Exploring the subsurface structure and stratification of Mars advances our understanding of Martian geology, hydrological evolution and palaeoclimatic changes, and has been a main task for past and continuing Mars exploration missions^1–10. Utopia Planitia, the smooth plains of volcanic and sedimentary strata that infilled the Utopia impact crater, has been a prime target for such exploration as it is inferred to have hosted an ancient ocean on Mars^11–13. However, 45 years have passed since Viking-2 provided ground-based detection results. Here we report an in situ ground-penetrating radar survey of Martian subsurface structure in a southern marginal area of Utopia Planitia conducted by the Zhurong rover of the Tianwen-1 mission. A detailed subsurface image profile is constructed along the roughly 1,171 m traverse of the rover, showing an approximately 70-m-thick, multi-layered structure below a less than 10-m-thick regolith. Although alternative models deserve further scrutiny, the new radar image suggests the occurrence of episodic hydraulic flooding sedimentation that is interpreted to represent the basin infilling of Utopia Planitia during the Late Hesperian to Amazonian. While no direct evidence for the existence of liquid water was found within the radar detection depth range, we cannot rule out the presence of saline ice in the subsurface of the landing area.

As the largest basin on Mars, Utopia Planitia has both experienced and recorded variations of the Martian palaeoclimate. Layered subsurface structures have been identified by ground-penetrating radar in southern Utopia Planitia but lateral variations of the subsurface, potentially linked to the Martian palaeoclimatic evolution, have not been investigated. Here we report the lateral frequency-variation patterns of Zhurong radar reflections and interpret them as buried polygonal terrain below a depth of 35 m. Sixteen polygonal wedges were identified within ∼1.2 km distance, suggesting a wide distribution of such terrain under Utopia Planitia. The contrast above and below ∼35 m depth represents a notable transformation of aqueous activity or thermal conditions in the Late Hesperian–Early Amazonian. The interpreted buried polygons, possibly generated by freeze–thaw cycles, imply that there was a strong palaeoclimatic variability at low-to-mid latitudes (∼25° N), potentially due to the high obliquity of ancient Mars.

The high-frequency channel of lunar penetrating radar (LPR) onboard Yutu-2 rover successfully collected high quality data on the far side of the Moon, which provide a chance for us to detect the shallow subsurface structures and thickness of lunar regolith. However, traditional methods cannot obtain reliable dielectric permittivity model, especially in the presence of high mix between diffractions and reflections, which is essential for understanding and interpreting the composition of lunar subsurface materials. In this paper, we introduce an effective method to construct a reliable velocity model by separating diffractions from reflections and perform focusing analysis using separated diffractions. We first used the plane-wave destruction method to extract weak-energy diffractions interfered by strong reflections, and the LPR data are separated into two parts: diffractions and reflections. Then, we construct a macro-velocity model of lunar subsurface by focusing analysis on separated diffractions. Both the synthetic ground penetrating radar (GPR) and LPR data shows that the migration results of separated reflections have much clearer subsurface structures, compared with the migration results of un-separated data. Our results produce accurate velocity estimation, which is vital for high-precision migration; additionally, the accurate velocity estimation directly provides solid constraints on the dielectric permittivity at different depth.

Above-surface diffractions (ASDs) received by unshielded ground-penetrating radar (GPR) antennas are known to contaminate subsurface reflections and diffractions. Existing ASD-removal methods either leave relatively strong residual ASDs within subsurface reflections or attenuate them excessively. We have developed an iterative migration-based ASD-removal method to address this issue that separates ASDs from subsurface reflections via surgical mute. First, we isolate ASDs within the GPR profile using an optimal window function, generated using the focal center of ASDs within the migrated domain. The remainder signifies the zeroth-order separation of subsurface reflections. Second, we perform Stolt migration on the isolated ASDs using the speed of light in air. Then, we mute out the regions dominated by ASDs from the migration results, characterized by highly focused ASDs that are well separated from the majority of subsurface reflections. Following that, we demigrate the separated ASDs and subsurface reflections back to the unmigrated domain using the speed of light in air. Next, we combine the demigrated subsurface reflections with the zeroth-order subsurface reflections, thereby completing the first iteration of the separation process. The entire aforementioned process is then repeated twice on the residual data to obtain further residual subsurface reflections from ASDs, using a lower velocity and a higher velocity in sequence. Our method is verified to suppress ASDs more effectively than existing approaches by retaining a greater proportion of subsurface reflections, and its residual error is negligible. It is robust with respect to energy levels, window widths of ASDs, and clustered ASDs.

The Lunar Penetrating Radar (LPR) onboard the Yutu-2 rover from China’s Chang’E-4 (CE-4) mission is used to probe the subsurface structure and the near-surface stratigraphic structure of the lunar regolith on the farside of the Moon. Structural analysis of regolith could provide abundant information on the formation and evolution of the Moon, in which the rock location and property analysis are the key procedures during the interpretation of LPR data. The subsurface velocity of electromagnetic waves is a vital parameter for stratigraphic division, rock location estimates, and calculating the rock properties in the interpretation of LPR data. In this paper, we propose a procedure that combines the regolith rock extraction technique based on local correlation between the two sets of LPR high-frequency channel data and the common offset semblance analysis to determine the velocity from LPR diffraction hyperbola. We consider the heterogeneity of the regolith and derive the relative permittivity distribution based on the rock extraction and semblance analysis. The numerical simulation results show that the procedure is able to obtain the high-precision position and properties of the rock. Furthermore, we apply this procedure to CE-4 LPR data and obtain preferable estimations of the rock locations and the properties of the lunar subsurface regolith.

The ocean bottom nodes (OBN) acquire seismic data at a challenging depth to explore the subsurface structures. The recorded body wave and Scholte wave are highly mixed and are difficult to be separated. The strong body wave would influence the high-order modes extraction using Scholte wave, while the Scholte wave would degrade the imaging of sedimentary structures using body wave. The lacking of effective methods for separating both waves prevents their application. We developed a migration-based method to accurately separate Scholte wave and body wave in the OBN data. First, we use high-pass filtering to divide the original OBN data into three parts: background noise, high-frequency body wave, and the mixture of Scholte wave and low-frequency body wave. Then, we separate Scholte wave and low-frequency body wave using migration and demigration based on the fact that they have different limits of reversible migration velocity. Finally, we generate the separated body wave by subtracting the Scholte wave from the denoised OBN data. For the off-line data, the local orthogonalization method is required to retrieve the weak leakage of Scholte wave around the apices. Theoretical analyses and numerical experiments show that the proposed method can accurately separate Scholte wave and body wave without any visible artifacts while retaining most of their inherent properties respectively. The separated body wave provides a high-quality input for imaging sedimentary structures, and the separated Scholte wave enables the extraction of high-order modes of dispersion curve that are crucial for high-resolution surface-wave inversion.