Articles | Volume 7, issue 6
https://doi.org/10.5194/se-7-1521-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/se-7-1521-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
07 Nov 2016
Research article |
| 07 Nov 2016
Fully probabilistic seismic source inversion – Part 2: Modelling errors and station covariances
Simon C. Stähler
CORRESPONDING AUTHOR
Department of Earth and Environmental Sciences, Ludwig-Maximilians-Universität (LMU), Theresienstr. 41, 80333 Munich, Germany
Munich Centre of Advanced Computing, Department of Informatics, Technische Universität München, Munich, Germany
Leibniz Institute for Baltic Sea Research (IOW), Seestr. 15, 18119 Rostock, Germany
Karin Sigloch
Department of Earth Sciences, University of Oxford, South Parks Road, Oxford OX1 3AN, UK
Department of Earth and Environmental Sciences, Ludwig-Maximilians-Universität (LMU), Theresienstr. 41, 80333 Munich, Germany
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Cited
17 citations as recorded by crossref.
- Probabilistic Focal Mechanism Estimation Based on Body‐Wave Waveforms through Source‐Scanning Algorithm F. Massin & A. Malcolm 10.1785/0120170346
- The Marsquake Service: Securing Daily Analysis of SEIS Data and Building the Martian Seismicity Catalogue for InSight J. Clinton et al. 10.1007/s11214-018-0567-5
- Point‐Source Inversion of Small and Moderate Earthquakes From P‐wave Polarities and P/S Amplitude Ratios Within a Hierarchical Bayesian Framework: Implications for the Geysers Earthquakes X. Shang & H. Tkalčić 10.1029/2019JB018492
- Estimation of Full Moment Tensors, Including Uncertainties, for Nuclear Explosions, Volcanic Events, and Earthquakes C. Alvizuri et al. 10.1029/2017JB015325
- On the robustness of seismic moment tensor inversions for mid-ocean earthquakes: the Azores archipelago M. Frietsch et al. 10.1093/gji/ggy294
- Measuring Fundamental and Higher Mode Surface Wave Dispersion on Mars From Seismic Waveforms H. Xu et al. 10.1029/2020EA001263
- Crustal earthquakes in the Cook Inlet and Susitna region of southern Alaska V. Silwal et al. 10.1016/j.tecto.2018.08.013
- Estimation of Seismic Moment Tensors Using Variational Inference Machine Learning A. Steinberg et al. 10.1029/2021JB022685
- Bayesian ISOLA: new tool for automated centroid moment tensor inversion J. Vackář et al. 10.1093/gji/ggx158
- Bayesian Seismic Source Inversion With a 3‐D Earth Model of the Japanese Islands S. Simutė et al. 10.1029/2022JB024231
- Resolvability of the Centroid‐Moment‐Tensors for Shallow Seismic Sources and Improvements From Modeling High‐Frequency Waveforms B. Hejrani & H. Tkalčić 10.1029/2020JB019643
- Accounting for theory errors with empirical Bayesian noise models in nonlinear centroid moment tensor estimation H. Vasyura-Bathke et al. 10.1093/gji/ggab034
- Hamiltonian Monte Carlo Inversion of Seismic Sources in Complex Media A. Fichtner & S. Simutė 10.1002/2017JB015249
- Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism D. Piras et al. 10.1093/gji/ggac385
- A new automated procedure to obtain reliable moment tensor solutions of small to moderate earthquakes (3.0 ≤ M ≤ 5.5) in the Bayesian framework Y. Halauwet et al. 10.1093/gji/ggae309
- Waveform-based Bayesian full moment tensor inversion and uncertainty determination for the induced seismicity in an oil/gas field C. Gu et al. 10.1093/gji/ggx517
- Sensitivity of InSAR and teleseismic observations to earthquake rupture segmentation A. Steinberg et al. 10.1093/gji/ggaa351
17 citations as recorded by crossref.
- Probabilistic Focal Mechanism Estimation Based on Body‐Wave Waveforms through Source‐Scanning Algorithm F. Massin & A. Malcolm 10.1785/0120170346
- The Marsquake Service: Securing Daily Analysis of SEIS Data and Building the Martian Seismicity Catalogue for InSight J. Clinton et al. 10.1007/s11214-018-0567-5
- Point‐Source Inversion of Small and Moderate Earthquakes From P‐wave Polarities and P/S Amplitude Ratios Within a Hierarchical Bayesian Framework: Implications for the Geysers Earthquakes X. Shang & H. Tkalčić 10.1029/2019JB018492
- Estimation of Full Moment Tensors, Including Uncertainties, for Nuclear Explosions, Volcanic Events, and Earthquakes C. Alvizuri et al. 10.1029/2017JB015325
- On the robustness of seismic moment tensor inversions for mid-ocean earthquakes: the Azores archipelago M. Frietsch et al. 10.1093/gji/ggy294
- Measuring Fundamental and Higher Mode Surface Wave Dispersion on Mars From Seismic Waveforms H. Xu et al. 10.1029/2020EA001263
- Crustal earthquakes in the Cook Inlet and Susitna region of southern Alaska V. Silwal et al. 10.1016/j.tecto.2018.08.013
- Estimation of Seismic Moment Tensors Using Variational Inference Machine Learning A. Steinberg et al. 10.1029/2021JB022685
- Bayesian ISOLA: new tool for automated centroid moment tensor inversion J. Vackář et al. 10.1093/gji/ggx158
- Bayesian Seismic Source Inversion With a 3‐D Earth Model of the Japanese Islands S. Simutė et al. 10.1029/2022JB024231
- Resolvability of the Centroid‐Moment‐Tensors for Shallow Seismic Sources and Improvements From Modeling High‐Frequency Waveforms B. Hejrani & H. Tkalčić 10.1029/2020JB019643
- Accounting for theory errors with empirical Bayesian noise models in nonlinear centroid moment tensor estimation H. Vasyura-Bathke et al. 10.1093/gji/ggab034
- Hamiltonian Monte Carlo Inversion of Seismic Sources in Complex Media A. Fichtner & S. Simutė 10.1002/2017JB015249
- Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism D. Piras et al. 10.1093/gji/ggac385
- A new automated procedure to obtain reliable moment tensor solutions of small to moderate earthquakes (3.0 ≤ M ≤ 5.5) in the Bayesian framework Y. Halauwet et al. 10.1093/gji/ggae309
- Waveform-based Bayesian full moment tensor inversion and uncertainty determination for the induced seismicity in an oil/gas field C. Gu et al. 10.1093/gji/ggx517
- Sensitivity of InSAR and teleseismic observations to earthquake rupture segmentation A. Steinberg et al. 10.1093/gji/ggaa351
Discussed (final revised paper)
Latest update: 14 Dec 2024
Short summary
Seismic source inversion is the method of inferring the spatial orientation of an earthquake source from seismic records. The results come with large uncertainties, which we try to estimate in a Bayesian approach. We propose an empirical relationship for the likelihood function based on a large dataset of deterministic solutions. This allows using the cross-correlation coefficient as a misfit criterion, which is better suited for waveform comparison than the popular root mean square or L2 norm.
Seismic source inversion is the method of inferring the spatial orientation of an earthquake...
