Elena Donini

Characterizing the planetary subsurface through Radar Sounder (RS) data is fundamental to reconstructing the evolutionary history of the Solar System. However, the increasing volume of RS archives, which will be further expanded by upcoming and prospective missions to the Jovian and Uranian icy moons, necessitates a shift toward automated extraction methods. Although recent machine learning applications in RS stratigraphy have successfully automated the detection of isochronous layers, a significant methodological gap remains in detecting localized non-horizontal reflections. Features such as Martian troughs and Trough Migration Paths (TMPs) can serve as analogs for oblique geometries. Troughs are migratory valleys in the Martian North Polar Layered Deposit (NPLD), and their evolution provides a unique record of the history, including katabatic winds, sublimation, and cyclical climate forcing [1]. Their historical migration is encoded in the subsurface as Trough Migration Paths (TMPs), which are morpho-stratigraphic unconformities that truncate isochronous layers and track the trough floor over geological timescales [1]. While the joint analysis of high-resolution optical imagery (HiRISE/CTX) and RS (SHARAD) is essential for mapping these features [1], it relies heavily on manual tracing, which limits scalability.We frame the detection of dipping targets considering troughs and TMPs as analogues in RS data as a semantic segmentation task stratigraphy). We introduce a two-stage deep learning strategy based on a physics-informed contrastive pre-training phase, followed by supervised fine-tuning that mitigates class imbalance between troughs, TMPs, and horizontal stratigraphy. For the pre-training stage, we utilize a Momentum Contrast (MoCo) framework. MoCo forces a network to learn discriminative feature representations by maximizing the similarity between distinct views of the same image [2]. However, rather than relying on computer vision augmentations, we leverage squinted Synthetic Aperture Radar (SAR) focusing to generate physically consistent views of the same subsurface at distinct squint angles [3]. This forces the network to learn latent structural representations that are strictly invariant to angle-dependent radiometry. This approach desensitizes the model to the overwhelmingly dominant horizontal stratigraphy of the Martian polar regions, enabling it to capture the dipping signatures of troughs and TMPs without human supervision. The model is fine-tuned on a limited set of labeled radargrams for trough and TMPs. To prevent the model from being biased by dominant horizontal stratigraphy, we use a weighted binary cross-entropy loss function. Finally, detected troughs and TMPs are georeferenced and integrated into an interactive geospatial interface. Beyond simply visualizing the regional distribution of troughs and TMPs, this interface facilitates the geomorphological analysis. The interface allows users to navigate to the original radargrams for detailed morphological analysis, including depth and steepness of the trough walls and the TMPs.We consider SHARAD Experimental Data Record (EDR) radargrams acquired over the Martian NPLD (latitude > 77.5 N) between 2007 and 2021during solar occultation, yielding a set of 488 radargrams [4]. These were SAR-focused with 11 distinct squint angles that capture the diverse geometric slopes of the targets [3]. The data were partitioned into coregistered 11-channel patches (256×256 pixels) and standardized. The contrastive pre-training dataset comprises 1519 patches (split 70% for training, 15% for validation, and 15% for test). For the fine-tuning phase, experts annotated 50 radargrams (yielding 210 patches split 70% for training, 15% for validation, and 15% for test), cross-referencing with cluttergrams to decouple off-nadir clutter and subsurface reflections. Applying the proposed deep learning method to the SHARAD dataset yielded an automated map of the Martian NPLD troughs and TMPs. The model successfully isolated the troughs and TMPs from the dominant background stratigraphy, automatically detecting more than 95% of them across the NPLD. Figure 1 displays the results projected onto a polar Digital Elevation Model (DEM), where blue tracks indicate the spatial extent of TMPs and red markers denote the troughs. The geographical distribution of troughs and TMPS over the NPLD aligns with the spiral pattern. The interactive geospatial interface links the regional map to radargrams (Figure 2). Selecting map features displays the corresponding radargrams overlaid with a segmentation map that delineates the V-shaped topography of the troughs (Figure 2, left) and the stratigraphic interruptions in the TMPs (Figure 2, right). The segmentation enables automated extraction of morphological metrics, such as average trough depth and wall steepness. The extracted values are consistent with measurements reported in the literature [1].This work presents a dual-stage training methodology that leverages the physics of the dipping targets to map through and TMPS in RS data. We also deliver a geospatial interface that links the target's geographic position to the radargram. The methodology can be extended, with appropriate modifications, to map and characterize other geological dipping targets, including subsurface faults on Jovian or Uranian moons, providing a robust tool for investigating the cryotectonic evolution of icy satellites. Figure 1: Map with the track of the radargram portions imaging TMPs in blue and the position of the deepest point of the troughs in red. Figure 2: Example of trough (on the left) and TMPs (on the right) detected by the proposed method and visualized in the geospatial interface. ReferencesSmith, I. B., and J. W. Holt. "Onset and migration of spiral troughs on Mars revealed by orbital radar." Nature, 2010.He, K., et al. "Momentum contrast for unsupervised visual representation learning." Proceedings of the IEEE/CVF...

Ongoing planetary missions and future endeavors, such as JUICE to the Jovian moons, EnVision to Venus, and envisioned missions to the moons of Uranus, highlight the growing importance of Radar Sounders (RSs) for planetary science. RSs are active remote sensors capable of imaging dielectric discontinuities in the subsurface and recording them in depth images known as radargrams. While manual radargram analysis has led to important discoveries, such as the evolution of Martian troughs [1], efficiently exploiting the increasingly vast RS datasets necessitates automated methods. Currently, most automated approaches focus on segmentation or detection of linear reflections, yet there remains a need for methods that can analyze and semantically aggregate radargrams performing a large scale analysis.We conduct large scale analysis by leveraging on Martian RS data as a primary planetary analogue for future planned and envisioned missions to the icy moons of Jupiter and Uranus. We utilize Shallow Radar (SHARAD) data [5] acquired over the North Polar Layered Deposits (NPLD) (latitude > 77.5°). SHARAD achieves a vertical resolution of about 15 m in vacuum with a maximum penetration of about 1 km [6]. At the NPLD, SHARAD images icy targets, characterized by horizontal stratigraphy and oblique structures, that closely resemble the data expected from future and envisioned RS missions. Our analysis focuses on mapping troughs, which are depressions following a spiral pattern with depths of 400-1000 m [1]. Troughs expose the NPLD internal stratigraphy and record Martian climate processes within the Trough Migration Paths (TMPs). TMPs are subsurface unconformities that trace the trough displacement since their formation [2]. Their development followed distinct evolutionary paths across eight geographical regions, each characterized by unique morphological traits of TMPs and troughs.We selected 488 EDR SHARAD radargrams that are processed with squinted SAR focusing [7]. This approach optimizes the detection of non-horizontal reflections by aligning the RS line of sight with the subsurface dipping interfaces, such as trough walls and TMPs, which might otherwise be poorly resolved. We consider 11 squint angles, ranging from -2.5° to +2.5° in 0.5° steps. Radargrams are partitioned into patches of dimension 512 x 128 x 11, in the range, azimuth, and squint angle directions, and are divided into training and validation sets of 6,871 and 2,017 patches, respectively. Each patch is linked to one of the NPLD region [3] based on the morphological characteristics of the troughs and TMPs in the patch.Given a query radargram imaging a trough and/or a TMP, the goal is to retrieve radargrams with similar geological signatures, enabling the mapping of known and previously uncharacterized troughs and TMPs across the NPLD [3]. The task is addressed using the Content-Based Image Retrieval (CBIR) method in [4], where a Masked Autoencoder (MAE)[8], composed of a Vision Transformer (ViT) encoder [9] and a lightweight decoder, is trained in a self-supervised manner using a reconstruction proxy task. By reconstructing missing parts of the radargrams from their surrounding context, the model learns compact embeddings that encode the morphology of troughs, horizontal stratigraphy, and TMPs. Multi-squint radargrams are incorporated as input channels, providing the network with a physics-aware and multi-view representation that enhances oblique features. In inference, the pre-trained encoder maps each radargram into an embedding space where those with similar surface and subsurface signatures are grouped together. Retrieval is performed by ranking all embeddings in the validation set by cosine similarity with the selected query.The proposed method achieves a mean Average Precision (mAP) of 67.15% across the eight regions on the validation set. Figure 1 shows a representative query from region 5 and its five most similar retrievals, which exhibit trough and TMP patterns consistent with those of the query. Figure 2 presents maps of 15 representative trough queries from NPLD regions 3 and 7, together with their top-5 retrievals projected onto the MOLA topographic map. Most retrievals cluster within the corresponding query region, indicating that the method captures region-specific trough and TMP characteristics. Misretrievals mainly occur near region boundaries, likely reflecting both annotation uncertainty and similarity between geological features across adjacent regions.The results demonstrate that the proposed method facilitates large-scale subsurface mapping of troughs and TMPs by efficiently processing vast RS datasets. The method distinguishes between subtly different trough and TMP morphologies, effectively categorizing them in the distinct regions. This approach provides a tool for the large-scale analysis of subsurface data, supporting the management of the vast datasets expected from future and envisioned missions to the icy moons of Jupiter and Uranus. In these contexts, where specific geological targets may be unknown, grouping them by semantic similarity helps identify recurrent subsurface features.Figure 1: Query radargram and five most similar retrievals from Region 5. Figure 2: Retrieval maps for NPLD regions 3 and 7. Queries (stars), correct retrievals (circles), and incorrect retrievals (crosses) are shown.References[1] Smith, I. B., et al. (2013), The spiral troughs of Mars as cyclic steps, J. Geophys. Res. Planets, 118.[2] Smith, I., Holt, J. (2010), Onset and migration of spiral troughs on Mars revealed by orbital radar, Nature, 465.[3] Smith, I. B., and J. W. Holt (2015), Spiral trough diversity on the north pole of Mars, as seen by Shallow Radar (SHARAD), J. Geophys. Res. Planets, 120. [4] Donini, E., et al. (2026), A deep learning method for dual-frequency radar sounder data retrieval, in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS). [5] Seu, R.,et al. (2007), SHARAD sounding...

Radar sounder profiles are essential for imaging the subsurface of planetary bodies and the Earth as they provide valuable geological insights. However, the limited availability of high-resolution radargrams poses challenges. This paper proposes a novel method based on generative models to super-resolve radargrams. Our approach addresses the ill-posed and ill-conditioned nature of the super-resolution problem by training a neural network to learn the correlation between radargrams at different scales. The network learns a proxy for the mapping function between ambiguous low-resolution radargrams and more detailed high-resolution ones, considering the data’s geological and statistical properties. The mapping function enables the super-resolution of previously unseen low-resolution radargrams acquired in comparable conditions to those in the training and imaging similar underlying geology. To achieve this, we adopt a generative Cycle-Consistent Adversarial Network (CycleGAN), explicitly designed to match properties between low- and high-resolution radargrams, accounting for variations in dimensions and radiometric properties. Furthermore, we enhance the network performance by incorporating skip connections, a ResNet module, and attention mechanisms. The proposed method is validated using MCoRDS3 radargrams acquired in Greenland and Antarctica as high-resolution data. As low-resolution data, we used simulated radargrams representing what is expected by an Earth-orbiting low-resolution radar sounder to have a controlled experiment. The results are evaluated qualitatively and quantitatively, focusing on the areas with reflections with complex shapes that may generate artifacts and unrealistic geological features.

Context.Multispacecraft and multiwavelength observations of solar eruptions, such as flares and coronal mass ejections, are essential to understanding the complex processes behind these events. The study of solar burst events in the radio frequency spectrum has relied almost exclusively on data from ground-based observations and a few dedicated heliophysics missions such as STEREO or Wind.Aims.By reanalysing existing data from the Mars Reconnaissance Orbiter (MRO) Shallow Radar (SHARAD) instrument, a Martian planetary radar sounder, we discovered the instrument was also capable of detecting solar radio bursts and that it was able to do so with unprecedented resolution for a space-based solar instrument. In this study, we aim to demonstrate the reliability and value of SHARAD as a new solar radio observatory.Methods.We characterised the sensitivity of the instrument to type III solar radio bursts through a statistical analysis of correlated observations using STEREO and Wind as references. Using 38 correlated detections, we established the conditions under which SHARAD can observe solar bursts in terms of acquisition geometry. As an example of scientific application, we also present the first analysis of type III characteristic times at high resolution beyond 1 AU.Results.A simple logistic model based purely on geometrical acquisition parameters can predict burst show versus no-show in SHARAD data with an accuracy of 79.2%, demonstrating the reliability of the instrument in detecting solar bursts and laying the foundation for using SHARAD as a solar radio observatory. The extremely high resolution of the instrument, both in temporal and frequency directions; its bandwidth; and its position in the Solar System enable SHARAD to make significant contributions to heliophysics. Notably, it could provide data on plasma processes on the site of the burst generation and along the propagation path of associated fast electron beams.

Blockchain is currently a core technology for developing new types of decentralized applications. With the unique properties of blockchain, unique challenges and characteristics are introduced to the system. Among these characteristics, the infrastructure costs and benefits of the system are critical to evaluate the feasibility of any system and have yet to be addressed in the current literature. This work presents a framework for evaluating blockchain applications’ infrastructure costs and benefits. The framework includes a taxonomy to classify the related transactions, a model to evaluate the infrastructure costs and benefits in applications using public or private blockchains, and a methodology to guide the use of the model. The model is based on simple parameters that describe the systems, and the methodology helps to identify and estimate these parameters at any stage of the application life cycle. We quantitatively analyze three real use cases to demonstrate the framework’s merit. The analyses highlight the model’s accuracy by achieving the same results presented in the use cases. Furthermore, the use-case analyses emphasize the framework’s potential to evaluate different scenarios across the entire life cycle of blockchain-based applications.

Airborne Radar Sounders (RSs) are active sensors that acquire subsurface data for Earth observation. RS data (radargrams) provide information on buried geology by imaging subsurface dielectric discontinuities. Recently, several automatic RS target identification techniques have been proposed, being convolutional neural network (CNN)-based methods the most promising. However, they require numerous labeled data that are hard to retrieve in the subsurface environment targeted by RS. Further, they are not designed to effectively deal with problems showing unbalanced classes like RS segmentation. We introduce newer cryosphere subsurface targets in the inland and coastal areas that can have a very low probability. To deal with the higher complexity and variability than previous works, we propose a transfer learning framework for RS data to mitigate the need for a large amount of labeled data and handle extremely unbalanced target classes. Herewith, we propose two transfer learning-based mechanisms for radargram segmentation. The first uses a lightweight architecture whose pre-training is supervised with a large labeled dataset from other domains. The second mechanism uses a deep architecture pre-trained in the RS domain, considering the pretest task of radargram reconstruction. The architectures are modified to deal with the characteristics of RS data and the radargram segmentation task. Finally, both methods are fine-tuned with a few labeled radargrams to learn radargram features useful for segmentation. We reveal experimental results on radargrams acquired in Antarctica by MCoRDS-1 and MCoRDS-3. The results demonstrate the effectiveness of transfer learning for radargram segmentation.

The data economy is based on data and information sharing and tremendously impacts society as it facilitates innovative collaborations and decision-making strategies. Nonetheless, most dataset-sharing solutions rely on a centralized authority that rules data ownership, availability, and accessibility. Recent works have explored the integration of distributed storage and blockchain to enhance decentralization, data access, and smart contracts for automating the interactions between actors and data. However, current solutions propose a smart contract design limiting the system’s scalability in terms of actors and shared datasets. Furthermore, little is known about the performance of these architectures when using distributed storage instead of centralized storage approaches. This paper proposes a scalable architecture called DeBlock for data sharing in a trusted way among unreliable actors. The architecture integrates a public blockchain that provides a transparent record of datasets and interactions, with a distributed storage for data storage in a completely decentralized way. Furthermore, the architecture provides a smart-contract design for a transparent catalog of datasets, actors, and interactions with efficient search and retrieval capabilities. To assess the system’s feasibility, robustness, and scalability, we implement a prototype using the Ethereum blockchain and leveraging two decentralized storage protocols, Swarm and IPFS. We evaluate the performance of our proposed system in different scenarios (e.g., varying the amount and size of the shared datasets). Our results demonstrate that our proposal outperforms benchmarks in gas consumption, latency, and resource requirements, especially when increasing the number of actors and shared datasets.

During the last decades, radar sounders provided direct measurements (radargrams) of the Earth’s polar caps’ subsurface. Radargrams are of critical importance for a better understanding of glaciologic structures and processes of the ice sheet in the framework of climate change. This article aims to automatically extract information on basal boundary conditions given their substantial relevance for modeling the ice-sheet processes, such as the sliding. We introduce a novel automatic method based on deep learning to detect the basal layer and basal units in radargrams acquired in the inland of icy areas. Radargrams are segmented into englacial layers, bedrock, basal units, and noise-limited regions; the latter includes the echo-free zone (EFZ), thermal noise, and signal perturbation. The network is a U-Net with attention gates and the Atrous Spatial Pyramid Pooling (ASPP) module that automatically extract semantically meaningful features at different scales. Experimental results on two datasets acquired in north Greenland and west Antarctica by the Multichannel Coherent Radar Depth Sounder (MCoRDS3) indicate a high overall segmentation accuracy. The accuracy of basal ice and signal perturbation detection is high, and that of the other classes is comparable with the literature techniques based on handcrafted features. The results show the effectiveness of the proposed method in automatically extracting semantically meaningful features to segment radargrams and map the basal layer and basal units.

A satellite mission onboard a radar sounder for the observation of the earth’s polar regions can greatly support the monitoring of the cryosphere and climate change analyses. Several studies are in progress proposing the design and demonstrating the performance of such an earth-orbiting radar sounder (EORS). However, one critical aspect of the cryospheric targets that are often ignored and simplified in these studies is the complex geoelectrical nature of the polar ice. In this article, we present a performance assessment of the polar ice target detectability by focusing on their realistic representation. This is obtained by simulating the orbital radargrams corresponding to different regions of the polar cryosphere by leveraging the data available from airborne campaigns in Antarctica and Greenland. We propose novel performance metrics to analyze the detectability of the internal reflecting horizons (IRHs), the basal interface, and to analyze the nature of the basal interface. This performance assessment strategy can be applied to guide the design of the signal-to-noise ratio (SNR) budget at the surface, which can further support the selection of the main orbital instrument parameters, such as the transmitted power, the two-way antenna gain, and the processing gains.