基于残差网络的海洋温跃层分析方法

首先, 以世界海洋地图集2013（WOA13）海洋数据为实验数据, 提出将不等距微分法、 垂直梯度法应用于海洋数据预处理、 海洋区域划分和跃层分析中, 并通过对多种神经网络在基于WOA13海洋三维数据二分类实验的性能分析, 选取残差网络作为二分类实验的网络模型, 在三层残差网络模型基础上增加了Dropout保留层以防止过拟合. 其次, 将残差网络模型用于温跃层分析判定, 并针对改进模型进行超参数优化、 残差单元改进、 保留率调整等对比实验. 实验结果表明, 改进的ResNet 26网络对WOA13海洋区域数据的温跃层数据分类有效, 分类准确率超过94%.

Analytical Method of Oceanic Thermocline Based on Residual Network

Firstly, we selected the world ocean atlas 2013 (WOA13) ocean dataas the experimental data, the unequal distance differential method and vertical gradient method were applied to the preprocessing of ocean data, the division of ocean area and analysis of thermocline. Through the performance analysis of various neural networks based on the three dimensional WOA13 ocean data in the binary classification experiment, we chose the residual network as the network model of the binary classification experiment, and added the Dropout retention layer on the basis of the three layer residual network modelto prevent over-fitting. Secondly, the residual network model was used for thermocline analysis and determination, and the comparative tests such as the super parameters optimization, the residual unit improvement and the retention rate adjustment were carried out for the improved model. The experimental results show that the improved ResNet 26 network is effective for the thermocline data classifica tion of WOA13 ocean area data, and the classification accuracy is more than 94%.