A new prediction method of seafloor hydrothermal active field based on wavelet neural network

The study of modern seafloor hydrothermal activity and its mineralization has become one of the focuses of global geoscience. Accurate prediction of possible seafloor hydrothermal active fields is the basis of all research work. The detecting method for new seafloor hydrothermal activity still is mainly dependent on marine site investigation. This includes a series of temperature, turbidity, and geochemical anomaly investigations using submarine cameras and manned diving investigation. Both of these require expensive financial, human, and material resources. In order to realize the accurate prediction of potential hydrothermal activity areas in a low cost manner, with strong pertinence, and a wide range, we propose a prediction method of seafloor hydrothermal active region based on Wavelet Neural Network. First, we integrated the hydrothermal position information from the InterRidge Vents Database with the hydrothermal temperature information form the Argo Database to construct a data set. Then, we combined wavelet analysis with an artificial neural network to create a wavelet neural network optimization algorithm. Finally, the temperature and salinity data were input to the wavelet neural network to predict the seafloor hydrothermal active region. Sevenfold cross validation was used to evaluate the performance of the model and 90.43% prediction accuracy was achieved. The results of experiments show that salinity is not related to the existence of hydrothermal activity fields but rather that the surrounding water temperature has a strong correlation with hydrothermal existence. Therefore, it is effectively feasible to use the wavelet neural network model with an input of seawater temperature to predict seafloor hydrothermal activity fields. Although artificial neural network cannot completely replace traditional hydrothermal exploration technology, it can provide a valuable reference with a strong target.