- Ground-motion model is:
after trying various other functional forms. Fix a5 to 0.42 from previous study due to lack of near-field
data and unstable regression results.
- Use two site classes:
- V s,30 ≥ 760m∕s. S1 = 1, S2 = 0.
- V s,30 < 760m∕s. S2 = 1, S1 = 0.
Classify station using V s,30 and surface geology data, if available. Otherwise use empirical H/V
- Response parameter is acceleration for 5% damping.
- Investigate differences in ground motions between Alborz-Central Iran and Zagros regions using analysis
of variance (ANOVA) (Douglas, 2004b) to check whether data can be combined into one dataset. Find
that for only one magnitude-distance interval out of 30 is there a significant difference in ground motions
between the two regions. Hence, combine two datasets.
- Check that data from West Eurasia and Kobe from Fukushima et al. (2003) can be combined with data
from Iran using ANOVA. Find that for only one magnitude-distance interval is there a significant difference
in ground motions and, therefore, the datasets are combined.
- Only retain data from R < 100km to avoid bias due to non-triggered instruments and because data from
greater distances is of low engineering significance.
- Process uncorrected records by fitting quadratic to velocity data and then filtering acceleration using a
fourth-order acausal Butterworth filter after zero padding. Choose filter cut-offs by using the signal-to-noise
ratio using the pre-event noise for digital records and the shape of the Fourier amplitude spectra for
analogue records. Only use records for periods within the passband of the filters applied.
- Exclude data from earthquakes with Mw < 5 because of risk of misallocating records to the wrong small
events and because small events can be poorly located. Also records from earthquakes with Mw < 5 are
unlikely to be of engineering significance.
- Cannot find negative anelastic coefficients for periods > 1s and therefore exclude this term for all periods.
- Try including a M2 term but find that it is not statistically significant so remove it.
- Examine residuals (display graphs for 0.1 and 1s) w.r.t. M and R. Find no significant (at 5% level) trends.
- Examine histograms of residuals for 0.1 and 1s and find that expected normal distribution fits the