1 / 12

Bayesian kinematic earthquake source models

Bayesian kinematic earthquake source models. S. E. Minson, M. Simons, J. L. Beck, J. F. Genrich , J. E. Galetzka , F. Chowdhury , S. E. Owen, F. Webb, D. Comte, B. Glass, C. Leiva , F. H. Ortega. T14A-06. Motivation.

halen
Download Presentation

Bayesian kinematic earthquake source models

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Bayesian kinematic earthquake source models S. E. Minson, M. Simons, J. L. Beck, J. F. Genrich, J. E. Galetzka, F. Chowdhury, S. E. Owen, F. Webb, D. Comte, B. Glass, C. Leiva, F. H. Ortega T14A-06

  2. Motivation • We want a robust & tractable method for combining geodetic and seismic data to constrain slip models for large earthquakes • We recognize inherent ill-posedness of the problem • There are many optimization techniques • Optimization only yields one solution • Requires prior decision on form of regularization • Full Bayesian methods produce PDFs with ensemble of models and their likelihoods • No functional regularization required • Allows quantitative questions to be asked of posterior PDFs

  3. Tempered Markov Chain Monte Carlo • Bayes theorem • p(θ|D) ∝ p(D|θ)• p(θ) • Tempering (cooling) • Fm(θ) ∝ p(D|θ)β p(θ) • Tempering helps sample highly peaked distributions • Amenable for exchange of information when parallelized • MCMC sampling by parallel Metropolis algorithm • Parallel = Efficient • Chains share information at each cooling step • Algorithm optimizes its sampling

  4. How to sampleunknown PDFs?

  5. Fm(θ) ∝ p(D|θ)β p(θ)

  6. Synthetic sampling tests Input Same Mw Under parameterized Over parameterized Output

  7. Sampling efficiency

  8. 2007 Mw 7.8 Tocopilla, Chile Track 96 Track 368 Data Model Misfit

  9. Chile-Peru Trench

  10. Cascaded sampling • Earthquake models can be divided into static and kinematic parameters • P(θ|D) ∝ p(Dk|θk,θs)• p(Ds|θs) • p(θs) • p(θk) • Cascade  • Sample static parameters • Use results as a priori distribution and sample kinematic parameters to produce full a posteriori distribution

  11. Kinematic inversion 10 sec 30 sec Slip [m] • Synthetic static data and regional waveforms 10 sec 30 sec

  12. Conclusions • Presented an efficient technique for high-dimensional Bayesian PDF sampling • Applicable to a wide range of geophysical models • Most important for under-determined problems • Application of Bayesian techniques to finite fault earthquake source models illuminates full model uncertainties without smoothing or regularization (beyond basic model parameterization) • Next up: Kinematic solutions with real data…

More Related