Geosciences Journal

, Volume 12, Issue 4, pp 419–428 | Cite as

Local crustal structures of southern Korea from joint analysis of waveforms and travel times

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Abstract

A joint inversion technique of waveforms and travel times is applied to broadband seismic data from a local earthquake in order to estimate local crustal velocity structures of southern Korea. Combining the waveform and travel time inversion techniques, we can surmount the demerits of the techniques: the distortion of deep velocity structure in the waveform inversion caused by low signal-to-noise ratio (SNR), the velocity-depth trade-off, and errors in picking phases in the travel time inversion. The purpose of this study is not only for verifying whether the technique is performed well in the local areas where the number of stations is limited, but also for estimating the velocity structures of the areas that have been little investigated. We adopted the genetic algorithm (GA) as a search algorithm, since we could not expect appropriate initial models due to little a priori information about crustal velocity structure. Both broadband waveforms bandpassed between 0.05 Hz and 0.3 Hz and travel times of Pg, Pn, and PmP waves from the 26 April 2004 Daegu earthquake (ML=3.9) were used as input data. We performed the joint inversion ten times or more for each local area, and adopted the averaged model of optimal models to acquire credible crustal structure. Synthetic waveforms and travel time curves obtained from the estimated velocity models were generally agreed with observed seismograms, and the estimated source depths from the velocity models of the three local areas are similar to and consistent with each other. Therefore, we believe that the joint inversion technique is still applicable to local areas where the number of stations is limited.

Key words

crustal velocity structure joint analysis genetic algorithm southern Korea 
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References

  1. Aki, K. and Richards, P.G., 1980, Quantitative seismology: Theory and methods. W.H. Freeman and company, San Francisco, California, 700 p.Google Scholar
  2. Baag, C.E. and Langston, C.A., 1985, A WKBJ spectral method for computation of SV synthetic seismograms in a cylindrically symmetric medium. Geophysical Journal of the Royal Astronomical Society, 80, 387–417.Google Scholar
  3. Baag, C.E. and Baag, C., 1988, An extended reflectivity method for the P-SV-Rayleigh wave system: theory. Journal of the Geological Society of Korea, 24, 489–499.Google Scholar
  4. Berteussen, K.A., 1977, Moho depth determinations based on spectral ratio analysis of NORSAR long-period P waves, Physics of the Earth and Planetary Interiors, 15, 13–27.CrossRefGoogle Scholar
  5. Bhattacharyya, J., Sheehan, A.F., Tiampo, K., and Rundle J., 1999, Using a Genetic Algorithm to model broadband regional waveforms for crustal structure in the western United States. Bulletin of the Seismological Society of America, 89, 202–214.Google Scholar
  6. Billings, S., Kennett, B., and Sambridge, M., 1994, Hypocenter location: genetic algorithms incorporating problem specific information. Geophysical Journal International, 118, 693–706.CrossRefGoogle Scholar
  7. Bondár, I., Myers, S.C., Engdahl, E.R., and Bergman, E.A., 2004, Epicentre accuracy based on seismic network criteria, Geophysical Journal International, 156, 483–496.CrossRefGoogle Scholar
  8. Boschetti, F., Dentith, M.C., and List, R.D., 1996, Inversion of seismic refraction data using genetic algorithms. Geophysics, 61, 1715–1727.CrossRefGoogle Scholar
  9. Carroll, D.L., 1996, Genetic Algorithms and Optimizing Chemical Oxygen-Iodine Lasers. In: Wilson, H.B., Batra, R.C., Bert, C.W., Davis, A.M.J., Schapery, R.A., Stewart, D.S., and Swinson, F.F. (eds), Developments in Theoretical and Applied Mechanics, Vol. 18, School of Engineering, The University of Alabama, Tuscaloosa, p. 411–424.Google Scholar
  10. Chang, S.-J., Baag, C.-E., and Langston, C.A., 2004, Joint analysis of teleseismic receiver functions and surface wave dispersion using the genetic algorithm. Bulletin of the Seismological Society of America, 94, 691–704.CrossRefGoogle Scholar
  11. Chang, S.-J. and Baag, C.-E., 2006, Crustal structure in southern Korea from joint analysis of teleseismic receiver functions and surface wave dispersion, Bulletin of the Seismological Society of America, 95 1516–1534.CrossRefGoogle Scholar
  12. Chang, S.-J. and Baag, C.-E., 2007, Moho depth and crustal VP/VS variation in southern Korea from teleseismic receiver functions: Implication for tectonic affinity between the Korean peninsula and China. Bulletin of the Seismological Society of America, 97, 1621–1631.CrossRefGoogle Scholar
  13. Chang, S.-J. and Baag, C.-E., 2006, Crustal structure in southern Korea from joint analysis of regional broadband waveforms and travel times. Bulletin of the Seismological Society of America, 96, 856–870.CrossRefGoogle Scholar
  14. Cho, H.-M., Baag, C.-E., Lee, J.M., Moon, W.M., Jung, H., Kim, K.Y., and Asudeh, I., 2006, Crustal velocity structure across the southern Korean Peninsula from seismic refraction survey. Geophysical Research Letters, 33, L06307, doi:10.1029/2005GL025145.CrossRefGoogle Scholar
  15. Choi, K.-S. and Shin, Y.-H., 1996, Isostasy in and around the Korean Peninsula by analyzing gravity and topography data. Journal of the Geological Society of Korea, 32, 407–420. (in Korean)Google Scholar
  16. Crosson, R.S., 1976a, Crustal structure modeling of earthquake data 1, Simultaneous least squares estimation of hypocenter and velocity parameters. Journal of Geophysical Research, 81, 3036–3046.CrossRefGoogle Scholar
  17. Crosson, R.S., 1976b, Crustal structure modeling of earthquake data 2, Velocity structure of the Puget Sound region, Washington. Journal of Geophysical Research, 81, 3047–3054.CrossRefGoogle Scholar
  18. Crosson, R.S. and Koyanagi, R.Y., 1979, Seismic velocity structure below the Island of Hawaii from local earthquake data. Journal of Geophysical Research, 84, 2331–2342.CrossRefGoogle Scholar
  19. Curtis, A., Dost, B., Trampert, J., and Snieder, R., 1995, Shear wave velocity structure beneath Eurasia from surface wave group and phase velocities in an inverse problem reconditioned using the genetic algorithm. EOS, 76, 386.CrossRefGoogle Scholar
  20. Dreger, D.S. and Helmberger, D.V., 1990, Broadband modeling of local earthquakes. Bulletin of the Seismological Society of America, 80, 1162–1179.Google Scholar
  21. Drijkoningen, G.G. and White, R.S., 1995, Seismic velocity structure of oceanic crust by inversion using genetic algorithms. Geophysical Journal International, 123, 653–664.CrossRefGoogle Scholar
  22. Du, Z.J. and Panza, G.F., 1999, Amplitude and phase differentiation of synthetic seismograms: a must for waveform inversion at regional scale. Geophysical Journal International, 136, 83–98.CrossRefGoogle Scholar
  23. Fuchs, K. and Müller, G., 1971, Computation of synthetic seismograms with the reflectivity method and comparison with observations. Geophysical Journal of the Royal Astronomical Society, 23, 417–433.Google Scholar
  24. Hileman, J.A., 1979, Inversion of phase times for hypocenters and shallow crustal velocities, Mojave Dessert, California. Bulletin of the Seismological Society of America, 69, 387–396.Google Scholar
  25. Jin, S. and Madariaga, R., 1993, Background velocity inversion with a genetic algorithm. Geophysical Research Letters, 20, 93–96.CrossRefGoogle Scholar
  26. Kanasewich, E.R., 1981, Time sequence analysis in geophysics. University of Alberta Press, Edmonton, page number.Google Scholar
  27. Kim, W. and Baag, C.-E., 2002, Rapid and Accurate Two-Point Ray Tracing Based on a Quadratic Equation of Takeoff Angle in Layered Media with Constant or Linearly Varying Velocity Functions. Bulletin of the Seismological Society of America, 92, 2251–2263.CrossRefGoogle Scholar
  28. Kim, S.J. and Kim, S.G., 1983, A study on the crustal structure of South Korea by using seismic waves. Journal of the Korean Institute of Mining Geology, 16, 51–61. (in Korean)Google Scholar
  29. Kim, S.K., 1995, A study on the crustal structure of the Korean Peninsula. Journal of the Geological Society of Korea, 31, 393–403. (in Korean)Google Scholar
  30. Kind, R., 1976, Computation of reflection coefficients for layered media, Journal of Geophysics, 42, 191–200.Google Scholar
  31. Kobayashi, R., and Nakanishi, I., 1994, Application of genetic algorithms to focal mechanism determination. Geophysical Research Letters, 21, 729–732.CrossRefGoogle Scholar
  32. Krishnakumar, K., 1989, Micro-Genetic Algorithms for Stationary and Non-Stationary Function Optimization. SPIE: Intelligent Control and Adaptive Systems, Vol. 1196, Philadelphia — The international society for optical engineering, 289–296.Google Scholar
  33. Kwon, B.D. and Yang, S.Y., 1985, A study on the crustal structure of the southern Korea peninsula through gravity analysis. Journal of the Korean Institute of Mining Geology, 18, 309–320.Google Scholar
  34. Lawrence, J.F. and Wiens, D.A., 2004, Combined receiver-function and surface wave phase-velocity inversion using a niching genetic algorithm: Application to Patagonia. Bulletin of the Seismological Society of America, 94, 977–987.CrossRefGoogle Scholar
  35. Lay, T. and Wallace, T.C., 1995, Modern global seismology. Academic press, San Diego, California, 521 p.Google Scholar
  36. Lee, W.H.K. and Valdes, C.M., 1985, HYP071PC: A personal com puter version of the HYPO71 earthquake location program. U.S. Geological Survey Open File Report 85-749, p. 43.Google Scholar
  37. Lomax, A. and Snieder, R., 1995, The contrast in upper mantle shearwave velocity between the East European Platform and tectonic Europe obtained with genetic algorithm inversion of Rayleighwave group dispersion. Geophysical Journal International, 123, 169–182.CrossRefGoogle Scholar
  38. Mellman, G.R., 1980, A method of body-wave waveform inversion for the determination of earth structure. Geophysical Journal of the Royal Astronomical Society, 62, 481–504.Google Scholar
  39. Moya, A., Aguirre, J., and Irikura, K., 2000, Inversion of source parameters and site effects from strong ground motion records using genetic algorithms. Bulletin of the Seismological Society of America, 90, 977–992.CrossRefGoogle Scholar
  40. Neves, F.A., Singh, S.C., and Priestley, K.F., 1996, Velocity structure of upper-mantle transition zones beneath central Eurasia from seismic inversion using genetic algorithms. Geophysical Journal International, 125, 869–878.CrossRefGoogle Scholar
  41. Rodgers, A.J. and Schwartz, S.Y., 1997, Low crustal velocities and mantle lithospheric variations in southern Tibet from regional Pnl waveforms. Geophysical Research Letters, 24, 9–12.CrossRefGoogle Scholar
  42. Rodgers, A.J. and Schwartz, S.Y., 1998, Lithospheric structure of the Qiangtang Terrane, northern Tibetan Plateau, from complete regional waveform modeling: evidence for partial melt. Journal of Geophysical Research, 103, 7137–7152.CrossRefGoogle Scholar
  43. Sambridge, M. and Gallagher, K., 1993, Earthquake hypocenter location using genetic algorithms. Bulletin of the Seismological Society of America, 83, 1467–1491.Google Scholar
  44. Sen, M.K. and Stoffa, P.L., 1992, Rapid sampling of model space using genetic algorithms: examples from seismic waveform inversion. Geophysical Journal International, 108, 281–292.CrossRefGoogle Scholar
  45. Sen, M.K. and Stoffa, P.L., 1995, Global optimization methods in geophysical inversion. Elsevier science, Amsterdam, 281 p.CrossRefGoogle Scholar
  46. Snoke, J.A., 2003, FOCMEC: FOcal MEChanism determinations, International Handbook of Earthquake and Engineering Seismology. In: Lee, W.H.K., Kanamori, H., Jennings, P.C., and Kisslinger, C. (eds.), Academic Press, San Diego, Chapter 85.12, 1000 p.Google Scholar
  47. Steppe, J.A. and Crosson, R.S., 1978, P-velocity models of the southern Diablo Range, California, from inversion of earthquake and explosion arrival times. Bulletin of the Seismological Society of America, 61, 729–746.Google Scholar
  48. Stoffa, P.L. and Sen, M.K., 1991, Nonlinear multiparameter optimization using genetic algorithms: inversion of plane-wave seismograms. Geophysics, 56, 1794–1810.CrossRefGoogle Scholar
  49. Stoffa, P.L., Sen, M.K., Valera, C., and Chunduru, R.K., 1994, Geophysical applications of global optimization methods. European Assoc. Explor. Geophys., 56, 134.Google Scholar
  50. Syswerda, G., 1989, Uniform Crossover in Genetic Algorithms. In: Schaffer, J. (ed.), Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Los Altos, California, p. 2–9.Google Scholar
  51. Yamanaka, H and Ishida, H., 1996, Application of genetic algorithms to an inversion of surface-wave dispersion data. Bulletin of the Seismological Society of America, 86, 436–444.Google Scholar
  52. Zeng, Y. and Anderson, J.G., 1996, A composite source model of the 1994 Northridge earthquake using genetic algorithms. Bulletin of the Seismological Society of America, 86, S71–S83.Google Scholar
  53. Zhou, R., Tajima, F., and Stoffa, P.L., 1995, Application of genetic algorithms to constrain near-source velocity structure for the 1989 Sichuan earthquakes. Bulletin of the Seismological Society of America, 85, 590–605.Google Scholar
  54. Zhu, L. and Kanamori, H., 2000, Moho depth variation in southern California from teleseismic receiver functions. Journal of Geophyscal Research, 105, 2969–2980.CrossRefGoogle Scholar

Copyright information

© The Association of Korean Geoscience Societies 2008

Authors and Affiliations

  1. 1.School of Earth and Environmental SciencesSeoul National UniversitySeoulKorea

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