## 4 Results
For this reason, taking 5% uncertainty as a difference between standard least-square regression without consideration of errors in variables and error-in-variables, yield to site kappa value that is more conveniently estimated as shown in Table 1. With less data, large data scatter and lack of data at shorter epicentral distances, differences between two regression methods could be significant, and that can affect final \(\kappa_{0}\) and \(\kappa_{R}\) values. Less data and lack of data at shorter epicentral distances are related to the seismicity of an area and operational years of seismological stations. However, large data scatter in \(\kappa\) and \(R_{e}\) is not only sensitive to differences in methods and data, and to the local and regional characteristics of study area (the effect of distance and local site effects), but also to complex \(Q\) structure, different ray paths across faulting structure for different earthquakes [36; 37] and anisotropic attenuation properties [1]. Therefore, we choose an area within about 20 km of the epicentre as near-field attenuation [38] to constrain \(\kappa_{0}\) value to \(\kappa\) value at shortest \(R_{e}\) (Figure 6) using nearby records (hockey-stick model proposed by [36]). This enabled us to determine site kappa value affected primarily by upper local site condition that would not be influenced by changes in slope of \(\kappa_{R}\) related to \(Q\); in order to minimize influence of regional effects (deeper local site effect + source and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**PTJ Station**} \\ \hline  & & & **HOR** & & & & **VER** \\ \hline  & & **\(\kappa_{0}\) (s)** & **SE-\(\kappa_{0}\)** & **\(\kappa_{R}\) (skm\({}^{-1}\))** & **SE-\(\kappa_{R}\)** & **\(\kappa_{0}\) (s)** & **SE-\(\kappa_{0}\)** & **\(\kappa_{R}\) (skm\({}^{-1}\))** & **SE-\(\kappa_{R}\)** \\ \hline  & **LSE \(\rightarrow\)** & **0.0283** & 0.0028 & **0.000327** & 0.000024 & **0.0249** & 0.0024 & **0.000265** & 0.000021 \\ \hline
**Error Set** & & & & **[PERSON] et al. **[35] & **Regression Test** & & & & \\ \hline  & R = 2 km & 0.0287 & 0.0054 & 0.000323 & 0.000048 & 0.0252 & 0.0054 & 0.000263 & 0.000048 \\ \(\pm\)**1 std** & R = 5 km & 0.0291 & 0.0053 & 0.000319 & 0.00047 & 0.0256 & 0.0053 & 0.000259 & 0.000047 \\  & R = 10 km & 0.0298 & 0.0051 & 0.000314 & 0.000045 & 0.0261 & 0.0005 & 0.000254 & 0.000046 \\ \hline  & R = 2 km & 1.39 & & 1.24 & & 1.19 & 0.80 & \\
**Diff. \%** & R = 5 km & 2.75 & & 2.51 & 2.73 & & 2.36 & \\  & R = 10 km & 5.03 & & 4.14 & & 4.60 & 4.37 & \\ \hline  & R = 2 km & 0.0290 & 0.0027 & 0.000321 & 0.000023 & 0.0255 & 0.0027 & 0.000261 & 0.000024 \\ \(\pm\)**2 std** & **R = 5 km** & **0.0298** & 0.0026 & **0.000313** & 0.000022 & **0.0261** & 0.0026 & **0.00025** & 0.00023 \\  & R = 10 km & 0.0300 & 0.0024 & 0.000311 & 0.000021 & 0.0266 & 0.0025 & 0.000250 & 0.000022 \\ \hline  & R = 2 km & 2.41 & & 1.87 & & 2.35 & 1.57 & \\
**Diff. \%** & R = 5 km & 5.03 & & 4.47 & & 4.60 & 4.37 & \\  & R = 10 km & 5.67 & & 5.14 & 6.39 & & 6.04 & \\ \hline  & & & & & & & & & \\ \hline  & & & & & & & & & \\ \hline  & & & & & & & & & \\ \hline \multicolumn{6}{c}{**ZAG Station**} \\ \hline  & & & **HOR** & & & & & & & \\ \hline  & & **\(\kappa_{0}\)** & **SE-\(\kappa_{0}\)** & **\(\kappa_{R}\)** & **SE-\(\kappa_{R}\)** & **\(\kappa_{0}\)** & **SE-\(\kappa_{0}\)** & **\(\kappa_{R}\)** & **SE-\(\kappa_{R}\)** \\ \hline  & **LSE \(\rightarrow\)** & **0.0411** & 0.0032 & **0.000265** & 0.000029 & **0.0391** & 0.0026 & **0.000223** & 0.000024 \\ \hline
**Error Set** & & & & **[PERSON] et al. **[35] & **Regression Test** & & & & \\ \hline  & R = 2 km & 0.0414 & 0.0063 & 0.000262 & 0.000058 & 0.0393 & 0.0063 & 0.000221 & 0.000058 \\ \(\pm\)**1 std** & R = 5 km & 0.0419 & 0.0062 & 0.000256 & 0.000057 & 0.0397 & 0.0062 & 0.000217 & 0.000057 \\  & R = 10 km & 0.0426 & 0.0060 & 0.000250 & 0.000054 & 0.0402 & 0.0060 & 0.000213 & 0.000055 \\ \hline  & R = 2 km & 0.72 & & 1.15 path effects, [32]) on \(\kappa_{0}\).