Predicting the Spatial Distribution of VLF Transmitter Signals Using Transfer Learning Models

The D-region ionosphere (60–100 km altitude) is critical for radio communication and space weather research but cannot be easily measured because it is too low for satellites and too high for balloons. The most effective technique is to remotely sense by measuring Very-Low-Frequency (VLF, 3–30 kHz) waves emitted from man-made transmitters, a technique that was traditionally utilized to estimate the average ionospheric condition between the transmitter and receiver. Recently, various methods have been proposed to remotely sense the D-region ionosphere in large areas using network observation of VLF transmitter signals. The key component of these methods is the VLF propagation model, and the Long-Wavelength Propagation Capability (LWPC) model is employed in most cases due to its relatively fast computation speed. However, it is still too long and thus insufficient for real-time remote sensing. To overcome this limitation, we have proposed a neural network model to replace the LWPC model and to shorten the computation time of VLF propagation. This model is specifically obtained using the transfer learning method by retraining the last three layers of the well-established VGG16, GoogLeNet, and ResNet architectures. We have tested different methods to organize the input data for these neural network models and verified their performance using the validation dataset and real measurements. Among the three models, GoogLeNet outperforms the other two, and the root mean squared error (RMSE), with respect to LWPC results, is as low as 0.334. Moreover, the proposed neural network model can dramatically reduce the computation time. The computation time to calculate the signal distribution near the transmitter is 1184 s if one uses the LWPC model but 0.87 s if the present neural network model is used. The performance of this model is also excellent for ionospheric conditions that are not included in the validation dataset. Therefore, this model is robust and can be used to remotely sense, in real time, the D-region ionosphere in large areas, as well as various scientific and engineering needs.