Abstract
In this study, we investigate the dependencies between ground-motion intensity measures (GMIM) and earthquake magnitudes (M), in order to evaluate the dynamic stress parameter (Δσ) magnitude scaling. To achieve this, two types of datasets are used: a large subset of the NGA-West 2 (next generation attenuation) dataset including 1700 records from 426 sites and 271 earthquakes. The other datasets are generated through the stochastic method (Boore 2003) assuming various magnitude dependencies (constant and variable) of the stress parameter with magnitude. Adaptive neuro-fuzzy inference systems (ANFIS) are used to derive data-driven ground-motion prediction models (Ameur et al. 2018). Stiff soil (Vs30 > 500 m/s) data are selected and the ground-motion models are depending on two input parameters: the moment magnitude (Mw) and the hypocentral distance (Rhyp). Following Molkenthin et al. (2014), we assume that Δσ is the dominating controlling factor of GMIM for stiff site conditions at Rhyp = 30 km, at the frequency (f) = 3.33 Hz for moderate earthquakes in the magnitude range Mw = [4.5–6.5]. This study confirms that the relations between magnitude and stress parameter control the scaling of ground motions. We show that the magnitude-dependent stress drops better fit the latest generation of NGA-West 2 datasets and empirical ground-motion equations. We finally calibrate a relation between dynamic stress parameter and earthquake magnitude in the magnitude range Mw = [4.5–6.5].
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Acknowledgments
The authors thank Prof. Hiroshi Kawase and Dr. Sanjay Singh Bora for their very valuable and detailed comments. We also thank Dr. Mourad Ameur for his generous help. The authors also thank the participants of the NGA-West 2 program for providing high-quality data and stimulating ideas. Finally, we want to thank Dr. David M. Boore for providing the SMSIM programs on his website.
Data and resources
The ground-motion simulations that are presented in this study are based on the stochastic method simulation (SMSIM) programs (FORTRAN source code), provided by David M. Boore (http://www.daveboore.com/software_online.html) (last accessed November 2017). The observed data have been collected and disseminated by the Pacific Earthquake Engineering Research (PEER) Center.
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Dif, Z., Derras, B., Cotton, F. et al. Data-driven testing of the magnitude dependence of earthquake stress parameters using the NGA-West 2 dataset. J Seismol 24, 1095–1107 (2020). https://doi.org/10.1007/s10950-020-09952-1
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DOI: https://doi.org/10.1007/s10950-020-09952-1