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Exploratory Data Analysis. July 19, 2004 NGA Workshop. Step 1: For each earthquake, fit data to the following model (functional form used by Sadigh et al.). M = Earthquake magnitude R j = Closest distance to fault All soil types are lumped together.
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Exploratory Data Analysis July 19, 2004 NGA Workshop
Step 1: For each earthquake, fit data to the following model (functional form used by Sadigh et al.) M = Earthquake magnitude Rj = Closest distance to fault All soil types are lumped together
C5 and C6 can’t be reliably estimated for most earthquakes fixed to the values of Sadigh et al. (1997) • 172 earthquakes 172 x 2 = 344 unknown coefficients • Rj < 70 km • Freefield records • California data vs. worldwide data
Step 2: Examine estimated C1 and C4 for magnitude effects and functional form.
Step 3: • Modify the basic model to • C1 C1 + C2 M + C3 (M-8.5)2.5 • C4 C4 + C5 M • Repeat regression
Step 4: • Examine estimated coefficientsfor • Magnitude scaling at short distances to large-magnitude earthquakes • Style of faulting effect • Effects of coseismic surface faulting
Step 4: • Examine residualsfor • Site classification and site effects • FW/HW effects • Directivity effects
Observed trend may be explained by more than one explanatory variables • Z1.5 ~ Vs30 (NEHRP) • Style of faulting ~ Surface Rupture • FW/HW ~ Directivity
No Yes Unknown SS 17 17 38 NM 3 5 14 RV 18 4 11 RV-OB 7 3 1 NM-OB 1 1 4 Unknown 0 0 28
Exploratory • Outliers do show up