## 4 Results and Discussion
Kriging was applied to extract interpolated sublayer planes and the Euclidian distances between discontinuity points and interpolated planes. The best fit underground sublayers were extracted using an iterative procedure to attain an optimal number of clusters and distances. In this research, manual intervention was required to accomplish this task; however, the development of an automated/semi-automated routine is under development. The above procedure to reconstruct the underlying discontinuity planes revealed the gridded subsurface structures of Euripus Mons and anonymous LDAs, as shown in Figure 11. We discovered three clear discontinuity sublayers underneath the Euripus Mons LDA, represented by the yellow, green, and blue face. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Region** & **Accuracy** & **AUC** & \
\begin{tabular}{c} **Balanced** \\ **Accuracy** \\ \end{tabular} & **Precision** & **Recall** & **F Score** &
\begin{tabular}{c} **Jaccard** \\ **Index** \\ \end{tabular} \\ \hline Euripus Mons & 0.99878 & 0.92241 & 0.68422 & 0.36885 & 0.54973 & 0.44148 & 0.288 \\ Anonymous LDA & 0.99966 & 0.72016 & 0.54820 & 0.09649 & 0.27160 & 0.14239 & 0.076 \\ Phoenix L. S. & 0.99979 & 0.49951 & - & - & - & - \\ Polar Regions & 0.99975 & - & 0.50000 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The metrics and statistics for each region. Figure 10: Epochs vs. loss for training and validation (**left**); epochs vs. accuracy for training and validation (**right**). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region** & **True Positive** & **False Positive** & **False Negative** & **True Negative** \\ \hline Euripus Mons & 315 & 539 & 258 & 654,248 \\ Anonymous LDA & 22 & 206 & 59 & 786,145 \\ Phoenix L. S. & 0 & 0 & 67 & 327,613 \\ Polar Regions & 0 & 65 & 0 & 262,079 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The distribution of test data, and confusion matrices for each region. We also identified two discontinuity layers under anonymous LDA, as shown in Figure 11, and ascertained different morphologies in northern and southern aprons. We propose that such different morphologies of the two subsurface layers imply a certain temporal/contextual disconnection between paleoclimate events and progressive developments during intermediate periods, perhaps involving sublimation. This is a significant clue towards identifying the morphological evolution of LDAs. We did not observe three discontinuity layers, which follow the shape of surface topography, as claimed in a previous study [61]. This may be due to sparsity in the reconstructed discontinuity points. Moreover, the integration of several studies [25,26] for the estimation of dielectric constants will be considered for future discontinuity layer reconstruction algorithms. The depth of discontinuity layers will be adjusted to precisely depict subsurface structures using varying dielectric constants. Finally, the assessment of our automated discontinuity detection approach guarantees sufficient accuracy to replace manual detection, given that proper training and pre-processing are preceded. Moreover, the reconstructed discontinuity planes over LDAs and the automated MV algorithms on SHARAD signals can provide meaningful geological information. The latter implies the potential of our method to mine significant clues regarding Martian evolution.