**5. Conclusion**

18 Will-be-set-by-IN-TECH

2010). Optimal classification systems for the automated identification of volcanic earthquakes

Multiway data analysis has been extensively used in chemometrics and psychometrics. It extends classical multivariate statistical techniques such as component analysis, factor analysis, cluster analysis, correspondence analysis, and multidimensional scaling to multiway data (Kroonenberg, 2008). Multiway means that data are arranged in high-order arrays instead of the usual two-dimensional matrices, in which each row represents an object and each column is associated to a feature or measurement. Data collected at different times, conditions or locations are suitable to be considered as multiway data sets (Porro-Muñoz et al.,

Porro-Muñoz et al. (2010a;b; 2011) derived intuitive multiway representations for classifying seismic volcanic signals. Spectrograms and scalograms are computed for each segmented seismic signal and, afterwards, the whole set is arranged by stacking those initial two-dimensional representations. As a result, a so-called profile-data configuration is obtained, where the three dimensions are associated to signals, time and frequency; respectively. Further studies on the design of custom classifiers for multiway data sets are needed. Moreover, other multiway arrangements might be created by considering, for instance, the recording stations or the sensor components (vertical, North-South, and

This section is devoted to a discussion on the difficulties and challenges for the design and deployment of custom solutions at the volcano observatories. Technical challenges and non-technical constraints are summarized. Lastly, a few remarks concerning industrial and

Technical challenges in the deployment of PR systems for the automated recognition of seismic volcanic signals are mainly related to the following issues: (1) computational aspects and (2) local conditions. The first issue depends on the actual computational requirements of classification algorithms and their associated demands for data storage. The latter is becoming less relevant since disk storage capacity has grown exponentially and hardware prices have declined. In spite of that, processing the stored data may still be cumbersome, especially when dealing with continuous recording as commented by Langer & Falsaperla (2003). Classification speed is of crucial importance for real-time applications. Computational complexities of all stages in the PR pipeline (see Fig. 5) must be carefully estimated in terms of orders or FLOPS<sup>3</sup> in order to guarantee fast execution. Such a condition implies a reasonable

The second issue —local conditions— includes the consideration of several volcano-specific factors as those mentioned at the beginning of Sec. 3.3. In addition, the so-called source, path and local site effects require special attention. They cause that waveforms of the same seismic

might be designed by using those novel ROC approaches.

East-West) as additional ways, i.e. dimensions.

commercial implementation alternatives are made.

**4.1 Technical challenges and non-technical constraints**

trade-off between complexity and classification performance.

<sup>3</sup> Floating point operations per second.

**4. Challenges and constraints in deploying automated systems**

**3.5 Multiway representations**

2009).

Multiple research studies have shown that PR tools can be successfully used in the volcano-seismic monitoring task. Several data representations have been explored, including raw and processed signals in the time- and/or frequency-domain as well as other measurements related to geophysical wave properties. ANNs and HMMs have been preferred to be used in the classification stage, thanks to their flexibility and in spite of being heavily parameterized. Other classifiers, on the contrary, do not demand much

<sup>4</sup> http://perclass.com/index.php/html/applications/

parameter adjustments and have being used in combination with novel representations such as dissimilarities and multiway configurations.

The state-of-the-art in PR offers a number of new techniques and methods that might be suitably applied to the automated recognition of volcanic earthquakes. Such technological trends and research directions could effectively incorporate inherent properties of the problem, e.g. multiple channels (stations and components), variations over time and multiclass unbalanced nature. Results obtained by different research teams are unfortunately not comparable because different data sets were used across the studies. A rigorous and comprehensive comparison has not yet been made. If undertaken, defining a benchmark set of problems would be mandatory.

Transferring research achievements to the seismological practice demands careful feasibility evaluations of implementation alternatives and would greatly benefit from working cooperations agreements between volcano observatories and universities. One of the ways to achieve an effective technology transfer is the provision of grants and scholarships.

#### **6. Acknowledgments**

This chapter was carried out within the research program "Fortalecimiento de capacidades conjuntas para el procesamiento y análisis de información ambiental" (code Hermes 12677), which is funded by Universidad Nacional de Colombia.

#### **7. References**


parameter adjustments and have being used in combination with novel representations such

The state-of-the-art in PR offers a number of new techniques and methods that might be suitably applied to the automated recognition of volcanic earthquakes. Such technological trends and research directions could effectively incorporate inherent properties of the problem, e.g. multiple channels (stations and components), variations over time and multiclass unbalanced nature. Results obtained by different research teams are unfortunately not comparable because different data sets were used across the studies. A rigorous and comprehensive comparison has not yet been made. If undertaken, defining a benchmark set

Transferring research achievements to the seismological practice demands careful feasibility evaluations of implementation alternatives and would greatly benefit from working cooperations agreements between volcano observatories and universities. One of the ways to achieve an effective technology transfer is the provision of grants and scholarships.

This chapter was carried out within the research program "Fortalecimiento de capacidades conjuntas para el procesamiento y análisis de información ambiental" (code Hermes 12677),

Aksela, M. (2007). *Adaptive Combinations of Classifiers with Application to On-Line Handwritten*

Alasonati, P., Wassermann, J. & Ohrnberger, M. (2006). Signal classification by wavelet-based

Avossa, C., Giudicepietro, F., Marinaro, M. & Scarpetta, S. (2003). Supervised and

Bendat, J. S. & Piersol, A. G. (2010). *Random Data: Analysis and Measurement Procedures*, Wiley

Benítez, M. C., Ramírez, J., Segura, J. C., Ibáñez, J. M., Almendros, J., García-Yeguas, A. &

Beyreuther, M., Carniel, R. & Wassermann, J. (2008). Continuous hidden Markov models:

2859 of *Lecture Notes in Computer Science*, Springer, pp. 173–178.

*Character Recognition*, PhD thesis, Helsinki University of Technology, Espoo, Finland.

hidden Markov models: application to seismic signals of volcanic origin, *in* H. Mader, C. Connor & S. Coles (eds), *Statistics in Volcanology*, number 1 in *Special Publications of IAVCEI*, Geological Society of London, Trowbridge, UK, chapter 13, pp. 161–174. Ansari, A., Noorzad, A. & Zafarani, H. (2009). Clustering analysis of the seismic catalog of

unsupervised analysis applied to Strombolian E.Q., *in* B. Apolloni, M. Marinaro & R. Tagliaferri (eds), *14th Italian Workshop on Neural Nets, WIRN VIETRI 2003*, Vol.

Series in Probability and Statistics, 4 edn, John Wiley & Sons, Hoboken, New Jersey.

Cortés, G. (2007). Continuous HMM-based seismic-event classification at Deception Island, Antarctica, *IEEE Transactions on Geoscience and Remote Sensing* 45(1): 138–146.

Application to automatic earthquake detection and classification at Las Cañadas caldera, Tenerife, *Journal of Volcanology and Geothermal Research* 176(4): 513 – 518. Beyreuther, M. & Wassermann, J. (2008). Continuous earthquake detection and

classification using discrete hidden Markov models, *Geophysical Journal International*

as dissimilarities and multiway configurations.

which is funded by Universidad Nacional de Colombia.

Iran, *Computers & Geosciences* 35(3): 475–486.

175(3): 1055–1066.

of problems would be mandatory.

**6. Acknowledgments**

**7. References**


Esposito, A. M., Giudicepietro, F., D'Auria, L., Scarpetta, S., Martini, M. G., Coltelli, M. &

Esposito, A. M., Giudicepietro, F., Scarpetta, S., D'Auria, L., Marinaro, M. & Martini,

Esposito, A. M., Scarpetta, S., Giudicepietro, F., Masiello, S., Pugliese, L. & Esposito, A.

Falsaperla, S., Graziani, S., Nunnari, G. & Spampinato, S. (1996). Automatic classification

Gayar, N. E., Kittler, J. & Roli, F. (eds) (2010). *Multiple Classifier Systems: 9th International*

Gil-Cruz, F. (1999). Observations of two special kinds of tremor at Galeras volcano, Colombia

Gil-Cruz, F. & Chouet, B. A. (1997). Long-period events, the most characteristic seismicity

Galeras Volcano, Colombia: Interdisciplinary Study of a Decade Volcano. Guillier, B. & Chatelain, J.-L. (2006). Evidence for a seismic activity mainly constituted of

Gutiérrez, L., Ibañez, J., Cortés, G., Ramírez, J., Benítez, C., Tenorio, V. & Álvarez, I.

Gutiérrez, L., Ramírez, J., Benítez, C., Ibañez, J., Almendros, J. & García-Yeguas, A.

Harrington, R. M. & Brodsky, E. E. (2007). Volcanic hybrid earthquakes that are brittle-failure

Havskov, J. & Alguacil, G. (2004). *Instrumentation in Earthquake Seismology*, Vol. 22 of *Modern Approaches in Geophysics*, 1 edn, Springer, Dordrecht, the Netherlands.

*of America* 98(5): 2449–2459.

*the Seismological Society of America* 96(4): 1230–1240.

Springer-Verlag, London, UK, pp. 140–145.

(1989-1991), *Annali di Geofisica* 42(3): 437–449.

context, *Comptes Rendus Geosciences* 338(8): 499 – 506.

events, *Geophysical Research Letters* 34(L06308): 1–5.

13(3): 205–208.

Springer, Berlin, Germany.

pp. IV–522 –IV–525.

pp. 2765–2768.

Marinaro, M. (2008). Unsupervised neural analysis of very-long-period events at Stromboli Volcano using the self-organizing maps, *Bulletin of the Seismological Society*

M. (2006). Automatic discrimination among landslide, explosion-quake, and microtremor seismic signals at Stromboli Volcano using neural networks, *Bulletin of*

(2005). Nonlinear exploratory data analysis applied to seismic signals, *in* B. Apolloni, M. Marinaro, G. Nicosia & R. Tagliaferri (eds), *16th Italian Workshop on Neural Nets and International Workshop on Natural and Artificial Immune Systems, WIRN/NAIS 2005*, Vol. 3931 of *Lecture Notes in Computer Science*, Springer, Berlin Heidelberg, pp. 70–77. Ezin, E. C., Giudicepietro, F., Petrosino, S., Scarpetta, S. & Vanacore, A. (2002). Automatic

discrimination of earthquakes and false events in seismological recording for volcanic monitoring, *WIRN VIETRI 2002: Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers*, Vol. 2486/2002 of *Lecture Notes in Computer Science*,

of volcanic earthquakes by using multi-layered neural networks, *Natural Hazards*

*Workshop, MCS 2010 (Cairo, Egypt)*, Vol. 5997 of *Lecture Notes in Computer Science*,

accompanying the emplacement and extrusion of a lava dome in Galeras Volcano, Colombia, in 1991, *Journal of Volcanology and Geothermal Research* 77(1-4): 121 – 158.

hybrid events at Cayambe volcano, Ecuador. Interpretation in a iced-domes volcano

(2009). Volcano-seismic signal detection and classification processing using hidden Markov models. application to San Cristóbal volcano, Nicaragua, *Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009*, Vol. 4,

(2006). HMM-based classification of seismic events recorded at Stromboli and Etna volcanoes, *IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2006*,


Laverde, C. A. & Manzo, Ó. (2009). Manual del procesamiento de la información sismológica:

Lesage, P. (2009). Interactive Matlab software for the analysis of seismic volcanic signals,

Lesage, P., Glangeaud, F. & Mars, J. (2002). Applications of autoregressive models and

Londoño-Bonilla, J. M. (2010). Activity and Vp/Vs ratio of volcano-tectonic seismic

Martinelli, B. (1997). Volcanic tremor and short-term prediction of eruptions, *Journal of*

McNutt, S. R. (2005). Volcanic seismology, *Annual Review of Earth and Planetary Sciences*

Narváez-M., L., Torres C, R. A., Gómez M., D. M., Cortés J., G. P., Cepeda V., H. & Stix, J. (1997).

Neuberg, J. (2000). Characteristics and causes of shallow seismicity in andesite volcanoes,

Ohrnberger, M. (2001). *Continuous Automatic Classification of Seismic Signals of Volcanic Origin*

Orozco-Alzate, M. & Castellanos-Domínguez, C. G. (2007). Clustering on dissimilarity

Orozco-Alzate, M., García-Ocampo, M. E., Duin, R. P. W. & Castellanos-Domínguez, C. G.

Orozco-Alzate, M., Skurichina, M. & Duin, R. P. W. (2008). Spectral characterization of volcanic

Ottemöller, L., Voss, P. & Havskov, J. (2011). SEISAN: Earthquake analysis software for

Paclík, P., , Lai, C., Landgrebe, T. C. W. & Duin, R. P. W. (2010). ROC analysis and

*Pattern Recognition (ICPR2010)*, IEEE Computer Society, pp. 2977 –2980. Pekalska, E. & Duin, R. P. W. (2005). *The Dissimilarity Representation for Pattern Recognition:*

*Philosophical Transactions of the Royal Society* 358(1770): 1533–1546.

*Journal of Volcanology and Geothermal Research* 114(3-4): 391 – 417.

y Minería - INGEOMINAS.

14(1): 111–124.

33(1): 461–491.

*Computers & Geosciences* 35(10): 2137–2144.

Interdisciplinary Study of a Decade Volcano.

Interdisciplinary Study of a Decade Volcano.

*Earth Sciences Research Journal* 11(2): 131–138.

*Science*, IAPR, Springer, pp. 708–715.

University of Bergen, Norway.

World Scientific, Singapore.

Ruiz volcano, *Earth Sciences Research Journal* 10(2): 57–65.

Sistema PROVIG - INGEOMINAS, *Technical report*, Instituto Colombiano de Geología

time-frequency analysis to the study of volcanic tremor and long-period events,

swarm zones at Nevado del Ruiz volcano, Colombia, *Earth Sciences Research Journal*

*Volcanology and Geothermal Research* 77(1-4): 305 – 311. Galeras Volcano, Colombia:

'Tornillo'-type seismic signals at Galeras volcano, Colombia, 1992-1993, *Journal of Volcanology and Geothermal Research* 77(1-4): 159 – 171. Galeras Volcano, Colombia:

*at Mt. Merapi, Java, Indonesia*, PhD thesis, University of Potsdam, Postdam, Germany.

representations for detecting mislabelled seismic signals at Nevado del Ruiz volcano,

(2006). Dissimilarity-based classification of seismic volcanic signals at Nevado del

earthquakes at Nevado del Ruiz Volcano using spectral band selection/extraction techniques, *in* J. Ruiz-Shulcloper & W. G. Kropatsch (eds), *Progress in Pattern Recognition, Image Analysis and Applications. Proceedings of the 13th Iberoamerican Congress on Pattern Recognition CIARP 2008*, Vol. 5197 of *Lecture Notes in Computer*

Windows, Solaris, Linux and MacOSX. Version 9.0.1. Department of Earth Science,

cost-sensitive optimization for hierarchical classifiers, *Proc. of the 20th Int. Conf. on*

*Foundations and Applications*, Vol. 64 of *Machine Perception and Artificial Intelligence*,


402 Earthquake Research and Analysis – Seismology, Seismotectonic and Earthquake Geology

Scherbaum, F. (1994). *Basic Concepts in Digital Signal Processing for Seismologists*, Vol. 53 of

Scherbaum, F. (2002). Analysis of digital earthquake signals, *in* W. H. K. Lee, H. Kanamori,

Scherbaum, F. (2007). *Of Poles and Zeros: Fundamentals of Digital Seismology*, Vol. 15 of *Modern Approaches in Geophysics*, revised 2nd edn, Springer, Dordrecht, The Netherlands. Tax, D. & Duin, R. P. W. (2008). Learning curves for the analysis of multiple instance

Tax, D. M. J. (2001). *One-class classification*, PhD thesis, Delft University of Technology, Delft,

Temes, L. & Schultz, M. E. (1998). *Electronic Communication*, Schaum's Outlines series, 2 edn,

Theodoridis, S. & Koutroumbas, K. (2006). *Pattern Recognition*, 3 edn, Academic Press, London,

Trombley, R. B. (2006). *Probability Contribution Due To Seismic Analysis: Types of Volcanic*

Ursino, A., Langer, H., Scarfì, L. & Giuseppe di Grazia, S. G. (2001). Discrimination of quarry

van der Heijden, F., Duin, R. P. W., de Ridder, D. & Tax, D. M. J. (2004). *Classification,*

Vapnik, V. N. (1998). *Statistical Learning Theory*, Adaptive and Learning Systems for Signal Processing, Communication and Control, John Wiley & Sons, New York, NY. Vargas-Jimenez, C. A. & Rincón-Botero, S. (2003). Portable digital seismological AC station over mobile telephone network and internet, *Computers & Geosciences* 29: 685–694. Veeramachaneni, S. & Nagy, G. (2003). Adaptive classifiers for multisource OCR, *International*

Webb, A. R. (2002). *Statistical Pattern Recognition*, 2nd edn, John Wiley & Sons, West Sussex,

Werner-Allen, G., Lorincz, K., Ruiz, M., Marcillo, O., Johnson, J., Lees, J. & Welsh, M. (2006).

Yıldırım, E., Gülbag, A., Horasan, G. & Dogan, E. (2011). Discrimination of quarry blasts and

Zobin, V. M. (2003). *Introduction to Volcanic Seismology*, Vol. 6 of *Developments in Volcanology*,

Deploying a wireless sensor network on an active volcano, *IEEE Internet Computing*

earthquakes in the vicinity of Istanbul using soft computing techniques, *Computers &*

*Journal on Document Analysis and Recognition* 6: 154–166.

*Earthquakes*, iUniverse, chapter 3 in The Forecasting of Volcanic Eruptions, pp. 14–17.

blasts from tectonic microearthquakes in the Hyblean Plateau (Southeastern Sicily),

*Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB*,

P. C. Jennings & C. Kisslinger (eds), *International Handbook of Earthquake and Engineering Seismology*, Vol. 81, Part A of *International Geophysics Series*, Academic

classifiers, *in* N. da Vitoria Lobo, T. Kasparis, F. Roli, J. Kwok, M. Georgiopoulos, G. Anagnostopoulos & M. Loog (eds), *Structural, Syntactic, and Statistical Pattern Recognition. Proceedings of the Joint IAPR International Workshop, SSPR & SPR 2008*, Vol. 5342 of *Lecture Notes in Computer Science*, Springer, Berlin, Germany, pp. 724–733.

*Lecture Notes in Earth Sciences*, Springer, Berlin / Heidelberg.

Press, San Diego, CA, chapter 22, pp. 349 – 355.

The Netherlands.

*Annali di Geofisica* 44(4): 703–722.

Wiley, Chichester, UK.

England.

10(2): 18 – 25.

*Geosciences* 37(9): 1209 – 1217.

Elsevier, Amsterdam.

McGraw-Hill.

UK.
