## 4 Discussion  
The local optimization algorithm performed optimally in delineating both resistivity and velocity anomalies regarding the tested models. This result is attributed to the application of the smoothing term and appropriate regularization parameters in the objective function to mitigate the effect of the non-uniqueness of the inverse problem. Tables 2-4 summarize the performance of both LOM and CGO algorithms in the inversion involving both DCR and SR methods. Generally, the results from Tables 2-4 show that the CGO improved the misfits compared with the LOM in both the DCR and SR methods but at a relatively high computation cost. Table 5 summarizes the results of the CGO for both (DCR and SR) synthetic models. Despite using a parallel computing approach, the GOM results showed a run time of 6642.15 to 13,962.00 min. This suggests that the most significant challenge with applying the GOM algorithm is the run time. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Inversion Type**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{2}{c}{**No. of Iterations/Generations**} & \multicolumn{1}{c}{**Time (min)**} & \multicolumn{1}{c}{**Misfit (\%)**} & \multicolumn{1}{c}{**Target’s Reconstruction (\%)**} \\ \hline \multirow{4}{*}{LOM} & IIM & SR & 11 & 2.034 & 0.31 & 80 & 90 \\  & ID & DC & 8 & 0.93 & 1.55 & 70 & 90 \\  & JIM & SR & 8 & 20.08 & 1.46 & 95 & 95 \\  & DC & 8 & 20.08 & 1.46 & 98 & 95 \\  & SOO & SR & 1500 & 1193.68 & 0.14 & 85 & 95 \\ CGO & DC & 1500 & 150.22 & 0.12 & 80 & 95 \\  & MOO & SR & 3502 & 2891.43 & 0.13 & 97 & 100 \\ \cline{3-6} \cline{8-8}  & & DC & 3502 & 2891.43 & 0.10 & 80 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Local and global optimization inversion results parameters for both DCR and SR (synthetic positive and negative anomalies model) methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Inversion Type**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{2}{c}{**No. of Iterations/Generations**} & \multicolumn{1}{c}{**Time (min)**} & \multicolumn{1}{c}{**Misfit (\%)**} & \multicolumn{1}{c}{**Target’s Reconstruction (\%)**} \\ \cline{3-6}  & & & **Generations** & & & **Geometry** & **Amplitude** \\ \hline \multirow{4}{*}{LOM} & IIM & SR & 10 & 1.69 & 0.68 & 80 & 95 \\  & ID & DC & 8 & 0.81 & 10.61 & 70 & 97 \\  & JIM & SR & 10 & 10.07 & 8.83 & 60 & 95 \\  & DC & 10 & 10.07 & 8.83 & 80 & 97 \\  & SOO & SR & 1000 & 747.85 & 0.58 & 85 & 100 \\  & DC & 1500 & 175.57 & 0.48 & 65 & 100 \\  & MOO & SR & 4000 & 3136.42 & 0.62 & 90 & 100 \\  & DC & 4000 & 3136.42 & 2.13 & 70 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Local and global optimization inversion results parameter for both DCR and SR (synthetic dyke anomaly model) methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Inversion Type**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{2}{c}{**No. of Iterations/Generations**} & \multicolumn{1}{c}{**Time (min)**} & \multicolumn{1}{c}{**Misfit (\%)**} & \multicolumn{1}{c}{**Target’s Reconstruction (\%)**} \\ \cline{3-6}  & & & **Generations** & & & **Geometry** & **Amplitude** \\ \hline \multirow{4}{*}{LOM} & IIM & SR & 10 & 1.69 & 0.68 & 80 & 95 \\  & ID & DC & 8 & 0.81 & 10.61 & 70 & 97 \\  & JIM & SR & 10 & 10.07 & 8.83 & 60 & 95 \\  & DC & 10 & 10.07 & 8.83 & 80 & 97 \\  & SOO & SR & 1000 & 747.85 & 0.58 & 85 & 100 \\  & DC & 1500 & 175.57 & 0.4 The results showed that the CGO algorithm inherited some features of the model, such as the geometry and amplitude of the anomaly from the local optimization that reflects in its optimum performance. For example, the amplitude and geometry of the DCR in the Dyke model was reflected in the final output of global optimization (Figure 6). Notice that in Figures 9d and 10d, the ray path avoided the negative anomaly; thus, the ideal model structure cannot be reconstructed with high resolution.