Internship in sea state characterisation and wave breaking statistics (F/M)

  • Reference : FEM-SAS-2021-288
  • Position type: Internship
  • Duration: 6 month
  • Localisation: Brest (29) - France Energies Marines
  • Download the offer

Job Description

Offshore structures are subjected to numerous stresses due to the physical environment in which they are located. Among these, the forces related to waves, and in particular to breaking waves, are highly dimensioning. Thus, a good knowledge of the sea states, including reliable statistics of breakers, as well as of the resulting forces are necessary to optimize the design of the machines. In addition, breaking waves eject sea spray modifying air-sea momentum and heat fluxes which influence the wind in the marine atmospheric boundary layer. Therefore, characterization of the breaking statistics is necessary, both for the design of the machines and for the evaluation of the resource. However, few wave breaking observations exist and data sets only cover low to moderate sea states. The proposed internship aims at building a database of wave breaking statistics particularly under high wind in storm conditions. This database will then be used in two ongoing projects at France Energies Marines:

  • The DIMPACT project, which aims to quantify (occurrence and intensity) the slamming forces for the dimensioning of floating wind turbines;
  • The CASSIOWPE project, which aims to develop a coupled numerical model Ocean-Wave-Atmosphere integrating sea-spray  impacts on the exchange of heat and momentum at the air-sea interface.

To meet the objectives of these projects, France Energies Marines has deployed various stereo-video systems, allowing stereo triangulation to reconstruct sea surfaces (Bergamasco et al., 2017. Filipot et al., 2019). In addition, France Energies Marines has developed an artificial intelligence algorithm to identify breaking waves in video images (Stringari at al., 2021). Used together, these two methods allow characterizing the sea state and producing various breaking wave statistics.

The objective of the internship will be to exploit stereo-video databases of breaking waves collected from the lighthouse of La Jument (France), the floating wind turbine Zefyros (Norway), and the oceanographic research vessel Atalante (SUMOS field campaign, France).

The originality of the internship will be to investigate statistics of breaking crest lengths distributions (introduced by Phillips, 1985) and of the curling factor (geometry of the breakers which controls the slamming forces) under high-wind conditions. The innovative character of the results could therefore lead to a publication on the generated database.


The candidate will process the stereo-video images acquired during the aforementioned field campaigns. S/He will set up the processing chain allowing to reconstruct the temporal evolution of the sea surface and implement the method proposed by Stringari et al (2020) to identify breaking waves.


The analysis of the data set will then focus on two aspects:

  • The characterization of the wave geometry, in particular of the wave front which controls the intensity of the slamming forces;
  • The quantification of the breaking statistics, distributed according to the speeds of the breaking fronts (Phillips, 1985).


In practice, the candidate will have to:

  • Select the most relevant situations (image quality, sea state severity, etc)
  • Implement the WASS processing chain proposed by Bergamasco et al. (2017) for the 3D reconstruction of sea surfaces
  • Apply the algorithm proposed by Stringari et al. (2020) to detect and track breaking fronts:
    • Create a training dataset of wave breaking images
    • Carry out the training phase
    • Process the whole dataset
  • Analyze the obtained data to produce:
    • Breaking crest length distributions and derived statistics
    • Curling factor statistics

Required Skills

Initial training
Final year of a master’s degree or an engineering programme

Specific knowledge

• Signal analysis, image analysis
• Machine learning approach (neural network)
• Programming with Python or equivalent, (machine learning libraries like Pytorch, TensorFlow, etc)

• Knowledge of sea states in general, and of wave breaking processes in particular

Professional qualities
• Good interpersonal skills
• Fluency (reading) in English is essential


Job : Internship in sea state characterisation and wave breaking statistics (F/M)

Reference : FEM-SAS-2021-288

    Closed search overlay screen