Semi-Airborne Electromagnetic Line Signal Denoising Based on Recurrent Self-Coding Neural Network

Semi-airborne electromagnetic method (SAEM) is an emerging geophysical exploration method, which can be efficiently carried out in complex terrain conditions. SAEM signals are often mixed with various noise interference, which seriously affects the signal quality. However, the amount of SAEM data is extremely large, the number of signals of a single survey line is in the millions, and the existing noise elimination methods usually intercept the signals in segments and then process them, which is inefficient and has strong subjective factors. To address the above problems, this article proposes a recurrent self-coding neural network (RSCN) for one-stop noise cancellation of the whole measurement line signal. The method adopts the gated recurrent unit (GRU) to obtain the full waveform state and adopts the data segmentation technique to obtain the small-scale data and perform the convolutional coding. The state information as well as the coded data is used to extract important features using an attention mechanism and decoded. The self-encoding structure is updated cyclically to realize one-stop noise cancellation for the whole measurement line data. The test results of simulated and measured data show that the method proposed in this article has a good denoising effect on the full survey line SAEM data and is more adaptable to the SAEM data than the existing noise reduction methods.