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.

对现代海底热液活动及其成矿作用的研究已成为全球地球科学的重点之一。准确预测可能存在的海底热液活动场是所有研究工作的基础。新的海底热液活动的检测方法仍主要取决于海洋现场调查。这包括使用潜水相机和载人潜水调查进行的一系列温度，浊度和地球化学异常调查。两者都需要昂贵的财力，人力和物力。为了以低成本，针对性强，范围广的方式实现对潜在热液活动区域的准确预测，提出了一种基于小波神经网络的海底热液活动区域预测方法。第一，我们将InterRidge Vents数据库中的热液位置信息与Argo数据库中的热液温度信息集成在一起，以构建数据集。然后，我们将小波分析与人工神经网络相结合，创建了一个小波神经网络优化算法。最后，将温度和盐度数据输入到小波神经网络中，以预测海底热液活动区域。七重交叉验证用于评估模型的性能，并达到90.43％的预测准确性。实验结果表明，盐度与热液活动场的存在无关，而与水温的存在密切相关。因此，利用小波神经网络模型输入海水温度来预测海底热液活动场是切实可行的。人工神经网络虽然不能完全替代传统的热液勘探技术，但可以为有力的目标提供有价值的参考。