Algorithm for separating microseism noise mixed signal by utilizing SVD-EMD algorithm

Abstract

The invention discloses an algorithm for separating micro-seismic noise mixed signals by using an SVD-EMD algorithm. The algorithm comprises the following steps: (S1) preprocessing the micro-seismic noise mixed signals by using a singular value decomposition method SVD; (2) obtaining a signal f (x, t) subjected to SVD preprocessing, carrying out EMD decomposition on the signal, calculating currentstep signals f0 (x, t) of all extreme points by initializing a residual function res (x, t), and then removing an average envelope value from the original signal to enable the remaining part of signals f1 (x, t) to become signals of the next step; (S3) placing the current step signal f1 (x, t) and the previous step signal f0 (x, t) in a stop state, and repeating the screening process through an intrinsic mode function and a final remaining function; and (S4) utilizing Hilbert transform to obtain frequency characteristics of each intrinsic mode function decomposed through EMD, and calculatinga correlation coefficient Ri so as to complete signal reconstruction. By means of the scheme, the purposes of improving microseism seismic phase recognition and realizing the optimal P wave picking effect are achieved, and high practical value and promotional value are achieved.

Description

technical field [0001] The invention specifically relates to an algorithm for separating microseismic noise mixed signals by using the SVD-EMD algorithm. Background technique [0002] The microseismic data collected by the microseismic monitoring system are all weak signals, which makes the signal characteristics of microseismic signals difficult to be effectively identified or submerged in noise signals, which are easily ignored by monitoring personnel. Because micro-seismic signals are not easy to be monitored, the sensitivity of sensors in micro-seismic monitoring systems is generally set to be high, which results in a particularly large amount of monitoring data, making it difficult for monitoring personnel to screen from these monitoring signals in a timely manner. Therefore, the separation of microseismic noise is the basis of microseismic detection technology, and it can provide reliable data for early warning of dynamic disasters such as rock bursts and mine earthqua...

AI Technical Summary

Problems solved by technology

[0002] The microseismic data collected by the microseismic monitoring system are all weak signals, which makes the signal characteristics of the microseismic signal difficult to be effectively identified or submerged in the noise signal, which is easily ignored by the monitoring personnel.

Because micro-seismic signals are not easy to be monitored, the sensitivity of sensors in micro-seismic monitoring systems is generally set to be high, which results in a particularly large amount of monitoring data, making it difficult for monitoring personnel to screen from these monitoring signals in a timely manner. Therefore, microseismic noise separation is the basis of microseismic detection technology, and it can provide reliable data for early warning of dynamic disasters such as rock bursts and mine earthquakes.

[0003] At present, there are many microseismic event identification methods. Allen et al. proposed the long-short time-average ratio method (STA / LTA). The LTA ratio becomes larger instantly. This method has a simple algorithm and high identification efficiency, but this method needs to determine multiple parameters, which is not easy to control, and the identification effect on low signal-to-noise ratio signals is poor, and often identification errors; Maeda et al. Based on the theory that there are significant differences in the statistical properties of data before and after the arrival of earthquake events, the Chichi Information Criterion (AIC method) was proposed. This method is also simple in algorithm and high in identification efficiency, but the method requires a large amount of calculation; Zhang Haijing et al. based on wavelet transform and AIC method The W-AIC method is proposed. This method first uses wavelet transform to decompose the signal into waveforms of different frequency bands, and then applies the AIC method to each frequency-divided waveform to pick up the time. If the picking results of each frequency-divided waveform are consistent, it is considered that there is Effective microseismic events, although this method overcomes the defect of single AIC picking, the complexity of the algorithm is high; in 2002, Saragiotis et al. proposed a new picking method called the PAI-S / K method, the principle of which is based on It is the theory of seismic waveform skewness and kurtosis. This method has high identification efficiency, but this method is greatly affected by the size of the time window. When the time window length is not selected, event identification will be wrong.

It can be seen that the above methods can effectively realize the identification and separation of microseismic events, but there are varying degrees of defects in the algorithm accuracy and algorithm calculation amount, which reduces the accuracy and feasibility of microseismic data identification.

Images