Shock and Vibration

Volume 2018 (2018), Article ID 9753028, 10 pages

https://doi.org/10.1155/2018/9753028

## Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning

School of Resources and Safety Engineering, Central South University, Changsha 410000, China

Correspondence should be addressed to Ruishan Cheng

Received 28 September 2017; Accepted 23 January 2018; Published 22 February 2018

Academic Editor: Xinglin Lei

Copyright © 2018 Zilong Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The automatic discrimination of rock fracture and blast events is complex and challenging due to the similar waveform characteristics. To solve this problem, a new method based on the signal complexity analysis and machine learning has been proposed in this paper. First, the permutation entropy values of signals at different scale factors are calculated to reflect complexity of signals and constructed into a feature vector set. Secondly, based on the feature vector set, back-propagation neural network (BPNN) as a means of machine learning is applied to establish a discriminator for rock fracture and blast events. Then to evaluate the classification performances of the new method, the classifying accuracies of support vector machine (SVM), naive Bayes classifier, and the new method are compared, and the receiver operating characteristic (ROC) curves are also analyzed. The results show the new method obtains the best classification performances. In addition, the influence of different scale factor and number of training samples on discrimination results is discussed. It is found that the classifying accuracy of the new method reaches the highest value when = 8–15 or 8–20 and .

#### 1. Introduction

In laboratory rock tests, in situ rock excavation, and a lot of other rock engineering, signals of rock fracture events are often mixed with other signals such as environmental noise, impact and vibration, and blast signal. When these signals are monitored by microseismic or acoustic emission machines [1–4], the presence of jamming signals, especially blast signals, may result in the wrong interpretation, for example, erroneous state evaluation and disaster prediction [5, 6]. Consequently, it is necessary to ensure a clean database of rock fracture signals. Although the discrimination of rock fracture and blast events can be performed by experts, manual discrimination of rock fracture and blast signals is time-consuming and subjective due to the fact that it depends on the experience. Therefore, discrimination of rock fracture and blast signals, in particular large quantities of signals, requires a reliable and automatic method.

In recent years, machine learning has been widely applied to realize automatic identification and classification about signals. Machine learning [7–10] includes many methods, such as neural network [11–13], support vector machine [14, 15], and naive Bayes classifier [16, 17]. Currently, several recognition methods of rock fracture or similar signals were proposed in some studies. For example, Shang et al. [18] classified microseismic events and quarry blasts according to artificial neural networks (ANN) based on principal component analysis. Yildirim et al. [12] used the extracted peak amplitude ratio ( ratio) of quarry blasts and earthquakes to contrast classification accuracies of FFNN, PNN, and ANFIS. Liu et al. [19] proposed a method of wavelet transform and ANN to recognize acoustic emission signals for different rocks. Del Pezzo et al. [20] used ANN based on seismogram signatures to classify earthquakes and underwater explosions. Peng et al. [21] used improved BPNN and combined feature extraction method to recognize seismic signal.

All the aforementioned methods usually conduct feature extraction before feature recognition. Waveform parameters of signals, such as amplitude, frequency, and total radiated energy, are extracted as eigenvectors. However, those waveform parameters are sometimes impossible to reflect the characteristic of the total waveform absolutely. In addition, the process of extracting parameters also consumes much time and effort. In order to classify signals more precisely and easily, it is vital to find a classification method that need not depend on waveform parameters of rock fracture and blast signals.

In this study, a new method based on signal complexity analysis and machine learning has been proposed to achieve automatic identification of rock fracture and blast signals without waveform parameter. The method calculates signal complexity based on multiscale permutation entropy (MPE) and uses back-propagation neural network (BPNN) as a tool of machine learning. To calibrate and validate the proposed method, the signal complexity values of predetected events were also input into support vector machine (SVM) and naive Bayes classifier to classify signal category. In addition, the influence of scale factors and number of training samples on classifying accuracy was also analyzed for the new method.

#### 2. Methodology

##### 2.1. Signal Complexity Analysis with Multiscale Permutation Entropy

Feature extraction of signals is usually required before signal discrimination. Almost all the previous studies used waveform parameters as discrimination features. For example, Vallejos and McKinnon [22] used 13 parameters of seismic full waveform as discrimination feature vectors of blast and microseismic events. Mousavi et al. [23] extracted 40 features from time, frequency, and time–frequency domains to classify deep and shallow microearthquakes. However, the commonly used characteristic parameters are difficult to obtain automatically, which limits the automatic identification of rock fracture events. Furthermore, the above waveform parameters are obtained from single scale analysis, which reflects less information of signals. To solve the above problems, this paper extracts feature vectors of signals based on signal complexity standpoint. Signal complexity is expressed primarily by correlation and random degree of time series for a signal, which reflects the overall feature of a signal. The complexity of a signal can be described by many methods, such as permutation entropy (PE) [24, 25], multiscale permutation entropy (MPE) [26, 27], Lempel-Ziv complexity [28], and multiscale Lempel-Ziv complexity [29]. MPE is more robust due to the only use of the order of time series values; meanwhile MPE can obtain multiscale signal information. This paper applies thus MPE to calculate signal complexity as signal recognition features. The basic principles are introduced as follows.

A one-dimensional time series is given as follows:

Coarse graining of the above time series can be expressed bywhere stands for the scale factor and stands for the multiscale time series. When , the coarse graining time series stands for the original time series.

Phase space reconstruction of coarse graining series is performed:where is the embedded dimension and is the time delay. If the number of real values contained in each can be arranged in ascending order as

and if there exist two or more elements in that have the same value, for example, , their original positions can be sorted such that, for ,

Accordingly, any vector can be mapped onto a group of symbols aswhere and ; is the largest number of permutations. The permutation entropy of time series at scale is expressed as follows:

If , will reach a maximum and will be normalized; then

Thenwhere in represents signal complexity when the scale factor is equal to . The size of value indicates the degree of randomness of time series. The smaller the value of is, the more regular the time sequence states are. The greater the value of is, the more random the time series is.

##### 2.2. Signal Identification with Back-Propagation Neural Network

After signal features are extracted by signal complexity, then discrimination of rock fracture and blast signals is performed by feature recognition. However, manual identification is time-consuming and easily influenced by individual factors. In order to reliably discriminate rock fracture and blast signals automatically, BP neural network [30] as an identification tool is applied. It is made up of an input layer, a hidden layer, and an output layer.

There are two kinds of signals flowing between layers in BP neural network. The working signals spread forward and other error signals between actual outputs and expected outputs are back-propagated. The basic process is shown as follows.

The hidden layer input of the th node:where represents the hidden layer input of the th node, stands for the weight value from the th node of the hidden layer and the th node of the input layer, is the th input of input layer, and is the th threshold of the hidden layer. The hidden layer output of the th node:

In the formula, is the hidden layer output of the th node and stands for the inspirit function of the hidden layer. The output layer input of the th node: where represents the output layer input of the th node, stands for the weight value from the th node of the out layer and the th node of the input layer, and is the th threshold of the output layer. The output layer’s output of the th node:In the formula, is the output layer’s output of the th node and stands for the inspirit function of the output layer.

The error function is given by (14) and the BP ANN stops when is satisfied, where is a given precision. where is expected value of output node .

A learning process updates the weights for each neuron based on the following equation:where is learning rate, .

#### 3. Discrimination Process and Performance of the New Method

##### 3.1. Discrimination Process

This section describes the process of whole discrimination of rock fracture and blast signals based on the proposed method. The process divides signals waveform data into training and test and validation sets. The specific steps of the new method are as follows.

*Step 1 (sample selection). *Choose training samples of numbers from 200 sets of samples that are named . The samples are composed of blasting and rock fracture signal samples. And the remaining data in are regarded as test and validation data.

*Step 2 (feature extraction). *Use MPE to calculate permutation entropies of training samples with different scale factors to form feature vectors of training sets; the remaining data are also extracted to form features vectors of test and validation sets.

*Step 3 (train machine learning tools). *Input the feature vectors of training samples to train BPNN and make it adjust the weight value constantly until the error is below the set error value.

*Step 4 (classification of test and validation data). *Input the feature vectors of test and validation samples to the BP neural network that has been trained. Through network internal calculation, the accuracies of test and validation data can be derived.

According to the above operation, the classification results are derived. The whole process sketch is shown in Figure 1.