
Application of Uniform Slip Models to Tsunami Early Warning: A Case Study of 2021 M w 8.2 Alaska Peninsula Earthquake
Zhu Yifan, An Chao
Application of Uniform Slip Models to Tsunami Early Warning: A Case Study of 2021 M w 8.2 Alaska Peninsula Earthquake
To issue tsunami warnings in real-time, complex earthquake sources are usually simplified to uniform slip models for tsunami prediction. Althought this approach of simplification is widely used, its accuracy in predicting tsunami waves in actual events hasnot been fully evaluated and recognized. In this paper, a finite-fault model and various uniform slip models are constructed for the 2021 M w 8.2 Alaska Peninsula earthquake, and their prediction errors for tsunami waves are compared. The finite-fault model inverted from tsunami datareveals that the coseismic slip of this event was distributed over a depth range of 15 to 40 km, and the ~6m maximum slip occurred near the hypocenter. Besides, the optimum uniform slip model obtained from global search provides very similar tsunami predictions to those given by the finite-fault model, both of which agree well with the observations. Two uniform slip models located at the gCMT centroid but using different scaling relations yield almost the same far-field waveforms.Results of this study show that the optimum predicting ability of uniform slip modelis almost equivalent to that of the finite-fault model. The uniform slip models based on gCMT centroids and scaling relations are relatively reliable for far-field tsunami warning, and difference in scaling relations may not significantly impact the far-field predictions.
tsunami warning / uniform slip model / tsunami inversion / 2021 Alaska Peninsula earthquake / earthquake
An, C., Liu, H., Ren, Z. Y., et al., 2018. Prediction of Tsunami Waves by Uniform Slip Models. Journal of Geophysical Research: Oceans, 123(11): 8366-8382. https://doi.org/10.1029/2018jc014363
|
An, C., Sepúlveda, I., Liu, P. L. F., et al., 2014. Tsunami Source and its Validation of the 2014 Iquique, Chile, Earthquake. Geophysical Research Letters, 41(11): 3988-3994. https://doi.org/10.1002/2014gl060567
|
Bernard, E., Titov, V., 2015. Evolution of Tsunami Warning Systems and Products. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences,373(2053): 20140371. https://doi.org/10.1098/rsta.2014.0371
|
Blaser, L., Kruger, F., Ohrnberger, M., et al., 2010. Scaling Relations of Earthquake Source Parameter Estimates with Special Focus on Subduction Environment. Bulletin of the Seismological Society of America, 100(6): 2914-2926. https://doi.org/10.1785/0120100111
|
Elliott, J. L., Grapenthin, R., Parameswaran, R. M., et al., 2022. Cascading Rupture of a Megathrust. Science Advances, 8(18): eabm4131. https://doi.org/10.1126/sciadv.abm4131
|
Fujii, Y., Satake, K., Sakai, S., et al., 2011. Tsunami Source of the 2011 off the Pacific Coast of Tohoku Earthquake. Earth, Planets and Space, 63(7): 815-820. https://doi.org/10.5047/eps.2011.06.010
|
Geist, E. L., 2002. Complex Earthquake Rupture and Local Tsunamis. Journal of Geophysical Research: Solid Earth,107(B5): ESE2-1-ESE2-15. https://doi.org/10.1029/2000JB000139
|
Gonzalez, F. I., Milburn, H. M., Bernard, E. N., et al., 1998. Deep-Ocean Assessment and Reporting of Tsunamis (Dart): Brief Overview and Status Report. Proceedings of the international workshop on Tsunami Disaster Mitigation,
|
Greenslade, D. J. M., Allen, S. C. R., Simanjuntak, M. A., 2011. An Evaluation of Tsunami Forecasts from the T2 Scenario Database. Pure and Applied Geophysics, 168(6/7): 1137-1151. https://doi.org/10.1007/s00024-010-0229-3
|
Greenslade, D. J. M., Titov, V. V., 2008. A Comparison Study of Two Numerical Tsunami Forecasting Systems. Pure and Applied Geophysics, 165(11/12): 1991-2001. https://doi.org/10.1007/s00024-008-0413-x
|
Hayes, G. P., Moore, G. L., Portner, D. E.,et al., 2018. Slab2, a Comprehensive Subduction Zone Geometry Model. Science, 362(6410): 58-61. https://doi.org/10.1126/science.aat4723
|
Kamigaichi, O., 2022. Tsunami Forecasting and Warning. Complexity in Tsunamis, Volcanoes, and Their Hazards, 335-371.
|
Lawson, C. L., Hanson, R. J., 1995. Solving Least Squares Problems (Vol. 161). SIAM, Englewood Cliffs, N. J. https://doi.org/10.1137/1.9781611971217
|
Li, L. L., Qiu, Q., Li, Z. G., et al., 2022. Tsunami Hazard Assessment in the South China Sea: A Review of Recent Progress and Research Gaps. Science China Earth Sciences, 65(5): 783-809. https://doi.org/10.1007/s11430-021-9893-8
|
Li, L., Qiu, Q., Li, Z., et al., 2022. Tsunami Hazard Assessment in the South China Sea: A Review of Recent Progress and Research Gaps. Science China Earth Sciences, 52(5): 803-831 (in Chinese with English abstract).
|
Liu, C. L., Lay, T., Xiong, X., 2022. The 29 July 2021 M W 8.2 Chignik, Alaska Peninsula Earthquake Rupture Inferred from Seismic and Geodetic Observations: Re‐Rupture of the Western 2/3 of the 1938 Rupture Zone. Geophysical Research Letters, 49(4): e2021GL096004. https://doi.org/10.1029/2021gl096004
|
Liu, P. L.F., Woo, S.B., Cho, Y.S., 1998. Computer Programs for Tsunami Propagation and Inundation (Technical Report). Cornell University, Ithaca, N.Y.
|
Mai, P. M., Beroza, G. C.,2000. Source Scaling Properties from Finite-Fault-Rupture Models. Bulletin of the Seismological Society of America, 90(3): 604-615. https://doi.org/10.1785/0119990126
|
Melgar, D., Williamson, A. L., Salazar-Monroy, E. F., 2019. Differences between Heterogenous and Homogenous Slip in Regional Tsunami Hazards Modelling. Geophysical Journal International,219(1): 553-562. https://doi.org/10.1093/gji/ggz299
|
Mueller, C., Power, W., Fraser, S., et al., 2015. Effects of Rupture Complexity on Local Tsunami Inundation: Implications for Probabilistic Tsunami Hazard Assessment by Example. Journal of Geophysical Research: Solid Earth, 120(1): 488-502. https://doi.org/10.1002/2014jb011301
|
Mulia, I. E., Gusman, A. R., Heidarzadeh, M., et al., 2022. Sensitivity of Tsunami Data to the Up-Dip Extent of the July 2021 Mw 8.2 Alaska Earthquake. Seismological Research Letters, 93(4): 1992-2003. https://doi.org/10.1785/0220210359
|
Murotani, S., Miyake, H., Koketsu, K., 2008. Scaling of Characterized Slip Models for Plate-Boundary Earthquakes. Earth, Planets and Space, 60(9): 987-991. https://doi.org/10.1186/bf03352855
|
Okada, Y., 1985. Surface Deformation Due to Shear and Tensile Faults in a Half-Space. Bulletin of the Seismological Society of America, 75(4): 1135-1154. https://doi.org/10.1785/bssa0750041135
|
Ren, J., Sun, M., Bing, H., 2021. A Giant Submarine Landslide and Its Triggering Mechanisms on the Nansha Trough Margin, South China Sea. Earth Science, 46(3): 1058-1071 (in Chinese with English Abstract).
|
Rudloff, A., Lauterjung, J., Münch, U., et al., 2009. Preface: The GITEWS Project (German-Indonesian Tsunami Early Warning System). Natural Hazards and Earth System Sciences, 9(4): 1381-1382. https://doi.org/10.5194/nhess-9-1381-2009
|
Setiyono, U., Gusman, A. R., Satake, K., et al., 2017. Pre-Computed Tsunami Inundation Database and Forecast Simulation in Pelabuhan Ratu, Indonesia. Pure and Applied Geophysics, 174(8): 3219-3235. https://doi.org/10.1007/s00024-017-1633-8
|
Tanioka, Y., Satake, K., 1996. Tsunami Generation by Horizontal Displacement of Ocean Bottom. Geophysical Research Letters, 23(8): 861-864. https://doi.org/10.1029/96gl00736
|
Wang, X. M., Liu, P. L. F., 2006. An Analysis of 2004 Sumatra Earthquake Fault Plane Mechanisms and Indian Ocean Tsunami. Journal of Hydraulic Research, 44(2): 147-154. https://doi.org/10.1080/00221686. 2006. 9521671
|
Wang, Y. C., Su, H. Y., Ren, Z. Y., et al., 2022. Source Properties and Resonance Characteristics of the Tsunami Generated by the 2021 M 8.2 Alaska Earthquake. Journal of Geophysical Research: Oceans, 127(3): e2021JC018308. https://doi.org/10.1029/2021jc018308
|
Wei, Y., Cheung, K. F., Curtis, G. D., et al., 2003. Inverse Algorithm for Tsunami Forecasts. Journal of Waterway, Port, Coastal, and Ocean Engineering, 129(2): 60-69. https://doi.org/10.1061/(asce)0733-950x(2003)129:2(60)
|
Wells, D. L., Coppersmith, K. J., 1994. New Empirical Relationships among Magnitude, Rupture Length, Rupture Width, Rupture Area, and Surface Displacement. Bulletin of the Seismological Society of America, 84(4): 974-1002. https://doi.org/10.1785/bssa0840040974
|
Ye, L. L., Bai, Y. F., Si, D. J., et al., 2022. Rupture Model for the 29 July 2021 MW 8.2 Chignik, Alaska Earthquake Constrained by Seismic, Geodetic, and Tsunami Observations. Journal of Geophysical Research: Solid Earth, 127(7): e2021JB023676. https://doi.org/10.1029/2021jb023676
|
Yue, H., Lay, T., Rivera, L., et al., et al., 2014. Localized Fault Slip to the Trench in the 2010 Maule, Chile Mw = 8.8 Earthquake from Joint Inversion of High-Rate Gps, Teleseismic Body Waves, Insar, Campaign Gps, and Tsunami Observations. Journal of Geophysical Research: Solid Earth, 119(10): 7786-7804. https://doi.org/10.1002/2014JB011340
|
Zhu, Y., An, C., Wang, T., et al., 2021. Time-Dependent Tsunami Source Following the 2018 Anak Krakatau Volcano Eruption Inferred from Nearby Tsunami Recordings. China Ocean Engineering, 35(1): 145-152. https://doi.org/10.1007/s13344-021-0013-4
|
任金锋, 孙鸣, 韩冰, 2021. 南海南沙海槽大型海底滑坡的发育特征及成因机制. 地球科学, 46(3):1058-1071.
|
李琳琳, 邱强, 李志刚, 等, 2022. 南海海啸灾害研究进展及展望. 中国科学: 地球科学, 52(5):803-831.
|
/
〈 |
|
〉 |