Low-frequency magnetotelluric signal denoising method based on shift invariant sparse coding

A sparse coding, magnetotelluric technology, applied in the field of exploration geophysical signal processing, can solve problems such as difficulty in data processing, loss of low-frequency effective signals in strong interference segments, and few methods for signal-to-noise separation

Active Publication Date: 2019-08-20
EAST CHINA UNIV OF TECH
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Problems solved by technology

These two types of methods can effectively protect low-frequency signals in high-quality segments, but the processing of noisy segments is still to directly extract human noise, which loses effective low-frequency signals in strong interference segments.
[0006] In general, since the frequency of the magnetotelluric signal is very wide (10 -4 ~10 5 Hz), the existing magnetotelluric data processing methods are difficult to meet the processi

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  • Low-frequency magnetotelluric signal denoising method based on shift invariant sparse coding
  • Low-frequency magnetotelluric signal denoising method based on shift invariant sparse coding
  • Low-frequency magnetotelluric signal denoising method based on shift invariant sparse coding

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Embodiment 1

[0067] Applying the signal-to-noise separation method described in the present invention to the processing of synthetic data, it is obtained as figure 2 The experimental results shown. figure 2 Middle (a) is a time series segment collected in the Qaidam Basin in Qinghai, with a sampling frequency of 15 Hz. Obviously, there is no obvious strong human noise in this time series, and the signal changes smoothly, which is high-quality data; figure 2 (b) is the noisy signal obtained after adding the simulated strong humanistic noise in (a), figure 2 (c) is the result of denoising directly using the mathematical morphological filtering method. It is not difficult to find that the morphological filtering method removes almost all low-frequency slow-changing parts while denoising, and loses a large amount of low-frequency effective signals, and there are still a small amount of Impulse noise; figure 2 (d) is the signal obtained after using the method described in the present inv...

Embodiment 2

[0069] Similar to Example 1, the signal-to-noise separation method of the present invention is applied to the processing of the measured time series in Qinghai, and the following results are obtained: image 3 The experimental results shown. It is not difficult to find that before denoising image 3 (a), the time series contains a large amount of impulse noise, and its amplitude is significantly larger than the effective signal. After denoising image 3 (b), the impulse noise is suppressed, and the low-frequency slowly changing signal is well preserved.

Embodiment 3

[0071] Similar to Embodiment 1 and Embodiment 2, the signal-to-noise separation method of the present invention is applied to the measured magnetotelluric data of the Tongling ore concentration area in Anhui. get as Figure 4 The experimental results shown. Such as Figure 4 As shown in (a), the pre-processing time series contains a lot of pulse and square wave noise, and its amplitude is significantly larger than the effective signal. Such as Figure 4 As shown in (b), after using the present invention for denoising, square wave noise and impulse noise are accurately removed. Likewise, there is no noticeable loss of low frequency signals.

[0072] The beneficial effects of the present invention are illustrated by comparing the apparent resistivity-phase curves before and after processing using the signal-to-noise separation method of the present invention. Such as Figure 5 Shown are the apparent resistivity-phase curves before and after processing of a measured point i...

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Abstract

The invention provides a low-frequency geomagnetic signal denoising method based on shift-invariant sparse coding. The method comprises the following steps: firstly, decomposing a noise-containing magnetotelluric time sequence into a low-frequency effective signal and a noise-containing high-frequency signal by using mathematical morphology filtering; and then dividing the high-frequency signal containing noise into a plurality of segments, autonomously learning a feature structure of human noise from the high-frequency signal containing noise by using shift invariant sparse coding to construct a learning type redundant dictionary, and performing signal-noise separation on the high-frequency effective signal containing noise by using the learned redundant dictionary to obtain a denoised high-frequency effective signal; and adding the low-frequency effective signal and the high-frequency effective signal to obtain a full-band magnetotelluric effective signal. Strong human noise in low-frequency magnetotelluric data can be effectively removed, the quality of the magnetotelluric data is remarkably improved, and the deep detection effect of a magnetotelluric method is improved.

Description

technical field [0001] The invention belongs to the field of exploration geophysical signal processing, and relates to a method for separating magnetotelluric signal-noise based on a shift-invariant sparse coding machine learning algorithm, especially for 10 -4 ~10 1 Processing methods for low frequency magnetotelluric data in the Hz range. 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 mineral resource exploration and deep electrical structure detection. However, the natural magnetotelluric signal has strong randomness, weak amplitude, and wide frequency range (10 -4 ~10 5 Hz), it is highly susceptible to human noise pollution. [0003] For decades, scholars at home and abroad have proposed many methods to suppress or separate the noise in the magnetotelluric signal. Among them, the far reference method and Robust method have been widely recognized and ...

Claims

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Application Information

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IPC IPC(8): G06K9/00
CPCG06F2218/04Y02A90/30
Inventor 李广刘晓琼汤井田邓居智李进
Owner EAST CHINA UNIV OF TECH
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