Using a grid-search approach to validate the Graves-Pitarka broadband simulation method
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Abstract
This work assesses the ability of the Graves–Pitarka simulation approach to reproduce observed ground motions for 12 California and Baja California earthquakes. A total of 240 realizations are computed for each earthquake and compared with recorded strong motions from near-fault sites. In addition to spatial variability in slip, each realization samples from discrete combinations of average rupture speed, rise time, and down-dip fault width. Ground motions for each realization of a given earthquake are compared to the observations using a pseudo-spectral acceleration goodness-of-fit (GoF) metric. Parameters for the lowest misfit cases are then tabulated to develop relations for estimating median values and ranges for future applications. The results are generally consistent with published scaling relations for estimating rupture dimensions as a function of magnitude. Additionally, I find the average rise time scales as 2.3×αT×10-9×Mo1/3 (Mo is seismic moment in dyne-cm) and average rupture speed as (0.765±0.075)×αT-1×Vs (Vs is local shear wave velocity), where αT is a mechanism adjustment ranging from a value of 1.0 for pure strike-slip to 0.9 for pure reverse-slip cases. There are cases where slightly different combinations of parameters produce equally good fits to the observations, demonstrating the non-uniqueness of using a single GoF metric in this approach. Nonetheless, the results reinforce the importance of adequately sampling ranges of rupture parameters when performing validations, as well as when simulating ground motions for future events.
Publication type | Article |
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Publication Subtype | Journal Article |
Title | Using a grid-search approach to validate the Graves-Pitarka broadband simulation method |
Series title | Earth, Planets and Space |
DOI | 10.1186/s40623-022-01742-y |
Volume | 74 |
Year Published | 2022 |
Language | English |
Publisher | Springer |
Contributing office(s) | Earthquake Science Center |
Description | 186, 26 p. |
Google Analytic Metrics | Metrics page |