Abstract
Sea wave monitoring is key in many applications in oceanography such as the validation of weather and wave models. Conventional in situ solutions are based on moored buoys whose measurements are often recognized as a standard. However, being exposed to a harsh environment, they are not reliable, need frequent maintenance, and the datasets feature many gaps. To overcome the previous limitations, we propose a system including a buoy, a micro-seismic measuring station, and a machine learning algorithm. The working principle is based on measuring the micro-seismic signals generated by the sea waves. Thus, the machine learning algorithm will be trained to reconstruct the missing buoy data from the micro-seismic data. As the micro-seismic station can be installed indoor, it assures high reliability while the machine learning algorithm provides accurate reconstruction of the missing buoy data. In this work, we present the methods to process the data, develop and train the machine learning algorithm, and assess the reconstruction accuracy. As a case of study, we used experimental data collected in 2014 from the Northern Tyrrhenian Sea demonstrating that the data reconstruction can be done both for significant wave height and wave period. The proposed approach was inspired from Data Science, whose methods were the foundation for the new solutions presented in this work. For example, estimating the period of the sea waves, often not discussed in previous works, was relatively simple with machine learning. In conclusion, the experimental results demonstrated that the new system can overcome the reliability issues of the buoy keeping the same accuracy.
Highlights
The complexity of the sea waves is mathematically described by the directional wave spectrum as a combination of waves propagating in different directions with different wavelengths (Talley et al, 2011)
The objective of this work has been to develop a sea wave monitoring system consisting of a moored buoy, a micro-seismic station, and a machine learning (ML) algorithm to automatically reconstruct missing buoy data using micro-seismic data
We have shown that the performance assessments, in particular the validation metrics, depend on the validation dataset
Summary
The complexity of the sea waves is mathematically described by the directional wave spectrum as a combination of waves propagating in different directions with different wavelengths (Talley et al, 2011). The knowledge of the directional wave spectrum is key in several applications such as coastal management and design of coastal and offshore structures (e.g., ports and renewable energy platforms). Seismic Based Wave Data Reconstruction assimilation into weather and sea waves models to improve their accuracy (Krogstad et al, 2005; Mentaschi et al, 2015). If in the middle of the twentieth century the measurements of the directional wave spectrum were a major achievement, nowadays many systems based on different measuring principles are affordable for operational use. In Krogstad et al (2005); Souza et al (2011), good reviews of the available sea state monitoring systems are provided distinguishing two families: remote sensing and in situ. Examples of remote sensing systems are those based on radars. Examples of in situ systems are the subsurface devices, such as pressure and acoustic sensors, but the most common are those based on moored buoys instrumented with motion sensors such as accelerometers, gyroscopes, or GPS as described in Herbers et al (2012); Andrews and Peach (2019) and in Datawell website. The directional wave spectrum is calculated from the raw measurements by using algorithms based on the hydrodynamics characteristics of the hull
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