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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.
Rheology describes the flow of matter under the influence of stress, and - related to solids- it investigates how solids subjected to stresses deform. As the deformation of the Earth’s outer layers, the lithosphere and the crust, is a major focus of rheological studies, rheology in the geosciences describes how strain evolves in rocks of variable composition and temperature under tectonic stresses. It is here where deformation processes shape the form of ocean basins and mountain belts that ultimately result from the complex interplay between lithospheric plate motion and the susceptibility of rocks to the influence of plate-tectonic forces. A rigorous study of the strength of the lithosphere and deformation phenomena thus requires in-depth studies of the rheological characteristics of the involved materials and the temporal framework of deformation processes.
This dissertation aims at analyzing the influence of the physical configuration of the lithosphere on the present-day thermal field and the overall rheological characteristics of the lithosphere to better understand variable expressions in the formation of passive continental margins and the behavior of strike-slip fault zones. The main methodological approach chosen is to estimate the present-day thermal field and the strength of the lithosphere by 3-D numerical modeling. The distribution of rock properties is provided by 3-D structural models, which are used as the basis for the thermal and rheological modeling. The structural models are based on geophysical and geological data integration, additionally constrained by 3-D density modeling. More specifically, to decipher the thermal and rheological characteristics of the lithosphere in both oceanic and continental domains, sedimentary basins in the Sea of Marmara (continental transform setting), the SW African passive margin (old oceanic crust), and the Norwegian passive margin (young oceanic crust) were selected for this study.
The Sea of Marmara, in northwestern Turkey, is located where the dextral North Anatolian Fault zone (NAFZ) accommodates the westward escape of the Anatolian Plate toward the Aegean. Geophysical observations indicate that the crust is heterogeneous beneath the Marmara basin, but a detailed characterization of the lateral crustal heterogeneities is presented for the first time in this study. Here, I use different gravity datasets and the general non-uniqueness in potential field modeling, to propose three possible end-member scenarios of crustal configuration. The models suggest that pronounced gravitational anomalies in the basin originate from significant density heterogeneities within the crust. The rheological modeling reveals that associated variations in lithospheric strength control the mechanical segmentation of the NAFZ. Importantly, a strong crust that is mechanically coupled to the upper mantle spatially correlates with aseismic patches where the fault bends and changes its strike in response to the presence of high-density lower crustal bodies. Between the bends, mechanically weaker crustal domains that are decoupled from the mantle are characterized by creep.
For the passive margins of SW Africa and Norway, two previously published 3-D conductive and lithospheric-scale thermal models were analyzed. These 3-D models differentiate various sedimentary, crustal, and mantle units and integrate different geophysical data, such as seismic observations and the gravity field. Here, the rheological modeling suggests that the present-day lithospheric strength across the oceanic domain is ultimately affected by the age and past thermal and tectonic processes as well as the depth of the thermal lithosphere-asthenosphere boundary, while the configuration of the crystalline crust dominantly controls the rheological behavior of the lithosphere beneath the continental domains of both passive margins.
The thermal and rheological models show that the variations of lithospheric strength are fundamentally influenced by the temperature distribution within the lithosphere. Moreover, as the composition of the lithosphere significantly influences the present-day thermal field, it therefore also affects the rheological characteristics of the lithosphere. Overall my studies add to our understanding of regional tectonic deformation processes and the long-term behavior of sedimentary basins; they confirm other analyses that have pointed out that crustal heterogeneities in the continents result in diverse lithospheric thermal characteristics, which in turn results in higher complexity and variations of rheological behavior compared to oceanic domains with a thinner, more homogeneous crust.
Towards unifying approaches in exposure modelling for scenario-based multi-hazard risk assessments
(2023)
This cumulative thesis presents a stepwise investigation of the exposure modelling process for risk assessment due to natural hazards while highlighting its, to date, not much-discussed importance and associated uncertainties. Although “exposure” refers to a very broad concept of everything (and everyone) that is susceptible to damage, in this thesis it is narrowed down to the modelling of large-area residential building stocks. Classical building exposure models for risk applications have been constructed fully relying on unverified expert elicitation over data sources (e.g., outdated census datasets), and hence have been implicitly assumed to be static in time and in space. Moreover, their spatial representation has also typically been simplified by geographically aggregating the inferred composition onto coarse administrative units whose boundaries do not always capture the spatial variability of the hazard intensities required for accurate risk assessments. These two shortcomings and the related epistemic uncertainties embedded within exposure models are tackled in the first three chapters of the thesis. The exposure composition of large-area residential building stocks is studied on the scope of scenario-based earthquake loss models. Then, the proposal of optimal spatial aggregation areas of exposure models for various hazard-related vulnerabilities is presented, focusing on ground-shaking and tsunami risks. Subsequently, once the experience is gained in the study of the composition and spatial aggregation of exposure for various hazards, this thesis moves towards a multi-hazard context while addressing cumulative damage and losses due to consecutive hazard scenarios. This is achieved by proposing a novel method to account for the pre-existing damage descriptions on building portfolios as a key input to account for scenario-based multi-risk assessment. Finally, this thesis shows how the integration of the aforementioned elements can be used in risk communication practices. This is done through a modular architecture based on the exploration of quantitative risk scenarios that are contrasted with social risk perceptions of the directly exposed communities to natural hazards.
In Chapter 1, a Bayesian approach is proposed to update the prior assumptions on such composition (i.e., proportions per building typology). This is achieved by integrating high-quality real observations and then capturing the intrinsic probabilistic nature of the exposure model. Such observations are accounted as real evidence from both: field inspections (Chapter 2) and freely available data sources to update existing (but outdated) exposure models (Chapter 3). In these two chapters, earthquake scenarios with parametrised ground motion fields were transversally used to investigate the role of such epistemic uncertainties related to the exposure composition through sensitivity analyses. Parametrised scenarios of seismic ground shaking were the hazard input utilised to study the physical vulnerability of building portfolios. The second issue that was investigated, which refers to the spatial aggregation of building exposure models, was investigated within two decoupled vulnerability contexts: due to seismic ground shaking through the integration of remote sensing techniques (Chapter 3); and within a multi-hazard context by integrating the occurrence of associated tsunamis (Chapter 4). Therein, a careful selection of the spatial aggregation entities while pursuing computational efficiency and accuracy in the risk estimates due to such independent hazard scenarios (i.e., earthquake and tsunami) are discussed. Therefore, in this thesis, the physical vulnerability of large-area building portfolios due to tsunamis is considered through two main frames: considering and disregarding the interaction at the vulnerability level, through consecutive and decoupled hazard scenarios respectively, which were then contrasted.
Contrary to Chapter 4, where no cumulative damages are addressed, in Chapter 5, data and approaches, which were already generated in former sections, are integrated with a novel modular method to ultimately study the likely interactions at the vulnerability level on building portfolios. This is tested by evaluating cumulative damages and losses after earthquakes with increasing magnitude followed by their respective tsunamis. Such a novel method is grounded on the possibility of re-using existing fragility models within a probabilistic framework. The same approach is followed in Chapter 6 to forecast the likely cumulative damages to be experienced by a building stock located in a volcanic multi-hazard setting (ash-fall and lahars). In that section, special focus was made on the manner the forecasted loss metrics are communicated to locally exposed communities. Co-existing quantitative scientific approaches (i.e., comprehensive exposure models; explorative risk scenarios involving single and multiple hazards) and semi-qualitative social risk perception (i.e., level of understanding that the exposed communities have about their own risk) were jointly considered. Such an integration ultimately allowed this thesis to also contribute to enhancing preparedness, science divulgation at the local level as well as technology transfer initiatives.
Finally, a synthesis of this thesis along with some perspectives for improvement and future work are presented.
The plasmasphere is a dynamic region of cold, dense plasma surrounding the Earth. Its shape and size are highly susceptible to variations in solar and geomagnetic conditions. Having an accurate model of plasma density in the plasmasphere is important for GNSS navigation and for predicting hazardous effects of radiation in space on spacecraft. The distribution of cold plasma and its dynamic dependence on solar wind and geomagnetic conditions remain, however, poorly quantified. Existing empirical models of plasma density tend to be oversimplified as they are based on statistical averages over static parameters. Understanding the global dynamics of the plasmasphere using observations from space remains a challenge, as existing density measurements are sparse and limited to locations where satellites can provide in-situ observations. In this dissertation, we demonstrate how such sparse electron density measurements can be used to reconstruct the global electron density distribution in the plasmasphere and capture its dynamic dependence on solar wind and geomagnetic conditions.
First, we develop an automated algorithm to determine the electron density from in-situ measurements of the electric field on the Van Allen Probes spacecraft. In particular, we design a neural network to infer the upper hybrid resonance frequency from the dynamic spectrograms obtained with the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrumentation suite, which is then used to calculate the electron number density. The developed Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm is applied to more than four years of EMFISIS measurements to produce the publicly available electron density data set.
We utilize the obtained electron density data set to develop a new global model of plasma density by employing a neural network-based modeling approach. In addition to the location, the model takes the time history of geomagnetic indices and location as inputs, and produces electron density in the equatorial plane as an output. It is extensively validated using in-situ density measurements from the Van Allen Probes mission, and also by comparing the predicted global evolution of the plasmasphere with the global IMAGE EUV images of He+ distribution. The model successfully reproduces erosion of the plasmasphere on the night side as well as plume formation and evolution, and agrees well with data.
The performance of neural networks strongly depends on the availability of training data, which is limited during intervals of high geomagnetic activity. In order to provide reliable density predictions during such intervals, we can employ physics-based modeling. We develop a new approach for optimally combining the neural network- and physics-based models of the plasmasphere by means of data assimilation. The developed approach utilizes advantages of both neural network- and physics-based modeling and produces reliable global plasma density reconstructions for quiet, disturbed, and extreme geomagnetic conditions.
Finally, we extend the developed machine learning-based tools and apply them to another important problem in the field of space weather, the prediction of the geomagnetic index Kp. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere, the radiation belts and the plasmasphere. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast and make short-term predictions of Kp, basing their inferences on the recent history of Kp and solar wind measurements at L1. We analyze how the performance of neural networks compares to other machine learning algorithms for nowcasting and forecasting Kp for up to 12 hours ahead. Additionally, we investigate several machine learning and information theory methods for selecting the optimal inputs to a predictive model of Kp. The developed tools for feature selection can also be applied to other problems in space physics in order to reduce the input dimensionality and identify the most important drivers.
Research outlined in this dissertation clearly demonstrates that machine learning tools can be used to develop empirical models from sparse data and also can be used to understand the underlying physical processes. Combining machine learning, physics-based modeling and data assimilation allows us to develop novel methods benefiting from these different approaches.
The near-Earth space environment is a highly complex system comprised of several regions and particle populations hazardous to satellite operations. The trapped particles in the radiation belts and ring current can cause significant damage to satellites during space weather events, due to deep dielectric and surface charging. Closer to Earth is another important region, the ionosphere, which delays the propagation of radio signals and can adversely affect navigation and positioning. In response to fluctuations in solar and geomagnetic activity, both the inner-magnetospheric and ionospheric populations can undergo drastic and sudden changes within minutes to hours, which creates a challenge for predicting their behavior. Given the increasing reliance of our society on satellite technology, improving our understanding and modeling of these populations is a matter of paramount importance.
In recent years, numerous spacecraft have been launched to study the dynamics of particle populations in the near-Earth space, transforming it into a data-rich environment. To extract valuable insights from the abundance of available observations, it is crucial to employ advanced modeling techniques, and machine learning methods are among the most powerful approaches available. This dissertation employs long-term satellite observations to analyze the processes that drive particle dynamics, and builds interdisciplinary links between space physics and machine learning by developing new state-of-the-art models of the inner-magnetospheric and ionospheric particle dynamics.
The first aim of this thesis is to investigate the behavior of electrons in Earth's radiation belts and ring current. Using ~18 years of electron flux observations from the Global Positioning System (GPS), we developed the first machine learning model of hundreds-of-keV electron flux at Medium Earth Orbit (MEO) that is driven solely by solar wind and geomagnetic indices and does not require auxiliary flux measurements as inputs. We then proceeded to analyze the directional distributions of electrons, and for the first time, used Fourier sine series to fit electron pitch angle distributions (PADs) in Earth's inner magnetosphere. We performed a superposed epoch analysis of 129 geomagnetic storms during the Van Allen Probes era and demonstrated that electron PADs have a strong energy-dependent response to geomagnetic activity. Additionally, we showed that the solar wind dynamic pressure could be used as a good predictor of the PAD dynamics. Using the observed dependencies, we created the first PAD model with a continuous dependence on L, magnetic local time (MLT) and activity, and developed two techniques to reconstruct near-equatorial electron flux observations from low-PA data using this model.
The second objective of this thesis is to develop a novel model of the topside ionosphere. To achieve this goal, we collected observations from five of the most widely used ionospheric missions and intercalibrated these data sets. This allowed us to use these data jointly for model development, validation, and comparison with other existing empirical models. We demonstrated, for the first time, that ion density observations by Swarm Langmuir Probes exhibit overestimation (up to ~40-50%) at low and mid-latitudes on the night side, and suggested that the influence of light ions could be a potential cause of this overestimation. To develop the topside model, we used 19 years of radio occultation (RO) electron density profiles, which were fitted with a Chapman function with a linear dependence of scale height on altitude. This approximation yields 4 parameters, namely the peak density and height of the F2-layer and the slope and intercept of the linear scale height trend, which were modeled using feedforward neural networks (NNs). The model was extensively validated against both RO and in-situ observations and was found to outperform the International Reference Ionosphere (IRI) model by up to an order of magnitude. Our analysis showed that the most substantial deviations of the IRI model from the data occur at altitudes of 100-200 km above the F2-layer peak. The developed NN-based ionospheric model reproduces the effects of various physical mechanisms observed in the topside ionosphere and provides highly accurate electron density predictions.
This dissertation provides an extensive study of geospace dynamics, and the main results of this work contribute to the improvement of models of plasma populations in the near-Earth space environment.
Volcanoes are one of the Earth’s most dynamic zones and responsible for many changes in our planet. Volcano seismology aims to provide an understanding of the physical processes in volcanic systems and anticipate the style and timing of eruptions by analyzing the seismic records. Volcanic tremor signals are usually observed in the seismic records before or during volcanic eruptions. Their analysis contributes to evaluate the evolving volcanic activity and potentially predict eruptions. Years of continuous seismic monitoring now provide useful information for operational eruption forecasting. The continuously growing amount of seismic recordings, however, poses a challenge for analysis, information extraction, and interpretation, to support timely decision making during volcanic crises. Furthermore, the complexity of eruption processes and precursory activities makes the analysis challenging.
A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contribute to improving our understanding of the underlying physical processes. Some similar issues (data reduction, source separation, extraction, and classification) are addressed in the context of music information retrieval (MIR). The signal characteristics of acoustic and seismic recordings comprise a number of similarities. This thesis is going beyond classical signal analysis techniques usually employed in seismology by exploiting similarities of seismic and acoustic signals and building the information retrieval strategy on the expertise developed in the field of MIR.
First, inspired by the idea of harmonic–percussive separation (HPS) in musical signal processing, I have developed a method to extract harmonic volcanic tremor signals and to detect transient events from seismic recordings. This provides a clean tremor signal suitable for tremor investigation along with a characteristic function suitable for earthquake detection. Second, using HPS algorithms, I have developed a noise reduction technique for seismic signals. This method is especially useful for denoising ocean bottom seismometers, which are highly contaminated by noise. The advantage of this method compared to other denoising techniques is that it doesn’t introduce distortion to the broadband earthquake waveforms, which makes it reliable for different applications in passive seismological analysis. Third, to address the challenge of extracting information from high-dimensional data and investigating the complex eruptive phases, I have developed an advanced machine learning model that results in a comprehensive signal processing scheme for volcanic tremors. Using this method seismic signatures of major eruptive phases can be automatically detected. This helps to provide a chronology of the volcanic system. Also, this model is capable to detect weak precursory volcanic tremors prior to the eruption, which could be used as an indicator of imminent eruptive activity. The extracted patterns of seismicity and their temporal variations finally provide an explanation for the transition mechanism between eruptive phases.
One third of the world's population lives in areas where earthquakes causing at least slight damage are frequently expected. Thus, the development and testing of global seismicity models is essential to improving seismic hazard estimates and earthquake-preparedness protocols for effective disaster-risk mitigation. Currently, the availability and quality of geodetic data along plate-boundary regions provides the opportunity to construct global models of plate motion and strain rate, which can be translated into global maps of forecasted seismicity. Moreover, the broad coverage of existing earthquake catalogs facilitates in present-day the calibration and testing of global seismicity models. As a result, modern global seismicity models can integrate two independent factors necessary for physics-based, long-term earthquake forecasting, namely interseismic crustal strain accumulation and sudden lithospheric stress release.
In this dissertation, I present the construction of and testing results for two global ensemble seismicity models, aimed at providing mean rates of shallow (0-70 km) earthquake activity for seismic hazard assessment. These models depend on the Subduction Megathrust Earthquake Rate Forecast (SMERF2), a stationary seismicity approach for subduction zones, based on the conservation of moment principle and the use of regional "geodesy-to-seismicity" parameters, such as corner magnitudes, seismogenic thicknesses and subduction dip angles. Specifically, this interface-earthquake model combines geodetic strain rates with instrumentally-recorded seismicity to compute long-term rates of seismic and geodetic moment. Based on this, I derive analytical solutions for seismic coupling and earthquake activity, which provide this earthquake model with the initial abilities to properly forecast interface seismicity. Then, I integrate SMERF2 interface-seismicity estimates with earthquake computations in non-subduction zones provided by the Seismic Hazard Inferred From Tectonics based on the second iteration of the Global Strain Rate Map seismicity approach to construct the global Tectonic Earthquake Activity Model (TEAM). Thus, TEAM is designed to reduce number, and potentially spatial, earthquake inconsistencies of its predecessor tectonic earthquake model during the 2015-2017 period. Also, I combine this new geodetic-based earthquake approach with a global smoothed-seismicity model to create the World Hybrid Earthquake Estimates based on Likelihood scores (WHEEL) model. This updated hybrid model serves as an alternative earthquake-rate approach to the Global Earthquake Activity Rate model for forecasting long-term rates of shallow seismicity everywhere on Earth.
Global seismicity models provide scientific hypotheses about when and where earthquakes may occur, and how big they might be. Nonetheless, the veracity of these hypotheses can only be either confirmed or rejected after prospective forecast evaluation. Therefore, I finally test the consistency and relative performance of these global seismicity models with independent observations recorded during the 2014-2019 pseudo-prospective evaluation period. As a result, hybrid earthquake models based on both geodesy and seismicity are the most informative seismicity models during the testing time frame, as they obtain higher information scores than their constituent model components. These results support the combination of interseismic strain measurements with earthquake-catalog data for improved seismicity modeling. However, further prospective evaluations are required to more accurately describe the capacities of these global ensemble seismicity models to forecast longer-term earthquake activity.