Chiou and Youngs PEER-NGA Empirical Ground Motion Model for ...
Chiou and Youngs PEER-NGA Empirical Ground Motion Model for ... Chiou and Youngs PEER-NGA Empirical Ground Motion Model for ...
ecorded within 70 km of earthquake ruptures, the region of primary concern for application of the model. We also believe that the model properly represents the attenuation of ground motion at larger distances in California. The issue we have raised points out the need to systematically collect and process all recordings from large earthquakes if there is interest in correctly modeling the attenuation of ground motions at large distances. Equation (19) provides a relationship for γ for pga. The relationships for other spectral periods was constructed by using the period-dependence of γ computed from analysis of the broadband data from the three small southern California earthquakes and scaling the values by the relative difference in γ at pga, that is: γ ( pga) California γ ( T ) California = γ ( T ) Anza− YL−BBC × (20) γ ( pga) Anza−YL−BBC Figure 30 compares the model developed for γ with values obtained from direct regression of the PEER-NGA data without imposing any distance truncation and without using truncated regression. The variation of γ with spectral period T is similar. Figure 30: Comparison of the model developed for γ (Equations 19 and 20) versus values of γ obtained from regression without truncation (black points) of the PEER-NGA data set. MODELING STEPS The parameters of the ground motion model were developed through an iterative process that involved performing regressions for the entire spectral period range with some parts of the model fixed, examining the trend of remaining parameters with spectral period, developing smoothing models for these parameters with period, and then repeating the analysis to examine the variation of the remaining parameters. The major steps in the development of the parameters are summarized below. All analyses were performed using the PEER-NGA C&Y2006 Page 39
data truncated at a maximum distance of RRUP = 70 km. In addition, the magnitude scaling parameters c2 and c3 were fixed at 1.06 and 3.45 for all analyses and in the final model. As discussed in the model development section, c3 = 3.45 represents scaling of low-frequency amplitudes proportional to seismic moment. Parameter c2 was fixed at 1.06, the value derived from the Atkinson and Silva (2006) seismic source model. All previous applications of the general function form used by Ross Sadigh have yielded far-source high-frequency magnitude scaling coefficients that are consistent with this value. Preliminary analysis of the PEER-NGA data also produced values near 1.06. Parameter c4 was examined by analyses of PEER-NGA data and TriNet data. It was concluded that a range of values for c4 would provide satisfactory fits to the data with adjustments to the rate of attenuation occurring through changes of parameters c5 and c6. The change in attenuation rate with distance was set to occur at cRB = 50 km. A value near 50 km was consistently found in all preliminary analyses. After extensive exploratory analysis of the data, we concluded that the PEER-NGA dataset does not sample a sufficiently wide range of motion and Vs30 to allow the simultaneous estimation of all 3 nonlinear soil parameters (φ2, φ3,, and φ4) We thus decided to fix φ4 through other supporting information. Both our preliminary analysis of the PEER-NGA data and our analysis of the simulated soil amplification factors from Silva (2004) suggested that φ4 is about 0.1g in the case of pga. The empirical study of Choi and Stewart (2005) also indicate 0.1g is a viable value for φ4. We thus fix pga’s φ4 at 0.1g. To fix φ4 of other periods, we anchor the rock spectral shape of M6.5 at 10 km (Silva et a. Add referrecne) at 0.1g pga. The remaining two nonlinear parameters will adjust to the fixed value of φ4 and they are more than sufficient to capture the nonlinear soil behavior existing in the PEER-NGA dataset. In the preliminary analyses it was found that the data exhibited a statistically significant dependence on source depth parameterized as the depth to top of rupture, ZTOR and that aftershocks showed a stronger dependence on depths than main shocks. Also, the data indicated that the style of faulting effects were weaker for aftershocks than for main shocks. Therefore, the analyses were performed with separate depth dependence and style of faulting effects for main shocks and aftershocks. First Phase: Step 1: The first phase of the analysis used only data from neutral sites. Sites with the hanging wall/foot wall flags set to ‘fw’ or ‘hw’ in the PEER-NGA database were removed. This simplification was performed to examine other portions of the model without interaction with the hanging wall function fHW. In particular, regression with the full model showed high correlation between estimates of the hanging wall parameters and the parameters for style of faulting and soil nonlinearity. Using a simple model for the reference motion (without style of faulting, depth of rupture, and variable magnitude scaling) the parameters of the non-linear soil model (φ2 and φ3) were obtained from fits to the NGA data. At longer periods when the soil response becomes nearly linear, estimates of the nonlinear soil parameter φ3 fluctuated wildly and had very large errors of estimation. Therefore parameter φ3 for periods longer than 1.0 seconds were fixed to produce a smooth transition to linear behavior at spectral periods near 1.0 sec and greater. C&Y2006 Page 40
- Page 1 and 2: Chiou and Youngs PEER-NGA Empirical
- Page 3 and 4: data are consistent with strong mot
- Page 5 and 6: Figure 1: Magnitude-distance-region
- Page 7 and 8: Figure 2: Empirical ground motion d
- Page 9 and 10: EQID Earthquake M Table 3: Inferred
- Page 11 and 12: Site Average Shear Wave Velocity: A
- Page 13 and 14: Figure 6: Relationship between VS30
- Page 15 and 16: 1 ) ∝ C2 × M + ( C2 − C ) × l
- Page 17 and 18: Figure 9: Peak acceleration data fr
- Page 19 and 20: C4+C5M slowly and the value of the
- Page 21 and 22: allows the interpretation of the co
- Page 23 and 24: Figure 13: Coefficients resulting f
- Page 25 and 26: the top of rupture located at x = 0
- Page 27 and 28: Figure 18: Intra-event residuals fo
- Page 29 and 30: Figure 21: Variation of HW* with ma
- Page 31 and 32: The interpretation of the parameter
- Page 33 and 34: to the PEER-NGA pga data selected f
- Page 35 and 36: EFFECT OF DATA TRUNCATION The initi
- Page 37 and 38: term [ 1 Φ( y ( θ ) + τ ⋅ z ,
- Page 39: Table 4: Estimate of Anelastic Atte
- Page 43 and 44: faulting earthquakes at long period
- Page 45 and 46: Slope -1.5 -1.0 -0.5 0.0 0.5 1.0 0.
- Page 47 and 48: C&Y2006 Page 46 Table 5: Coefficien
- Page 49 and 50: c1 of T0.010S c1 of T1.000S MODEL R
- Page 51 and 52: esid 1 0 -1 -2 resid resid 1 0 -1 -
- Page 53 and 54: esid resid resid 1 0 -1 -2 1 0 -1 -
- Page 55 and 56: esid 2 1 0 -1 -2 SCEC Version 2 0 2
- Page 57 and 58: Amplification w.r.t. Vs30 = 1130 m/
- Page 59 and 60: Sa(g) Sa(g) 10 1 0.1 0.01 10 1 0.1
- Page 61 and 62: Sa (g) Sa (g) 1 0.1 0.01 0.001 0.00
- Page 63 and 64: Sa (g) Sa (g) 1 0.1 0.01 0.001 1 0.
- Page 65 and 66: EXAMPLE CALCULATIONS FORTRAN routin
- Page 67 and 68: Table 6: Example Calculations Perio
- Page 69 and 70: REFERENCES Abrahamson, N.A., and Si
- Page 71 and 72: Frankel, A., A. McGarr, J. Bicknell
- Page 73 and 74: Appendix A Recordings from PEER-NGA
- Page 75 and 76: RSN EQID Earthquake M Station No, S
- Page 77 and 78: RSN EQID Earthquake M Station No, S
- Page 79 and 80: RSN EQID Earthquake M Station No, S
- Page 81 and 82: RSN EQID Earthquake M Station No, S
- Page 83 and 84: RSN EQID Earthquake M Station No, S
- Page 85 and 86: RSN EQID Earthquake M Station No, S
- Page 87 and 88: RSN EQID Earthquake M Station No, S
- Page 89 and 90: RSN EQID Earthquake M Station No, S
ecorded within 70 km of earthquake ruptures, the region of primary concern <strong>for</strong> application<br />
of the model. We also believe that the model properly represents the attenuation of ground<br />
motion at larger distances in Cali<strong>for</strong>nia. The issue we have raised points out the need to<br />
systematically collect <strong>and</strong> process all recordings from large earthquakes if there is interest in<br />
correctly modeling the attenuation of ground motions at large distances.<br />
Equation (19) provides a relationship <strong>for</strong> γ <strong>for</strong> pga. The relationships <strong>for</strong> other spectral<br />
periods was constructed by using the period-dependence of γ computed from analysis of the<br />
broadb<strong>and</strong> data from the three small southern Cali<strong>for</strong>nia earthquakes <strong>and</strong> scaling the values<br />
by the relative difference in γ at pga, that is:<br />
γ ( pga)<br />
Cali<strong>for</strong>nia<br />
γ ( T ) Cali<strong>for</strong>nia = γ ( T ) Anza−<br />
YL−BBC<br />
×<br />
(20)<br />
γ ( pga)<br />
Anza−YL−BBC<br />
Figure 30 compares the model developed <strong>for</strong> γ with values obtained from direct regression of<br />
the <strong>PEER</strong>-<strong>NGA</strong> data without imposing any distance truncation <strong>and</strong> without using truncated<br />
regression. The variation of γ with spectral period T is similar.<br />
Figure 30: Comparison of the model developed <strong>for</strong> γ (Equations 19 <strong>and</strong> 20) versus values of γ<br />
obtained from regression without truncation (black points) of the <strong>PEER</strong>-<strong>NGA</strong> data set.<br />
MODELING STEPS<br />
The parameters of the ground motion model were developed through an iterative process that<br />
involved per<strong>for</strong>ming regressions <strong>for</strong> the entire spectral period range with some parts of the<br />
model fixed, examining the trend of remaining parameters with spectral period, developing<br />
smoothing models <strong>for</strong> these parameters with period, <strong>and</strong> then repeating the analysis to<br />
examine the variation of the remaining parameters. The major steps in the development of<br />
the parameters are summarized below. All analyses were per<strong>for</strong>med using the <strong>PEER</strong>-<strong>NGA</strong><br />
C&Y2006 Page 39