EGU2020-22619, updated on 12 Jun 2020
https://doi.org/10.5194/egusphere-egu2020-22619
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Predicting ocean activity from seismic data using machine learning techniques

Susana Custódio, Francisco Bolrão1, Tan Bui2, Céline Hadziioannou3, Miguel Lima1, Diogo Rodrigues1, Sheroze Sheriffdeen2, Graça Silveira4, and Joana Carvalho1
Susana Custódio et al.
  • 1Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Portugal
  • 2Department of Aerospace Engineering and Engineering Mechanics, Institute for Computational Engineering and Sciences, The University of Texas at Austin, USA
  • 3University of Hamburg, Institute of Geophysics, Center for Earth System Research and Sustainability (CEN), Germany
  • 4Instituto Dom Luiz, Instituto Superior de Engenharia de Lisboa, Portugal

The most pervasive seismic signal recorded on our planet – microseismic ambient noise -results from the coupling of energy between atmosphere, oceans and solid Earth. Because it carries information on ocean waves (source), the microseismic wavefield can be advantageously used to image ocean storms. Such imaging is of interest both to climate studies – by extending the record of oceanic activity back into the early instrumental seismic record – and to real-time monitoring – where real-time seismic data can potentially be used to complement the spatially dense but temporally sparse satellite meteorological data.

In our work, we develop empirical transfer functions between seismic observations and ocean activity observations. We start by following the classical approach of Bromirski et al (1999), who computed an empirical transfer function between ground-motion recorded at a coastal seismic station and significant wave height measured at a nearby ocean buoy. We explore further developments by considering other seismic data observations – such as the polarization of seismic ambient noise – and other indicators of ocean activity observations, including the spectra of ocean waves.

In addition to employing the classical approach of empirical transfer functions, we further present preliminary tests using machine learning techniques to: 1) infer which seismic and ocean activity observables are better predictors of each other, and 2) to predict ocean activity given observed ground motion.

The analysis is made using selected datasets around the North Atlantic, namely using seismic data from North America (west Atlantic), the Azores (central Atlantic) and Portugal (east Atlantic).

This work is supported by FCT through projects UIDB/50019/2020 – IDL and UTAP-EXPL/EAC/0056/2017 - STORM.

References:

Bromirski, P. D., Flick, R. E., & Graham, N. (1999). Ocean wave height determined from inland seismometer data: Implications for investigating wave climate changes in the NE Pacific. Journal of Geophysical Research: Oceans, 104(C9), 20753-20766.

 

How to cite: Custódio, S., Bolrão, F., Bui, T., Hadziioannou, C., Lima, M., Rodrigues, D., Sheriffdeen, S., Silveira, G., and Carvalho, J.: Predicting ocean activity from seismic data using machine learning techniques , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22619, https://doi.org/10.5194/egusphere-egu2020-22619, 2020

This abstract will not be presented.