A Multi-Scale Attention Network for Uncertainty Analysis of Ground Penetrating Radar Modeling

A multi-scale attention-based model (MSAM) is proposed as a surrogate model for uncertainty analysis (UA) in ground penetrating radar (GPR) simulation. Instead a thousand of full-wave simulations, the surrogate model converts the uncertain inputs to electric fields, and the output uncertainty is effectively quantified. Global feature aggregation (GFA) module and local affinity reconstruction (LAR) are presented to improve the model representation capability by Affinity calculation under different receptive fields. In addition, a new loss function is proposed to accelerate the convergence of the model for training data with a wider range of input disturbances. The effectiveness and accuracy of the surrogate model are verified by comparing the UA results with the Monte Carlo method (MCM). In comparison with existing deep learning methods, the proposed method can efficiently get higher quality predictions. Meanwhile, the Sobol indices evaluated by MSAM accord with those of MCM, and the mean square error between them is only 0.0005. However, the MCM needs to run the full-wave simulation one thousand times to converge, which is much more time consuming than the proposed surrogate model.