海洋学研究 ›› 2024, Vol. 42 ›› Issue (3): 2-27.DOI: 10.3969/j.issn.1001-909X.2024.03.001
人工智能海洋学发展前景
董昌明1,2,3(
), 王子韵2, 谢华荣2, 徐广珺4, 韩国庆5, 周书逸6, 谢文鸿7, 沈向宇2, 韩磊8
- 1.南京信息工程大学 天长研究院,江苏 南京 210044
2.南京信息工程大学 海洋科学学院,江苏 南京 210044
3.南方海洋科学与工程广东省实验室(珠海),广东 珠海 519000
4.广东海洋大学 电子与信息工程学院,广东 湛江 524088
5.浙江海洋大学 海洋科学与技术学院,浙江 舟山 316022
6.清华大学 地球系统科学系,北京 100084
7.南京星遥科技有限公司,江苏 南京 210012
8.南京信息工程大学 大气科学学院,江苏 南京 210044
-
收稿日期:2023-10-08修回日期:2024-02-23出版日期:2024-09-15发布日期:2024-11-25 -
作者简介:董昌明(1967—),男,安徽省宣城市人,教授,主要从事人工智能海洋学研究,E-mail:cmdong@nuist.edu.cn。 -
基金资助:国家重点研发计划项目(2023YFC3008200)
Prospect of artificial intelligence in oceanography
DONG Changming1,2,3(), WANG Ziyun2, XIE Huarong2, XU Guangjun4, HAN Guoqing5, ZHOU Shuyi6, XIE Wenhong7, SHEN Xiangyu2, HAN Lei8
- 1. Tianchang Research Institute, Nanjing University of Information Science & Technology, Nanjing 210044, China
2. School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
3. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
4. College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
5. Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
6. Department of Earth System Science, Tsinghua University, Beijing 100084, China
7. Nanjing Xingyao Technology Co., LTD., Nanjing 210012, China
8. School of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
-
Received:2023-10-08Revised:2024-02-23Online:2024-09-15Published:2024-11-25
摘要：
随着海洋观测数据和数值模式产品的爆发式增长,人工智能方法在海洋学研究中展现出巨大的潜能。该文首先回顾了海洋大数据科学的发展历程,并详细介绍了人工智能在海洋现象识别、海洋要素与现象预报、海洋动力参数估算、海洋预报误差订正和海洋动力方程求解中的研究现状。具体地,阐述了海洋涡旋、海洋内波和海冰等海洋现象的智能识别研究,海面温度、厄尔尼诺-南方涛动、风暴潮、海浪和海流的智能预测研究,数值模式中海洋湍流过程参数化方案的智能估算研究以及海浪、海流等海洋现象预报误差的智能订正研究。此外,还讨论了物理机制融合和傅里叶神经算子在海洋运动方程智能求解中的研究进展。该文立足于当前人工智能海洋学的发展现状,旨在全面展示人工智能技术在海洋学领域的优势和潜力,并聚焦于海洋数字孪生和人工智能大模型两个新兴的研究热点,展望未来人工智能海洋学的发展方向,为海洋学者提供启示和参考。
中图分类号:
引用本文
董昌明, 王子韵, 谢华荣, 徐广珺, 韩国庆, 周书逸, 谢文鸿, 沈向宇, 韩磊. 人工智能海洋学发展前景[J]. 海洋学研究, 2024, 42(3): 2-27.
DONG Changming, WANG Ziyun, XIE Huarong, XU Guangjun, HAN Guoqing, ZHOU Shuyi, XIE Wenhong, SHEN Xiangyu, HAN Lei. Prospect of artificial intelligence in oceanography[J]. Journal of Marine Sciences, 2024, 42(3): 2-27.
图1 2009—2030 年全球海洋数据量增长趋势[9]
Fig.1 Global trends in the growth of ocean data volume from 2009 to 2030[9]
图1 2009—2030 年全球海洋数据量增长趋势[9]
Fig.1 Global trends in the growth of ocean data volume from 2009 to 2030[9]
图2 遥感数据采集过程[14]
Fig.2 Remote sensing data acquisition process[14]
图2 遥感数据采集过程[14]
Fig.2 Remote sensing data acquisition process[14]
图3 前馈神经网络重构的2016年1月全球盐度异常的空间分布 (图件改绘自文献[27]。图a为盐度异常(IAP 0.25°);图b为盐度异常(IAP 1°);图c为盐度异常(ARMOR3D);图d为盐度异常(实观测资料);图e为盐度异常(IAP 0.25°减 IAP 1°在100 m);图f为ADT异常(遥感资料);图g为SST异常(遥感资料)。)
Fig.3 Spatial distribution of global salinity anomalies in January 2016 reconstructed by feedforward neural networks (Figure is repainted from reference [27]. Fig.a shows the salinity anomaly (IAP 0.25°); Fig.b shows the salinity anomaly (IAP 1°); Fig.c shows the salinity anomaly (ARMOR3D); Fig.d shows the salinity anomaly (observations); Fig.e shows the salinity anomaly (IAP 0.25° minus IAP 1° at 100 m); Fig.f shows the ADT anomaly (remote sensing); Fig.g shows the SST anomaly (remote sensing).)
图3 前馈神经网络重构的2016年1月全球盐度异常的空间分布 (图件改绘自文献[27]。图a为盐度异常(IAP 0.25°);图b为盐度异常(IAP 1°);图c为盐度异常(ARMOR3D);图d为盐度异常(实观测资料);图e为盐度异常(IAP 0.25°减 IAP 1°在100 m);图f为ADT异常(遥感资料);图g为SST异常(遥感资料)。)
Fig.3 Spatial distribution of global salinity anomalies in January 2016 reconstructed by feedforward neural networks (Figure is repainted from reference [27]. Fig.a shows the salinity anomaly (IAP 0.25°); Fig.b shows the salinity anomaly (IAP 1°); Fig.c shows the salinity anomaly (ARMOR3D); Fig.d shows the salinity anomaly (observations); Fig.e shows the salinity anomaly (IAP 0.25° minus IAP 1° at 100 m); Fig.f shows the ADT anomaly (remote sensing); Fig.g shows the SST anomaly (remote sensing).)
图4 CNN模型中ENSO预测的相关系数 (图件改绘自文献[72]。图a为CNN模型(红色)、SINTEX-F动态预测系统(蓝色)和北美多模式集成项目中包含的动态预测系统(其他颜色)3个月滑动平均Ni?o3.4指数的全季节相关系数;图b和图c为CNN模型(b)和SINTEX-F动态预测系统(c)中预测每月的Ni?o3.4指数与提前预测时间的相关系数,阴影线突出显示了相关系数超过0.5的预测。)
Fig.4 Correlation coefficients for ENSO forecasts using the CNN model (Figure is repainted from reference [72]. Fig.a shows the all-season correlation skill of the three-month moving average Ni?o3.4 index as a function of the forecast lead month in the CNN model (red), SINTEX-F dynamical forecast system (blue), and dynamical forecast systems included in the North American Multi-Model Ensemble project (the other colors); Fig.b and Fig.c show the correlation skill of the Ni?o3.4 index targeted to each calendar month in the CNN model (b) and the SINTEX-F dynamical forecast system (c), hatching highlights the forecasts with correlation skill exceeding 0.5.)
图4 CNN模型中ENSO预测的相关系数 (图件改绘自文献[72]。图a为CNN模型(红色)、SINTEX-F动态预测系统(蓝色)和北美多模式集成项目中包含的动态预测系统(其他颜色)3个月滑动平均Ni?o3.4指数的全季节相关系数;图b和图c为CNN模型(b)和SINTEX-F动态预测系统(c)中预测每月的Ni?o3.4指数与提前预测时间的相关系数,阴影线突出显示了相关系数超过0.5的预测。)
Fig.4 Correlation coefficients for ENSO forecasts using the CNN model (Figure is repainted from reference [72]. Fig.a shows the all-season correlation skill of the three-month moving average Ni?o3.4 index as a function of the forecast lead month in the CNN model (red), SINTEX-F dynamical forecast system (blue), and dynamical forecast systems included in the North American Multi-Model Ensemble project (the other colors); Fig.b and Fig.c show the correlation skill of the Ni?o3.4 index targeted to each calendar month in the CNN model (b) and the SINTEX-F dynamical forecast system (c), hatching highlights the forecasts with correlation skill exceeding 0.5.)
图5 ANN预测风暴潮水位的验证 (图件改绘自文献[86]。图a~c分别为Masan站点2003年“Maemi”台风(a),Tongyoung站点2002年“Rusa”台风(b),Yeosu站点2012年“Bolaven”台风(c)风暴潮验证结果。MA是指文献中的Levenberg-Marquardt反向传播算法。)
Fig.5 Verification of ANN forecast storm surge level (Figure is repainted from reference [86]. Figures a-c show the storm surge verification results of 2003 Typhoon “Maemi” at Masan Station (a),2002 Typhoon “Rusa” at Tongyoung Station (b), and 2012 Typhoon “Bolaven” at Yeosu Station (c), respectively. MA is the Levenberg-Marquardt backpropagation algorithm in the reference.)
图5 ANN预测风暴潮水位的验证 (图件改绘自文献[86]。图a~c分别为Masan站点2003年“Maemi”台风(a),Tongyoung站点2002年“Rusa”台风(b),Yeosu站点2012年“Bolaven”台风(c)风暴潮验证结果。MA是指文献中的Levenberg-Marquardt反向传播算法。)
Fig.5 Verification of ANN forecast storm surge level (Figure is repainted from reference [86]. Figures a-c show the storm surge verification results of 2003 Typhoon “Maemi” at Masan Station (a),2002 Typhoon “Rusa” at Tongyoung Station (b), and 2012 Typhoon “Bolaven” at Yeosu Station (c), respectively. MA is the Levenberg-Marquardt backpropagation algorithm in the reference.)
图6 ConvLSTM算法智能预测台风浪浪高 (图件改绘自文献[104]。图a~c为2019年8月8日18时,台风“Lekima”在东海上空时的有效波高(SWH);图d~f为2017年12月24日21时,台风“Tembin”在南海上空时的有效波高。)
Fig.6 ConvLSTM algorithm intelligently predicts typhoon wave height (Figure is repainted from reference [104]. Figures a-c show the SWH(significant wave height) of Typhoon “Lekima” over the East China Sea at 18:00 on August 8, 2019; figures d-f show the SWH of Typhoon “Tembin” over the South China Sea at 21:00 on December 24, 2017.)
图6 ConvLSTM算法智能预测台风浪浪高 (图件改绘自文献[104]。图a~c为2019年8月8日18时,台风“Lekima”在东海上空时的有效波高(SWH);图d~f为2017年12月24日21时,台风“Tembin”在南海上空时的有效波高。)
Fig.6 ConvLSTM algorithm intelligently predicts typhoon wave height (Figure is repainted from reference [104]. Figures a-c show the SWH(significant wave height) of Typhoon “Lekima” over the East China Sea at 18:00 on August 8, 2019; figures d-f show the SWH of Typhoon “Tembin” over the South China Sea at 21:00 on December 24, 2017.)
图7 不同输入参数驱动的人工智能参数化方案与KPP参数化方案混合系数比较 (图件改绘自文献[144]。图a的驱动参数为温度、盐度和流速,图b的驱动参数为温度和盐度,图c的驱动参数为温度。图例中“corr”表示相关系数。)
Fig.7 Comparison of mixing coefficients between artificial intelligence parameterization schemes driven by different input parameters and KPP parameterization schemes (Figure is repainted from reference [144]. The driving parameters in figures a-c are temperature, salinity, flow rate (a), temperature and salinity (b),and temperature (c), respectively. In figure, ‘corr’ refers to the correlation coefficient.)
图7 不同输入参数驱动的人工智能参数化方案与KPP参数化方案混合系数比较 (图件改绘自文献[144]。图a的驱动参数为温度、盐度和流速,图b的驱动参数为温度和盐度,图c的驱动参数为温度。图例中“corr”表示相关系数。)
Fig.7 Comparison of mixing coefficients between artificial intelligence parameterization schemes driven by different input parameters and KPP parameterization schemes (Figure is repainted from reference [144]. The driving parameters in figures a-c are temperature, salinity, flow rate (a), temperature and salinity (b),and temperature (c), respectively. In figure, ‘corr’ refers to the correlation coefficient.)
图8 PINN训练数据对应的圆柱体尾迹中的非对称旋涡脱落模式 (图件改绘自文献[161]。图a表示Re=100时不可压缩流和动态涡脱落经过圆柱体时的涡度,所述时空训练数据对应于所描述的圆柱体尾迹中的矩形区域。图b和c表示流向和横向速度分量u(t,x,y)和v(t,x,t)的训练数据点位置。)
Fig.8 Asymmetrical vortex shedding pattern in the cylinder wake to PINN training data (Figure is repainted from reference [161]. Figure a shows the incompressible flow and dynamic vortex shedding past a circular cylinder at Re=100. The spatio-temporal training data correspond to the depicted rectangular region in the cylinder wake. Figures b-c show the locations of training datapoints for the stream-wise and transverse velocity components, u(t, x, y) and v(t, x, t), respectively.)
图8 PINN训练数据对应的圆柱体尾迹中的非对称旋涡脱落模式 (图件改绘自文献[161]。图a表示Re=100时不可压缩流和动态涡脱落经过圆柱体时的涡度,所述时空训练数据对应于所描述的圆柱体尾迹中的矩形区域。图b和c表示流向和横向速度分量u(t,x,y)和v(t,x,t)的训练数据点位置。)
Fig.8 Asymmetrical vortex shedding pattern in the cylinder wake to PINN training data (Figure is repainted from reference [161]. Figure a shows the incompressible flow and dynamic vortex shedding past a circular cylinder at Re=100. The spatio-temporal training data correspond to the depicted rectangular region in the cylinder wake. Figures b-c show the locations of training datapoints for the stream-wise and transverse velocity components, u(t, x, y) and v(t, x, t), respectively.)
图9 零样本超分辨率模拟: Navier-Stokes方程[170] (上:真实案例;下:神经算子预测案例,在64×64×20分辨率数据集上训练,在256×256×80分辨率上求值。)
Fig.9 Zero-shot super-resolution simulation: Navier-Stokes Equation[170] (Top: truth case; bottom: neural operator prediction case. Trained on 64×64×20 dataset, evaluated on 256×256×80.)
图9 零样本超分辨率模拟: Navier-Stokes方程[170] (上:真实案例;下:神经算子预测案例,在64×64×20分辨率数据集上训练,在256×256×80分辨率上求值。)
Fig.9 Zero-shot super-resolution simulation: Navier-Stokes Equation[170] (Top: truth case; bottom: neural operator prediction case. Trained on 64×64×20 dataset, evaluated on 256×256×80.)
图10 1952—2022年里程碑式机器学习模型的发展趋势[193]
Fig.10 Trends of milestone ML models from 1952 to 2022[193]
图10 1952—2022年里程碑式机器学习模型的发展趋势[193]
Fig.10 Trends of milestone ML models from 1952 to 2022[193]
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