Detection and Identification of Seismic P-Waves using Artificial Neural Networks

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1 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. INTRODUCTION 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 ( kkaur003@odu.edu). Manish Wadhwa is with the Department of Information Technology, South University, Virginia Beach, VA ( mwadhwa@southuniversity.edu). E. K. Park is with the Department of Graduate Studies and Office of Research and Sponsored Programs, California State University, Chico, CA ( ekpark@csuchico.edu) 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 S- Waves 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 /13/$ IEEE 2949

2 Multi layer Perceptron (MLP) Neural Network model was employed for detection of seismic S-Wave. 74% of the S- phase 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 P- Waves. 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 SEISMIC SIGNAL 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] C = Cov(x, x) Cov(x, y) Cov(x, z) 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) = (λ 1 λ 2 ) 2 + (λ 2 λ 3 ) 2 + (λ 3 λ 1 ) 2 2(λ 1 + λ 2 + λ 3 ) 2 (1) where λ 1, λ 2 and λ 3 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, F m (t), of DOP [14] must be used as given in Eq. 2. F m (t) = F (t) M(t) (2) where M(t) = (x 2 (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. P- wave 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. RV 2T = N t=1 x2 (t) N t=1 (x2 (t) + y 2 (t) + z 2 (t) 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) = w 1(t)w 2 (t) w 1 (t 1)w 2 (t 1) w 1 (t)w 2 (t) where w k (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. (3) (4) 2950

3 C. Energy Analysis Phase detection using energy analysis is based on the ratio of a Short-Time Average of the energy content to a Long- Time Average energy level (STA/LTA) [11]. It is defined as given by Eq. 5. β = ST A LT A where STA and LTA are defined by Eq. 6 and 7 respectively. ST A = LT A = 0 (5) t= N s (x 2 (t) + y 2 (t) + z 2 (t)) (6) 0 t= N l (x 2 (t) + y 2 (t) + z 2 (t)) (7) where N s and N l are the time windows for STA and LTA respectively. N s is much smaller as compared to N l 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. METHODOLOGY The methodology to detect and identify seismic P-Waves using Artificial Neural Network can be divided into various steps as follows. 1) Data Collection 2) Selection of inputs 3) Preprocessing of the input data 4) Designing network architecture 5) 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 ( km) 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 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. 2951

4 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. (a) Degree of Polarization D. Designing Network Architecture 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 (c) Ratio of Vertical Power to Total Power (d) Short Term Average to Long Term Average Fig. 2. Attributes of Seismic Signal E. Training and Testing 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. P- Wave 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. SIMULATION RESULTS 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. 2952

5 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 P- wave 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 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 P- Waves 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. picking The time difference between picking by the ANN and by manual Fig. 4. Detected and Identified P-wave for Local Earthquakes 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 P- wave is given in Table I. TABLE I OPTIMUM ANN MODEL FOR IDENTIFICATION AND DETECTION OF SEISMIC P-WAVES Parameter Value Number of Hidden layers 1 Number of Nodes in Hidden layers 20 Number of Input Nodes 100 Number of Output Nodes 1 Transfer Function for Input Layer Log-sigmoid Transfer Function for Hidden layer Tan-sigmoid Transfer Function for Output layer Log-sigmoid Training Function Scaled Conjugate Gradient Target Error for Training 10 5 Moving Window Length 2 seconds Filter Type IIR Butterworth Band Pass Frequency Band 1 8 Hz 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 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 2953

6 that the ANN approach compares favorably with the manual approach. V. CONCLUSIONS In this paper, we presented the detection of seismic P- Waves 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 , [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 ,1997. [14] Amadej Trnkoczy, Understanding and Parameter Setting of STA/LTA Trigger Algorithm, CH1028 preverenges, Switzerland, 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. REFERENCES [1] William Lowrie, Fundamentals of Geophysics, Cambridge University Press, [2] G.J. Nair, Falguni Roy, Seismology - An Introduction, An International Journal of Nuclear Power, Vol 15, pp 1-4, [3] Zamani, Abu Sarwar, Response Prediction of Earthquake motion using Artificial Neural Networks, International Journal of Applied Research in Computer Science and Information Technology, Vol 1, no 2, [4] Diersen, Steve, En-Jui Lee, Diana Spears, Po Chen, Liqiang Wang, Classification of Seismic Windows using Artificial Neural Networks, Procedia Computer Science, Vol 4, pp , [5] James A. Anderson, An Introduction to Neural Networks, Prentice Hall of India Pvt. Ltd [6] S. Rajasekaran, G.A.Vijayalakshmi Pai, Neural Networks, Fuzzy logic, and Genetic algorithms: Synthesis and Applications, Prentice Hall of India Pvt. Ltd [7] Brian Russell, Hampsono-Russell, Neural Network Applications in Geophysics, CSEG National Convention, [8] Chao-ying Bai, B.L.N Kennett, Automatic Phase-Detection and Identification by Full Use of a Single Three-component Broadband Seismogram, Bulletin of Seismological Society of America, Vol 90, pp , [9] Haijiang Zhang, Clifford Thurber, Charlotte Rowe, Automatic P-Wave Arrival Detection and Picking with Multi Scale Wavelet Analysis for Single Component Recordings, Bulletin of the Seismological Society of America, Vol 93, pp , [10] Henchang Dai, Colin D.Macbeth, Identifying P and S Waves using Artificial Neural, EAGE 57th Conference and Technical Exhibition, Scotland, [11] Henchang Dai, Colin D.Macbeth, Arrival Type Identification in Local Earthquake Data using an Artificial Neural Network, Journal of Geophysical Exhibition, Vol 12, pp ,