Trans-dimensional finite-fault inversion

Geophysical Journal International

Volumn 199, Issue 2, 2014, Pages 735-751

  Dettmer, Jan ^a,b   Benavente, Roberto ^a   Cummins, Phil R ^a   Sambridge, Malcolm ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b UNIVERSITY OF VICTORIA   (Canada)

Author keywords

Computational seismology; Early warning; Earthquake source observations; Inverse theory; Probability distributions; Statistical seismology

Indexed keywords

BAYESIAN NETWORKS; COMPUTATION THEORY; COVARIANCE MATRIX; EARTHQUAKES; GEOMETRICAL OPTICS; ITERATIVE METHODS; PROBABILITY DISTRIBUTIONS; UNCERTAINTY ANALYSIS; VELOCITY;

COMPUTATIONAL SEISMOLOGIES; EARLY WARNING; EARTHQUAKE SOURCE OBSERVATIONS; INVERSE THEORY; STATISTICAL SEISMOLOGIES;

FAULTING;

ALGORITHM; BAYESIAN ANALYSIS; DATA INVERSION; EARLY WARNING SYSTEM; EARTHQUAKE EVENT; EARTHQUAKE RUPTURE; FAULT SLIP; SEISMIC DATA; SEISMIC SOURCE; SEISMOLOGY;

CHILE; MAULE;

EID: 84961947070     PISSN: 0956540X     EISSN: 1365246X     Source Type: Journal    
DOI: 10.1093/gji/ggu280     Document Type: Article

References (63)

1. Aster, R.C., Borchers, B. & Thurber, C.H., 2005. Parameter Estimation and Inverse Problems, pp. 195-402, International Geophysics, Elsevier.
2. Benavente, R. & Cummins, P.R., 2013. Simple and reliable finite fault solutions for large earthquakes using theW-phase: the Maule (Mw = 8.8) and Tohoku (Mw = 9.0) earthquakes, Geophys. Res. Lett., 40, 3591-3595.
3. Beresnev, I.A., 2003. Uncertainties in finite-fault slip inversions: to what extent to believe? (a critical review), Bull. seism. Soc. Am., 93, 2445- 2458.
4. Bodin, T. & Sambridge, M., 2009. Seismic tomography with the reversible jump algorithm, Geophys. J. Int., 178, 1411-1436.
5. Bodin, T., Sambridge,M.&Gallagher, K., 2009. A self-parametrizing partition model approach to tomographic inverse problems, Inverse Problems, 25, 055009, doi:10.1088/0266-5611/25/5/055009.
6. Bodin, T., Sambridge, M., Tkalčić, H., Arroucau, P., Gallagher, K. & Rawlinson, N., 2012. Transdimensional inversion of receiver functions and surface wave dispersion, J. geophys. Res., 117, B02301, doi:10.1029/2011JB008560.
7. Brooks, S., Gelman, A., Jones, G. & Meng, X., Eds., 2011. Handbook of Markov Chain Monte Carlo, pp. 1-592, Springer.
8. Dettmer, J. & Dosso, S.E., 2012. Trans-dimensional matched-field geoacoustic inversion with hierarchical error models and interacting Markov chains, J. acoust. Soc. Am., 132, 2239-2250.
9. Dettmer, J. & Dosso, S.E., 2013. Probabilistic two-dimensional watercolumn and seabed inversion with self-adapting parameterizations, J. acoust. Soc. Am., 133, 2612-2623.
10. Dettmer, J., Dosso, S.E. & Holland, C.W., 2007. Uncertainty estimation in seismo-acoustic reflection travel-time inversion, J. acoust. Soc. Am., 122, 161-176.
11. Dettmer, J., Dosso, S.E. & Holland, C.W., 2008. Joint time/frequencydomain inversion of reflection data for seabed geoacoustic profiles, J. acoust. Soc. Am., 123, 1306-1317.
12. Dettmer, J., Dosso, S.E. & Holland, C.W., 2009. Model selection and Bayesian inference for high resolution seabed reflection inversion, J. acoust. Soc. Am., 125, 706-716.
13. Dettmer, J., Dosso, S.E. & Holland, C.W., 2010a. Trans-dimensional geoacoustic inversion, J. acoust. Soc. Am., 128, 3393-3405.
14. Dettmer, J., Dosso, S.E. & Osler, J.C., 2010b. Bayesian evidence computation for model selection in non-linear geoacoustic inference problems, J. acoust. Soc. Am., 128, 3406-3415.
15. Dettmer, J., Molnar, S., Steininger, G.A.M.W., Dosso, S.E. & Cassidy, J.F., 2012. Trans-dimensional inversion of microtremor array dispersion data with hierarchical autoregressive error models, Geophys. J. Int., 188, 719- 734.
16. Dettmer, J., Holland, C.W. & Dosso, S.E., 2013. Trans-dimensional uncertainty estimation for dispersive seabed sediments, Geophysics, 78, WB63-WB76.
17. Dosso, S.E., 2002. Quantifying uncertainty in geoacoustic inversion. I. A fast Gibbs sampler approach, J. acoust. Soc. Am., 111, 129-142.
18. Dosso, S.E. &Wilmut, M.J., 2006. Data uncertainty estimation in matchedfield geoacoustic inversion, IEEE J. Ocean. Eng., 31, 470-479.
19. Dosso, S.E. &Wilmut,M.J., 2008. Uncertainty estimation in simulataneous Bayesian tracking and environmental inversion, J. acoust. Soc. Am., 124, 82-97.
20. Duputel, Z., Agram, P.S., Simons, M., Minson, S.E. & Beck, J.L., 2014. Accounting for prediction uncertainty when inferring subsurface fault slip, Geophys. J. Int., 197, 464-482.
21. Dziewonski, A.M. & Anderson, D.L., 1981. Preliminary reference Earth model, Phys. Earth planet. Inter., 25, 297-356.
22. Fukuda, J. & Johnson, K.M., 2008. A fully Bayesian inversion for spatial distribution of fault slip with objective smoothing, Bull. seism. Soc. Am., 98, 1128-1146.
23. Fukuda, J. & Johnson, K.M., 2010. Mixed linear-non-linear inversion of crustal deformation data: Bayesian inference of model, weighting and regularization parameters, Geophys. J. Int., 181, 1441-1458.
24. Geyer, C.&Moller, J., 1994. Simulation procedures and likelihood inference for spatial point processes, Scand. J. Stat., 21, 359-373.
25. Geyer, C.J., 1991. Markov chain Monte Carlo maximum likelihood, in Computing Science and Statistics: Proceedings of the 23rd Symposium on the Interface, pp. 156-163.
26. Godsill, S.&Clapp, T., 2001. Improvement strategies forMonte Carlo particle filters, in Sequential Monte Carlo in Practice, pp. 139-158, Springer.
27. Green, P.J., 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, 82, 711-732.
28. Green, P.J., 2003. Trans-dimensional Markov chain Monte Carlo, in Highly Structured Stochastic Systems, pp. 179-198, Oxford Statistical Science Series, Oxford University Press.
29. Guo, R., Dosso, S., Liu, J., Dettmer, J. & Tong, X., 2011. Nonlinearity in Bayesian 1-D magnetotelluric inversion, Geophys. J. Int., 185, 663-675.
30. Hartzell, S.H., 1989. Comparison of seismic waveform inversion results for the rupture history of a finite fault: application to the 1986 North Palm Springs, California, earthquake, J. geophys. Res., 94, 7515- 7534.
31. Hartzell, S.H. & Heaton, T.H., 1983. Inversion of strong ground motion and teleseismic waveform data for the fault rupture history of the 1979 Imperial Valley, California, earthquake, Bull. seism. Soc. Am., 73, 1553- 1583.
32. Hartzell, S.H., Liu, P. & Mendoza, C., 1996. The 1994 Northridge, California, earthquake: investigation of rupture velocity, risetime, and highfrequency radiation, J. geophys. Res., 101, 20 091-20 108.
33. Holland, C.W.,Dettmer, J.&Dosso, S.E., 2005. Remote sensing of sediment density and velocity gradients in the transition layer, J. acoust. Soc. Am., 118, 163-177.
34. Ide, K., 2007. Slip inversion, in Treatise on Geophysics, Vol. 4, pp. 193-223, Elsevier.
35. Jarzynski, C., 1997. Nonequilibrium equality for free energy differences, Phys. Rev. Lett., 78, 2690-2693.
36. Jasra, A., Stephens,D.A.&Holmes, C.C., 2007. Population-based reversible jump Markov chain Monte Carlo, Biometrika, 94, 787-807.
37. Kanamori, H., 1993. W phase, Geophys. Res. Lett., 20, 1691-1694.
38. Kanamori, H. & Rivera, L., 2008. Source inversion of W phase: speeding up seismic tsunami warning, Geophys. J. Int., 175, 222-238.
39. Koper, K.D., Hutko, A.R., Lay, T. & Sufri, O., 2012. Imaging shortperiod seismic radiation from the 27 February 2010 Chile (Mw 8.8) earthquake by back-projection of P, PP, and PKIKP waves, J. geophys. Res., 117, B02308, doi:10.1029/2011JB008576.
40. MacKay, D.J.C., 2003. Information Theory, Inference, and Learning Algorithms, pp. 343-386, Cambridge Univ. Press.
41. Malinverno, A., 2002. Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem, Geophys. J. Int., 151, 675- 688.
42. Malinverno, A.&Briggs,V.A., 2004. Expanded uncertainty quantification in inverse problems: hierarchichal Bayes and empirical Bayes, Geophysics, 69, 1005-1016.
43. Matsu'ura, M., Noda, A. & Fukahata, Y., 2007. Geodetic data inversion based on Bayesian formulation with direct and indirect prior information, Geophys. J. Int., 171, 1342-1351.
44. Menke, W., 2012. Geophysical Data Analysis: Discrete Inverse Theory, Academia Press.
45. Minsley, B.J., 2011. A trans-dimensional Bayesian Markov chain Monte Carlo algorithm for model assessment using frequency-domain electromagnetic data, Geophys. J. Int., 187, 252-272.
46. Minson, S.E., Simons, M. & Beck, J.L., 2013. Bayesian inversion for finite fault earthquake source models I-theory and algorithm, Geophys. J. Int., 194, 1701-1726.
47. Minson, S.E. et al., 2014. Bayesian inversion for finite fault earthquake source models II: the 2011 great Tohoku-Oki, Japan earthquake, Geophys. J. Int., 198, 922-940.
48. Neal, R.M., 2001. Annealed importance sampling, Stat. Comp., 11, 125- 139.
49. Olsen, A.H. & Apsel, R.J., 1982. Finite faults and inverse theory with applications to the 1979 Imperial Valley earthquake, Bull. seism Soc. Am., 72, 1969-2001.
50. Podvin, P. & Lecomte, I., 1991. Finite difference computation of traveltimes in very contrasted velocity models: a massively parallel approach and its associated tools, Geophys. J. Int., 105, 271-284.
51. Ray, A. & Key, K., 2012. Bayesian inversion of marine CSEM data with a trans-dimensional self parametrizing algorithm, Geophys. J. Int., 191, 1135-1151.
52. Sambridge, M., 2014. A parallel tempering algorithm for probabilistic sampling and multimodal optimization, Geophys. J. Int., 196, 357- 374.
53. Sambridge, M., Brain, J. & McQueen, H., 1995. Geophysical parametrization and interpolation of irregular data using natural neighbours, Geophys. J. Int., 122, 837-857.
54. Sambridge, M., Gallagher, K., Jackson, A. & Rickwood, P., 2006. Transdimensional inverse problems, model comparison and the evidence, Geophys. J. Int., 167, 528-542.
55. Schwarz, G., 1978. Estimating the dimension of a model, Ann. Stat., 6, 461-464.
56. Simons, M. et al., 2011. The 2011 magnitude 9.0 Tohoku-Oki earthquake: mosaicking the megathrust from seconds to centuries, Science, 332, 1421- 1425.
57. Sivia, D.S. & Skilling, J., 2006. Data Analysis: A Bayesian Tutorial, Oxford Science Publications.
58. Steininger, G.A.M.W., Holland, C.W., Dosso, S.E. & Dettmer, J., 2013. Seabed roughness parameters from joint backscatter and reflection inversion at the Malta Plateau, J. acoust. Soc. Am., 134, 1833-1842.
59. Tarantola, A., 2005. Inverse Problem Theory and Methods for Model Parameter Estimation, pp. 1-57, SIAM.
60. Vigny, C. et al., 2011. The 2010 Mw 8.8 Maule megathrust earthquake of Central Chile, monitored by GPS, Science, 332, 1417-1421.
61. Voronoi, M.G., 1908. Nouvelles applications des parametres continus a la theorie des formes quadratiques, J. rein. Angew. Math., 134, 198- 287.
62. Yabuki, T. & Matsu'ura, M., 1992. Geodetic data inversion using a Bayesian information criterion for spatial distribution of fault slip, Geophys. J. Int., 109, 363-375.
63. Yagi, Y. & Fukahata, Y., 2011. Introduction of uncertainty of Green's function into waveform inversion for seismic source processes, Geophys. J. Int., 186, 711-720.