Unravelling the Relationship Between Microseisms and Spatial Distribution of Sea Wave Height by Statistical and Machine Learning Approaches

Abstract

Global warming is making extreme wave events more intense and frequent. Hence, the importance of monitoring the sea state for marine risk assessment and mitigation is increasing day-by-day. In this work, we exploit the ubiquitous seismic noise generated by energy transfer from the ocean to the solid earth (called microseisms) to infer the sea wave height data provided by hindcast maps. To this aim, we use a combined approach based on statistical analysis and machine learning. In particular, a random forest model shows very promising results in the spatial and temporal reconstruction of sea wave height by microseisms. The observed dependence of input importance from the distance sea grid cell-seismic station suggests how the reliable monitoring of the sea state in a wide area by microseisms needs data recorded by dense networks, comprising stations evenly distributed along the coastlines.

Seismic evidence of the COVID-19 lockdown measures: a case study from eastern Sicily (Italy)

“…At the same time, different studies showed the effectiveness of seismic noise monitoring as a tool to quantify human activity and its changes over time (Dias et al, 2020;Hong et al, 2020;Lindsey et al, 2020;Poli et al, 2020). Indeed, the Earth is continuously vibrating due to a wide spectrum of elastic energy sources including tectonic forces (Stein and Wysession, 2003), volcanic processes (Chouet and Matoza, 2013), the ocean (Cannata et al, 2020b) and human (Diaz et al, 2017) activity. As for the last point, it typically generates a high-frequency continuous signal (>1 Hz), called anthropogenic or cultural seismic noise, associated with phenomena such as traffic, construction, industrial operations and mining (Diaz et al, 2017;Hong et al, 2020).…”

Measurement of Sea Waves

“…Other authors have applied a more physics-based approach to quantitatively link microseism and sea wave height [ 170 , 171 , 193 , 194 ]. Cannata et al [ 180 ] analyzed the microseism recorded by six seismic stations located close to the Eastern Sicilian coastline, and proposed a machine learning-based approach to calculate a regression model for reconstructing the temporal and spatial variation in sea wave height using the microseism recorded at multiple seismic stations in different frequency bands.…”

“…In addition, microseism is recorded continuously with a sampling frequency from tens to hundreds of Hz and is acquired at a very high temporal resolution. The spatial resolution depends on the number of stations installed close to the coastline [ 180 ]. Furthermore, in most areas, it is not necessary to install a seismic network specifically to record the microseism and then for sea wave height monitoring, but it is possible to use the seismic stations installed to monitor seismic and volcanic activities.…”

“…SPSM, SM, and PM and the corresponding dashed rectangles indicate the frequency bands characterized by short period secondary microseism, secondary microseism, and primary microseism, respectively. The acronym PSD in both color bar of ( a ) and y -axis of ( b ) indicates power spectral density (modified from [ 180 ]).…”

Sea Wave Data Reconstruction Using Micro-Seismic Measurements and Machine Learning Methods

“…In Serafino et al (2021), simultaneous measurements of a micro-seismic based system (called OS−IS) and those of a radar system were compared for the first time. In Cannata et al (2020), a machine learning method (specifically, a random forest) was proposed to reconstruct the spatial distribution of sea wave height, as provided by hindcast maps of sea wave models, by using micro-seismic data from multiple seismic stations. In Moschella et al (2020), a network of broadband seismic stations was used to investigate the microseismic signals from Ionian and Tyrrhenian Sea and, importantly, it was demonstrated that the signal detected by seismic stations closer to the sea contain more information concerning the sea state than the others.…”