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.

This article analyzes the composition and characteristics of echo signals in a pseudo-random-coded ground -penetrating radar (GPR). Based on these characteristics, an innovative low-rank sparse matrix decomposition (LRSD) method is developed using equivalent matrix gamma norm (EMGN) and equivalent minimax-concave penalty (EMCP) to suppress clutter in the echo signals. The proposed method employs non-convex alternatives to the matrix rank and sparsity functions. Therefore, it can alleviate biased estimates of the matrix rank and sparsity of the pseudo-random-coded radar’s received signals. The proposed method is verified by simulations and experiments on a real dataset. The results demonstrate that the proposed method can achieve better performance in terms of signal-to-clutter ratio (SCR) than the robust nonnegative matrix factorization (RNMF), and robust principal component analysis (RPCA) methods.

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.

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.

Among the inversion methods for airborne transient electromagnetic (ATEM) data, the hybrid inversion method integrates the iterative optimization framework with artificial neural networks (ANNs), ensuring inversion accuracy while enhancing the generalization capability of neural networks. However, this method faces challenges in terms of slow computation speeds due to its lower updated step length and the lack of consideration for electromagnetic response laws. Our method adopts a supervised descent method (SDM) framework to supervise the ANNs, obtaining a longer updated step length. On the basis of the SDM framework, we have considered the electromagnetic response laws and designed RNN-ResNet and 1-D-UNet networks to update the conductivity model, improving the computing speed. Through numerical ablation experiments, we validated the effectiveness of our proposed method and compared the inversion results with those obtained using the traditional hybrid method. Additionally, we conducted tests on bundle fringe distribution, inversion fitting loss, noise sensitivity, and inversion speed for both methods using measured data to evaluate their performance in practical applications. The experimental findings demonstrate that our method achieves the same level of inversion accuracy and generalization ability as the traditional hybrid method while enhancing inversion speed by up to 68.5%.

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.

This paper presents the ice thickness, fine resolution internal reflecting horizons (IRHs), and distinct bottom topography measurements of Chinese Kunlun Station and Grove Mountains, Antarctica, derived from sounding these glaciers with a high-resolution radar. To enable the development of next-generation ice-sheet models, we need information on IRHs, bottom topography, and basal conditions. To this end, we performed measurements with the progressively improved ice-sounding radar system, currently known as the high-resolution ice-sounding radar developed by the Key Laboratory of Electromagnetic Radiation and Sensing Technology of Institute of Electronics, Chinese Academy of Sciences, Beijing, China. We processed the collected data using focused synthetic aperture radar (SAR) algorithm named the modified range migration algorithm using curvelets and the modified nonlinear chirp scaling algorithm to improve radar sensitivity and reduce along-track surface clutter. Representative results from selected transects indicate that we successfully sounded 3-km-thick ice with a fine resolution of 0.75 m. In this paper, we provide a brief description of the radar system, discuss the focused SAR processing algorithms, and provide sample results to demonstrate the successful sounding of the ice sheet in Antarctica.

We develop and then demonstrate a modified planar subarray processing algorithm based on the inverse scaled Fourier transform applied to very high frequency ice-sounding data that produces swath measurements of ice sheet surface topography, ice thickness, and radar reflectivity of both internal reflecting horizons and bedrock of the ice sheet. It is a real-time ice-sounding imaging method. First, theory analysis has been carried on the proposed algorithm. Then, we give the particular realizing steps to implement this algorithm. Finally, we apply this algorithm to the simulation point targets and real data collected during the 29th Chinese Antarctic Research Expedition to prove its validity of imaging of ice sheets. Furthermore, compared with two previous algorithms in two major aspects—the power of azimuth clutter reduction and calculating time—the proposed algorithm could considerably reduce the imaging time to meet the requirement of real-time imaging of ice-sounding data without degrading the ability of azimuth clutter reduction via C+MPI language on a parallel computer system.

In this paper, we propose an approach to integrate the radio-frequency interference (RFI) mitigation technique into a real-time synthetic aperture radar (SAR) imaging algorithm applied to very high frequency (VHF) ice-sounding data that produces swath measurements of ice sheet surface topography, ice thickness, and radar reflectivity of both Internal Reflecting Horizons (IRHs) and bedrock of the ice sheet. According to the proposal, the RFI suppression method-an adaptive line enhancer (ALE) controlled by the normalized least mean square (NLMS) , is incorporated into the real-time imaging algorithm named modified Planar Subarray Processing (PSAP) algorithm based on inverse scaled Fourier transform (ISFT). The approach is tested successfully on real data recorded over Chinese Kunlun Station during the 29thChinese Antarctic Research Expedition (CHINARE 29). The experimental result indicates that the narrowband RFI could be subtracted from the desired chirp signal return during the imaging processing, and the computational efficiency of the imaging processing is preserved.

In this paper, we propose a new algorithm to address the speckle noise problem in imaging of ice sheets. It is a wave-equation-based ice-sounding imaging method using curvelets as building blocks of ice-sounding data, which successfully images the topography of ice sheets. First, theory analysis has been carried on to the proposed algorithm. Then, we give the specific steps to implement this algorithm. Finally, we apply this algorithm to the simulation point targets and High-Resolution Ice-Sounding Radar data to prove its validity of imaging of ice sheets. Furthermore, compared with five previous methods in two major aspects—the power of clutter reduction and the equivalent number of looks—the proposed algorithm reduces the speckle noise during the imaging processing without degrading the ability in clutter reduction.