Blasting Vibration Signal Research Articles

Deep Neural Network for Distinguishing Microseismic Signals and Blasting Vibration Signals Based on Deep Learning of Spectrum Features

ABSTRACT Similar waveforms and overlapping frequency ranges make distinguishing blasting vibrations and microseismic signals challenging, causing interference with coal mine microseismic monitoring systems. To address this problem, we propose a spectrum dataset (MSData) reflecting the spectrum features of both signal types and present a signal classification network (SCNet) combining CNNs and Transformers for signal classification. The network can learn multi-dimensional features of both signals from MSData and automatically and efficiently identify the two signal types. Experimental results yield F1-scores of 0.991 for microseismic signals and 0.993 for blasting vibration signals, meeting engineering application requirements.

Analysis of blasting vibration duration considering frequency and energy and its application

Blasting vibration's effect on ground vibration is essentially an energy transfer and transformation process (Hongtao et al., 2010) [1]. In engineering, the blasting vibration duration is often used as an objective index to evaluate the blasting vibration hazard. Thus, this study on the factors influencing blasting vibration duration is essential. In this study, the blasting vibration duration prediction formula uses frequency and energy as independent parameters, which are analysed using a statistical t probability distribution. With the blasting vibration duration prediction formula, the delay time taken for the structure to reach the resonance frequency is obtained by calculating the dominant frequency of the structure. The results indicate that the frequency and energy of the blasting vibration signal are highly correlated with the duration of blasting vibration, and their introduction as independent parameters in the prediction formula can better improve the prediction accuracy. The dominant frequency should not be neglected in blasting vibration safety evaluations, and the use of blasting vibration duration prediction to calculate the dominant frequency can better avoid the resonance phenomenon in the structure.

Analysis of Noise Reduction Methods for Blasting Vibration Signals in Tunnels Separated by Small Clear Distances

Analysis of Noise Reduction Methods for Blasting Vibration Signals in Tunnels Separated by Small Clear Distances

Denoising of blasting vibration signals based on CEEMDAN-ICA algorithm.

Monitoring of blasting vibration signals can make the collected blasting signals noisy due to various factors such as on-site actual construction conditions, equipment, and instruments. Thus, the acquired signals should be preprocessed before analyzing the blasting vibration signals. The current study proposes a blasting vibration denoising method based on CEEMDAN-ICA to alleviate the noise component in the blasting signals effectively. The collected signal is first decomposed through the CEMMDAN algorithm to extract the IMF components of different frequency bands. Next, the collected signal is estimated using the ICA algorithm to attain corresponding ICA components. Finally, the arrangement entropy of the ICA components is calculated for signal reconstruction to attain a small noise blasting vibration signal. Simulations are performed to evaluate the feasibility of the presented algorithm and compare its efficiency with the traditional algorithms. The results demonstrate that this algorithm has specific advantages over other algorithms, which can more accurately denoise the original signal and retain the effective signals, providing a new denoising method for subsequent signal analysis.

GNSS blasting vibration monitoring and feature recognition method

Aiming at the problems of the current blasting vibration monitoring, such as unstable low-frequency performance, vulnerability to environmental interference, complex operation, and easily damaged, a new blasting vibration monitoring and feature recognition method of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multiscale permutation entropy (MPE) and singular spectrum analysis (SSA) is proposed based on global navigation satellite system (GNSS). Initially, a new method of GNSS blasting vibration monitoring is proposed according to the blasting environment and blasting characteristics, it can provide a new reference for GNSS technology to be used in blasting vibration monitoring. Then, signal simulation proved that the proposed feature recognition method can effectively remove the low-frequency and high-frequency background noise as well as trend items of blasting vibration signals and accurately extract the dominant frequency of vibration compared with the traditional processing methods. Finally, the proposed method is demonstrated by factory open blasting and tunnel excavation blasting compared with the monitoring results of the conventional blasting recorder, it can accurately recognize the characteristics of blasting initiation time, particle peak velocity (PPV), initiation period, and dominant frequency of vibration. The RMSE of PPVs is 0.086, and other errors are within 5%, which is highly consistent with the monitoring results of blasting recorder. The GNSS blasting vibration monitoring and feature recognition method can improve the low-frequency stability, field adaptability, simple operation, and information richness of blasting monitoring, which can provide a new approach for blasting monitoring; it can also be an important supplement to existing blasting monitoring methods.

The influence of the number of free surfaces on the energy distribution and attenuation law of blasting vibration signals from peripheral holes: field experiment and simulation

Tunnels are commonly excavated using drilling and blasting methods, and the surrounding rock is greatly affected by the vibration of surrounding hole blasting. To study the influence of the number of free surfaces on the energy distribution and attenuation law of surrounding hole blasting vibration signals, on-site experiments and numerical simulation experiments were conducted. The research results indicate that the higher the number of free surfaces, the smaller the peak vibration velocity. The longitudinal Fourier main frequency decreases with the distance from the monitoring point. The more free surface, the greater the centroid frequency and zero crossing frequency. In addition, numerical simulation shows that the degree of rock fragmentation after blasting increases with the increase of the number of free surface of rock.

CNN-Transformer for Microseismic Signal Classification

The microseismic signals of coal and rock fractures collected by underground sensors contain masses of blasting vibration signals generated by coal mine blasting, and the waveforms of the two signals are highly similar. In order to identify the true microseismic signals with a microseismic monitoring system quickly and accurately, this paper proposes a lightweight network model that combines a convolutional neural network (CNN) and transformer, named CCViT. Of these, the CNN is used to extract shallow features locally, and the transformer is used to extract deep features globally. Moreover, a modified channel attention module provides important channel information for the model and suppresses useless information. The experimental results on the dataset used in this paper show that the proposed CCViT model has significant advantages for floating point operations (FLOPs), parameter quantity, and accuracy compared to many advanced network models.

Characteristics and Energy Distribution of Blast-Induced Ground Vibration in Deep-Hole Blasting

This study proposes an incremental extreme extraction method based on the waveform characteristics of ground vibration signals obtained from open-pit mines to investigate the distribution and characteristics of ground vibration from deep-hole blasting. Firstly, an incremental extreme extraction method is proposed based on the waveform characteristics of borehole blasting vibration signals in open-pit mines. The proposed method could extract and screen the extreme values of blasting vibration signals and effectively improve the utilization rate of the data. The space vector of particle vibration is introduced to analyze the angle change between the particle velocity vector and the ground surface when the extreme value increases. Finally, the relation between the particle velocity vector and the angle between the ground plane and the increasing extremum position of several sets of measured signals is studied. Based on the statistical analysis, the results show that the particle velocity in the vertical direction has a significant advantage over that of the other two directions, and the angle between the extreme particle velocity vector direction and the ground plane is primarily distributed in the range of 60°~90°. After an unstable distribution of particle velocities in the transition zone, the particle velocities in each direction gradually attain a relatively balanced and stable attenuation condition as the distance increases. This proves the reliability of the proposed vector analysis of particle velocity in understanding the mechanism of rock blasting.

Study on Denoising Method of Vibration Signal Induced by Tunnel Portal Blasting Based on WOA-VMD Algorithm

Because of the impact of the complex environment of tunnel portals, the measured blasting vibration signals in a tunnel portal contains a lot of high-frequency noise. To achieve effective noise reduction, a novel method of noise reduction for blasting vibration signals based on the whale optimization algorithm (WOA) optimized with the variational mode decomposition (VMD) algorithm was proposed. The WOA algorithm is used to optimize globally for the mode number K and penalty factor α of VMD for measured signals and to determine the optimal parameters of [K, α], and to obtain the intrinsic mode function (IMF). Multi-scale permutation entropy (MPE) was used to identify and remove noise components in IMF, and then the reserved IMF was reconstructed to achieve a denoised signal. The method is applied to the blasting vibration analysis of the Xiali tunnel of the Jixin expressway in the Henan Province. Results indicate that the novel method can acquire the optimal decomposition mode number and identify the high frequency noise. Its denoising effect is better than the conventional VMD algorithm and the complete ensemble empirical mode decomposition with adaptive ability (CEEMDAN), which verifies the self-adaptivity and effectiveness of the WOA-VMD denoising method.

Improving purity of blasting vibration signals using advanced Empirical Mode Decomposition and Wavelet packet technique

The study proposes a denoising method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Wavelet packet threshold denoising (WPTD) to reduce the noise of blasting vibration signals. CEEMDAN is first employed to decompose noisy blasting vibration signals into different intrinsic mode functions (IMFs), i.e., different frequencies of subcomponents. By using correlation coefficients, all of IMFs are automatically categorized as the dominant IMFs with higher portions of vibration components and the secondary IMFs with less vibration contents. Then, with further decomposing two groups of IMFs into different scales of Wavelet packet coefficient series, WPTD based on the fixed-threshold technique and the soft threshold function method is applied to minimize the level of noise in two groups of IMFs. Two groups of denoised IMFs are finally reconstructed as the pure blasting vibration waveform. Numerical waveforms with different types and intensities of noise and measured blasting vibration signals with different main frequency bands are used to verify the accuracy and capability of the proposed method. The numerical and field results show that the proposed method can not only effectively reduce noise components, but also well remain components of blasting vibrations.

Analysis of blasting vibration signal of high steep anti-dip layered rock slope

Analysis of blasting vibration signal of high steep anti-dip layered rock slope

Study on Blasting Vibration Control of Brick-Concrete Structure under Subway Tunnel

In order to study the impact of the blasting vibration of subway tunnels on adjacent buildings, taking the tunnel mining method construction of the section between Zhifang Street Station and Metro Town Station of Wuhan Metro Line 27 as the engineering background, the blasting scheme is optimized by reducing the maximum single section charge, multi-section and densifying the surrounding holes. The HHT method and wavelet analysis are used to evaluate the advantages and disadvantages of the optimization scheme from the perspective of energy. The results show that the peak velocity of the blasting vibration is significantly reduced and the frequency is significantly increased after the blasting scheme is optimized. After the blasting scheme is optimized, when the working face is directly below the external wall of the building, the peak vibration velocity is the largest; from the back of the working face to the front of the working face, the peak velocity of the surface particle vibration first increases and then decreases. The frequency band of the optimized blasting vibration signal energy distribution is wider and the energy is more dispersed. This study can provide some practical experience for the design and construction of similar projects.

Frequency spectrum and energy refinement characteristics of blasting vibration signals in raw water pipeline tunnel excavation

The analysis of time-frequency variation and energy refinement characteristics of blasting vibration signals has contributed to understanding the propagation law of blasting vibration wave and reducing the possible losses. Combined with the measured data of tunnel blasting excavation and based on the newly constructed wavelet function, the spectrum distribution and energy refinement characteristics of tunnel blasting vibration signals are deeply explored and studied. The results demonstrated that compared to the Fourier spectrum, the innovative method of scale energy spectrum can not only acquire the dominant frequency of the blasting vibration signals, but also the obtained spectrum curve is smoother and can clearly reflect the change trend of the signal spectrum. The newly constructed biorthogonal wavelet has the characteristics of high vanishing moment, high regularity and matching with the waveform variation of the measured blasting vibration signals, and can describe the subtle variation characteristics of blasting vibration signal frequency. The continuous wavelet transform energy spectrum can reflect the three-dimensional energy distribution of blasting vibration signal in the time-scale domain, and the occurrence time of frequency, the frequency duration interval and time range of blasting vibration signal can also be acquired. Wavelet packet algorithm can precisely calculate the energy distribution of each frequency component in the signal, the tunnel blasting vibration signals (YBJ1, YBJ2) generated near the power tower presents low frequency, while the signals (YBF3,YBF4) far away from the power tower presents relatively high frequency. Measures should be taken to control the vibration and resonance of power tower caused by tunnel blasting. This research is of great significance for recognizing the propagation law of vibration waves, reducing the impact of blasting on surrounding buildings, and ensuring the safety of tunnel construction and surrounding buildings.

Mine-Microseismic-Signal Recognition Based on LMD–PNN Method

The effective recognition of microseismic signal is related to the accuracy of mine-dynamic-disaster precursor-information processing, which is a difficult method of microseismic-data processing. A mine-microseismic-signal-identification method based on LMD energy entropy and the probabilistic neural network (PNN) is proposed. First, the Local-Mean-Decomposition (LMD) method is used to decompose the mine microseismic signal. Considering the problem of vector redundancy, combined with the correlation-coefficient method, the energy entropy of the effective product-function component (PF) is extracted as the feature vector of mine-microseismic-signal classification. Furthermore, the probabilistic neural network (PNN) is used for learning and training, and the blasting-vibration signal and the coal–rock-mass-rupture signal are effectively identified. The test results show that the recognition accuracy of the PNN is up to 90%, the calculation time and classification effect of the PNN are better, and the recognition accuracy is increased by 15% and 7.5%, respectively, compared with the traditional PBNN and GRNN. This method can accurately and effectively identify the microseismic signals of mines and has good generalization performance.

Application of Low-Frequency Processing Method Based on VMD Algorithm in Blasting Signal Processing

In the field of blasting vibration signal analysis, accurate removal of low-frequency noise in the signal has important practical significance. In view of the shortcomings of the existing methods, such as cumbersome calculation and limited scope of application, a method based on variational mode decomposition (VMD) to remove low-frequency noise was proposed. In this paper, the parameter selection of VMD algorithm to identify low-frequency noise of blasting signal is optimized by simulation experiment. The results show that frequency has little influence on the decomposition effect. When the frequency of low-frequency noise is between 0 and 5 Hz, the influence of frequency on the decomposition effect remains basically unchanged. The amplitude has a significant effect on the decomposition effect. The smaller the amplitude, the worse the decomposition effect of the VMD method. Taking the excavation of the Badaling Great Wall Station as the background, the LSM method, EMD method, and VMD method are used to remove the low-frequency noise of the randomly selected test signals in order. Compared with the LSM method and the EMD method, the signal processed by the VMD method does not have modal mixing and does not have much low-frequency noise residue. It has more extensive applicability in removing low-frequency noise of blasting signal.

Hilbert spectrum analysis method of blast vibration signal based on HHT instantaneous phase optimization

Compared with wavelet analysis method of signal data, HHT (Hilbert-Huang Transform) method is widely used for the local signal data analysis in the time–frequency domain associated with adaptable capacity, which has been recognized as one of the most efficient method to address the non-stationary signals. In this study, Matlab software was used as the calculation code and field millisecond delay blasting data was collected as the research objects, the Hilbert time–frequency spectrum, marginal spectrum, instantaneous energy spectrum and Hilbert energy spectrum scatter were obtained for the signal IMF components under the standard instfreq.m function (as shown in Section 3.3) frequency modulation with the identified bandwidth of 1 Hz. The reasons of negative frequency for digital signals were discussed based on the theoretical analysis and the digitial signals were then summarized as two types of negative frequency according to the instantaneous phase characteristics. The field collected blasting vibration signals were used to illustrate the features of the negative frequency points such as large number, correlated relation between the signal amplitude extent and frequency band, and non-negligible of the total vibration energy. In addition, a theoretical equation was proposed for the instantaneous phase optimization function based on the HHT method, and a triple identification approach of millisecond time was proposed based on instantaneous phase optimization method. The process of Hilbert spectrum analysis based on HHT instantaneous phase optimization was implemented using Matlab, which was used to analyze Hilbert spectrum characteristics. Further, a theoretical comparison and analysis between the standard instfreq.m time–frequency modulation and the proposed phase optimized HHT spectrum were performed, and the results shows that the optimized HHT spectrum analysis improves the accuracy of time–frequency spectrum and Hilbert spectrum calculation, besides, the frequency domain recognition width is about 2∙r times of that from the standard solution. Finally, the phase optimization was used to analyze the amplitude spectrum, instantaneous velocity energy spectrum and 3D time–frequency seismic velocity energy spectrum features, application of HHT-based instantaneous phase optimization method is given for the accurate recognition application of multi-segment detonation millisecond time in underground blasting.

Reduction of Blasting Induced Ground Vibrations Using High-Precision Digital Electronic Detonators

This study aims to effectively control the effects of blast vibrations and improve the safety factor for personnel and facilities in blasting engineering. Using high-precision digital electronic detonators according to the propagation and superposition principle of blasting vibration signals, a superposition prediction method based on single-hole blasting vibration waveform is proposed to determine the group-hole blasting vibration waveform. Experimental results show that the variation law of the predicted blasting vibration wave is consistent with that of the measured blasting vibration wave, and the error rate is less than 16%. Based on the proposed superposition prediction method, the variation law of particle vibration velocity peak with micro-differences in time is studied. Consequently, an optimal differential time interval (delay time) is obtained with regard to the blasting vibration reduction. The results are applied in a site leveling project, and the results show that the slight time difference between zones has an evident effect on vibration reduction. The maximum reduction achieved with the proposed method is superior (69.7%) to that obtained via other controlled blasting delay methods. This result has been successfully applied in a flat project in China.

Blasting Vibration Signal Transform in Geotechnical Engineering Applied HHT and S Transform for Time-Frequency Analysis

Based on the same blasting vibration signal, the S transform and HHT method have been applied for the time and frequency analysis. Both the S transform and HHT method are analyzed and compared from the point of decomposition path and analysis function. It expressed that these two methods are complete and effective and have the characteristics of adaptive multiresolution. Especially, the time resolution of the HHT method is invariable and its accuracy is high. Moreover, its frequency resolution can be adaptively adjusted with the inherent characteristics of the signal. However, the time and frequency resolution of the S transform are limited by the uncertainty principle, and the resolution improvement is at the cost of reducing the resolution of another. Furthermore, the HHT method has much more adaptability and is superior to S transform in the analysis of nonstationary signals. In advance, the waveform curve and power spectral density of each detonator are extracted, which indicates that it is not scientific to use different segment detonators for the same circle of the auxiliary hole. Under the premise of the same charge quantity, the vibration energy peak value produced by the same segment detonator is less than that of several segments of detonators. Therefore, it is believed that the HHT method has broad application prospects in the study of wave propagation, attenuation law, structural dynamic response characteristics, and blasting vibration damage in rocks.

Cross-Term Suppression Method for Time-Frequency Spectrum of Engineering Blasting Signals

Abstract Engineering blasting vibration signals are affected by the test environment and contain noise and trend components, which leads to significant cross-term in the time-frequency (TF) distribution. To address these problems, a combined algorithm of matching pursuit (MP) and smooth pseudo-Wigner-Ville distribution (SPWVD) is proposed to suppress the TF spectrum cross-term and extract the signal features. An overcomplete atom library is constructed for signal feature matching according to sparse signal representation theory. Atomic reconstruction of the subsignal is achieved by MP decomposition, and the SPWVD distribution of the reconstructed subsignal is calculated and superimposed, yielding a more accurate TF expression of the blasting signal. The results show that the combined MP-SPWVD method can accurately suppress cross-term and improve the TF identification and feature analysis ability, which verifies the accuracy of its application in signal TF analysis. The combined algorithm is thus suitable for TF feature extraction of engineering blasting signals.

Wavelet packet analysis on blasting vibration signal under different cutting method

For the smooth blasting method, the straight parallel hole cutting method, single wedge hole cutting method and double wedge hole cutting method have been devised. Corresponding to the devised cutting methods, the blasting test have been carried out in site. By the blasting vibration monitor technology, the blasting effect and peak blasting vibration velocity have been considered and compared. From the points of blasting footage, half hole ratio of the peripheral hole, rock slag throwing distance and the homogenization degree of rock slag, the blasting effect has been assessed separately. In advance, for straight parallel hole cutting, there is a rather higher ratio in the low frequency band area, the Power Spectral Density (PSD) mainly concentrated in the range of 0-100 Hz. However, for the wedge hole cutting, the PSD mainly ranges from 0 to 800 Hz, concentrated in a higher frequency band area. Meantime, for straight parallel hole cutting, the overall energy distribution is in the low frequency part, and the distribution difference is a bit large. For the single wedge hole cutting, about 25 % of the energy in the second layer is concentrated in the range of 1000-2000 Hz. In the third layer, about 20 % of the energy is concentrated in the range of 1500-2000 Hz. Furthermore, for the double wedge hole cutting, the energy distribution is basically uniform, and there is equivalent vibration energy distribution in each frequency band. In the second layer, about 50 % of the energy is concentrated in the high frequency part, that is in the 1000-2000 Hz. Even in the range of 1750-2000 Hz, there is about 20% of the energy still distributed here.