WP3: The Generation of Global Seismological Noise
This WP will develop an integrated model of seafloor and land seismological noise validated by measurements. We will use broadband datasets from the Pacific, Indian Ocean and Atlantic oceans to generate noise models and evaluate the key features of each ocean.
Work Package Management Site
- Develop an integrated model of broadband (0.02-1 Hz) seafloor noise validated by measurements
- Better understand the physics of the sources of broadband noise and the noise floor in this frequency band
- Improve the quality of ambient noise data selection for compliance analysis
- Provide a catalogue of broadband sources that can be used for tomographic application
|T3.1||Sources and effects of spatio-temporal variations in seafloor noise:||@Stutzmann||not started|
|T3.2||Sources of seafloor/global noise||@Stutzmann||not started|
|T3.3||An integrated seafloor/global noise model||@Stutzmann, FabriceArdhuin||not started|
|D3.1||Catalog of wave-generated noise sources||M36||not started|
|D3.2||Scientific articles||M24-48||not started|
Task 3.1: Spatio-temporal variations in seafloor noise, both sources and effects.
- T3.1.2: Variations of infragravity waves. Static (compliance) & dynamic (seismic wave) effects.
- T3.1.1: Variations of currents and other noise sources. Using topographic current models and seafloor spectra.
Task 3.2: Sources of seafloor/global noise
- T3.2.1: Data cleaning. Remove local effects using tools developed in WP4
- T3.2.2: Quantify variability in three principal frequency bands. Use spectrograms and polarisation analysis to quantify the intensity and azimuth variability of the sources in the primary microseism, secondary microseism and infragravity wave bands. Analyze datasets from three oceans to locate sources and analyse in detail the strongest sources.
- T3.2.3: Classifying the sea floor signal using machine-learning algorithms. Starting with primary and secondary microseisms, which record characteristic signals related to ocean wave dispersion, we will identify source clusters and investigate wave origin using algorithms such as blind source separation (Comon & Jutten, 2010, Moni et al., 2013, Meschede et al., 2019), and classify signals using machine learning (Malfante et al. 2018a, 2018b).
Task 3.3: An integrated seafloor/global noise model
- T3.3.1: Improving modelling tools. The current microseism and hum modelling tools were developed for seismic surface and body wave noise on land. We will modify the station site effect to account for the reverberation of acoustic waves in the water column and possibly also in the sediment layer, separately considering body and surface waves.
- T3.3.2: Modelling seafloor seismic noise between 0.003 and 1 Hz. We will model the Pacific, Atlantic and Indian ocean data sets and analyse the data fit, progressively mproving the model as indicated by the fit.
- T3.3.3: An integrated model of sea floor noise. Based on the above models and physical mechanisms.
Risks are fairly low as we developed the techniques and know in what areas they can be improved or modified to apply to the seafloor environment.