process.
We analyze a large transient strainmeter signal recorded at 62.5 m depth along the southern shore of the eastern Sea of Marmara region in northwestern Turkey. This region represents a passage of stress transfer from the Izmit rupture to the Marmara seismic gap. The strain signal was recorded at the Esenkoy site by one of the ICDP-GONAF (International Continental Drilling Programme - Geophysical Observatory at the North Anatolian Fault) strainmeters on the Armutlu peninsula with a maximum amplitude of 5 microstrain and lasting about 50 days. The onset of the strain signal coincided with the origin time of a M-w 4.4 earthquake offshore Yalova, which occurred as part of a seismic sequence including eight M-w >= 3.5 earthquakes. The Mw 4.4 event occurred at a distance of about 30 km from Esenkoy on June 25th 2016 representing the largest earthquake in this region since 2008. Before the event, the maximum horizontal strain was subparallel to the regional maximum horizontal stress derived from stress inversion of local seismicity. During the strain transient, we observe a clockwise rotation in the local horizontal strain field of about 20 degrees. The strain signal does not correlate with known environmental parameters such as annual changes of sea level, rainfall or temperature. The strain signal could indicate local slow slip on the Cinarcik fault and thus a transfer of stress to the eastern Marmara seismic gap.
Earthquakes occurring close to hydrocarbon fields under production are often under critical view of being induced or triggered. However, clear and testable rules to discriminate the different events have rarely been developed and tested. The unresolved scientific problem may lead to lengthy public disputes with unpredictable impact on the local acceptance of the exploitation and field operations. We propose a quantitative approach to discriminate induced, triggered, and natural earthquakes, which is based on testable input parameters. Maxima of occurrence probabilities are compared for the cases under question, and a single probability of being triggered or induced is reported. The uncertainties of earthquake location and other input parameters are considered in terms of the integration over probability density functions. The probability that events have been human triggered/induced is derived from the modeling of Coulomb stress changes and a rate and state-dependent seismicity model. In our case a 3-D boundary element method has been adapted for the nuclei of strain approach to estimate the stress changes outside the reservoir, which are related to pore pressure changes in the field formation. The predicted rate of natural earthquakes is either derived from the background seismicity or, in case of rare events, from an estimate of the tectonic stress rate. Instrumentally derived seismological information on the event location, source mechanism, and the size of the rupture plane is of advantage for the method. If the rupture plane has been estimated, the discrimination between induced or only triggered events is theoretically possible if probability functions are convolved with a rupture fault filter. We apply the approach to three recent main shock events: (1) the M-w 4.3 Ekofisk 2001, North Sea, earthquake close to the Ekofisk oil field; (2) the M-w 4.4 Rotenburg 2004, Northern Germany, earthquake in the vicinity of the Sohlingen gas field; and (3) the M-w 6.1 Emilia 2012, Northern Italy, earthquake in the vicinity of a hydrocarbon reservoir. The three test cases cover the complete range of possible causes: clearly human induced, not even human triggered, and a third case in between both extremes.
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.
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.
Earthquake faults interact with each other in many different ways and hence earthquakes cannot be treated as individual independent events. Although earthquake interactions generally lead to a complex evolution of the crustal stress field, it does not necessarily mean that the earthquake occurrence becomes random and completely unpredictable. In particular, the interplay between earthquakes can rather explain the occurrence of pronounced characteristics such as periods of accelerated and depressed seismicity (seismic quiescence) as well as spatiotemporal earthquake clustering (swarms and aftershock sequences). Ignoring the time-dependence of the process by looking at time-averaged values – as largely done in standard procedures of seismic hazard assessment – can thus lead to erroneous estimations not only of the activity level of future earthquakes but also of their spatial distribution. Therefore, it exists an urgent need for applicable time-dependent models. In my work, I aimed at better understanding and characterization of the earthquake interactions in order to improve seismic hazard estimations. For this purpose, I studied seismicity patterns on spatial scales ranging from hydraulic fracture experiments (meter to kilometer) to fault system size (hundreds of kilometers), while the temporal scale of interest varied from the immediate aftershock activity (minutes to months) to seismic cycles (tens to thousands of years). My studies revealed a number of new characteristics of fluid-induced and stress-triggered earthquake clustering as well as precursory phenomena in earthquake cycles. Data analysis of earthquake and deformation data were accompanied by statistical and physics-based model simulations which allow a better understanding of the role of structural heterogeneities, stress changes, afterslip and fluid flow. Finally, new strategies and methods have been developed and tested which help to improve seismic hazard estimations by taking the time-dependence of the earthquake process appropriately into account.