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Spatial b-value distribution based on Gaussian process inference using a geological prior with focus on California and Turkey

Authors

von Specht,  Sebastian
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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

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

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

/persons/resource/hainzl

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

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Citation

von Specht, S., Holschneider, M., Zöller, G., Molkenthin, C., Hainzl, S. (2023): Spatial b-value distribution based on Gaussian process inference using a geological prior with focus on California and Turkey, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-4748


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5021156
Abstract
As a population parameter, reliable estimation of the b-value is intrinsically complicated, particularly when spatial variability is considered. We approach this issue by treating the spatial b-value distribution as a non-stationary Gaussian process for the underlying earthquake-realizing Poisson process. For Gaussian process inference the covariance—which describes here the spatial correlation of the b-value—must be specified a priori. We base the covariance on the local fault structure, i.e. the covariance is anisotropic: elongated along the dominant fault strike and shortened when normal to the fault trace. This adaptive feature captures the geological structure better than an isotropic covariance or similarly defined and commonly used running-window estimates of the b-value. We demonstrate the Bayesian inference of the Gaussian process b-value estimation for two regions: California based on SCEDC earthquake and Turkey based on the AFAD earthquake catalog. The covariances in the inferences are calibrated with the SCEC community fault model the GEM fault model for California and Turkey, respectively. Our model provides a continuous b-value estimate (including its uncertainties) which reflects the local fault structure to a very high degree. We are able to associate the b-value with the local seismicity distribution and link it to major faults. In light of the recent Turkish earthquake sequence, we also assess the temporal evolution of the b-value of recent seismicity before and after major events.