Proceedings of International Joint Conference on Neural Networks, Dallas, Texas, USA, August 4-9, 2013
Detection and Identification of Seismic P-Waves using Artificial
Neural Networks
Komalpreet Kaur, Manish Wadhwa and E. K. Park
Abstract—Detection and identification of seismic P-Wave is
useful in event location and event detection. This involves an
intensive amount of pattern recognition. For the recognition
of seismic phases, no probabilistic distribution model performs
as well as Artificial Neural Network(ANN). Back Propagation
Neural Network (BPNN) was applied for the automatic detection and identification of local and regional seismic P-Waves.
For a set of three-component seismic data, four attributes
were used as input to the ANN: Degree of Polarization (DOP),
Auto Regression Coefficient (ARC), Ratio between Short time
average and Long time average (STA/LTA) and Ratio of Vertical
power to Total power (RV2T). These four attributes were
calculated in the frequency band of 1-8 Hz with a 2 second
moving window. The results of preliminary training and testing
with a set of various local and regional earthquake recordings
show that the ANN achieved 95% correct rate of P-Wave
detection and identification. 90% of the P-Waves were detected
with a maximum deviation of 0.1 sec from correct manual pick
up.
I. I NTRODUCTION
The occurrence of an earthquake gives rise to seismic
waves. Identification of seismic P-Waves helps to locate
earthquake source [1], [2]. P-Waves are longitudinal waves,
that is, they travel along the direction of wave propagation.
These waves alternately compress and pull out the solid rock.
These waves travel through massive rock and liquid material
like magma and water. This is the fastest kind of seismic
wave and can travel up to speed of 4 miles/sec.
To minimize the colossal losses which take place as a
result of major earthquakes, the design and configuration
of a seismic alert system has become the urgent need of
the hour. For this, the automatic identification and detection
of first arrival is required, avoiding false alerts, and for
logging correct arrival time of P-Wave. This is necessary for
identification of an event. Quickly detecting and accurately
identifying the first P-Wave is of great importance in event
location, event identification, source mechanism analysis and
spectral analysis. There are various methods available for
identification of P-Wave. While all these algorithms work
well for selected seismic data, the difficulties of how to
define characteristic function and set threshold are still major
drawbacks that reflect a compromise between rate of false
picking and rate of missing real signals. Artificial Neural
Komalpreet Kaur is with the Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA (email:
[email protected]).
Manish Wadhwa is with the Department of Information Technology, South
University, Virginia Beach, VA (email: [email protected]).
E. K. Park is with the Department of Graduate Studies and Office of
Research and Sponsored Programs, California State University, Chico, CA
(email: [email protected])
978-1-4673-6129-3/13/$31.00 ©2013 IEEE
Networks (ANNs) are emerging as potential tool for the
detection and identification of seismic signals owing to the
fact that they can be adapted to fit more complex decision
surface compared with the conventional techniques.
Identification and detection of P-Wave from a seismogram
is a problem of pattern recognition. ANN handles pattern
information by performing several pattern recognition tasks.
Several authors have used ANNs using different attributes
of seismic waves for automatic detection of phases [3]–[9].
ANN is used in pattern association, pattern classification, and
pattern grouping etc. [5], [6]. Authors in [10], [11] employed
an ANN to identify P- and S-Waves from seismic threecomponent data. They used degree of polarization (DOP)
to distinguish P- and S-Waves. It was shown that ANN is
capable of classifying the arrival type based on characteristic
patterns of polarization state of a seismic wave. They showed
that this method was adaptive and training sets can be altered
to enhance particular features of different data sets. 84% of
P-Waves and 63% of S-waves were precisely picked by this
algorithm. It had a limitation due to inter-station complexity
of DOP. The quality of polarization information deteriorates
with increasing noise level. Instead of using DOP as the only
attribute, this paper takes into consideration four different
attributes as discussed in section II. The decision is thus
based on weighted decision using four different attributes
that reduces the error component caused by increased levels
of noise.
In this paper, we use Back Propagation Neural Network
(BPNN) with three-component seismic data. Authors in [12]
also applied BPNN for automatic seismic arrival pick up
using single component recordings. Absolute value of the
single-component seismic recordings was used as an input
to ANN. Compared with other single-component methods
this method is flexible and can pick up both P- and SWaves simultaneously. But performance of single-component
picking is lower as compared to three-component picking
because of the dependency of the single-component data
on ray direction. Three-component recordings are important
for regional seismic monitoring because regional phases can
exhibit large horizontal motions. The use of an array of
three-component sensors reduces the variance of polarization
attributes.
In [13], the authors applied ANN to the automatic picking
of local and regional S-phase. Three-component data was
used and variety of attributes namely STA/LTA, ratio between horizontal and total power, short axis incidence angle
and ARC were analyzed. The attributes were calculated in
frequency band of 2 to 8 Hz with a 2.56 sec-moving window.
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Multi layer Perceptron (MLP) Neural Network model was
employed for detection of seismic S-Wave. 74% of the Sphase was precisely picked with less than 0.1sec onset time.
This paper accomplishes the following three objectives.
• The first objective is to optimize the neural network
model with respect to its various parameters like momentum, learning rate, number of hidden layers, and
number of nodes in the hidden layers etc. to achieve
least error in the learning data as well as test data.
• The second objective is to find out the neural network’s
ability to accurately identify and detect the seismic PWaves.
• The third objective is to compare the performance of
the ANN model with the manual techniques.
The rest of the paper is organized as follows. In section II,
we discuss the attributes of seismic signal. The methodology
to detect and identify seismic P-Waves using ANNs is then
presented in section III. Section IV presents the simulation
results in which the optimum ANN model for identification
and detection of seismic P-Waves is presented. Section V
finally concludes the paper.
II. ATTRIBUTES OF S EISMIC S IGNAL
The identification of different seismic waves is accomplished by recognizing different characteristics of the signal.
For a set of local three-component seismic data, variety of
features for signal detection and identification are analyzed.
These attributes are as follows.
A. Polarization
Polarization is the main source of information derived
from three-component seismic data [x, y, z]. The covariance
matrix C is defined as follows [1]
Cov(x, x) Cov(x, y) Cov(x, z) C = Cov(y, x) Cov(y, y) Cov(y, z) Cov(z, x) Cov(z, y) Cov(z, z) The diagonalization of the covariance matrix gives principal
axis of the matrix. The direction of polarization is measured
by considering the Eigen vector of the largest axis. This
direction is parallel to propagation direction of P-wave and
is perpendicular to the propagation direction of S-wave. It
is difficult to use this direction as a decision parameter
for arrival identification. Therefore the parameters that are
independent of source location are required. The parameters
used in this paper are as follows.
1) Degree of Polarization (DOP): P- and S-Waves can
be identified by using a combination of the degree of polarization vector and vector modulus of its three-component
motion. The DOP is calculated from the covariance matrix
of three-component recording that is a useful measure of the
polarization of seismic signal. DOP [2] is defined as given
in Eq. 1.
F (t) =
(λ1 − λ2 )2 + (λ2 − λ3 )2 + (λ3 − λ1 )2
2(λ1 + λ2 + λ3 )2
(1)
where λ1 , λ2 and λ3 are Eigen values of the covariance
matrix at central time t of a moving window with N samples.
These are independent of the co-ordinate system, so they
are also independent of source location and depend only on
the polarization state. According to this definition, if only
one eigenvalue is nonzero, then F = 1, and the signal is
linearly polarized; if all of the Eigen Values are equal then
F = 0, and the signal can be considered as completely
unpolarized or circularly polarized. Most P-Waves have high
values of F (t) and S-Waves, low values. F (t) patterns
of P-arrivals differ from those of S-Waves. To calculate
the DOP, all three components must have same frequency,
same bandwidth, same scale and same noise level. If one
of the three-component has a different property, DOP is
highly biased. Different arrival types not only have different
polarization characteristics but also have different amplitude
characteristics. Therefore, the modified function, Fm (t), of
DOP [14] must be used as given in Eq. 2.
Fm (t) = F (t) × M (t)
(2)
where M (t) = (x2 (t) + y 2 (t) + z 2 (t))1/2
and x(t), y(t) and z(t) represent displacements with time.
2) Ratio of Vertical Power to Total Power: Ratio of
Vertical Power to Total Power (RV 2T ) is used to measure
the power in the vertical component of the seismic signal. Pwave has its major component in Vertical Power. Thus with
the arrival of P-wave, increase in RV 2T is observed. RV 2T
[7] is defined as given by Eq. 3.
PN
RV 2T = PN
t=1 (x
t=1
2 (t)
x2 (t)
+ y 2 (t) + z 2 (t)
(3)
where x(t), y(t) and z(t) are amplitudes of three component recordings and N is the total points in the window.
B. Auto Regression Coefficient (ARC) Representation
For each seismic signal, each phase has its own characteristic and lasts at least for a few seconds. The difference
in nature of the Auto Regression (AR) representation of
signal and noise can be used to make a separation of phase
contributed from background and hence permit onset time
detections. For seismic signals second order AR model [7]
is used and is expressed in Eq. 4.
ARC(t) =
|w1 (t)w2 (t) − w1 (t − 1)w2 (t − 1)|
|w1 (t)w2 (t)|
(4)
where wk (t) are AR coefficients of data at time t. AR
technique is based on the assumption that the seismograms
can be divided into locally stationary segments as an AR
process, and the intervals before and after the onset are two
different stationary processes.
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C. Energy Analysis
Phase detection using energy analysis is based on the ratio
of a Short-Time Average of the energy content to a LongTime Average energy level (STA/LTA) [11]. It is defined as
given by Eq. 5.
β=
ST A
LT A
(5)
where STA and LTA are defined by Eq. 6 and 7 respectively.
v
u 0
u X
ST A = t
(x2 (t) + y 2 (t) + z 2 (t))
(6)
t=−Ns
v
u 0
u X
LT A = t
(x2 (t) + y 2 (t) + z 2 (t))
(7)
t=−Nl
where Ns and Nl are the time windows for STA and LTA
respectively. Ns is much smaller as compared to Nl and that
is why the names short-term and long-term respectively.
β is calculated recursively in a moving window at a time
and its value will change if amplitude of signal changes.
The Short-Time-Average to Long-Time-Average (STA/LTA)
is the most broadly used algorithm. It continuously calculates
the average values of the absolute amplitude of a seismic
signal in two consecutive moving-time windows. The STA is
sensitive to seismic events while LTA provides information
about the temporal amplitude of seismic noise. When the
ratio of both exceeds a pre-set value, an event is declared.
The STA/LTA algorithm processes filter seismic signals in
two moving time windows- a Short Time Average window
(STA) and a Long-Time Average window (LTA). The STA
measures the instant amplitude of the seismic signal and
watches for earthquakes. The LTA takes care of the current
average seismic noise amplitude. First, the absolute amplitude of each data sample of an incoming signal is calculated.
Next, the average of absolute amplitudes in both windows
is calculated. Then a ratio of both values that is, STA/LTA
ratio is calculated. This ratio is continuously compared to a
threshold value. If the ratio exceeds this threshold, event is
declared [14].
III. M ETHODOLOGY
The methodology to detect and identify seismic P-Waves
using Artificial Neural Network can be divided into various
steps as follows.
1)
2)
3)
4)
5)
Data Collection
Selection of inputs
Preprocessing of the input data
Designing network architecture
Training and Testing of the network to optimize the
neural network topology to minimize error in learning
as well as test data.
A. Data Collection
The data used in this paper was taken from Central Scientific Instruments Organization (CSIO), Chandigarh, India.
160 events of the earthquake have been considered for the
identification and detection purposes. The 160 events include
the various different types of seismic waves that are produced
because of the difference in the distance of the seismometer
from the source or because of the difference in the depth of
the originating earthquake. According to variations in depth
three types exist as follows.
1) Shallow-focus Earthquakes (0 to 70m deep)
2) Intermediate Earthquakes (70 to 300m deep)
3) Deep-focus Earthquakes (300 to 700m deep)
According to the distance from the seismometer they are
categorized into three types as follows.
1) Local Shocks (0-200km)
2) Regional Shocks (200-1500km)
3) Distant Shocks (more than 1500km)
The epicentral data, i.e. data including the necessary
parameters like Magnitude and Epicenter etc. was obtained
from the United States Earthquake Information Center (see
www.usgs.com). Fig. 1 shows sample of a three-component
raw waveform for an event.
Fig. 1.
Sample of a three-component raw waveform for an event
B. Selection of Inputs
It is the most important aspect for detection and identification of waves. Choice of inputs influences the accuracy with
which the phase will be identified and detected. In this paper
four attributes were considered that greatly affect the P- Wave
and hence make it easy to identify and detect these from
raw data. These attributes are, Degree of Polarization (DOP),
Auto Regression Coefficient (ARC), Short Time Average to
Long Time Average Ratio (STA/LTA), and Ratio of Vertical
power to total power (RV2T) as shown in Figs. 2(a), 2(b),
2(c), and 2(d) respectively and as discussed in Section II.
There are peaks corresponding to P-Wave arrival in all the
four attributes. The combinations of these four attributes give
a clear response to P-Wave arrival.
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C. Preprocessing of Input Data
The purpose of preprocessing the input data is to simplify
the patterns to be recognized in order to avoid a huge amount
of computation and to improve the network’s generalization
ability. For detection and identification of P-Wave, only data
of 20 sec is required. Thus, a moving window of 2 sec was
designed that extracts required amount of data and rejects
the rest of the data. Data was low pass filtered to 8 Hz using
a Butterworth Filter.
D. Designing Network Architecture
(a) Degree of Polarization
BPNN was designed that uses four attributes of the seismic
signal as an input as shown in Fig. 3. It consists of four
preprocessors, four neural subnets, and one final decision
neuron. When three-component seismograms are input to the
system, four attributes are computed by the four preprocessors. Four neural subnets have identical three layer BPNN
structure. Outputs of the four subnets are the inputs of the
final decision unit. When the summation of inputs at time
t is greater than a threshold, a P-wave is declared, and the
time t is picked as the onset time of the P-phase.
(b) Auto Regression Coefficient
Fig. 3.
Block Diagram of Back Propagation Neural Network
E. Training and Testing
(c) Ratio of Vertical Power to Total Power
Input data was divided into two parts: training data and
test data. Training data consists of 30 P-wave arrival and
30 background noises. These 60 waveform segments were
extracted from local and regional earthquake recordings. PWave waveform segments were extracted starting from 49
points before and 51 points after P-Wave arrivals. Background noise segments were extracted prior to P-Wave arrivals. The chosen topology was trained with the training data
by using MATLAB. When it reached the minimum training
error of 10−5 , the trained network was applied to the test
set and the testing error was noted. In order to minimize the
testing error, the trained network was optimized by applying
various topologies with different parameters. Table I gives
the final optimum ANN model used for training and testing.
IV. S IMULATION R ESULTS
(d) Short Term Average to Long Term Average
Fig. 2.
Attributes of Seismic Signal
This paper identifies the existence of P-Waves and also the
time of occurrence of these. The results are thus presented
for both the identification and occurrence as follows.
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A. Identification of P-Waves
Four neural networks were trained with the training set
i.e. 60 samples containing 30 P-Waves and 30 background
noises. After that 100 local and regional three-component
seismograms, each of which was 20 second long, were
used to test the best neural network topology for P-wave
identification. The signal was continuously checked for a Pwave with a moving window of 1 sec and updated every 0.01
sec. One 20 second long seismic recording has 1901 windows
to be examined. For the 100 testing seismograms, there are
190100 windows to be tested. For all 100-test seismograms,
a 95% correct identification was achieved. Fig. 4 shows the
detected P-Waves using trained neural network for various
types of local earthquakes. Fig. 5 shows the detected PWaves using neural network for various types of regional
earthquakes.
ANN and manual picking is plotted versus the signal to
noise ratio in Fig. 6. The time difference decreases with
the increasing signal-to-noise ratio and becomes insignificant
when the signal-to-noise ratio is larger than 15. Out of 100
seismograms, 90% of the P-Waves were detected with a
maximum deviation of 0.1 sec from correct manual pick up
without doing retiming.
Fig. 6. The time difference between picking by the ANN and by manual
picking
As discussed earlier in this paper, the three objectives of
this paper were accomplished. The first objective was to
optimize the neural network model with respect to its various
parameters. Finding the best ANN model for a particular
problem is always a very difficult task. There is no theoretical
or empirical formula to model a network as an optimum
one. This task must be performed by trial and observation
technique as is done in this paper. After fair amount of
training and testing various network parameters, an optimum
ANN model for identification and detection of seismic Pwave is given in Table I.
Fig. 4.
Detected and Identified P-wave for Local Earthquakes
TABLE I
O PTIMUM ANN MODEL FOR I DENTIFICATION AND D ETECTION OF
S EISMIC P-WAVES
Parameter
Number of Hidden layers
Number of Nodes in Hidden layers
Number of Input Nodes
Number of Output Nodes
Transfer Function for Input Layer
Transfer Function for Hidden layer
Transfer Function for Output layer
Training Function
Target Error for Training
Moving Window Length
Filter Type
Frequency Band
Fig. 5.
Detected and Identified P-wave for Regional Earthquakes
B. Detection of P-Waves
After correct phase identification, the accuracy of onset
estimation is important. The picking time difference between
Value
1
20
100
1
Log-sigmoid
Tan-sigmoid
Log-sigmoid
Scaled Conjugate Gradient
10−5
2 seconds
IIR Butterworth Band Pass
1 − 8 Hz
The second objective was to find out the neural network’s
ability to accurately identify and detect the seismic P-Waves.
From the results, it is observed that neural network is able to
identify and detect the P-Waves with good accuracy despite
its highly random nature. The optimum network with above
features gives accuracy greater than 94% in the 100 test
seismograms, which is fairly satisfactory to identify and
detect the P-wave.
The third objective was to compare the performance of
the ANN model with the manual techniques. It is observed
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that the ANN approach compares favorably with the manual
approach.
V. C ONCLUSIONS
In this paper, we presented the detection of seismic PWaves using Back Propagation Neural Network for threecomponent seismic data. Four attributes of the seismic wave,
viz. Degree of Polarization, Autoregression Coefficient, Ratio
of Vertical to Total Power and Ratio of Short Term Average
to Long Term Average Energy Level were equally weighted
to decide the occurrence of seismic P-Wave. Because of
the non-stationarity of the seismic signal, it is not possible
to compute the threshold for different attributes for the
occurrence of the P-Wave. Thus, Artificial Neural Network
helps in the detection of these waves.The results obtained
showed 95% accuracy in determining the P-Waves.
The network has been tested on data obtained for local
and regional earthquakes only, in our study, India is the
point of reference for earthquake study. The future work
would be to apply this technique over data obtained from
distant earthquakes. Regarding performance measures, some
refinements are expected to be fruitful, for example, more
attributes can be used for the identification and detection
of the seismic P-Waves, for example, spectral analysis of
seismic wave. Considering more attributes would strengthen
the credibility of the attributes.
[12] Henchang Dai, Colin D.Macbeth, The application of Back Propagation
Neural Network to Automate Picking Seismic Arrivals from Single
Component Recordings, Journal of Geophysical Research, Vol 102, pp
15105-15113, 1997.
[13] Jin Wang, Ta-ling Teng, Identification and Picking of S-phase using
an Artificial Neural Networks, Bulletin of the Seismological Society of
America, Vol 87, pp 1140-1149,1997.
[14] Amadej Trnkoczy, Understanding and Parameter Setting of STA/LTA
Trigger Algorithm, CH1028 preverenges, Switzerland, 1999.
ACKNOWLEDGMENTS
The authors would like to thank Central Scientific Instruments Organization (CSIO), Chandigarh, India, for guidance
and for the perusal of the data provided by them for this
study.
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