A Domain-adversarial Wavelet Residual Network for Crevasse Detection Using Ground-penetrating Radar

Crevasse detection using Ground-Penetrating Radar (GPR) is crucial for glacier and climate studies and for ensuring safety in glacial regions. To address challenges including large texture variability in crevasses in polar environments, high false-alarm rates, limited real-time performance, and poor generalization, this study proposes a domain-adversarial learning–based automatic crevasse detection method that balances high accuracy and real-time efficiency. Using GPR data acquired from diverse regions and complex scenarios, an adversarial learning mechanism is established between a feature extractor and a domain discriminator, enabling the method to maintain discriminative feature extraction while effectively reducing interdomain distribution discrepancies. This leads to cross-domain feature alignment, enhancing robustness and generalization across heterogeneous data sources. In the feature extraction stage, a wavelet-residual-network-based crevasse feature extractor is designed. By introducing a learnable multiscale wavelet convolution module into the first layer of the residual network, the model adaptively extracts multiscale crevasse features from GPR data, significantly enhancing the separability between crevasse regions and continuous snow layers in the feature space. Experiments were conducted on two GPR datasets: the 2015 McMurdo Shear Zone dataset from Antarctica and a Greenland dataset from the Arctic region. The experimental results demonstrate that the proposed model achieves an average detection accuracy of 95.70%, an F1-score of 95.50%, and a false-alarm rate of 1.87%, with an average inference time of 5.26 ms per sample, thereby meeting the requirements for real-time crevasse warning during field GPR acquisition. Overall, the proposed method achieves a favorable balance among high accuracy, low false-alarm rate, and real-time performance across multiscene and cross-regional GPR data, demonstrating its suitability for safety assurance during Antarctic expeditions and for glacier crevasse detection in polar research.