WMSAN: Wave Model Sources of Ambient Noise Python Library. From Modeling to Applications.

Interactions between oceanic waves and the seafloor generate seismic waves recorded globally and referred to as natural ambient “noise.” In particular, the 3-10s period band, known as the secondary microseismic band, corresponds to non-linear oceanic wave-wave interaction and represents the highest peak in a seismic station PSD. While surface waves are prominent in this period band, body waves, which sample deeper areas and are less scattered, can also be identified. These body waves are valuable for examining the properties of the deep Earth due to their sensitivity to the inner medium.

In the last decade, improvements in global oceanographic hindcast, such as the WAVEWATCHIII model, have allowed seismologists to track the spatiotemporal behavior of these ocean-generated seismic sources. Since these unconventional sources are non-impulsive, interferometric methods, by correlating signals between stations for a few hours, are necessary to highlight surface and body waves from local to global scale.

We introduce the WMSAN Python library for Wave Model Sources of Ambient Noise, which allows for the visualization of oceanic sources of ambient noise distribution and computation of proxy for seismic observables in a user-friendly fashion. This library provides functions and simple examples to map secondary microseismic source distributions for Rayleigh, P, and SV waves using oceanographic data. Seismic data counterparts are then inferred from these source distributions, such as synthetic spectrograms and cross- or auto-correlation functions. We will detail the benchmark examples of this library and its application to extract body wave interference (PP-P) differential travel times from a single secondary microseismic event occurring 8-11 December 2014 in the Northern Atlantic Ocean.