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Exploratory Data Analysis

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

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  1. Exploratory Data Analysis July 19, 2004 NGA Workshop

  2. 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

  3. 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

  4. Step 2: Examine estimated C1 and C4 for magnitude effects and functional form.

  5. Step 3: • Modify the basic model to • C1 C1 + C2 M + C3 (M-8.5)2.5 • C4 C4 + C5 M • Repeat regression

  6. Step 4: • Examine estimated coefficientsfor • Magnitude scaling at short distances to large-magnitude earthquakes • Style of faulting effect • Effects of coseismic surface faulting

  7. Step 4: • Examine residualsfor • Site classification and site effects • FW/HW effects • Directivity effects

  8. Observed trend may be explained by more than one explanatory variables • Z1.5 ~ Vs30 (NEHRP) • Style of faulting ~ Surface Rupture • FW/HW ~ Directivity

  9. Y Cos()

  10. 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

  11. Exploratory • Outliers do show up

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