@phdthesis{Lilienkamp2024,
  author    = {Lilienkamp, Henning},
  title     = {Enhanced computational approaches for data-driven characterization of earthquake ground motion and rapid earthquake impact assessment},
  doi       = {10.25932/publishup-63195},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-631954},
  school      = {Universit{\"a}t Potsdam},
  pages     = {x, 145},
  year      = {2024},
  abstract  = {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.},
  language  = {en}
}
@article{MatzkaSiddiquiLilienkampetal.2017,
  author    = {Matzka, J{\"u}rgen and Siddiqui, Tarique Adnan and Lilienkamp, Henning and Stolle, Claudia and Veliz, Oscar},
  title     = {Quantifying solar flux and geomagnetic main field influence on the equatorial ionospheric current system at the geomagnetic observatory Huancayo},
  series = {Journal of Atmospheric and Solar-Terrestrial Physics},
  volume    = {163},
  journal   = {Journal of Atmospheric and Solar-Terrestrial Physics},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {1364-6826},
  doi       = {10.1016/j.jastp.2017.04.014},
  pages     = {120 -- 125},
  year      = {2017},
  abstract  = {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.},
  language  = {en}
}
@article{GomezZapataPittoreCottonetal.2022,
  author    = {Gomez-Zapata, Juan Camilo and Pittore, Massimiliano and Cotton, Fabrice and Lilienkamp, Henning and Shinde, Simantini and Aguirre, Paula and Santa Maria, Hernan},
  title     = {Epistemic uncertainty of probabilistic building exposure compositions in scenario-based earthquake loss models},
  series = {Bulletin of Earthquake Engineering},
  volume    = {20},
  journal   = {Bulletin of Earthquake Engineering},
  number    = {5},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1570-761X},
  doi       = {10.1007/s10518-021-01312-9},
  pages     = {2401 -- 2438},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{LilienkampvonSpechtWeatherilletal.2022,
  author    = {Lilienkamp, Henning and von Specht, Sebastian and Weatherill, Graeme and Caire, Giuseppe and Cotton, Fabrice},
  title     = {Ground-Motion modeling as an image processing task},
  series = {Bulletin of the Seismological Society of America},
  volume    = {112},
  journal   = {Bulletin of the Seismological Society of America},
  number    = {3},
  publisher = {Seismological Society of America},
  address   = {Albany},
  issn      = {0037-1106},
  doi       = {10.1785/0120220008},
  pages     = {1565 -- 1582},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}