US Japan Collaboration in Ground Motion Simulation

US – Japan Collaboration in Ground Motion Simulation Hiroshi Kawase JSPS - Kyoto University Paul Somerville and Jeff Bayless SCEC - URS

Topics • JSPS-SCEC comparison of strong motion prediction techniques for disaster risk evaluation – Empirical Method – Simulation Method • Implementation of the Irikura Recipe for Strong Motion Simulation on the SCEC Broadband Platform

Comparison of Strong Motion Prediction Techniques - Empirical Method • We will select data for the commonly agreed magnitude and distance ranges from the PEER NGA database, and obtain average attenuation characteristics for peak acceleration, peak velocity, and response spectral acceleration using the method of Sato (2010) • The median ground motion levels and standard deviations will be compared with the Japanese results. The comparison will focus on event terms and site amplification factors.

Comparison of Strong Motion Prediction Techniques - Simulation Method • We will select existing strong ground motion simulation results for standard earthquake scenarios that have already been done in Southern California and Japan. • These may include the suites of broadband simulations that we done for the Tall Buildings Initiative. • We will exchange all the source parameters that were used in generating the simulations. • We will then repeat the simulations of the scenarios using the other side’s source parameters in our simulation method • These simulations will be done on the SCEC Broadband Platform.

Comparison of Strong Motion Prediction Techniques - Simulation Method (cont. ) • We will compare the average ground motion levels and standard deviations of the simulated strong motions obtained by each side, and with the empirical ground motion models for each country • If the comparisons reveal that significant bias exists in the simulated ground motions, we will seek the source of the bias and delineate the issues that we must resolve for further improvement in strong motion simulation techniques.

Implementation of the Irikura Recipe on the SCEC Broadband Platform • The Irikura Recipe is used in the seismic licensing of nuclear power plants in Japan, and in the development of scenario ground motion maps by the Earthquake Research Committee. • The following presentation is based on a presentation prepared by Kojiro Irikura and Hiroe Miyake • Irikura, K. and H. Miyake (2011). Recipe for Predicting Strong Ground Motion from Crustal Earthquake Scenarios, Pure Appl. Geophys. 168 (2011), 85– 104. DOI 10. 1007/s 00024 -010 -0150 -9.

Logistics for Implementation of the Irikura Recipe on the SCEC Platform • Download the code and its Japanese language documentation from NIED (Fujiwara) • Code is professionally written (Mitsubishi Aerospace) • Find a SCEC grad/postdoc to translate the documentation into English • SCEC – URS interaction on implementation and testing • URS verification against Japanese scenario

GMPE vs. Broadband Simulation for Scenario Earthquake: Itoigawa-Shizuoka Tectonic-Line Earthquake ↓Ground Motion Prediction Equation ↑ Ground Motion Simulation by the Hybrid Method (Irikura Recipe) Earthquake Research Committee (2002)

Hybrid Method for Ground Motion Time History stochastic Green’s function method (SGF) Matching filter crustal: ~ 1 s subduction: > 2 s Computational codes: Long-period: Graves (1996), Aoi and Fujiwara (1999), Pitarka (1999), and etc. Short-period by SGF: Kamae et al. (1991), Dan and Sato (1999)

Recipe for Strong Ground Motion Prediction Outer Fault Parameters Rupture area S is given. Seismic moment Mo from the empirical relation of Mo-S. Average static stress-drop Dsc from appropriate physical model (e. g. , circular crack model, tectonic loading model, etc. ) Inner Fault Parameters Combined area of asperities Sa from the empirical relations of S-Sa or Mo-Ao. Stress drop on asperities Dsa based on the multiple asperity model. Number of asperities from fault segments. Average slip of asperities Da from dynamic simulations. Effective stress for asperities sa and background area sb are given. Slip velocity time function given as Kostrov-like function. Extra Fault Parameters Rupture nucleation and termination are related to fault geometry. Irikura and Miyake (2001, 2011)

Source Characterization & Scaling Mo-S: Outer Scaling (Fault Area) Slip inversion Outer Fault Parameters S(=LW), Mo, Dsc Mo-Sa: Inner Scaling (Asperity Area) Inner Fault Parameters Sa, Dsa , etc Somerville et al. (1999)

Characterized Source Model Miyake et al. (2003)

Characteristics of the Recipe • Easy to link active fault studies. • To adopt the characterized source model, not k-squared. • To avoid the usage of random parameters, everybody can reproduce ground motion time histories. • To adopt the stochastic Green’s function method providing ground motion time histories following the omega-squared source model in a broadband period range (e. g. , 0. 1 -20 s). • Well calibrated to match GMPEs for past earthquakes.

Empirical Relationships for the Recipe (1) - Seismic Moment (Mo) vs. Total Rupture Area (S) - inland crustal eq. (Wmax=20 km) subduction eq. (Wmax=100 km)

Empirical Relationships for the Recipe (2 -2) - Mo vs. Acceleration Source Spectral Level (Ao) - Theoretical relationship shows Aoa ∝ Mo 1/3 (from Madariaga, 1977; Boatwright, 1988) S ∝ Mo 2/3 Sa ∝ Mo 2/3 const. Empirical relationship shows Ao ∝ Mo 1/3

Outer Fault Parameters characterizing entire source area Inland crustal earthquake Step 1: Give total rupture area (S=LW) Fault length (L) is related to grouping of active faults from geological and geomophological survey. Fault width (W) is related to thickness of seismogenic zones. Step 2: Assume average static stress-drop (Dsc) on the fault (about 2. 3 MPa from the empirical relationship by Somerville et al. (1999)) Step 3: Estimate total seismic moment (Mo) from S and Dsc assuming a circular-crack model (Eshelby, 1957) for smaller L or a loading model (Fujii and Matsu’ura, 2000) for larger L. 　　　　　　　

Inner Fault Parameters Slip heterogeneity or roughness of faulting Inland crustal earthquake Step 4: Estimate combined area of asperities (Sa) from empirical relation Sa-S (Somerville et al. , 1999; Irikura and Miyake, 2001, 2011 ) Sa/S = 0. 22 Sa: combined area of asperities (inner) S : total rupture area (outer) 　　　 Step 5: Estimate Stress Drop on Asperities (Dsa) from multi-asperity model (Madariaga, 1979) 　　　 Dsa: stress drop on asperity (inner) Dsc: average stress drop (outer)

Inner Fault Parameters Slip heterogeneity or roughness of faulting Inland crustal earthquake Step 6: Estimate number of asperities (N): The asperities in the entire fault rupture are related to the active-fault segments 　　location from surface offsets measured along fault Step 7: Estimate average slip on asperities (Da) based on Step 6 and empirical relationships from dynamic simulations 　　　　 (ex. N=1 Da/D=2. 3, N=2 Da/D=2. 0, N=3 Da/D=1. 8) reference: average Da/D = 2. 0 (Somerville et al. , 1999)

Extra Fault Parameters Propagation pattern of rupture Rupture starting point Rupture propagation pattern Rupture velocity Inland crustal earthquakes Rupture nucleation and termination are related to geomorphology of active faults Subduction-zone earthquakes Information from historical earthquakes Nakata et al. (1998)