Noise reduction algorithm for transient electromagnetic detection signal based on variational mode decomposition

Abstract

The invention relates to a noise reduction algorithm for a transient electromagnetic detection signal based on variational mode decomposition. The decomposition process of a goaf strong-interference transient electromagnetic signal is converted into solving of a variational problem. A constraint variation problem is converted into a non-constraint problem by carrying out variation construction onstrong interference transient electromagnetism and introducing a secondary penalty factor and a Lagrange penalty operator, limited modal components are obtained through a multiplication operator alternating direction algorithm, effective separation of signals is achieved, and noise and effective signals are separated. According to the invention, signals can be decomposed in a self-adaptive manner,priori knowledge is not needed, and the actual operation is simpler and more convenient; the variational mode decomposition algorithm is simple, the computer operation time is short, and the storagespace and the calculation time are saved; the modal aliasing problem in empirical mode decomposition is effectively solved, the extracted signals are more accurate, and better noise robustness is shown.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a noise reduction method for transient electromagnetic detection signals based on variational mode decomposition. Background technique [0002] As a new geophysical prospecting method, transient electromagnetic method has been widely used all over the world due to its advantages such as small random interference, sensitive response to low-resistance bodies, strong ability to penetrate high-resistance, large exploration depth and convenient construction. In China, the transient electromagnetic method is mainly used to accurately detect water-bearing goafs to prevent water inrush accidents and ensure the safe operation of coal mines. However, transient electromagnetic detection will always be interfered by surrounding noise in the field implementation, which will lead to distortion or distortion of TEM signal, which will affect the processing and interpretation of data, t...

AI Technical Summary

Problems solved by technology

Digital filtering regards the transient electromagnetic signal and its noise as a random signal, and uses its statistical algorithm and Fourier transform to estimate the signal itself, thereby improving the signal-to-noise ratio. Although the reliability is high, it requires prior knowledge of the noise and the effective signal , not suitable for non-stationary process

Independent component analysis denoising can separate the received transient electromagnetic signal into independent statistical components through a certain linear decomposition, remove the noise base vector, retain the effective signal, and achieve the purpose of separating the source signal. It has good adaptability, The advantage of fast convergence speed, but due to the limitation of its algorithm, the magnitude and order of decomposition are uncertain

Kalman filter regards the transient electromagnetic signal and its noise as a random signal, and uses its statistical algorithm and Fourier transform to estimate the signal itself, thereby improving the signal-to-noise ratio. Although the reliability is high, it requires prior knowledge of the noise and the effective signal Knowledge, poor numerical stability, low computational efficiency

Images