Low-frequency magnetotelluric data denoising method based on over-complete dictionary and compressed sensing reconstruction algorithm

Abstract

The invention provides a low-frequency magnetotelluric data denoising method based on an over-complete dictionary and a compressed sensing reconstruction algorithm. The method comprises the followingsteps of firstly, extracting a rough low-frequency effective signal from a noisy magnetotelluric time sequence by using mathematical morphological filtering; then, using complementary set empirical mode decomposition to smooth the rough low-frequency effective signal so as to obtain an accurate low-frequency effective signal, and acquiring a noisy high frequency signal by subtracting the extractedlow frequency effective signal from the noisy magnetotelluric time sequence; and finally, through designing a suitable over-complete dictionary, using the compressed sensing reconstruction algorithmto carry out signal-noise separation on the noisy high frequency signal, and acquiring a de-noise high-frequency effective signal; and acquiring a full spectrum band magnetotelluric effective signal through adding the low-frequency effective signal and the high-frequency effective signal. In the invention, under the condition that the magnetotelluric effective signal is well reserved, a strong human noise in low-frequency magnetotelluric data is removed, a signal-to-noise ratio of the magnetotelluric data is significantly increased, and an apparent resistivity and a phase curve are improved.

Description

technical field [0001] The invention belongs to the field of geophysical signal processing, and relates to a denoising method for magnetotelluric data based on an overcomplete dictionary and a compressed sensing reconstruction algorithm, in particular to a denoising method for low-frequency magnetotelluric data below 10 Hz. Background technique [0002] The magnetotelluric method has the advantages of large detection depth and no need for artificial field sources, and is widely used in deep earth detection. However, the natural magnetotelluric signal has strong randomness and weak amplitude, and is easily affected by noise. With the continuous progress of human civilization, all kinds of human noise are increasing day by day, and it is becoming more and more difficult to obtain high-quality magnetotelluric signals. [0003] In the face of severe human noise pollution, the general approach is to set up a remote reference station and perform Robust robust estimation. However...

AI Technical Summary

Problems solved by technology

These two types of methods can effectively protect low-frequency signals in high-quality segments, but the processing of strong interference segments is still to directly extract human noise, which loses part of the effective low-frequency signals in strong interference segments. Therefore, when processing low-frequency ground below 10Hz Difficult to achieve good results with electromagnetic data

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