Lisa Tomasetto, ; Tomasetto, Lisa ; Boué, Pierre ; Ardhuin, Fabrice ; Stutzman, Eleonore ; Xu, Zonbo ; De Plaen, Raphaël ; Stehly, Laurent

published in Seismica, 4 issue 1 (2025)

Abstract: Seismic ambient noise spectra ubiquitously show two amplitude peaks corresponding to distinct oceanic wave interaction mechanisms called primary (seismic period (T) ~ 14 s) and secondary (T ~ 7 s) microseism. Seismic noise records are used in a wide range of applications including crustal monitoring, imaging of the Earth's deep interior using noise correlations, and studies on the coupling between oceans and solid Earth. All of these applications could benefit from a robust knowledge of spatiotemporal dynamics of microseismic sources. Consequently, seismologists have been studying how to model microseismic sources of ambient noise with the recent improvements in ocean wave models. Global sea state and its derivative products are now covering the past decades in models such as the WAVEWATCHIII hindcast. This paper introduces the Wave Model Sources of Ambient Noise (WMSAN, pronounced [wam-san]) Python package. This modular package uses standardized wave model outputs to visualize ambient noise source maps and efficiently compute synthetics of seismic spectrograms and cross-correlations for surface waves (Rayleigh) and body waves (P, SV), in a user-friendly way.

Keyword(s): Ambient seismic noise ; Python ; Modeling ; Oceanography ; Microseisms
DOI: 10.26443/seismica.v4i1.1483
Funding: BRAIN-be 2.0 SeismoStorm/BRAIN-be 2.0 SeismoStorm/BRAIN-be 2.0 SeismoStorm