Ground penetrating radar (GPR) is used to image the shallow subsurface as evident in earth and planetary exploration. Electromagnetic (EM) velocity (permittivity) models are inverted from GPR data for accurate migration. While conventional velocity analysis methods are designed for multioffset GPR data, to our knowledge, the velocity analysis for zero-offset GPR has been underexplored. Inspired by recent deep learning seismic impedance inversion, we propose a deep learning guided technique, GPRNet, that is based on convolutional neural networks to directly learn the intrinsic relationship between GPR data and EM velocity. GPRNet takes in GPR data and outputs the corresponding EM velocity. We simulate numerous GPR data from a range of pseudo-random velocity models and feed the datasets into GPRNet for training. Each training data set comprises of a pair of one-dimensional GPR data and EM velocity. During training phase, the neural network's weights are updated iteratively until convergence. This process is analogous to full-waveform inversion in which the best model is found by iterative optimization until simulated data matches observed data. We test GPRNet on synthetic testing datasets and the predicted velocity models are accurate. A case study is presented where this method is applied on a GPR data collected at the former Wurtsmith Air Force Base in Michigan. The inversion results agree with velocity models established by previous GPR inversion studies of the similar area. We expect the GPRNet open-source software to be useful in imaging the subsurface for earth and planetary exploration.
All Science Journal Classification (ASJC) codes
- Geochemistry and Petrology
- Earth and Planetary Sciences (miscellaneous)
- Space and Planetary Science