Aagaard, B. T., Brocher, T. M., Dolenc, D., Dreger, D., Graves, R. W., Harmsen, S., Hartzell, S., Larsen, S., and Zoback, M. L. 2008. Ground-Motion Modeling of the 1906 San Francisco Earthquake, Part I: Validation Using the 1989 Loma Prieta Earthquake. Bulletin of the Seismological Society of America, 98 (2), 989–1011.Google Scholar

Aagaard, B. T., Hall, J. F., and Heaton, T. H. 2001. Characterization of Near-Source Ground Motions with Earthquake Simulations. Earthquake Spectra, 17 (2), 177–207.Google Scholar

Abrahamson, N. A. 1993. Spatial Variation of Multiple Support Inputs. In: Proc of 1st US Seminar on Seismic Evaluation and Retrofit of Steel Bridges.Google Scholar

Abrahamson, N. A. 2006. Seismic Hazard Assessment: Problems with Current Practice and Future Developments. In: First European Conference on Earthquake Engineering and Seismology.Google Scholar

Abrahamson, N., Atkinson, G., Boore, D., Bozorgnia, Y., Campbell, K., Chiou, B., Idriss, I. M., Silva, W., and Youngs, R. 2008. Comparisons of the NGA Ground-Motion Relations. Earthquake Spectra, 24 (1), 45–66.Google Scholar

Abrahamson, N. A., and Bommer, J. J. 2005. Probability and Uncertainty in Seismic Hazard Analysis. Earthquake Spectra, 21 (2), 603–607.Google Scholar

Abrahamson, N. A., Kuehn, N. M., Walling, M., and Landwehr, N. 2019. Probabilistic Seismic Hazard Analysis in California Using Nonergodic Ground-Motion Models. Bulletin of the Seismological Society of America, 109 (4), 1235–1249.Google Scholar

Abrahamson, N., Schneider, J. F., and Stepp, J. C. 1991. Spatial Coherency of Shear Waves from the Lotung, Taiwan Large-Scale Seismic Test. Structural Safety, 10 (1), 145–162.Google Scholar

Abrahamson, N., and Silva, W. 2008. Summary of the Abrahamson & Silva NGA Ground-Motion Relations. Earthquake Spectra, 24 (1), 67–97.Google Scholar

Abrahamson, N. A., Silva, W. J., and Kamai, R. 2014. Summary of the ASK14 Ground Motion Relation for Active Crustal Regions. Earthquake Spectra, 30 (3), 1025–1055.Google Scholar

Abrahamson, N., and Youngs, R. R. 1992. A Stable Algorithm for Regression Analysis Using the Random Effects Model. Bulletin of the Seismological Society of America, 82 (1), 505–510.Google Scholar

Adachi, T., and Ellingwood, B. 2007. Impact of Infrastructure Interdependency and Spatial Correlation of Seismic Intensities on Performance Assessment of a Water Distribution System. In: Proceedings of the 10th International Conference on Applications of Statistics and Probability in Civil Engineering.Google Scholar

Adachi, T., and Ellingwood, B. R. 2009. Serviceability Assessment of a Municipal Water System under Spatially Correlated Seismic Intensities. Computer-Aided Civil and Infrastructure Engineering, 24 (4), 237–248.CrossRefGoogle Scholar

Aki, K. 1980. Scattering and Attenuation of Shear Waves in the Lithosphere. Journal of Geophysical Research: Solid Earth, 85 (B11), 6496–6504.Google Scholar

Aki, K. 2003. A Perspective on the History of Strong Motion Seismology. Physics of the Earth and Planetary Interiors, 137 (May), 5–11.CrossRefGoogle Scholar

Aki, K., and Richards, P. G. 2002. Quantitative Seismology. University Science Books.Google Scholar

Akkar, S., and Bommer, J. J. 2006. Influence of Long-Period Filter Cut-off on Elastic Spectral Displacements. Earthquake Engineering & Structural Dynamics, 35 (9), 1145–1165.Google Scholar

Akkar, S., Sandıkkaya, M. A., and Ay, B. Ö. 2014. Compatible Ground-Motion Prediction Equations for Damping Scaling Factors and Vertical-to-Horizontal Spectral Amplitude Ratios for the Broader Europe Region. Bulletin of Earthquake Engineering, 12 (1), 517–547.Google Scholar

Al Atik, L., and Abrahamson, N. 2010. An Improved Method for Nonstationary Spectral Matching. Earthquake Spectra, 26 (3), 601–617.Google Scholar

Al Atik, L., Abrahamson, N., Bommer, J. J., Scherbaum, F., Cotton, F., and Kuehn, N. 2010. The Variability of Ground-Motion Prediction Models and Its Components. Seismological Research Letters, 81 (5), 794–801.CrossRefGoogle Scholar

Al Atik, L., and Youngs, R. R. 2014. Epistemic Uncertainty for NGA-West2 Models. Earthquake Spectra, 30 (3), 1301–1318.Google Scholar

Alavi, B., and Krawinkler, H. 2004. Behavior of Moment-Resisting Frame Structures Subjected to Near-Fault Ground Motions. Earthquake Engineering & Structural Dynamics, 33 (6), 687–706.Google Scholar

Aldor-Noiman, S., Brown, L. D., Buja, A., Rolke, W., and Stine, R. A. 2013. The Power to See: A New Graphical Test of Normality. The American Statistician, 67 (4), 249–260.Google Scholar

Alexander, N. A., Chanerley, A. A., Crewe, A. J., and Bhattacharya, S. 2014. Obtaining Spectrum Matching Time Series Using a Reweighted Volterra Series Algorithm (RVSA). Bulletin of the Seismological Society of America, 104 (4), 1663–1673.Google Scholar

Allen, T. I., and Hayes, G. P. 2017. Alternative Rupture-Scaling Relationships for Subduction Interface and Other Offshore Environments. Bulletin of the Seismological Society of America, 107 (3), 1240–1253.Google Scholar

Ameri, G., Gallovic, F., Pacor, F., and Emolo, A. 2009. Uncertainties in Strong Ground-Motion Prediction with Finite-Fault Synthetic Seismograms: An Application to the 1984 M 5.7 Gubbio, Central Italy, Earthquake. Bulletin of the Seismological Society of America, 99 (2A), 647–663.Google Scholar

American Society of Civil Engineers. 2010. Minimum Design Loads for Buildings and Other Structures, ASCE 7-10. American Society of Civil Engineers/Structural Engineering Institute.Google Scholar

Amrhein, V., Greenland, S., and McShane, B. 2019. Scientists Rise up against Statistical Significance. Nature, 567 (7748), 305–307.Google Scholar

Anagnos, T., and Kiremidjian, A. S. 1984. Stochastic Time-Predictable Model for Earthquake Occurrences. Bulletin of the Seismological Society of America, 74 (6), 2593–2611.CrossRefGoogle Scholar

Anagnos, T., and Kiremidjian, A. S. 1988. A Review of Earthquake Occurrence Models for Seismic Hazard Analysis. Probabilistic Engineering Mechanics, 3 (1), 3–11.Google Scholar

Ancheta, T. D., Darragh, R. B., Stewart, J. P., Seyhan, E., Silva, W. J., Chiou, B. S.-J., Wooddell, K. E., Graves, R. W., Kottke, A. R., Boore, D. M., Kishida, T., and Donahue, J. L. 2014. NGA-West2 Database. Earthquake Spectra, 30 (3), 989–1005.Google Scholar

Ancheta, T. D., Stewart, J. P., and Abrahamson, N. A. 2010. Engineering Characterization of Spatially Variable Ground Motions. PhD Thesis, University of California, Los Angeles.Google Scholar

Anderson, E. L. 1983. Quantitative Approaches in Use to Assess Cancer Risk. Risk Analysis, 3 (4), 277–295.Google Scholar

Anderson, J. G., and Brune, J. N. 1999. Probabilistic Seismic Hazard Analysis without the Ergodic Assumption. Seismological Research Letters, 70 (1), 19–28.Google Scholar

Anderson, J. G., and Hough, S. E. 1984. A Model for the Shape of the Fourier Amplitude Spectrum of Acceleration at High Frequencies. Bulletin of the Seismological Society of America, 74 (5), 1969–1993.Google Scholar

Anderson, J. G., and Uchiyama, Y. 2011. A Methodology to Improve Ground-Motion Prediction Equations by Including Path Corrections. Bulletin of the Seismological Society of America, 101 (4), 1822–1846.Google Scholar

Anderson, J. G., Wesnousky, S. G., and Stirling, M. W. 1996. Earthquake Size as a Function of Fault Slip Rate. Bulletin of the Seismological Society of America, 86 (3), 683–690.Google Scholar

Andrews, D. J., Hanks, T. C., and Whitney, J. W. 2007. Physical Limits on Ground Motion at Yucca Mountain. Bulletin of the Seismological Society of America, 97 (6), 1771–1792.Google Scholar

Andrews, D. J., and Schwerer, E. 2000. Probability of Rupture of Multiple Fault Segments. Bulletin of the Seismological Society of America, 90 (6), 1498–1506.CrossRefGoogle Scholar

Ang, A. H.-S., and Tang, W. 2007. Probability Concepts in Engineering Emphasis on Applications in Civil & Environmental Engineering. New York: Wiley.Google Scholar

Apostolakis, G. 1990. The Concept of Probability in Safety Assessments of Technological Systems. Science, 250 (4986), 1359–1364.Google Scholar

Arias, A. 1970. A Measure of Earthquake Intensity. Pages 438–483 of: Hansen, R. (ed.), Seismic Design for Nuclear Power Plants. Cambridge, MA: MIT Press.Google Scholar

Arroyo, D., and Ordaz, M. 2011. On the Forecasting of Ground-Motion Parameters for Probabilistic Seismic Hazard Analysis. Earthquake Spectra, 27 (1), 1–21.Google Scholar

ASCE. 2016. Minimum Design Loads for Buildings and Other Structures, ASCE 7-16. Reston, VA: American Society of Civil Engineers/Structural Engineering Institute.Google Scholar

Aspinall, W. 2010. A Route to More Tractable Expert Advice. Nature, 463 (7279), 294–295.Google Scholar

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.Google Scholar

Atkinson, G. M. 2008. Ground-Motion Prediction Equations for Eastern North America from a Referenced Empirical Approach: Implications for Epistemic Uncertainty. Bulletin of the Seismological Society of America, 98 (3), 1304–1318.Google Scholar

Atkinson, G. M., and Beresnev, I. 1997. Don’t Call It Stress Drop. Seismological Research Letters, 68 (1), 3–4.Google Scholar

Atkinson, G. M., Bommer, J. J., and Abrahamson, N. A. 2014. Alternative Approaches to Modeling Epistemic Uncertainty in Ground Motions in Probabilistic Seismic-Hazard Analysis. Seismological Research Letters, 85 (6), 1141–1144.Google Scholar

Atkinson, G. M., and Silva, W. 2000. Stochastic Modeling of California Ground Motions. Bulletin of the Seismological Society of America, 90 (2), 255–274.Google Scholar

Atwater, B. F., Tuttle, M. P., Schweig, E. S., Rubin, C. M., Yamaguchi, D. K., and Hemphill-Haley, E. 2003. Earthquake Recurrence Inferred from Paleoseismology. Pages 331–350 of: Developments in Quaternary Sciences. The Quaternary Period in the United States, vol. 1. Elsevier.Google Scholar

Azarbakht, A., Mousavi, M., Nourizadeh, M., and Shahri, M. 2014. Dependence of Correlations between Spectral Accelerations at Multiple Periods on Magnitude and Distance. Earthquake Engineering & Structural Dynamics, 43 (8), 1193–1204.Google Scholar

Baker, J. W. 2007. Probabilistic Structural Response Assessment Using Vector-Valued Intensity Measures. Earthquake Engineering & Structural Dynamics, 36 (13), 1861–1883.Google Scholar

Baker, J. W. 2011. Conditional Mean Spectrum: Tool for Ground Motion Selection. Journal of Structural Engineering, 137 (3), 322–331.Google Scholar

Baker, J. W. 2015. Efficient Analytical Fragility Function Fitting Using Dynamic Structural Analysis. Earthquake Spectra, 31 (1), 579–599.Google Scholar

Baker, J. W., Abrahamson, N. A., Whitney, J. W., Board, M. P., and Hanks, T. C. 2013. Use of Fragile Geologic Structures as Indicators of Unexceeded Ground Motions and Direct Constraints on Probabilistic Seismic Hazard Analysis. Bulletin of the Seismological Society of America, 103 (3), 1898–1911.Google Scholar

Baker, J. W., and Bradley, B. A. 2017. Intensity Measure Correlations Observed in the NGA-West2 Database, and Dependence of Correlations on Rupture and Site Parameters. Earthquake Spectra, 33 (1), 145–156.Google Scholar

Baker, J. W., and Chen, Y. 2020. Ground Motion Spatial Correlation Fitting Methods and Estimation Uncertainty. Earthquake Engineering & Structural Dynamics, 49 (15), 1662–1681.Google Scholar

Baker, J. W., and Cornell, C. A. 2006a. Correlation of Response Spectral Values for Multi-component Ground Motions. Bulletin of the Seismological Society of America, 96 (1), 215–227.Google Scholar

Baker, J. W., and Cornell, C. A. 2006b. Spectral Shape, Epsilon and Record Selection. Earthquake Engineering & Structural Dynamics, 35 (9), 1077–1095.Google Scholar

Baker, J. W., and Cornell, C. A. 2006c. Which Spectral Acceleration Are You Using? Earthquake Spectra, 22 (2), 293–312.Google Scholar

Baker, J. W., and Jayaram, N. 2008. Correlation of Spectral Acceleration Values from NGA Ground Motion Models. Earthquake Spectra, 24 (1), 299–317.Google Scholar

Baker, J. W., and Lee, C. 2018. An Improved Algorithm for Selecting Ground Motions to Match a Conditional Spectrum. Journal of Earthquake Engineering, 22 (4), 708–723.Google Scholar

Baker, J. W., Lin, T., Shahi, S. K., and Jayaram, N. 2011. New Ground Motion Selection Procedures and Selected Motions for the PEER Transportation Research Program. PEER Technical Report 2011/03.Google Scholar

Bao, H., Bielak, J., Ghattas, O., Kallivokas, L. F., O’Hallaron, D. R., Shewchuk, J. R., and Xu, J. 1998. Large-Scale Simulation of Elastic Wave Propagation in Heterogeneous Media on Parallel Computers. Computer Methods in Applied Mechanics and Engineering, 152 (1), 85–102.Google Scholar

Båth, M. 1965. Lateral Inhomogeneities of the Upper Mantle. Tectonophysics, 2 (6), 483–514.Google Scholar

Bauer, P., Thorpe, A., and Brunet, G. 2015. The Quiet Revolution of Numerical Weather Prediction. Nature, 525 (7567), 47–55.Google Scholar

Bayless, J., and Abrahamson, N. A. 2018. Evaluation of the Interperiod Correlation of Ground-Motion Simulations. Bulletin of the Seismological Society of America, 108 (6), 3413–3430.Google Scholar

Bayless, J., and Abrahamson, N. A. 2019. An Empirical Model for the Interfrequency Correlation of Epsilon for Fourier Amplitude Spectra. Bulletin of the Seismological Society of America, 109 (3), 1058–1070.Google Scholar

Bazzurro, P., and Cornell, C. A. 1999. Disaggregation of Seismic Hazard. Bulletin of the Seismological Society of America, 89 (2), 501–520.Google Scholar

Bazzurro, P., and Cornell, C. A. 2002. Vector-Valued Probabilistic Seismic Hazard Analysis. In: 7th U.S. National Conference on Earthquake Engineering. Boston: Earthquake Engineering Research Institute.Google Scholar

Bazzurro, P., and Cornell, C. A. 2004a. Ground-Motion Amplification in Nonlinear Soil Sites with Uncertain Properties. Bulletin of the Seismological Society of America, 94 (6), 2090–2109.Google Scholar

Bazzurro, P., and Cornell, C. A. 2004b. Nonlinear Soil-Site Effects in Probabilistic Seismic-Hazard Analysis. Bulletin of the Seismological Society of America, 94 (6), 2110–2123.CrossRefGoogle Scholar

Bazzurro, P., and Luco, N. 2005. Accounting for Uncertainty and Correlation in Earthquake Loss Estimation. In: 9th International Conference on Structural Safety and Reliability (ICOSSAR05).Google Scholar

Bazzurro, P., and Luco, N. 2006. Do Scaled and Spectrum-Matched Near-Source Records Produce Biased Nonlinear Structural Responses? In: Proceedings, 8th National Conference on Earthquake Engineering.Google Scholar

Beauval, C., Bard, P. Y., Hainzl, S., and Gueguen, P. 2008. Can Strong-Motion Observations Be Used to Constrain Probabilistic Seismic-Hazard Estimates? Bulletin of the Seismological Society of America, 98 (2), 509–520.Google Scholar

Beauval, C., Honoré, L., and Courboulex, F. 2009. Ground-Motion Variability and Implementation of a Probabilistic–Deterministic Hazard Method. Bulletin of the Seismological Society of America, 99 (5), 2992–3002.Google Scholar

Beauval, C., Tasan, H., Laurendeau, A., Delavaud, E., Cotton, F., Guéguen, P., and Kuehn, N. 2012. On the Testing of Ground-Motion Prediction Equations against Small-Magnitude Data. Bulletin of the Seismological Society of America, 102 (5), 1994–2007.Google Scholar

Bedford, T., and Cooke, R. 2001. Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press.Google Scholar

Bender, B. 1983. Maximum-Likelihood Estimation of b-Values for Magnitude Grouped Data. Bulletin of the Seismological Society of America, 73 (3), 831–851.Google Scholar

Benjamin, J. R., and Cornell, C. A. 2014. Probability, Statistics, and Decision for Civil Engineers. Mineola, NY: Dover Publications.Google Scholar

Berrill, J. B. 1988. Diversion of Faulting by Hills. Quarterly Journal of Engineering Geology and Hydrogeology, 21 (4), 371–374.Google Scholar

Berryman, K., Hamling, I., Kaiser, A., and Stahl, T. 2018. Introduction to the Special Issue on the 2016 Kaikōura Earthquake. Bulletin of the Seismological Society of America, 108 (3B), 1491–1495.Google Scholar

Beyer, K., and Bommer, J. J. 2007. Selection and Scaling of Real Accelerograms for Bi-directional Loading: A Review of Current Practice and Code Provisions. Journal of Earthquake Engineering, 11 (suppl. 1), 13–45.Google Scholar

Biasi, G. P. 2013. Appendix H: Maximum Likelihood Recurrence Intervals for California Paleoseismic Sites. Open File Report 2013-1165. United States Geological Survey.Google Scholar

Biasi, G. P., and Weldon, R. J. I. 2006. Estimating Surface Rupture Length and Magnitude of Paleoearthquakes from Point Measurements of Rupture Displacement. Bulletin of the Seismological Society of America, 96 (5), 1612–1623.Google Scholar

Biasi, G. P., and Weldon, R. J. 2009. San Andreas Fault Rupture Scenarios from Multiple Paleoseismic Records: Stringing Pearls. Bulletin of the Seismological Society of America, 99 (2A), 471–498.Google Scholar

Biasi, G. P., Weldon, R. J. I., and Dawson, T. E. 2013. Appendix F: Distribution of Slip in Ruptures. Open File Report 2013-1165. United States Geological Survey.Google Scholar

Biasi, G. P., Weldon, R. J., Fumal, T. E., and Seitz, G. G. 2002. Paleoseismic Event Dating and the Conditional Probability of Large Earthquakes on the Southern San Andreas Fault, California. Bulletin of the Seismological Society of America, 92 (7), 2761–2781.Google Scholar

Bielak, J., Graves, R. W., Olsen, K. B., Taborda, R., Ramírez-Guzmín, L., Day, S. M., Ely, G. P., Roten, D., Jordan, T. H., Maechling, P. J., Urbanic, J., Cui, Y., and Juve, G. 2010. The ShakeOut Earthquake Scenario: Verification of Three Simulation Sets. Geophysical Journal International, 180 (1), 375–404.Google Scholar

Bielak, J., Karaoglu, H., and Taborda, R. 2011. Memory-Efficient Displacement-Based Internal Friction for Wave Propagation Simulation. Geophysics, 76 (6), T131–T145.Google Scholar

Bielak, J., Loukakis, K., Hisada, Y., and Yoshimura, C. 2003. Domain Reduction Method for Three-Dimensional Earthquake Modeling in Localized Regions, Part I: Theory. Bulletin of the Seismological Society of America, 93 (2), 817–824.Google Scholar

Bird, P. 2003. An Updated Digital Model of Plate Boundaries. Geochemistry Geophysics Geosystems, 4 (3), 1027.Google Scholar

Bizzarri, A. 2011. On the Deterministic Description of Earthquakes. Reviews of Geophysics, 49 (RG3002).Google Scholar

Bocchini, P., and Frangopol, D. M. 2011. Generalized Bridge Network Performance Analysis with Correlation and Time-Variant Reliability. Structural Safety, 33 (2), 155–164.Google Scholar

Bommer, J., Abrahamson, N., Strasser, F., A, P., Bard, P., Bungum, H., Cotton, F., Fah, D., Sabetta, F., Scherbaum, F., and Studer, J. 2004. The Challenge of Defining Upper Bounds on Earthquake Ground Motions. Seismological Research Letters, 75 , 82–95.Google Scholar

Bommer, J., Douglas, J., and Strasser, F. 2003. Style-of-Faulting in Ground-Motion Prediction Equations. Bulletin of Earthquake Engineering, 1 (2), 171–203.Google Scholar

Bommer, J. J. 2012. Challenges of Building Logic Trees for Probabilistic Seismic Hazard Analysis. Earthquake Spectra, 28 (4), 1723–1735.Google Scholar

Bommer, J. J., and Abrahamson, N. A. 2006. Why Do Modern Probabilistic Seismic-Hazard Analyses Often Lead to Increased Hazard Estimates? Bulletin of the Seismological Society of America, 96 (6), 1967–1977.Google Scholar

Bommer, J. J., and Acevedo, A. B. 2004. The Use of Real Earthquake Accelerograms as Input to Dynamic Analysis. Journal of Earthquake Engineering, 8 (Special Issue 1), 43–91.Google Scholar

Bommer, J. J., and Akkar, S. 2012. Consistent Source-to-Site Distance Metrics in Ground-Motion Prediction Equations and Seismic Source Models for PSHA. Earthquake Spectra, 28 (1), 1–15.Google Scholar

Bommer, J. J., Coppersmith, K. J., Coppersmith, R. T., Hanson, K. L., Mangongolo, A., Neveling, J., Rathje, E. M., Rodriguez-Marek, A., Scherbaum, F., Shelembe, R., Stafford, P. J., and Strasser, F. O. 2015. A SSHAC Level 3 Probabilistic Seismic Hazard Analysis for a New-Build Nuclear Site in South Africa. Earthquake Spectra, 31 (2), 661–698.Google Scholar

Bommer, J. J., and Crowley, H. 2017. The Purpose and Definition of the Minimum Magnitude Limit in PSHA Calculations. Seismological Research Letters, 88 (4), 1097–1106.Google Scholar

Bommer, J. J., Douglas, J., Scherbaum, F., Cotton, F., Bungum, H., and Fah, D. 2010. On the Selection of Ground-Motion Prediction Equations for Seismic Hazard Analysis. Seismological Research Letters, 81 (5), 783–793.Google Scholar

Bommer, J. J., and Martinez-Pereira, A. 1999. The Effective Duration of Earthquake Strong Motion. Journal of Earthquake Engineering, 3 (2), 127–172.Google Scholar

Bommer, J. J., and Scherbaum, F. 2008. The Use and Misuse of Logic Trees in Probabilistic Seismic Hazard Analysis. Earthquake Spectra, 24 (4), 997–1009.Google Scholar

Bommer, J. J., and Stafford, P. J. 2016. Chapter 2: Seismic Hazard and Earthquake Actions. Pages 7–40 of: Seismic Design of Buildings to Eurocode 8. CRC Press.Google Scholar

Bommer, J. J., and Stafford, P. J. 2020. Selecting Ground-Motion Models for Site-Specific PSHA: Adaptability versus Applicability. Bulletin of the Seismological Society of America, 110 (6), 2801–2815.Google Scholar

Bommer, J. J., Stafford, P. J., and Alarcon, J. E. 2009. Empirical Equations for the Prediction of the Significant, Bracketed, and Uniform Duration of Earthquake Ground Motion. Bulletin of the Seismological Society of America, 99 (6), 3217–3233.Google Scholar

Bommer, J. J., Stafford, P. J., Alarcon, J. E., and Akkar, S. 2007. The Influence of Magnitude Range on Empirical Ground-Motion Prediction. Bulletin of the Seismological Society of America, 97 (6), 2152.Google Scholar

Bommer, J. J., Stafford, P. J., Edwards, B., Dost, B., van Dedem, E., Rodriguez-Marek, A., Kruiver, P., van Elk, J., Doornhof, D., and Ntinalexis, M. 2017. Framework for a Ground-Motion Model for Induced Seismic Hazard and Risk Analysis in the Groningen Gas Field, the Netherlands. Earthquake Spectra, 33 (2), 481–498.Google Scholar

Bommer, J. J., Strasser, F. O., Pagani, M., and Monelli, D. 2013. Quality Assurance for Logic-Tree Implementation in Probabilistic Seismic-Hazard Analysis for Nuclear Applications: A Practical Example. Seismological Research Letters, 84 (6), 938–945.Google Scholar

Boore, D. M. 2003. Simulation of Ground Motion Using the Stochastic Method. Pure and Applied Geophysics, 160 , 635–676.Google Scholar

Boore, D. M. 2004. Estimating (or NEHRP Site Classes) from Shallow Velocity Models (Depths < 30 m). Bulletin of the Seismological Society of America, 94 (2), 591–597.Google Scholar

Boore, D. M. 2009. Comparing Stochastic Point-Source and Finite-Source Ground-Motion Simulations: SMSIM and EXSIM. Bulletin of the Seismological Society of America, 99 (6), 3202–3216.Google Scholar

Boore, D. M. 2010. Orientation-Independent, Nongeometric-Mean Measures of Seismic Intensity from Two Horizontal Components of Motion. Bulletin of the Seismological Society of America, 100 (4), 1830–1835.Google Scholar

Boore, D. M. 2013. The Uses and Limitations of the Square-Root-Impedance Method for Computing Site Amplification. Bulletin of the Seismological Society of America, 103 (4), 2356–2368.Google Scholar

Boore, D. M. 2016. Determining Generic Velocity and Density Models for Crustal Amplification Calculations, with an Update of the Boore and Joyner (1997) Generic Site Amplification for VS(Z) = 760 m/s. Bulletin of the Seismological Society of America, 106 (1), 313–317.Google Scholar

Boore, D. M., and Boatwright, J. 1984. Average Body-Wave Radiation Coefficients. Bulletin of the Seismological Society of America, 74 (5), 1615–1621.Google Scholar

Boore, D. M., and Bommer, J. J. 2005. Processing of Strong-Motion Accelerograms: Needs, Options and Consequences. Soil Dynamics and Earthquake Engineering, 25 , 93–115.Google Scholar

Boore, D. M., Gibbs, J. F., Joyner, W. B., Tinsley, J. C., and Ponti, D. J. 2003. Estimated Ground Motion from the 1994 Northridge, California, Earthquake at the Site of the Interstate 10 and La Cienega Boulevard Bridge Collapse, West Los Angeles, California. Bulletin of the Seismological Society of America, 93 (6), 2737–2751.Google Scholar

Boore, D. M., and Joyner, W. B. 1997. Site Amplifications for Generic Rock Sites. Bulletin of the Seismological Society of America, 87 (2), 327–341.Google Scholar

Boore, D. M., Joyner, W. B., and Fumal, T. E. 1997. Equations for Estimating Horizontal Response Spectra and Peak Acceleration from Western North American Earthquakes: A Summary of Recent Work. Seismological Research Letters, 68 (1), 128–153.Google Scholar

Boore, D. M., Joyner, W. B., and Wennerberg, L. 1992. Fitting the Stochastic ω⁻² Source Model to Observed Response Spectra in Western North America: Trade-offs between Δσ and κ. Bulletin of the Seismological Society of America, 82 (4), 1956–1963.Google Scholar

Boore, D. M., Stewart, J. P., Seyhan, E., and Atkinson, G. M. 2014. NGA-West2 Equations for Predicting PGA, PGV, and 5% Damped PSA for Shallow Crustal Earthquakes. Earthquake Spectra, 30 (3), 1057–1085.Google Scholar

Boore, D. M., and Thompson, E. M. 2014. Path Durations for Use in the Stochastic-Method Simulation of Ground Motions. Bulletin of the Seismological Society of America, 104 (5), 2541–2552.Google Scholar

Boore, D. M., and Thompson, E. M. 2015. Revisions to Some Parameters Used in Stochastic-Method Simulations of Ground Motion. Bulletin of the Seismological Society of America, 105 (2A), 1029–1041.Google Scholar

Boore, D., Watson-Lamprey, J., and Abrahamson, N. 2006. Orientation-Independent Measures of Ground Motion. Bulletin of the Seismological Society of America, 96 (4A), 1502–1511.Google Scholar

Bora, S. S., Scherbaum, F., Kuehn, N., and Stafford, P. J. 2014. Fourier Spectral- and Duration Models for the Generation of Response Spectra Adjustable to Different Source-, Propagation-, and Site Conditions. Bulletin of Earthquake Engineering, 12 , 467–493.Google Scholar

Bora, S. S., Scherbaum, F., Kuehn, N., and Stafford, P. J. 2016. On the Relationship between Fourier and Response Spectra: Implications for the Adjustment of Empirical Ground-Motion Prediction Equations (GMPEs). Bulletin of the Seismological Society of America, 106 (3), 1235–1253.Google Scholar

Bourne, S. J., Oates, S. J., and van Elk, J. 2018. The Exponential Rise of Induced Seismicity with Increasing Stress Levels in the Groningen Gas Field and Its Implications for Controlling Seismic Risk. Geophysical Journal International, 213 (3), 1693–1700.Google Scholar

Box, G. E. P. 1980. Sampling and Bayes’ Inference in Scientific Modelling and Robustness. Journal of the Royal Statistical Society. Series A (General), 143 (4), 383–430.Google Scholar

Boyd, O. S. 2012. Including Foreshocks and Aftershocks in Time-Independent Probabilistic Seismic-Hazard Analyses. Bulletin of the Seismological Society of America, 102 (3), 909–917.Google Scholar

Bozorgnia, Y., and Campbell, K. 2004. The Vertical-to-Horizontal Response Spectral Ratio and Tentative Procedures for Developing Simplified V/H and Vertical Design Spectra. Journal of Earthquake Engineering, 8 (2), 175–207.Google Scholar

Bradley, B. 2009. Seismic Hazard Epistemic Uncertainty in the San Francisco Bay Area and Its Role in Performance-Based Assessment. Earthquake Spectra, 25 (4), 733–753.Google Scholar

Bradley, B. 2010a. Epistemic Uncertainty in Component Fragility Functions. Earthquake Spectra, 26 (1), 41–62.Google Scholar

Bradley, B. A. 2010b. A Generalized Conditional Intensity Measure Approach and Holistic Ground-Motion Selection. Earthquake Engineering & Structural Dynamics, 39 (12), 1321–1342.Google Scholar

Bradley, B. A. 2011. Correlation of Significant Duration with Amplitude and Cumulative Intensity Measures and Its Use in Ground Motion Selection. Journal of Earthquake Engineering, 15 (6), 809–832.Google Scholar

Bradley, B. A. 2012a. Empirical Correlations between Cumulative Absolute Velocity and Amplitude-Based Ground Motion Intensity Measures. Earthquake Spectra, 28 (1), 37–54.Google Scholar

Bradley, B. A. 2012b. A Ground Motion Selection Algorithm Based on the Generalized Conditional Intensity Measure Approach. Soil Dynamics and Earthquake Engineering, 40 , 48–61.Google Scholar

Bradley, B. A. 2012c. The Seismic Demand Hazard and Importance of the Conditioning Intensity Measure. Earthquake Engineering & Structural Dynamics, 41 (11), 1417–1437.Google Scholar

Bradley, B. A. 2013a. A Comparison of Intensity-Based Demand Distributions and the Seismic Demand Hazard for Seismic Performance Assessment. Earthquake Engineering & Structural Dynamics, 42 (15), 2235–2253.Google Scholar

Bradley, B. A. 2013b. A Critical Examination of Seismic Response Uncertainty Analysis in Earthquake Engineering. Earthquake Engineering & Structural Dynamics, 42 (11), 1717–1729.Google Scholar

Bradley, B. A. 2013c. Practice-Oriented Estimation of the Seismic Demand Hazard Using Ground Motions at Few Intensity Levels. Earthquake Engineering & Structural Dynamics, 42 (14), 2167–2185.Google Scholar

Bradley, B. A. 2015. Correlation of Arias Intensity with Amplitude, Duration and Cumulative Intensity Measures. Soil Dynamics and Earthquake Engineering, 78 , 89–98.Google Scholar

Bradley, B. A., Burks, L. S., and Baker, J. W. 2015. Ground Motion Selection for Simulation-Based Seismic Hazard and Structural Reliability Assessment. Earthquake Engineering & Structural Dynamics, 44 (13), 2321–2340.Google Scholar

Bradley, B. A., and Dhakal, R. P. 2008. Error Estimation of Closed-Form Solution for Annual Rate of Structural Collapse. Earthquake Engineering & Structural Dynamics, 37 (15), 1721–1737.Google Scholar

Bradley, B. A., Dhakal, R. P., MacRae, G. A., and Cubrinovski, M. 2010. Prediction of Spatially Distributed Seismic Demands in Specific Structures: Ground Motion and Structural Response. Earthquake Engineering & Structural Dynamics, 39 (2), 501–520.Google Scholar

Bradley, B. A., Pettinga, D., Baker, J. W., and Fraser, J. 2017. Guidance on the Utilization of Earthquake-Induced Ground Motion Simulations in Engineering Practice. Earthquake Spectra, 33 (3), 809–835.Google Scholar

Bray, J. D., and Travasarou, T. 2007. Simplified Procedure for Estimating Earthquake-Induced Deviatoric Slope Displacements. Journal of Geotechnical and Geoenvironmental Engineering, 133 (4), 381–392.Google Scholar

Brillinger, D. R., and Preisler, H. K. 1984. An Exploratory Analysis of the Joyner–Boore Attenuation Data. Bulletin of the Seismological Society of America, 74 (4), 1441–1450.Google Scholar

Brillinger, D. R., and Preisler, H. K. 1985. Further Analysis of the Joyner–Boore Attenuation Data. Bulletin of the Seismological Society of America, 75 (2), 611–614.Google Scholar

Brocher, T. M. 2005. Empirical Relations between Elastic Wavespeeds and Density in the Earth’s Crust. Bulletin of the Seismological Society of America, 95 (6), 2081–2092.Google Scholar

Brune, J. 1970. Tectonic Stress and the Spectra of Seismic Shear Waves from Earthquakes. Journal of Geophysical Research, 75 (26), 4997–5009.Google Scholar

Brune, J. 1971. Correction. Journal of Geophysical Research, 76 (20), 5002.Google Scholar

Brune, J. 1999. Precarious Rocks along the Mojave Section of the San Andreas Fault, California: Constraints on Ground Motion from Great Earthquakes. Seismological Research Letters, 70 (1), 29–33.Google Scholar

BSSC. 2009. NEHRP Recommended Seismic Provisions for New Buildings and Other Structures. FEMA P-750. Building Seismic Safety Council, Washington, DC.Google Scholar

BSSC. 2015. NEHRP Recommended Seismic Provisions for New Buildings and Other Structures. FEMA P-1050. Building Seismic Safety Council, Washington, DC.Google Scholar

Budnitz, R. J., Amico, P. J., Cornell, C. A., Hall, W. J., Kennedy, R. P., Reed, J. W., and Shinozuka, M. 1985. An Approach to the Quantification of Seismic Margins in Nuclear Power Plants. NUREG/CR 4334. US Nuclear Regulatory Commission, Washington, DC.Google Scholar

Bullock, Z. 2019. Log-Logistic Uncertainty Is More Durable than Lognormal Uncertainty in Ground-Motion Prediction Equations. Bulletin of the Seismological Society of America, 109 (2), 567–574.Google Scholar

Buratti, N., Stafford, P. J., and Bommer, J. J. 2011. Earthquake Accelerogram Selection and Scaling Procedures for Estimating the Distribution of Drift Response. Journal of Structural Engineering, 137 (3), 345–357.Google Scholar

Burbank, D. W., and Anderson, R. S. 2011. Tectonic Geomorphology, 2nd ed. Chichester, UK: John Wiley & Sons.Google Scholar

Burridge, R., and Knopoff, L. 1964. Body Force Equivalents for Seismic Dislocations. Bulletin of the Seismological Society of America, 54 (6), 1875–1888.CrossRefGoogle Scholar

Burridge, R., and Knopoff, L. 1967. Model and Theoretical Seismicity. Bulletin of the Seismological Society of America, 57 (3), 341–371.Google Scholar

Calvi, G. M., Pinho, R., Magenes, G., Bommer, J. J., Restrepo-Vílez, L. F., and Crowley, H. 2006. Development of Seismic Vulnerability Assessment Methodologies over the Past 30 Years. ISET Journal of Earthquake Technology, 43 (3), 75–104.Google Scholar

Campbell, K. W. 2003. Prediction of Strong Ground Motion Using the Hybrid Empirical Method and Its Use in the Development of Ground-Motion (Attenuation) Relations in Eastern North America. Bulletin of the Seismological Society of America, 93 (3), 1012–1033.Google Scholar

Campbell, K. W., and Bozorgnia, Y. 2014. NGA-West2 Ground Motion Model for the Average Horizontal Components of PGA, PGV, and 5% Damped Linear Acceleration Response Spectra. Earthquake Spectra, 30 (3), 1087–1115.Google Scholar

Carballo, J. E. 2000. Probabilistic Seismic Demand Analysis: Spectrum Matching and Design. Dept. of Civil and Environmental Engineering. Stanford, CA: Stanford University.Google Scholar

Carlson, J. M., and Langer, J. S. 1989. Mechanical Model of an Earthquake Fault. Physical Review A, 40 (11), 6470–6484.Google Scholar

Carlson, J. M., Langer, J. S., Shaw, B. E., and Tang, C. 1991. Intrinsic Properties of a Burridge– Knopoff Model of an Earthquake Fault. Physical Review A, 44 (2), 884–897.Google Scholar

Carlton, B., and Abrahamson, N. 2014. Issues and Approaches for Implementing Conditional Mean Spectra in Practice. Bulletin of the Seismological Society of America, 104 (1), 503–512.Google Scholar

CEN. 2004. Eurocode 8: Design of Structures for Earthquake Resistance—Part 1: General Rules, Seismic Actions and Rules for Buildings. Comité Européen de Normalisation, European Standard EN 1998-1:2004: E. Brussels.Google Scholar

Cesca, S., Zhang, Y., Mouslopoulou, V., Wang, R., Saul, J., Savage, M., Heimann, S., Kufner, S. K., Oncken, O., and Dahm, T. 2017. Complex Rupture Process of the Mw 7.8, 2016, Kaikoura Earthquake, New Zealand, and Its Aftershock Sequence. Earth and Planetary Science Letters, 478 , 110–120.Google Scholar

Chaljub, E., Maufroy, E., Moczo, P., Kristek, J., Hollender, F., Bard, P.-Y., Priolo, E., Klin, P., de Martin, F., Zhang, Z., Zhang, W., and Chen, X. 2015. 3-D Numerical Simulations of Earthquake Ground Motion in Sedimentary Basins: Testing Accuracy through Stringent Models. Geophysical Journal International, 201 (1), 90–111.Google Scholar

Chaljub, E., Moczo, P., Tsuno, S., Bard, P.-Y., Kristek, J., Käser, M., Stupazzini, M., and Kristekova, M. 2010. Quantitative Comparison of Four Numerical Predictions of 3D Ground Motion in the Grenoble Valley, France. Bulletin of the Seismological Society of America, 100 (4), 1427–1455.Google Scholar

Chandramohan, R., Baker, J. W., and Deierlein, G. G. 2016. Impact of Hazard-Consistent Ground Motion Duration in Structural Collapse Risk Assessment. Earthquake Engineering & Structural Dynamics, 45 (8), 1357–1379.Google Scholar

Chang, S. E., Shinozuka, M., and Moore, J. E. 2000. Probabilistic Earthquake Scenarios: Extending Risk Analysis Methodologies to Spatially Distributed Systems. Earthquake Spectra, 16 (3), 557–572.Google Scholar

Chen, Y., and Baker, J. W. 2019. Spatial Correlations in CyberShake Physics-Based Ground-Motion Simulations. Bulletin of the Seismological Society of America, 109 (6), 2447–2458.Google Scholar

Chen, P., Zhao, L., and Jordan, T. H. 2007. Full 3D Tomography for the Crustal Structure of the Los Angeles Region. Bulletin of the Seismological Society of America, 97 (4), 1094–1120.Google Scholar

Chen, Y.-H., and Tsai, C.-C. P. 2002. A New Method for Estimation of the Attenuation Relationship with Variance Components. Bulletin of the Seismological Society of America, 92 (5), 1984–1991.Google Scholar

Chiou, B., Darragh, R., Gregor, N., and Silva, W. 2008. NGA Project Strong-Motion Database. Earthquake Spectra, 24 (1), 23–44.Google Scholar

Chiou, B., Youngs, R., Abrahamson, N., and Addo, K. 2010. Ground-Motion Attenuation Model for Small-to-Moderate Shallow Crustal Earthquakes in California and Its Implications on Regionalization of Ground-Motion Prediction Models. Earthquake Spectra, 26 (4), 907–926.Google Scholar

Chiou, B. S.-J., and Youngs, R. R. 2008. An NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra. Earthquake Spectra, 24 (1), 173–215.Google Scholar

Chiou, B. S.-J., and Youngs, R. R. 2014. Update of the Chiou and Youngs NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra. Earthquake Spectra, 30 (3), 1117–1153.Google Scholar

Choi, Y., and Stewart, J. P. 2005. Nonlinear Site Amplification as Function of 30 m Shear Wave Velocity. Earthquake Spectra, 21 (1), 1–30.Google Scholar

Choi, Y., Stewart, J. P., and Graves, R. W. 2005. Empirical Model for Basin Effects Accounts for Basin Depth and Source Location. Bulletin of the Seismological Society of America, 95 (4), 1412–1427.CrossRefGoogle Scholar

Chopra, A. 2016. Dynamics of Structures, 5th ed. Hoboken, NJ: Pearson.Google Scholar

Clough, R., and Penzien, J. 1975. Dynamics of Structures. New York: McGraw-Hill.Google Scholar

Coburn, A., and Spence, R. 2002. Earthquake Protection. John Wiley & Sons.Google Scholar

Cochran, W. G. 1977. Sampling Techniques, 3rd ed. New York: John Wiley & Sons.Google Scholar

Console, R., and Murru, M. 2001. A Simple and Testable Model for Earthquake Clustering. Journal of Geophysical Research, 106 (B5), 8699–8711.Google Scholar

Cook, R., Malkus, D., and Plesha, M. 1989. Concepts and Applications of Finite Element Analysis, 3rd ed. New York: John Wiley and Sons.Google Scholar

Cooke, R. 1991. Experts in Uncertainty: Opinion and Subjective Probability in Science. Oxford University Press on Demand.Google Scholar

Cordova, P. P., Deierlein, G. G., Mehanny, S. S., and Cornell, C. 2001. Development of a Two-Parameter Seismic Intensity Measure and Probabilistic Assessment Procedure. Pages 187–206 of: The Second U.S.–Japan Workshop on Performance-Based Earthquake Engineering Methodology for Reinforced Concrete Building Structures.Google Scholar

Cornell, C. A. 1968. Engineering Seismic Risk Analysis. Bulletin of the Seismological Society of America, 58 (5), 1583–1606.Google Scholar

Cornell, C. A. 1994. Statistical Analysis of Maximum Magnitudes. In: The Earthquakes of Stable Continential Regions. EPRI Report, nos. TR–102261s–V1–V5. Electric Power Research Institute.Google Scholar

Cornell, C. A., Banon, H., and Shakal, A. F. 1979. Seismic Motion and Response Prediction Alternatives. Earthquake Engineering & Structural Dynamics, 7 (4), 295–315.Google Scholar

Cornell, C. A., Jalayer, F., Hamburger, R. O., and Foutch, D. A. 2002. Probabilistic Basis for 2000 SAC Federal Emergency Management Agency Steel Moment Frame Guidelines. Journal of Structural Engineering, 128 (4), 526–533.Google Scholar

Cornell, C. A., and Krawinkler, H. 2000. Progress and Challenges in Seismic Performance Assessment. PEER Center News, 3 (2).Google Scholar

Cornell, C. A., and Vanmarcke, E. H. 1969. The Major Influences on Seismic Risk. Pages 69–83 of: Proceedings of the Fourth World Conference on Earthquake Engineering, vol. 1.Google Scholar

Cornell, C. A., and Winterstein, S. R. 1988. Temporal and Magnitude Dependence in Earthquake Recurrence Models. Bulletin of the Seismological Society of America, 78 (4), 1522–1537.Google Scholar

Cotton, F., Scherbaum, F., Bommer, J., and Bungum, H. 2006. Criteria for Selecting and Adjusting Ground-Motion Models for Specific Target Regions: Application to Central Europe and Rock Sites. Journal of Seismology, 10 , 137–156.Google Scholar

Crempien, J. G. F., and Archuleta, R. J. 2015. UCSB Method for Simulation of Broadband Ground Motion from Kinematic Earthquake Sources. Seismological Research Letters, 86 (1), 61–67.Google Scholar

Crowley, H., and Pinho, R. 2011. Global Earthquake Model: Community-Based Seismic Risk Assessment. Pages 3–19 of: Protection of Built Environment against Earthquakes. Springer.Google Scholar

Crowley, H., Pinho, R., and Bommer, J. J. 2004. A Probabilistic Displacement-Based Vulnerability Assessment Procedure for Earthquake Loss Estimation. Bulletin of Earthquake Engineering, 2 (2), 173–219.Google Scholar

Crowley, H., Stafford, P. J., and Bommer, J. J. 2008. Can Earthquake Loss Models Be Validated Using Field Observations? Journal of Earthquake Engineering, 12 (7), 1078–1104.Google Scholar

Daniell, J. E., Khazai, B., Wenzel, F., and Vervaeck, A. 2011. The CATDAT Damaging Earthquakes Database. Natural Hazards and Earth System Sciences, 11 (8), 2235.Google Scholar

Day, S. M., and Bradley, C. R. 2001. Memory-Efficient Simulation of Anelastic Wave Propagation. Bulletin of the Seismological Society of America, 91 (3), 520–531.Google Scholar

Day, S. M., Graves, R., Bielak, J., Dreger, D., Larsen, S., Olsen, K. B., Pitarka, A., and Ramirez-Guzman, L. 2008. Model for Basin Effects on Long-Period Response Spectra in Southern California. Earthquake Spectra, 24 (1), 257–277.Google Scholar

D’Ayala, D., Meslem, A., Vamvatsikos, D., Porter, K., Rossetto, T., Crowley, H., and Silva, V. 2014. Guidelines for Analytical Vulnerability Assessment of Low-to Mid-Rise Buildings—Methodology. GEM Technical Report 2014-12 V1.0.0. Global Earthquake Model.Google Scholar

de la Puente, J., Dumbser, M., Käser, M., and Igel, H. 2008. Discontinuous Galerkin Methods for Wave Propagation in Poroelastic Media. Geophysics, 73 (5), T77–T97.Google Scholar

de la Torre, C. A., Bradley, B. A., and Lee, R. L. 2020. Modeling Nonlinear Site Effects in Physics-Based Ground Motion Simulations of the 2010–2011 Canterbury Earthquake Sequence. Earthquake Spectra, 36 (2), 856–879.Google Scholar

DeBock, D. J., Garrison, J. W., Kim, K. Y., and Liel, A. B. 2014. Incorporation of Spatial Correlations between Building Response Parameters in Regional Seismic Loss Assessment. Bulletin of the Seismological Society of America, 104 (1), 214–228.Google Scholar

DeMets, C., Gordon, R. G., Argus, D. F., and Stein, S. 1990. Current Plate Motions. Geophysical Journal International, 101 (2), 425–478.Google Scholar

Der Kiureghian, A. 2005. Non-Ergodicity and PEER’s Framework Formula. Earthquake Engineering and Structural Dynamics, 34 (13), 1643–1652.Google Scholar

Der Kiureghian, A., and Ditlevsen, O. 2008. Aleatory or Epistemic? Does It Matter? Structural Safety, 31 (2), 105–112.Google Scholar

Detweiler, S. T., and Wein, A. M. 2017. The HayWired Earthquake Scenario. USGS Numbered Series 2017-5013. US Geological Survey, Reston, VA.Google Scholar

Dieterich, J. H. 1979. Modeling of Rock Friction: 1. Experimental Results and Constitutive Equations. Journal of Geophysical Research, 84 (B5), 2161–2168.Google Scholar

Dolsek, M. 2009. Incremental Dynamic Analysis with Consideration of Modeling Uncertainties. Earthquake Engineering & Structural Dynamics, 38 (6), 805–825.Google Scholar

Donahue, J. L., and Abrahamson, N. A. 2014. Simulation-Based Hanging Wall Effects. Earthquake Spectra, 30 (3), 1269–1284.Google Scholar

Dorra, E. M., Stafford, P. J., and Elghazouli, A. Y. 2013. Earthquake Loss Estimation for Greater Cairo and the National Economic Implications. Bulletin of Earthquake Engineering, 11 (4), 1217–1257.Google Scholar

Douglas, J. 2003. Earthquake Ground Motion Estimation Using Strong Motion Records: A Review of Equations for the Estimation of Peak Ground Acceleration and Response Spectral Ordinates. Earth Science Reviews, 61 , 43–104.Google Scholar

Douglas, J. 2019. Ground Motion Prediction Equations 1964–2018. Online Report www.gmpe.org.uk. University of Strathclyde, Glasgow.Google Scholar

Douglas, J., and Aochi, H. 2008. A Survey of Techniques for Predicting Earthquake Ground Motions for Engineering Purposes. Surveys in Geophysics, 29 (3), 187–220.Google Scholar

Douglas, J., and Aochi, H. 2016. Assessing Components of Ground-Motion Variability from Simulations for the Marmara Sea Region (Turkey). Bulletin of the Seismological Society of America, 106 (1), 300–306.Google Scholar

Douglas, J., and Boore, D. 2011. High-Frequency Filtering of Strong-Motion Records. Bulletin of Earthquake Engineering, 9 (2), 395–409.Google Scholar

Douglas, J., and Edwards, B. 2016. Recent and Future Developments in Earthquake Ground Motion Estimation. Earth-Science Reviews, 160 (Sept.), 203–219.Google Scholar

Douglas, J., Ulrich, T., Bertil, D., and Rey, J. 2014. Comparison of the Ranges of Uncertainty Captured in Different Seismic-Hazard Studies. Seismological Research Letters, 85 (5), 977–985.Google Scholar

Douglas, J., Ulrich, T., and Negulescu, C. 2013. Risk-Targeted Seismic Design Maps for Mainland France. Natural Hazards, 65 (3), 1999–2013.Google Scholar

Dreger, D. S., and Jordan, T. H. 2015. Introduction to the Focus Section on Validation of the SCEC Broadband Platform V14.3 Simulation Methods. Seismological Research Letters, 86 (1), 15–16.Google Scholar

Eads, L., Miranda, E., Krawinkler, H., and Lignos, D. G. 2013. An Efficient Method for Estimating the Collapse Risk of Structures in Seismic Regions. Earthquake Engineering & Structural Dynamics, 42 (1), 25–41.Google Scholar

Eaton, J. P. 1992. Determination of Amplitude and Duration Magnitudes and Site Residual from Short-Period Seismographs in Northern California. Bulletin of the Seismological Society of America, 82 (2), 533–579.Google Scholar

Ebel, J. E., and Kafka, A. L. 1999. A Monte Carlo Approach to Seismic Hazard Analysis. Bulletin of the Seismological Society of America, 89 (4), 854–866.Google Scholar

Eberly, D. 2020. Distance between Point and Triangle in 3D. Geometric Tools, Online Report. www.geometrictools.com/Documentation/DistancePoint3Triangle3.pdf.Google Scholar

Edwards, B., and Fäh, D. 2013. A Stochastic Ground Motion Model for Switzerland. Bulletin of the Seismological Society of America, 103 (1), 78–98.Google Scholar

Edwards, B., Zurek, B., van Dedem, E., Stafford, P. J., Oates, S., van Elk, J., deMartin, B., and Bommer, J. J. 2019. Simulations for the Development of a Ground Motion Model for Induced Seismicity in the Groningen Gas Field, the Netherlands. Bulletin of Earthquake Engineering, 17 , 4441–4456.Google Scholar

Ellingwood, B. R., and Kinali, K. 2009. Quantifying and Communicating Uncertainty in Seismic Risk Assessment. Structural Safety, 31 (2), 179–187.Google Scholar

Ellsworth, W. L., Matthews, M. V., Nadeau, R. M., Nishenko, S. P., Reasenberg, P. A., and Simpson, R. W. 1999. A Physically Based Earthquake Recurrence Model for Estimation of Long-Term Earthquake Probabilities. Open File Report 99-522.Google Scholar

Elms, D. G. 2004. Structural Safety: Issues and Progress. Progress in Structural Engineering and Materials, 6 (2), 116–126.Google Scholar

EPRI. 1991. Standardization of the Cumulative Absolute Velocity. EPRI TR-100082-T2. Electric Power Research Institute, Palo Alto, CA.Google Scholar

Esposito, S., and Iervolino, I. 2011. PGA and PGV Spatial Correlation Models Based on European Multievent Datasets. Bulletin of the Seismological Society of America, 101 (5), 2532–2541.Google Scholar

Evison, F. F., and Rhoades, D. A. 2004. Demarcation and Scaling of Long-Term Seismogenesis. Pure and Applied Geophysics, 161 (1), 21–45.Google Scholar

Felzer, K. R. 2013a. Appendix K: The UCERF3 Earthquake Catalog. Open File Report 2013-1165. United States Geological Survey.Google Scholar

Felzer, K. R. 2013b. Appendix L: Estimate of the Seismicity Rate and Magnitude-Frequency Distribution of Earthquakes in California from 1850 to 2011. Open File Report 2013-1165. United States Geological Survey.Google Scholar

Felzer, K. R., and Brodsky, E. E. 2005. Testing the Stress Shadow Hypothesis. Journal of Geophysical Research: Solid Earth, 110 (B5), 702.Google Scholar

Felzer, K. R., and Brodsky, E. E. 2006. Decay of Aftershock Density with Distance Indicates Triggering by Dynamic Stress. Nature, 441 (7094), 735–738.Google Scholar

FEMA. 2005. Improvement of Nonlinear Static Seismic Procedures. FEMA-440. Federal Emergency Management Agency, FEMA, Washington, DC.Google Scholar

FEMA. 2009. Quantification of Building Seismic Performance Factors (FEMA P695, ATC-63). FEMA P695. Federal Emergency Management Agency, prepared by the Applied Technology Council.Google Scholar

FEMA. 2012. Seismic Performance Assessment of Buildings. FEMA P-58. Prepared by Applied Technology Council for the Federal Emergency Management Agency.Google Scholar

FEMA. 2015. Hazus-MH 2.1. Multi-hazard Loss Estimation Methodology Technical and User Manuals. Federal Emergency Management Agency.Google Scholar

Field, E. H. 2015. “All Models Are Wrong, but Some Are Useful.” Seismological Research Letters, 86 (2A), 291–293.Google Scholar

Field, E. H., Arrowsmith, R. J., Biasi, G. P., Bird, P., Dawson, T. E., Felzer, K. R., Jackson, D. D., Johnson, K. M., Jordan, T. H., Madden, C., Michael, A. J., Milner, K. R., Page, M. T., Parsons, T., Powers, P. M., Shaw, B. E., Thatcher, W. R., Weldon, R. J., and Zeng, Y. 2014. Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3)—The Time-Independent Model. Bulletin of the Seismological Society of America, 104 (3), 1122–1180.Google Scholar

Field, E. H., Biasi, G. P., Bird, P., Dawson, T. E., Felzer, K. R., Jackson, D. D., Johnson, K. M., Jordan, T. H., Madden, C., Michael, A. J., Milner, K. R., Page, M. T., Parsons, T., Powers, P. M., Shaw, B. E., Thatcher, W. R., Weldon, R. J. I., and Zeng, Y. 2013. Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3): The Time-Independent Model. Open File Report 2013-1165. United States Geological Survey.Google Scholar

Field, E. H., Biasi, G. P., Bird, P., Dawson, T. E., Felzer, K. R., Jackson, D. D., Johnson, K. M., Jordan, T. H., Madden, C., Michael, A. J., Milner, K. R., Page, M. T., Parsons, T., Powers, P. M., Shaw, B. E., Thatcher, W. R., Weldon, R. J., and Zeng, Y. 2015. Long-Term Time-Dependent Probabilities for the Third Uniform California Earthquake Rupture Forecast (UCERF3). Bulletin of the Seismological Society of America, 105 (2A), 511–543.Google Scholar

Field, E. H., Dawson, T. E., Felzer, K. R., Frankel, A. D., Gupta, V., Jordan, T. H., Parsons, T., Petersen, M. D., Stein, R. S., Weldon, R. J., and Wills, C. J. 2009. Uniform California Earthquake Rupture Forecast, Version 2 (UCERF 2). Bulletin of the Seismological Society of America, 99 (4), 2053–2107.Google Scholar

Field, E. H., and Jordan, T. H. 2015. Time-Dependent Renewal-Model Probabilities When Date of Last Earthquake Is Unknown. Bulletin of the Seismological Society of America, 105 (1), 459–463.Google Scholar

Field, E. H., Jordan, T. H., and Cornell, C. A. 2003. OpenSHA: A Developing Community-Modeling Environment for Seismic Hazard Analysis. Seismological Research Letters, 74 (4), 406–419.Google Scholar

Field, E. H., Jordan, T. H., Page, M. T., Milner, K. R., Shaw, B. E., Dawson, T. E., Biasi, G. P., Parsons, T., Hardebeck, J. L., Michael, A. J., Weldon, R. J. I., Powers, P. M., Johnson, K. M., Zeng, Y., Felzer, K. R., van der Elst, N., Madden, C., Arrowsmith, R., Werner, M. J., and Thatcher, W. R. 2017a. A Synoptic View of the Third Uniform California Earthquake Rupture Forecast (UCERF3). Bulletin of the Seismological Society of America, 88 (5), 1259–1267.Google Scholar

Field, E. H., Milner, K. R., Hardebeck, J. L., Page, M. T., van der Elst, N., Jordan, T. H., Michael, A. J., Shaw, B. E., and Werner, M. J. 2017b. A Spatiotemporal Clustering Model for the Third Uniform California Earthquake Rupture Forecast (UCERF3-ETAS): Toward an Operational Earthquake Forecast. Bulletin of the Seismological Society of America, 107 (3), 1049–1081.Google Scholar

Field, E. H., and Page, M. T. 2011. Estimating Earthquake-Rupture Rates on a Fault or Fault System. Bulletin of the Seismological Society of America, 101 (1), 79–92.Google Scholar

Finn, W. D. L., Ventura, C. E., and Wu, G. 1993. Analysis of Ground Motions at Treasure Island Site during the 1989 Loma Prieta Earthquake. Soil Dynamics and Earthquake Engineering, 12 (7), 383–390.Google Scholar

Foulser-Piggott, R., and Goda, K. 2015. Ground-Motion Prediction Models for Arias Intensity and Cumulative Absolute Velocity for Japanese Earthquakes Considering Single-Station Sigma and Within-Event Spatial Correlation. Bulletin of the Seismological Society of America, 105 (4), 1903–1918.Google Scholar

Foulser-Piggott, R., and Stafford, P. J. 2012. A Predictive Model for Arias Intensity at Multiple Sites and Consideration of Spatial Correlations. Earthquake Engineering & Structural Dynamics, 41 (3), 431–451.Google Scholar

Fox, M. J., Stafford, P. J., and Sullivan, T. J. 2016. Seismic Hazard Disaggregation in Performance-Based Earthquake Engineering: Occurrence or Exceedance? Earthquake Engineering & Structural Dynamics, 45 (5), 835–842.Google Scholar

Frankel, A. 1995. Simulating Strong Motions of Large Earthquakes Using Recordings of Small Earthquakes: The Loma Prieta Mainshock as a Test Case. Bulletin of the Seismological Society of America, 85 (4), 1144–1160.CrossRefGoogle Scholar

Frankel, A., and Vidale, J. 1992. A Three-Dimensional Simulation of Seismic Waves in the Santa Clara Valley, California, from a Loma Prieta Aftershock. Bulletin of the Seismological Society of America, 82 (5), 2045–2074.Google Scholar

Frankel, A., Wirth, E., Marafi, N., Vidale, J., and Stephenson, W. 2018. Broadband Synthetic Seismograms for Magnitude 9 Earthquakes on the Cascadia Megathrust Based on 3D Simulations and Stochastic Synthetics, Part 1: Methodology and Overall Results. Bulletin of the Seismological Society of America, 108 (5A), 2347–2369.Google Scholar

Fukushima, Y. 1996. Scaling Relations for Strong Ground Motion Prediction Models with M2 Terms. Bulletin of the Seismological Society of America, 86 (2), 329–336.Google Scholar

Gardner, J. K., and Knopoff, L. 1974. Is the Sequence of Earthquakes in Southern California, with Aftershocks Removed, Poissonian? Bulletin of the Seismological Society of America, 64 (5), 1363–1367.Google Scholar

Gardoni, P., Der Kiureghian, A., and Mosalam, K. M. 2002. Probabilistic Capacity Models and Fragility Estimates for Reinforced Concrete Columns Based on Experimental Observations. Journal of Engineering Mechanics, 128 (10), 1024–1038.Google Scholar

Gasparini, D., and Vanmarcke, E. H. 1976. SIMQKE: A Program for Artificial Motion Generation. MIT Department of Civil Engineering, Cambridge, MA.Google Scholar

Gatz, D. F., and Smith, L. 1995. The Standard Error of a Weighted Mean Concentration—I. Bootstrapping vs Other Methods. Atmospheric Environment, 29 (11), 1185–1193.Google Scholar

Geller, R. J. 2011. Shake-up Time for Japanese Seismology. Nature, 472 (Apr.), 407.Google Scholar

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. 2013. Bayesian Data Analysis, 3rd ed. Chapman and Hall/CRC.Google Scholar

Gentle, J. E. 2003. Random Number Generation and Monte Carlo Methods, 2nd ed. New York: Springer-Verlag.Google Scholar

Gerstenberger, M. C., Rhoades, D. A., and McVerry, G. H. 2016. A Hybrid Time-Dependent Probabilistic Seismic-Hazard Model for Canterbury, New Zealand. Seismological Research Letters, 87 (6), 1311–1318.Google Scholar

Ghafory-Ashtiany, M., Mousavi, M., and Azarbakht, A. 2011. Strong Ground Motion Record Selection for the Reliable Prediction of the Mean Seismic Collapse Capacity of a Structure Group. Earthquake Engineering & Structural Dynamics, 40 (6), 691–708.Google Scholar

Gledhill, K., Ristau, J., Reyners, M., Fry, B., and Holden, C. 2011. The Darfield (Canterbury, New Zealand) Mw 7.1 Earthquake of September 2010: A Preliminary Seismological Report. Seismological Research Letters, 82 (3), 378–386.Google Scholar

Goda, K. 2011. Interevent Variability of Spatial Correlation of Peak Ground Motions and Response Spectra. Bulletin of the Seismological Society of America, 101 (5), 2522–2531.Google Scholar

Goda, K., and Atkinson, G. M. 2010. Intraevent Spatial Correlation of Ground-Motion Parameters Using SK-Net Data. Bulletin of the Seismological Society of America, 100 (6), 3055–3067.Google Scholar

Goda, K., and Atkinson, G. M. 2014. Variation of Source-to-Site Distance for Megathrust Subduction Earthquakes: Effects on Ground Motion Prediction Equations. Earthquake Spectra, 30 (2), 845–866.Google Scholar

Goda, K., and Hong, H. P. 2008. Spatial Correlation of Peak Ground Motions and Response Spectra. Bulletin of the Seismological Society of America, 98 (1), 354–365.Google Scholar

González Ortega, J. A., González García, J. J., and Sandwell, D. T. 2018. Interseismic Velocity Field and Seismic Moment Release in Northern Baja California, Mexico. Seismological Research Letters, 89 (2A), 526–533.Google Scholar

Goovaerts, P. 1997. Geostatistics for Natural Resources Evaluation. Applied Geostatistics Series. New York: Oxford University Press.Google Scholar

Gospodinov, D. 2017. Spatio-Temporal Evolution of Aftershock Energy Release Following the 1989, Mw6.9, Loma Prieta Earthquake in California. Acta Geophysica, 65 (3), 565–573.Google Scholar

Graves, R. 1993. Modeling Three-Dimensional Site Response Effects in the Marina District Basin, San Francisco, California. Bulletin of the Seismological Society of America, 83 (4), 1042–1063.Google Scholar

Graves, R., Jordan, T. H., Callaghan, S., Deelman, E., Field, E., Juve, G., Kesselman, C., Maechling, P., Mehta, G., Milner, K., et al. 2011a. CyberShake: A Physics-Based Seismic Hazard Model for Southern California. Pure and Applied Geophysics, 168 (3–4), 367–381.Google Scholar

Graves, R., Jordan, T., Callaghan, S., Deelman, E., Field, E., Juve, G., Kesselman, C., Maechling, P., Mehta, G., Milner, K., Okaya, D., Small, P., and Vahi, K. 2011b. CyberShake: A Physics-Based Seismic Hazard Model for Southern California. Pure and Applied Geophysics, 168 (3), 367–381.Google Scholar

Graves, R., and Pitarka, A. 2015. Refinements to the Graves and Pitarka (2010) Broadband Ground-Motion Simulation Method. Seismological Research Letters, 86 (1), 75–80.Google Scholar

Graves, R., and Pitarka, A. 2016. Kinematic Ground-Motion Simulations on Rough Faults Including Effects of 3D Stochastic Velocity Perturbations. Bulletin of the Seismological Society of America, 106 (5), 2136–2153.Google Scholar

Graves, R. W. 1996. Simulating Seismic Wave Propagation in 3D Elastic Media Using Staggered-Grid Finite Differences. Bulletin of the Seismological Society of America, 86 (4), 1091–1106.Google Scholar

Graves, R. W., and Pitarka, A. 2010. Broadband Ground-Motion Simulation Using a Hybrid Approach. Bulletin of the Seismological Society of America, 100 (5A), 2095–2123.Google Scholar

Gregor, N., Abrahamson, N. A., Atkinson, G. M., Boore, D. M., Bozorgnia, Y., Campbell, K. W., Chiou, B. S.-J., Idriss, I. M., Kamai, R., Seyhan, E., Silva, W., Stewart, J. P., and Youngs, R. 2014. Comparison of NGA-West2 GMPEs. Earthquake Spectra, 30 (3), 1179–1197.Google Scholar

Grossi, P., Kunreuther, H., and Patel, C. C. 2005. Catastrophe Modeling: A New Approach to Managing Risk. Springer.Google Scholar

Guatteri, M., Mai, P. M., and Beroza, G. C. 2004. A Pseudo-Dynamic Approximation to Dynamic Rupture Models for Strong Ground Motion Prediction. Bulletin of the Seismological Society of America, 94 (6), 2051–2063.Google Scholar

Gulerce, Z., and Abrahamson, N. A. 2011. Site-Specific Design Spectra for Vertical Ground Motion. Earthquake Spectra, 27 (4), 1023–1047.Google Scholar

Gutenberg, B., and Richter, C. F. 1944. Frequency of Earthquakes in California. Bulletin of the Seismological Society of America, 34 (4), 185–188.Google Scholar

Haarala, M., and Orosco, L. 2016. Analysis of Gutenberg-Richter b-Value and Mmax. Part I: Exact Solution of Kijko–Sellevoll Estimator of Mmax. Cuadernos de Ingeniería, Nueva Serie, 9 , 51–77.Google Scholar

Hale, C., Abrahamson, N., and Bozorgnia, Y. 2018. Probabilistic Seismic Hazard Analysis Code Verification. PEER Report 2018/03. Berkeley, CA.Google Scholar

Halsey, L. G., Curran-Everett, D., Vowler, S. L., and Drummond, G. B. 2015. The Fickle P Value Generates Irreproducible Results. Nature Methods, 12 (3), 179–185.Google Scholar

Han, Y., and Davidson, R. A. 2012. Probabilistic Seismic Hazard Analysis for Spatially Distributed Infrastructure. Earthquake Engineering & Structural Dynamics, 41 (15), 2141–2158.Google Scholar

Hancock, J., and Bommer, J. J. 2006. A State-of-Knowledge Review of the Influence of Strong-Motion Duration on Structural Damage. Earthquake Spectra, 22 , 827.Google Scholar

Hancock, J., and Bommer, J. J. 2007. Using Spectral Matched Records to Explore the Influence of Strong-Motion Duration on Inelastic Structural Response. Soil Dynamics and Earthquake Engineering, 27 (4), 291–299.Google Scholar

Hancock, J., Bommer, J. J., and Stafford, P. J. 2008. Numbers of Scaled and Matched Accelerograms Required for Inelastic Dynamic Analyses. Earthquake Engineering & Structural Dynamics, 37 (14), 1585–1607.Google Scholar

Hanks, T., and McGuire, R. 1981. The Character of High-Frequency Strong Ground Motion. Bulletin of the Seismological Society of America, 71 (6), 2071–2095.Google Scholar

Hanks, T. C. 1977. Earthquake Stress Drops, Ambient Tectonic Stresses and Stresses That Drive Plate Motions. Pure and Applied Geophysics, 115 , 441–458.Google Scholar

Hanks, T. C., and Abrahamson, N. 2008. A Brief History of Extreme Ground Motions. Seismological Research Letters, 79 , 282–283.Google Scholar

Hanks, T. C., Abrahamson, N. A., Boore, D. M., Coppersmith, K. J., and Knepprath, N. E. 2009. Implementation of the SSHAC Guidelines for Level 3 and 4 PSHAs—Experience Gained from Actual Applications. Open-File Report 2009-1093. US Geological Survey.Google Scholar

Hanks, T. C., and Bakun, W. H. 2002. A Bilinear Source-Scaling Model for M-logA Observations of Continental Earthquakes. Bulletin of the Seismological Society of America, 92 (5), 1841–1846.Google Scholar

Hanks, T. C., and Bakun, W. H. 2008. M-logA Observations for Recent Large Earthquakes. Bulletin of the Seismological Society of America, 98 (1), 490–494.Google Scholar

Hanks, T. C., Beroza, G. C., and Toda, S. 2012. Have Recent Earthquakes Exposed Flaws in or Misunderstandings of Probabilistic Seismic Hazard Analysis? Seismological Research Letters, 83 (5), 759–764.Google Scholar

Hanks, T. C., and Kanamori, H. 1979. A Moment Magnitude Scale. Journal of Geophysical Research, 84 (B5), 2348.Google Scholar

Harichandran, R. S., Hawwari, A., and Sweidan, B. N. 1996. Response of Long-Span Bridges to Spatially Varying Ground Motion. Journal of Structural Engineering, 122 (5), 476–484.Google Scholar

Hartford, D. N. D. 2009. Legal Framework Considerations in the Development of Risk Acceptance Criteria. Structural Safety, 31 (2), 118–123.Google Scholar

Hartzell, S., Frankel, A., Liu, P., Zeng, Y., and Rahman, S. 2011. Model and Parametric Uncertainty in Source-Based Kinematic Models of Earthquake Ground Motion. Bulletin of the Seismological Society of America, 101 (5), 2431–2452.Google Scholar

Hartzell, S., Harmsen, S., and Frankel, A. 2010. Effects of 3D Random Correlated Velocity Perturbations on Predicted Ground Motions. Bulletin of the Seismological Society of America, 100 (4), 1415–1426.Google Scholar

Hartzell, S., Harmsen, S., Frankel, A., and Larsen, S. 1999. Calculation of Broadband Time Histories of Ground Motion: Comparison of Methods and Validation Using Strong-Ground Motion from the 1994 Northridge Earthquake. Bulletin of the Seismological Society of America, 89 (6), 1484–1504.Google Scholar

Hashash, Y. M., Hook, J. J., Schmidt, B., John, I., and Yao, C. 2001. Seismic Design and Analysis of Underground Structures. Tunnelling and Underground Space Technology, 16 (4), 247–293.Google Scholar

Hastie, T., Tibshirani, R., and Friedman, J. 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer.Google Scholar

Hawkes, A. G., and Adamopoulos, L. 1973. Cluster Models for Earthquakes: Regional Comparisons. Bulletin of the International Statistical Institute, 45 (3), 454–461.Google Scholar

Hayes, G. P., Earle, P. S., Benz, H. M., Wald, D. J., Briggs, R. W., and the USGS/NEIC Earthquake Response Team. 2011. 88 Hours: The U.S. Geological Survey National Earthquake Information Center Response to the 11 March 2011 Mw 9.0 Tohoku Earthquake. Seismological Research Letters, 82 (4), 481–493.Google Scholar

Headquarters for Earthquake Research Promotion. 2005. National Seismic Hazard Maps for Japan. www.jishin.go.jp/main/index-e.html.Google Scholar

Heaton, T. H. 1990. Evidence for and Implications of Self-Healing Pulses of Slip in Earthquake Rupture. Physics of the Earth and Planetary Interiors, 64 (1), 1–20.Google Scholar

Heresi, P., and Miranda, E. 2019. Uncertainty in Intraevent Spatial Correlation of Elastic Pseudo-Acceleration Spectral Ordinates. Bulletin of Earthquake Engineering, 17 (3), 1099–1115.Google Scholar

Holt, W. E., and Haines, A. J. 1995. The Kinematics of Northern South Island, New Zealand, Determined from Geologic Strain Rates. Journal of Geophysical Research: Solid Earth, 100 (B9), 17991–18010.Google Scholar

Hough, S. E., and Anderson, J. G. 1988. High-Frequency Spectra Observed at Anza, California: Implications for Q Structure. Bulletin of the Seismological Society of America, 78 (2), 692–707.Google Scholar

Husen, S., and Hardebeck, J. 2010. Earthquake Location Accuracy. Community Online Resource for Statistical Seismicity Analysis, CORSSA, www.corssa.org.Google Scholar

Hutchings, L. 1994. Kinematic Earthquake Models and Synthesized Ground Motion Using Empirical Green’s Functions. Bulletin of the Seismological Society of America, 84 (4), 1028–1050.Google Scholar

Ibarra, L. F., and Krawinkler, H. 2005. Global Collapse of Frame Structures under Seismic Excitations. 152. John A. Blume Earthquake Engineering Center, Stanford, CA.Google Scholar

Idriss, I., and Boulanger, R. 2008. Soil Liquefaction during Earthquakes. Earthquake Engineering Research Institute.Google Scholar

Idriss, I., and Seed, H. 1968. Seismic Response of Horizontal Soil Layers. Journal of Soil Mechanics and Foundations (ASCE), 94 (SM4), 1003–1031.Google Scholar

Iervolino, I., De Luca, F., and Cosenza, E. 2010. Spectral Shape-Based Assessment of SDOF Nonlinear Response to Real, Adjusted and Artificial Accelerograms. Engineering Structures, 32 (9), 2776–2792.Google Scholar

Iervolino, I., Giorgio, M., and Polidoro, B. 2014. Sequence-Based Probabilistic Seismic Hazard Analysis. Bulletin of the Seismological Society of America, 104 (2), 1006–1012.Google Scholar

Imperatori, W., and Mai, P. M. 2013. Broad-band Near-Field Ground Motion Simulations in 3-Dimensional Scattering Media. Geophysical Journal International, 192 (2), 725–744.Google Scholar

Imperatori, W., and Mai, P. M. 2015. The Role of Topography and Lateral Velocity Heterogeneities on Near-Source Scattering and Ground-Motion Variability. Geophysical Journal International, 202 (3), 2163–2181.Google Scholar

Imtiaz, A., Causse, M., Chaljub, E., and Cotton, F. 2015. Is Ground-Motion Variability Distance Dependent? Insight from Finite-Source Rupture Simulations. Bulletin of the Seismological Society of America, 105 (2A), 950–962.Google Scholar

Inoue, T., and Cornell, C. A. 1990. Seismic Hazard Analysis of Multi-Degree-of-Freedom Structures. RMS-8. Reliability of Marine Structures, Stanford, CA.Google Scholar

Ishihara, K., and Cubrinovski, M. 2005. Characteristics of Ground Motion in Liquefied Deposits during Earthquakes. Journal of Earthquake Engineering, 9 (S1), 1–15.Google Scholar

Jaeger, J. C., Cook, N. G. W., and Zimmerman, R. W. 2007. Fundamentals of Rock Mechanics. Blackwell Publishing.Google Scholar

Jaiswal, K., Wald, D., and D’Ayala, D. 2011b. Developing Empirical Collapse Fragility Functions for Global Building Types. Earthquake Spectra, 27 (3), 775–795.Google Scholar

Jaiswal, K. S., Aspinall, W., Perkins, D., Wald, D., and Porter, K. A. 2012. Use of Expert Judgment Elicitation to Estimate Seismic Vulnerability of Selected Building Types. In: 15th World Conference on Earthquake Engineering.Google Scholar

Jaiswal, K. S., Wald, D. J., Earle, P. S., Porter, K. A., and Hearne, M. 2011a. Earthquake Casualty Models within the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) System. Pages 83–94 of: Spence, R., So, E., and Scawthorn, C. (eds.), Human Casualties in Earthquakes: Progress in Modelling and Mitigation. Advances in Natural and Technological Hazards Research. Dordrecht: Springer Netherlands.Google Scholar

Jalayer, F. 2003. Direct Probabilistic Seismic Analysis: Implementing Non-linear Dynamic Assessments. PhD thesis, Dept. of Civil and Environmental Engineering, Stanford University.Google Scholar

Jayaram, N., and Baker, J. W. 2008. Statistical Tests of the Joint Distribution of Spectral Acceleration Values. Bulletin of the Seismological Society of America, 98 (5), 2231–2243.Google Scholar

Jayaram, N., and Baker, J. W. 2009. Correlation Model for Spatially Distributed Ground-Motion Intensities. Earthquake Engineering & Structural Dynamics, 38 (15), 1687–1708.Google Scholar

Jayaram, N., and Baker, J. W. 2010a. Considering Spatial Correlation in Mixed-Effects Regression, and Impact on Ground-Motion Models. Bulletin of the Seismological Society of America, 100 (6), 3295–3303.Google Scholar

Jayaram, N., and Baker, J. W. 2010b. Efficient Sampling and Data Reduction Techniques for Probabilistic Seismic Lifeline Risk Assessment. Earthquake Engineering & Structural Dynamics, 39 (10), 1109–1131.Google Scholar

Jayaram, N., Lin, T., and Baker, J. W. 2011. A Computationally Efficient Ground-Motion Selection Algorithm for Matching a Target Response Spectrum Mean and Variance. Earthquake Spectra, 27 (3), 797–815.Google Scholar

Jayaram, N., Park, J., Bazzurro, P., and Tothong, P. 2010. Estimation of Spatial Correlation between Spectral Accelerations Using Simulated Ground-Motion Time Histories. In: 9th US National and 10th Canadian Conference on Earthquake Engineering.Google Scholar

Jayaram, N., Shome, N., and Rahnama, M. 2012. Development of Earthquake Vulnerability Functions for Tall Buildings. Earthquake Engineering & Structural Dynamics, 41 (11), 1495–1514.Google Scholar

Jeon, S.-S., and O’Rourke, T. D. 2005. Northridge Earthquake Effects on Pipelines and Residential Buildings. Bulletin of the Seismological Society of America, 95 (1), 294–318.Google Scholar

Johnston, A. C., Coppersmith, K. J., Kanter, L. R., and Cornell, C. A. 1994. The Earthquakes of Stable Continental Regions. EPRI Report TR-102261s-V1-V5. Electric Power Research Institute, Palo Alto, CA.Google Scholar

Jordan, T. H. 2006. Earthquake Predictability, Brick by Brick. Seismological Research Letters, 77 (1), 3–6.Google Scholar

Journel, A. G., and Huijbregts, C. J. 1978. Mining Geostatistics. London: Academic Press.Google Scholar

Kagan, Y. Y. 2006. Why Does Theoretical Physics Fail to Explain and Predict Earthquake Occurrence? Pages 303–362 of: Bhattacharyya, P., and Chakrabarti, B. K. (eds.), Modelling Critical and Catastrophic Phenomena in Geoscience. Springer.Google Scholar

Kagan, Y. Y., and Knopoff, L. 1981. Stochastic Synthesis of Earthquake Catalogs. Journal of Geophysical Research: Solid Earth, 86 (B4), 2853–2862.Google Scholar

Kagan, Y. Y., and Jackson, D. D. 2013. Tohoku Earthquake: A Surprise? Bulletin of the Seismological Society of America, 103 (2B), 1181–1194.Google Scholar