Presenting a Model of Earthquake Wave Velocity Changes (VPn ) Based on Genetic Algorithm (Case Study - Iran)

Document Type : Research Note

Authors

1 M.Sc. Graduate, Seismological Research Center, International Institute of Earthquake Engineering and Seismology (IIEES), Tehran, Iran

2 Assistant Professor, Faculty of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran

Abstract

Earthquake is one of the most dangerous natural disasters of the present age, which has always shown its importance objectively. An earthquake is a natural disaster that, depending on its magnitude, can cause massive catastrophes in a short time. In this study, the authors seek to provide a simple analytical form for the propagation speed of waves, which despite previous studies, has not received much attention. Therefore, the purpose of this paper is to extract and present a model for earthquake wave velocity changes ( VPn) using Genetic Algorithm (GA).
Research Methods
 The data used in this study were received from the National Seismological Center of the Institute of Geophysics, University of Tehran. In this study, three provinces of Kermanshah, East Azerbaijan and Kerman were selected. Earthquake event characteristics of each of these three provinces in the period between 2006 and the end of 2018, with a focal depth of up to 30 km and magnitude between 4 and 8 were selected. In order to use the Genetic Algorithm (GA), first the data received from the National Seismological Center for these three provinces were merged, which was estimated at 1863 earthquake events. After extracting the relevant data, the earthquake wave velocity (VPn ) was calculated. Then, ignoring about 25% of this data, a mathematical model for earthquake velocity was extracted. Finally, the obtained formula was applied to the initial ignored data (25%), which had similar results. To model the changes in wave velocity according to distance changes, a mathematical relation was considered as an exponential function and the unknown parameters of the model were determined using a Genetic Algorithm (GA). To find a suitable model between distance and speed, the following relation is considered for it.
V(X)=a+b-kX                                                                                                                                                        (1)         
In this regard, a, b and k are constant coefficients and x is the distance from the earthquake site in terms of one thousand kilometers. In the above equation, the coefficients must be determined so that the output of this model with the recorded data has the least amount of error. For this purpose, the Genetic Algorithm (GA) optimization method is used, and the error between the model output and the actual data was considered as the objective function of the optimization problem, and the optimization variables were determined with the aim of minimizing this objective function. The objective function is defined as follows:
                                                                                                                                        (2)
In this connection,Vi is the velocity obtained as a measure and V(Xi) the amount of speed obtained according to the Equation (1). For implementation, the Genetic Algorithm (GA) has been used 2000 populations and 30 generations. Also the coefficient of crossover is equal to 70% and coefficient mutation is equal to 2%.
 The output of the model for training and test data showed that the proposed model has acceptable accuracy for modeling the velocity of longitudinal waves. This model can be used to determine the arrival time of waves of an earthquake to different points. It is also possible to estimate the location of the earthquake by recording the occurrence of the earthquake at several different points and using the provided relationship.

Keywords

Main Subjects

References

  1. Ashayeri, I., Biglari, M., and Rezaei Sefat, M. (2015) Deriving rayleigh wave velocity equation at surface of semi-infinite unsaturated media. Bulletin of Earthquake Science and Engineering, 1(1), 51-57 (in Persian).
  2. Ansaripour M. and Rezapour M. (2015) Crustal velocity in the Busher region and analyses of 2013 Mw 6.3 Kaki-Busher Earthquake. Journal of the Earth and Space Physics, 41(3), 351-361 (in Persian).
  3. Javan Mehri, M., Bayramnejad, I., Gheitanchi, M.R., and Azhari, S.M. (2012) Crustal seismic velocity structure study in Kope Dagh using simultaneous inversion modeling. Journal of Earth and Space Physics, 41(3), 351-361 (in Persian).
  4. Zhao, Z. and Zeng, R. (1993) The P and S wave velocity structures of the crust and upper mantle beneath Tibetan Plateau. Acta Seismologica Sinica, 6, 299-304, https://doi.org/10.1007/BF02650942.
  5. Abd Etedal, M. and Gheitanchi, M.R. (2011) Investigation of one dimensional upper crust velocity structure in northeast Khorasan by the travel time inversion of P waves. Journal of Earth and Space Physics, 37(2), 139 (in Persian).
  6. Ghods, A. and Sobouti, F. (2005) Quality assessment of seismic recording: Tehran seismic telemetry network. Asian Journal of Earth Sciences, 25, 687-694.
  7. Eliassy, M., Biglari, M., and Ashayeri, I. (2018) Empirical Modeling of Compressional aveVelocity of Fine Grained Unsaturated Soils Subject to Drying. Modares Civil Engineering Journal (M.C.E.J), 18(4) (in Persian).
  8. Bayramnejad, I., Mirzaei, M., and Gheitanchi, R. (2007) Determination of improved velocity model for the north west Iran region, using simultaneous inversion of local earthquake travel times. Journal of Earth and Space Physics, 33(3), 47-59 (in Persian).
  9. Nowrouzi, Gh., Priestley, K.F., Ghafory-Ashtiany, Mohsen, Javan Doloei, Gh., and Rham, D.J. (2007) Crustal velocity structure in Iranian Kopeh-Dagh, from analysis of P-waveform receiver functions. JSEE, 8(4), 187-194.
  10. Lin, G. and Wang, Y. (2005) The P-wave velocity structure of thecrust–mantle transition zone in the continent of China. Geophys. Eng., 2, 268-276.
  11. Fatemizadeh, A. and Tatar, M. (2006) Estimation of crustal velocity structure of the central zagros using refracted waves. Journal of Earth Sciences, 15(60), 2-11 (in Persian).
  12. Rostami, Sh., Sepahvand, M.R., Mahood, M., and Nasrabadi, (2021) Quick estimation of the magnitude and epicentral distance of the earthquake in eastern half of Kerman Province by single station (B-Δ) method. Journal of Research on Applied Geophysics, 7(1), 91-102 (in Persian).
  13. Paul, A., Hatzfeld, D., Kaviani, A., Tatar, M., and Péquegnat, C. (2010) Seismic imaging of the lithospheric structure of the Zagros mountain belt(Iran). Geological Society, London, Special Publications.
  14. Mousavian, S. and Tatar, M. (2014) Crustal Structure of the Western Alborz by joint invesion of the receiver function and surface wave dispersion curve. Iranian Journal of Geophysics, 7(4), 94-81 (in Persian).
  15. Mohammadipour, Z., Yaminifard, F., and Tatar, (2016) Three-dimensional upper structure of  the eastern zagros (Iran) using local tomography. Journal of Earth Sciences, 25(98), 72-67 (in Persian).
  16. Yaminifard, F. and Moradi, A. (2011) Crustal velocity structure beneath Tehran based on teleseismic and mining explosion data recorded by Tehran City seismic network (TCSN). Quarterly Journal of Earth and Space Physics, 37(3), 59 (in Persian).
  17. Afra, M., Naghavi, M., and Shirzad, T. (2016) Velocity structure in Tehran using P-Wave tomography. Proceedings of the 18th Iranian Geophysical Conference, 63-60 (in Persian).
  18. Azhari, S.M., Rezapour, M., and Mottaghi, A.A. (1397) Determination of upper crustal velocity structure in north eastern part of iran between Kashfarud and Binalood faults. Journal of Earth Sciences, 27(108), 95-104.
  19. Whitley, D. (1994) A genetic algorithm tutorial. Statistics and Computing, 4(2), 65-85.

Files

History

  • Receive Date: 15 May 2020
  • Revise Date: 24 August 2020
  • Accept Date: 03 February 2021

Share

How to cite

Statistics