A deep learning-based parametric inversion for forecasting water-filled bodies position using electromagnetic method

We design a tunnel electromagnetic joint scan observation system and present a deep learning-based parametric inversion for improved tunnel electromagnetic imaging, designed specifically for tunnel prediction of water filled structures. It utilizes a configuration wherein transmitters scan along the surface while receivers are positioned within the tunnel, employing time-domain and frequency-domain transmitters and a multi-component receiver. The DL model for the first time provides parametric imaging of two different view, forming a self-checking mechanism, which can help constrain the predictions and reduce the non-uniqueness of the inversion. Trained by synthetic data, our system shows impressive adaptability to predict the 3D spatial position of water-filled anomalies and strong robustness in the tunnel environment with metal interference.

Before prediction, you need to download the pre-trained weight model which contain UNet_model/FTEM.ckpt.data-00000-of-00001, UNet_model/FTEM.ckpt.index, UNet_model/FTEM.h5. Then place the directory containing the weight model in the same directory as the prediction code. Then run: python predi.py