A Survey on Mathematical Aspects of Machine Learning in GeoPhysics: The Cases of Weather Forecast, Wind Energy, Wave Energy, Oil and Gas Exploration
This survey provides a comprehensive review for researchers and practitioners interested in applying machine learning to geophysical problems, highlighting current progress and future opportunities.
This paper surveys the application of machine learning techniques across various geophysical domains, including weather forecasting, wind energy, wave energy, and oil and gas exploration. It reviews past successes and identifies future research directions, aiming to accelerate the integration of novel ML approaches in geophysics.
This paper reviews the most notable works applying machine learning techniques (ML) in the context of geophysics and corresponding subbranches. We showcase both the progress achieved to date as well as the important future directions for further research while providing an adequate background in the fields of weather forecast, wind energy, wave energy, oil and gas exploration. The objective is to reflect on the previous successes and provide a comprehensive review of the synergy between these two fields in order to speed up the novel approaches of machine learning techniques in geophysics. Last but not least, we would like to point out possible improvements, some of which are related to the implementation of ML algorithms using DataFlow paradigm as a means of performance acceleration.