Abstract
Acquisition, classification, and analysis of seismic data are crucial tasks in volcano monitoring. The large number of seismic signals that are continuously acquired during the first monitoring stage poses a huge challenge for the human experts that must classify and analyze them. Several automatic classification systems have been proposed in the literature to alleviate such an overwhelming workload, each one characterized by different levels of accuracy, computational complexity, and interpretability. Considering this last perspective, which represents one of the recent key issues in geoscience, it is possible to find many accurate methods (in terms of classification accuracy) which however represent black boxes, not permitting a clear interpretation. On the other hand, there are other approaches, such as those based on support vector machines (SVM), random forests (RF), and K-nearest neighbor (KNN), which permit the interpretation of results, rules, and models at different levels. Among these last techniques, KNN approaches for volcanic signal classification typically do not achieve the satisfactory classification results obtained with RF and SVM. One possible reason is that in this context, the KNN rule has usually been applied in its basic version, not exploiting the different advanced KNN variants that have been introduced in recent years. This paper takes one step along this direction, investigating the suitability of a number of advanced versions of the KNN rule for the problem of classifying seismic-volcanic signals. The usefulness of these rules, in comparison with the original KNN rule as well as other interpretable classifiers, is evaluated within a real-world scenario involving a five-class dataset of seismic signals acquired at the Nevado del Ruiz volcano, Colombia. The results show that the classification accuracy of basic KNN is largely improved by these advanced variants, even surpassing that obtained with other classifiers like RF and SVM.
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Notes
The code is available at https://www.mathworks.com/matlabcentral/fileexchange/43156-dynamic-time-warping-dtw.
However, it was very recently shown that the integration of the three components can be very useful (Orozco-Alzate et al. 2019b).
References
Adadi A, Berrada M (2018) Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6:52138–52160
Bicego M, Baldo S (2016) Properties of the Box-Cox transformation for pattern classification. Neurocomputing 218:390–400
Bicego M, Loog M (2016) Weighted k-nearest neighbor revisited. In: Proceedings of international conference on pattern recognition, pp 1642–1647
Bicego M, Orozco-Alzate M (2020) PowerHC: non linear normalization of distances for advanced nearest neighbor classification. In: Proceedings of international conference on pattern recognition, pp 1205–1211
Bicego M, Acosta-Muñoz C, Orozco-Alzate M (2013) Classification of seismic volcanic signals using hidden-Markov-model-based generative embeddings. IEEE Trans Geosci Remote Sens 51(6):3400–3409
Bicego M, Londoño-Bonilla JM, Orozco-Alzate M (2015) Volcano-seismic events classification using document classification strategies. In: Proceedings of international conference on image analysis and processing, pp 119–129
Bishop CM (2006) Pattern recognition and machine learning. Springer, New York
Bramer M (2016) Principles of data mining, 3rd edn., Chap 7: estimating the predictive accuracy of a classifier, Springer, pp 79–92
Canário JP, Mello R, Curilem M, Huenupan F, Rios R (2020) In-depth comparison of deep artificial neural network architectures on seismic events classification. J Volcanol Geoth Res 401(106):881
Cárdenas-Peña D, Orozco-Alzate M, Castellanos-Domínguez G (2013) Selection of time-variant features for earthquake classification at the Nevado-del-Ruiz volcano. Comput Geosci 51:293–304
Carniel R, Guzmán SR (2020) Machine learning in volcanology: a review. In: Volcanoes: updates in volcanology, IntechOpen
Castro-Cabrera PA, Orozco-Alzate M, Adami A, Bicego M,Londoño-Bonilla JM, Castellanos-Domínguez G (2014)A comparison between time-frequency and cepstral feature representations for seismic-volcanic pattern classification. In: Proceedings of Iberoamerican congress on pattern recognition, pp 440–447
Castro-Cabrera P, Castellanos-Dominguez G, Mera-Banguero C,Franco-Marín L, Orozco-Alzate M (2021) Adaptive classification using incremental learning for seismic-volcanic signals with concept drift. J Volcanol Geoth Res 413(107):211
Chouet B, Matoza R (2013) A multi-decadal view of seismic methods for detecting precursors of magma movement and eruption. J Volcanol Geoth Res 252:108–175
Cover T, Hart P (1967) The nearest neighbor decision rule. IEEE Trans Inform Theory IT 13:21–27
Cox TF, Cox MAA (1994) Multidimensional scaling. Chapman & Hall, London
Curilem M, Soto R, Huenupan F, Cardona C,Franco L, San Marin C(2019) Hierachical classification structure based on SVM for volcano seismic events. In: Proceedings of IEEE Latin American conference on computational intelligence (LA-CCI), pp 1–6
Duin RPW, Bicego M, Orozco-Alzate M et al (2014) Metric learning in dissimilarity space for improved nearest neighbor performance. In: Proceedings of joint international workshop on structural, syntactic and statistical pattern recognition, pp 183–192
Fukanaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic press, San Diego
Grijalva F, Ramos W, Peréz N, Benítez D, Lara-Cueva RA, Ruiz M (2021) ESeismic-GAN: a generative model for seismic events from Cotopaxi volcano. IEEE J Sel Top Appl Earth Observ Remote Sens 14:7111–7120
Karpatne A, Ebert-Uphoff I, Ravela S,Babaie HA, Kumar V (2018) Machine learning for the geosciences: challenges and opportunities. In: IEEE transactions on knowledge and data engineering, pp 1–12
Kostorz W (2021) A practical method for well log data classification. Comput Geosci 25(1):345–355
Lara-Cueva RA, Benítez DS, Carrera EV Ruiz M, Rojo-Álvarez JL(2016) Automatic recognition of long period events from volcano tectonic earthquakes at Cotopaxi Volcano. IEEE Trans Geosci Remote Sens 54(9):1–11
Lara-Cueva R, Benítez DS, Paillacho V, Villalva M, Rojo-Álvarez JL (2017) On the use of multi-class support vector machines for classification of seismic signals at Cotopaxi volcano. In: Proceedings of IEEE international autumn meeting on power, electronics and computing, pp 1–6
Lin J, Williamson S, Borne KD (2012) Pattern recognition in time series. In: Advances in machine learning and data mining for astronomy, chap 28, CRC Press, pp 617–646
Lopes N, Ribeiro B (2015) Incremental hypersphere classifier (IHC). In: Machine learning for adaptive many-core machines: a practical approach, vol 7, Springer, chap 6, pp 107–123
Malfante M, Dalla Mura M, Métaxian JP Mars JI, Macedo O, Inza A(2018) Machine learning for volcano-seismic signals: challenges and perspectives. IEEE Signal Process Mag 35(2):20–30
Orozco-Alzate M, Acosta-Muñoz C, Londoño-Bonilla JM (2012) The automated identification of volcanic earthquakes: concepts, applications and challenges. In: Earthquake research and analysis: seismology, seismotectonic and earthquake geology. InTech, chap 19, pp 345–370
Orozco-Alzate M, Castro-Cabrera PA, Bicego M, Londoñ-Bonilla JM (2015) The DTW-based representation space for seismic pattern classification. Comput Geosci 85:86–95
Orozco-Alzate M, Duin RPW, Bicego M (2016) Unsupervised parameter estimation of non linear scaling for improved classification in the dissimilarity space. In: Proceedings of joint international workshop on structural, syntactic, and statistical pattern recognition, pp 74–83
Orozco-Alzate M, Baldo S, Bicego M (2019a) Relation, transition and comparison between the adaptive nearest neighbor rule and the hypersphere classifier. In: Proceedings of international conference on image analysis and processing, pp 141–151
Orozco-Alzate M, Londoño-Bonilla JM, Nale V, Bicego M (2019) Towards better volcanic risk-assessment systems by applying ensemble classification methods to triaxial seismic-volcanic signals. Eco Inform 51:177–184
Pal AK, Mondal PK, Ghosh AK (2016) High dimensional nearest neighbor classification based on mean absolute differences of inter-point distances. Pattern Recogn Lett 74:1–8
Peréz N, Venegas P, Benítez D Lara-Cueva R, Ruiz M (2020) A new volcanic seismic signal descriptor and its application to a data set from the Cotopaxi volcano. IEEE Trans Geosci Remote Sens 58(9):6493–6503
Talebi H, Peeters LJM, Mueller U, Tolosana-Delgado R,van den Boogaart KG(2020) Towards geostatistical learning for the geosciences: a case study in improving the spatial awareness of spectral clustering. Math Geosci 52(8):1035–1048
Titos M, Bueno A, García L, Benitez C (2018) A deep neural networks approach to automatic recognition systems for volcano-seismic events. IEEE J Sel Top Appl Earth Observ Remote Sens 11(5):1533–1544
Titos M, Bueno Á, García L, Benítez MC, Ibañez J (2019) Detection and classification of continuous volcano-seismic signals with recurrent neural networks. IEEE Trans Geosci Remote Sens 57(4):1936–1948
Triguero I, Derrac J, García S, Herrera F (2012) A taxonomy and experimental study on prototype generation for nearest neighbor classification. IEEE Trans Syst Man Cybern Part C Appl Rev 42(1):86–100
Trombley RB (2006) The forecasting of volcanic eruptions. Ed. iUniverse
Trujillo-Castrillón N, Valdés-González CM, Arámbula-Mendoza R, Santacoloma-Salguero CC (2018) Initial processing of volcanic seismic signals using Hidden Markov Models: Nevado del Huila, Colombia. J Volcanol Geoth Res 364:107–120
Venegas P, Peréz N, Benítez D, Lara-Cueva R, Ruiz M (2019) Combining filter-based feature selection methods and Gaussian mixture model for the classification of seismic events from Cotopaxi volcano. IEEE J Sel Top Appl Earth Observ Remote Sens 12(6):1991–2003
Wang J, Neskovic P, Cooper LN (2007) Improving nearest neighbor rule with a simple adaptive distance measure. Pattern Recogn Lett 28(2):207–213
Wang X, Mueen A, Ding H, Trajcevski G, Scheuermann P, Keogh E (2013) Experimental comparison of representation methods and distance measures for time series data. Data Min Knowl Disc 26(2):275–309
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The authors acknowledge the Cooperation and Academic Exchange Agreement between Universidad Nacional de Colombia and Università degli Studi di Verona, which is available at https://tinyurl.com/ae4dt7d5.
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Bicego, M., Rossetto, A., Olivieri, M. et al. Advanced KNN Approaches for Explainable Seismic-Volcanic Signal Classification. Math Geosci 55, 59–80 (2023). https://doi.org/10.1007/s11004-022-10026-w
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DOI: https://doi.org/10.1007/s11004-022-10026-w