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A nonlinear identification method of rock mass rupture signal and blasting vibration signal

A blasting vibration and identification method technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of signal aliasing and overlapping effects of different frequency bands

Active Publication Date: 2017-02-22
CENT SOUTH UNIV
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Problems solved by technology

Wavelet analysis, wavelet packet analysis and frequency slice wavelet analysis have good adaptability, but are easily affected by the overlap of adjacent harmonic components in the signal, resulting in aliasing of signals in different frequency bands
[0005] It can be seen that the existing identification methods for rock mass rupture signals and blasting vibration signals have relatively large limitations, and it is necessary to study an automatic identification method with strong applicability and high accuracy

Method used

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  • A nonlinear identification method of rock mass rupture signal and blasting vibration signal
  • A nonlinear identification method of rock mass rupture signal and blasting vibration signal
  • A nonlinear identification method of rock mass rupture signal and blasting vibration signal

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Embodiment 1

[0063] figure 2 It is the EMD decomposition process diagram of rock mass microseismic signal. Among them, (a) is the original rock mass microseismic signal, (b) is the normalized rock mass microseismic signal, and (c) is the eigenmode component obtained by EMD decomposition of the normalized rock mass microseismic signal. In the figure, the total number of sampling points of rock mass microseismic signals is N=5000, and the sampling frequency f=6000Hz. Depend on figure 2 (c) EMD decomposition of the normalized rock mass microseismic signal obtained 7 intrinsic mode components IMF j (j=1,2,...,7), the analysis scale of the signal is expanded, but the amount of data is increased, and false components may exist.

[0064] image 3 is the correlation coefficient map between each eigenmode component and the normalized rock mass microseismic signal. Depend on image 3 IMF 1 ~IMF 3 The correlation coefficient with the normalized signal is large, and the IMF 4 ~IMF 7 The co...

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Abstract

The invention discloses a nonlinear identification method for rock mass rupture signals and blasting vibration signals. Step 1: Import the time series x(n) of rock mass microseismic signals, n=1, 2,...,N; Step 2: EMD The eigenmode components of the normalized rock mass microseismic signal are obtained by decomposing, and the main eigenmode components c1,c2,...,cr are obtained by screening. Among them, r is the number of eigenmode components after screening; Step 3: SVD decomposes the matrix [c1 c2…cr]T to obtain its singular value σi (i=1,2,…,r); Step 4: Logistic The model calculates the probability p(Z) of the blasting vibration signal; step 5: identify the rock mass microseismic signal: p(Z)>0.5 blasting vibration signal; p(Z)≤0.5 rock mass rupture signal. This method has the characteristics of strong applicability and high accuracy.

Description

technical field [0001] The invention relates to a method for identifying rock mass rupture signals and blasting vibration signals, in particular to a non-linear identification method for rock mass rupture signals and blasting vibration signals. Background technique [0002] Microseismic monitoring, as an effective means of ground pressure monitoring, has been widely used at home and abroad, and the identification of rock mass rupture signals and blasting vibration signals is of great significance to microseismic monitoring. However, the rock mass rupture signal is quite similar to the blasting vibration signal, and is interfered by many noise signals, so automatic identification is difficult. At present, artificial identification of rock mass microseismic signals is mainly used, but manual identification is easily affected by personal factors, and the number of identifications is limited, which limits the real-time analysis of microseismic monitoring. [0003] At present, t...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
CPCG06F2218/20
Inventor 董陇军李夕兵尚雪义王泽伟周勇勇刘栋
Owner CENT SOUTH UNIV
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