Identifying microseismic events in a mining scenario using a convolutional neural network

Wilkins, Andy; Strange, Andrew; Duan, Yi; X, Luo

doi:10.1016/j.cageo.2020.104418

Cited by 60 publications

(11 citation statements)

References 33 publications

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“…This research will use a more general and different dataset but with the same sampling frequency. There will be a change in the network architecture, such as the number and the type of layers [8]. Then, the proposed system will include a subsystem in one of the micro-seismic mining systems [9], which has included the frequency domain of the signal as a feature of the neural network.…”

Section: Cpicmentioning

confidence: 99%

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P-Wave detection using deep learning in time and frequency domain for imbalanced dataset

Sugondo

Machbub

2021

Heliyon

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Convolutional neural networks with time and frequency domain can detect Earthquake Primary Wave (P-Wave) signals well.• The Synthetic Minority Oversampling Technique (SMOTE) can increase the imbalanced earthquake dataset training performances. • P-Wave detection with several time windows and classifiers can increase overall performance, surpassing traditional method. • The proposed system can achieve a 0.17second execution time with 0.4 seconds periodic time.

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Section: Cpicmentioning

confidence: 99%

“…Fig. 6 shows the architecture of the phase picker and phase detectors, based on previous research, with several changes [8]. The changes themselves consist of the input dimension (because in this research, we use three-timedomain channels and three-frequency-domain channels) and the zero paddings to maintain the dimension.…”

Section: Classifier Architecturementioning

confidence: 99%

P-Wave detection using deep learning in time and frequency domain for imbalanced dataset

Sugondo

Machbub

2021

Heliyon

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“…Several studies have explored ML methods, such as artificial neural networks (ANNs), support vector machines (SVM), and convolutional neural networks (CNNs) for predicting rock strength parameters, 29,30 locating seismic events within faults, 31 monitoring rock failure events in mining, 32 and for crack patterns recognition in rocks 33 (see the cited references around each topic). Miah et al 29 applied ANNs and SVMs to predict the unconfined compression strength of rocks based on field log data and identified the most relevant predictors.…”

Section: Introductionmentioning

confidence: 99%

“…They showed that the ML methods can provide accurate assessments, as a robust alternative to complex inversion methods. Wilkins et al 32 developed CNNs to detect, classify, and locate micro-seismic events related to rock failure from sonic signals captured by geophones installed in boreholes. They demonstrated that CNNs outperform human microseismic expert in recognizing both true and false events.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the post‐failure behavior of rocks: Combining artificial intelligence and acoustic emission sensing

Yousefpour

Pouragha

2022

Num Anal Meth Geomechanics

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Acoustic emission (AE) reading is among the most common methods for monitoring the behavior of brittle materials such as rock and concrete. This study uses discrete element method (DEM) simulations to explore the correlations between the pre-failure AE readings with the post-failure behavior and residual strength of rock masses. The deep learning (DL) method based on long short-term memory (LSTM) algorithms has been applied to generate predictive models based on the data from DEM simulations of biaxial compression. The dataset has been populated by varying interparticle friction while keeping bond cohesion constant. Various configurations of the LSTM algorithm were evaluated considering different scenarios for input features (strain, stress, and AE energy records) and a range of values for the key hyperparameters. The prime AI models show promising accuracy in predicting residual strength decay with strain based on pre-failure patterns in AE readings. The results indicate that the pre-failure AE indeed encapsulates information about the developing failure mechanisms and the post-failure response in rocks, which can be captured through artificial intelligence.

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“…However, the results showed that RNN overfit the small data set. Wilkins et al [27] trained a CNN using hand-labeled, multichannel microseismic data from a coal mine to detect events. e results showed that the CNN outperformed the human microseismic expert in picking more true events and in eliminating more false events.…”

Section: Introductionmentioning

confidence: 99%

Microseismic Signal Classification Based on Artificial Neural Networks

Xin

Jiang

Jin

2021

Shock and Vibration

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The classification of multichannel microseismic waveform is essential for real-time monitoring and hazard prediction. The accuracy and efficiency could not be guaranteed by manual identification. Thus, based on 37310 waveform data of Junde Coal Mine, eight features of statistics, spectrum, and waveform were extracted to generate a complete data set. An automatic classification algorithm based on artificial neural networks (ANNs) has been proposed. The model presented an excellent performance in identifying three preclassified signals in the test set. Operated with two hidden layers and the Logistic activation function, the multiclass area under the receiver operating characteristic curve (AUC) reached 98.6%.

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Identifying microseismic events in a mining scenario using a convolutional neural network

Cited by 60 publications

References 33 publications

P-Wave detection using deep learning in time and frequency domain for imbalanced dataset

P-Wave detection using deep learning in time and frequency domain for imbalanced dataset

Prediction of the post‐failure behavior of rocks: Combining artificial intelligence and acoustic emission sensing

Microseismic Signal Classification Based on Artificial Neural Networks

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