Engineering Seismology

  • M. L. Sharma


Seismology is the branch of geophysics which deals with the occurrence of earthquakes and related phenomena on the planet earth. Seismology, as a science of earthquakes, includes the direct and inverse problems dealing with earthquake source, medium characteristics, data recording and interpretation of data. The outcome is the modeling and source characterization in terms of its location, geometry and potential, characterization of medium in terms of geometry, attenuation and other rheological properties and quantification of size of earthquakes in terms of magnitude and energy, modeling earthquake occurrence and study the earth structure etc. Seismology, in general, can be divided into four sections namely: observational seismology, theoretical seismology, strong motion seismology and engineering seismology.


Observational seismology Theoretical seismology Strong-motion seismology Engineering seismology Maximum credible earthquake Design basis earthquake Response spectra 


  1. Abrahamson, N. A., & Bommer, J. J. (2005). Probability and uncertainty in seismic hazard analysis. Earthquake Spectra, 21, 603–607. ISSN:8755-2930.CrossRefGoogle Scholar
  2. Akkar, S., & Bommer, J. J. (2007). New empirical prediction equations for peak ground velocity derived from strong-motion records from Europe and the Middle East. Bulletin of the Seismological Society of America, 97(2), 511–530.CrossRefGoogle Scholar
  3. Anagnos, T., & Kiremidjian, A. S. (1988). A review of earthquake occurrence models for seismic hazard analysis. Journal of Probabilistic Engineering Mechanics, V3, 3–11.CrossRefGoogle Scholar
  4. Anderson, J. G., & Trifunac, M. D. (1978). Uniform risk functionals for characterization of strong earthquake ground motion. Bulletin of the Seismological Society of America, 68(1), 205–218.Google Scholar
  5. Atkinson, G. M., & Cassidy, J. (2000). Integrated use of seismograph and strong motion data to determine soil amplification in the Fraser delta: Results from Duvall and Georgia state earthquakes. Bulletin of the Seismological Society of America, 90, 1028–1040.CrossRefGoogle Scholar
  6. Benioff, H. (1934). The physical evaluation of seismic destructiveness. Bulletin of Seismological Society of America, 24, 398–403.Google Scholar
  7. Boore, D. M., & Atkinson, G. M. (1987). Stochastic prediction of ground acceleration and spectral response parameters at hard rock sites in Eastern North America. Bulletin of Seismological Society of America, 77, 440–467.Google Scholar
  8. Bour, M., & Cara, M. (1997). Test of a simple empirical Green’s function method on moderate-sized earthquakes. Bulletin of the Seismological Society of America, 87, 668–683.Google Scholar
  9. Cluff, L. S., Patwardhan, A. S., & Coppersmith, K. J. (1980). Estimating the probability of occurrences of surface faulting earthquakes on the Wasatch Fault zone, Utah. Bulletin of the Seismological Society of America, 70, 463–478.Google Scholar
  10. Coppersmith, K. J. (1981). Probabilities of earthquake occurrences on San Andres fault based geologic risk. Eos, 19(17), 322.Google Scholar
  11. Cornell, C. (1968). Engineering seismic risk analysis. Bulletin of the Seismological Society of America, 58, 1583–1606.Google Scholar
  12. Cornell, C. A., & Winterstein, S. R. (1988). Temporal and magnitude dependence in earthquake recurrence models. Bulletin of the Seismological Society of America, 78(4), 1522–1537.Google Scholar
  13. CWC Guide Lines. (2011). Guidelines for preparation and submission of site specific seismic study report of river valley project to national committee on seismic design parameters. Central Water Commission, Govt. of India. Retrieved from
  14. Das, R., Wason, H. R., & Sharma, M. L. (2012a). Homoginisation of earthquake catalogue for North East India and adjoining region. Journal of Pure and Applied Geophysics (PAGEOPH), 169, 725–731.CrossRefGoogle Scholar
  15. Das, R., Wason, H. R., & Sharma, M. L. (2012b). Magnitude conversion to unified moment magnitude using orthogonal regression relation. Journal of Asian Earth Sciences, 50, 44–51.CrossRefGoogle Scholar
  16. Douglas, J. (2003). Earthquake ground motion estimation using strong-motion records: A review of equations for the estimation of peak ground acceleration and spectral ordinates. Earth Science Reviews, 61, 43–104.CrossRefGoogle Scholar
  17. Douglas, J. (2006). Difficulties in predicting earthquake ground motions in metropolitan France and possible ways forward. Géosciences, 4, 26–31.Google Scholar
  18. Douglas, J., Bungum, H., & Scherbaum, F. (2006). Ground-motion prediction equations for southern Spain and Southern Norway obtained using the composite model perspective. Journal of Earthquake Engineering, 10(1), 33–72.CrossRefGoogle Scholar
  19. Esteva, L. (1970). Seismic risk and seismic design decisions. In R. J. Hansens (Ed.), Seismic design of nuclear power plants. Cambridge, MA: MIT Press.Google Scholar
  20. Guagenti-Grandori, E., & Molina, D. (1984). Semi-Markov processes in seismic risk analysis. Proceedings, International Symposium of Semi-Markov Processes and Their Applications, Brussels. Google Scholar
  21. Hadley, D. M., & Helmberger, D. V. (1980). Simulation of ground motions. Bulletin of the Seismological Society of America, 70, 617–610.Google Scholar
  22. Hagiwara, Y. (1974). Probability of earthquake occurrence as obtained from a Weibull distribution analysis of crustal strain. Tectonophysics, 23(3), 313–318.CrossRefGoogle Scholar
  23. Hanks, T. C., & McGuire, R. K. (1981). Character of high frequency ground motion. Bulletin of the Seismological Society of America, 71, 2071–2095.Google Scholar
  24. Hartzell, S. H. (1978). Earthquake aftershocks as Green’s functions. Geophysical Research Letters, 5, 1–4.CrossRefGoogle Scholar
  25. Herbindu, A., Kamal, K., & Sharma, M. L. (2012a). Site amplification and frequency-dependent attenuation coefficient at rock sites of Himachal region in NW Himalaya, India. Bulletin of the Seismological Society of America, 102(4), 1497–1504.CrossRefGoogle Scholar
  26. Herbindu, A., Sharma, M. L., & Kamal, K. (2012b). Stochastic ground-motion simulation of two Himalayan earthquakes: Seismic hazard assessment perspectives. Journal of Seismology, 16, 345–369.CrossRefGoogle Scholar
  27. Housner, G.W. (1952) Spectrum intensities of strong motion earthquakes. Proceedings of the Symposium of Earthquake and Blast Effects on Structures, Earthquake Engineering Research Institute. Google Scholar
  28. Housner, G. W. (1959). Behavior of structures during earthquakes. Journal of the Engineering Mechanics Division, ASCE, 85, 109–129.Google Scholar
  29. Housner, G. W., & Jennings, P. C. (1964). Generation of artificial earthquakes. Journal of the Engineering Mechanics Division, ASCE, 90, 113–150.Google Scholar
  30. Huang, C. H., & Teng, T. L. (1999). An evaluation of H/V ratio vs. spectral ratio for the site-response estimation using the 1994 Northridge earthquake sequences. Pure and Applied Geophysics, 156, 631–649.CrossRefGoogle Scholar
  31. Hudson, D. E. (1956). Response spectrum techniques in engineering seismology. Proceedings of the World Conference on Earthquake Engineering. Earthquake Engineering Research Institute and the University of California, Berkeley. Google Scholar
  32. Hutchings, L. (1985). Modeling earthquakes with empirical Green’s functions (abs.) Earthquake Notes, 56, 14.Google Scholar
  33. Irikura, K. (1983). Semi-empirical estimation of strong ground motions during large earthquakes. Bulletin of the Disaster Prevention Research Institute, 33, 63–104.Google Scholar
  34. Irikura, K. (1986). Prediction of strong ground acceleration motion using empirical Green’s function. Proceedings of 7th Japan Earthquake Engineering Symposium, 151–156.Google Scholar
  35. Irikura, K., & Kamae, K. (1994). Estimation of strong ground motion in broad-frequency band based on a seismic source scaling model and an Empirical Green’s function technique. Annali di Geofisica, 7(6), 1721–1743.Google Scholar
  36. Jordanovski, L.R., Todorovska, M.I. and Trifunac, M.D. (1991). A model for assessment of the total loss in a building exposed to earthquake hazard (Rep. No. CE 92-05). Los Angeles, CA: Department of Civil Engineering, University of Southern California. Google Scholar
  37. Joshi, A., Kumari, P., Kumar, S., Sharma, M. L., Ghosh, A. K., Agrawal, M. K., & Ravikiran, R. (2012). Estimation of model parameter of Sumatra earthquake using empirical Green’s function technique and generation of hypothetical earthquake scenario for Andaman Island, India. Natural Hazards, 62, 1081–1108.CrossRefGoogle Scholar
  38. Joshi, G. C., & Sharma, M. L. (2008). Uncertainties in estimation of Mmax. Journal of Earth Sciences Systems, 117(S2), 671–682.Google Scholar
  39. Kanamori, H. (1979). A semi empirical approach to prediction of long period ground motions from great earthquakes. Bulletin of the Seismological Society of America, 69, 1645–1670.Google Scholar
  40. Kiremidjian, A. S., & Anagnos, T. (1984). Stochastic slip-predictable model for earthquake occurrences. Bulletin of the Seismological Society of America, 74(2), 739–755.Google Scholar
  41. Langston, C. A., Chi Chiu, S. C., & Lawrence, Z. (2010). Array observations of micro-seismic noise and the nature of H/V in the Mississippi embayment. Bulletin of the Seismological Society of America, 99(5), 2893–2911.CrossRefGoogle Scholar
  42. Lermo, J., & Chavez-Garcia, F. (1993). Site effect evaluation using spectral ratios with only one station. Bulletin of the Seismological Society of America, 83, 1574–1594.Google Scholar
  43. McGuire, R. K., Cornell, C. A., & Toro, G. R. (2005). The case for using mean seismic hazard. Earthquake Spectra, 21(3), 879–886.CrossRefGoogle Scholar
  44. Motazedian, D. (2006). Region specific key seismic parameters of earthquakes in northern Iran. Bulletin of the Seismological Society of America, 96(4A), 1383–1395.CrossRefGoogle Scholar
  45. Musson, R. M. W., Toro, G. R., Coppersmith, K. J., Bommer, J. J., Deichmann, N., Bungum, H., Cotton, F., Scherbaum, F., Slejko, D., & Abrahamson, N. A. (2005). Discussion—Evaluating hazard results for Switzerland and how not to do it: A discussion of ‘problems in the application of the SSHAC probability method for assessing earthquake hazards at Swiss nuclear power plants’ by J-U Klügel. Engineering Geology, 82(1), 43–55.CrossRefGoogle Scholar
  46. Nakamura, Y. (1989). A method for dynamic characteristics estimation of subsurface using microtremor on the ground surface. Quarterly Report of Railway Technical Research Institute (RTRI), 30(1), 25–33.Google Scholar
  47. Narayan, J. P., Sharma, M. L., & Kumar, A. (2002). A seismological report on the January 26, 2001 Bhuj, India earthquake. Seismological Research Letters, 73(3), 343–355.CrossRefGoogle Scholar
  48. Nishioka, T., & Shah, H. C. (1980). Application of the Markov chain on probability of earthquake occurrences. Proceedings of the Japanese Society of Civil Engineering, 1, 137–145.CrossRefGoogle Scholar
  49. Patwardhan, A. S., Kulkarni, R. B., & Tocher, D. (1980). A semi Markov model for characterizing recurrence of great earthquakes. Bulletin of the Seismological Society of America, 70, 323–347.Google Scholar
  50. Saikia, C. K. (1993). Ground motion studies in great Los Angles due to Mw = 7.0 earthquake on the Elysian thrust fault. Bulletin of the Seismological Society of America, 83, 780–810.Google Scholar
  51. Savy, H. B., Shah, H. C., & Boore, D. M. (1980). Nonstationary risk model with geophysical input. Journal of the Structural Division, ASCE, 106(ST1), 145–164.Google Scholar
  52. Sharma, M. L. (1998). Attenuation relationship for estimation of peak ground horizontal acceleration using data from strong motion arrays in India. Bulletin of the Seismological Society of America, 88, 1063–1069.Google Scholar
  53. Sharma, M. L. (2003). Seismic hazard in Northern India region. Seismological Research Letters, 74(2), 140–146.CrossRefGoogle Scholar
  54. Sharma, M. L., & Lindolhm, C. (2012). Earthquake hazard assessment for Dehradun, Uttarakhand, India, including a characteristic earthquake recurrence model for the Himalaya Frontal Fault (HFF). Pure and Applied Geophysics (PAGEOPH), 169, 1601–1617.CrossRefGoogle Scholar
  55. Sharma, M. L., Douglas, J., Bungum, H., & Kotadia, J. (2009). Ground motion predicting equations on data from the Himalayan and Zagros regions. Journal of Earthquake Engineering, 13(8), 1191–1210.CrossRefGoogle Scholar
  56. Somerville, P. G., Sen, M. K., & Cohece, B. P. (1991). Simulation of strong ground motions recorded during the 1985 Michoacan, Mexico and Valparaiso Chile earthquakes. Bulletin of the Seismological Society of America, 81, 1–27.Google Scholar
  57. Suzuki, T., Adachi, Y., & Tanaka, M. (1995). Application of micro tremor measurement to the estimation of earthquake ground motion in Kushiro City during the Kushiro-Oki earthquake of 15 January 1993. Earthquake Engineering and Structural Dynamics, 24, 595–613.CrossRefGoogle Scholar
  58. Theodulidis, N. P., & Bard, P.-Y. (1995). Horizontal to vertical spectral ratio and geological conditions: An analysis of strong motion data from Greece and Taiwan (SMART-1). Soil Dynamics and Earthquake Engineering, 14, 177–197.CrossRefGoogle Scholar
  59. Tripathi, J. N., Singh, P., & Sharma, M. L. (2012). Variation of seismic coda-wave attenuation in the Garhwal region, north western Himalaya. Journal of Pure and Applied Geophysics (PAGEOPH), 169(1–2), 71–88.CrossRefGoogle Scholar
  60. Vagliente, V. H. (1973). Forecasting risk inherent in earthquake-resistant design (Tech. Rep. No. 174), Stanford, CA: Department of Civil Engineering, Stanford University. Google Scholar
  61. Veneziano, D., & Cornell, C. A. (1974). Earthquake models with spatial and temporal memory for engineering seismic risk analysis (Research Rep. No. R 74–78). Cambridge, MA: Department of Civil Engineering, MIT. Google Scholar
  62. Vere-Jones, D., & Davies, R. B. (1966). A statistical survey of earthquakes in the main seismic region of New Zealand, Part 2, Time series analysis. New Zealand Journal of Geology and Geophysics, 9, 251–284.CrossRefGoogle Scholar
  63. Vere-Jones, D., & Ozaki, T. (1982). Some examples of statistical estimation applied to earthquake data, I. Cyclic poisson and self-exciting models. Annals of the Institute of Statistic and Mathematics, 34(Part B), 189–207.CrossRefGoogle Scholar
  64. Vladimir, Y. S., Chin-Hsiung, L., & Wen-Yun, J. (2007). Application of horizontal to-vertical (H/V) Fourier spectral ratio for analysis of site effect on rock (NEHRP-class B) sites in Taiwan. Soil Dynamics and Earthquake Engineering, 27(4), 314–323.CrossRefGoogle Scholar
  65. Wason, H. R., Das, R., & Sharma, M. L. (2012). Magnitude conversion problem using general orthogonal regression. Geophysical Journal International, 190(2), 1091–1109.CrossRefGoogle Scholar
  66. Yu, G., Khattri, K. N., Anderson, J. G., Brune, J. N., & Zeng, Y. (1995). Strong ground motion from the Uttarkashi, Himalaya, India, earthquake: Comparison of observations with synthetics using the composite source model. Bulletin of the Seismological Society of America, 85, 31–50.Google Scholar
  67. Zeng, Y., Anderson, J. G., & Su, F. (1994). A composite source model for computing realistic synthetic strong ground motions. Geophysical Research Letters, 21, 725–728.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • M. L. Sharma
    • 1
  1. 1.Department of Earthquake EngineeringIIT RoorkeeRoorkeeIndia

Personalised recommendations