Two-Stage Denoising of Ground Penetrating Radar Data Based on Deep Learning

Denoising is a crucial step in ground penetrating radar (GPR) data processing. Conventional denoising algorithms for GPR typically require selecting optimal processing parameters, which can be challenging to achieve in practical applications, resulting in unsatisfactory processing outcomes. In recent years, in order to address the issue of low accuracy in conventional GPR denoising algorithms, denoising neural networks have been applied in the field of GPR. Although conventional denoising neural networks have shown improvements in signal-to-noise ratio (SNR) in some cases, their performance is often inadequate when facing real GPR data with complex random noise, due to the training methods of the networks. To address the challenges in denoising of GPR data, a two-stage denoising method based on deep learning (DL) has been proposed. Initially, conventional GPR data processing is conducted, followed by training a denoising network model using both the processed and unprocessed signals. Leveraging the powerful nonlinear fitting capability of convolutional neural networks (CNNs), an end-to-end mapping relationship is established to obtain the final denoising network model, completing the two-stage denoising process. Finally, this letter validates the proposed two-stage denoising method using synthetic and field data. The radar data obtained through this two-stage denoising method not only improve mean squared error (mse) by 0.17 compared to conventional methods but also increase peak SNR (PSNR) by 8.1. Furthermore, there is a significant enhancement in the integrity of the waveform and the recovery of weak signals.