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Framework for Deterministic Earthquake Ground Motion Maps in Germany using Machine Learning

Authors

Steinberg,  Andreas
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Hobiger,  Manuel
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Gaebler,  Peter
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Vasyura-Bathke,  Hannes
2.1 Physics of Earthquakes and Volcanoes, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Azari Sisi,  Aida
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Steinberg, A., Hobiger, M., Gaebler, P., Vasyura-Bathke, H., Azari Sisi, A. (2023): Framework for Deterministic Earthquake Ground Motion Maps in Germany using Machine Learning, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-3261


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019785
Abstract
The surface effects of an earthquake are typically evaluated using ground motion maps. These ground motion maps are often based on empirical ground motion prediction equations (GMPEs), which however largely neglect source physics and, therefore, the radiation pattern of seismic energy. We present a framework for evaluating seismicity in Germany in which we calculate physics-based deterministic ground motion maps based on forward modeling of full seismic waveforms for different sources; isotropic sources for smaller earthquakes and full seismic moment tensors and finite rectangular sources for larger earthquakes. We use 1-D and 2-D Green’s function databases to rapidly consider a large number of statistically significant scenarios, including variations in Earth structure models, site effects, and source parameters. Basin effects are explicitly modelled. We apply machine learning to generate the ground motion maps even faster and incrementally improve the accuracy of our results with each earthquake based on a comparison between expected and measured peak ground velocity. To evaluate the accuracy of our approach, we compare the ground motion predictions from our waveform simulations to those obtained from regionalized probabilistic ground motion prediction equations (GMPEs) for a variety of synthetic cases. We quantify the differences between the two approaches. We carry out specific tests for an area in southern Germany with induced earthquakes around the two geothermal power plants of Insheim and Landau in the Upper Rhine Graben. We use locally measured passive measured velocity profiles to incorporate site effects in the form of VS30.