Classification of mine blasts and microseismic events using starting-up features in seismograms

Zhao, Guoyan; Ma, Jiangwei; Dong, Longjun; Li, Xibing; Chen, Guanghui; Zhang, Chuxuan

doi:10.1016/s1003-6326(15)63976-0

Cited by 49 publications

(17 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning, including Convolution neural network (CNN), is used to classify landslide-based seismic events and human-made events such as mine blasts. The microseismic events and quarry blast events are classified in [40] and [41] respectively, with good accuracy. SVM can solve the high dimensional non-linear classification problem with small training datasets [42].…”

Section: T46mentioning

confidence: 99%

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

Stanković

Pytharouli

2021

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Microseismic monitoring has been increasingly used in the past two decades to illuminate (sub)surface processes such as landslides, due to its ability to record small seismic waves generated by soil movement and/or brittle behaviour of rock. Understanding the evolution of landslide processes is of paramount importance in predicting or even avoiding an imminent failure. Microseismic monitoring recordings are often continuous, noisy and consist of signals emitted by various sources. Manually detecting and distinguishing the signals emitted by an unstable slope is challenging. Research on automated end-to-end denoising, detection, and classification of microseismic events, as an early warning system, is still in its infancy. To this effect, our work is focused on jointly evaluating and developing suitable approaches for signal denoising, accurate event detection, non site-specific feature construction, feature selection and event classification. We propose an automated end-to-end system that can process big data sets of continuous seismic recordings fast and demonstrate applicability and robustness to a wide range of events (distant and local earthquakes, slidequakes, anthropogenic noise etc.). Algorithmic contributions lie in novel signal processing and analysis methods with fewer tunable parameters than the state of the art, evaluated on two field datasets and benchmarked against the state of the art.

show abstract

Section: T46mentioning

confidence: 99%

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

Stanković

Pytharouli

2021

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Besides, based on large amounts of data and statistical methods, blasts and seismic events were identified. Dong et al [148,149] and Zhao et al [167] used the Fisher Classifier, Naive Bayesian Classifier, and logistic regression to establish discriminators. Databases from three Australian and Canadian mines were established for training, calibrating, and testing the discriminant models.…”

Section: Microseismic (Ms) Techniquementioning

confidence: 99%

Dynamic Stability Analysis of Rockmass: A Review

Dong

Wang

et al. 2018

Advances in Civil Engineering

View full text Add to dashboard Cite

ere are a series of human or natural activities, including earthquakes, explosions, and rockbursts, which have caused a number of safety accidents in geotechnical engineering. is review paper summarized the theories and methods for dynamic stability analysis of rockmass. First, numerical simulation methods, including finite element method (FEM), discrete element method (DEM), finite difference method (FDM), boundary element method (BEM), discontinuous deformation analysis method (DDA), and numerical manifold method (NMM), are summarized. Second, the laboratory experiments, containing shaking table test, split-Hopkinson pressure bar test (SHPB), improved true-triaxial test, dynamic centrifugal model test, acoustic emission (AE) technique, and dynamic infrared monitoring, are considered. ird, the in situ tests including microseismic (MS) technique, velocity tomography, stress-strain monitoring, and electromagnetic radiation monitoring are considered. Finally, some comprehensive analysis methods based on statistical theories are also provided. It is pointed out that the study foundation for the dynamic stability of rockmass is weak to explain the mechanism. So, a set of general comprehensive theories integrating the different methods, including theoretical analysis, numerical methods, laboratory experiments, and in situ tests, should be completely established. is is the most effective way for further investigation.

show abstract

“…The fitting precision of the following peak attenuation curve of the blasting signal is higher, with 84% of the signals falling in the range of 0.9∼1. Therefore, in most The basic principle of Fisher linear discriminant analysis is to project multidimensional and multiclass data to the same direction and separate two or more classes [15,17]. In fact, it is a dimension reduction process.…”

Section: Statistics Of Attenuation Coefficient and Fitting Precisionmentioning

confidence: 99%

“…Ma et al analyzed seven parameters to distinguish between mining MS signals and blasting signals in a phosphorite mine, based on which, two statistical discriminant models were proposed by the Bayesian classifier and the Fisher classifier. Their models were more accurate than traditional methods [16,17]. Based on the works of Booker and Taylor, Malovichko applied a multivariate maximum-likelihood Gaussian classifier technique to discriminate between mining MS and blasting events.…”

Section: Introductionmentioning

confidence: 99%

Discriminant Model of Coal Mining Microseismic and Blasting Signals Based on Waveform Characteristics

et al. 2017

Shock and Vibration

View full text Add to dashboard Cite

Generally, there are two important types of microseismic (MS) signals caused by mining and blasting activities at coal mines. The waveform characteristics of MS signals using FFT, STA/LTA method, and envelope analysis were studied to distinguish these two types of MS signals. The main results are as follows: the dominant frequency and duration of two types of signals are significantly different. The following peak envelope curves of two types of MS signals fit a power function. The power exponent was obtained to describe the attenuated speed of the MS signals. The attenuation of the coal mining MS signals is slower and more fluctuant than that of the blasting signal. Waveform characteristics consisting of the dominant frequency, duration, and attenuation coefficient were extracted as the discriminating parameters. The discriminating performance of these parameters was compared and discussed. Based on the waveform characteristics, a discriminant model for coal mining MS and blasting signals was established by using Fisher linear discriminant method and its performance was checked. The results show that the accuracy of the discriminant model is more than 85%, which can meet the requirements of MS monitoring at coal mines.

show abstract

Classification of mine blasts and microseismic events using starting-up features in seismograms

Cited by 49 publications

References 16 publications

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

Dynamic Stability Analysis of Rockmass: A Review

Discriminant Model of Coal Mining Microseismic and Blasting Signals Based on Waveform Characteristics

Contact Info

Product

Resources

About