- Ground-motion model is:
after trying various other functional forms. Fix a

_{5}to 0.42 from previous study due to lack of near-field data and unstable regression results. - Use two site classes:
- Rock
- V
_{s,30}≥ 760m∕s. S_{1}= 1, S_{2}= 0. - Soil
- V
_{s,30}< 760m∕s. S_{2}= 1, S_{1}= 0.

Classify station using V

_{s,30}and surface geology data, if available. Otherwise use empirical H/V classification scheme. - 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 M
_{w}< 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 M_{w}< 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 M
^{2}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 histograms closely.