Multi-frequency and multi-attribute GPR data fusion based on 2-D wavelet transform

•We implement a multi-frequency and multi-attribute GPR data fusion approach based on wavelet transform.•Our method is utilizing a dynamic fusion weight scheme derived from edge detection algorithm.•Information entropy and spatial frequency are developed as quantitative evaluation parameters to analyze the fusion outcomes.•We can obtain the advantage of penetration and resolution for the GPR characterization of subsurface.•The results demonstrate that the method can enhance the efficiency and accuracy of GPR data interpretation. High frequency GPR signals offer high resolution while low frequency GPR signals offer greater depth of penetration. Effective fusion of multiple frequencies can combine the advantages of both. In addition, GPR attribute analysis can improve subsurface imaging, but a single attribute can only partly highlight details of different physical and geometrical properties of subsurface potential targets. In order to overcome these challenges, we implement an advanced multi-frequency and multi-attribute GPR data fusion approach based on 2-D wavelet transform utilizing a dynamic fusion weight scheme derived from edge detection algorithm, which is tested on data from a small glacier in the north-eastern Alps by 250 & 500 MHz central frequency antennas. Besides, information entropy and spatial frequency are developed as quantitative evaluation parameters to analyze the fusion outcomes. The results demonstrate that the proposed approach can enhance the efficiency and scope of GPR data interpretation in an automatic and objective way.