Denoising method of observation signal of artificial-source electromagnetic-method

Abstract

The invention discloses a denoising method of an observation signal of an artificial-source electromagnetic-method. The method includes: firstly, carrying out fast Fourier transform (FFT) on the noised signal, removing power-frequency interference in a frequency-domain signal, and returning to a time domain after inverse Fourier transform; then carrying out EEMD (Ensemble Empirical Mode Decomposition) on a time-domain signal after removing the power-frequency interference, and attenuating signal baseline drift; and finally, further correcting the baseline drift of the signal on the basis of shift-invariant sparse coding (SISC), and removing other human noises. According to the method, power-frequency interference noises, baseline drift noises and the human noises of pulses and the like ofwhich amplitude is much larger than that of a useful signal can be effectively removed on the premise of not losing the useful signal, the strong noises are allowed to exist in an entire process fromobservation starting to ending, and thus the artificial-source electromagnetic-method is enabled to be used for observation under an environment of continuous and strong interference.

Description

technical field [0001] The invention belongs to the field of exploration geophysical signal processing, and relates to a denoising method for artificial source electromagnetic observation signals, in particular to a denoising method for periodic signals, including pseudo-random signals. Background technique [0002] In artificial source electromagnetic prospecting, the transmitter sends a controllable electromagnetic signal through a grounded electrode or an ungrounded return line, and then observes the signal through a receiver at a certain distance to obtain underground electrical structure information. Compared with the natural field source electromagnetic method, the signal-to-noise ratio of the artificial source electromagnetic method is significantly improved. However, in actual exploration, the signals collected by the receiver are still affected by various noises to varying degrees. Therefore, the collected signals are removed. Noise is still an essential task. [0...

AI Technical Summary

Problems solved by technology

[0003] Although the existing data processing methods such as data segment selection and robust estimation can achieve certain results, if the noise lasts for a long time, or even exists in the entire observation period from the beginning to the end, these methods will not be effective

Wavelet transform, morphological filtering and other methods are not ideal due to the loss of useful signals.

[0004] In addition, for the ubiquitous power frequency interference...

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