Improving microseismic event and quarry blast classification using Artificial Neural Networks based on Principal Component Analysis

Shang, Xueyi; Li, Xibing; Morales‐Esteban, Antonio; Chen, Guanghui

doi:10.1016/j.soildyn.2017.05.008

Cited by 72 publications

(38 citation statements)

References 25 publications

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“…The red fonts correspond to the sensor id, green grid means the ground surface, dark orange line is the Jinyang road, blue lines are different mining levels, and light blue lines are the mining levels with sensor distributed. The spheres are locations of microseismic events; (b) System structure diagram of the MS system modified from Shang et al [53]. The green line represents 4 core optical fiber, pink line represents 4 core signal cable, and the bold pink line represents 8 core signal cable.…”

Section: Engineering Background and Multi-scale Grid Generationmentioning

confidence: 99%

High-Accuracy Location of Microseismic Events in a Strong Inhomogeneous Mining Environment by Optimized Global Full Waveform Inversion

et al. 2020

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High-accuracy determination of a microseismic (MS) location is the core task in MS monitoring. In this study, a 3D multi-scale grid Green’s function database, depending on recording wavefield frequency band for the target mining area, is pre-generated based on the reciprocity theorem and 3D spectral element method (SEM). Then, a multi-scale global grid search strategy is performed based on this pre-stored Green’s function database, which can be effectively and hierarchically processed by searching for the spatial location. Numerical wavefield modeling by SEM effectively overcomes difficulties in traditional and simplified ray tracing modeling, such as difficult wavefield amplitude and multi-path modeling in 3D focusing and defusing velocity regions. In addition, as a key step for broadband waveform simulation, the source-time function estimated from a new data-driven singular value decomposition averaged fractional derivative based wavelet function (DD-SVD-FD wavelet) was proposed to generate high-precision synthetic waveforms for better fitting observed broadband waveform than those by simple and traditional source-time function. Combining these sophisticated processing procedures, a new robust grid search and waveform inversion-based location (GSWI location) approach is integrated. In the synthetic test, we discuss and demonstrate the importance of 3D velocity model accuracy to waveform inversion-based location results for a practical MS monitoring configuration. Furthermore, the average location error of the 3D GSWI location for eight real blasting events is only 15.0 m, which is smaller than error from 3D ray tracing-based location (26.2 m) under the same velocity model. These synthetic and field application investigations prove the crucial role of 3D velocity model, finite-frequency travel-time sensitivity kernel characteristics and accurate numerical 3D broadband wavefield modeling for successful MS location in a strong heterogeneous velocity model that are induced by the presence of ore body, host rocks, complex tunnels, and large excavations.

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Section: Engineering Background and Multi-scale Grid Generationmentioning

confidence: 99%

High-Accuracy Location of Microseismic Events in a Strong Inhomogeneous Mining Environment by Optimized Global Full Waveform Inversion

et al. 2020

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“…A considerable number of seismic signal discrimination methods based on statistical machine learning have been proposed [2][3][4][5][6][7]. Mousavi [2] developed a machine learning-based strategy to discriminate between deep microseismic events and shallow ones using logistic regression and artificial neural network models.…”

Section: Introductionmentioning

confidence: 99%

Seismic Discrimination between Earthquakes and Explosions Using Support Vector Machine

Kim

Lee

You

2020

Sensors

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The discrimination between earthquakes and explosions is a serious issue in seismic signal analysis. This paper proposes a seismic discrimination method using support vector machine (SVM), wherein the amplitudes of the P-wave and the S-wave of the seismic signals are selected as feature vectors. Furthermore, to improve the seismic discrimination performance using a heterodyne laser interferometer for seismic wave detection, the Hough transform is applied as a compensation method for the periodic nonlinearity error caused by the frequency-mixing in the laser interferometric seismometer. In the testing procedure, different kernel functions of SVM are used to discriminate between earthquakes and explosions. The outstanding performance of a laser interferometer and Hough transform method for precision seismic measurement and nonlinearity error compensation is confirmed through some experiments using a linear vibration stage. In addition, the effectiveness of the proposed discrimination method using a heterodyne laser interferometer is verified through a receiver operating characteristic curve and other performance indices obtained from practical experiments.

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“…Ruano et al build a classifier using support vector machines, aiming at distinguishing local and regional earthquakes and explosions from the other possibilities in earthquake early-warning system [17]. Shang et al propose a hybrid technique based on principal component analysis and artificial neural networks (PCA-ANN) to discriminate between microseismic events and quarry blasts [18]. e PCA-ANN is trained on a dataset with 1600 events, and 22 source parameters are extracted from each event, such as corner frequency, seismic moment, energy, source radius, and static stress drop.…”

Section: Introductionmentioning

confidence: 99%

“…However, there is still room for further improvement. Most of these classifiers are trained on a large number of parameters [18][19][20][21], which are acquired through experienced processing. In other words, these algorithms cannot classify an event unless basic processing procedures (e.g., P-wave arrival picking and epicenter location) are done.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines

Peng

Wang

2019

Shock and Vibration

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In order to mitigate economic and safety risks during mine life, a microseismic monitoring system is installed in a number of underground mines. The basic step for successfully analyzing those microseismic data is the correct detection of various event types, especially the rock mass rupture events. The visual scanning process is a time-consuming task and requires experience. Therefore, here we present a new method for automatic classification of microseismic signals based on the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) by using only Mel-frequency cepstral coefficient (MFCC) features extracted from the waveform. The detailed implementation of our proposed method is described. The performance of this method is tested by its application to microseismic events selected from the Dongguashan Copper Mine (China). A dataset that contains a representative set of different microseismic events including rock mass rupture, blasting vibration, mechanical drilling, and electromagnetic noise is collected for training and testing. The results show that our proposed method obtains an accuracy of 92.46%, which demonstrates the effectiveness of the method for automatic classification of microseismic data in underground mines.

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Improving microseismic event and quarry blast classification using Artificial Neural Networks based on Principal Component Analysis

Cited by 72 publications

References 25 publications

High-Accuracy Location of Microseismic Events in a Strong Inhomogeneous Mining Environment by Optimized Global Full Waveform Inversion

High-Accuracy Location of Microseismic Events in a Strong Inhomogeneous Mining Environment by Optimized Global Full Waveform Inversion

Seismic Discrimination between Earthquakes and Explosions Using Support Vector Machine

Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines

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