To achieve efficient ice sheet detection, this study develops a frequency-modulated continuous wave (FMCW) ice sounding radar imaging algorithm. The proposed algorithm represents an improved range-Doppler algorithm (RDA) that can effectively handle multi-layer medium imaging models and address the range migration variations caused by multi-layer media. The theoretical analysis is conducted to explain the imaging principle of the proposed algorithm, and a detailed description of its processing flow is provided. Moreover, point target simulation experiments and actual data processing tests are performed to demonstrate the effectiveness of the proposed algorithm in ice sheet imaging. The proposed algorithm is also compared with the frequency scaling algorithm (FSA), which is a commonly used FMCW ice sounding radar imaging algorithm, in terms of signal-to-noise ratio (SNR) and imaging time. The comparison results show that the proposed algorithm can improve imaging efficiency while maintaining accuracy.

Basal roughness is a crucial parameter for quantifying subglacial geomorphological landforms, which offers key insights into glacial geomorphic environments and ice sheet dynamics. Princess Elizabeth Land (PEL) in East Antarctica covers approximately 15% of the Antarctic Ice Sheet. However, to date, understanding the relationship between subglacial geomorphology and ice flow in the PEL has remained limited. In this study, we used airborne ice radar data from the Chinese National Antarctic Research Expedition (CHINARE) during the first five austral seasons and publicly available Antarctica’s Gamburtsev Province (AGAP) Project North data to calculate a two-parameter spectral roughness index of the subglacial topography. We analyzed the relationship between the spatial distribution of basal roughness and the speed and direction of ice velocity, while classifying the regional roughness results into four different combinations. We find that the subglacial environment in the PEL is more intricate than the one previously reported. The area near the polar record glacier (PRG) is characterized by locally rough geomorphology but fast ice flow. The beds in the slow ice flow area of PEL are characterized by both rough and flat landforms. Low-lying basins situated in the interior are of considerable interest because they may be characterized by preglacial active erosional landscapes.

The inversion of potential field data dispersed by nonstructured tetrahedron mesh is better applied for undulating terrain area, but it has a poor computational efficiency owing to the irregular distribution of the nodes of the mesh, which makes it challenging to potential field data fine inversion over a large area. We propose an equivalent geometric-discrete function fitting (EG-DFF) method for the fast inversion of potential field data with nonstructured tetrahedral mesh, and we prove that this mesh yields equivalent property of adjacent surface, which is used to accelerate the calculation of kernel function. The efficiency of inversion solution is improved by using a discrete function to fit the physical parameters of the grid cells to reduce the number of solution parameters. The test results showed that the EG-DFF method did not lose the inversion when 500 mesh cells are fit with a third-order function and 200 mesh cells are fit with a second-order function and can improve the computational efficiency by more than four times and reducing memory consumption by eight times compared to the traditional method. The proposed method also exhibited good resistance to noise. We applied the EG-DFF method to airborne gravity and magnetic data to analyze the origin mechanism of Kirin Lake, which is located in the subglacial rift valley of Princess Elizabeth Land. The density and magnetization structure showed that the upwelling of the mantle with the action of the rift accounted for the formation of Kirin Lake.

Understanding basal processes across ice-sheet grounding lines is crucial in accurately modeling ice-sheet dynamics and estimating global sea-level rise. The grounding line, which demarcates the specific boundary between a grounded ice sheet and a floating ice shelf, is notoriously challenging to locate precisely. Existing methods for determining grounding line location rely on indirect methods, such as tide-induced vertical ice-shelf motion (the point of flexure determining the grounding line) and ice-surface slope change (the sharp change in gradient toward being flat indicating the grounding line). In this study, we utilize ice-penetrating radar data to extract grounding line information from the Lambert-Amery glacier system. By incorporating ice bed topography and the reflection amplitude differences between ice-water and ice-bedrock interfaces, we establish an automated method to extract grounding line positions from radar survey lines. From 53 radar survey lines, we identified 85 grounding points. The comparison with the positions from an existing satellite InSAR-based grounding line product shows an average difference of 0.69±0.70 km. Tidally-induced migration of grounding lines at different points of the tidal cycle, and advance/retreat of grounding line with the evolution of the ice shelf, are the main reasons for the discrepancy between the radar-derived results and the existing grounding line products. In general, the results demonstrate the feasibility of ice-penetrating radar in confirming grounding line positions, and show great potential in constraining indirect satellite remote sensing or modelling evaluations at both regional and continental scales. Our work facilitates an ongoing effort of the Scientific Committee on Antarctic Research (SCAR)’s RINGS programme to develop gapless coverage of bed topography in the coastal regions around Antarctica.

The ice–bedrock interfaces at the bottoms of ice sheets and the internal ice layers record the historical evolution of the ice sheets and are important indicators for inferring glacier dynamics and explaining subglacial topographies. Radar-detected bedrock and internal ice layers can be used to observe conditions in the interiors and bottoms of ice sheets. The low contrast of the internal layers’ feature in radar images, as well as the effects of ice flow, makes it challenging to automatically extract and trace the layers and interfaces of ice sheets. Manual or semiautomatic methods are extensively applied in bedrock and internal ice layers’ extractions. However, the conventional methods require large amounts of time. Especially when processing a large number of radar observation data, the accuracy and efficiency of conventional methods are untoward. Therefore, we propose EisNet, a fusion system consisting of deep neural networks, to extract the multiple types of internal layers. Because of the rareness of high-precision extraction of ice layers, it is virtually impossible to train EisNet in observational data with the corresponded label of ice layers. To train EisNet, we design a novel synthetic method for radar images based on artifact noise and characteristic textures that are similar to those in the observation data. The validation in synthetic data proves EisNet’s capacity for layer extraction and noise removal. The field data application shows the generalization and adaptation abilities of EisNet and indicates that EisNet can significantly improve the efficiency of the automatic processing of ice sheet radiostratigraphy.

In the past decades, radio-echo sounding (RES) data have been used to predict basal dry-wet distributions in glaciated regions through manual inspection of the records. Extending such work, we propose a semi-automatic method for predicting such distributions. The method improves previous work in two ways: (1) subglacial water bodies are taken as reference to correct the thresholds of dry and wet beds identification at a regional scale; and (2) five distinct features are defined and used to automatically identify the dry-wet transition, allowing a classification model based on a support vector machine. To demonstrate its effectiveness, the method is applied to airborne RES data collected in recent years over Princess Elizabeth Land in East Antarctica. A comparative analysis of the new vs previous method was carried out in the Ridge B region of the East Antarctica and at the Thwaites Glacier region of West Antarctica. The results show the method can obtain more accurate subglacial dry-wet distribution results with larger coverage and has the potential to determine dry-wet transitions at a continental scale if applied to the full set of known Antarctic RES data.

The characterization of unconformities in wind-scoured areas of the Antarctic Ice Sheet (AIS) is important for the accurate estimation of Antarctic mass balance and can provide constraints on climate models. Here we apply radar data collected by the Chinese National Antarctic Research Expedition in the vicinity of the Dome A region in East Antarctica to trace six isochronous internal reflecting horizons. We identify 39 unconformities and map englacial stratigraphy 500 m below the ice surface through cross-validation among the data from several austral summer campaigns using different ice-penetrating radars. By estimating erosion time of buried unconformities using a one-dimensional ice flow model along local ice flowlines, we identify persistent wind-scoured zones on the scale of ten thousand years. We identify the earliest truncated climatic record through interpretation of the radar images and find that erosional activity can be dated back to approximately 100,000 years before present; all of the persistent wind scouring behaviors we present here began during the Last Interglacial- Last Glacial (128 ~ 10 ka B.P.). High-resolution records such as this that reveal englacial unconformities and englacial structural characteristics associated with erosion contribute to improved understanding of climate variability and ice sheet dynamics during the Late Pleistocene.

The spatial correlation of ice-sounding data can be used to trace internal isochronic layers and synchronise the age-depth relationship between different ice core sites, which is difficult using existing imaging methods. In this study, we propose a new algorithm to address the opportunity that applying spatial correlation to ice-sheet imaging offers. The algorithm is a spatial-correlation–based ice-sounding imaging method using fast back-projection that successfully improves the spatial correlation of imaging results with high efficiency. We give the specific steps to implement the algorithm and apply it to simulate both point targets and ice-sounding radar data to demonstrate its validity in imaging ice sheets. Furthermore, compared with two previous methods the proposed algorithm improves the spatial correlation without degrading the ability of signal-to-noise ratio improvement and processing efficiency.