The authors are to be congratulated for a systematic investigationof the accurate and non subjective classifying approach in seismic research. The authors have conducted several clustering algorithms to the seismic event records from Volcanological and SeismologicalObservatory at Manizales. Their objective was to improve the grouping of seismic data (i.e., volcano-tectonic earthquakes, long-period earthquakes and icequakes) digitized at 100.16 Hz sampling frequency.Their study seems adding new approach to their previous work of Langer et al. (2006) who applied different classification techniques to seismic data

Demirel, Mehmet C.

Kahya, Ercan

Rivera, Diego

English

Portal de Revistas UNAL (Univ. Nacional de Colombia)

DISCUSSION OF “CLUSTERING ON DISSIMILARITY REPRESENTATIONS FOR DETECTING MISLABELLED SEISMIC SIGNALS AT NEVADO DEL RUIZ VOLCANO” BY MAURICIO OROZCO-ALZATE, AND CÉSAR GERMÁN CASTELLANOS-DOMÍNGUEZ

Universidad Nacional De Colombia - Repositorio Institucional UN

EARTH SCIENCESRESEARCH JOURNALEarth Sci. Res. J. Vol. 12, No. 2 (December 2008): 265-268DISCUSSION OF “CLUSTERING ON DISSIMILARITYREPRESENTATIONS FOR DETECTING MISLABELLEDSEISMIC SIGNALS AT NEVADO DEL RUIZ VOLCANO” BYMAURICIO OROZCO-ALZATE, AND CÉSAR GERMÁNCASTELLANOS-DOMÍNGUEZMehmet C. Demirel1, Ercan Kahya2 and Diego Rivera31 Ph.D. Student, Department of Water Engineering and Management, University of Twente,PO Box 217, 7500 AE Enschede, The Netherlands. E-mail: m.c.demirel@utwente.nl2 Civil Engineering Department, American University of Sharjah,PO Box 26666, Sharjah, United Arab Emirates (corresponding author).E-mail: ekahya@aus.edu3 Professor, Department of Water Resources, University of Concepcion, Vicente Méndez 595,Chillán, Chile. E-mail: dirivera@udec.clThe authors are to be congratulated for a systematic in-vestigation of the accurate and non subjective classify-ing approach in seismic research. The authors haveconducted several clustering algorithms to the seismicevent records from Volcanological and SeismologicalObservatory at Manizales. Their objective was to im-prove the grouping of seismic data (i.e., volcano-tec-tonic earthquakes, long-period earthquakes andicequakes) digitized at 100.16 Hz sampling frequency.Their study seems adding new approach to their previ-ous work of Langer et al. (2006) who applied differentclassification techniques to seismic data.The discussers have the following suggestions toimprove the author’s investigation on Ruiz volcanodata and to be a guide for similar future studies.1. There are five empirical steps that should befollowed in the application of cluster analysiswhich are (i) selection of variables, (ii) selec-tion of standardization technique (if necessary),(iii) dissimilarity metric, (iv) selection of an ap-propriate method, and (v) test of stability orvalidation (Demirel 2004; Everitt 1993; Greenet al. 1990). These steps are difficult to distin-guish in the present study by Orozco-Alzateand Castellanos-Domínguez (2007). The users`experience and preferences in these steps mayradically affect the resultant cluster structures.For instance, the selected distance metric wasnot clearly mentioned in the text. Did the nota-tion kl refer to the correlation coefficient be-tween entities k and l? How many stations wereselected near the Olleta crater and the glacier atNevado del Ruiz volcanic complex? Werethere any scale issues in the dataset which mayperturb the dissimilarity matrices? Henceforth265NOV 10-DISCUSSION.prnD:\GEOCIENCIAS V-12-2-DIC 2008\GEOCIENCIAS-VOL 12-2 DIC.vpmartes, 10 de noviembre de 2009 20:50:03p p pComposite  133 lpi at 45 degreesthe span of the data and some statistical infor-mation on the data structure should be ex-plained for easy follow of readers and to avoidthe aforementioned questions. Standardizationpriori to analysis phase is necessary when thescale differences emerged in a dataset (Demirelet al. 2008; Everitt 1993; Gnanadesikan et al.1995; Milligan and Cooper 1988).2. The authors applied several algorithms on theirdata and reasoned the following statement: “thelack of a single appropriate clustering algo-rithm”. However most of the algorithms werealready tested in the literature and the relevantshortcomings are given in many text books(Bacher 2002; Everitt 1993). Single linkageproduces chain type cluster which is not be de-sirable for many applications, and completelinkage may create small and compact clusters(Demirel 2004; Everitt 1993). On the otherhand the Wards method emerged to make moresuccessive clusters with small inner variance.Hence it is herein suggested to use the Wardsmethod with the squared Euclidean metric toget more distinct clusters in future investiga-tions.3. In the context of text indications in notationwise, at page 133: the notation “DC” was notexplained in the text. At page 133: D(T,T) des-ignates to distance/dissimilarity measure; how-ever, the notation “d” was used for the samepurpose in table 1. It is important to maintainconsistent use of notations for the dissimilaritymeasure throughout the text.4. The mismatches in labeling were counted forthe performance comparison and number ofruns was given as 10. The author also men-tioned that “Hierarchical methods report thesame number of mismatches over the runs”. Itshould be noted that cluster structure in the hi-erarchical methods do not differ in any run asthe steps in dissimilarity calculations and clus-ter delineation has concrete algorithm; thus, itis herein encouraged that issues similar to theseunclear points should be justified in the manu-script.5. The clustering results were not given in thetext. The labels of clusters and statistics (i.e.,variance, mean) of each cluster should be sum-marized in the result section. Only the averagednumbers of mismatches between class labelswere presented but this was not adequate forthe readers to have appropriate insights regard-ing the main objective of the study. Since thearticle is about signal clustering, it would havebeen very illustrative to put 2 figures: Onegraph including 3 representative signals (e.g.Langer et al., 200) and one figure representingthe topological structure of the clusters, e.g.dendrogram. Both figures allow analyzing inan intuitive way dissimilarities among signals.6. At page 135: the authors mentioned that “eventhough the number of cluster is fixed, singlelinkage and average linkage find second andthird clusters of a few objects only”. The singlelinkage and average linkage methods are in thegroup of unsupervised clustering techniqueswhich has no priori knowledge on number ofclusters as partitioning methods; therefore, ajustification should have been indicated for thatmatter (Demirel and Kahya 2007; Kahya et al.2007).7. As was noted by Morlet et al. (1982), seismicsignal does vary in amplitude, shape, frequencyand phase, versus propagation time. Therefore,for clustering it is necessary to analyze signal’sfrequency content, as well as to localize in timechanges in both, frequency and amplitude. Forthis task, Wavelet transform is a joint time-fre-quency signal representation that can give thefrequency content of the signal at a particularinstant of time by filtering (Sheikholeslami etal., 1998). It is well suited for signal whose fre-quencies change with time, but also for signalcontaining noise and transients (Rouyer et al.,2008). Also, its multi-resolution property canhelp detecting the clusters at different levels ofaccuracy (Sheikholeslami et al., 1998). We266MEHMET C. DEMIREL, ERCAN KAHYA AND DIEGO RIVERANOV 10-DISCUSSION.prnD:\GEOCIENCIAS V-12-2-DIC 2008\GEOCIENCIAS-VOL 12-2 DIC.vpmartes, 10 de noviembre de 2009 20:50:04p p pComposite  133 lpi at 45 degreespropose for further research to apply this tech-nique to Ruiz volcano data. A good referenceare Kumar and Foufoula-Georgiou (1997) andTorrence and Compo (1998) for methods. In-deed, Arciniega-Ceballos et al. (2008) appliedbandpass filters before clustering in seismicdata and Rouyer et al., (2008) applied a wave-let-based clustering technique.ReferencesArciniega-Ceballos A, Chouet B, Dawson Ph. and GAsch (2008) Broadband seismic measurementsof degassing activity asociated with lava effu-sion at Popocatépetl Volcano, Mexico. Journalof Volcanology and Geothermal Research, 170:12-23.Bacher, J. (2002). “Cluster Analysis.” Lecture Notes,Nuremberg.Demirel, M. C. (2004). “Cluster Analysis ofStreamflow Data over Turkey,” Istanbul Techni-cal University, Istanbul.Demirel, M. C., and Kahya, E. “Hydrological determi-nation of hierarchical clustering scheme by usingsmall experimental matrix.” 27th AGU HydrologyDays, Fort Collins, Colorado, 161-168.Demirel, M. C., Kahya, E., and Rivera, D. (2008).Discussion of “Hydrologic Regionalization ofWatersheds in Turkey” by Sabahattin Isik; andVijay P. Singh in ASCE Journal of HydrologicEngineering, Sep 2008, Vol. 13, No. 9, pp.824-834. DOI: 10.1061/(asce) 1084-0699 (2008)13:9(824) (accepted for publication).Everitt, B. (1993). Cluster Analysis. 3rd edn. ,Halsted Press, Division of Wiley, New York.Gnanadesikan, R., Kettenring, J. R., and Tsao, S. L.(1995). “Weighting and selection of variablesfor cluster analysis.” Journal of Classification,12(1), 113-136.Green, P. E., Kim, J., and Carmone, F. J. (1990). “Apreliminary study of optimal variable weightingin k-means clustering”. Journal of Classifica-tion, 7(2), 271-285.Kahya, E., Demirel, M. C., and Piechota, T. C. “Spa-tial grouping of annual streamflow patterns inTurkey ” 27th AGU Hydrology Days, Fort Col-lins, Colorado, 169-176.Kumar, P. and Foufoula-Georgiou, E. (1997). Wave-let analysis for geophysical applications. Re-views of Geophysics, 35:385–412.Langer, H., Falsaperla, S., Powell, T., and Thomp-son, G. (2006). “Automatic classification anda-posteriori analysis of seismic event identifica-tion at Soufri?re Hills volcano, Montserrat.”Journal of Volcanology and Geothermal Re-search, 153(1-2), 1-10.Milligan, G. W., and Cooper, M. C. (1988). “A studyof standardization of variables in cluster analy-sis.” Journal of Classification, 5(2), 181-204.Morlet, J., Arens, G., Fourgeau, E. and Glard, D.(1982) Wave propagation and sampling the-ory-Part I: Complex signal and scattering inmultilayered media. Geophysics 47, 203-221.Orozco-Alzate, M., and Castellanos-Domínguez, C.G. (2007). “Clustering On Dissimilarity Repre-sentations For Detecting Mislabelled SeismicSignals At Nevado Del Ruiz Volcano.” EarthSci. Res. J., 11( 2), 131-138Rouyer T, Fromentin J-M, Stenseth N and BCazelles (2008) Analysing multiple tiem seriesand extending significance testing in waveletanalysis. Marine Ecology Progress Series,359:11-23.Sheikholeslami, Gh., Chatterjee, S. and Zhang, A.(1998) Wavecluster: A multi-resolution cluster-ing approach for very large spatial databases. InProceedings of the 24th VLDB conference.Torrence, C. and Compo, G. (1998). A practicalguide to wavelet analysis. The Bulletin of theAmerican Meteorological Society, 79:61–78.267DISCUSSION OF “CLUSTERING ON DISSIMILARITY REPRESENTATIONS FOR DETECTING MISLABELLED SEISMICSIGNALS AT NEVADO DEL RUIZ VOLCANO” BY MAURICIO OROZCO-ALZATE, AND CÉSAR GERMÁNCASTELLANOS-DOMÍNGUEZNOV 10-DISCUSSION.prnD:\GEOCIENCIAS V-12-2-DIC 2008\GEOCIENCIAS-VOL 12-2 DIC.vpmartes, 10 de noviembre de 2009 20:50:05p p pComposite  133 lpi at 45 degreesNOV 10-DISCUSSION.prnD:\GEOCIENCIAS V-12-2-DIC 2008\GEOCIENCIAS-VOL 12-2 DIC.vpmartes, 10 de noviembre de 2009 20:50:05p p pComposite  133 lpi at 45 degrees

Spanish

Discussion of “clustering on dissimilarity representations for detecting mislabelled seismic signals at nevado del ruiz volcano” by mauricio orozco-alzate, and césar germán castellanos-domínguez

LAReferencia - Red Federada de Repositorios Institucionales de Publicaciones Científicas Latinoamericanas

A practical guide to wavelet analysis.

A preliminary study of optimal variable weighting in k-means clustering”.

A study of standardization of variables in cluster analysis.”

Automatic classification and a-posteriori analysis of seismic event identification at Soufri?re Hills volcano,

Broadband seismic measurements of degassing activity asociated with lava effusion at Popocatépetl Volcano,

Cluster Analysis of StreamflowData over Turkey,” Istanbul Technical University, Istanbul. Demirel,M.C.,andKahya,E.“Hydrologicaldetermination of hierarchical clustering scheme by using small experimental matrix.” 27th AGU Hydrology Days, Fort Collins,

Cluster Analysis. 3rd edn.

Clustering On Dissimilarity Representations For Detecting Mislabelled Seismic Signals At Nevado Del Ruiz Volcano.” Earth Sci.

Discussion of “Hydrologic Regionalization of Watersheds in Turkey” by Sabahattin Isik; and Vijay P.

Wave propagation and sampling theory-Part I: Complex signal and scattering in multilayered media.

Wavecluster: A multi-resolution clustering approach for very large spatial databases.

Wavelet analysis for geophysical applications. Reviews of Geophysics,

Weighting and selection of variables for cluster analysis.”

http://purl.utwente.nl/publications/67900

Discussion of “clustering on dissimilarity Representations for detecting mislabelled Seismic signals at Nevado del Ruiz Volcano” by Mauricio Orozco-Alzate, and César Germán Castellanos-Domínguez

Discussion of “clustering on dissimilarity Representations for detecting mislabelled Seismic signals at Nevado del Ruiz Volcano” by Mauricio Orozco-Alzate, and César Germán Castellanos-Domínguez

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