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Construction of Logic Trees and Hazard Curves for Probabilistic Tsunami Hazard Analysis

확률론적 지진해일 재해도평가를 위한 로직트리 작성 및 재해곡선 산출 방법

  • Received : 2019.01.31
  • Accepted : 2019.04.23
  • Published : 2019.04.30

Abstract

Due to the difficulties in forecasting the intensity and the source location of tsunami the countermeasures prepared based on the deterministic approach fail to work properly. Thus, there is an increasing demand of the tsunami hazard analyses that consider the uncertainties of tsunami behavior in probabilistic approach. In this paper a fundamental study is conducted to perform the probabilistic tsunami hazard analysis (PTHA) for the tsunamis that caused the disaster to the east coast of Korea. A logic tree approach is employed to consider the uncertainties of the initial free surface displacement and the tsunami height distribution along the coast. The branches of the logic tree are constructed by reflecting characteristics of tsunamis that have attacked the east coast of Korea. The computational time is nonlinearly increasing if the number of branches increases in the process of extracting the fractile curves. Thus, an improved method valid even for the case of a huge number of branches is proposed to save the computational time. The performance of the discrete weight distribution method proposed first in this study is compared with those of the conventional sorting method and the Monte Carlo method. The present method is comparable to the conventional methods in its accuracy, and is efficient in the sense of computational time when compared with the conventional sorting method. The Monte Carlo method, however, is more efficient than the other two methods if the number of branches and the number of fault segments increase significantly.

지진해일 규모와 발생시기 예측의 어려움으로 인해 결정론적 방법으로 얻은 결과가 실제 재난을 반영하지 못하는 사례가 발생하고 있다. 따라서 지진해일의 불확실성을 확률론적으로 접근하는 확률론적 지진해일 재해도 분석(Probabilistic Tsunami Hazard Analysis) 연구의 중요성이 점차 증가하고 있다. 본 연구에서는 과거 동해안에 피해를 유발한 동해 동연부 지진에 대하여 확률론적 지진해일 재해도 분석을 위한 기초연구를 수행하였다. 초기수면변위와 해일고분포의 불확실성을 고려하기 위해 로직트리 기법을 사용하였으며, 우리나라에 내습하는 지진해일의 특성을 반영하여 분기를 구성하였다. 프랙타일 곡선을 산출하는 과정에서 분기의 수가 증가하면 시간소요가 비선형적으로 증가하므로 모든 분기를 고려하면서도 계산시간을 줄일 수 있는 개선된 방법을 제안하였다. 새로 제안된 이산가중치분포법(Discrete Weight Distribution)과 정렬기법 및 몬테카를로법으로 얻은 결과의 일관성과 소요시간을 비교하였다. 이산가중치분포법은 정렬기법을 적용한 경우보다 계산시간이 짧아 효율적인 것으로 평가되었으나, 다수의 분기나 세그먼트를 고려할 경우 몬테카를로 방법이 더 효율적인 것으로 판단된다.

Keywords

References

  1. Aida, I. (1978). Reliability of a tsunami source model derived from fault parameters. Journal of Physics of the Earth, 26(1), 57-73. https://doi.org/10.4294/jpe1952.26.57
  2. Annaka, T., Satake, K., Sakakiyama, T., Yanagisawa, K. and Shuto, N. (2007). Logic-tree approach for probabilistic tsunami hazard analysis and its applications to the japanese coasts. Pure and Applied Geophysics, 164(2), 577-592. https://doi.org/10.1007/s00024-006-0174-3
  3. Assatourians, K. and Atkinson, G.M. (2013). EqHaz: An open-source probabilistic seismic-hazard code based on the Monte Carlo simulation approach. Seismological Research Letters, 84(3), 516-524. https://doi.org/10.1785/0220120102
  4. Cramer, C.H., Petersen, M.D. and Reichle, M.S. (1996). A Monte Carlo approach in estimating uncertainty for a seismic hazard assessment of Los Angeles, Ventura, and Orange Counties, California. Bulletin of the Seismological Society of America, 86(6), 1681-1691.
  5. Cornell, C.A. (1968). Engineering seismic risk analysis. Bulletin of the Seismological Society of America, 58(5), 1583-1606. https://doi.org/10.1785/BSSA0580051583
  6. Fukutani, Y., Suppasri, A. and Imamura, F. (2015). Stochastic analysis and uncertainty assessment of tsunami wave height using a random source parameter model that targets a Tohoku-type earthquake fault. Stochastic Environmental Research and Risk Analysis, 29(7), 1763-1779. https://doi.org/10.1007/s00477-014-0966-4
  7. Horspool, N., Pranantyo, I., Griffin, J., Latief, H., Natawidjaja, D., Kongko, W., Cipta, A., Bustaman, B., Anugrah, S.D. and Thio, H. (2014). A probabilistic tsunami hazard assessment for Indonesia. Natural Hazards and Earth System Sciences, 14(11), 3105-3122. https://doi.org/10.5194/nhess-14-3105-2014
  8. Japan Society of Civil Engineers (2008). Survey result on the weight of logic trees (in japanese).
  9. Japan Society of Civil Engineers (2016). Tsunami assessment technique for Nuclear Power Plant 2016 (in japanese).
  10. Lorito, S., Selva, J., Basili, R., Romano, F., Tiberti, M.M. and Piatanesi, A. (2014). Probabilistic hazard for seismically induced tsunamis: accuracy and feasibility of inundation maps. Geophysical Journal International, 200(1), 574-588. https://doi.org/10.1093/gji/ggu408
  11. Mansinha, L. and Smylie, D. (1971). The displacement fields of inclined faults. Bulletin of the Seismological Society of America, 61(5), 1433-1440.
  12. Mori, N., Goda, K. and Cox, D. (2018). Recent process in probabilistic tsunami hazard analysis (PTHA) for mega thrust subduction earthquakes. In the 2011 Japan earthquake and tsunami: Reconstruction and restoration, 469-485.
  13. Musson, R.M. (1999). Determination of design earthquakes in seismic hazard analysis through Monte Carlo simulation. Journal of Earthquake Engineering, 3(4), 463-474. https://doi.org/10.1080/13632469909350355
  14. Musson, R.M. (2000). The use of Monte Carlo simulations for seismic hazard assessment in the UK. Annals of Geophysics, 43(1), 1-9. https://doi.org/10.4401/ag-3617
  15. Park, H. and Cox, D.T. (2016). Probabilistic assessment of nearfield tsunami hazards: Inundation depth, velocity, momentum flux, arrival time, and duration applied to Seaside. Oregon. Coastal Engineering, 117, 79-96. https://doi.org/10.1016/j.coastaleng.2016.07.011
  16. Rhee, H.-M., Kim, M.K., Sheen, D.-H. and Choi, I.-K. (2014). Estimation of wave parameters for probabilistic tsunami hazard analysis considering the fault sources in the Western Part of Japan. Journal of the Earthquake Engineering Society of Korea, 18(3), 151-160. https://doi.org/10.5000/EESK.2014.18.3.151
  17. Shapira, A. (1983). Potential earthquake risk estimations by application of a simulation process. Tectonophysics, 95(1-2), 75-89. https://doi.org/10.1016/0040-1951(83)90260-3
  18. Sugino, H., Iwabuchi, Y., Hashimoto, N., Matsusue, K., Ebisawa, K., Kameda, H. and Imamura, F. (2015). The characterizing model for tsunami source regarding the inter-plate earthquake tsunami. Journal of Japan Association for Earthquake Engineering, 15(3), 114-133.
  19. The Headquarters for Earthquake Research Promotion (2003). Long-term Evaluation of seismic activity in the eastern margin of Japan sea (in japanese).
  20. Yoon, S.B., Lim, C.H. and Choi, J. (2007). Dispersion-correction finite difference model for simulation of transoceanic tsunamis. Terrestrial Atmospheric and Oceanic Sciences, 18(1), 31-53. https://doi.org/10.3319/TAO.2007.18.1.31(T)