## 5 Conclusions and Future Work  
Although the data analysis of orbital radars such as MARSIS and SHARAD are producing many compelling clues about Martian surface and climate evolution, their interpretation still depends on 1D  
Figure 11: Euripus Mons LDA subsurface mapping result. (**a**) The distribution of discontinuity points with classified layer numbers, (**b**) 3D perspective view of two discontinuity planes gridded by kriging; (**c**) the structure of layer 1 overlaid on Euripus Mons DEM; (**d**) the structure of layer 2 overlaid on Euripus Mons DEM. Note that the discontinuity planes were rotated for the best view.  
profiling of signals, which are created manually by experienced interpreters. This manual profiling has reduced the potential for synthesized use of SHARAD and MARSIS data sets with other spatial information to extract scientific clues about the Martian surface and subsurface. Therefore, we have developed an automated 3D subsurface mapping procedure that enables us to automatically define surface discontinuity and reconstruct subsurface layers from SHARAD signals using ML/DL. We have applied the established scheme over several target areas such as LDAs in the Euripus Mons region, high altitude potential permafrost, and polar deposit. The developed method automatically identified several discontinuity layers with adequate accuracy. The detected discontinuity points were utilized to reconstruct subsurface discontinuity planes.  
In comparison to the precedent studies aimed at bringing partially automated improvement for the analysis of SHARAD and MARSIS data sets such as point detection, auto registration, filtering, and reconstruction, our method depicts further potential to be developed as a comprehensive SHARAD and MARSIS procedure with improved accuracy. We are planning to improve the established algorithms as follows.  
Employment of HRSC & Context Camera (CTX) stereo DTMs for better clutter simulation.  
Automated subsurface layer clustering based on more robust approaches, such as support vector machine (SVM) algorithms.  
ML algorithm improvement, by preparing more labelled data from spatially varied regions to increase the robustness of the model.  
We will publish these improvements in the public domain after the fully stabilized components are integrated with the MARSIS/SHARAD signal processor. However, for robust training, SHARAD tracks and discontinuity labels from several other areas will be required based on user feedback for the published software. The case will not be limited only to Mars, and may also apply to future planetary subsurface radars such as Radar for Europa Assessment and Sounding: Ocean to Near-surface (REASON) [62].  
[PERSON] was charged of major data processing and writing. [PERSON] developed ML algorithms and wrote corresponding text. [PERSON] designed the overall strategy and structured the draft. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors appreciate a number of professional supports and technical advices from CO-SHARPS administrators. [PERSON] and [PERSON] thank [PERSON], IIT Mandi for kindly providing computational facilities and financial support to [PERSON].  
The authors declare no conflicts of interest.