Abstract
Algorithms based on artificial intelligence (AI) have had a strong development in recent years in different research fields of earth science such as seismology and volcanology. In particular, they have been applied to the study of the volcanic eruptive products of the recent activity of Mount Etna volcano. This work presents an application of the self-organizing map (SOM) neural networks to perform a clustering analysis on petrographic patterns of rocks of Somma–Vesuvius and Campi Flegrei volcanoes, in the Neapolitan area. The goal is to highlight possible affinity between the magmatic reservoirs of these two volcanic complexes. The SOM is known for its ability to cluster data by using intrinsic similarity measures without any previous information about their distribution. Moreover, it allows an easy understandable data visualization by using a two-dimensional map. The SOM has been tested on a geochemical dataset of 271 samples, consisting of 134 samples of Campi Flegrei eruptions (named CF), 24 samples of Somma–Vesuvius effusive eruptions (VF), 73 samples of Somma–Vesuvius explosive eruptions (VX), and finally 40 samples of “foreign” eruptions (ET), included to verify the neural net classification capability. After a pre-processing phase, applied to have a more appropriate data representation as input for the SOM, each sample has been encoded through a vector of 23 features, containing information about major bulk components, trace elements, and Sr isotopic ratio. The resulting SOM identifies three main clusters, and in particular, the foreign patterns (ET) are well separated from the other ones being mainly grouped in a single node. In conclusion, the obtained results suggest the ability of SOM neural network to associate volcanic rock suites on the basis of their geochemical imprint and can be consistent with the hypothesis that there might be a common magma source beneath the whole Neapolitan area.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ali, M., Chawathé, A.: Using artificial intelligence to predict permeability from petrographic data. Comput. Geosci. 26(8), 915–925 (2000)
Aminian, K., Ameri, S.: Application of artificial neural networks for reservoir characterization with limited data. J. Petrol. Sci. Eng. 49(3–4), 212–222 (2005)
Corsaro, R.A., Falsaperla, S., Langer, H.: Geochemical pattern classification of recent volcanic products from Mt. Etna, Italy, based on Kohonen maps and fuzzy clustering. Int. J. Earth Sci. 102(4), 1151–1164 (2013)
Dowla, F. U., Rogers, L. L.: Solving problems in environmental engineering and geosciences with artificial neural networks. Mit Press (1995)
Esposito, A.M., Giudicepietro, F., D’Auria, L., Scarpetta, S., Martini, M.G., Coltelli, M., Marinaro, M.: Unsupervised neural analysis of very-long-period events at Stromboli volcano using the self-organizing maps. Bull. Seismol. Soc. Am. 98(5), 2449–2459 (2008)
Giudicepietro, F., Esposito, A.M., Ricciolino, P.: Fast discrimination of local earthquakes using a neural approach. Seismol. Res. Lett. 88(4), 1089–1096 (2017)
Goswami, S., Chakraborty, S., Ghosh, S., Chakrabarti, A., Chakraborty, B.: A review on application of data mining techniques to combat natural disasters. Ain Shams Eng. J. 9(3), 365–378 (2018)
Huffman, W. S.: Geographic information systems, expert systems and neural networks: disaster planning, mitigation and recovery. WIT Trans. Ecol. Environ. 50 (2001)
Kohonen T., Hynninen, J., Kangas, J., Laaksonen, J.: SOM_PAK: the self-organizing map program package, Report A31, Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland (1996) (http://www.cis.hut.fi/research/som_lvq_pak.shtml)
Kohonen, T.: Self-Organizing Maps. Series in information sciences, vol. 30, 2nd edn. Springer, New York (1997)
Pappalardo, L., Mastrolorenzo, G.: Rapid differentiation in a sill-like magma reservoir: a case study from the Campi Flegrei caldera. Sci. Rep. 2, 712 (2012)
Peccerillo, A., De Astis, G., Faraone, D., Forni, F., Frezzotti, M.L.: Compositional variations of magmas in the Aeolian arc: implications forpetrogenesis and geodynamics. In: From: Lucchi, F., Peccerillo, A., Keller, J., Tranne, C. A. Rossi, P.L. (eds.). The Aeolian Islands Volcanoes. Geological Society, vol. 37, pp. 491–510. London, Memoirs (2013)
Yu, M., Yang, C., Li, Y.: Big data in natural disaster management: a review. Geosciences 8(5), 165 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Esposito, A.M., De Bernardo, A., Ferrara, S., Giudicepietro, F., Pappalardo, L. (2020). SOM-Based Analysis of Volcanic Rocks: An Application to Somma–Vesuvius and Campi Flegrei Volcanoes (Italy). In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, vol 151. Springer, Singapore. https://doi.org/10.1007/978-981-13-8950-4_6
Download citation
DOI: https://doi.org/10.1007/978-981-13-8950-4_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8949-8
Online ISBN: 978-981-13-8950-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)