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Exploratory Data Analysis for Earthquake Modeling Workshop

In this workshop, data from 172 earthquakes are fitted to a model for magnitude and distance to fault. Adjustments are made to examine various coefficients and effects, including magnitude scaling and site influences. Residuals are analyzed for site classification, directivity, and faulting style. Outliers are identified and explanations sought for observed trends.

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Exploratory Data Analysis for Earthquake Modeling Workshop

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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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