  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.
