EGU21-14837, updated on 08 Mar 2022
https://doi.org/10.5194/egusphere-egu21-14837
EGU General Assembly 2021
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Inferring significant wave height from seismic data: A comparison between methods.

Francisco Bolrão1, Co Tran2, Miguel Lima1, Sheroze Sheriffdeen2, Diogo Rodrigues1, Graça Silveira1,3, Tan Bui-Thanh2, and Susana Custodio1
Francisco Bolrão et al.
  • 1Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Portugal (franciscobolrao@gmail.com)
  • 2Department of Aerospace Engineering and Engineering Mechanics, The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, USA
  • 3Instituto 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. This 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, in particular, significant wave height. We employ three different approaches: 1) The approach of Ferretti et al (2013), who compute a seismic significant wave height and invert only for the empirical conversion parameters between oceanic and seismic significant wave heights; 2) 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; and 3) A novel recurrent neural-network (RNN) approach to infer significant wave height from seismic data. 
We apply the three approaches to seismic and ocean buoy data recorded in the east coast of the United States. All three approaches are able to successfully predict ocean significant wave height from the seismic data. We compare the three approaches in terms of accuracy, computational effort and robustness. In addition, we investigate the regimes where each approach works best.  The results show that the RNN approach is able to predict well the significant wave height recorded at the buoy. The prediction is improved if several nearby seismic stations are used rather than just one. 
This work is supported by FCT through projects UIDB/50019/2020 – IDL and UTAP-EXPL/EAC/0056/2017 - STORM.

How to cite: Bolrão, F., Tran, C., Lima, M., Sheriffdeen, S., Rodrigues, D., Silveira, G., Bui-Thanh, T., and Custodio, S.: Inferring significant wave height from seismic data: A comparison between methods., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14837, https://doi.org/10.5194/egusphere-egu21-14837, 2021.

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