A denoising method of magnetotelluric signal based on noise discrimination

Abstract

The invention discloses a magnetotelluric signal denoising method based on noise discrimination, which comprises the following steps: calculating approximate entropy and LZ complexity of each electromagnetic signal sample; using the approximate entropy, LZ complexity and class value of each electromagnetic signal sample to train the preset classification model to get the noise discrimination classification model; acquiring magnetotelluric signals to be processed, and performing noise screening on the magnetotelluric signals to be processed according to the noise screening classification modelto obtain electromagnetic signal segments with non-strong interference and electromagnetic signal segments with strong interference; combining the empirical mode decomposition of complementary set andwavelet threshold method, the strong disturbance electromagnetic signal being suppressed; the reconstructed magnetotelluric signal being obtained by combining the de-noising suppressed electromagnetic signal with the non-strong disturbance electromagnetic signal. The method of the invention can more accurately discriminate the data segments with strong interference and non-strong noise interference, retain the real magnetotelluric signal, and improve the denoising effect of the magnetotelluric signal.

Description

technical field [0001] The invention belongs to the technical field of magnetotelluric signal processing, and in particular relates to a method for denoising magnetotelluric signals based on noise discrimination. Background technique [0002] Magnetotelluric (MT) method is an electrical prospecting method that uses natural field sources to study the electrical properties of underground strata and its distribution characteristics. This method has the advantages of large detection depth, simple construction and low cost, and has been widely used in many fields of geophysics. However, because the natural magnetotelluric field signal is weak and the frequency range is wide, the data collected in the field will inevitably be interfered to varying degrees, especially the increasingly serious human noise interference, resulting in a decline in the quality of the magnetotelluric data and excessive distortion of the apparent resistivity-phase curve. It greatly affects the reliabili...

AI Technical Summary

Problems solved by technology

Such as far reference method, Robust method, least square method, wavelet transform, Hilbert-Huang transform, morphological filter, etc., can suppress noise to a certain extent and improve the quality of magnetotelluric data, but these methods have certain limitations, and Focus on overall processing, lack of noise screening, the result often results in over-processing of magnetotelluric data and loss of a large amount of low-frequency slow-change information, resulting in poor denoising effect

Images