Research on the Noise Suppression of The TEM Signal by Neural Network

Abstract

The Groud-source Airborne Time-domain Electromagnetic(GATEM) system is susceptible to interference during flight, including motion noise (caused by factors such as wind direction, cable vibrations, and sensor attitude), power frequency noise, and atmospheric noise. To obtain field data, and enhance the precision of abnormal target identification, it is necessary to suppress noise to the field data. In this paper, a neural network approach is employed to reconstruct the GATEM signals. This includes the establishment of a sample sets, parallel numerical simulation method of GATEM responses based on the OpenMP, deployment and execution of parallel computing programs on cloud computing platforms, and neural networks implementation for noise suppression in noisy GATEM signals. When the signal-to-noise ratio is above 30dB, the error between the denoised signal and the original signal is very small, with an average relative error not exceeding 1%. This method can effectively improve the accuracy of interpretation and imaging of GATEM signals, opening up new research directions in noise suppression for electromagnetic signals.