Accounting for theory errors with empirical Bayesian noise models in nonlinear centroid moment tensor estimation

Hannes Vasyura-Bathke, J Dettmer, Rishabh Dutta, Paul Martin Mai, Sigurjon Jonsson

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Summary Centroid moment-tensor (CMT) parameters can be estimated from seismic waveforms. Since these data indirectly observe the deformation process, CMTs are inferred as solutions to inverse problems which are generally under-determined and require significant assumptions, including assumptions about data noise. Broadly speaking, we consider noise to include both theory and measurement errors, where theory errors are due to assumptions in the inverse problem and measurement errors are caused by the measurement process. While data errors are routinely included in parameter estimation for full CMTs, less attention has been paid to theory errors related to velocity-model uncertainties and how these affect the resulting moment-tensor (MT) uncertainties. Therefore, rigorous uncertainty quantification for CMTs may require theory-error estimation which becomes a problem of specifying noise models. Various noise models have been proposed, and these rely on several assumptions. All approaches quantify theory errors by estimating the covariance matrix of data residuals. However, this estimation can be based on explicit modelling, empirical estimation, and/or ignore or include covariances. We quantitatively compare several approaches by presenting parameter and uncertainty estimates in non-linear full CMT estimation for several simulated data sets and regional field data of the Ml 4.4, 13 June 2015 Fox Creek, Canada, event. While our main focus is at regional distances, the tested approaches are general and implemented for arbitrary source model choice. These include known or unknown centroid locations, full MTs, deviatoric MTs, and double-couple MTs. We demonstrate that velocity-model uncertainties can profoundly affect parameter estimation and that their inclusion leads to more realistic parameter uncertainty quantification. However, not all approaches perform equally well. Including theory errors by estimating non-stationary (non-Toeplitz) error covariance matrices via iterative schemes during Monte Carlo sampling performs best and is computationally most efficient. In general, including velocity-model uncertainties is most important in cases where velocity structure is poorly known.
Original languageEnglish (US)
JournalGeophysical Journal International
StatePublished - Jan 25 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-02-08
Acknowledged KAUST grant number(s): BAS/1/1339-01-1, BAS/1/1353-01-01
Acknowledgements: Seismic waveforms were originally downloaded from Incorporated Research Institutions for Seismology (IRIS) We thank Babak Hejrani, Simon St¨ahler and the editor Andrew Valentine for their comments that helped to improve the quality of this manuscript.
Special thanks go to Sebastian Heimann, Frank Kr¨uger, Mehdi Nikkhoo and Olaf Zielke for valuable discussions. Plots were produced with Matplotlib and the Generic Mapping Tools (Hunter 2007; Wessel et al. 2013). This work employed the open source library py rocko ( (Heimann et al. 2017) and the Bayesian Earthquake Analysis Tool ( The 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.


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