Volcano seismic events are a source of great hazards implicating human lives and material damage. Consequently, continuous monitoring of this natural phenomenon is of great importance to reduce their dramatic effects on people and nearby economy. A seismic network is usually deployed around the crater to achieve this monitoring task. The different produced volcano seismic events (e.g., long period LP, tremor TR, volcano tectonic VT) are related to physical phenomenon (explosion, eruption, depressurization …etc) occurring at the source. The seismic network may also record seismic events that are not related to volcanoes such as tectonic events (TC) produced by geological faults. The first vital task in volcano monitoring is to recognize the source of each detected event. This task should be performed automatically due to the large amount of data recorded daily. In this work, we propose an easy and straightforward method to classify volcano seismic events using the cross-correlation function in time domain. We applied this method using three approaches. The application of these approaches to the seismic database of the Llaima volcano (Chile) gives good results, particularly the third approach that achieves a global accuracy of 92.7%.

Volcano seismic events, Classification, Cross-correlation, Time domain.

[1] S. Scarpetta, F. Giudicepietro, E. Ezin, S. Petrosino, E. Del Pezzo, M. Martini, M. Marinaro, “Automatic classification of seismic signals at Mt. Vesuvius volcano, Italy, using neural networks”, Bulletin of the Seismological Society of America, Vol. 95, No. 1, pp. 185–196, 2005.
[2] H. Langer, S. Falsaperla, T. Powell, G. Thompson, “Automatic classification and a posteriori analysis of seismic event identification at Soufrière hills volcano, Montserrat”, Journal of Volcanology and Geothermal Research 153 (1–2 SPEC. ISS.), 1–10, 2006.
[3] M. Masotti, S. Falsaperla, H. Langer, S. Spampinato, R. Campanini, “Application of support vector machine to the classification of volcanic tremor at Etna, Italy”, Geophysical Research Letters, 33, L20304, 2006.
[4] M. Titos, A. Bueno, L. Garcia, & C. Benitez, “A Deep Neural Networks Approach to Automatic Recognition Systems for Volcano-Seismic Events”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5), 1533-1544, 2018.
[5] M. Malfante, M. D. Mura, J.-P. Métaxian, J. Mars, O. Macedo, A. Inza. “Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives”, IEEE Signal Processing Magazine, Institute of Electrical and Electronics Engineers, 35 (2), pp.20 – 30, 2018.
[6] J.P. Canário , R.F. de Mello , M. Curilem , F. Huenupan , R.A. Rios, “In-Depth Comparison of Deep Artificial Neural Network Architectures on Seismic Events Classification”, Journal of Volcanology and Geothermal Research, volume 30, 105627, 2020.
[7] M. Titos, A. Bueno, L. Garcia, M. C. Benitez, & J. Ibanez, “Detection and Classification of Continuous Volcano-Seismic Signals With Recurrent Neural Networks” IEEE Transactions on Geoscience and Remote Sensing, volume 57, Issue 4, pp.1936–1948, 2018.
[8] M. Titos, A. Bueno, L. Garcia, C. Benitez, & J. C. Segura, “Classification of Isolated Volcano-Seismic Events Based on Inductive Transfer Learning” IEEE Geoscience and Remote Sensing Letters, vol. 17, Issue. 5, pp.869–873, 2019.
[9] A. Salazar, R. Arroyo, N. Perez, & D. Benitez, “Deep-Learning for Volcanic Seismic Events Classification”, In the Proceedings of the 2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020), 2020.
[10] M. Lopez-Perez, L. Garcia, C. Benitez, & R. Molina, “A Contribution to Deep Learning Approaches for Automatic Classification of Volcano-Seismic Events: Deep Gaussian Processes”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 59, Issue. 5, pp. 3875 - 3890, 2020.
[11] P. Venegas, N. Perez, D. S. Benitez, R. Lara-Cueva, & M. Ruiz, “Building Machine Learning Models for Long-Period and Volcano-Tectonic Event Classification”, In the Proceedings of the 2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON 2019), Chile, pp. 1-6, 2019.
[12] R. Carnel, “Characterization of volcanic regimes and identification of significant transitions using geophysical data: a review”, Bulletin of Volcanology, 76(8), pp. 1-22, 2014.
[13] K. Mohabkumar, K. Sangeetha, “A Study on Earthquake Pediction Using Neural Network Algorithms”, International Journal of Computer Sciences and Engineering. Vol. 6, Issue.10, 2018.
[14] P. Venegas, N. Perez, D. Benitez, R. Lara-Cueva, & M. Ruiz, “Combining Filter-Based Feature Selection Methods and Gaussian Mixture Model for the Classification of Seismic Events From Cotopaxi Volcano”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 12, Issue. 6, pp. 1991 - 2003, 2019.
[15] R. Soto, F. Huenupan, P. Meza, M. Curilem, & L. Franco, “Spectro-temporal features applied to the automatic classification of volcanic seismic events”, Journal of Volcanology and Geothermal Research, 358, pp.194–206, 2018.
[16] N. Perez, P. Venegas, D. Benitez, F. Grijalva, R. Lara, & M. Ruiz, “Benchmarking Seismic-Based Feature Groups to Classify the Cotopaxi Volcanic Activity”, IEEE Geoscience and Remote Sensing Letters, Vol. 19, pp.1–5, 2020.
[17] T. Permana, T. Nishimura, H. Nakahara and N. Shapiro, “Classification of volcanic tremors and earthquakes based on seismic correlation: application at Sakurajima volcano, Japan” Geophysical Journal International, 229, pp. 1077–1097, 2022.
[18] M. Curilem, R.F. de Mello, F. Huenupan, C. San Martin, L. Franco, E. Hernández, R.A. Rios. “Discriminating seismic events of the Llaima volcano (Chile) based on spectrogram cross-correlations”, Journal of Volcanology and Geothermal Research 367, pp.63–78, 2018.
[19] E. H. Ait Laasri, E. Akhouayri, D. Agliz, A. Atmani, “Automatic detection and picking of P-wave arrival in locally stationary noise using cross-correlation”, Digital Signal Processing, Vol. 26, pp. 87- 100, 2014.
[20] F. Wagner, A. Tryggvason, R. Roberts and O. Gudmundsson. “Processing automatic seismic event detections: an iterative sorting algorithm improving earthquake hypocentres using interevent cross-correlation”, Geophysical Journal International. 219, pp. 1268–1280, 2019.
[21] R.O. Salvage, J.W. Neuberg. “Using a cross correlation technique to refine the accuracy of the Failure Forecast Method: Application to Soufrière Hills volcano, Montserrat”, Journal of Volcanology and Geothermal Research 324, pp.118–133, 2016.
[22] D. P. Schaff, W.-Y. Kim, P. G. Richards, E. Jo, and Y. Ryoo, “Using Waveform Cross Correlation for Detection, Location, and Identification of Aftershocks of the 2017 Nuclear Explosion at the North Korea Test Site”, Seismological Research Letters, Vol. 89, Number 6 pp. 2113-2119, 2018.
[23] F. R. Suratwala, S. G. Kejgir, “A Robust Multi-Channel Digital Image Watermarking Technique with SVD, DWT, DCT”, International Journal of Computer Sciences and Engineering. Vol. 7, Issue.5, 2019.
[24] J. P. Canario, R. Mello, M. Curilem, F. Huenupan , and R. Rios, “Llaima Volcano Dataset: In-Depth Comparison of Deep Artificial Neural Network Architectures on Seismic Events Classification”, Data in Brief, 30, 105627, 2020.
[25] E.H. Ait Laasri, A. Atmani, D. Agliz, E. Akhouayri, “Application of Cross-Correlation to Seismic Signal Database of Agadir”, International Journal of Computer Sciences and Engineering. Vol. 9, Issue.6, 2021.