Testing non-linear amplification factors of 1 ground-motion models
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[1] G. Weatherill,et al. An open-source site database of strong-motion stations in Japan: K-NET and KiK-net (v1.0.0) , 2021 .
[2] A. Rodriguez-Marek,et al. An updated database for ground motion parameters for KiK-net records , 2020 .
[3] R. Rotondi,et al. Ground motion models for the new seismic hazard model of Italy (MPS19): selection for active shallow crustal regions and subduction zones , 2020, Bulletin of Earthquake Engineering.
[4] S. Drouet,et al. A probabilistic seismic hazard map for the metropolitan France , 2020, Bulletin of Earthquake Engineering.
[5] G. Atkinson,et al. Significance of site natural period effects for linear site amplification in central and eastern North America: Empirical and simulation-based models , 2020 .
[6] Norman A. Abrahamson,et al. An Empirical Model for the Interfrequency Correlation of Epsilon for Fourier Amplitude Spectra , 2019, Bulletin of the Seismological Society of America.
[7] J. Bamber,et al. Subglacial roughness of the Greenland Ice Sheet: relationship with contemporary ice velocity and geology , 2019, The Cryosphere.
[8] Marco Pilz,et al. Does the One-Dimensional Assumption Hold for Site Response Analysis? A Study of Seismic Site Responses and Implication for Ground Motion Assessment Using KiK-Net Strong-Motion Data , 2019, Earthquake Spectra.
[9] W. Silva,et al. Site Amplification Functions for Central and Eastern North America – Part II: Modular Simulation-Based Models , 2019, Earthquake Spectra.
[10] F. Cotton,et al. Impact of Magnitude Selection on Aleatory Variability Associated with Ground‐Motion Prediction Equations: Part II—Analysis of the Between‐Event Distribution in Central Italy , 2019, Bulletin of the Seismological Society of America.
[11] J. Kaklamanos,et al. Challenges in Predicting Seismic Site Response with 1D Analyses: Conclusions from 114 KiK‐net Vertical Seismometer Arrays , 2018, Bulletin of the Seismological Society of America.
[12] M. Sandıkkaya. On linear site amplification behavior of crustal and subduction interface earthquakes in Japan: (1) regional effects, (2) best proxy selection , 2018, Bulletin of Earthquake Engineering.
[13] Debi Kilb,et al. Decomposing Leftovers: Event, Path, and Site Residuals for a Small‐Magnitude Anza Region GMPE , 2018, Bulletin of the Seismological Society of America.
[14] M. Sandıkkaya,et al. A Site Amplification Model for Crustal Earthquakes , 2018, Geosciences.
[15] Dino Bindi,et al. A new approach to site classification: Mixed-effects Ground Motion Prediction Equation with spectral clustering of site amplification functions , 2018, Soil Dynamics and Earthquake Engineering.
[16] J. D. Zechar,et al. The Collaboratory for the Study of Earthquake Predictability: Achievements and Priorities , 2018, Seismological Research Letters.
[17] J. D. Zechar,et al. Prospective CSEP Evaluation of 1‐Day, 3‐Month, and 5‐Yr Earthquake Forecasts for Italy , 2018, Seismological Research Letters.
[18] Dino Bindi,et al. Impact of Magnitude Selection on Aleatory Variability Associated with Ground‐Motion Prediction Equations: Part I—Local, Energy, and Moment Magnitude Calibration and Stress‐Drop Variability in Central Italy , 2018 .
[19] P. Bard,et al. Are the Standard VS-Kappa Host-to-Target Adjustments the Only Way to Get Consistent Hard-Rock Ground Motion Prediction? , 2018, Pure and Applied Geophysics.
[20] Dino Bindi,et al. The probabilistic seismic hazard assessment of Germany—version 2016, considering the range of epistemic uncertainties and aleatory variability , 2018, Bulletin of Earthquake Engineering.
[21] Julian J. Bommer,et al. Scenario dependence of linear site effect factors for short-period response spectral ordinates , 2017 .
[22] F. Cotton,et al. VS30, slope, H800 and f0: performance of various site-condition proxies in reducing ground-motion aleatory variability and predicting nonlinear site response , 2017, Earth, Planets and Space.
[23] Domniki Asimaki,et al. From Stiffness to Strength: Formulation and Validation of a Hybrid Hyperbolic Nonlinear Soil Model for Site‐Response Analyses , 2017 .
[24] D. Schorlemmer,et al. Empirical Evaluation of Hierarchical Ground‐Motion Models: Score Uncertainty and Model Weighting , 2017 .
[25] F. Cotton,et al. Site-Condition Proxies, Ground Motion Variability, and Data-Driven GMPEs: Insights from the NGA-West2 and RESORCE Data Sets , 2016 .
[26] L. Bonilla,et al. PGA-PGV/Vs considered as a stress–strain proxy for predicting nonlinear soil response , 2016 .
[27] Christine A. Goulet,et al. A Flatfile for the KiK-net Database Processed Using an Automated Protocol , 2016 .
[28] Frank Scherbaum,et al. On the Relationship between Fourier and Response Spectra: Implications for the Adjustment of Empirical Ground‐Motion Prediction Equations (GMPEs) , 2016 .
[29] Danijel Schorlemmer,et al. Validating Intensity Prediction Equations for Italy by Observations , 2015 .
[30] Norman A. Abrahamson,et al. Repeatable Site and Path Effects on the Ground‐Motion Sigma Based on Empirical Data from Southern California and Simulated Waveforms from the CyberShake Platform , 2015 .
[31] Ying-bin Zhang,et al. Nonlinear Site Models Derived from 1D Analyses for Ground‐Motion Prediction Equations Using Site Class as the Site Parameter , 2015 .
[32] Ellen M. Rathje,et al. Evaluation of one-dimensional site response techniques using borehole arrays , 2015 .
[33] Robert R. Youngs,et al. Update of the Chiou and Youngs NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra , 2014 .
[34] Norman A. Abrahamson,et al. Summary of the ASK14 Ground Motion Relation for Active Crustal Regions , 2014 .
[35] W. Silva,et al. NGA-West2 Database , 2014 .
[36] Jonathan P. Stewart,et al. NGA-West2 Equations for Predicting PGA, PGV, and 5% Damped PSA for Shallow Crustal Earthquakes , 2014 .
[37] Jonathan P. Stewart,et al. Semi-Empirical Nonlinear Site Amplification from NGA-West2 Data and Simulations , 2014 .
[38] Peter J. Stafford,et al. Crossed and Nested Mixed-Effects Approaches for Enhanced Model Development and Removal of the Ergodic Assumption in Empirical Ground-Motion Models , 2014 .
[39] J. D. Zechar,et al. Regional Earthquake Likelihood Models I: First-Order Results , 2013 .
[40] Sinan Akkar,et al. A Nonlinear Site‐Amplification Model for the Next Pan‐European Ground‐Motion Prediction Equations , 2013 .
[41] Naoshi Hirata,et al. CSEP Testing Center and the first results of the earthquake forecast testing experiment in Japan , 2012, Earth, Planets and Space.
[42] Aurore Laurendeau,et al. Nonlinear site response evidence of K-NET and KiK-net records from the 2011 off the Pacific coast of Tohoku Earthquake , 2011 .
[43] Julian J. Bommer,et al. The Variability of Ground-Motion Prediction Models and Its Components , 2010 .
[44] Frank Scherbaum,et al. Information-Theoretic Selection of Ground-Motion Prediction Equations for Seismic Hazard Analysis: An Applicability Study Using Californian Data , 2009 .
[45] Daniel Lavallée,et al. Hysteretic and Dilatant Behavior of Cohesionless Soils and Their Effects on Nonlinear Site Response: Field Data Observations and Modeling , 2005 .
[46] Hiroyuki Fujiwara,et al. Recent Progress of Seismic Observation Networks in Japan , 2004 .
[47] Jonathan P. Stewart,et al. Amplification Factors for Spectral Acceleration in Tectonically Active Regions , 2003 .
[48] W. Cleveland. Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .
[49] Hiroyuki Fujiwara,et al. STRONG-MOTION SEISMOGRAPH NETWORK OPERATED BY NIED: K-NET AND KiK-net , 2004 .
[50] Eduardo Kausel,et al. Seismic simulation of inelastic soils via frequency-dependent moduli and damping , 2002 .
[51] A. Jefferson Offutt,et al. An Empirical Evaluation , 1994 .