Blasting Vibration Signal Research Articles

R wave identification of blasting seismic waves in propagation process and its engineering application

The identification of R wave is helpful to analyze damage characteristics and damage analysis of nearby building caused by blasting seismic waves. According to the characteristics of low frequency, low speed and strong amplitude of R wave, a time-frequency filter for R-wave recognition is constructed and a R-wave recognition method based on S-transform is proposed. Firstly, the measured blasting vibration signal is transformed from time-space to time-frequency domain by S-transform. Secondly, according to the characteristics of R-wave, the R-wave filter is constructed and processed in time-frequency domain. Finally, the obtained R-wave signal in time-frequency domain is transformed into time-space signal by inverse transform. The results of numerical simulation and field measurement show that the R-wave identification method based on S-transform can effectively suppress and extract R-wave without destroying low-frequency body wave signal.

Analysis and Research on Blasting Network Delay of Deep-Buried Diversion Tunnel Crossing Fault Zone Based on EP-CEEMDAN-INHT

The Empirical Mode Decomposition (EMD) of blasting seismic wave monitoring signal with noise can get IMFs with serious modal confusion and endpoint divergence. The Hilbert transform is constrained by the Bedrosian theorem, when it dealing with such IMFs will get negative instantaneous frequency, which leads to serious error in the identification of non-electric millisecond detonator initiation delay. EP-CEEMDAN-INHT is proposed and applied to the delay analysis of blasting network of deep buried diversion tunnel crossing fault zone. Comparing EP-CEEMDAN-INHT with EMD-HHT, it is found that EP-CEEMDAN-INHT can clearly display the time–frequency information contained in the measured blasting vibration signal, and EMD modal confusion and endpoint effect are well suppressed. The actual millisecond time interval obtained by EP-CEEMDAN-INHT can judge whether the detonator is in normal service. At the same time, the blasting millisecond interval with the best damping effect is 54.51–59.75 ms, which can realize the optimization of blasting network and has important practical significance for blasting safety control.

Liquid CO2 Phase Change Fracturing and Vibration Monitoring in Roadbed Slope Excavation

In order to analyze the vibration impact of carbon dioxide fracturing tube blasting red sandstone, combined with the engineering conditions and surrounding environment of Anqing High-tech Zone, the rock-breaking blasting plan is carefully designed. The fracturing tube is arranged in two rows, the first row has 23 holes with a spacing of 1.5m, and the second batch of holes has 22 holes, which are arranged in a quincunx pattern with a distance of ‘1.3m between the front and rear rows. And design the corresponding vibration signal monitoring test to study the crack propagation caused by blasting, the attenuation law of vibration and its frequency spectrum distribution. The results show that after blasting, there is a relatively smooth crack in the plane area of the fracture zone 7m away from the critical surface. The width of the crack is between 20-40cm. The vibration velocity of the measuring point decays rapidly with the increase of the vibration source distance, and the vibration velocity decay curves in the X and Y directions are basically the same. Spectrum analysis shows that the main frequency band of each sub-velocity is generally below 100Hz, and the main vibration frequencies in the X and Y directions are concentrated in 30∼60Hz. The main vibration frequency band of the rock-breaking blasting vibration signal has little correlation with the direction and propagation distance of the vibration signal. And compared with the analysis of emulsion explosives, it is found that the particle vibration speed caused by the emulsion explosive rock-breaking blasting is much greater than that of the liquid CO2 fracturing tube rock-breaking blasting, indicating that the use of liquid carbon dioxide to break the rock can effectively reduce the vibration generated by blasting.

Study of S wave identification based on measured blasting vibration signals and its application under engineering scale

Study of S wave identification based on measured blasting vibration signals and its application under engineering scale

De-noising method of tunnel blasting signal based on CEEMDAN decomposition-wavelet packet analysis

Aiming at the measured vibration signals collected during tunnel blasting construction, a noise reduction method based on the overall average empirical mode decomposition method (CEEMDAN decomposition) combined with wavelet packet analysis was in troduced. First, a series of multiple intrinsic modal components were obtained by CEEMDAN decomposition, and the modal components containing noise were selected using correlation coefficients, checked by the spectrogram and the variance contribution rate of the modal components. Then, the wavelet packet threshold noise reduction method was used to process the modal components containing noise. Finally, the unprocessed modal components and the de-noised components were reconstructed to obtain the final pure blasting vibration signal. At the same time, the feasibility of this noise reduction method has been verified by wavelet packet energy spectrum analysis. This method combines the advantages of CEEMDAN decomposition and wavelet packet analysis. Compared with existing methods, the de-noising effect is better, and it can be applied to the de-noising processing of similar tunnel blasting signals.

Analysis of Fractal Features of Blasting Vibration Signal Based on EEMD Decomposition

In order to study the propagation characteristics of blasting seismic waves by using the analysis and research of blasting vibration monitoring data of Urumqi subway tunnel excavation, the blasting vibration signal has been decomposed into several IMF components by EEMD decomposition and fractal theory has been used to calculate the correlation dimension in this paper. The results show that the IMF component can reveal the frequency characteristics in the blasting vibration signal clearly. As the measuring point is far away from the explosion source, the complexity of the frequency component of the vibration signal gradually decreases. The seismic wave interacts with the rock and soil medium in the propagation process, make it shown non-linear characteristics, which are not easily to be observed by conventional measure methods. This paper introduces fractal dimension to study the nonlinear characteristics of blasting vibration signals and achieves the purpose of analysing the law of blasting seismic wave propagation. This study achieves a quantitative description of the effects of site media on seismic waves, and at the same time, it lays a theoretical foundation for predicting the vibration effect analysis of seismic waves reaching the target position.

Research on vibration effect of tunnel blasting based on an improved Hilbert–Huang transform

Through a tunnel-blasting project, the effect of tunnel-blasting vibration has been analyzed from the perspective of vibration energy transfer. The non-linear regression method was used to obtain the prediction equation for blast vibration velocity based on the field blast vibration data. Then, the maximum charge per delay of the blasting construction of the tunnel was obtained through the formula inversion. Based on the traditional Hilbert transform, a novel Hilbert–Huang transform (HHT) analysis method considering Complete Ensemble Empirical Mode Decomposition with Adaptive Noise decomposition (CEEMDAN) and wavelet packet threshold de-noising method has been proposed, the feasibility of which was verified using the field blast vibration signals. It has been proved that the improved HHT analysis method can be used to analyze the influence of the different blasting vibration parameters on the regular distribution of vibration energy. In addition, the dimensional analysis method was used to establish the blasting vibration energy prediction model. The results of this research show that the improved HHT analysis method can solve the problem of modal aliasing caused by the traditional decomposition method, and can obtain the purified main modal components, which improves the adaptability of HHT analysis. In addition, with the increase of the distance between blast area and the maximum charge per delay, the high-frequency energy of the blasting signal gradually weakens, while the dominant energy frequency band diverts to the low-frequency band. The methods and conclusions of this research can provide a certain reference for the controlled blasting construction in similar cases.

Study on blasting vibration characteristics of metro tunnel based on wavelet packet technology

Wavelet packet technology has been widely applied in the study on blasting vibration transmission law of overlapping tunnels. The previous research focused on vibration transmission law of the distant area---a range more than 20m, but for the close area---a range less than 10m, research seldom has been carried out. Therefore, this paper, based on a overlapping tunnel project in Qingdao, reveals the blasting vibration transmission law of the close field as well as how the numbers of free surfaces affect the law. Firstly, at the site,the blasting vibration data both in front and rear of the working faces including the cutting area (a free face) and the spreading area (two free faces) in a newly-built tunnel are obtained; Secondly, the wavelet packet technology is applied to analyze the data of blasting vibration frequency and energy distribution characteristics. Finally, it is concluded that different free facing with different numbers can lead to different main frequency distribution range and different law of main frequency band changing with distance when blasting vibration transmit within the close field; and the vibration signal energy in front and rear of the working face in cutting area and spreading area is differently distributed in the directions of 3 channels. Combining blasting vibration signal frequence with energy factors, this paper puts forward the constructive suggestions on the safety protection of existing tunnels and provides reference for studying the law of blasting vibration transmission of overlapping tunnels.CLC classification number: 0389 TD235 Document identification code: A

A Novel Denoising Model of Underwater Drilling and Blasting Vibration Signal Based on CEEMDAN

In underwater drilling and blasting engineering, the blasting vibration signal is mixed with a mass of noises due to the complexity of monitoring environment, the error of monitoring sensors and the reflection of propagation medium. In order to accurately obtain the characteristics of vibration signal, a novel denoising model is established. The complete ensemble empirical mode decomposition with adaptive noise is used to decompose the original signal, and the objective function of the filtering algorithm is used to obtain the optimal denoising signal. The results indicate that the model can not only successfully remove the high-frequency noise but also has no effect on the low-frequency signal components, which verifies the reliability and validity of the denoising model.

The Removal Method of the Blasting Vibration Signal Trend Item and Noise

Aiming at the problem that the blasting vibration signal contains trend items and noise interference, a signal processing method based on the Fourier decomposition method (FDM) is introduced. Firstly, based on the FDM theory, the original blasting vibration signal is decomposed to obtain several modal components; then, the dominant component of the signal is filtered according to the dominant frequency of the modal component and its correlation with the original signal; finally, the dominant component is reconstructed to remove the trend item and noise at the same time; spectrum analysis is carried out to verify the effectiveness of the method. The results show that FDM can effectively disperse low‐frequency trend items, high‐frequency noise, and useful signal information while decomposing signals containing trend terms and noise; compared with the existing common methods, it can more effectively retain the original information, which provides a reference for the trend item and noise removal of similar blasting vibration signals.

A New Zoning Method of Blasting Vibration Based on Energy Proportion and Its SVM Classification Models

The blasting vibration signals show obvious zoning propagation characteristics. Because of not considering the specific influences of geological and topographical conditions, the existing zoning methods of blasting vibration cannot fully describe the internal details of blasting vibration signals. Therefore, a new zoning method of blasting vibration based on energy proportion was proposed in this paper, in which the energy proportion in low, medium, and high frequency bands after multiresolution wavelet analysis is used as the zoning index to distinguish the different characteristics of blasting vibration signals in different zones. Based on the proposed zoning method, 343 sets of measured blasting vibration signals were used to train and test the SVM classification models with four different types of kernel functions. The testing results demonstrate that the zoning method of blasting vibration based on energy proportion has high feasibility, flexibility, and reliability, and the SVM classification models with RBF have higher accuracy than models with other kernel functions in blasting vibration zoning prediction.

Identifying Delay Time of Detonator for a Millisecond Blasting

Based on wavelet transform, the blasting vibration signals are analyzed here. For millisecond blasting, the blasting effect is mostly affected by the actual delay time. Local characteristics of the analyzed signals could be highlighted by the wavelet transform. The simultaneous initiation of large explosive quantity could be avoided by the use of multistage detonators, while the vibration resistance effect could be better. For the same level of detonator segment, the larger the arranged time interval, the less the possibility of initiation at the same time, which is not conducive to the vibration resistance. Therefore, it is suggested to use high‐level detonators with detonating cord or high‐precision digital electronic detonators to minimize the initiation error. Furthermore, by identifying the delay time using wavelet transform, the interval delay time of different detonator segments could be obtained. Moreover, the nominal delay time, actual delay time, and interval delay time are further compared and analyzed. It is suggested that the millisecond delay series of detonators should be selected in the whole section blasting, and the segment should be jumped as much as possible, so as to increase the secondary breakage time. Detonators with longer interval delay time should be avoided to the full.

Improving Purity of Blasting Vibration Signals Using Advanced Empirical Mode Decomposition and Wavelet Packet Technique

Improving Purity of Blasting Vibration Signals Using Advanced Empirical Mode Decomposition and Wavelet Packet Technique

An Improved Empirical Mode Decomposition Method for Vibration Signal

With the development of electronic measurement and signal processing technology, nonstationary and nonlinear signal characteristics are widely used in the fields of error diagnosis, system recognition, and biomedical instruments. Whether these features can be extracted effectively usually affects the performance of the entire system. Based on the above background, the research purpose of this paper is an improved vibration empirical mode decomposition method. This article introduces a method of blasting vibration signal processing—Differential Empirical Mode Decomposition (DEMD), combined with phosphate rock engineering blasting vibration monitoring test, and Empirical Mode Decomposition (EMD) to compare and analyze the frequency screening of blasting vibration signals, the aliasing distortion, and the power spectrum characteristics of the decomposed signal. The results show that compared with EMD, DEMD effectively suppresses signal aliasing and distortion, and from the characteristics of signal power spectrum changes, DEMD extracts different dominant frequency components, and the frequency screening effect of blasting vibration signals is superior to EMD. It can bring about an obvious improvement in accuracy, and the calculation time is about 4 times that of the EMD method. Based on the ground analysis of ground motion signals, this paper uses the EMD algorithm to analyze measured ground blast motion signals and study its velocity characteristics and differential time, which provides a new way of studying motion signals.

Method of S Wave Identification: Application to Measured blasting vibration signals

S wave identification is an indispensable step in the process of deriving the dynamic parameters of rock masses, which are highly significant in guiding the design and construction of hydraulic engineering projects. However, it is difficult to identify S waves accurately because of 1) the influence of overlapping within the P wave coda and 2) the incomplete separation between S and P waves owing to the characteristic short distances of blasting seismic wave propagation. This paper presents a modified method for accurately identifying S waves at the engineering scale using blasting vibration signals. The proposed method combines the application of the short-time average zero-over rate with polarization analysis. By using the short time average zero-over rate, it is possible to effectively reduce the computational time while improving the computational efficiency. Polarization analysis is used to improve the accuracy of the method. A comparison of numerical identification and theoretical results revealed that the improved method is clearly capable of S wave identification with errors of less than 2%. To demonstrate the capability and accuracy of the proposed method, measured vibration signals were introduced to the Fengning pumped-storage power station and the S wave velocities obtained from the first arrival times were compared to obtain an estimated S wave velocity.

Influence of free face numbers on energy distribution characteristics of blast vibration signal in underwater drilling blasting

Aiming to the problem of free face not only affect the blasting effects but also the blasting vibration affects, an analytical method of exploring the free face’ influences of the blasting vibration and the attenuation law of seismic wave is proposed. Taking the field monitoring data of underwater blasting seismic wave between the river reach of the Three Gorges Dam and Gezhouba Dam as the study object, utilize the wavelet analysis and decompose method to research the blasting vibration signal’s total energy and energy distribution characteristics in variety of frequency band on different free face. The results indicated that underwater drilling blasting vibration has the characteristic of low frequency, short duration and fast attenuation. Most of the energy of the explosion will be consumed as seismic energy in the slotted blasting due to the restricted of a single free face. However, after the subsequent excavation blasting, the free face number increases but the total energy of blasting vibration and PPV decreases. Therefore, in addition to the usual seismic mitigation measures of underwater drilling and blasting, the number of free faces can be increased to achieve the effect of shock absorption control without reducing section charge.

Identification of Microseismic Signals Based on Multiscale Singular Spectrum Entropy

The accurate identification of effective microseismic events has great significance in the monitoring, early warning, and forecasting of rockburst hazards. However, the conventional identification methods have displayed difficulties in achieving satisfactory results. A microseismic signal identification method which combines variational mode decomposition (VMD) and multiscale singular spectrum entropy was proposed in this paper. The original signal was firstly broken down into a given numberKvariational mode components, which are ranked by frequency in descending order. Then, the characteristic pattern matrix was constructed according to the mode component signals, and the identification model of the microseismic signals based on the support vector machine was built by performing a multiscale singular spectrum entropy calculation of the collected vibration signals, constructing eigenvectors of signals. Finally, a comparative analysis of the microseismic events and blasting vibration signals in the experiment proved that the different characteristics of the two kinds of signals can be fully expressed by using multiscale singular spectrum entropy. Experimental results further confirmed the effective identification performance of this proposed method.

Vibration Signal Analysis of Water Seal Blasting Based on Wavelet Threshold Denoising and HHT Transformation

The blasting vibration signal obtained from tunnel construction monitoring is affected by the external environment, which contains a lot of noise that causes distortion during signal processing. To analyse the blasting vibration signal and determine the appropriate water seal blasting charge structure for construction, combined with wavelet threshold denoising method and HHT transformation, the blasting vibration signals of the four charge structures of conventional charge, water interval charge at both ends, water interval charge at the orifice, and water interval charge at the hole bottom are denoised and HHT is analysed. The results show that the wavelet threshold method can effectively eliminate high‐frequency noise in the blasting vibration signals and retain information carried by the vibration signal itself. The frequency and energy of the blasting vibration signals of the water interval charge at both ends are densely distributed in the range of 0 s to 0.9 s and below 100 Hz. The frequency and energy of the blasting signals of the other three charging structures are reduced within the same range, sparse areas appear, and the instantaneous total energy is smaller than that with a water interval charge at both ends, which shows that the water interval charge at both ends can effectively apply explosive energy to the surrounding rock and reduce energy loss in the explosive. The blasting vibration signal energy of the water interval charge at both ends is mainly concentrated in components IMF2 to IMF5, and the corresponding frequencies are concentrated at 6 Hz to 11 Hz and 20 Hz to 70 Hz, while the blasting vibration signal energy of other three charge structures is mainly distributed in components IMF2 to IMF4, corresponding frequencies are concentrated within 20 Hz to 70 Hz, and the distribution at low frequencies is not obvious. Therefore, when using the water interval charge at both ends, it is necessary to increase the main vibration frequency of the original vibration signals by reducing the single section charge and using frequency shift technology to avoid the natural frequency of the structure and reduce resonance‐induced damage.

An Automatic Recognition Method of Microseismic Signals Based on S Transformation and Improved Gaussian Mixture Model

The microseismic signals in the coal minefield are very complex because of its special environment with a large number of blast vibration signals, and how to effectively identify the microseismic signals is still a big problem. S transform (ST) and Manifold Learning (ML) methods are introduced to extract the characteristics of the microseismic signals, and Gaussian Mixture Model based on the improved Bee Colony optimization algorithm (IBC‐GMM) is established to identify the microseismic signals accurately. Firstly, the time‐frequency characteristics of microseismic signals in coal mine are extracted by ST analysis. It is found that there are obvious time‐frequency differences between rock‐fracturing signals and blast vibration signals. Blast vibration signals have short duration, high frequency, and complex frequency spectrum, and their dominant frequencies are mainly over 100 Hz. However, rock‐fracturing signals are relatively slow, with low frequency and stable spectrum change, and their dominant frequencies are generally below 100 Hz. Then, combining with the microseismic data of Xiashinjie coal mine in Tongchuan, China, the feature dimension reduction is carried out by Manifold Learning (ML) method, and the processed feature vectors are automatically recognized by IBC‐GMM. Field test results show that the method summarizes the characteristics of the microseismic wave which are difficult to emerge as the learning vector, and the features reflect the key features of microseismic signals well. The identification accuracy is as high as 94%, and its recognition effect is superior to other recognition models (such as traditional Gaussian Mixture Model based on Expectation‐Maximum (EM‐GMM), Backpropagation (BP) neural network, Random Forests (RF), Bayes (Bayes) methods, and Logistic Regression (LR) method). Therefore, IBC‐GMM could be used to mine engineering microseismic monitoring waveform recognition to provide the reference.

Recognition of Microseismic and Blasting Signals in Mines Based on Convolutional Neural Network and Stockwell Transform

The microseismic monitoring signals which need to be determined in mines include those caused by both rock bursts and by blasting. The blasting signals must be separated from the microseismic signals in order to extract the information needed for the correct location of the source and for determining the blast mechanism. The use of a convolutional neural network (CNN) is a viable approach to extract these blast characteristic parameters automatically and to achieve the accuracy needed in the signal recognition. The Stockwell Transform (or S-Transform) has excellent two-dimensional time-frequency characteristics and thus to obtain the microseismic signal and blasting vibration signal separately, the microseismic signal has been converted in this work into a two-dimensional image format by use of the S-Transform, following which it is recognized by using the CNN. The sample data given in this paper are used for model training, where the training sample is an image containing three RGB color channels. The training time can be decreased by means of reducing the picture size and thus reducing the number of training steps used. The optimal combination of parameters can then be obtained after continuously updating the training parameters. When the image size is $180\times140$ pixels, it has been shown that the test accuracy can reach 96.15% and that it is feasible to classify separately the blasting signal and the microseismic signal based on using the S-Transform and the CNN model architecture, where the training parameters were designed by synthesizing LeNet-5 and AlexNet.