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LEQNet: Light Earthquake Deep
Neural Network for Earthquake
Detection and Phase Picking
Jongseong Lim
1
†
, Sunghun Jung
2
†
, Chan JeGal
2
†
, Gwanghoon Jung
3
, Jung Ho Yoo
4
,
Jin Kyu Gahm
1
,
2
* and Giltae Song
1
,
2
*
1
Division of Artificial Intelligence, Pusan National University, Busan, South Korea,
2
School of Computer Science and Engineering,
Pusan National University, Busan, South Korea,
3
Department of Industrial Engineering, Pusan National University, Busan, South
Korea,
4
FACDAMM, Busan, South Korea
Developing seismic signal detection and phase picking is an essential step for an on-site
early earthquake warning system. A few deep learning approaches have been developed
to improve the accuracy of seismic signal detection and phase picking. To run the existing
deep learning models, high-throughput computing resources are required. In addition, the
deep learning architecture must be optimized for mounting the model in small devices
using low-cost sensors for earthquake detection. In this study, we designed a lightweight
deep neural network model that operates on a very small device. We reduced the size of
the deep learning model using the deeper bottleneck, recursive structure, and depthwise
separable convolution. We evaluated our lightweight deep learning model using the
Stanford Earthquake Dataset and compared it with EQTransformer. While our model
size is reduced by 87.68% compared to EQTransformer, the performance of our model is
comparable to that of EQTransformer.
Keywords: seismic wave, earthquake detection, lightweight technology, deep learning, convolutional neural
network
1 INTRODUCTION
Detection of seismic signals is essential for an on-site early earthquake warning system (EEWS).
Major approaches for seismic signal detection, such as short-time average/long-time average (STA/
LTA) (Allen, 1978), require an analysis of the ambient noise and structural vibration of sites in
advance and the optimization of threshold values via multiple trials and errors for reducing false
detection. Since the on-site EEWS needs multiple seismic sensors, data acquisition, and management
systems, it is important that sensing systems are cost-effective, and detection is performed with no
pre-analysis on on-site ambient noises.
Traditional approaches to detect P/S waves, such as STA/LTA and AIC (Maeda, 1985) techniques,
process seismic signals using short-term and long-term averages for the amplitudes of continuous
seismic waveforms. This process cannot be fully automated because various noises caused by the
geographical locations and environments of the seismic monitoring equipment need to be removed.
To overcome these issues, several studies have detected seismic signals using machine learning and
deep learning (Ross et al., 2019;Zhu et al., 2019;Mousavi et al., 2020). Among these methods,
EQTransformer is considered to perform the best (Mousavi et al., 2020). The EQTransformer
structure consists of one encoder and three decoders that compress and restore data and is connected
using long short-term memory (LSTM) and an attention mechanism. The EQTransformer model
outputs probabilistic statistics for earthquakes and P/S waves Figure 1.
Edited by:
Said Gaci,
Algerian Petroleum Institute, Algeria
Reviewed by:
Stephen Wu,
Institute of Statistical Mathematics,
Japan
Chong Xu,
Ministry of Emergency Management,
China
*Correspondence:
Jin Kyu Gahm
gahmj@pusan.ac.kr
Giltae Song
gsong@pusan.ac.kr
†
These authors have contributed
equally to this work and share first
authorship
Specialty section:
This article was submitted to
Geohazards and Georisks,
a section of the journal
Frontiers in Earth Science
Received: 04 January 2022
Accepted: 17 May 2022
Published: 04 July 2022
Citation:
Lim J, Jung S, JeGal C, Jung G,
Yoo JH, Gahm JK and Song G (2022)
LEQNet: Light Earthquake Deep
Neural Network for Earthquake
Detection and Phase Picking.
Front. Earth Sci. 10:848237.
doi: 10.3389/feart.2022.848237
Frontiers in Earth Science | www.frontiersin.org July 2022 | Volume 10 | Article 8482371
ORIGINAL RESEARCH
published: 04 July 2022
doi: 10.3389/feart.2022.848237
Although the EQTransformer model detects earthquakes and
P/S phase pickings with high accuracy, it is almost impossible to
mount the model on small IoT (Internet of Things) devices such
as earthquake detection sensors that equip limited computing
resources.
In this study, we proposed a lightweight seismic signal
detection model called LEQNet to operate even in ultra-small
devices. We applied various lightweight deep learning techniques,
such as the recursive, deeper bottleneck, depthwise, and pointwise
separable structures, to reduce the size of the deep learning
model. We evaluated our lightweight deep learning system
using STEAD datasets and compared them with
EQTransformer. Compared to EQTransformer, our LEQNet
reduced the number of parameters substantially, with no
significant performance degradation (Mousavi et al., 2020).
Our LEQNet model can be operated to detect seismic signals
even in small devices. The source code of LEQNet is available at
https://github.com/LEQNet/LEQNet.
2 MATERIALS AND METHODS
2.1 Datasets and Preprocessing
STEAD (Mousavi et al., 2019), a high-quality, large-scale, global
dataset was used in this experiment. STEAD consists of seismic
waveforms with an epicenter of less than 350 km and noise
waveforms without seismic signals. Regarding the
configuration of the data, 120M data are provided, including
450K time series seismic data and noise data for 19,000 h.
One data include 6,000 data points, collected at 100 Hz for
1 min, and three channels, namely, E on the east–west axis, N on
the north–south axis, and Z perpendicular to the ground. Based
on the discussion of EQTransformer, in which the size of the
training dataset did not significantly affect the performance,
earthquake data and noise data were under-sampled by 50,000
each. The two classes were in equal ratio to resolve data imbalance
and build a deep learning detection model. The ratio of training,
validation, and test data set was divided into a general ratio of 8:
1:1.
To add more diverse situations to the training data, we applied
data aggregation techniques (Van Dyk and Meng, 2001).
Augmented techniques include adding events, moving
sequences in parallel, adding noise, deleting channels, and
adding empty sequences with random probabilities.
2.2 Performance Evaluation
2.2.1 Confusion Matrix
The confusion matrix is typically used as an evaluation index
in binary classification problems. When real seismic waves are
detected as earthquakes, it is regarded as a true positive (TP).
Conversely, if they are not detected, it is a false negative (FN).
Noise data are true negative (TN) when predicted as noise and
false positive (FP) when called earthquakes. For phase picking,
it is counted as TP when the actual arrival time of P and S
waves and time of the model are within 0.5 s. When actual P
and S waves do not exist and our model does not call P and S
arrivals, it is regarded as TN. We calculated precision and
recall, as shown in Eqs 1 and 2.F1-score,thatis,theharmonic
average of precision and recall, was calculated as presented in
Eq. 3.
precision TP
TP +FP,(1)
recall TP
TP +FN,(2)
F1−score 2×precision ×recall
precision +recall.(3)
FIGURE 1 | EQTransformer to input signals, detect earthquakes in earthquake waveforms, mark P/S wave arrivals, and output their probabilistic statistics. There
are four plots in the output. Light blue lines indicate the earthquake locations in the three upper plots. In the last output plot, a green dotted line indicates the probability of
an earthquake, a blue dotted line indicates the probability for the starting point of the P wave, and a red dotted line indicates the probability for the starting point of the S
wave.
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Lim et al. Light Earthquake Deep Neural Network
2.2.2 Information Density
Information density is used to evaluate the lightweight
technology. When the accuracy of the model is denoted as
a(N), the number of parameters of the model as p(N) and the
information density D(N) is calculated as shown in Eq. 4. This
evaluation considers the number of parameters and accuracy.
DN
()
aN
()
pN
()
.(4)
2.2.3 Netscore
To consider the amount of computation and inference speed, we
measured Netscore Ω(N), as proposed in Eq. 5.(Wong, 2019).
The multiply-accumulate of the model is denoted by m(N). α,β,
and γare coefficients for controlling the influence of network
accuracy, model complexity, and computational complexity,
respectively. They are usually set to α=2,β= 0.5, and γ=
0.5 (Wong, 2019). When the model became lighter, it had a
smaller number of parameters and fewer operations. If the
performance of the model remains unchanged, the Netscore
increases. The higher the value of Netscore, the lighter is the
model.
ΩN
()
20 × log aN
()
α
pN
()
β×mN
()
γ
.(5)
2.3 Baseline Model EQTransformer
Figure 2A illustrates the structure of EQTransformer that was
designed with encoder, decoder, ResNet, and LSTM &
transformer. The encoder consists of seven convolutional
neural network (CNN) layers. This reduces STEAD seismic
signal data into a lower dimension in terms of seismic signal
length and allows to reduce the number of parameters in the
model. The decoder consists of seven CNN layers. This decoder
restores the extracted features to the original dimension. In the
EQTransformer, there are three decoders for P, S, and earthquake
detection. The ResNet part of the EQTransformer consists of five
residual blocks. Each residual block outputs the addition of input
and output in two linearly connected CNN layers. This allows to
avoid performance degradation caused by a high number of
layers. LSTM learns sequential features extracted by the
encoder. The transformer carries the characteristics of the
seismic signals to each decoder (for P, S, and earthquake
detection). EQTransformer comprises several layers and
learning parameters, which exceed 300k as it focuses on
maximizing the predictive accuracy.
Figure 2A and Table 1 show the structure of EQTransformer
that uses a total of 323,063 parameters. A total of 34,672
parameters were used for the encoder, 109,696 for ResNet, and
42,372 for LSTM & transformer. As EQTransformer uses
separate decoders in earthquake, P-wave, and S-wave
detections, 136,323 parameters are used. The ratio of the total
number of parameters is 11% for the encoder, 34% for ResNet,
13% for LSTM & transformer, and 42% for the decoder. The
layers with the most parameters in the structure include an
encoder, a decoder, and ResNet composed of CNN layer-based
FIGURE 2 | Structure of the EQTransformer. (A) EQTransformer is divided into four main parts: encoder, decoder, ResNet, and LSTM & transformer. (B) Structure
of our LEQNet. LEQNet comprises four main parts: an encoder that uses the depthwise separable CNN with recurrent CNN, a decoder that uses the depthwise
separable CNN with recurrent CNN, residual Bottleneck CNN, and LSTM & Transformer.
TABLE 1 | Number of parameters in the EQTransformer and LEQNet.
Section EQTransformer LEQNet
Encoder 34,672 1,289
ResNet 109,696 3,712
Decoder 136,323 4,131
LSTM & transformer 42,372 30,644
Frontiers in Earth Science | www.frontiersin.org July 2022 | Volume 10 | Article 8482373
Lim et al. Light Earthquake Deep Neural Network
structures, accounting for 87% of the total model parameters. The
lightweight methods used in this study simplify the CNN-based
layer structure with no significant performance degradation.
2.4 Model Lightweight Techniques
2.4.1 Depthwise Separable CNN
Depthwise separable convolution is a CNN that combines the
depthwise and pointwise methods. It was first introduced in
mobile net v1 (Howard et al., 2017)(Chollet, 2017).
Depthwise convolution extracts channel features by
performing a convolution operation per channel.
Consequently, the input and output channels comprise the
same number of channels. In addition, computations are
exponentially reduced by skipping convolution between
channels. Pointwise convolution compresses data at the same
location inside each channel. It extracts the features between
channels and controls the number of channels between the input
and output. (Hua et al., 2018).
When convolution operations in the CNN perform
redundantly among multiple channels, the depthwise separable
convolution reduces the huge amount of computation required
among the channels. (Paoletti et al., 2020).
2.4.2 Deeper Bottleneck Architecture
Deeper bottleneck architecture was proposed in ResNet to reduce
training time (He et al., 2016). In this method, an additional
convolution is performed before the convolution to reduce the
size of the input channel. After convolution with input channels
of reduced size, a convolution operation to restore the number of
channels is executed. When one residual block consists of two
layers in the EQTransformer, we changed it to three layers. While
we have an additional layer, we used a kernel of size one in the
first and last layers. This reduces the number of parameters
substantially in the intermediate layer that performs the actual
operation.
2.4.3 Recurrent CNN
Recurrent CNN is a concept that reuses the output of the CNN
layer. To use the recurrent CNN, the number of channels in input
and output needs to be equal with the same kernel shape. The
layers also need to be continuous. We applied the recurrent CNN
to the encoder and the decoder, respectively. This reduced the
model size by reusing parameters.
The use of the recurrent CNN has an additional benefitof
decreasing memory access costs needed for loading initial
parameters, increasing nonlinearity, and updating gradients in
multiple parts for learning the model (Köpüklü et al., 2019).
3 RESULTS
We learned a detection model using 50,000 sets of earthquake
data and noise data sampled from STEAD and repeated the
learning process for 10 epochs. The threshold values were set for
the probability of seismic detection and P- and S-phase pickings
as detection = 0.5, p= 0.3, and S = 0.3, which were equal to
EQTransformer.
3.1 LEQNet
Figure 2B illustrates the structure of LEQNet that was designed
using the depthwise separable CNN, deeper bottleneck, and
recurrent CNN. Depthwise separable CNN was applied to the
encoder and decoder which were used in the feature compression
and decompression steps. In addition, memory access time in the
encoder and decoder was reduced using the recurrent CNN layers
in LEQNet. These recurrent CNN layers also decreased the
inference time and model size via the reuse of existing
parameters.
Table 1 summarizes the reduction in the number of
parameters. The number of parameters in the encoder was
reduced from 34,672 to 1,289, and from 136,323 to 4,131 in
the decoder. ResNet for central feature extraction in the
EQTransformer was replaced by the deeper bottleneck CNN
in our LEQNet. This reduced the number of parameters from
109,696 to 3,712. Although there was no structural change in
LSTM & transformer, the decrease in the number of input
parameters itself reduced the number of parameters from
42,372 to 30,644 in LSTM & transformer.
Figure 3 illustrates the LEQNet architecture. Each encoder
and decoder of LEQNet consists of five layers, and the deeper
bottleneck consists of four layers; however, EQTranformer has
seven layers in each encoder and decoder and five layers for
ResNet. The number of output channels in the encoder in the
LEQNet has changed to 32, similar to the number of input
channels, while the number of channels in the EQTransformer
was 64.
3.2 Model Size Reduction
LEQNet reduces the number of parameters and computation
time substantially via the lightweight techniques (2 and
Figure 4A). The number of parameters for the LEQNet in this
study decreased by about 88% compared to the EQTransformer.
The amount of computation floating-point operations (FLOPs)
decreased from 79,687,040 to 5,271,488 (Table. 2). These results
reduced the model size by about 79% compared to the
EQTransformer (from 4.5MB to 0.94 MB). This suggests that
our LEQNet model can operate in small devices with tiny
memory.
Table 2 shows the degree of model size reduction for the
EQTransformer, Yews (Zhu et al., 2019) earthquake detection
deep learning model, and LEQNet in terms of information
density and Netscore. As the accuracy between these two
models does not differ significantly, the information density
score only depends on the number of parameters. LEQNet has
improved the information density by about 8.09 times compared
to EQTransformer.
Figure 4A shows Netscore, which measures the amount of
computation according to FLOPs. When Netscore coefficients are
usually set to α=2,β= 0.5, and γ= 0.5, we changed γto 0.1
because the Netscore was measured according to FLOPs in CNN
layers. The theoretical maximum value of Netscore is 80, while
EQTransformer showed 8.29 and Yews 9.82 in Netscore, LEQNet
scored 20.30 (see Figure 4A), indicating that our LEQNet
improves the Netscore by 2.44 times compared to the
EQTransformer.
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Lim et al. Light Earthquake Deep Neural Network
3.3 Detection Performance
We evaluated the detection performance of LEQNet and
compared it with other existing methods, such as
EQTransformer, PhaseNet (Ross et al., 2019), Yews, and STA/
LTA (Table 3). F1-scores for earthquake detection, P-phase
picking, and S-phase picking were also compared.
EQTransformer showed F1-scores of 1.0, 0.99, and 0.98 in
these three tasks. F1-scores (0.99, 0.98, and 0.97) of our
LEQNet were almost similar to those of EQTransformer. Our
F1-scores were higher than those of PhaseNet by 0.02 in P-phase
picking and by 0.03 in S-phase picking, higher than those of Yews
by 0.37 in P-phase picking and by 0.31 in S-phase picking, and by
FIGURE 3 | LEQNet model architecture. Depthwise separable CNN and recurrent CNN were applied to the encoder and decoder, respectively. The number of
layers in the encoder was reduced to five, while the EQTransformer had seven layers in the encoder. Features can be extracted via four blocks using deeper bottleneck
and via five blocks in the EQTransformer. Detailed description of each block is provided in the Methods section. The convolutional layers represent the number of kernels,
and kr is the kernel size.
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Lim et al. Light Earthquake Deep Neural Network
0.14 in earthquake detection. Interestingly, FLOPs in our LEQNet
was similar to those in Yews, although our LEQNet performed
better than Yews in terms of the F1-score.
4 DISCUSSION
EQTransformer and PhaseNet improved the performance of
earthquake detection, P-phase picking, and S-phase picking
substantially using deep neural networks. These models are
difficult to operate on low-cost embedded devices for earthquake
detection. The models should be processed in low-cost embedded
devices using EEWS. Therefore, we aimed to construct a lightweight
model while maintaining high performance.
Compared to existing deep neural network models, the size
of our LEQNet model is reduced while maintaining high
performance, as supported by the finding of this study.
Remarkably, we reduced the number of parameters by
87.68% and FLOPs in CNN layers by 93.38%, compared to
the EQTransformer. This model optimization was achieved by
applying the depthwise separable CNN and recurrent CNN
and by removing some layers in the encoder and decoder,
which occupy 53% of the model. In addition, decreasing the
number of output channels in the ResNet also contributed to
reducing the number of parameters in our LEQNet. However,
performance degradation was observed when some
parameters were reduced in the encoder and decoder. To
resolve this issue, we applied the deeper bottleneck
architecture to our model, as in the EQTransformer. This
increased the information density and Netscore of our
LEQNet model: information density increased from 3.06e-4
to 24.76e-4, 8.09 times higher than that of EQTransformer,
and Netscore increased from 8.29 to 20.30, 2.44 times higher
than that of EQTransformer.
Although we reduced the model size substantially, there
remains room for improvements. Our model needs to run on
the traditional TensorFlow-like environments (Abadi et al.,
2016), which may not be suitable for small devices. Therefore,
we aimed to address this issue using TensorFlow Lite (Google,
2020) in future works.
FIGURE 4 | (A) Comparison of the Netscore, LEQNet, EQTransformer, and Yews. (B) Comparison of detection, S-phase, and P-phase scores between LEQNet
and EQTransformer.
TABLE 2 | Model size comparison results.
Model Parameter FLOP Information density Netscore
EQTransformer 323,063 79,687,040 3.06e-4 8.29
Yews 108,691 7,806,976 6.47e-4 9.82
LEQNet 39,776 5,271,488 24.76e-4 20.30
TABLE 3 | Model result of detection and P-phase and S-phase scores.
Model Detection F1-score P-phase F1-score S-phase F1-score Training data Training size Reference
EQTransformer 1.0 0.99 0.98 Global 1.2M Mousavi et al. (2020)
PhaseNet - 0.96 0.94 North California 780K Ross et al. (2019)
CDRP (DetNet) 0.94 0.90 0.95 China and Japan 30K Zhou et al. (2019)
Yews 0.85 0.61 0.66 Taiwan 1.4M Zhu et al. (2019)
STA/LTA 0.95 - - - - Allen, (1978)
LEQNet 0.99 0.98 0.97 Global 100K
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Lim et al. Light Earthquake Deep Neural Network
5 CONCLUSION
A model for seismic signal detection for the EEWS needs to
operate in low-cost sensors in an environment without the
support of a server and interconnection with other
management systems. As the EQTransformer has a model size
of about 4.5 MB and needs a lot of computation, it is difficult to
operate in limited environments such as low-power wireless
communication devices and Arduino.
To resolve this issue, we developed LEQNet using lightweight
deep learning techniques. LEQNet reduced the number of
parameters of the detection model by 88% and the model size
by 79% (from 4.5 MB to 0.94 MB), as compared to the
EQTransformer, with no significant performance degradation.
Our model can be mounted on IoT devices that include
embedded RAM of less than 1 MB with no special external
management systems.
Although this LEQNet model reduces the model size drastically
compared to the EQTransformer, there remains room for
improvement. Recently, tiny AI models, which can operate in
smaller devices with memory less than 256 kB, are in high
demand. Therefore, our model needs to be reduced more than the
current model size to meet this requirement. Our LEQNet package is
easy to install and is available for public use. We believe that this
lightweight earthquake deep neural network can be a useful tool in a
community burdened with geohazards and georisks.
DATA AVAILABILITY STATEMENT
Publicly available datasets were analyzed in this study. These data
can be found at: https://github.com/smousavi05/STEAD.
AUTHOR CONTRIBUTIONS
JL, SJ, CJ, JG, and GS contributed to the conception and design of the
study. JL, SJ, and CJ wrote the first draft of the manuscript. GS, JG,
GJ, and JY wrote sections of the manuscript. All authors contributed
to manuscript revision, read, and approved the submitted version.
FUNDING
This work was supported by the Institute of Information and
Communications Technology Planning and Evaluation (IITP)
grant funded by the Korean government (MSIT) (No. 2020-0-
01450, Artificial Intelligence Convergence Research Center
(Pusan National University)) to GS.
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Conflict of Interest: Author JY was employed by FACDAMM.
The remaining authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a potential
conflict of interest.
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