The Ranges of Uncertainty among the Use of NGA-West1 and NGA-West 2 Ground Motion Prediction Equations

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1 The Ranges of Uncertainty among the Use of NGA-West1 and NGA-West 2 Ground otion Prediction Equations T. Ornthammarath Assistant Professor, Department of Civil and Environmental Engineering, Faculty of Engineering, ahidol University THAILAND P. Warnitchai Professor., Asian Institute of Technology, Pathumthani, THAILAND ABSTRACT: In this study, a comparison of the use of NGA-West1 and NGA-West2 ground motion prediction equations (GPEs) to estimate peak ground acceleration (PGA) and spectral acceleration (SA) at 1.0s for moderate to high seismic hazard area have been presented. This paper focuses on updated estimated ground motion due to the use of NGA-West2, and their impact on the hazard map related to those estimated by NGA-West1 for 2 cities in South East Asia with different level of seismic hazard. In addition, comparison of the range of epistemic uncertainty between NGA-West1 and NGA-West2 have also been determined. In general, the combined effects of lower medians and increased standard deviations in the new GPEs have caused only small changes, within 20%, in the probabilistic ground motions for considered sites compared to the previous results. In addition, the results illustrate that the variation in seismic hazard due to GPEs seems to be lower for NGA-West2 comparing to NGA-West1 for area with controlling earthquake magnitude of However, for area with controlling earthquakes of small magnitudes in the range.-6.0 or very strong earthquakes (>.), the variations in seismic hazard seems to be similar for both NGA-West1 and NGA-West2. Keywords: NGA-West1, NGA-West2, Seismic hazard, Epistemic Uncertainty 1. INTRODUCTION The development of seismic hazard models and their characteristics are obviously the combination of a wide range of possible outcomes and their uncertainties. any past studies emphasize and address uncertainties in PSHA (e.g. cguire and Shedlock, 1981; Senior Seismic Hazard Analysis Committee SSHAC, 199). There are two types of variability that are formalized and included in PSHA. Epistemic uncertainty, or modeling uncertainty, is derived from the recognition that diverse alternative models could describe specific phenomena equally well. This uncertainty is due to insufficient knowledge about the validity of alternative assumptions, mathematical models, and values of the parameters of each model. Aleatory variability, or randomness, is uncertainty in the data used in an analysis and generally accounts for randomness associated with the prediction of a parameter from a specific model, assuming that the model is correct. The standard deviation (σ) of an individual GPE is a representation of aleatory variability. In PSHA, the epistemic uncertainty is mostly considered by applying logic trees that handle the use of alternative models and parameter valuess of each model. Contrary to aleatory variability, the epistemic uncertainty might be reduced by acquiring a better understanding that is, by acquiring additional data and improved information. Recognition of the two kinds of uncertainty is initially useful when selecting and combining inputs. Hazard evaluators need to be aware of the sources of uncertainties (e.g., limitations of available data) so that they can make informed assessments of the validity of alternative hypotheses, the accuracy of alternative models, and the value of data and then transmit these uncertainties to the end users. For example, epistemic uncertainty would generally be much greater for the assessment of seismic hazard in regions where there are relatively few ground-motion records and

2 undetermined locations of slow-slip-rate faults to constrain the selection of appropriate models. The calculated mean seismic hazard from different source models (area or fault sources) could give similar results irrespective of different source models; however, the fractile hazard curves that represent epistemic uncertainty would be differing greatly. For many regions, where a limited number of strong-motion records are available, one solution to this limitation in order to perform seismic hazard analysis is to assume that some existing GPEs developed for other regions with similar seismotectonic characteristics can adequately represent ground-motion scaling in this region. For Thailand and the rest of South East Asia (SE Asia), Next Generation Attenuation West 1 (NGA-West 1) models developed for shallow crustal earthquakes in the western United States has generally been used in the past few years. However, with recent development of the NGA West 2 models, some improvement from the NGA-West 1 equations involved adding data at small-to moderate magnitudes, the richer database available for NGA-West 2 allows NGA West 2 developers to improve on prior work by considering additional variables that could not previously be adequately resolved. In this study, a comparison of the use of NGA-West1 and NGA-West2 ground motion prediction equations (GPEs) to estimate peak ground acceleration (PGA) and spectral acceleration (SA) at 1.0 at different soil classes for moderate to high seismic hazard area have been presented. This paper focuses on updated estimated ground motion due to the use of NGA-West2, and their impact on the hazard map related to those estimated by NGA-West1 for 2 cities in SE Asia with different level of seismic hazard. In addition, comparison of the range of epistemic uncertainty between NGA-West1 and NGA-West2 have also been determined and discussed. 2. SELECTED STUDY AREA In this study, 2 cities in SE Asia with different level of seismic hazard have been selected. The PGAs and SA at 1s at % of critical damping had been determined for the mean, median, and 16 th and 84 th percentile for 4 and 24 year return periods based on the model developed by Ornthammarath et al. (2011). The model is a mixture of background smooth seismicity, crustal faults, and subduction area. The background seismicity model represents random earthquakes in the whole study region except the subduction zones. The model accounts for all earthquakes in areas with no mapped seismic faults and for smaller earthquakes in areas with mapped faults. The magnitude-dependent characteristic of the seismicity rate in each background seismicity zone is modelled by a truncated exponential model (Gutenberg-Richter model). The obtained regional b-value is 0.90, and The a-value varies from place to place within each grid. In the truncated Gutenberg-Richter models of both background smooth seismicity inside Thailand (BG-I) and outside Thailand (BG-II), the minimum earthquake magnitude is set equal to 4. because earthquakes with smaller magnitude than this are judged not to cause damage to buildings and structures (Bommer et al. 2001). The maximum (upper bound) magnitude is set to 6. for BG-I and. for BG-II. Table 1 shows PGA and SA for 1 s at % critical damping for 10-perent and 2-percent probability of exceedance in 0 years (4 and 2,4-year, respectively) based on selected NGA West1. These selected NGA-West1 models were Boore and Atkinson (2008), Campbell and Bozorgnia (2008), and Chiou and Youngs (2008). Equal probabilities (i.e. 1/3) have been assigned to each of these three models in the logic tree analysis. Of the selected cities in SE Asia, Yangon has by far is one of the greatest seismic hazard, primarily due to observed seismicity and its proximity to the potential large earthquake fault, Sagaing fault. The estimated PGA value of 0 percent g for 2 percent in 0 years is comparable to the seismically active regions of the intermountain west in the United States. For Chiang ai, the observed seismicity contributes mainly to moderate hazard with PGA value of 2 percent g for 2 percent in 0 years.

3 Table 1. Probabilistic ground motions for selected cities by using NAG-West1 based on Ornthammarath et al. (2011) City PGA (g) SA (T = 1s) T = 4 T = 24 T = 4 T = 24 Chiang ai Yangon DEAGGREGATION Furthermore, deaggregation analysis is applied for selected sites to assess the combined effect of all magnitudes and distances on the probability of exceeding a given ground motion level. Considering a return period of 4 and 24 year and PGA and SA at 1s for Chiang ai and Yangon, the deaggregation was computed and the controlling earthquakes are shown in Table 2 in terms of mean moment magnitude and mean rupture distance,. The deaggregation results are found to be similar for both soil classes. Observed local seismicity in and around Chiang ai seems to govern the hazard for both short and long structural periods at considered return periods; however, for Yangon, the deaggregation shows a large contribution for the controlling earthquake scenarios from Sagaing fault. It can also be noticed that there is similar controlling earthquake scenarios between different site classes. For deaggregation of PGA at a 2,4-year return period, the controlling earthquake scenario for Chiang ai leads to larger earthquake size at a closer distance. While the deaggregation results of Yangon again show a large contribution from large earthquake magnitudes at 0 km distance. City Chiang ai Yangon Table 2. Controlling earthquake scenarios for PGA and SA at 1s for Chiang ai and Yangon Soil Class PGA (g) SA (T = 1s) T = 4 T = 24 T = 4 T = 24 B D B D COPARISON BETWEEN NGA-WEST1 AND NGA-WEST2 In this section, seismic hazard analysis has been computed for two selected cities based on selected NGA-West2. These selected NGA-West2 models were Boore et al. (2014), Campbell and Bozorgnia (2014), and Chiou and Youngs (2014). Equal probabilities (i.e. 1/3) have been assigned to each of these three models in the logic tree analysis. Figure 1 displays hazard results and the range of uncertainty observed from the use of NGA-West1 and NGA-West2 for both considered sites for rock and soft soil condition at 4- and 24-year return periods. It can be noticed that the hazard results among the use of NGA-West1 and NGA-West2 are comparable for all considered cases, within 10%, in the probabilistic ground motions for considered sites compared to the previous results. For area of moderate hazard in Chiang ai, the observed epistemic uncertainty for NGA-West2 at rock site condition in the range between g.are much smaller than that for NGA-West1 in the range between g.especially where the controlling earthquake magnitude is in the range between. and 6. from 10 to 60 km distance. However, for the case of soft soil condition, both NGA-West1 and NGA-West2 show similar dispersion in the range between g.

4 Unc NGA-W1 (Rock) 30 2 NGA-W2 20 (Soil) Yangon NGA-W1 (Rock) NGA-W2 (Soil) Chiang ai PGA (g) (g) Figure 1. Comparison of expected PGAs (AFEs of 1= 4 (black) and 1= 24 (gray) return periods) from different probabilistic seismic-hazard assessments (PSHAs) (bars, th 8th fractiles; square, medians; and triangles, means) for moderate seismic hazard area (Chiang ai) and high seismic hazard area (Yangon), the uncertainty metric 100 log(sa 8/ SA ). To more objectively compare epistemic uncertainty among NGA-West1 and NGA-West2, the range of uncertainty in Yangon, where the high level of seismic hazard is observed with large controlling earthquake magnitude ( >.) at 0 km distance, has been computed. The distribution of predicted ground motion at 4- and 2,4-year return period at rock site for Yangon by NGA-West2 in the range between 0.03 and 0.06g shows lower uncertainty than the previous analysis with NGA-West1 in the range between 0.11 and 0.18g, where the controlling earthquake magnitude is in the range of at 0 km distance. However, the observed uncertainty is still higher than that observed in the area of moderate controlling earthquake magnitude (. < <.8) as previously observed in Chiang ai. In addition, the uncertainty for soft site at high level hazard are larger than that observed in moderate hazard for both NGA-West1 and NGA West2 models in the range between g.

5 Unc Ratio NGA-W1 (Rock) Yangon NGA-W2 (Soil) NGA-W1 (Rock) 10 NGA-W2 (Soil) Chiang ai SA (T = 1s) (g) (g) Figure 2. Comparison of expected SAs (T = 1.0s) (AFEs of 1= 4 (black) and 1= 24 (gray) return periods) from different probabilistic seismic-hazard assessments (PSHAs) (bars, th 8th fractiles; square, medians; and triangles, means) for moderate seismic hazard area (Chiang ai) and high seismic hazard area (Yangon), the uncertainty metric 100 log(sa 8/ SA ) and the ratio of the uncertainty metrics for SA(1 s) and PGA. oreover, dispersion in GPEs appear to be only weakly dependent on structural period, it would be expected that the uncertainties in the PSHA would also not show strong period dependency. This is examined by comparing Figure 1 (for PGA) and Figure 2 (for SA (1 s)) and particularly by examining the range of uncertainty. In general, the hazard results for both selected sites at all considered return periods and soil conditions displays similar results, within 12%, in the probabilistic ground motions. However, large changes within 22-29%, in the case for soft soil site could be observed in moderate seismic hazard area. For the uncertainties, in general the expected SA (1 s) values are show similar dispersion pattern as it is observed in the expected PGA. For moderate hazard area, the uncertainty is lower than 0.02 g and for both NGA-West1 and NGA-West2 and for both rock and soft soil conditions. For high hazard area, the dispersion is higher than that of moderate hazard area for both NGA-West1 and NGA West2 models at rock condition in the range between g and for soft soil condition the uncertainty is in the range between 0.0g and 0.1g for both NGA-West1 and NGA-West2.

6 . DISCUSSION AND CONCLUSION Since Kulkarni et al. (1984) first introduced the logic tree approach, it has been regularly employed as a means to account for epistemic uncertainty in seismic hazard analysis. The use of logic trees provides a convenient framework for the explicit treatment of model (i.e. epistemic) uncertainty. Apart from alternative models, e.g. different GPEs, the logic tree approach could also consider ranges of parameters required for them and their associated uncertainties, which results in a set of hazard curves from a logic tree that can be represented using a set of percentile (or fractile) curves and a mean curve (i.e. mean with respect to the epistemic uncertainties). In practice, the mean hazard curve is generally employed. A characteristic of the mean hazard curve that is significantly different from the median curve is that the hazard distribution at very low annual rates of exceedance has a positive skewness toward larger values that is, there is a long upper tail to the distribution. Abrahamson and Bommer (200) also question the applicability of the mean hazard curve at long return periods (e.g. ν 10 - mean annual rate of exceedance), since the mean hazard curve at very long return period is strongly influenced by excessively large fractile hazard curves, and the mean hazard curve tends to climb across the fractile curves and can result in very high design ground motions. Considering all sources of epistemic uncertainty can lead to very complex logic trees, making the final hazard calculations difficult to implement by reviewers or anyone not part of the project, thus resulting in a serious lack of transparency of the results. As the complexity of the logic trees grows, the number of possible combinations of their different branches becomes enormous, on the order of 1020 in some past studies, (Abrahamson et al., 2002). In the past the requirement has been to fully sample the logic tree, which is a huge computational effort. Hence, sensitivity analysis is sometimes introduced to discriminate which parameters contribute the most to the hazard and its uncertainty and can be used as a preliminary step for the construction of logic trees focusing efforts on the parameters found to be most important (Rabinowitz et al., 1998; Scherbaum et al.,200) Perus and Fajfar (2009) use a non-parametric approach (CAE method) to investigate the possible reasons for difference among NGA-West1 equations. The results suggest that the predictions depend substantially on the selection of the effective database and on the adopted functional forms. These observed variations (i.e. the epistemic uncertainty) inevitably influence the application of these wellconstrained GPEs, especially in PSHA. Computed hazard curves from well-constrained GPEs do not necessarily produce lower epistemic uncertainty. This could be due to the adopted functional form, selected database, and the determination of sigma. Current analysis shows that despite the fact that the development of NGA-West2 models is a collaborative effort with many interaction and exchange of ideas among the developers the similar level of epistemic uncertainties still could be observed from both NGA-West1 and NGA-West2. The large differences are observed for the areas in which the NGA-West2 database is sparse, such as for large ( >.9) earthquakes at close distances and larger total standard deviation values for the smaller earthquakes comparting to those of moderate earthquake magnitudes. Based on this information, additional data collection or analysis should be undertaken to reduce this uncertainty in the future. ACKNOWLEDGEENTS This study was sponsored by Thailand Research Fund and Faculty of Engineering, ahidol University under contract No. TRG80243 & RG REFERENCES Abrahamson, N.A., Bommer, J.J., [200] Opinion Papers: Probability and Uncertainty in Seismic Hazard Analysis, Earthquake Spectra, Vol. 21, pp Abrahamson, N. A., Birkhauser, P., Koller,., ayer-rosa, D., Smit, P., Sprecher, C., Tinic, S., Graf, R. [2002] PEGASOS a comprehensive probabilistic seismic hazard assessment for nuclear power plants in Switzerland, Proceedings of the Twelfth European Conference on Earthquake Engineering, London, Paper no. 633

7 Boore, D.., Atkinson, G.. [2008] Ground-motion prediction equations for the average horizontal component of PGA, PGV, and %-damped PSA at spectral periods between 0.01 s and 10.0 s, Earthquake Spectra Vol. 24, pp Boore, D.., Stewart, J. P., Seyhan, E., and Atkinson, G.., NGA-West2 equations for predicting PGA, PGV, and % damped PSA for shallow crustal earthquakes, Earthquake Spectra 30, Campbell KW, Bozorgnia Y (2008) NGA ground motion model for the geometric mean horizontal component of PGA, PGV, PGD and % damped linear elastic response spectra for periods ranging from 0.01 to 10 s. Earthq Spectra 24: Campbell, K. W., and Bozorgnia, Y., (2014). NGA-West2 ground motion model for the average horizontal components of PGA, PGV, and % damped linear acceleration response spectra, Earthquake Spectra 30, Chiou BSJ, Youngs RR (2008) Chiou-Youngs NGA ground motion relations for the geometric mean horizontal component of peak and spectral ground motion parameters. Earthq Spectra 24: Chiou BSJ, Youngs RR (2014). Update of the Chiou and Youngs NGA model for the average horizontal component of peak ground motion and response spectra, Earthquake Spectra 30, Kulkarni, R. B., Youngs, R. R., Coppersmith, K. J., [1984] Assessment of confidence intervals for results of seismic hazard analysis, Proceedings, Eighth World Conference on Earthquake Engineering, Vol. 1, San Francisco, pp cguire, R.K., Shedlock, K.., [1981] Statistical Uncertainties in Seismic Hazard Evaluations in the United States, Bull. Seismol. Soc. Am., Vol. 1, pp Ornthammarath T, Warnitchai P, Worakanchana K, Zaman S, Sigbjörnsson R, Lai CG (2011) Probabilistic seismic hazard assessment for Thailand Bull Earthq Eng DOI:10.100/s Peruš, I., Fajfar, P., [2009] How reliable are the ground motion prediction equations?, Proc., 20 th International Conference on Structural echanics in Reactor Technology (SiRT 20), Espoo, Finland, Paper No Rabinowitz, N., Steinberg, D.., Leonard G. [1998] Logic trees, sensitivity analyses, and data reduction in probabilistic seismic hazard assessment, Earth. Spectra Vol. 14, pp Scherbaum, F., Bommer, J.J., Bungum, H., Cotton, F., Abrahamson, N.A. [200] Composite Ground-otion odels and Logic Trees: ethodology, Sensitivities, and Uncertainties, Bulletin of the Seismological Society of America, Vol. 9, No., pp. 93 SSHAC (Senior Seismic Hazard Assessment Committee), [199] Recommendations for PSHA: Guidanceon Uncertainty and Use of Experts, Report NUREG/CR-632, U.S. Nuclear Regulatory Commission, Washington, D.C.