Wave height quantification using land based seismic data with grammatical evolution

Abstract

Abstract-Accurate, real time, continuous ocean wave height measurements are required for the initialisation of ocean wave forecast models, model hindcasting, and climate studies. These measurements are usually obtained using in situ ocean buoys or by satellite altimetry, but are sometimes incomplete due to instrument failure or routine network upgrades. In such situations, a reliable gap filling technique is desirable to provide a continuous and accurate ocean wave field record. Recorded on a land based seismic network are continuous seismic signals known as microseisms. These microseisms are generated by the interactions of ocean waves and will be used in the estimation of ocean wave heights. Grammatical Evolution is applied in this study to generate symbolic models that best estimate ocean wave height from terrestrial seismic data, and the best model is validated against an Artificial Neural Network. Both models are tested over a five month period of 2013, and an analysis of the results obtained indicates that the approach is robust and that it is possible to estimate ocean wave heights from land based seismic data.

Assessing swells in La Réunion Island from terrestrial seismic observations, oceanographic records and offshore wave models

“…Estimating the significant wave height using seismic land station, have been successfully used before using different methods (e.g. Bromirski et al 1999;Donne et al 2014).…”

Ambient Seismic Source Inversion in a Heterogeneous Earth: Theory and Application to the Earth's Hum

“…Besides their influence on ambient noise tomography, noise sources have been extensively studied with the aims of understanding their excitation mechanism (e.g., Haubrich & McCamy, 1969;Hillers et al, 2012;Juretzek & Hadziioannou, 2016;Landès et al, 2010;Stehly et al, 2006;Traer et al, 2012) and of using them as proxy for present and past ocean wave states or Antarctic sea ice cover (e.g., Aster et al, 2010;Ebeling & Stein, 2011;Donne et al, 2014;Stutzmann et al, 2009).…”

North Atlantic Oscillation (NAO) Climate Index Hidden in Ocean Generated Secondary Microseisms

“…Whereas previous studies linking changing wave climate with secondary microseisms have mainly used seismic noise amplitudes (Anthony et al, 2017;Aster et al, 2008;Donne et al, 2014;Essen et al, 1999;Grevemeyer et al, 2000;Stutzmann et al, 2009;Turner et al, 2020), we investigate temporal changes in the relative dominance of Rayleigh surface wave modes associated with secondary microseisms, recorded both in the ocean and on land. To do so, we focus on daily vertical component cross-correlations of ambient seismic noise which enable the extraction of Rayleigh waves (Shapiro & Campillo, 2004).…”

“…In contrast to primary microseisms (10-20 s) caused by the direct interaction of ocean waves with bathymetry, secondary microseisms (3-10 s) originate from pressure fluctuations at the seafloor as a result of non-linear interactions of opposing ocean wavefronts (Hasselmann, 1963;Longuet-Higgins, 1950). Due to their generation mechanisms, the energy of secondary microseisms is closely related to ocean wave amplitudes (Bromirski et al, 1999;Donne et al, 2014), globally and through the seasons (Stutzmann et al, 2009), and has therefore been linked to climate variability (Aster et al, 2008;Grevemeyer et al, 2000). In fact, atmospheric teleconnection patterns have been associated with temporal changes in microseisms intensity, including the NAO (Essen et al, 1999) and the El Niño Southern Oscillation (Aster et al, 2008), or more recently the Southern Annular Mode (SAM) or Antarctic oscillation in the southern hemisphere (Anthony et al, 2017;Turner et al, 2020).…”