PU et al 2020 IJMST

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International Journal of Mining Science and Technology 29 (2019) 565–570
Contents lists available at ScienceDirect
International Journal of Mining Science and Technology
journal homepage: www.elsevier .com/locate / i jmst
Machine learning methods for rockburst prediction-state-of-the-art
review
https://doi.org/10.1016/j.ijmst.2019.06.009
2095-2686/� 2019 Published by Elsevier B.V. on behalf of China University of Mining & Technology.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
⇑ Corresponding author.
E-mail address: dapel@ualberta.ca (D.B. Apel).
Yuanyuan Pu a, Derek B. Apel a,⇑, Victor Liu a, Hani Mitri b
a School of Mining and Petroleum Engineering, University of Alberta, Edmonton T6G 2R3, Canada
bDepartment of Mining and Materials Engineering, McGill University, Montreal H3A 2T6, Canada
a r t i c l e i n f o
Article history:
Received 1 October 2018
Received in revised form 28 December 2018
Accepted 2 January 2019
Available online 24 June 2019
Keywords:
Rockburst prediction
Burst liability
Artificial neural network
Support vector machine
Deep learning
a b s t r a c t
One of the most serious mining disasters in underground mines is rockburst phenomena. They can lead to
injuries and even fatalities as well as damage to underground openings and mining equipment. This has
forced many researchers to investigate alternative methods to predict the potential for rockburst occur-
rence. However, due to the highly complex relation between geological, mechanical and geometric
parameters of the mining environment, the traditional mechanics-based prediction methods do not
always yield precise results. With the emergence of machine learning methods, a breakthrough in the
prediction of rockburst occurrence has become possible in recent years. This paper presents a state-of-
the-art review of various applications of machine learning methods for the prediction of rockburst poten-
tial. First, existing rockburst prediction methods are introduced, and the limitations of such methods are
highlighted. A brief overview of typical machine learning methods and their main features as predictive
tools is then presented. The current applications of machine learning models in rockburst prediction are
surveyed, with related mechanisms, technical details and performance analysis.
� 2019 Published by Elsevier B.V. on behalf of China University of Mining & Technology. This is an open
access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
Rockburst is a common geological hazard encountered in min-
ing engineering and rock engineering and can damage equipment
and lead to injuries and death [1–8]. Virtually all mining countries
have records about rockburst hazards. In Canada, more than 15
mines reported rockburst case histories, including the Brunswick
lead-zinc mine at Bathurst, the Lake Shore mine, Teck-Hughes
mine, Wright-Hargreaves mine, and Macassa gold mines at Kirk-
land Lake [9]. In the United States, from 1936 to 1993, 172 rock-
burst cases were recorded. These cases resulted in more than 78
fatalities and 158 injuries [10,11]. During November 1996, rock-
bursts causing three fatalities and five additional serious injuries
occurred in a two-week period [12]. Rockburst occurrences in Ger-
many have declined in recent years, not because of better tech-
niques that can predict or limit the occurrence and severity but
because of a decrease in underground mining. Despite the decrease
in underground mining activity, Germany still recorded rockbursts
which led to injuries and fatalities; between 1983 and 2007, more
than 40 cases have been recorded with injuries and deaths [13]. In
Australia, the first rockburst event with related fatalities and inju-
ries occurred in 1917 at the GoldenMile underground working face
in Kalgoorlie. Hundreds of rockbursts and mine seismicity were
observed. Between 1996 and 1998, three fatalities in west Aus-
tralian underground mines occurred as a result of falls of ground
potentially associated with large seismic events [14]. Due to
high-stress mining conditions, rockburst hazards have become an
increasingly frequent problem in Australia [15]. China is currently
the world’s largest coal producer. With its high rate of under-
ground coal production, China has seen a steady increase in the
number of recorded rockbursts. More than 100 Chinese mines have
recorded rockbursts [16–18]. In November 2011, a serious rock-
burst occurred in the Qianqiu Mine in Henan province, injuring
64 miners and killing 10. Fig. 1 shows a historical rockburst map
of 1108 events during the period from 1995 to 2000. All these
losses are a proof that rockbursts are a serious problem and should
be given attention.
2. Traditional rockburst prediction methods
Rockburst prediction may be classified into two categories:
long-term prediction, which can be used at the design stage of
engineering, and short-term prediction, which is helpful during
the life of an engineering project [20].
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Fig. 1. A historical rockburst map for the period of 1995–2000 [19].
566 Y. Pu et al. / International Journal of Mining Science and Technology 29 (2019) 565–570
Short-term rockburst prediction mainly includes field monitor-
ing such as micro-seismic method, electromagnetic radiation
method, drilling cutting method, micro-gravity method, infrared
thermal imaging method. Butt et al. used high frequency micro-
seismic system to capture high frequency microseismic events at
Creighton Mine in Sudbury, Canada [21]. These high frequency
events were caused by microfractures of the overstressed rock.
Similar findings were reported by Wang and his colleagues who
used microseismic monitoring techniques to predict rockbursts
in Jinping II hydropower station, and proposed that precursory
microcracking exists prior to most rockbursts, which could be cap-
tured by the micro-seismic monitoring system [22]. The stress con-
centration is evident near structural discontinuities, which should
be the focus of rockburst monitoring. Frid et al. proposed that
higher stress associated with increased rockburst hazard in rocks
near a working mine generates an increase in their natural electro-
magnetic radiation (EMR) [23]. Petukhov first put forward the idea
of using the volume of ‘‘drilled coal rubble” to estimate a rockburst
hazard at coal mines [24]. The corresponding mechanism of this
method is drilling a borehole that leads to excitation of an inten-
sive process of cracking in the zone of its influence. Fajklewic
explained the relationship between a micro-gravity anomaly and
the occurrence of a rockburst, and pointed out that during the pro-
cess of a rockburst the variation of micro-gravity anomaly changes
from a positive value to a negative value [25]. At the point before a
Table 1
Frequently used rockburst potential assessment indicators.
Number Name Description
1 Strain energy storage index (WET) [27] A ratio between strain
strain energy dissipate
2 Strain energy density (SED) [28] SED ¼ r2
c
2ES
rc is uniaxial compress
unloading elastic modu
3 Rock brittleness (B) [29] B ¼ rc
rT
rc is uniaxial compress
the tensile strength (M
4 Criterion of tangential stress (Ts) [30] Ts ¼ rh
rc
rh is the tangential str
the excavations and rc
5 Failure duration time (Dt) [30] The failure duration tim
peak strength to total
6 Energy-based burst potential index (BPI) [31] BPI ¼ ESR
ec
� 100%
ESR (kJ/m3) is the energ
mass and ec (kJ/m3) is t
rockburst, the negative value ofa micro-gravity anomaly will be an
extreme value. Some researchers measured moisture content in
coal seams and proposed that when the moisture content in a coal
seam is greater than 3%, there is no rockburst hazard. Zhang et al.
employed a thermal infrared radiation systemmonitoring the tem-
perature variation in a tunnel floor surface at coal mines and found
that the infrared radiation temperature on the left and right walls
of tunnels shows a sudden increase before rockbursts [26]. Short-
term rockburst prediction methods can be implemented after the
completion of underground development. Only after excavating
an underground tunnel or drift can the monitoring equipment be
installed at underground excavations. On the other hand, in order
to avoid areas with high rockburst hazard during excavation, a
long-term rockburst prediction method should be employed at
the design stage of future excavations.
Long-term rockburst prediction is based on a combination of
evaluating rockburst potential and field conditions. To evaluate
burst potential, some scholars put forward several indicators. The
strain energy storage index (WET), which refers to a ratio between
strain energy retained (Wsp) and strain energy dissipated (Wst), is
proposed by Kidybinski [27]. Wattimena et al. used elastic strain
energy density as a burst potential indicator [28]. The rock brittle-
ness coefficient, based on the ratio between UCS (uniaxial com-
pressive stress) and tensile stress, is another widely used burst
liability indicator [29]. A criterion of tangential stress, the ratio
between tangential stress around underground excavations, rh,
and the UCS of rock, rc can be used to assess the rock burst ten-
dency [30]. Mitri et al. developed an energy-based burst potential
index (BPI) to diagnose the burst proneness [31]. Table 1 shows
some frequently used burst potential evaluation indicators.
However, rockburst occurrence relates to a number of factors,
including geologic structure, mining or excavation methods,
mechanical properties of rocks, and in-situ stress [32]. Further-
more, the mutual effects of these impact factors for occurrence of
rockburst are still not clear. As such, current prediction methods
have significant limitations in engineering. Given this situation,
some scholars have tried to use machine learning methods to pre-
dict rockbursts.
Classification criteria
energy and retained (Wsp)
d (Wst)
WET � 2.0, no burst potential;
2.0 200, Strong burst potential.
ion stress (MPa), and ES is
lus (GPa)
B > 40, No burst potential;
26.7 500 ms, No burst potential;
50 msof a rockburst for similar conditions.
Zhou et al. employed a support vector machine (SVM) to deter-
mine the classification of long-term rockburst for underground
openings [49]. Two optimization methods: genetic algorithm and
swarm optimization algorithm, are adopted to automatically
determine the optimal hyper-parameters for SVMs. The results
indicated that the heuristic algorithm of GA and PSO can speed
up SVM’s parameter optimization search. This proposed method
might hold high potential to become a useful tool in rockburst
prediction.
learning development.
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Fig. 4. Performance comparison between traditional machine learning and deep
learning.
Fig. 3. Rockburst classification accuracy from twelve machine learning algorithms
[42].
568 Y. Pu et al. / International Journal of Mining Science and Technology 29 (2019) 565–570
Su et al. proposed a new method based on k-Nearest Neighbor
case reasoning technology [50]. The results of the prediction of a
mining induced rockburst at a great depth in South Africa show
that this method is feasible and reliable for rockburst prediction
with high precision.
A Bayes discriminant analysis model is used by Fu et al. to pre-
dict the possibility and classification of rockbursts [51]. Three fac-
tors are the discriminating factors of the model. Rockbursts in
Dongyu Mine and Pingdingshan deep development opening were
predicted using this model. The predicted results are consistent
with the observed ones.
Cai et al. combined a principal component analysis and fuzzy
comprehensive evaluation model for coal burst potential assess-
ment [1]. These two methods can decrease the correlation of orig-
inal data, which can avoid interaction among original data.
Zhou et al. summarized 12 machine learning algorithms includ-
ing artificial neural network (ANN), distance discriminant analysis
(DDA), support vector machine (SVM), Bays discriminant analysis
(BDA), Fisher linear discriminant analysis (LDA), etc., in long-
term rockburst prediction and compared their prediction accura-
cies [44]. These algorithms used different rockburst indicators as
input features and their training samples sizes were different.
Fig. 3 shows various accuracies obtained from 12 machine learning
algorithms.
These applied machine learning methods in rockburst predic-
tion mainly focus on burst potential evaluation which can be
regarded as long-term rockburst prediction. Some other scholars
applied machine learning in microseismic monitoring, a field mon-
itoring method that can predict rockburst within a short-term.
Microseismic signals are critical evidence for rockburst occur-
rence. However, many noise sources characterized by an abrupt
amplitude, including human walking, passing vehicles, and espe-
cially blasting, increasing give the appearance of microseismic
events [48]. Hence, the first step to use microseismic signal to pre-
dict rockburst is extracting genuine rock microseismic signals from
received monitoring signals.
Zhao and Gross demonstrated how to use a support vector
machine (SVM) to distinguish genuine microseismic from noise
events [48]. 16 input attributes were extracted from 71 original
time-domain and frequency-domain features to train the SVM
model based on a dimensionality reduction method called neigh-
borhood component analysis (NCA). Four different kernel functions
(linear, Gaussian, Quadratic, Cubic) were embedded in the SVM
model to compare accuracy. However, SVM is a binary classifier
which can only distinguish microseismic events and non-
microseismic events. We can anticipate a multi-classifier to further
classify noise events into more subclasses such as walking noise,
vehicle noise and blasting noise
Dong et al. compared the three machine learning models (Fisher
classifier, naive Bayesian classifier, and logistic regression) in dif-
ferentiating seismic events and blasts generate seismic waveform
[52]. The results showed that the logistic regression model had
the best discriminating performance in these three mines. How-
ever, database from three mines were used as training as well as
testing. The generalization performance of the model might be
doubtful. In other words, this model only guaranteed empirical risk
minimization instead of structural risk minimization.
Shang et al. used a BP neural network to distinguish rock mass
fracturing signals and blasting vibration signals [53]. A combined
method: frequency slice wavelet transform (FSWT) plus singular
value decomposition (SVD) was adopted to extract relevant infor-
mation from original microseismic signals as input parameters
for BP neural network. The results showed 86.67% of the signals
could be precisely identified. This BP neural model had 70 training
samples as well as 50 test samples, which was not an optimal pro-
portion. In general, the proportion between training samples and
test samples should be 2:1.
Yıldırım et al. used three different neural network models (feed-
forward neural networks, adaptive neural fuzzy inference system,
and probabilistic neural network) to discriminate between seismic
events and quarry blasts [54]. He found that the feedforward neu-
ral network performs better than other two neural networks with a
classification accuracy 99% against 96% for adaptive neural net-
work and 97% for probabilistic neural network under a support
of 175 seismic events data.
5. Conclusion, discussion and future research
A significant number of lab tests and engineering projects show
a highly nonlinear relationship between rockburst occurrence and
corresponding control factors. Machine learning is a prospective
way to simulate such relationship because it does not need any
prior knowledge about the nature of the relationship between
the input/output variables, which is one of the benefits that
machine learning has over most empirical and statistical methods.
However, problems do exist with the application of machine learn-
ing in rockburst prediction.
Most existing machine learning methods in rockburst predic-
tion use a shallow machine learning model like SVM, decision tree,
and logistic regression. Although these models enjoy computation
convenience, they can only show relatively simple relationships
between rockburst control factors and rockburst occurrence.
Highly nonlinear relationship may not locate in the scope of shal-
lowmachine learning models. In future research, deep learning can
be involved in rockburst prediction. A multi-layer neural network
can tackle any function with arbitrary precision, which ensures a
more accurate relationship between rockburst control factors and
rockburst occurrence. Furthermore, deep learning has demon-
strated its superior performance with more supportive data.
Fig. 4 compares performances between deep learning and tradi-
tional shallow machine learning methods under different data
sizes. Traditional machine learning methods perform slightly
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Y. Pu et al. / International Journal of Mining Science and Technology 29 (2019) 565–570 569
better than deep learning with small data size. However, this
advantage will totally reverse when data size is large enough.
Another advantage of deep learning is automatic feature selec-
tion. In general, when we apply machine learning to rockburst pre-
diction, we must assign features for input vector. For example, in
burst potential assessment with machine learning, some indicators
in Table 1 are determined as features for training samples. How-
ever, manual feature determination usually cannot reveal all char-
acteristics of a problem. Feature engineering in deep learning is
able to decide features automatically, create featuresand improve
features, which is very helpful in microseismic signal identification
in short-term rockburst prediction. As of now, there is no research
referring to this topic.
Based on the machine learning principle ‘Garbage in, garbage
out’, the selection of training samples directly influences the suc-
cess or failure of prediction. Some deficiencies exist in current
long-term rockburst prediction with machine learning. Firstly, a
common problem is lack of training samples. A small amount of
training samples cannot feed enough features into a machine
learning model, which leads to inferior prediction accuracy.
Another problem for training samples is imbalance, which means
there are significant differences among the number of labels in
the training set. For example, if the expected prediction results
are ‘rockburst happen’ and ‘no rockburst’, an optimal training set
should consist of 50% ‘rockburst happen’ labelled samples and
50% ‘no rockburst, labelled samples. However, the ‘‘rockburst hap-
pen” records in engineering projects are much less than ‘no rock-
burst’ records, which usually results in an unbalanced training
set. One common solution to solve training set imbalance is
over-sampling for ‘rockburst happen’ samples and under-
sampling for ‘no rockburst’ samples.
As a short-term prediction method, microseismic monitoring
can relatively predict the burst location and burst time, which sug-
gests a hopeful prospect in rockburst control. However, current
research about machine learning in microseismic monitoring
mainly focuses on distinguishing burst signals from other noises.
There is little research about the subsequent steps on how to build
a model between burst signals and rockburst occurrence. This job
is pending for future research. The new breakthrough of rockburst
prediction applying machine learning based on field monitoring
may reside in the monitoring signal anomaly detection. All types
of field monitoring signal are expected to show anomalies before
a real rockburst happens. If we can build the relation between sig-
nal anomalies with rockburst, somehow can determine the rock-
burst happen time. Now, anomaly detection with machine
learning is a prospective technology with many proposed methods.
The introduction of this kind of technology may provide an alterna-
tive method for rockburst prediction.
In summary, research about machine learning in rockburst pre-
diction have made some achievements although some flaws exist
in current research. Future achievements can be obtained if more
advanced machine learning methods are involved in addressing
this issue.
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	Machine learning methods for rockburst prediction-state-of-the-art review
	1 Introduction
	2 Traditional rockburst prediction methods
	3 Brief introduction of machine learning methods
	4 Rockburst prediction with machine learning methods
	5 Conclusion, discussion and future research
	References