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Seamount age prediction machine learning model based on multiple geophysical observables: methods and applications in the Pacific Ocean
Marine Geophysical Research ( IF 1.4 ) Pub Date : 2021-09-15 , DOI: 10.1007/s11001-021-09451-z
Yongliang Bai 1 , Yilin Rong 1 , Diya Zhang 1 , Jihong Sun 2 , Leiming Chen 3 , Zhengtong Yin 4
Affiliation  

Seamount ages are important for understanding crust-mantle interactions and exploring sea bottom ore resources. Rock sampling and laboratory measurements are time-consuming and expensive, and are currently the main methods for dating seamounts. Thus, the ages of many seamounts in the Pacific Ocean are still unknown. To address this problem, gravity anomalies, magnetic anomalies, oceanic crustal ages, sediment thicknesses and seamount heights are chosen as the input parameters for seamount age prediction based on the potential relationship between geophysical observables and seamount ages. A Back-propagation (BP) neural network is constructed using the currently known seamount ages in the Pacific Ocean. Then, the weight and threshold of the BP network are optimized by a Genetic algorithm (GA); finally, the GA-BP model for seamount age production is derived. In addition, Convolutional neural network (CNN), BP model, and Support vector regression (SVR) methods are also used to predict seamount ages. The uncertainties in the prediction results decrease in the order of the GA-BP model, CNN, BP model, and SVR methods. The RMSE of the GA-BP prediction results is 10.22 Ma, and R2 is 0.90.



中文翻译:

基于多地球物理观测的海山年龄预测机器学习模型:方法与应用在太平洋

海山年龄对于了解壳幔相互作用和探索海底矿石资源具有重要意义。岩石取样和实验室测量既费时又费钱,是目前确定海山年代的主要方法。因此,太平洋中许多海山的年龄仍然未知。为了解决这个问题,根据地球物理可观测数据与海山年龄之间的潜在关系,选择重力异常、磁异常、海洋地壳年龄、沉积物厚度和海山高度作为海山年龄预测的输入参数。使用当前已知的太平洋海山年龄构建了一个反向传播 (BP) 神经网络。然后,通过遗传算法(GA)优化BP网络的权重和阈值;最后,推导出海山年龄生产的GA-BP模型。此外,卷积神经网络(CNN)、BP模型和支持向量回归(SVR)方法也用于预测海山年龄。预测结果的不确定性按照GA-BP模型、CNN、BP模型、SVR方法的顺序递减。GA-BP 预测结果的 RMSE 为 10.22 Ma,并且R 2是0.90。

更新日期:2021-09-16
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