Scaling relations of seismic moment, rupture area, average slip, and asperity size for M~9 subduction‐zone earthquakes

Abstract

Scaling relations for seismic moment M0, rupture area S, average slip D, and asperity size Sa were obtained for large, great, and giant (Mw = 6.7–9.2) subduction‐zone earthquakes. We compiled the source parameters for seven giant (Mw~9) earthquakes globally for which the heterogeneous slip distributions were estimated from tsunami and geodetic data. We defined Sa for subfaults exhibiting slip greater than 1.5 times D. Adding 25 slip models of 10 great earthquakes around Japan, we recalculated regression relations for 32 slip models: S = 1.34 × 10−10 M02/3, D = 1.66 × 10−7 M01/3, Sa = 2.81 × 10−11 M02/3, and Sa/S = 0.2, where S and Sa are in square kilometers, M0 is in newton meters, and D is in meters. These scaling relations are very similar to those obtained by Murotani et al. (2008) for large and great earthquakes. Thus, both scaling relations can be used for future tsunami hazard assessment associated with a giant earthquake.

Impact of a tsunami generated at the Lesser Antilles subduction zone on the Northern Atlantic Ocean coastlines

“…This magnitude value, set within the range of the estimated maximum of 8.0 < M Wmax < 8.5, and the corresponding fault plane parameters have been chosen following the recent studies of Feuillet et al (2011a), Roger et al (2013), and Hayes et al (2013) about the 8 February 1843 Lesser Antilles earthquake, and in agreement with used dataset and empirical relations obtained by Strasser et al (2010), Blaser et al (2010), and Murotani et al (2013). For information, a M w ∼ 8.4 earthquake corresponds approximately to a moment magnitude (M 0 ) of 6 × 10 21 N m according to the relation M w = 2 3 Log (M 0 ) − 6.0 (Hanks and Kanamori, 1979) leading to a maximum rupture area of ∼ 4 × 10 4 km 2 and a maximum rupture slip of 5 m conforming to these relations.…”

Influence of Flow Velocity on Tsunami Loss Estimation

“…In probabilistic tsunami risk analysis, several studies adopted a stochastic approach where the seismic source is characterised by magnitude and average slip [32], while probabilistic scaling relationships can be used to characterise earthquake source through multiple source parameters in a comprehensive way [33]. The effects due to uncertainty of tsunami source characterisation on tsunami loss estimation can be evaluated through Monte Carlo tsunami simulation using numerous synthesised source models [31,34]. Tsunami loss estimated in a stochastic approach is beneficial to assess the importance of velocity for tsunami inundation in different situations.…”

Tsunami hazard warning and risk prediction based on inaccurate earthquake source parameters

“…For a M w 9.0-class mega-thrust subduction event, the fault plane of the earthquake rupture extends to distances over several hundred kilometers (Murotani et al, 2013). Moreover, spatial distribution of earthquake slip varies significantly and these rupture characteristics, which are not captured by the earthquake magnitude and location, have major influence on tsunami waves and inundation in coastal cities and towns (Goda et al, 2014(Goda et al, , 2015Fukutani et al, 2015;Mueller et al, 2015).…”

“…In particular, a stochastic source modeling method (Mai and Beroza, 2002;Goda et al, 2014) is incorporated, and nonlinear shallow water equations with run-up are evaluated for each source model, enabling accurate inundation simulation. To extend the analyses to different earthquake scenarios, scaling relationships for the source parameters (Mai and Beroza, 2002;Murotani et al, 2013;Thingbaijam and Mai, 2016) are employed to generate stochastic source models that correspond to different moment magnitudes. Subsequently, Monte Carlo tsunami simulation is carried out, and inundation results at building locations are integrated with tsunami fragility curves and damage cost models that are applicable to the buildings of interest (Goda and Song, 2016).…”