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  BEAT : Bayesian Earthquake Analysis Tool

Vasyura-Bathke, H., Dettmer, J., Steinberg, A., Heimann, S., Isken, M. P., Zielke, O., Mai, P. M., Sudhaus, H., Jónsson, S.(2019): BEAT: Bayesian Earthquake Analysis Tool, Potsdam : GFZ Data Services.
https://doi.org/10.5880/fidgeo.2019.024

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Item Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5000161 Version Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5000161_1
Genre: Software

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 Creators:
Vasyura-Bathke, Hannes1, Author
Dettmer, Jan1, Author
Steinberg, Andreas2, Author              
Heimann, Sebastian2, Author              
Isken, Marius Paul2, Author              
Zielke, Olaf1, Author
Mai, Paul Martin1, Author
Sudhaus, H.2, Author              
Jónsson, Sigurjón1, Author
Affiliations:
1External Organizations, ou_persistent22              
22.1 Physics of Earthquakes and Volcanoes, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146029              

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Free keywords: Seismological, geodetic software, Python framework, Earthquake source parameter estimation, Bayesian Inference, Uncertainty Quantification, Finite Fault Inversion
 Abstract: BEAT is an open-source software tool for the robust characterization of the temporal and spatial evolution of earthquake rupture processes. It uses kinematic rupture models that include low-parametric models like Moment Tensors but also complex high-parametric, finite-extent sources. In other words, BEAT allows studying earthquakes on a first-order level as points with location, size and mechanisms. In consecutive steps, the complexity of the source model may be increased by various details up to the potential to resolve rupture dimension, fault segmentation, slip-distribution and slip-history. The source model parameters and their uncertainties are estimated based on seismic waveforms, and/or geodetic observations like InSAR and GNSS data. Rapid forward modeling is enabled by using pre-computed Green's function databases, handled through the Pyrocko software library. Based on these, synthetic data are provided for arbitrary earthquake rupture models embedded in heterogeneous media. For an extensive exploration of the often high-dimensional model parameter space, BEAT offers a suite of sampling algorithms for high-standard Bayesian inference. The implementations of these sampling algorithms exploit the parallel architecture of modern computers for optimal performance. Finally, BEAT offers easy configuration and automatic visualization of relevant results. The software relies on functionality from PYROCKO (Heimann et al., 2017) and KITE (optionally, Isken et al., 2017).

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Language(s): eng - English
 Dates: 2019
 Publication Status: Finally published
 Pages: -
 Publishing info: Potsdam : GFZ Data Services, V. 1.0
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.5880/fidgeo.2019.024
 Degree: -

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