The Bayesian Earthquake Analysis Tool

Hannes Vasyura-Bathke, Jan Dettmer, Andreas Steinberg, Sebastian Heimann, Marius Paul Isken, Olaf Zielke, Paul Martin Mai, Henriette Sudhaus, Sigurjon Jonsson

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Abstract : The Bayesian earthquake analysis tool (BEAT) is an open-source Python software to conduct source-parameter estimation studies for crustal deformation events, such as earthquakes and magma intrusions, by employing a Bayesian framework with a flexible problem definition. The software features functionality to calculate Green’s functions for a homogeneous or a layered elastic half-space. Furthermore, algorithm(s) that explore the solution space may be selected from a suite of implemented samplers. If desired, BEAT’s modular architecture allows for easy implementation of additional features, for example, alternative sampling algorithms. We demonstrate the functionality and performance of the package using five earthquake source estimation examples: a full moment-tensor estimation; a double-couple moment-tensor estimation; an estimation for a rectangular finite source; a static finite-fault estimation with variable slip; and a full kinematic finite-fault estimation with variable hypocenter location, rupture velocity, and rupture duration. This software integrates many aspects of source studies and provides an extensive framework for joint use of geodetic and seismic data for nonlinear source- and noise-covariance estimation within layered elastic half-spaces. Furthermore, the software also provides an open platform for further methodological development and for reproducible source studies in the geophysical community.
Original languageEnglish (US)
JournalSeismological Research Letters
StatePublished - Jan 22 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): BAS/1/1339-01-1, BAS/1/1353-01-01
Acknowledgements: The authors thank Jiří Vackář, Romain Jolivet, one anynomous reviewer, and the editors for their comments that helped to improve the quality of this article. This research was supported by King Abdullah University of Science and Technology (KAUST), under Award Numbers BAS/1/1353-01-01 and BAS/1/1339-01-1. H. V.-B. was partially supported by Geo.X, the Research Network for Geosciences in Berlin and Potsdam under the Project Number SO_087_GeoX. Henriette Sudhaus, Andreas Steinberg, and Marius Paul Isken acknowledge founding by the German Research Foundation (DFG) through an Emmy-Noether Young Researcher Grant Number 276464525. Hannes Vasyura-Bathke owes the most gratitude to his belove wife Olha for her tireless support and tolerance during many evenings and nights spent writing the code and this article.


Dive into the research topics of 'The Bayesian Earthquake Analysis Tool'. Together they form a unique fingerprint.

Cite this