La Jument lighthouse: a real-scale laboratory for the study of giant waves and their loading on marine structures

Deep neural networks for active wave breaking classification

Wave breaking is an important process for energy dissipation in the open ocean and coastal seas. It drives beach morphodynamics, controls air-sea interactions, determines when ship and offshore structure operations can occur safely, and influences on the retrieval of ocean properties from satellites. Still, wave breaking lacks a proper physical understanding mainly due to scarce observational field data. Consequently, new methods and data are required to improve our current understanding of this process. In this paper we present a novel machine learning method to detect active wave breaking, that is, waves that are actively generating visible bubble entrainment in video imagery data. The present method is based on classical machine learning and deep learning techniques and is made freely available to the community alongside this publication. The results indicate that our best performing model had a balanced classification accuracy score of [Formula: see text] 90% when classifying active wave breaking in the test dataset. An example of a direct application of the method includes a statistical description of geometrical and kinematic properties of breaking waves. We expect that the present method and the associated dataset will be crucial for future research related to wave breaking in several areas of research, which include but are not limited to: improving operational forecast models, developing risk assessment and coastal management tools, and refining the retrieval of remotely sensed ocean properties.

A Low-Cost Stereo Video System for Measuring Directional Wind Waves

Typical oceanographic instruments are expensive, complex to build, and hard to deploy and require constant and specialized maintenance. In this paper, we present a cheap and simple technique to estimate a three-dimensional surface elevation map, η(x,y,t), the directional spectrum, and the main sea state parameters using inexpensive smartphones. The proposed methodology uses Time Lagged Cross Correlation (TLCC) between the audio signals from two independent video records to perform the frame synchronization. This makes the system much easier to deploy, where the main requirement is a fixed or moving platform close to the sea. The time records are mostly limited by the equipment storage space and battery life, although it can be easily replaced or recharged. Here, we pose the basis for an inexpensive yet powerful stereo reconstruction device and discuss its capabilities and limitations. The smartphone system capabilities were illustrated here by near shore experiment, at Leme beach in the Southeast of Brazil, and the results were compared against a pressure sensor. For this particular setup, the root mean square error in terms of significant wave height is of the order of 11% with perfect estimation of the peak period. The results are promising and demonstrate the validity and applicability of the technique.

A data set of sea surface stereo images to resolve space-time wave fields

Stereo imaging of the sea surface elevation provides unique field data to investigate the geometry and dynamics of oceanic waves. Typically, this technique allows retrieving the 4-D ocean topography (3-D space + time) at high frequency (up to 15-20 Hz) over a sea surface region of area ~104 m2. Stereo data fill the existing wide gap between sea surface elevation time-measurements, like the local observation provided by wave-buoys, and large-scale ocean observations by satellites. The analysis of stereo images provides a direct measurement of the wavefield without the need of any linear-wave theory assumption, so it is particularly interesting to investigate the nonlinearities of the surface, wave-current interaction, rogue waves, wave breaking, air-sea interaction, and potentially other processes not explored yet. In this context, this open dataset aims to provide, for the first time, valuable stereo measurements collected in different seas and wave conditions to invite the ocean-wave scientific community to continue exploring these data and to contribute to a better understanding of the nature of the sea surface dynamics.

Structural response of French offshore heritage lighthouses

Offshore lighthouses are a remarkable historical heritage often over 100 years old. The management of their ageing is a challenge and requires a thorough understanding of their structural response under wave loading. With this aim in view, the dynamic response of the 'La Jument' lighthouse in the French Iroise Sea was characterized in situ, with accelerometers, under the action of a breaking wave, during a winter storm in January 2018. Detailed exploitation of the measurement data complemented by photographic documentation of the same wave event helped determine eigenfrequencies and damping coefficients of the structure as well as the likely orientation of the wave and the position of its crest with respect to the lighthouse tower. The results of these analyses were put into perspective with those of literature and more specifically from three-dimensional finite element modelling of the structural response of the lighthouse under the action effects of breaking waves. This article is part of the theme issue 'Environmental loading of heritage structures'.