Development of a drone-based ground-penetrating radar system for efficient and safe 3D and 4D surveying of alpine glaciers

Abstract

Recent research has highlighted the potential for high-resolution, high-density, 3D and 4D ground-penetrating radar (GPR) acquisitions on alpine glaciers. When carried out on foot, such surveys are laborious and time consuming, which limits their application to small domains of limited glaciological interest. Further, crevasses and other hazards make the data acquisition risky. To address these issues, we have developed a drone-based GPR system. The system has a payload weight of 2.2 kg and a data output rate of 14 traces per second. An 80-MHz antenna and a recording time of 2800 ns mean that depths of over 100 m can be reached in temperate ice. Differential GPS positioning assures accurate flight paths. At a speed of 4 m s−1 and height of 5 m above the glacier surface, our system can acquire over 4 line-km of GPR data in 20 min on a single set of drone batteries. After presenting the technical specifications of the system and tests required to optimize its performance, we showcase a recently acquired 3D dataset from the Otemma glacier in Switzerland, where 462 parallel GPR profiles were surveyed at a 1-m line spacing, totaling over 112 line-km of data, in only 4 days.

Reconstruction of High-Resolution 3D GPR Data from 2D Profiles: A Multiple-Point Statistical Approach

“…We are currently exploring the use of deep-learning-based tools for 3D GPR trace reconstruction, which have gained increasing popularity in recent years for both GPR and seismic applications. After proper training of the corresponding convolutional neural network, these tools could offer a highly computationally efficient means of simulating data in the along-line and across-line directions, thereby permitting the application of the approach considered here to larger 3D GPR datasets, such as those recently acquired by drones over glaciers [45]. Part of this work involves investigating whether, by considering a rich training database consisting not only of the available along-line profiles but also synthetically generated datasets, we can reduce the dependency on the additional collection of across-line TIs.…”