## 3 Results  
### 1D Model Optimization  
Data processing starts with preliminary source localization and 1D model optimization. The best 1D model that describes our dataset was determined using several starting velocity models (Figure 10A). We tested several 1D models previously developed by [4] (model 2), [46] (model 3) or [47] (model 4), and we also developed a model (model 1) based on our data using the VELEST code [48]. Figure 10A shows the starting models and their optimizations, and Table 1 shows the RMS residuals after the first and fifth iterations.  
Figure 9: Synthetic test with free-shaped anomalies defined in the horizontal projection. Upper row presents the initial synthetic models for the P- and S-wave velocity anomalies. Lower row shows the recovered anomalies. The shapes of the initial anomalies are highlighted with dotted lines.  
The process for obtaining the 1D minimum velocity model for the Targu-Jiu region started with the selection of the initial realistic velocity models already tested for the western part of Romania [13, 36, 46, 49]. The concept of the 1D minimum velocity model involves several parameters such as station corrections, reference station and velocity. Station corrections play an essential role in improving the accuracy and reliability of subsequent 3D tomographic inversion. As recommended in previous studies [48, 50], the VELEST algorithm is typically employed to initiate the 1D inversion using a stratified model with numerous shallow layers. These layers are later combined during the iterative inversion process as velocity values converge, ensuring a stable and representative starting model for 3D tomography. The 1D minimum velocity model is obtained from the inversion of the arrival times recorded at the station for each event.  
Analyzing the values of the RMS residuals presented in Table 1, we concluded that the most likely 1D model for our data is model 1, presented in Table 2. The stability of the model was assessed by the \"odd/even\" test [51]. The test consists of running inversions on subsets of the data obtained by separating events with even and odd identification numbers. The starting model for the odd and even subsets of data and for the entire dataset is the same and corresponds to model 1 in Figure 10A. The optimization results are similar for all cases and are shown in Figure 10B. This test reveals that the dataset does not affect the stability of the 1D model optimization.  
As shown in Figure 10, the model 4 [47], which is a global model, greatly underestimated the velocity values over the entire depth range and for this reason deviates the 1D model obtained by inversion toward low values. On the contrary, the model proposed by [46] (model 3) for the intra-Carpathian area overestimated the velocities, especially for  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model Description**} & **RMS of P Residuals, One Iteration (s)** & **RMS of S Residuals, One Iteration (s)** & **RMS of P Residuals (s)** & **RMS of S Residuals, Five Iterations (s)** \\ \hline MODEL 1 & 0.3149 & 0.4755 & 0.1955 & 0.2142 \\ \hline MODEL 2 & 0.3302 & 0.5013 & 0.2154 & 0.2698 \\ \hline MODEL 3 & 0.5441 & 0.7611 & 0.2753 & 0.3316 \\ \hline MODEL 4 & 0.4422 & 0.6086 & 0.2609 & 0.3135 \\ \hline \hline \end{tabular}
\end{table}
Table 1: RMS values for P- and S-wave residuals after the first and fifth iterations for different starting models.  
Figure 10: Optimization of the 1D model for observed data. (**A**) Optimization results with observed data and different starting 1D models. Different colors indicate different models, dotted lines are starting models and bold lines of corresponding colors are the optimization results. (**B**) Optimization results for halved data subsets in the odd/even test.  
larger depths (below 10 km). The most suitable models were located between these two extremes (models 1 and 2). We note that model 2 varies quasi-continuously with depth (no jumps in the velocity).