Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : IFREMER Brest
Durée : 16 months
Contact : firstname.lastname@example.org
Date limite de publication : 2022-12-26
Marine seismology has made tremendous technological advances in the past decades: data recorded at the seafloor by ocean bottom seismometers (OBSs) are becoming widely available (eg IRIS consortium). An OBS is a multicomponent instrument able to continuously record pressure and earth motion. There are two types of OBSs: short-period instruments for recording high-frequency motions, and long-period instruments for acquiring a wider range of motions (cf. INSU-IPGP national OBS facility). With both instruments, OBSs record a superposition of a broad variety of signals generated by solid earth, ocean wave, biologic, ship sources and noise. These signals can be very different in amplitude, duration and frequency content. They however also overlap in those domains, making them hard to isolate from each other. That is why OBS data cannot yet be fully exploited by the seismological community, as they require more advanced processing and identification techniques.
This postdoctoral position funded by the BRUIT-FM project primarily aims at developing signal processing and machine learning techniques to classify and separate signals recorded by OBSs and to enhance earthquake waveforms and microseismic noise. It devotes to a better exploitation of non-seismological signals for defining a seafloor soundscape. Hence the moniker ”Ocean Bottom Noise Shazam”, from the famous music retrieval/identification application
Profil du candidat :
PhD with strong interest in spectral analysis, adaptive filtering, machine learning, data science with a taste of physics.
Formation et compétences requises :
Languages: C/C++, Python/Matlab or similar. Seismology is a plus
Adresse d’emploi :
France, IFREMER, Brest