Insights Into Microseism Sources by Array and Machine Learning Techniques: Ionian and Tyrrhenian Sea Case of Study

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