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E
Seismic Event and Phase Detection Using
Time–Frequency Representation and
Convolutional Neural Networks
by Ramin M. H. Dokht, Honn Kao, Ryan Visser, and Brindley Smith
ABSTRACT
The availability of abundant digital seismic records and suc-
cessful application of deep learning in pattern recognition and
classification problems enable us to achieve a reliable earth-
quake detection framework. To overcome the limitations and
challenges of conventional methods, which are mainly due to
an incomplete set of template waveforms and low signal-to-
noise ratio, we design a generalized model to improve discrimi-
nation between earthquake and noise recordings using a deep
convolutional network (ConvNet). Exclusively based on a
dataset of over 4900 earthquakes recorded over a period of
3 yrs in western Canada, a multilayer ConvNet is trained to
learn general characteristics of background noise and earth-
quake signals in the time–frequency domain. In the next step,
we train a secondary network using the wavelet transform of
the major seismic arrivals to separate Pfrom Swaves and esti-
mate their approximate arrival times. The results of validation
experiments demonstrate promising performance and achieve
an average accuracy of nearly 99% for both networks. To inves-
tigate the applicability of our algorithm, we apply the trained
model on an independent dataset recently recorded in
northeastern British Columbia (NE BC). It is found that
deep-learning-based methods are superior to traditional tech-
niques in detecting a higher number of seismic events at sig-
nificantly less computational cost.
Electronic Supplement: Tables reporting the performance of
convolutional networks trained directly in the time domain,
and figures showing the accuracy of the validation set and
P- and S-wave error measurements.
INTRODUCTION
The recent increase in the rate of earthquake occurrence in
western Canada, a region with a historically low level of back-
ground seismicity, has been largely attributed to the develop-
ment of unconventional hydrocarbon resources (Horner et al.,
1994;Schultz et al., 2014;Farahbod et al., 2015;Rubinstein
and Mahani, 2015;Atkinson et al., 2016). Reliable ground-
motion analyses and seismic hazard assessments require a com-
plete earthquake catalog containing both natural and induced
earthquakes. However, most of the conventional automated
techniques fail to identify induced events of low magnitudes,
which makes characterizing and locating these events a chal-
lenging task. In comparison, manual picking of seismic events
has a higher detection rate, but the process is extremely labo-
rious and remains subjective to the analyst’s experience.
Among the various detection algorithms, the short-term
average/long-term average (STA/LTA) technique, which mea-
sures the signal-to-noise ratio (SNR) function, has been widely
used for detecting moderate-to-large earthquakes if a certain
triggering threshold is exceeded (Allen, 1978;Withers et al.,
1998). On the other hand, cross-correlation-based techniques
(also called template matching) have been extensively used to
identify repeating earthquakes of lower magnitudes based on sim-
ilarity measurements of the entire waveforms (Gibbons and
Ringdal, 2006;Skoumal et al., 2015;Caffagni et al., 2016).
Although less sensitive to high noise levels, template matching
is computationally intensive and its application is limited to iden-
tifying earthquakes sharing the same source region and mecha-
nism (Eisner et al., 2006).Thiscanbepartiallyremediedby
generalizing template matching using clustering of similar wave-
form fingerprints through a set of hash functions (Yoon et al.,
2015;Bergen et al., 2016), and subspace analysis of a set of rep-
resentative waveforms (Barrett and Beroza, 2014). Recent studies
take advantage of spectral information of seismic records to
improve the accuracy associated with detection of weak micro-
seismic events (Galiana-Merino et al., 2008;Va ez i a nd Va n d er
Baan, 2015;Mousavi et al., 2016), though aprioriknowledge of
noise characteristics is required.
Deep learning is a set of representation-learning algorithms
with multiple layers of nonlinear transformations (Bengio et al.,
2013;LeCun et al.,2015;Najafabadi et al., 2015). Unlike con-
ventional machine-learning techniques, which rely on carefully
hand-engineered features (Wa n g a n d T e n g , 1 9 9 5 ;Gentili and
Michelini, 2006;Maity et al.,2014), deep learning allows a
model to learn a general-purpose representation of raw data
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using a training set (Dahl et al., 2013;Wang and Yeung,
2013). Deep representation learning has been widely applied
to several research areas such as natural language understanding
(Collobert et al., 2011;Mikolov et al.,2013), image classification
(Krizhevsky et al.,2012), and speech recognition (Dahl et al.,
2012;Hinton et al.,2012). Recent studies have investigated
the application of deep-learning techniques for earthquake
detection (e.g., Perol et al.,2018;Ross et al.,2018;Zhu and
Beroza, 2018) and seismic imaging (Araya-Polo et al., 2018;
Moseley et al., 2018).
Our research is motivated by the successful application of
deep convolutional networks (ConvNets) to overcome the lim-
itations of traditional techniques in studying induced seismic-
ity in Oklahoma, United States (Perol et al., 2018). The basic
idea is to train a ConvNet model on a large dataset of previ-
ously recorded earthquakes, so that the classifier can be gener-
alized to identify seismic events different from those used in
training. In comparison with the model proposed by Perol et al.
(2018), the current study presents additional improvements
by including both temporal and spectral information of three-
component seismograms to enhance the detection accuracy. We
divide the automatic earthquake detection process into two steps.
A pretrained model is first adopted to separate earthquakes from
nonearthquake signals in the time–frequency domain. Then, we
build up a secondary supervised classification system using
higher-resolution spectral images of earthquake records to dis-
criminate between Pand Swaves.
METHOD AND DATASET
Convolutional Neural Networks
ConvNets are feed-forward, multilayer neural networks, which
were introduced to process multidimensional arrays (LeCun
et al., 1998). A ConvNet transforms an input volume of data
features to output class probabilities through a sequence of sev-
eral hidden units consisting of convolutional, pooling, and fully
connected layers. Each convolutional layer contains a bank of
linear filters to extract local features at all parts on the previous
layer (Cireşan et al.,2012) and passes the resulting convolutional
responses through a nonlinear activation unit. ConvNets have
been found to train several times faster by mapping all negative
responses to zero using a rectified linear unit (Nair and Hinton,
2010;Glorot et al.,2011):
EQ-TARGET;temp:intralink-;df1;40;229xjmax0;b
jX
i
wijxi;1
in which bjand wij are the bias and weights of the jth neuron
passing the input from the previous layer xito the output feature
map xj, respectively. It is common to spatially downsample the
resulting activations by merging similar local features into one
(LeCun et al.,2015). The subsampling, also known as pooling,
operation can significantly reduce the number of free parame-
ters, thereby improving the performance of the network and
avoiding overfitting (Krizhevsky et al.,2012). Max pooling is the
most widely used subsampling operator; this calculates the
maximum activation over nonoverlapping local neighborhoods
(Serre et al., 2005;Cireşan et al., 2012), and thus reduces the
variability to small temporal/spatial transformations (LeCun
et al.,1990;Ya ng et al., 2010;Farabet et al.,2013). The output
feature maps, resulted from a sequence of convolutional, non-
linear activation, and pooling layers, are concatenated and passed
to a fully connected layer in which every neuron is linearly con-
nected to all activations in the previous layer. The output of the
last fullyconnected layer isfed to a normalized exponential func-
tion (softmax classifier), which calculates a probability distribu-
tion over Cdifferent possible classes (Peterson and Söderberg,
1989):
EQ-TARGET;temp:intralink-;df2;311;601pjexpxj
PC
k1expxkk1;…;C: 2
The objective of the current study is to find a set of learn-
able free parameters, which minimizes the misfit between the
predicted pand ground-truth scores gof Ninstances using a
L2-regularized multinomial logistic loss function:
EQ-TARGET;temp:intralink-;df3;311;507J1
NX
N
n1
X
C
k1
−gn
klogpn
kλX
i
kWik2
2:3
The regularization parameter λcontrols the trade-off
between the data misfit and model constraints, and Wiare the
model parameters (bias and weights) of the ith layer. The sol-
ution to the objective function is found using the gradient
descent technique, which calculates the updates on network
parameters at each iteration tby a linear combination of the
negative gradient of the loss function and the model update
from the previous iteration t−1:
EQ-TARGET;temp:intralink-;df4;311;346ΔWtμΔWt−1−α∇JWt;4
in which the learning rate αis the weight of the negative gra-
dient, and the momentum μcontrols the network parameter
update at each iteration. For a large training set, it would be
more efficient to estimate the stochastic approximation of the
cost function by drawing a small random selection (mini-
batch) of the training set at each iteration.
Earthquake Detection
In the first step of our earthquake detection framework, we
design a ConvNet to scan continuous seismic records and sep-
arate between noise and earthquake signals. Exclusively based
on a comprehensive earthquake catalog from western Canada,
a classifier is trained to identify coherent high-power earthquake
signals between three-component seismograms. In this region,
the Geological Survey of Canada has reported 4914 earthquakes
with local magnitudes MLranging from 0.1 to 4.9 between
January 2014 and December 2016 (Fig. 1;Visser et al.,2017).
After deconvolving the instrument response from the ground
velocity records, we apply a 2 Hz high-pass filter and resample
the data to 20 Hz. The resulting waveforms are visually
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inspected and low-quality data are discarded. The final dataset
contains 13,949 earthquake–receiver pairs with epicentral dis-
tances up to 275 km (Fig. 1c) and a total of 148,000 noise
records selected from time segments free of earthquake arrivals.
To avoid overfitting due to the uneven number of observations
per category, the earthquake signals are randomly repeated, time-
shifted, and contaminated with Gaussian noise.
Unlike the previous studies (Perol et al., 2018;Ross et al.,
2018), which perform the classification task in the time
domain, the current model attempts to learn the general char-
acteristics associated with the earthquake and nonearthquake
signals in the time–frequency domain. Because seismic event
and noise recordings have intrinsically different spectral con-
tents, the performance of the network can be further improved
by learning from the spectro-temporal representations of wave-
form data (Wang and Teng, 1995;Vaezi and Van der Baan,
2015). The first layer of the ConvNet is represented by spectro-
grams of 100-s-long data segments calculated using the short-
time Fourier transform (STFT; Portnoff, 1980). The spectro-
grams are computed using a moving Hanning window of 40
samples with 50% overlap between successive windows (Fig. 2).
The resulting spectrograms are then normalized by their maxi-
mum spectral values and therefore become independent of
earthquake magnitude.
The architecture of earthquake detection ConvNet con-
sists of a sequence of four convolutional layers, each followed
by a pooling layer (Table 1). The supervised classifier is trained
on a random subset consisting of 75% of spectrograms (train-
ing set) and its performance is evaluated in terms of detection
accuracy of the remaining 25% observations
(validation set). The training is run for 50,000
iterations with a trade-off parameter λ10−4,
an initial learning rate of 0.01 decaying to
0.005, and a fixed moment rate of 0.9. The
training process is performed using a mini-
batch of 200 samples per iteration and takes
approximately 4 hrs on an Intel Core i7
CPU (4 GHz).
Phase Identification
The goal of the next experiment is to design a
secondary classifier using the existing earthquake
catalog for separating between different body
waves. Robust measurements of phase arrivals are
achieved by analyzing the nonstationary, multi-
component seismic signals using the wavelet
transform (WT) (Zhang et al.,2003;Ahmed
et al.,2007;Galiana-Merino et al., 2008).
Compared with the STFT spectrogram, the WT
has higher temporal resolution, but it suffers
from the energy spread out along the frequency
axis. The synchrosqueezing wavelet transform
(SWT) is a new time–frequency analysis tech-
nique that combines the conventional WT with
a frequency reassignment method to enhance the
time–frequency localization (Daubechies and
Maes, 1996;Daubechies et al.,2011). Assuming that the energy
of a seismic event is concentrated in a few high-amplitude wave-
let coefficients, Mousavi et al. (2016) introduced an SWT-based
technique to simultaneously suppress nonstationary random
noise and detect onset times of weak microseismic events using
a characteristic function of the thresholded wavelet coefficients.
In this step, we train a separate network using a dataset of
high-resolution wavelet power spectra of major seismic arrivals
picked by expert analysts. The new dataset consists of 11,500
P-wave, 11,500 S-wave, and 45,000 noise windows of 5 s long
each. The windows containing both Pand Swaves are repeated
and centered on the arrival time of each phase separately. We
applied the same data augmentation strategy described in the
Earthquake Detection section to ensure that there is an equal
number of observations in each class. Then, we employed a
Morlet wavelet, product of a complex exponential and a
Gaussian envelope, as the basis function to calculate the SWT
of windowed data (Fig. 3). The final dataset is eventually
divided into the training and validation sets including time–
frequency representations of waveform data normalized by the
maximum value of wavelet coefficients of all three compo-
nents. The architecture of phase identification network and its
model parameters are similar to those of earthquake detection.
However, the input layer consists of three maps of 80 ×101
neurons, and all convolutional layers have a fixed filter size
of 3×5. The pooling layers have local receptive fields of size
2×2with a constant stride of 2 in both dimensions, the first
fully connected layer has 192 neurons and the output layer is a
vector of 3 class labels (Table 2).
(a) (b)
(c)
▴Figure 1. (a) Distributions of earthquakes (circles) and seismic stations (white
triangles) used in this study. The warm and cold colors correspond to shallow
and deep earthquakes, respectively. The circle sizes represent the earthquake
magnitudes. (b) A histogram of event local magnitude distribution with mean and
standard deviation values of 1.8 and 0.55, respectively. (c) Distribution of source–
receiver distances using a 20 km bin size.
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RESULT
We evaluate the performance of the proposed
framework on validation datasets selected inde-
pendent of training sets. While the solver tries
to minimize the objective function using the
training samples (learning curves in Fig. 4a,b),
validation data are employed to measure the gen-
eralization capability of the trained network for
correct classification of new samples that have
never been considered during the training phase
(Table 3).Thelearningcurvesshowthatthe
earthquake detection algorithm rapidly converges
to the optimal solution after 20,000 iterations,
but it takes ∼40;000 iterations until the objec-
tive function becomes flat for the seismic phase
identification model. The neural network produ-
ces a vector of confidence scores for all class
labels, indicating the probability of a particular
input belonging to a given category. The accuracy
of correct classification is calculated every 5000
iterations and the model with the highest accu-
racy on the validation set is selected as the final
classifier (ⒺFig. S1, available in the electronic
supplement to this article).
For earthquake detection, the network accu-
racy reaches 99.8% on the validation set and a
total number of only 40 earthquake windows are
mislabeled. This is comparable to that obtained
from the training dataset, which yields respective
accuracies of 99.7% and 99.95% for earthquake
and nonearthquake categories (see Table 3). On
the other hand, the phase identification network
results in a slightly reduced accuracy on average.
The second classifier gives an average error of
0.7% for the entire training set and predicts
Table 1
Architecture of Convolutional Network (ConvNet) Used for Seismic Event Detection
Layer Type Kernel Size Stride Output
1 Input data ——3mapsof33 ×99 neurons
2 ConvReLU 5×7—16 maps of 29 ×93 neurons
3 Maxpool 1×2(1, 2) 16 maps of 29 ×47 neurons
4 ConvReLU 5×5—16 maps of 25 ×43 neurons
5 Maxpool 1×2(1, 2) 16 maps of 25 ×22 neurons
6 ConvReLU 3×3—16 maps of 23 ×20 neurons
7 Maxpool 2×2(2, 2) 16 maps of 12 ×10 neurons
8 ConvReLU 3×3—16 maps of 10 ×8neurons
9 Maxpool 2×2(2, 2) 16 maps of 5×4neurons
10 FC —— 320 neurons
11 FC —— 2 neurons
ConvReLU, a convolutional layer followed by a rectified linear unit; Maxpool, a max pooling layer; FC, a fully connected layer.
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Frequency (Hz) Frequency (Hz) Frequency (Hz)
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CN.BMBC.BHECN.BMBC.BHE
CN.BMBC.BHNCN.BMBC.BHN
CN.BMBC.BHZ CN.BMBC.BHZ
▴Figure 2. Three-component waveforms of (a) a 2.4 magnitude earthquake
recorded by CN.BMBC at 164 km distance on 25 February 2014, and (b) a postevent
noise window recorded at the same station. (c–e) The short-time Fourier transform
of earthquake records in (a). (f–h) Same as (c–e) but calculated for the noise win-
dow in (b).
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correct labels for approximately 98.7% and 98.4% of P-andS-
wave windows in the validation set, respectively.
The performance of the proposed model is compared with
that obtained from a model that is trained directly in the time
domain using the same dataset.Although the difference between
the earthquake detection networks is insignificant, the total
number of misclassified samples obtained from our model is
nearly 40% of that of the time-domain classifier (see classifica-
tion accuracies in Table 3and ⒺTable S1 and confusion matri-
ces in Table 4and ⒺTable S2). However, the difference
becomes more pronounced for the phase identification process.
On average, the model trained using the time–frequency features
improves the accuracy by up to 3.2%. Although
the time-domain model misidentifies 580 of P
phases and 588 of Sphases in the validation set,
our model provides a higher recall rate and
misses only 137 and 79 of Pand Swaves, respec-
tively (see confusion matrices in Table 5and
ⒺTable S3).
The earthquake detection and phase iden-
tification results indicate that the classification
performance can be affected by the minimum
accepted confidence score (Fig. 4). We define a
hard-thresholding function, which discards
earthquakes whose scores are less than a given
probability threshold (LeCun et al., 1990). A
careful selection of probability threshold is
required because a low threshold value can
increase the number of false detections, while
higher threshold values may result in erroneous
rejection of true events. The distribution of the
number of earthquakes as a function of detec-
tion score shows a gradual increase for confi-
dence scores greater than 0.6 with a sudden
rise at a detection score of 0.95 (Fig. 4c). The
same observations are made for the phase sep-
aration results as nearly 98.5% of correctly pre-
dicted examples fall in the last probability bin
(Fig. 4d).
Induced Seismicity in Northeast British
Columbia
After successful training and validation, the
ConvNet models were employed for monitor-
ing the seismic activity in Fort St. John and near
Dawson Creek, an area with an increased num-
ber of potentially induced earthquakes in recent
years (Horner et al., 1994;British Columbia Oil
and Gas Commission, 2014;Visser et al., 2017).
The network was implemented on continuous
records from an array of nine broadband sta-
tions, deployed in August 2017, which are not
included in either training or validation set. For
each station, the daily records are first divided
into 100-s-long windows (with 50% overlap
between windows) and the STFT representa-
tions of windowed data are calculated. If the first classifier
detects a seismic event at a probability threshold of 0.95,
the SWT of the corresponding time window will be calculated
and scanned by the second network to identify Pand Swaves.
The earthquake detection step takes ∼5sfor a three-compo-
nent daily recording, which is ∼30 times faster than the STA/
LTA technique. To assess the detection performance and gen-
eralization power of the model, all detected events are visually
inspected. It turns out that only 13 events are false detections
(2% of the total number of detected events), 9 of which are
eventually eliminated by the phase identification classifier. In
comparison, the time-domain model returns ∼25% more false
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▴Figure 3. (a) Three-component waveforms of the earthquake shown in
Figure 2a. Red and blue dashed lines indicate the P- and S-wave arrival times,
respectively. (b–d) The normalized synchrosqueezing wavelet transform (SWT) of
a 5-s-long window centered on the P-wave arrival. (e–g) The normalized SWT of a
5-s-long window centered on the S-wave arrival.
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positives than its time–frequency counterpart. The phase iden-
tification network is capable of detecting multiple P-andS-wave
arrivals if more than one event exists within a time window
(Fig. 5and ⒺFig. S2). Our technique finds 652 events
with epicentral distances ranging from 1 to 28 km on
5 September 2017 (Fig. 6). The local magnitudes of detected
earthquakes vary from −0:5to 1.1, which remain below the
minimum magnitude in the training catalog
for nearly 30% of events.
The automatic detection results corroborate
the observations obtained from manual picking
and show significant temporal variations (Fig. 6).
The ConvNet model detects ∼20% more earth-
quakes than previously reported by an expert
analyst, though Pwaves are identified for only
40% of detected events. A possible explanation
for this observation includes the difficulties in
separating Pand Sphases for earthquakes
recorded at shorter distances. For the remaining
60%, the P-wave arrivals are included mostly in
the S-wave windows because the differential
times between the two phases are less than 1.5 s
(nearly 80% of detected events are recorded at
epicentral distances ≤10 km). It is worth noting
that the time–frequency phase classifier identifies
52 more P-wave windows than the model trained
in the time domain. The ConvNet models
trained in the time–frequency and time domains
miss 31 and 39 of the events identified by a
human analyst, respectively. A lower probability
threshold allows the network to detect smaller
events, but this may result in false detections.
DISCUSSION
Most of the existing earthquake detection tech-
niques are poorly suited for identifying low-
magnitude events, and their application in areas with no record
of seismicity is limited by the absence of template earthquake
waveforms (Yoon et al., 2015). The main goal of this research
is to present a detection framework independent of earthquake
magnitude, epicentral distance, and noise level. We propose a
method utilizing multiresolution time–frequency analysis and
advances in deep learning to achieve a robust earthquake detec-
tion. In comparison with the time-domain analysis, the
0.0
0.2
0.4
0.6
Objective value
0 10k 20k 30k 40k 50k
Iteration
100
101
102
103
104
105
106
0.5 0.6 0.7 0.8 0.9 1.0
Number of earthquakes
Earthquake detection probability
Training set
Validation set
0.0
0.4
0.8
0 10k 20k 30k 40k 50k
Iteration
0.5 0.6 0.7 0.8 0.9 1.0
101
102
103
104
105
Phase identification probability
Number of windows containing P- or S-wave
Earthquake detection
learning curve
(a) (b)
(c) (d)
Objective value
Phase identification
learning curve
Training set
Validation set
▴Figure 4. Learning curves of the (a) earthquake detection, and (b) phase iden-
tification networks showing the reduction in objective function value during the
training process. Distributions of correctly labeled (c) earthquakes and (d) seismic
phases as a function of detection score.
Table 2
Architecture of ConvNet Used for Seismic Phase Identification
Layer Type Kernel Size Stride Output
1 Input data ——3 maps of 80 ×101 neurons
2 ConvReLU 3×5—16 maps of 78 ×97 neurons
3 Maxpool 2×2(2, 2) 16 maps of 39 ×49 neurons
4 ConvReLU 3×5—16 maps of 37 ×45 neurons
5 Maxpool 2×2(2, 2) 16 maps of 19 ×23 neurons
6 ConvReLU 3×5—16 maps of 17 ×19 neurons
7 Maxpool 2×2(2, 2) 16 maps of 9×10 neurons
8 ConvReLU 3×5—16 maps of 7×6neurons
9 Maxpool 2×2(2, 2) 16 maps of 4×3neurons
10 FC —— 192 neurons
11 FC —— 3 neurons
ConvReLU, a convolutional layer followed by a rectified linear unit; Maxpool, a max pooling layer; FC, a fully connected layer.
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application of SWT enables the network to learn the spectral
structures of noise and seismic signals, which has been found to
increase the accuracy of phase identification process by up to
∼3%. However, the calculation of WTs of continuous wave-
forms is computationally expensive and may not efficiently
scale to the large-array data. To reduce the processing time, the
phase identification network scans only the earthquake time
windows that are detected by the preliminary ConvNet using
the STFT-based spectrograms.
The learning ability of a neural network can vary with
changes in its structure (Wang and Teng, 1995). The sensitiv-
ity of the proposed method with respect to the network archi-
tecture was explored by (1) increasing the number of filters in
convolutional layers, and (2) replacing the pooling with strided
convolutions (Perol et al., 2018). We found no significant
variation in model performance in both cases, though the
Table 3
Classification Accuracies of the Earthquake Detection and
Seismic Phase Identification ConvNets Trained in the
Time–Frequency Domain
Training Set (%) Validation Set (%)
Earthquake detection accuracy
Seismic event 99.7 99.6
Noise 99.95 99.9
Phase identification accuracy
P-wave 99.1 98.7
S-wave 99.1 98.4
Noise 99.7 99.4
XL.MG03.HHE
XL.MG03.HHN
XL.MG03.HHZ
Detection score
0
1
0
5
10
Frequency (Hz)
0
5
10
Frequency (Hz)
0
5
10
Frequency (Hz)
P-wave
S-wave
02:26:27 02:26:47 02:27:07 02:27:27 02:27:47
02:26:27 02:26:47 02:27:07 02:27:27 02:27:47
XL.MG03.HHE
XL.MG03.HHN
XL.MG03.HHZ
(a)
(b)
(c)
(d)
(e)
0 0.2 0.4 0.6 0.8 1.0
▴Figure 5. (a) A 100-s-long time segment labeled as seismic event
by the earthquake detection network. The second network identi-
fies two separate events within this time segment using a 5-s-long
sliding window. Red and blue colors represent windows containing
Pand Swaves, respectively. Vertical red and blue lines mark the
P-andS-wave arrival times using their corresponding detection
scores, respectively. Dashed gray and black lines indicate the man-
ually picked Pand Swaves, respectively. (b) Continuous functions
of the P- (red) and S-wave (blue) detection scores. (c–e) The SWT
of waveform records presented in (a).
Table 5
Validation Set Confusion Matrix Calculated for the Phase
Identification Network Trained Based on the
Time–Frequency Feature Maps
Predicted Labels
True labels
P-wave S-wave Noise
P-wave 11,363 90 47
S-wave 58 11,421 21
Noise 87 91 11,322
04
812
16
20
0
20
40
60
80
100 ConvNet
Manual
Number of events
Time (hour)
▴Figure 6. Distribution of detected earthquakes over the period
of one day, on 5 September 2017, in the Dawson Creek area.
Convolutional network (ConvNet) detects a total number of 652
events (red bars), which is ∼20% more than the manually picked
earthquakes (gray bars).
Table 4
Validation Set Confusion Matrix Calculated for the
Earthquake Detection Network Trained Based
on the Time–Frequency Feature Maps
Predicted Labels
True labels
Seismic event Noise
Seismic event 37,460 40
Noise 142 36,882
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training time dramatically rises with the increasing number of
free parameters (the former case). To avoid overtraining, a con-
stant dropout rate of 10% was applied to the input and con-
volutional layers, which forces the network to learn more
robust features by ignoring a random subset of neurons during
the training process. An optimum dropout rate provides the best
trade-off between the training data misfit and validation set
accuracy. The near-identical performance of the proposed model
on both training and validation sets reflects the generalization
power of the model beyond the existing earthquake catalog.
In addition to phase separation, the second classifier pro-
vides a means to estimate the phase arrival times, which are
required to determine earthquake location and other source
parameters. Ross et al. (2018) suggest that the location of maxi-
mum detection score of each phase can be used to measure its
approximate travel time if multiple successive windows identify
the same phase (see Fig. 5b). The maximum difference between
the manual picks and those obtained from the detection scores
is on the order of one half of the sliding window length
(Fig. 7). The accuracy of automatic time picking is generally
affected by the SNR, and weaker signals result in larger errors
(ⒺFig. S3). However, the implementation of the WT enables
noise reduction, which can further reduce the picking error and
possibly automate the earthquake location process (a topic
beyond the scope of this article, but we refer readers to
Mousavi et al., 2016, for more detailed discussion).
CONCLUSION
Based on recent advances in deep learning, we propose a
ConvNet algorithm for robust detection of seismic events to
address the shortcomings of existing methods. Our technique
relies on the detectability of earthquake signals in the time–fre-
quency domain and takes advantage of the spectral characteris-
tics of phase arrivals to separate Pand Swaves. In comparison
with manual detection, our technique can identify ∼20% more
events while significantly reducing the processing time and
improving the efficiency. In addition to event detection, this
approachprovides initial estimates of phase onset
times, which can be used to determine prelimi-
nary earthquake locations. Highly improved
accuracy and reliable rejection of false detections
are achieved by joint application of the event
detection and phase identification ConvNets.
The proposed approach can be potentially uti-
lized to enhance real-time monitoring of both
natural and induced seismicity.
DATA AND RESOURCES
The regional earthquake catalog used in this
study was compiled by the Geological Survey
of Canada (http://publications.gc.ca/site/eng/
9.856883/publication.html, last accessed
September 2018). Waveform data can be collected
from the Incorporated Research Institutions for
Seismology (IRIS) Data Management Center at https://
ds.iris.edu/ds/nodes/dmc/ (last accessed September 2018). We
used Mocha, a deep-learning framework for Julia, to train the
convolutional networks (the latest version of Mocha is available
at https://mochajl.readthedocs.io/en/latest/, last accessed
September 2018). Some figures were generated using the
Generic Mapping Tools (GMT) v.5.4.2 (www.soest.hawaii.edu/
gmt, last accessed September 2018; Wessel and Smith, 1998).
ACKNOWLEDGMENTS
The authors wish to thank Guest Editor Karianne Bergen and
two anonymous reviewers for useful comments and suggestions
that helped improve the quality of this article. This is Natural
Resources Canada (NRCan) Contribution Number 20180263.
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Ramin M. H. Dokht
Honn Kao
Ryan Visser
Brindley Smith
Pacific Geoscience Centre
Natural Resources Canada
Geological Survey of Canada
9860 West Saanich Road
Sidney, British Columbia
Canada V8L 4B2
ramin.mohammadhosseinidokht@canada.ca
Published Online 16 January 2019
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