An Automatic Layer Extraction Algorithm for Ice Sounding Radar Data Based on Curvelet Transform (CT) and Minimum Spanning Tree (MST)

Accurate and continuous extraction of ice layer structures from airborne ice sounding radar data is critical for analyzing subglacial hydrothermal environments to support key applications such as geothermal heat flux mapping, subglacial hydrology delineation, ice flow dynamics prediction, ice temperature profile reconstruction, and Antarctic expedition efficiency. However, existing algorithms often suffer from missed detections, false positives, and poor layer continuity—particularly in complex datasets with dense internal layers or noise interference—limiting their applicability in regional-scale subglacial research. To address these limitations, this paper proposes a fully automatic ice layer extraction algorithm: It employs 2D multi-scale curvelet transform for local orientation extraction and denoising, integrates a weight-optimized Minimum Spanning Tree to construct a robust layer-connection framework, and supplements these with turn-back point detection and sub-layer merging for independent layer segmentation. Validated on field data from the CHINARE 32 expedition and compared against state-of-the-art methods on a standardized CReSIS dataset, the algorithm demonstrates superior performance in comprehensive evaluations. It achieves higher layer detection completeness, better continuity, and improved alignment with actual stratigraphy compared to existing approaches, while effectively eliminating hallucinated layers. The proposed method maintains robust performance across diverse ice conditions without requiring manual intervention, establishing an effective bridge between local feature detection and global optimization in radar data processing.