TY - JOUR A1 - Gomez-Zapata, Juan Camilo A1 - Pittore, Massimiliano A1 - Cotton, Fabrice A1 - Lilienkamp, Henning A1 - Shinde, Simantini A1 - Aguirre, Paula A1 - Santa Maria, Hernan T1 - Epistemic uncertainty of probabilistic building exposure compositions in scenario-based earthquake loss models JF - Bulletin of Earthquake Engineering N2 - In seismic risk assessment, the sources of uncertainty associated with building exposure modelling have not received as much attention as other components related to hazard and vulnerability. Conventional practices such as assuming absolute portfolio compositions (i.e., proportions per building class) from expert-based assumptions over aggregated data crudely disregard the contribution of uncertainty of the exposure upon earthquake loss models. In this work, we introduce the concept that the degree of knowledge of a building stock can be described within a Bayesian probabilistic approach that integrates both expert-based prior distributions and data collection on individual buildings. We investigate the impact of the epistemic uncertainty in the portfolio composition on scenario-based earthquake loss models through an exposure-oriented logic tree arrangement based on synthetic building portfolios. For illustrative purposes, we consider the residential building stock of Valparaiso (Chile) subjected to seismic ground-shaking from one subduction earthquake. We have found that building class reconnaissance, either from prior assumptions by desktop studies with aggregated data (top-down approach), or from building-by-building data collection (bottom-up approach), plays a fundamental role in the statistical modelling of exposure. To model the vulnerability of such a heterogeneous building stock, we require that their associated set of structural fragility functions handle multiple spectral periods. Thereby, we also discuss the relevance and specific uncertainty upon generating either uncorrelated or spatially cross-correlated ground motion fields within this framework. We successively show how various epistemic uncertainties embedded within these probabilistic exposure models are differently propagated throughout the computed direct financial losses. This work calls for further efforts to redesign desktop exposure studies, while also highlighting the importance of exposure data collection with standardized and iterative approaches. KW - Epistemic uncertainty KW - Sensitivity analysis KW - Scheme KW - Faceted taxonomy KW - Probabilistic exposure modelling KW - Earthquake scenario KW - Data collection KW - Earthquake loss modelling KW - Spatially cross-correlated ground motion KW - fields Y1 - 2022 U6 - https://doi.org/10.1007/s10518-021-01312-9 SN - 1570-761X SN - 1573-1456 N1 - Update notice Correction to: Epistemic uncertainty of probabilistic building exposure compositions in scenario-based earthquake loss models (Bulletin of Earthquake Engineering, (2022), 20, 5, (2401-2438), https://doi.org/10.1007/s10518-021-01312-9) Bulletin of Earthquake Engineering, Volume 20, Issue 5, Pages 2439, March 2022, https://doi.org/10.1007/s10518-022-01340-z VL - 20 IS - 5 SP - 2401 EP - 2438 PB - Springer CY - Dordrecht ER - TY - THES A1 - Lilienkamp, Henning T1 - Enhanced computational approaches for data-driven characterization of earthquake ground motion and rapid earthquake impact assessment T1 - Fortgeschrittene Berechnungsansätze für die datengestützte Charakterisierung von Erdbeben-Bodenbewegungen und die schnelle Einschätzung von Erdbebenauswirkungen N2 - 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. N2 - Das rapide Wachstum seismischer und makroseismischer Datenbanken und der vereinfachte Zugang zu fortschrittlichen Methoden aus dem Bereich des maschinellen Lernens haben in den letzen Jahren die datenfokussierte Betrachtung von Fragestellungen in der Seismologie ermöglicht. In dieser Arbeit erforsche ich das Potenzial solcher Betrachtungsweisen im Hinblick auf die Modellierung erdbebenbedingter Bodenerschütterungen und der raschen Einschätzung von gesellschaftlichen Erdbebenauswirkungen, Disziplinen von erheblicher Bedeutung für den langfristigen Erdbebenkatastrophenschutz in seismisch aktiven Regionen. In meiner ersten Studie nutze ich die Vielzahl an Bodenbewegungsdaten aus der Kanto Region in Japan, sowie eine spezielle neuronale Netzwerkarchitektur (U-Net) um ein Bodenbewegungsmodell zu entwickeln. Der einsatzbereite Prototyp liefert auf Basis der Charakterisierung von Erdbebenherden, Wellenausbreitungspfaden und Bodenbeschaffenheiten statistische Schätzungen der zu erwartenden Bodenerschütterungen. Das U-Net interpretiert Bodenbewegungsdaten im räumlichen Kontext, sodass etwa die geologischen Beschaffenheiten in der Umgebung von Messstationen mit einbezogen werden können. Auch die absoluten Koordinaten von Erdbebenherden und Messstationen werden berücksichtigt. Die zweite Studie behandelt die explizite Berücksichtigung richtungsabhängiger Verstärkungseffekte in der Bodenbewegungsmodellierung. Obwohl solche Effekte starke, impulsartige Erschütterungen in der Nähe von Erdbebenherden erzeugen, die eine erhebliche seismische Beanspruchung von Gebäuden darstellen, wird deren explizite Modellierung in der seismischen Gefährdungsabschätzung aufgrund des nicht vertretbaren Rechenaufwandes ausgelassen. Mit meinem, auf einem neuronalen Netzwerk basierenden, Ansatz schlage ich eine Methode vor, umdieses Vorhaben effizient für Erdbebenszenarien aus dem neuseeländischen seismischen Gefährdungsmodell für 2022 (NSHM) umzusetzen. Die Implementierung in einer seismischen Gefährdungsrechnung unterstreicht die Praktikabilität meines Modells. In einer anschließenden Machbarkeitsstudie untersuche ich einen alternativen Ansatz der auf die Anwendbarkeit auf beliebige Erdbebeszenarien abzielt. Die abschließende dritte Studie befasst sich mit dem potenziellen Nutzen der von makroseismischen Beobachtungen abgeleiteten pseudo-Erschütterungsintensitäten für die rasche Abschätzung von gesellschaftlichen Erdbebenauswirkungen. Ich zeige, dass sich aus den Merkmalen solcher Daten Schlussfolgerungen über die gesellschaftlichen Folgen eines Erdbebens ableiten lassen. Basierend darauf formuliere ich ein statistisches Modell, welches innerhalb weniger Minuten nach einem Erdbeben die Wahrscheinlichkeit für das Auftreten beachtlicher gesellschaftlicher Auswirkungen liefert. Ich komme zu dem Schluss, dass ein solches Modell, unter bestimmten Bedingungen, hilfreich sein könnte, um EntscheidungsträgerInnen in ihren Bestrebungen Hilfsmaßnahmen zu organisieren zu unterstützen. Die Anwendung von Methoden des maschinellen Lernens auf Datensätze die sich nur begrenzt als Big Data charakterisieren lassen, qualifizieren die Mehrheit der Ergebnisse dieser Arbeit als explorative Einblicke und weniger als einsatzbereite Lösungen für praktische Fragestellungen. Der praktische Nutzen dieser Arbeit wird sich in erst in Zukunft an der Anwendung der erarbeiteten Ansätze auf wachsende und zunehmend komplexe Datensätze final abschätzen lassen. KW - seismology KW - machine learning KW - deep learning KW - ground motion modeling KW - seismic hazard KW - rapid earthquake impact assessment KW - geophysics KW - Deep Learning KW - Geophysik KW - Bodenbewegungsmodellierung KW - maschinelles Lernen KW - schnelle Einschätzung von Erdbebenauswirkungen KW - seismische Gefährdung KW - Seismologie Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-631954 ER - TY - JOUR A1 - Lilienkamp, Henning A1 - von Specht, Sebastian A1 - Weatherill, Graeme A1 - Caire, Giuseppe A1 - Cotton, Fabrice T1 - Ground-Motion modeling as an image processing task BT - introducing a neural network based, fully data-driven, and nonergodic JF - Bulletin of the Seismological Society of America N2 - We construct and examine the prototype of a deep learning-based ground-motion model (GMM) that is both fully data driven and nonergodic. We formulate ground-motion modeling as an image processing task, in which a specific type of neural network, the U-Net, relates continuous, horizontal maps of earthquake predictive parameters to sparse observations of a ground-motion intensity measure (IM). The processing of map-shaped data allows the natural incorporation of absolute earthquake source and observation site coordinates, and is, therefore, well suited to include site-, source-, and path-specific amplification effects in a nonergodic GMM. Data-driven interpolation of the IM between observation points is an inherent feature of the U-Net and requires no a priori assumptions. We evaluate our model using both a synthetic dataset and a subset of observations from the KiK-net strong motion network in the Kanto basin in Japan. We find that the U-Net model is capable of learning the magnitude???distance scaling, as well as site-, source-, and path-specific amplification effects from a strong motion dataset. The interpolation scheme is evaluated using a fivefold cross validation and is found to provide on average unbiased predictions. The magnitude???distance scaling as well as the site amplification of response spectral acceleration at a period of 1 s obtained for the Kanto basin are comparable to previous regional studies. Y1 - 2022 U6 - https://doi.org/10.1785/0120220008 SN - 0037-1106 SN - 1943-3573 VL - 112 IS - 3 SP - 1565 EP - 1582 PB - Seismological Society of America CY - Albany ER - TY - JOUR A1 - Matzka, Jürgen A1 - Siddiqui, Tarique Adnan A1 - Lilienkamp, Henning A1 - Stolle, Claudia A1 - Veliz, Oscar T1 - Quantifying solar flux and geomagnetic main field influence on the equatorial ionospheric current system at the geomagnetic observatory Huancayo JF - Journal of Atmospheric and Solar-Terrestrial Physics N2 - In order to analyse the sensitivity of the equatorial ionospheric current system, i.e. the solar quiet current system and the equatorial electrojet, to solar cycle variations and to the secular variation of the geomagnetic main field, we have analysed 51 years (1935-1985) of geomagnetic observatory data from Huancayo, Peru. This period is ideal to analyse the influence of the main field strength on the amplitude of the quiet daily variation, since the main field decreases significantly from 1935 to 1985, while the distance of the magnetic equator to the observatory remains stable. To this end, we digitised some 19 years of hourly mean values of the horizontal component (H), which have not been available digitally at the World Data Centres. Then, the sensitivity of the amplitude Ali of the quiet daily variation to both solar cycle variations (in terms of sunspot numbers and solar flux F10.7) and changes of the geomagnetic main field strength (due to secular variation) was determined. We confirm an increase of Delta H for the decreasing main field in this period, as expected from physics based models (Cnossen, 2016), but with a somewhat smaller rate of 4.4% (5.8% considering one standard error) compared with 6.9% predicted by the physics based model. KW - Magnetic field KW - Equatorial ionosphere KW - Geomagnetic secular variation KW - Solar cycle Y1 - 2017 U6 - https://doi.org/10.1016/j.jastp.2017.04.014 SN - 1364-6826 SN - 1879-1824 VL - 163 SP - 120 EP - 125 PB - Elsevier CY - Oxford ER -