Title

以基因演算法同步決定一維構造之各層速度與厚度

Translated Titles

Simultaneous Determination of Velocities and Thicknesses of Layered Model with Genetic Algorithm

DOI

10.6342/NTU.2008.00576

Authors

林秉延

Key Words

速度構造 ; 基因演算法 ; 嘉義 ; velocity model ; genetic algorithm ; chiayi

PublicationName

臺灣大學地質科學研究所學位論文

Volume or Term/Year and Month of Publication

2008年

Academic Degree Category

碩士

Advisor

吳逸民

Content Language

繁體中文

Chinese Abstract

一維速度構造是地震研究之重要基礎資料,舉凡快速定位、波形反演等工作皆有賴於此,於是需要以可靠且有效率的演算法建立可信賴的速度構造。全域搜尋的演算方式雖然最為直覺,也最為可靠,但在資料量龐大的地球物理研究中,大量計算資源需求往往成為實際應用上的最大窒礙。現存的應用程式中,則大多使用線性計算的逆推方式。然而,這種方式除了有著容易受到先驗知識影響的缺點,且難以同時決定各層之厚度與速度。因此,本研究採用基因演算法,期望以此非線性的逆推方式,有效率地建立可靠的一維速度構造。本研究取用臺灣嘉義地區1991年至2006年地震的P波與S波資料,並以福傳程式語言撰寫程式來分析。結果顯示地震之分布密度會嚴重影響演算結果的可靠程度,但在地震資料充足之區域,基因演算法確實可在穩定的演算下大幅節省計算資源與時間,快速找出速度不連續面之深度。另外,藉由演算結果的比較,推測CHY測站底下存在迥異於周遭的區域構造。然而,因為基因演算法帶有隨機過程,難以確保得到最佳解與否,因此本研究建議應該將基因演算法的結果做為初始模型,進一步結合別的逆推方式做最佳化為宜。

English Abstract

Layered velocity model is important basic information for seismology. Global search and calculus-based inversion are commonly used to determine layered models. Global search offers accurate solutions but usually takes much time calculating. On the contrary, calculus-based inversion saves time but usually comes with the local minimum problem. Genetic algorithm (GA), which has been used in many fields, probably is an appropriate method to balance between accuracy and time saving. The purpose of our research was to determine velocities and thicknesses of layered velocity model simultaneously with GA method. In this study, P phase and S phase arrivals were used. Fortran program codes were written and tested with earthquake data of Chia-Yi area, Taiwan from1991 to 2006. The results showed that the velocity models calculated by GA are strongly affected by the density distribution of earthquakes. However, with enough data, our approach balanced well between efficacy and efficiency. The results suggest that GA could be a good way to obtain reliable initial velocity models for geophysical studies.

Topic Category 基礎與應用科學 > 地球科學與地質學
理學院 > 地質科學研究所
Reference
  1. 林豐澤,2005。演化式計算上篇:演化式演算法的三種理論模式。智慧科技與應用統計學報第三卷第一期,1-28。
    連結:
  2. 林豐澤,2005。演化式計算下篇:基因演算法以及三種應用實例。智慧科技與應用統計學報第三卷第一期,29-56。
    連結:
  3. An, M. and Assumpção, M., 2006. Crustal and upper mantle structure in the intracratonic Paraná Basin, SE Brazil, from surface wave dispersion using genetic
    連結:
  4. algorithms. Journal of South American Earth Sciences 21, 173-184. doi:10.1016/j.jsames.2006.03.001.
    連結:
  5. Backus, G. E., Gilbert, J. F., 1967. Numerical applications of a formalism for geophysical inverse problems. Geophysical Journal International, Vol. 13(1-3), 247-276. doi:10.1111/j.1365-246X.1967.tb02159.x
    連結:
  6. Davis, L., Steenstrup, M., 1990. Genetic algorithms and simulated annealing: An overview. In Davis, L., Genetic algorithms and simulated annealing, Morgan Kaufmann Publishers.
    連結:
  7. Fogel, L. J., Owens, A. J. and Walsh, M. J., 1966. Artificial Intelligence through simulated evolution. New York: Wiley.
    連結:
  8. Gallagher, K., Sambridge, M. and Drijkoningen, G., 1991. Genetic algorithms: An evolution from Monte Carlo methods for strongly non-linear geophysical optimization problem. Geophysical Research Letters, Vol. 18, NO. 12, 2177-2180.
    連結:
  9. Goldberg, D. E., 1989. Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Publishing Company.
    連結:
  10. Holland, J. H., 1975. Adaptation in natural and artificial systems. Ann Arbor: The University of Michigan Press.
    連結:
  11. Kirkpatrick, S., Gelatt, C. D., Jr. and Vecchi, M. P., 1983. Optimization by Simulated Annealing. Science, Vol. 220, NO. 4598, 671-680. doi:10.1126/science.220.4598.671.
    連結:
  12. Kissling, E., Ellsworth, W. L., Eberhart-Phillips, D.and Kradolfer, U., 1994. Initial reference models in local earthquake tomography. Journal of Geophysical Research, Vol. 99, NO. B10, 19635-19646.
    連結:
  13. Metropolis, N. and Ulam, S., 1949. The Monte Carlo method. Journal of the American Statistical Association, Vol. 44, NO. 247, 335-341.
    連結:
  14. Stoffa, P. L. and Sen, M. K., 1991. Nonlinear multiparameter optimization using genetic algorithms: Inversion of plane-wave seismograms. Geophysics, Vol. 56, NO. 11, 1794-1810. doi:10.1190/1.1442992.
    連結:
  15. Wu, Y. M., Chang, C. H., Zhao, L., Shyu, J. B. H., Chen, Y. G., Sieh, K., Avouac, J. P., 2007. Seismic tomography of Taiwan: Improved constraints from a dense network of strong-motion stations. Journal of Geophysical Research, Vol. 112, B08312. doi:10.1029/2007JB004983.
    連結:
  16. Wu, Y. M., Zhao, L., Chang, C. H., Hsu, Y. J., 2008. Focal mechanism determination in Taiwan by genetic algorithm. Bulletin of the Seismological Society of America 98,
    連結:
  17. 651–661. doi:10.1785/0120070115.
    連結:
  18. Wu, Y. M., Chang, C. H., Zhao, L., Teng, T. L. and Nakamura, M., 2008. A Comprehensive Relocation of Earthquakes in Taiwan from 1991 to 2005. Bulletin of the Seismological Society of America 98, 1471–1481. doi:10.1785/0120070166.
    連結:
  19. 何美儀,1994。台灣西部地區三維速度構造。中央大學地球物理研究所碩士論文。
  20. 辛在勤與何美儀,1994。台灣西部地區三維速度構造。氣象學報第四十卷第三期,216-234。
  21. Baker, J. E., 1985. Adaptive selection methods for genetic algorithms. In Grefenstette, J. J., Proceeding of the first international conference on genetic algorithms and their applications. Lawrence Erlbaum Associates, Publishers.
  22. Rechenberg, I., 1965. Cybernetic solution path of an experimental problem. Aircr. Establ., libr. Transl. 1122. Franborough, Hants., UK.
  23. 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, Vol. 86, NO. 2, 436-444.
Times Cited
  1. 吳懿倫(2014)。使用基因演算法優化LTE-Advanced系統之不連續頻段載波聚合資源配置。臺北科技大學電腦與通訊研究所學位論文。2014。1-54。