Reconstruction of Thermocline Depth based on Machine Learning

Abstract

The thermocline, as a special structure in the ocean temperature field, has an important impact on sound propagation, material transport, and human underwater activities in the ocean. Using existing methods, the thermocline is distributed underwater and cannot be observed in real-time and with high spatiotemporal accuracy. It can only be calculated from measured data. In previous studies, it was demonstrated that the ocean thermocline is significantly correlated with its sea surface elements. Based on machine learning methods, this paper uses sea level anomaly, sea surface temperature, sea surface salinity, time, longitude and latitude, as well as the vertical gradient method to calculate the depth of the thermocline as data. By constructing a Random Forest model, the regression relationship between the thermocline and sea surface elements and spatiotemporal elements is obtained, this thermocline reconstruction model has a good fitting effect on thermocline depth, with a fitting coefficient of 0.85, and its accuracy has been evaluated. This paper also evaluated the feature importance of the six input data of the model. The results showed that the three sea surface parameters had similar importance, with sea surface temperature having the most significant impact on the depth of the thermocline. The final model can achieve rapid reconstruction of thermocline depth using sea surface information.