Subglacial water bodies are critical components in analyzing the instability of the Antarctic ice sheet. Their detection and identification normally rely on geophysical and remote sensing methods such as airborne radar echo sounding (RES), ground seismic, and satellite/airborne altimetry and gravity surveys. In particular, RES surveys are able to detect basal terrain with a relatively high accuracy that can assist with the mapping of subglacial hydrology systems. Traditional RES processing methods for the identification of subglacial water bodies mostly rely on their brightness in radargrams and hydraulic flatness. In this study, we propose an automatic method with the main objective to differentiate the basal materials by quantitatively evaluating the shape of the A-scope waveform near the basal interface in RES radargrams, which has been seldom investigated. We develop an automatic algorithm mainly based on the traditional short-time Fourier transform (STFT) to quantify the shape of the A-scope waveform in radargrams. Specifically, with an appropriate window width applied on the main peak of each A-scope waveform in the RES radargram, STFT shows distinct and contrasting frequency responses at the ice-water interface and ice-rock interface, which is largely dependent upon their different reflection characteristics at the basal interface. We apply this method on 882 RES radargrams collected in the Antarctic’s Gamburtsev Province (AGAP) in East Antarctica. There are 8822 identified A-scopes with the calculated detection value larger than the set threshold, out of the overall 1,515,065 valid A-scopes in these 882 RES radargrams. Although these identified A-scopes only takes 0.58% of the overall A-scope population, they show exceptionally continuous distribution to represent the subglacial water bodies. Through a comprehensive comparison with existing inventories of subglacial lakes, we successfully verify the validity and advantages of our method in identifying subglacial water bodies using the detection probability for other basal materials of theoretically the highest along-track resolution. The frequency signature obtained by the proposed joint time–frequency analysis provides a new corridor of investigation that can be further expanded to multivariable deep learning approaches for subglacial and englacial material characterization, as well as subglacial hydrology mapping.

Address geolocation aims to associate address texts to the geographic locations. In China, due to the increasing demand for LBS applications such as take-out services and express delivery, automatically geolocating the unstructured address information is the key issue that needs to be solved first. Recently, a few approaches have been proposed to automate the address geolocation by directly predicting geographic coordinates. However, such point-based methods ignore the hierarchy information in addresses which may cause poor geolocation performance. In this paper, we propose a hierarchical region-based approach for geolocating Chinese addresses. We model the address geolocation as a Sequence-to-Sequence (Seq2Seq) learning task, that is, the input sequence is a textual address, and the output sequence is a GeoSOT grid code which exactly represents multi-level regions covered by the address. A novel coarse-to-fine model, which combines BERT and LSTM, is designed to learn the task. The experimental results demonstrate that our model correctly understands the Chinese addresses and achieves the highest geolocation accuracy among all the baselines.