## 4 Results and Discussion  
The training was performed on the tracks over Euripus Mons, which also contained the portion beyond the geographic extent of Euripus mons. Several new tracks, with almost no similarity to the other tracks in the region, were processed for testing. The results obtained are shown in Figure 9. Tables 3 and 4 show the confusion matrix and performance metrics over the respective target areas. The testing performed on the Euripus Mons tracks illustrate the best overall results as expected. It is important to note that a substantial proportion of the total labelled discontinuities belonged to the Euripus Mons region when compared to the Anonymous LDA and Polar region (refer to columns 1 and 3 of Table 3). The testing on the Anonymous LDA region also yielded fair results considering the true class size ratio. The model was able to predict 22 of the total 81 discontinuity pixels in the area. A few tracks in the polar region were also processed for testing. It can be inferred from Table 3 that there was no true class in the actual data for this region. However, the model predicted a small number of false positives. Despite the vast size of the Polar region, not many tracks which cover the Euripus Mons region also pass through it (refer Table 2). The model, when tested on tracks from the Phoenix landing site, did not predict any pixel as discontinuity even though there were 67 manually detected true class pixels. While preparing the labelled dataset for this region, some tracks were omitted, as there was no/very little discontinuity in the region. Since the Phoenix landing site does not contain significant topographical variation, thediscontinuities are not considerable. The test results were not very accurate, as expected in this region, indicating that geographically more distributed labelled data is required for training, to enhance the robustness of the model. However, it is important to note that the manually detected labelled data had only 1 of 3000 pixels as discontinuity pixels. Moreover, the Phoenix landing site lies in the northern lowland of Mars, and the training dataset belongs to the southern hemisphere, which has a different geological origin. Due to significant variation in the subsurface strata of Euripus Mons, a southern hemispheric highland, and the Phoenix landing site, a primary northern lowland of Mars, the training may not be spatially robust. To overcome this problem, and improve the model accuracy, more training data must be prepared covering a wide array of the geographical locations of Mars. The model was trained for 20 epochs with a batch size of 16, making it approximately 135 iterations. The epoch refers to a complete cycle of training, i.e., one forward pass and one backward pass of all the training samples. The training and validation loss as well as accuracy plotted at each epoch are shown in Figure 10. The training loss is initially high and decreases monotonically in subsequent epochs, demonstrating that the model did not present a bias (or no-skill) and was trained adequately. The accuracy increases with the number of epochs. Although some undulations occur in training accuracy, however, they may be attributed to a random sampling of training data in each epoch. Figure 9: SHARAD tracks in Euripus Mons (**a–d**) and Phoenix landing site (**e**). Target locations (1 st column), testing images (2 nd column), results of the model (3 rd column) and manually labelled data (4 th column) are presented. It is essential to assess the outcomes of the model in the geological context, as well as in terms of quantitative metrics for further scientific applications. The efforts towards geological applications were accomplished using discontinuity plane reconstruction, together with contextual interpretation. After collecting all discontinuity points from the sublayers, the points were clustered using the Balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm [60] into a few initial groups according to their spatial distributions.