insights into microseism sources by array and machine learning techniques: ionian and tyrrhenian sea case of study

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ID: 140079
2020
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Abstract
In this work, we investigated the microseism recorded by a network of broadband seismic stations along the coastline of Eastern Sicily. Microseism is the most continuous and ubiquitous seismic signal on Earth and is mostly generated by the ocean–solid earth interaction. On the basis of spectral content, it is possible to distinguish three types of microseism: primary, secondary, and short-period secondary microseism (SPSM). We showed how most of the microseism energy recorded in Eastern Sicily is contained in the secondary and SPSM bands. This energy exhibits strong seasonal patterns, with maxima during the winters. By applying array techniques, we observed how the SPSM sources are located in areas of extended shallow water depth: the Catania Gulf and a part of the Northern Sicily coastlines. Finally, by using the significant wave height data recorded by two buoys installed in the Ionian and Tyrrhenian Seas, we developed an innovative method, selected among up-to-date machine learning techniques (MLTs), able to reconstruct the time series of sea wave parameters from microseism recorded in the three microseism period bands by distinct seismic stations. In particular, the developed model, based on random forest regression, allowed estimating the significant wave height with a low average error (∼0.14–0.18 m). The regression analysis suggests that the closer the seismic station to the sea, the more information concerning the sea state are contained in the recorded microseism. This is particularly important for the future development of an experimental monitoring system of the sea state conditions based on microseism recordings.
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moschella2020frontiersinsights Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Salvatore Moschella;Andrea Cannata;Andrea Cannata;Flavio Cannavò;Giuseppe Di Grazia;Gabriele Nardone;Arianna Orasi;Marco Picone;Maurizio Ferla;Stefano Gresta
Journal ratio juris
Year 2020
DOI
10.3389/feart.2020.00114
URL
https://www.frontiersin.org/article/10.3389/feart.2020.00114/full
Keywords
Machine learning

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