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Today, near-surface investigations are frequently conducted using non-destructive or minimally invasive methods of applied geophysics, particularly in the fields of civil engineering, archaeology, geology, and hydrology. One field that plays an increasingly central role in research and engineering is the examination of sedimentary environments, for example, for characterizing near-surface groundwater systems. A commonly employed method in this context is ground-penetrating radar (GPR). In this technique, short electromagnetic pulses are emitted into the subsurface by an antenna, which are then reflected, refracted, or scattered at contrasts in electromagnetic properties (such as the water table). A receiving antenna records these signals in terms of their amplitudes and travel times. Analysis of the recorded signals allows for inferences about the subsurface, such as the depth of the groundwater table or the composition and characteristics of near-surface sediment layers. Due to the high resolution of the GPR method and continuous technological advancements, GPR data acquisition is increasingly performed in three-dimensional (3D) fashion today.
Despite the considerable temporal and technical efforts involved in data acquisition and processing, the resulting 3D data sets (providing high-resolution images of the subsurface) are typically interpreted manually. This is generally an extremely time-consuming analysis step. Therefore, representative 2D sections highlighting distinctive reflection structures are often selected from the 3D data set. Regions showing similar structures are then grouped into so-called radar facies. The results obtained from 2D sections are considered representative of the entire investigated area. Interpretations conducted in this manner are often incomplete and highly dependent on the expertise of the interpreters, making them generally non-reproducible.
A promising alternative or complement to manual interpretation is the use of GPR attributes. Instead of using the recorded data directly, derived quantities characterizing distinctive reflection structures in 3D are applied for interpretation. Using various field and synthetic data sets, this thesis investigates which attributes are particularly suitable for this purpose. Additionally, the study demonstrates how selected attributes can be utilized through specific processing and classification methods to create 3D facies models. The ability to generate attribute-based 3D GPR facies models allows for partially automated and more efficient interpretations in the future. Furthermore, the results obtained in this manner describe the subsurface in a reproducible and more comprehensive manner than what has typically been achievable through manual interpretation methods.
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.