Comparison of Stochastic Ground-Motion Simulation: SMSIM, EXSIM, and GMSS2.0

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Abstract
Reliable ground-motion models are fundamental to seismic hazard assessment, yet their development is often limited by the scarcity of strong-motion recordings, particularly for large-magnitude earthquakes. In such cases, stochastic ground-motion simulations provide an efficient and versatile means to augment empirical data sets and to investigate the influence of source, path, and site effects. This study presents a comprehensive comparative evaluation of three widely used stochastic simulation tools: the Stochastic-Method SIMulation (SMSIM), which employs a point-source representation, and two finite-fault models, EXtended SIMulation (EXSIM) and Ground-Motion Simulation System. Simulations were performed under two input configurations: (1) default conditions, using regionally calibrated parameters specific to each model, and (2) identical conditions, where uniform input parameters were applied across all three models. The analysis reveals that apparent discrepancies among model predictions arise primarily from differences in parameterization rather than inherent methodological distinctions. Specifically, SMSIM requires higher stress-drop and kappa values to produce results comparable to EXSIM. When site amplification and crustal filters are treated consistently, the simulated ground motions from all three models converge closely. These findings underscore the critical importance of consistent and well-calibrated parameterization of source, path, and site effects, demonstrating that robust hazard estimation depends more on parameter harmonization than on the choice of stochastic simulation code itself.

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