Recognition And Locaion Of Rock Microfracure Signals Detected Using Acive Seismic Sourse

The rock burst in deep tunnel engineering has the characteristics of sudden and violent,which will bring serious security risks to the tunnel construction of expressway and high-speed railway.The real-time monitoring and prediction of rockburst has become a common topic in the deep underground engineering construction and rockburst mechanism research.Microseismic monitoring is one of the methods of rockburst monitoring and early warning.It inverses the location of rock fracture by monitoring microseismic signals.The positioning accuracy is affected by many factors.It can be improved by adding active source technology.This method uses the known active source as the standard to detect the unknown rock fracture position,which is essential in the positioning method.However,adding active source signal will bring more complex problems to the microseismic monitoring signal.This dissertation focuses on how to effectively decompose and identify microseismic signals from complex mixed microseismic signals.On the basis of domestic and foreign researchers,active source detection technology is added to observe the rock micro-fracture phenomenon.This dissertation focuses on the decomposition and identification algorithm of microseismic signals under the active source and noise,and uses the identified microseismic and active source signals for accurate positioning.The main work and achievements are as follows:1.Aiming at the problems of difficulty in decomposition of complex mixed microseismic signals in the detection of rock microfracture based on active source technology.This dissertation proposes an algorithm based on Singular Value Empirical Mode Decomposition(SVEMD).SVEMD algorithm can effectively decompose complex mixed signal including active source signal,microseismic signal and noise signal.This algorithm has the characteristics of high resolution,low residual power and high SNR.It can decompose the mixed signal effectively in the frequency domain to obtain the noise,active source and microseismic signal in turn2.Aiming at the problems of low efficiency and low precision by manual identification microseismic signal in in complex microseismic mixed signals which has a lot of microseismic,active source and noise signals.This dissertation proposes a method of transforming signal recognition into image recognition and Convolutional Neural Network(CNN)is applied to microseismic signal recognition.It improves the existing CNN algorithm by adding the Inception structure to form the Deep Convolutional Neural Network-Inception(DCNN-Inception).The network realizes the automatic identification of microseismic signals.Using the test set to test the algorithm,the recognition accuracy of the signal is 92.4%,compared with the existing CNN network,the recognition efficiency is improved by 7.1%,and the loss rate of the signal is 17.4%,which is 13% lower than the existing CNN network.The feature fitting ability of DCNN-Inception network is obviously better than that of CNN network,the feature extraction ability is strong,and the accuracy is obviously improved.3.Aiming at the problems of the location and fine description of microseismic events in rock microfracture.This dissertation proposes the active source technology which can optimize the velocity model,and get the first break time and position information.Meanwhile,the active source technology improves the solution method of traditional TDOA location algorithm,establishes linear mathematical equations,improves the description accuracy of rock micro fracture,avoids the problem that the initial value has great influence in the traditional Geiger method,and optimizes the Geiger method.It provides reliable data support for further accurate positioning of microseismic events and analysis of rockburst.With the help of numerical simulation,the microseismic signal data are constructed,and the active source data collected by the Civil Engineering Laboratory of Southwest University of science and technology and the effective microseismic data obtained from the Baihetan Hydropower Station rockburst monitoring.Load the SVEMD algorithm and verify the feasibility and effectiveness.A large number of microseismic data monitored in Baihetan are used to build data sets to train and verify the DCNN-Inception algorithm,which can effectively identify the microseismic signals.At last,loading the active source location algorithm verify the positioning results.It is proved that the algorithm can improve the calculation efficiency and positioning accuracy of the existing Geiger algorithm...