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Procedia Computer Science 13 (2012) 7 – 17

Proceedings of the International Neural Network Society Winter Conference (INNS-WC 2012)

A Neurocomputing Approach for Monitoring Plinian Volcanic Eruptions Using Infrasound Fredric M. Hama,*, Ishwarya Iyengara, Bereket M. Hambeboa, Milton Garcesb, John Deatona, Anna Perttub, Brian Williamsb a

Florida Institute of Technology, 150 W. University Blvd., Melbourne, FL 32901 b Infrasound Laboratory, HIGP, SOEST, University of Hawaii at Manoa

Abstract Plinian volcanic eruptions can inject a substantial amount of volcanic ash and gas into the stratosphere, which can present a severe hazard to commercial air traffic. A hazardous volcanic ash eruption was reported on April 14, 2010, and London’s aviation authority issued an alert that an ash plume was moving from an eruption in Iceland towards northwestern Europe. This eruption resulted in the closure of large areas of European airspace. Large plinian volcanic eruptions radiate infrasonic signals that can be detected by a global infrasound array network. To reduce potential hazards for commercial aviation from volcanic ash, these infrasound sensor arrays have been used to detect infrasonic signals released by sustained volcanic eruptions that can inject ash into the stratosphere at aircraft’s cruising altitudes, typically in the order of 10km. A system that is capable of near, real-time eruption detection and discrimination of plinian eruptions from other natural phenomena that can produce infrasound with overlapping spectral content (0.01 to 0.1 Hz) is highly desirable to provide ash-monitoring for commercial aviation. In this initial study, cepstral features are extracted from plinian volcanic eruption and mountain associated wave infrasound signals. These feature vectors are then used to train and test a two-module neural network classifier. One module is dedicated to classifying plinian volcano eruptions, the other mountain associated waves. Using an independent validation dataset, the classifier’s correct classification rate is 91.5%. © 2012 2012 The Authors.byPublished Elsevier B.V. and/or peer-review under responsibility © Published Elsevierby B.V. Selection Selection and/or peer-review under responsibility of Program Committee of INNS-WC 2012 of the Program Committee of INNS-WC 2012. Keywords: Plinian volcanic eruptions, infrasound, parallel neural network classifier bank, cepstral feature vectors, 3D ROC curve

* Corresponding author. Tel.: +1-321-674-7318; fax: +1-321-674-7270. E-mail address: [email protected]

1877-0509 © 2012 Published by Elsevier B.V. Selection and/or peer-review under responsibility of Program Committee of INNS-WC 2012 doi:10.1016/j.procs.2012.09.109

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Fredric M. Ham et al. / Procedia Computer Science 13 (2012) 7 – 17

1. Introduction Plinian [1] volcanic eruptions can inject a substantial column of gas and volcanic ash into the stratosphere. The resulting ash cloud presents a severe hazard to air traffic [2]. The first recorded impact of volcanic activity on aviation was on March 22, 1944 when Mount Vesuvius produced more damage to an airfield than that created by opposing forces during World War II. It was 36 years later that the next event occurred when a civil Lockheed C-130 Hercules inadvertently penetrated an ash cloud from Mount St. Helens following its second eruption. A recent hazardous volcanic ash eruption was reported on April 14, 2010 when London’s aviation authority issued an alert that an ash plume was moving from an eruption in Iceland towards northwestern Europe. This eruption resulted in the immediate closure of large areas of European airspace, around 10 million passengers were affected, and total economic damage reached almost 5 billion dollars [3]. According to reports, this was the largest air-traffic shut-down since World War II. Over 95,000 flights had been cancelled all across Europe during the six-day travel ban [4], with later figures suggesting 107,000 flights cancelled during an eight day period, accounting for 48% of total air traffic and roughly 10 million passengers [5]. The danger of aircraft damage or flameout disrupted air traffic for over two weeks. After the Icelandic volcanic crisis, the International Civil Aviation Organization (ICAO) began working more closely with European aviation officials to enhance the forecasting abilities of volcanic ash trajectory and dispersion. A mobile radar unit will soon be stationed in Iceland to improve the accuracy and speed of predicting the height of any future ash cloud. According to Rodrigues and Cusick [3], there is now a coordinated European approach to dealing with this issue that was in process during 2011 under the “Single European Sky” initiative [6]. Large plinian volcanic eruptions radiate infrasonic signals that can be detected by the global infrasound array network [7]. In an effort to reduce potential hazards for commercial aviation from volcanic ash, these infrasound sensor arrays have been used to detect infrasonic signals released by sustained volcanic eruptions that may inject ash into the stratosphere at the aircraft’s cruising altitudes, typically in the order of 10km [8]. So systems capable of near, real-time eruption detection which can report low latency notifications are necessary to provide ash monitoring for aviation. These systems should be capable of detecting hazardous eruptions and also discriminating the eruption intensity based on the volcano's infrasound signature. Here, volcanic infrasound signals are used to develop near, real-time neural-classifiers that are capable of discriminating between plinian volcano eruptions and the other natural phenomena with overlapping spectral content. This work is an extension of the initial study that focused on discriminating between three different volcanoes that illustrated the feasibility of using neural-classifiers to classify different types of eruptive activity from their cepstral-based feature vectors [9]. Fig. 1 shows the infrasound frequency spectrum with some events that can produce infrasound. As indicated in Fig. 1, a plinian volcano eruption observed at telesonic (> 250 km) ranges will typically have frequency content in the range from 0.01 Hz to 0.1 Hz. However, also shown in the same figure are four other natural events that can generate infrasound and have frequency content that overlap with the plinian eruption band. Namely, mountain associated waves (MAW), tsunamis, bolides (meteors) and avalanches, referred to collectively as not-of-interest (NOI) events. Therefore, when it is desired to confirm that a plinian volcano eruption has occurred, it must be discriminated against those NOI events that can obscure the proper determination of the plinian eruption if not appropriately taken in account. The event classifier developed here uses a Parallel Neural Network Classifier Bank (PNNCB) architecture. It consists of individual classifier modules constructed using Radial Basis Function Neural Networks (RBF NN) [10]. The modules work in parallel to identify the signals belonging to a particular class. The number of modules in the PNNCB corresponds to the number of classes of interest. The general PNNCB architecture is presented in Fig 2.

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Fig.1. F Fi i 1 IInfrasound ig.1. nffrasoundd fr ffrequency equency spectrum spectr t um Fig.1. Infrasound frequency spectrum Optimal Threshold Value Set by 3-D ROC Curve

Pre-filter 1

Pre-processor 1

Infrasound Neural Module 1

1 0 Optimal Threshold Value Set by 3-D ROC Curve

Pre-filter 2 Infrasound Signals

Pre-processor 2


. . . Pre-filter N

1 0 Optimal Threshold Value Set by 3-D ROC Curve

Pre-processor N

Infrasound Neural Module N

1 0

Fig. 2. PNNCB architecture

As shown in Fig. 2, each module of the PNNCB is trained independently to recognize or reject the signals of a particular class. A negative reinforcement approach is used to train the PNNCB modules. This entails each block is trained not only to identify a signal of a particular class, but also to reject signals belonging to the other classes. The performance of the classifier is measured with respect to the Correct Classification Rate (CCR) [11]. That is: ‫ ܴܥܥ‬ൌ

௡௨௠௕௘௥௢௙௖௢௥௥௘௖௧௣௥௘ௗ௜௖௧௜௢௡௦ି௡௨௠௕௘௥௢௙௠௨௟௧௜௣௟௘௖௟௔௦௦௜௙௜௖௔௧௜௢௡௦ ௡௨௠௕௘௥௢௙௣௥௘ௗ௜௖௧௜௢௡௦

(1)

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Another performance measure that can be used is the accuracy (ACC), that is: ‫ ܥܥܣ‬ൌ

௡௨௠௕௘௥௢௙௖௢௥௥௘௖௧௣௥௘ௗ௜௖௧௜௢௡௦

(2)

௡௨௠௕௘௥௢௙௣௥௘ௗ௜௖௧௜௢௡௦

From equations (1) and (2) it is evident that the CCR is a more conservative performance measure as it takes into account multiple classifications. The steps involved in the development of the event classifier presented here are described in sections 2 to 4. The dataset to train the neural-classifier is selected to include a wide variety of signals, at the same time allocating sufficient data for testing and validation. The raw data is filtered appropriately and cepstral-based features [12] are extracted from the signals for classification. As compared to the time-domain signals (see Fig. 3), the cepstral feature vectors provide better uniformity among the infrasonic characteristics of a particular class, and distinctiveness between sets of feature vectors for different classes. This serves to enhance the overall performance of the classifier.

(a)

(b)

Fig. 3. Time-domain infrasound signals for: (a) plinian volcano eruptions, (b) Mountain Associated Waves

2. Data set Infrasound signals travel long distances in the Earth’s atmosphere making it possible to detect and record them using sensors located at large distances from the source. The dataset used in this work is obtained from various sensor arrays deployed in the global infrasound monitoring network [3]. These sensors can monitor and record infrasound activities such as volcanic eruptions, mountain associated waves, avalanches, tsunamis, bolides (meteors), and many man-made events. As discussed in section 1, plinian volcanic eruptions must be clearly distinguished from the NOI events for the purpose of possibly using this information as early warning for commercial aircraft. A list of plinian volcano and NOI events is provided in Table 1. In this initial study, to obtain an understanding of the neuralclassifier’s ability to distinguish between the volcanic events and other natural events, the NOI events are considered to be made up of only the MAW infrasound signals.

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Table 1. List of plinian volcano and NOI events

(a) Plinian volcano Plinian volcano Event

Manam

Kasatochi

Lascar

Augustine

Year of eruption

2005

2008

2005

2006

Sarychev Peak 2009

Tungurahua 2006

(b) Mountain associated wave Mountain Associated Wave Alaska & Aleutian Ranges 2005

Event Year

St. Elias Range

St. Elias Range

Alaska & Aleutian Ranges

2007

2008

2009

(c) Avalanche Avalanche Event Year

Mt. McKinley 2005

Mt. Steele 2007

Mt. Steller 2005

(d) Bolide Bolide Event Year

Central Pacific 2001

Acapulco Fireball 2000

Christmas Isl. 2006

South Sulawesi 2009

(e) Tsunami Tsunami Event Year

Sumatra 2004

Tohoku 2011

The sensor systems employ a sampling frequency of 20 Hz. The time-domain signals from the sensors in the array are partitioned into events with duration 200 sec each (that is, the signals are segmented with a window of length 200 sec). The sampling frequency and the event duration used result in 4000 samples for each training and testing signal. The events have a 50 % overlap on the windowed segments. From the total dataset, 60 % of the signals are used for training, 20 % for testing and 20 % for validation. The signals for training and testing are chosen randomly, however, for a particular event the signals from all the sensors in an array are kept together either in the training set or in the test set. This ensures statistical uniformity. The dataset distribution for this work is given in Table 2. Table 2. Number of training and test patterns for the plinian volcano – MAW classifier No. of training signals Plinian Volcano Mountain Associated Wave

2205 744

No. of testing signals 713 248

Total No. of Signals 2918 992

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3. Filtering and feature extraction The plinian volcano and MAW signals exhibit strong spectral characteristics in the 0.01 Hz – 0.1 Hz range, as mentioned in section 1. The signals presented to the classifier are restricted to this frequency range by a 4th order Butterworth bandpass filter with cutoff frequencies: 0.01 Hz and 0.1 Hz. Filtering in this spectral region also ensures that the signal is devoid of any microbarom [14] noise. Figure 4 shows a noisy raw infrasound signal associated with a plinian volcano eruption and its filtered version. Plinian volcano signal 0.2

Amplitude

0 -0.2 -0.4 -0.6 -0.8

0

1000

2000

3000

4000 No. of samples

5000

6000

7000

8000

6000

7000

8000

Filtered plinian volcano signal 0.2

Amplitude

0.1 0 -0.1 -0.2 -0.3

0

1000

2000

3000

4000 No. of samples

5000

Fig. 4. Unfiltered and filtered signal plots for Plinian Volcanoes (Sampling freq. = 20 Hz)

As discussed in section 1, cepstral features are used to extract the salient content from the time domain signals, and these cepstral feature vectors are then used for developing the neural classifier to discriminate between a plinian volcano eruption and MAWs. The following steps are taken to extract the cepstral features [7]: x

Every filtered signal is normalized by the absolute value of its largest sample. It is then divided by its standard deviation and the signal mean is subtracted. For a signal ‘y’ normalized by the maximum amplitude sample and standard deviation, we compute: ‫ ݕ‬ൌ ‫ ݕ‬െ ݉݁ܽ݊ሺ‫ݕ‬ሻ

x x x

x x

(3)

The power spectral density (PSD) of each signal, Syy(kω) is determined. The average of all the PSDs for a class is calculated (μ i). The maximum value of these averages is designated as μmax. Only the spectral components that maximize the mean (determined by parameter ε1) are retained, while the rest are set to a fixed parameter ε2. That is, if μi > ε1 x μmax, then μi μi else μi ε2. The value of ε2 is typically set to 0.00001, from previous simulations. This is a frequency selection process that can serve to maximize the classifier’s overall performance. The variances of the components selected in the previous step are calculated and arranged in ascending order. Then, ε3 percent of these spectral components are retained while the rest are set to ε2. Mel-frequency scaling is applied to these components.

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Sm(kω) = α loge [β * Syy(kω)] x

(4)

The inverse Discrete Cosine Transform for the Mel-frequency scaled values is computed to obtain:

xmel (n)

x x x x

1 N 1 ¦ Sm (kZ ) cos(2S kZ n / N ), for n 0, 1, 2, n kZ 0

, N 1.

(5)

The consecutive differences of the sequence xmel(n) are computed to get x'mel(n). xmel(n) and x'mel(n) are concatenated to form the augmented sequence, xamel = [x'mel(i) | xmel(j)]. The absolute value of xamel,abs = | xamel| is determined. The natural logarithm is applied to the above value such that: xamel,abs,log = loge[xamel,abs]

(6)

The final feature pool is made up of cepstral coefficients and their associated derivatives. The parameters, α, β, ε1 and ε3 for the pre-processing steps are adjusted through a numerical optimization procedure. This procedure determines the optimal values for the pre-processing parameters to select the best features, and thus maximize the classifier’s performance. The optimized parameter values and the cepstral feature vectors for the plinian volcano and MAW classifier are shown in Table 3 and Fig. 5, respectively. Comparing the feature vectors in Fig. 5 to their associated time-domain signals in Fig. 3, it is evident that the feature vectors show significant consistency within a class, and distinctiveness between the classes. Table 3. Pre-processing parameters for the plinian volcano and MAW classifier

Plinian Volcano Mountain Associated Wave

Α

β

100

10-9

4*10-11

100

-3

-11

10

ε1

4*10

Number of Features

ε3 0.98 0.98

Spread

Threshold

6.7 1

0.5636 0.4598

40 40

Feature set - Plinian volcano

Feature set - MAW

13

12 11.5

12

11 10.5

Amplitude

Amplitude

11

10

10 9.5 9

9

8.5 8

8

7.5 7

0

5

10

15

20 25 Feature number

(a)

30

35

40

7

0

5

10

15

20 25 Feature number

(b)

Fig. 5. Cepstral feature vectors for (a) Plinian volcano, and (b) MAW

30

35

40

14


Only a subset of the values from the feature pool is used to train or test the neural network modules in the PNNCB. The number of these values used (or the number of features) is selected such that the CCR mean is maximized, at the same time the CCR variance is minimized [11] as shown in Fig. 6(a) and Fig. 6(b). The mean and variance are calculated for the CCR values obtained from using different spread parameters for the neural network modules. This is illustrated in Fig. 5(c). Maximizing the CCR mean enhances the classifier performance and minimizing the variance guarantees that the variation within the feature set for each class is reduced.

(a)

(b)

(c) Fig. 6. (a) CCR mean, (b) CCR variance, (c) 3-D performance plot for different values of spread the RBF spread parameter

4. Neural-Classifier The PNNCB described in section 1 forms the basis of the neural-classifier. The architecture for the plinian volcano and MAW classifier is shown in Fig. 7.

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(Plinian Volcano) Pre-filter 1

Pre-processor 1


1 0

(MAW)

Optimal Threshold Value Set by 3-D ROC Curve


1 0

Infrasound Signals

Pre-filter 2

Pre-processor 2

Optimal Threshold Value Set by 3-D ROC Curve

Fig. 7. PNNCB architecture for plinian volcano and MAW classifier

Each neural module in the PNNCB produces an output which is compared to the threshold for the corresponding class. If the output is found to be greater than or equal to the threshold, the signal belongs to that class and vice-versa. The optimal threshold for a class is selected using a 3-D Receiver Operating Characteristic (ROC) curve [11] [13]. Along with the standard axes for the True Positive (TP) rate and the False Positive (FP) rate, the 3-D ROC curve incorporates a third axis representing the misclassifications between neural modules in the PNNCB. The ideal point on this curve is where the false positive rate and the misclassification are “0” while the true positive rate is “1”. The point on the 3-D ROC curve that is closest to the ideal point (0, 1, 0) is chosen to obtain the threshold. Fig. 8 shows the 3-D ROC curve for the plinian volcano and MAW classes. The associated threshold values selected are provided in Table 3. 3D ROC curve

1

1

0.8

0.8

MC(Mis-Classified)

MC(Mis-Classified)

3D ROC curve

0.6 0.4 0.2 0 1

0.6 0.4 0.2 0 1

1 0.8

0.5

0.6

1 0.8

0.5

0.6

0.4 TP(Sensitivity)

0

0.4

0.2 0

FP(1-Specificity)

TP(Sensitivity)

0

(a)

0.2 0

(b)

Fig. 8. 3-D ROC curve for (a) plinian volcano (b) MAW

FP(1-Specificity)

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5. Final simulation results The event classifier developed here to distinguish between plinian volcanic eruptions and mountain associated waves has a CCR of 91.46 % with a 95 % confidence interval of (0.8990, 0.9303). The accuracy for this classifier is 92.01 % with a 95 % confidence interval of (0.9058, 0.9360). The confusion matrix is shown in Table 4. Table 4. Confusion Matrix for plinian volcano and MAW classifier

Actual

Predicted Plinian Volcano Plinian Volcano Mountain Associated Wave

672 21

Mountain Associated Wave 18 213

Unclassified 25 18

A total of 2949 signals are used for training and 961 signals are used for testing. Of the 961 test signals, 713 are for plinian volcano eruptions and 248 are MAW. The classifier is able to correctly determine 672 plinian volcano events and 213 MAW events. The scatter plot for the classifier outputs is shown in Fig. 9.

Fig. 9. Scatter plot for neural-classifier outputs

6. Conclusions The classifier presented here is capable of distinguishing between plinian volcano and MAW infrasound signals with a CCR of 91.46 %. The bandpass filter cut-offs are set to the frequency range of interest, that is, 0.01 Hz to 0.1 Hz. The pre-processing parameters are optimized in order to select the best features for training and testing the neural network modules. The neural module threshold values for each class are optimized by use of a 3-D ROC curve. As an extension to this work, the NOI class will be expanded to include the other natural phenomena such as tsunamis, bolides (meteors) and avalanches which have infrasonic characteristics in the same frequency range


as the plinian volcano signatures. With the development of a robust classifier capable of discriminating plinian volcano events from a more comprehensive set of NOI events, a near, real-time warning system may be developed for use by the commercial aviation industry. Acknowledgements This work is supported in part by the National Oceanic and Atmospheric Administration (NOAA), contract 09-09-022. References [1] Marzano FS, Lamantea M, Cimini D, Montopoli M, Herzog M, Graf H. Passive microwave remote sensing of Plinian eruption due to the Grímsvötn Icelandic volcano. IEEE Conference Microwave Radiometry and Remote Sensing of the Environment (MicroRad) 2012; p. 1-4. [2] Garces M, Fee D, Steffke A, McCormack D, Servranckx R, Bass H, Hetzer C, Hedlin M, Matoza R, Yepes H, Ramon P. Prototype ASHE volcano monitoring system captures the acoustic fingerprint of stratospheric ash injection. EOS Sep. 2008; 89, p. 377-388. [3] Rodrigues,C., & Cusick, S. (2011). Commercial aviation safety (5th ed.). New York: McGraw-Hill (pp. 217-218). [4] “Ash Cloud Chaos: Airlines Face Huge Task as Ban Ends” http://news.bbc.co.uk/2/hi/uk_news/8633892.stm ). Retrieved May 6, 2012. [5] “Volcanic Eruptions: Science and Risk Management” (http://www.science20.com/planetbye/volcanic_eruptions _science_and_risk_management-79456). Retrieved May 6, 2012. [6] “Single European Sky” (http://ec.europa.eu/transport/air/single_european_sky /single_european_sky_en.htm). Retrieved May 6, 2012. [7] Garces M, Ham F, Bellesiles A, Williams B, Iyengar I, Fee D et al. Machine learning for infrasonic classification of hazardous volcanic eruptions. (Abstract), Infrasound Technology Workshop October 30 – November 4 2011; Dead Sea, Jordan. [8] Fee D, Garces M, Steffke A. Infrasound from Tungurahua Volcano (2006 - 2008): Strombolian to Plinian eruptive activity. Journal of Volcanology and Geothermal Research 2012; 193, p. 67-81. [9] Iyer A, Ham F, and Garces M. Neural classification of infrasonic signals associated with hazardous volcanic eruptions. Int’l. Joint Conference on Neural Networks July 31 - August 5 2011; San Jose, California, p. 336-341. [10] Ham, F.M. and Kostanic, I. Principles of Neurocomputing for Science and Engineering. New York: McGraw-Hill; 2001, p. 140-152. [11] Ham, F. M., Rekab K., Acharyya, R. and Lee, Y-C. Infrasound signal classification using parallel RBF Neural Networks, Int. J. Signal and Imaging Systems Engineering 2008; Vol. 1, Nos. ¾, p. 155-167. [12] Mammone, R.J., Zhang, X. and Ramachandran, R.P. (1996) ‘Robust speaker recognition: a feature-based approach’, IEEE Signal Processing Mag.; 1996, Vol. 13, No. 5, p. 58–71. [13] Fawcett, T. An Introduction to ROC Analysis, Pattern Recognition Letters 27; 2006, p. 861-874. [14] National Research Council, Comprehensive Nuclear Test Ban Treaty Monitoring, Washington, DC, National Academy Press, 1997.

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