- Ground-motion model is:
where y is in m∕s2, a

_{1}= -0.703, a_{2}= 0.392, a_{3}= -0.598, a_{4}= -0.100, a_{5}= -7.063, a_{6}= 0.186, a_{7}= 0.125, a_{8}= 0.082, a_{9}= 0.012 and a_{10}= -0.038 (do not report σ but unbiased mean square error) for horizontal PGA; and a_{1}= 0.495, a_{2}= 0.027, a_{3}= -2.83, a_{4}= 0.235, a_{5}= 7.181, a_{6}= 1.150, a_{7}= 1.103, a_{8}= -0.074, a_{9}= 0.065 and a_{10}= -0.170 (do not report σ but unbiased mean square error). - Use three site categories:
- Soft soil
- S
_{S}= 1, S_{A}= 0. - Stiff soil
- S
_{A}= 1, S_{S}= 0. - Rock
- S
_{S}= 0, S_{A}= 0.

- Use four faulting mechanisms:
- Normal
- F
_{N}= 1, F_{T }= 0, F_{O}= 0. - Strike-slip
- F
_{N}= 0, F_{T }= 0, F_{O}= 0. - Thrust
- F
_{T }= 1, F_{N}= 0, F_{O}= 0. - Odd
- F
_{O}= 1, F_{N}= 0, F_{T }= 0.

- Use same data and functional form as Ambraseys et al. (2005a) and Ambraseys et al. (2005b) but exclude six records that were not available.
- Use genetic (global optimization) algorithm to find coefficients so as to find the global (rather than a local) minimum. Use the unbiased mean square error as the error (cost or fitness) function in the algorithm. Use 20 chromosomes as initial population, best-fitness selection for offspring generation, uniform random selection for mutation of chromosomes and heuristic crossover algorithm for generation of new offspring.
- Find smaller (by 26% for horizontal and 16.66% for vertical) unbiased mean square error than using standard regression techniques.