Nonlinear identification method for rock fracture signal and blasting vibration signal

Abstract

The present invention discloses a nonlinear identification method for a rock fracture signal and a blasting vibration signal. The method comprises: step 1: importing a time sequence x(n) of a rock microseismic signal, wherein n=1, 2,..., N; step 2: performing decomposition to obtain intrinsic modality components of a normalized rock microseismic signal by EMD decomposition, and screening the intrinsic modality components to obtain main intrinsic modality components c1, c2,..., cr, wherein r is the number of the intrinsic modality components after screening; step 3: performing decomposition on a matrix [c1 c2... cr]T by SVD, so as to obtain a singular value signma i of the matrix, wherein i=1, 2,..., r; step 4:calculating a probability p(Z) of a blasting vibration signal by using a Logistic model; and step 5: identifying the rock microseismic signal, wherein p(Z)>= 0.5*(blasting vibration signal), and p(Z)<=0.58(rock fracture signal). The method has the characteristics of strong suitability and high accuracy and the like.

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...

AI Technical Summary

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

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