Crevasse detection is crucial for glacier and climate research, and provides essential guidance for activities in glacier regions. Ground-penetrating radar(GPR) and machine learning are used to automatically detect crevasse. In this study, a Siam-Gabor-ResNet deep learning framework is proposed to detect crevasse automatically using GPR data. A contrast learning with Siamese network framework is proposed to improve the accuracy of crevasse detection, which aims to increase the feature similarity between crevasses while simultaneously enhancing the feature distinctiveness between crevasse and continuous snow layers. Additionally, a trainable Gabor-ResNet feature extraction module is built by integrating the Gabor filter bank into the ResNet network and used to further reduce the complexity of model training while extracting multi-scale features in a real-time manner. Experiments are performed on the Greenland dataset and the 2015 McMurdo dataset, which illustrate the effectiveness of the proposed method. The average accuracy rate of crevasse detection reaches 94.38%, which can detect the narrowest crevasse (0.6 meters) in two datasets, with an average detection time of only 6.8 milliseconds. Experimental results show that the proposed method can detect crevasse in real-time, automatically, and accurately.

In recent decades, radio-echo sounding (RES) has been extensively applied to study the flow and form of polar ice sheets. In certain regions within the ice sheet, the RES reveals a structure referred to as a “basal unit,” which differs from the overlying ice in terms of its characteristics, structure, and origin, and plays a significant role in the ice’s rheology and flow dynamics. However, methods for detecting basal units in RES data are semiquantitative and can lead to inconsistent identification. To address this issue, we propose an automatic “basal unit identification method” based on residual network (ResNet) and weight control. The method improves upon previous works in three aspects: 1) it simultaneously uses the signal and image features of RES and reduces inaccuracies associated with image analysis; 2) this method assigns weight to signal features that are affected by backscatter consistent with high particle concentrations in basal units, reduces the interference of concentration on signal characteristics, and improves the ability to identify basal unit; and 3) it provides weights related to signal feature recognition and calculates a composite recognition result that automatically identifies basal units. To validate the method’s effectiveness, we applied it to airborne RES data collected in recent years from the Gamburtsev Subglacial Mountains (GSMs) and Princess Elizabeth Land (PEL) regions in East Antarctica. A comparative analysis of the new method and previous methods indicates more accurate basal unit identification due to stronger resistance to interference from backscatter consistent with high particle concentrations.

Synthetic aperture processing of radar sounding in ice is made complicated by the curvature of the ice surface. In order to tackle this issue, this study presents a fast back-projection (FBP) ice exploration imaging method based on the ice-surface curvature, aimed at reducing its effect on the ice-sounding radar imaging results. The specific implementation steps of the proposed method are applied in the simulation of point targets and ice-sounding radar data. Experiments verify the effectiveness of the proposed algorithm in ice sheet imaging. The experimental results demonstrate that the algorithm can enhance imaging quality (increase the signal-to-noise ratio (SNR) by approximately 15–20 dB) while only slightly increasing time complexity.

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.

Subglacial lakes are an important part of the Antarctic basal hydrological system, with many active subglacial lakes distributed in the steep topography of eastern Antarctica. When the ice surface has a slope, the horizontal geolocation error and elevation error of ICESat-2 altimetry data introduce additional uncertainty in estimating the ice surface elevation change, which will affect our ability to precisely monitor the activities of subglacial lakes. Therefore, based on the classical repeat-track analysis, we use weighted total least-squares adjustment to quantify the impact of horizontal geolocation and elevation errors on the fitted ice surface elevation. Through an iterative process, we derive precise time series of elevation changes to meet the need for monitoring subglacial lake hydrological activities. Using this improved method, we obtained the volume changes of the CookE2 subglacial lake in East Antarctica from March 2019 to March 2023. The results showed that Lake CookE2 was continuously recharged by subglacial water during this period, with an average equivalent recharge rate of 0.054 km³/yr. The water supply in the main lake accounted for about 68.5% of the total. The SW lobe, located away from the main basin, exhibited hydrological activity again after the drainage event and was reclassified into the active lake domain. The new lake area is ~ 118.73 km2. Compared to the traditional method, the improved approach reduces the variance of unit weight by ~0.044 m in the lake basin and by ~0.062 m on the steep southern basin margin. The precision of elevation change in rugged topography or steep slope areas has been significantly improved, aiding in precisely monitoring of subglacial lakes water volume changes and in determining their outlines.

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.

Heat flow is a key parameter for revealing the Earth’s internal structure and the distribution of geothermal resources, playing a crucial role in geoscientific research and resource assessment. Current heat flow prediction typically relies on statistical regression or machine learning methods, which model and estimate heat flow by uncovering its relationships with geological and geophysical features. However, existing studies still face two core challenges: first, key modeling features generally suffer from low spatial resolution at the global scale, which limits the resolution of heat flow predictions; second, current machine learning methods often exhibit insufficient feature extraction and generalization capabilities under limited data conditions. To address these issues, various high-resolution geological feature data were systematically integrated and analyzed, and a Geographic-Climatic Proximity Feature Combination (GeoClimaProx) was proposed to balance effectiveness with accessibility. In addition, the Tabular Prior-data Fitted Network (TabPFN), an advanced tabular foundation model characterized by strong feature extraction and generalization capabilities, is introduced to enhance the performance of continental heat flow prediction. To validate the effectiveness of the proposed approach, comparative experiments were conducted at multiple spatial resolutions. The results demonstrate that GeoClimaProx outperforms traditional feature combinations, contributing to improved accuracy and spatial resolution in heat flow prediction, and TabPFN exhibits superior generalization ability and higher predictive accuracy across various spatial resolutions and training data sizes. GeoClimaProx and TabPFN provide a novel technical pathway for accurate heat flow prediction using only easily accessible surface observations and tectonic distance features. Based on them, the first 0.2° resolution heat flow map covering global continental regions is constructed, improving the spatial resolution of heat flow modeling, which means the beginning of fine-scale modeling of continental heat flow at the global scale. The code, datasets, and resulting data products are publicly available at https://github.com/zhang152267/GCHF to facilitate reproducibility.

Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) carries a photon-counting laser altimeter with an unprecedented elevation accuracy of 2–4 cm. Since its data availability in 2018, there has been a challenge for the establishment of a new data processing model that can take advantage of this satellite for accurately estimating volumetric changes in Antarctica and associated contribution to global sea level rise (GSLR). We introduce an innovative multitemporal elevation change estimation model (MECEM) that separates precipitation effects from topographic influences to eliminate their correlations and estimates the elevation change rates effectively through a spatiotemporal iterative procedure. The MECEM results are validated by using GNSS in situ observations, snow stakes measurements, and airborne altimetric survey data. The results are also compared with those from ICESat and ESA multimission radar altimetric dataset. It is demonstrated that the model is capable of estimating small thickening of$1.8~\pm ~0.1$cm yr1 in the Vostok subglacial lake region. Using ICESat-2 ATL06 data from 2019 to 2023, the model is proven to be effective in the estimation of elevation change rates in Antarctic basins of different characteristics. Our results show that an increase of$0.103~\pm ~0.001$m yr1 in thickening is found from 2017–2021 to 2019–2023 in Dronning Maud Land. Furthermore, an accelerated thinning by$- 0.12~\pm ~0.035$m yr1 is witnessed from 2003–2019 to 2019–2023 in the fast-flowing Pine Island Glacial. With more ICESat-2 data acquired, the developed MECEM model can be applied for estimating the contribution of the entire Antarctic ice sheet (AIS) to GSLR.

The application of unmanned aerial vehicle (UAV)-borne ground penetrating radar (GPR) for subsurface detection has garnered considerable attention. The estimation of subsurface velocity using diffraction-focusing-based methods is integral to the reconstruction of subsurface structures. Nevertheless, such methods are challenged by the influence of reflections on diffraction analysis, and regions of noninterest impede the accuracy of media velocity estimation. In this study, we introduce a processing scheme to solve these problems. First, a common reflection surface (CRS)-based approach is implemented to identify and mitigate reflections from raw GPR B-scan data, leveraging a directional filter function derived from CRS wavefront characteristics. The optimization process is refined by selecting key parameter ranges based on the first Fresnel zone. Second, a weighted negative entropy-based velocity estimation approach is realized by integrating a weighting function into the statistical negative entropy (NE) calculation. These weight factors, derived from the air-coupled GPR travel time model, accentuate hyperbolic features, thereby elevating resolution in time-velocity (t–v) panels. The efficacy of our scheme in separating wavefields is demonstrated by the diffraction-to-reflection ratio (DRR) of 6.544 in simulations and 4.510 in field experiments, representing improvements of approximately fivefold and threefold, respectively. The resolution of t–v panels with the proposed scheme increases by more than 20%, highlighting its superiority in estimating media velocity. In the reconstructed images, the maximum depth-estimation error is 5.22%, and the root-mean-square relative error (RMSRE) is 3.37% in simulation. In the field test, the signal-to-noise ratio improvement shows the target’s return power at 14.8 dB above the noise floor, and the depth-estimation error is 4.41%.

Understanding subglacial bed topography is essential for learning about Antarctica in the geologic and glaciological fields. The primary method of investigating the Antarctic bed involves measuring the bed elevation by radio-echo sounding (RES) deployed on aircraft. Digital elevation models (DEMs) of the Antarctic bed generated by traditional interpolation methods usually lack resolution, precision, and roughness. To generate Antarctic bed DEMs by interpolating sparse RES bed elevation data, we use a two-stage coarse-to-fine fully convolutional neural network (CNN), which presents a deep generative elevation inpainting method that can extract, use in-depth features, and reconstruct the bed elevation conforming to the textural character of deglacial landscapes. Our method can generate a detailed and reasonable bed DEM with the full calculation of CNN and the training strategy of a generative adversarial network (GAN). The quantitative evaluation results show that a 250 m resolution elevation grid map with a 77 m mean absolute error (MAE) can be generated through elevation inpainting by a sparse data with 4 km RES survey spacing in Arctic test area. Our study also generates two realistic bed DEMs with a 250 m spatial resolution in the Gamburtsev Subglacial Mountains and Amundsen Sea Embayment. Compared with existing Antarctic bed DEM products, BedMachine_Antarctica, DeepBedMap_DEM and MB_DeepBedMap_DEM, our generated bed DEMs show more realistic terrain and elevation with low MAEs in test regions, which could better suit follow-up glaciological research. The code of this work will be available at https://github.com/Hecian/GEI_2022 for the sake of reproducibility.

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 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.

The accumulation rate is a key parameter in computing the mass balance of glaciers and ice sheets to estimate sea level rise. A shallow-layers-detection ice sounding radar (SLDISR) is developed to measure the accumulation rate and shape of near-surface internal layers with high resolution. With a transmitting frequency from 500 to 2000 MHz, this frequency-modulated continuous wave (FMCW) radar provides a range resolution of about 16 cm in free space by using a Hanning window and a penetrating depth about 150 m under polar ice. The spectral analysis and coherent integration techniques are used to obtain a high processing gain and to improve the signal-to-noise ratio of the system. A phase-locked loop with wideband yttrium iron garnet (YIG) oscillator is applied to generate a sweeping chirp signal as an input source for the transmitter. A stable, low-frequency reference chirp signal is generated with a direct digital synthesizer (DDS) integrated in field-programmable gate array (FPGA). To reduce the high-speed requirement to the analog-to-digital converter (ADC), dechirp technology is adopted at the RF section of the receiver. The implementation of the digital unit is based on an FPGA chip. The designed radar has been successfully deployed in Antarctica during the 31st Chinese Antarctic Research Expedition (CHINARE 31) and CHINARE 33, mainly over the East Antarctic Ice Sheet (EAIS). The echograms indicate the effectiveness of the radar system on detecting clear internal reflecting horizons (IRHs) over ice sheets.