A Method and System for Magnetotelluric Signal-Noise Separation Based on Multi-resolution Singular Value Decomposition

Abstract

The invention discloses a method and system for separating magnetotelluric signal and noise based on multi-resolution singular value decomposition. The method includes: obtaining the measured magnetotelluric data and segmenting them; constructing a Hankel matrix for each segment of the magnetotelluric signal, and using a layer of singular value decomposition to obtain approximate signals and detail signals with different resolutions, and using approximate signal standards with different resolutions The difference between the difference and the standard deviation of the detail signal divides the magnetotelluric data into useful signal segments or strong interference data segments; and then uses the multi-resolution singular value decomposition (MRSVD) algorithm to decompose the approximate signal of each strong interference data segment to obtain large-scale noise profile, and subtract the corresponding large-scale noise profile from the signal of the strong interference data segment to obtain a useful signal segment; reconstruct the useful signal segment and the denoised data segment to obtain a useful magnetotelluric signal. The method of the invention introduces the MRSVD algorithm for decomposition, the decomposition error is small, and more low-frequency useful signals can be reserved finely.

Description

A magnetotelluric signal-to-noise separation method based on multi-resolution singular value decomposition and system technical field The invention belongs to the technical field of magnetotelluric signal processing, be specifically related to a kind of singular value decomposition based on multi-resolution The method and system for separation of magnetotelluric signal-to-noise. Background technique The magnetotelluric sounding method (Magnetotelluric, MT) was developed by Tikhonow AN and Cagniard L in 1950. This paper proposes a Kanja resistance sounding method using natural magnetotelluric signals as the field source. The natural magnetotelluric signal is composed of different strengths It is produced by the comprehensive action of various field sources such as degrees, different distances, and different attributes, and has its remarkable characteristics. In general, the natural earth The electromagnetic signal is weak and the frequency range of the signal is w...

AI Technical Summary

Problems solved by technology

In recent years, some emerging modern digital signal processing techniques, such as mathematical morphological filtering, far reference method, narrow-band filtering method, coherence method, least square method, Robust method, synchronous time series dependence, signal subspace enhancement, signal-to-noise identification and recursive Analysis coefficient decomposition, sparse decomposition, wavelet transform and comprehensive algorithm, variational mode decomposition, empirical mode decomposition, etc. have all been applied to this field. Some methods have improved the quality of magnetotelluric signals from different angles and suppressed strong interference noise. However, the low-frequency useful signal is seriously lost

[0004] Among them, empirical mode decomposition has been widely applied to magnetotelluric denoising, and its research results have gradually deepened the understanding of magnetotelluric signals and noise, but the modal aliasing and end-point effects in the decomposition process have seriously affected the low-frequency part. influences

This leads to a decline in the quality of the processed magnetotelluric signal data, seriously affecting the reliability and interpretability of the magnetotelluric data

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