Time to Frequency Domain Conversion for Transient Electromagnetic Data With Neural Networks

To further improve the efficiency and accuracy of time-to-frequency domain conversion (TFC) for transient electromagnetic (TEM) data, a UNet++ framework was established to learn and model the complex nonlinear functional relationship between time-domain (t-d) and frequency-domain (FD) electromagnetic (EM) fields. The input and output of UNet++ were defined as the t-d and FD vertical magnetic field data derived from a one-dimensional (1-D) geoelectric model, respectively. The dataset consisted of 500,000 samples, encompassing 1-D models with 2 to 6 layers. Through this process, the hyperparameters characterizing the mapping relationship between t-d and FD magnetic fields were determined. To verify the validity of the proposed algorithm, 20 models were randomly selected from the test dataset. The experimental results demonstrate that the theoretical FD responses agree well with the recovered FD responses transformed from the t-d signals via TFC, with a relative error of less than 5% for all frequencies. In addition, the algorithm was tested on an H-type model to demonstrate its superiority over the regularization inversion method in achieving precise TFC. To further validate the effectiveness of TFC for TEM data using UNet++, the algorithm was applied to measured data collected from a mining area in western Qinghai Province, China. The results indicate that reliable FD EM responses can be obtained from t-d signals using UNet++ across all frequencies. The proposed algorithm offers robust technical support for TFC of TEM data and contributes to the advancement of geophysical exploration technology.