CN117350171A - Mesoscale vortex three-dimensional subsurface structure inversion method and system based on double-flow model - Google Patents
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Abstract
本发明提出了基于双流模型的中尺度涡三维次表层结构反演方法及系统,涉及深度学习与海洋反演交叉领域,通过卫星采集待反演的中尺度涡海表面信息;将中尺度涡海表面信息输入到训练好的双流模型中,反演出中尺度涡不同深度下的温度结果,得到中尺度涡次表层温度剖图;其中,所述双流模型引入Triplet attention注意力机制,采用三分支结构融合通道注意力和空间注意力进行跨维度交互;本发明采用双流模型实现中尺度涡次表层结构反演,发掘海表面参数间的数据关联,分别建立不同参数与次表层温度的关系模型,融合多源信息特征关系,实现特征融合,有效融合了多源数据,提升了反演效果。
The present invention proposes a method and system for inverting the three-dimensional subsurface structure of mesoscale vortices based on a two-flow model, which involves the intersection of deep learning and ocean inversion. The mesoscale vortex sea surface information to be inverted is collected through satellites; the mesoscale vortex sea is The surface information is input into the trained two-flow model, and the temperature results at different depths of the mesoscale eddy are inverted to obtain the subsurface temperature profile of the mesoscale eddy. Among them, the two-flow model introduces a triplet attention mechanism and adopts a three-branch structure. Fusion of channel attention and spatial attention for cross-dimensional interaction; the present invention uses a dual-flow model to achieve mesoscale vortex subsurface structure inversion, explores the data correlation between sea surface parameters, establishes relationship models between different parameters and subsurface temperature, and fuses Multi-source information feature relationships realize feature fusion, effectively integrate multi-source data, and improve the inversion effect.
Description
技术领域Technical field
本发明属于深度学习与海洋反演交叉领域,尤其涉及基于双流模型的中尺度涡三维次表层结构反演方法及系统。The invention belongs to the intersection of deep learning and ocean inversion, and in particular relates to a mesoscale eddy three-dimensional subsurface structure inversion method and system based on a two-flow model.
背景技术Background technique
本部分的陈述仅仅是提供了与本发明相关的背景技术信息,不必然构成在先技术。The statements in this section merely provide background technical information related to the present invention and do not necessarily constitute prior art.
中尺度涡作为一种常见的海洋现象,广泛存在于各个海域,其内部结构为一种封闭的涡流,在垂直结构上影响深度可达数千米;同时中尺度涡蕴含着巨大能量,带动海洋热量、盐度、营养物质的传输,对海洋化学及生物环境带来不可忽视的影响;中尺度涡的动能甚至占据整个海洋中大、中海动能的90%,其能量会造成水下三维声场紊乱,扰乱水下通信和探测,对潜艇、渔船等安全造成影响,为此研究中尺度涡三维结构将有效保障水下潜艇的航行安全,对海洋环境监测、国防军事等提供有力保障。As a common ocean phenomenon, mesoscale eddies widely exist in various sea areas. Its internal structure is a closed vortex, which can affect the vertical structure to a depth of several thousand meters. At the same time, mesoscale eddies contain huge energy and drive the ocean. The transmission of heat, salinity, and nutrients has a non-negligible impact on ocean chemistry and biological environment; the kinetic energy of mesoscale eddies even accounts for 90% of the kinetic energy of large and medium oceans in the entire ocean, and its energy will cause chaos in the underwater three-dimensional sound field. , disrupting underwater communications and detection, and affecting the safety of submarines, fishing boats, etc. Therefore, studying the three-dimensional structure of mesoscale vortices will effectively ensure the navigation safety of underwater submarines and provide strong support for marine environment monitoring, national defense and military, etc.
随着卫星遥感技术的发展,高分辨率的卫星遥感技术已广泛应用于气象水文学,目前海表面的涡旋特征提取主要依托高分辨的卫星遥感观测资料,但该方式仅能获取到涡旋表面数据,无法直接探测到海洋内部的信息。与遥感观测相比,基于海洋的原位观测数据(如Argo、CTD数据等)虽然可以探测海洋次表层的剖面信息,但存在时空分布不均匀、不连续,空间分辨率低等问题,不能满足中尺度涡内部动力过程的要求;因此,现有技术无法用高分辨率卫星资料反映次表层内部结构特征,妨碍对中尺度涡三维结构的进一步研究。With the development of satellite remote sensing technology, high-resolution satellite remote sensing technology has been widely used in meteorology and hydrology. At present, the extraction of eddy characteristics on the sea surface mainly relies on high-resolution satellite remote sensing observation data, but this method can only obtain eddies. Surface data cannot directly detect information about the interior of the ocean. Compared with remote sensing observations, although ocean-based in-situ observation data (such as Argo, CTD data, etc.) can detect the profile information of the ocean subsurface, they have problems such as uneven and discontinuous spatiotemporal distribution and low spatial resolution, and cannot meet the requirements of Requirements for the internal dynamic processes of mesoscale vortices; therefore, existing technology cannot use high-resolution satellite data to reflect the internal structural characteristics of the subsurface layer, hindering further research on the three-dimensional structure of mesoscale vortices.
发明内容Contents of the invention
为克服上述现有技术的不足,本发明提供了基于双流模型的中尺度涡三维次表层结构反演方法及系统,利用海表面信息作为输入参数、次表层温度数据作为标签,构建三维立体数据样本库,采用双流模型实现中尺度涡次表层结构反演,发掘海表面参数间的数据关联,分别建立不同参数与次表层温度的关系模型,融合多源信息特征关系,实现特征融合,有效融合了多源数据,提升了反演效果。In order to overcome the shortcomings of the above-mentioned existing technologies, the present invention provides a mesoscale eddy three-dimensional subsurface structure inversion method and system based on a two-flow model, using sea surface information as input parameters and subsurface temperature data as labels to construct three-dimensional three-dimensional data samples. The library uses a dual-flow model to achieve mesoscale eddy subsurface structure inversion, explores the data correlation between sea surface parameters, establishes relationship models between different parameters and subsurface temperature, integrates multi-source information feature relationships, realizes feature fusion, and effectively integrates Multi-source data improves the inversion effect.
为实现上述目的,本发明的一个或多个实施例提供了如下技术方案:To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:
本发明第一方面提供了基于双流模型的中尺度涡三维次表层结构反演方法。The first aspect of the present invention provides a mesoscale eddy three-dimensional subsurface structure inversion method based on a two-flow model.
基于双流模型的中尺度涡三维次表层结构反演方法,包括:The mesoscale eddy three-dimensional subsurface structure inversion method based on the two-flow model includes:
通过卫星采集待反演的中尺度涡海表面信息,包括海表面温度和海表面高度;Collect mesoscale eddy sea surface information to be retrieved through satellites, including sea surface temperature and sea surface height;
将中尺度涡海表面信息输入到训练好的双流模型中,反演出中尺度涡不同深度下的温度结果,得到中尺度涡次表层温度剖图;Input the mesoscale eddy sea surface information into the trained two-flow model, invert the temperature results at different depths of the mesoscale eddy, and obtain the mesoscale eddy subsurface temperature profile;
其中,所述双流模型引入Triplet attention注意力机制,采用三分支结构融合通道注意力和空间注意力进行跨维度交互,第一分支建立H维度与W维度之间的特征交互,第二分支建立C维度和W维度的特征交互,第三分支建立H维度和C维度的特征交互。Among them, the dual-stream model introduces a triplet attention mechanism, and uses a three-branch structure to fuse channel attention and spatial attention for cross-dimensional interaction. The first branch establishes the feature interaction between the H dimension and the W dimension, and the second branch establishes the C The feature interaction between dimension and W dimension, and the third branch establishes the feature interaction between H dimension and C dimension.
进一步的,所述双流模型,采用编码-解码结构,编码阶段用于海表面温度和海表面高度的特征提取,解码阶段用于特征融合。Furthermore, the two-stream model adopts an encoding-decoding structure. The encoding stage is used for feature extraction of sea surface temperature and sea surface height, and the decoding stage is used for feature fusion.
进一步的,所述编码阶段,以海表面温度和海表面高度为输入,分别探究海表面温度、海表面高度与次表层温度的关系;在海表面高度、海表面温度进行特征提取的同时,考虑到中尺度涡具备复杂非线性特征,其海表面高度信息与海表面温度信息存在必然联系,为融合SSH和SST的关系特征,构建数据特征融合网络,同时利用跳跃连接降低过拟合风险;Further, in the encoding stage, sea surface temperature and sea surface height are used as inputs to explore the relationship between sea surface temperature, sea surface height and subsurface temperature respectively; while performing feature extraction on sea surface height and sea surface temperature, consider Mesoscale eddies have complex nonlinear characteristics, and there is an inevitable connection between their sea surface height information and sea surface temperature information. In order to integrate the relationship characteristics of SSH and SST, a data feature fusion network is constructed, and jump connections are used to reduce the risk of over-fitting;
所述解码阶段,融合编码阶段每层的特征输出的同时,利用反卷积实现分辨率的恢复。In the decoding stage, while fusing the feature output of each layer in the encoding stage, deconvolution is used to restore the resolution.
进一步的,所述第一分支,引入identity残差分支结构,首先经过Z-Pool,融合平均池化以及最大池化特征,实现维度缩放;Further, the first branch introduces the identity residual branch structure, first passes through Z-Pool, and integrates the average pooling and maximum pooling features to achieve dimension scaling;
所述第二分支,将输入的特征图张量沿W轴旋转,实现C维度和W维度的特征交互;The second branch rotates the input feature map tensor along the W axis to achieve feature interaction in the C dimension and W dimension;
所述第三分支,将输入的特征图张量沿H轴旋转,实现H维度和C维度的特征交互。The third branch rotates the input feature map tensor along the H axis to achieve feature interaction in the H and C dimensions.
进一步的,所述双流模型,以构建的中尺度涡次表层结构反演样本库作为训练数据集进行训练;Further, the two-flow model is trained using the constructed mesoscale eddy subsurface structure inversion sample library as a training data set;
其中,以中尺度涡时间、涡心坐标位置对应的海表面信息作为输入参数,以中尺度涡时间、涡心坐标位置对应的次表层温度数据作为标签,构建所述的中尺度涡次表层结构反演样本库。Among them, the mesoscale eddy time and the sea surface information corresponding to the vortex center coordinate position are used as input parameters, and the mesoscale eddy time and the subsurface temperature data corresponding to the vortex center coordinate position are used as labels to construct the mesoscale eddy subsurface structure. Inversion sample library.
进一步的,所述双流模型的损失函数采用MAE损失,其表示真实值与预测值之间的绝对差值之和,公式为:Furthermore, the loss function of the two-stream model adopts MAE loss, which represents the sum of absolute differences between the true value and the predicted value. The formula is:
其中,为预测值,/>为真实值,n为一组温度场的数量。in, is the predicted value,/> is the real value, n is the number of a set of temperature fields.
进一步的,所述双流模型的评判标准采用R2、MAE以及explained_variance_score进行比较,所述R2用于衡量对预测数据的拟合程度,公式表示为:Further, the evaluation criteria of the two-stream model are compared using R2, MAE and explained_variance_score. The R2 is used to measure the degree of fit to the predicted data, and the formula is expressed as:
其中,f为估计值,y为真实值,为观测数据的平均值;Among them, f is the estimated value, y is the real value, is the average of the observed data;
所述explained_variance_score为解释方差,用于衡量模型对数据集波动的解释程度,计算真实值与预测值的可解释方差,公式如下:The explained_variance_score is the explained variance, which is used to measure the degree to which the model explains the fluctuations in the data set and calculate the explainable variance between the true value and the predicted value. The formula is as follows:
其中,var为方差,y为真实值,为预测值。Among them, var is the variance, y is the true value, is the predicted value.
本发明第二方面提供了基于双流模型的中尺度涡三维次表层结构反演系统。A second aspect of the present invention provides a mesoscale eddy three-dimensional subsurface structure inversion system based on a two-flow model.
基于双流模型的中尺度涡三维次表层结构反演系统,包括数据采集模块、温度反演模块:The mesoscale eddy three-dimensional subsurface structure inversion system based on the two-flow model includes a data acquisition module and a temperature inversion module:
数据采集模块,被配置为:通过卫星采集待反演的中尺度涡海表面信息,包括海表面温度和海表面高度;The data collection module is configured to: collect mesoscale eddy sea surface information to be inverted through satellites, including sea surface temperature and sea surface height;
温度反演模块,被配置为:将中尺度涡海表面信息输入到训练好的双流模型中,反演出中尺度涡不同深度下的温度结果,得到中尺度涡次表层温度剖图;The temperature inversion module is configured to: input the mesoscale eddy sea surface information into the trained two-flow model, invert the temperature results at different depths of the mesoscale eddy, and obtain the mesoscale eddy subsurface temperature profile;
其中,所述双流模型引入Triplet attention注意力机制,采用三分支结构融合通道注意力和空间注意力进行跨维度交互,第一分支建立H维度与W维度之间的特征交互,第二分支建立C维度和W维度的特征交互,第三分支建立H维度和C维度的特征交互。Among them, the dual-stream model introduces a triplet attention mechanism, and uses a three-branch structure to fuse channel attention and spatial attention for cross-dimensional interaction. The first branch establishes the feature interaction between the H dimension and the W dimension, and the second branch establishes the C The feature interaction between dimension and W dimension, and the third branch establishes the feature interaction between H dimension and C dimension.
本发明第三方面提供了计算机可读存储介质,其上存储有程序,该程序被处理器执行时实现如本发明第一方面所述的基于双流模型的中尺度涡三维次表层结构反演方法中的步骤。A third aspect of the present invention provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the mesoscale vortex three-dimensional subsurface structure inversion method based on the two-flow model described in the first aspect of the present invention is implemented. steps in.
本发明第四方面提供了电子设备,包括存储器、处理器及存储在存储器上并可在处理器上运行的程序,所述处理器执行所述程序时实现如本发明第一方面所述的基于双流模型的中尺度涡三维次表层结构反演方法中的步骤。A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the process based on the first aspect of the present invention is implemented. Steps in the mesoscale eddy three-dimensional subsurface structure inversion method for a two-flow model.
以上一个或多个技术方案存在以下有益效果:One or more of the above technical solutions have the following beneficial effects:
本发明通过双流神经网络结构,发掘海表面参数间的数据关联,分别建立不同参数与次表层温度的关系模型,融合多源信息特征关系,实现特征融合,有效融合了多源数据,提升了反演效果。This invention uses a dual-stream neural network structure to discover data correlations between sea surface parameters, establish relationship models between different parameters and subsurface temperatures, integrate multi-source information feature relationships, achieve feature fusion, effectively integrate multi-source data, and improve response performance. acting effect.
本发明引入Triplet attention注意力机制,采用三分支结构融合通道注意力和空间注意力,更有利于实现跨特征维度交互,提高中尺度涡空间特征交互能力,提升反演精度。The present invention introduces a Triplet attention mechanism and uses a three-branch structure to fuse channel attention and spatial attention, which is more conducive to realizing cross-feature dimension interaction, improving the interaction ability of mesoscale vortex space features, and improving the inversion accuracy.
本发明中研发的中尺度涡三维次表层结构反演方案是对海洋次表层领域的探索,更有利于提升海洋领域专家及研究人员的工作效率,具备实用性。The mesoscale vortex three-dimensional subsurface structure inversion scheme developed in this invention is an exploration of the ocean subsurface field, which is more conducive to improving the work efficiency of experts and researchers in the ocean field and is practical.
本发明附加方面的优点将在下面的描述中部分给出,部分将从下面的描述中变得明显,或通过本发明的实践了解到。Additional advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
附图说明Description of drawings
构成本发明的一部分的说明书附图用来提供对本发明的进一步理解,本发明的示意性实施例及其说明用于解释本发明,并不构成对本发明的不当限定。The description and drawings that constitute a part of the present invention are used to provide a further understanding of the present invention. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention.
图1为第一个实施例的方法流程图;Figure 1 is a method flow chart of the first embodiment;
图2 为第一个实施例温度剖面的获取流程图;Figure 2 is a flow chart for obtaining the temperature profile in the first embodiment;
图3 为第一个实施例的双流模型结构图;Figure 3 is a structural diagram of the dual-flow model of the first embodiment;
图4 为第一个实施例的Triplet Attention结构图。Figure 4 is a structural diagram of Triplet Attention in the first embodiment.
具体实施方式Detailed ways
应该指出,以下详细说明都是例示性的,旨在对本申请提供进一步的说明。除非另有指明,本发明使用的所有技术和科学术语具有与本申请所属技术领域的普通技术人员通常理解的相同含义。It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meanings commonly understood by one of ordinary skill in the art to which this application belongs.
需要注意的是,这里所使用的术语仅是为了描述具体实施方式,而非意图限制根据本申请的示例性实施方式。如在这里所使用的,除非上下文另外明确指出,否则单数形式也意图包括复数形式,此外,还应当理解的是,当在本说明书中使用术语“包含”和/或“包括”时,其指明存在特征、步骤、操作、器件、组件和/或它们的组合。It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular forms are also intended to include the plural forms unless the context clearly indicates otherwise. Furthermore, it will be understood that when the terms "comprises" and/or "includes" are used in this specification, they indicate There are features, steps, operations, means, components and/or combinations thereof.
实施例一Embodiment 1
本公开的一种实施例中提供了基于双流模型的中尺度涡三维次表层结构反演方法,包括如下步骤:An embodiment of the present disclosure provides a mesoscale eddy three-dimensional subsurface structure inversion method based on a two-flow model, which includes the following steps:
步骤S1:通过卫星采集待反演的中尺度涡海表面信息,包括海表面温度和海表面高度;Step S1: Collect mesoscale eddy sea surface information to be retrieved through satellites, including sea surface temperature and sea surface height;
步骤S2:将中尺度涡海表面信息输入到训练好的双流模型中,反演出中尺度涡不同深度下的温度结果,得到中尺度涡次表层温度剖图;Step S2: Input the mesoscale eddy sea surface information into the trained two-flow model, invert the temperature results of the mesoscale eddy at different depths, and obtain the mesoscale eddy subsurface temperature profile;
其中,所述双流模型引入Triplet attention注意力机制,采用三分支结构融合通道注意力和空间注意力进行跨维度交互,第一分支建立H维度与W维度之间的特征交互,第二分支建立C维度和W维度的特征交互,第三分支建立H维度和C维度的特征交互。Among them, the dual-stream model introduces a triplet attention mechanism, and uses a three-branch structure to fuse channel attention and spatial attention for cross-dimensional interaction. The first branch establishes the feature interaction between the H dimension and the W dimension, and the second branch establishes the C The feature interaction between dimension and W dimension, and the third branch establishes the feature interaction between H dimension and C dimension.
作为一种实施例,从模型训练的角度,对基于双流模型的中尺度涡三维次表层结构反演方法的具体实施方式进行说明,具体过程如图1所示:As an example, from the perspective of model training, the specific implementation of the mesoscale eddy three-dimensional subsurface structure inversion method based on the two-flow model is explained. The specific process is shown in Figure 1:
步骤一、构建中尺度涡次表层结构反演样本库Step 1. Construct a sample library for mesoscale eddy surface structure inversion
在本步骤中,获取数据样本,融合海表面数据以及次表层数据。海表面数据来源于AVISO官网中Meta3.2dt数据集,空间分辨率为0.25°×0.25°,时间分辨率为日平均,选取某区域(0-30°N,105-130°E),重点获取其涡心经纬度坐标、涡旋类型以及时间等关键信息;海表面信息来源于哥白尼海洋环境监测服务(CMEMS),选取数据产品为:Global OceanPhysics Reanalysis,数据ID为GLOBAL_MULTIYEAR_PHY_001_030,其空间和时间分辨率分别为1/12°×1/12°和日平均,次表层温度剖面数据也来源于该数据。In this step, data samples are obtained and sea surface data and subsurface data are fused. The sea surface data comes from the Meta3.2dt data set on the AVISO official website. The spatial resolution is 0.25° × 0.25°, and the temporal resolution is the daily average. A certain area (0-30°N, 105-130°E) is selected to focus on acquisition. Key information such as the longitude and latitude coordinates of the vortex center, vortex type and time; the sea surface information comes from the Copernicus Marine Environment Monitoring Service (CMEMS). The selected data product is: Global OceanPhysics Reanalysis, the data ID is GLOBAL_MULTIYEAR_PHY_001_030, and its spatial and temporal resolution The rates are 1/12° × 1/12° and the daily average respectively, and the subsurface temperature profile data are also derived from this data.
对获取的数据进行数据处理,该过程如图2所示,由AVISO的中尺度涡数据中获取涡心位置坐标、时间信息,建立气旋、反气旋样本库,利用该信息对应到哥白尼海洋表层信息中,实现数据对齐,选取4×4大小的网格,得到网格对应的海表面高度、海表面温度数据及次表层的温度剖面数据,构建中尺度涡次表层结构反演样本库。Perform data processing on the acquired data. The process is shown in Figure 2. The vortex center position coordinates and time information are obtained from AVISO's mesoscale eddy data, and a cyclone and anticyclone sample library is established. This information is used to correspond to the Copernican Ocean. In the surface information, data alignment is achieved, and a 4×4 grid is selected to obtain the sea surface height, sea surface temperature data and subsurface temperature profile data corresponding to the grid, and a mesoscale eddy surface structure inversion sample library is constructed.
步骤二、数据预处理实现分辨率统一、缺值处理Step 2: Data preprocessing to achieve unified resolution and missing value processing
对构建的样本库进行数据预处理,利用线性插值法将分辨率统一为0.25°×0.25°,并对缺失值做补0处理。Perform data preprocessing on the constructed sample library, use linear interpolation method to unify the resolution to 0.25° × 0.25°, and fill in missing values with zeros.
步骤三、将样本库输入至双流模型Step 3. Input the sample library into the dual-stream model
将处理后样本库输入至双流神经网络结构中进行模型训练及验证,模型参数更新优化后输出模型,输入测试集中海表面参数信息至输出模型中进行推理,得到中尺度涡次表三维温度反演结果。The processed sample library is input into the dual-stream neural network structure for model training and verification. The model parameters are updated and optimized to output the model. The sea surface parameter information in the test set is input into the output model for inference, and the three-dimensional temperature inversion of the mesoscale vorticity table is obtained. result.
具体的,所述双流模型,采用编码-解码结构,模型结构如图3所示,编码阶段用于海表面高度SSH和海表面温度SST的特征提取模块,解码阶段实现特征融合。Specifically, the dual-stream model adopts an encoding-decoding structure. The model structure is shown in Figure 3. The encoding stage is used for the feature extraction module of sea surface height SSH and sea surface temperature SST, and the decoding stage implements feature fusion.
编码阶段首先输入双流数据SST以及SSH,分别探究海表面温度、海表面高度与次表层温度的关系。在海表面高度、海表面温度进行特征提取的同时,考虑到中尺度涡具备复杂非线性特征,其海表面高度信息与海表面温度信息存在必然联系,为融合SSH和SST的关系特征,构建了数据特征融合网络,同时利用跳跃连接降低过拟合风险。该过程中,SSH和SST作为双流输入到神经网络的两个分支中,分别与次表层温度场建模,神经网络同一分支间进行特征提取,不同分支间也进行特征交互,实现数据特征融合。In the encoding stage, the dual-stream data SST and SSH are first input to explore the relationship between sea surface temperature, sea surface height and subsurface temperature respectively. While extracting features of sea surface height and sea surface temperature, considering that mesoscale eddies have complex nonlinear characteristics, there is an inevitable connection between their sea surface height information and sea surface temperature information. In order to integrate the relationship features of SSH and SST, a Data feature fusion network, while using skip connections to reduce the risk of overfitting. In this process, SSH and SST are input into two branches of the neural network as dual streams, and are modeled with the subsurface temperature field respectively. Feature extraction is performed between the same branch of the neural network, and feature interaction is also performed between different branches to achieve data feature fusion.
编码阶段通过加深网络实现特征图变小,以不同分辨率提取数据信息,在解码阶段融合编码阶段每层的特征输出的同时,利用反卷积实现分辨率的恢复;模型中引入Triplet attention注意力机制,采用三分支结构融合通道注意力和空间注意力,实现跨维度交互。In the encoding stage, the feature map is made smaller by deepening the network, and data information is extracted at different resolutions. In the decoding stage, while fusing the feature output of each layer in the encoding stage, deconvolution is used to restore the resolution; Triplet attention is introduced in the model. The mechanism adopts a three-branch structure to integrate channel attention and spatial attention to achieve cross-dimensional interaction.
本实施例采用Triplet attention用于实现跨维度交互,如图4所示,通过捕捉空间维度和输入张量通道维度的交互作用实现通道注意力和空间注意力的融合。This embodiment uses Triplet attention to achieve cross-dimensional interaction. As shown in Figure 4, the fusion of channel attention and spatial attention is achieved by capturing the interaction between the spatial dimension and the channel dimension of the input tensor.
具体的,给定输入张量,将其传递到Triplet attention模块的三个分支中,在第一分支中,引入identity残差分支结构,首先经过Z-Pool,融合平均池化以及最大池化特征,实现维度缩放。经过7×7大小的卷积(Conv)后进行Batch Norm归一化处理,经过Sigmoid激活函数生成注意力权值,并与identity残差分支点乘后得到第一分支输出;其中Z-Pool负责连接该维度的最大池化核平均池化特征,将张量的第0维减小至2维,保留实际张量丰富特征的同时缩小其深度,以达轻量的目的,Z-Pool公式可表示为:Specifically, given the input tensor , pass it to the three branches of the Triplet attention module. In the first branch, the identity residual branch structure is introduced. First, through Z-Pool, the average pooling and maximum pooling features are integrated to achieve dimension scaling. After a 7×7 size convolution (Conv), Batch Norm normalization is performed, and the attention weight is generated through the Sigmoid activation function, and the first branch output is obtained after dot multiplication with the identity residual branch; Z-Pool is responsible for the connection The maximum pooling kernel average pooling feature of this dimension reduces the 0th dimension of the tensor to 2 dimensions, retaining the rich features of the actual tensor while reducing its depth to achieve lightweight purposes. The Z-Pool formula can be expressed for:
Sigmoid激活函数公式可表示为:The Sigmoid activation function formula can be expressed as:
第二分支中输入特征图张量沿W轴逆时针旋转90°,实现维度变换张量表示为/>,经过Z-Pool后实现维度缩放,得到/>并经过7×7大小的矩阵,以及归一化处理得到/>的输出,利用Sigmoid激活函数后采用沿W轴顺时针旋转90°,得到输出/>。同理在第三分支中,旋转H轴实现H维度和C维度建立特征交互。Input the feature map tensor in the second branch Rotate 90° counterclockwise along the W axis to realize the dimension transformation tensor expressed as/> , achieve dimension scaling after Z-Pool, and get/> And after a 7×7 matrix and normalization processing, it is obtained/> The output, using the Sigmoid activation function, is rotated 90° clockwise along the W axis to obtain the output/> . Similarly, in the third branch, the H axis is rotated to establish feature interaction between the H dimension and the C dimension.
步骤四、训练及验证,迭代优化模型Step 4: Training and verification, iteratively optimize the model
在本实施例中,模型训练及模型验证,输出次表层温度结构。以预处理后的中尺度涡次表层结构反演样本库作为训练集,对用于次表层温度反演的双流模型进行训练,得到训练完成后的反演模型。在模型训练中,实验设置batch_size为64, epoch设置为50,优化器采用Adam,学习率为1e-3。In this embodiment, model training and model verification output the subsurface temperature structure. The preprocessed mesoscale eddy subsurface structure inversion sample library is used as a training set to train the two-flow model for subsurface temperature inversion, and the inversion model after training is obtained. In the model training, the experiment set batch_size to 64, epoch to 50, Adam was used as the optimizer, and the learning rate was 1e-3.
训练过程中,损失函数采用Mean absolute error (MAE)损失,表示真实值与预测值之间的绝对差值之和,损失函数用于训练过程中的模型优化,使预测结果逼近目标结果,其中如公式下,其中为预测值,/>为真实值,n为一组温度场的数量:During the training process, the loss function uses Mean absolute error (MAE) loss, which represents the sum of absolute differences between the real value and the predicted value. The loss function is used for model optimization during the training process to make the prediction result approach the target result, where as Under the formula, where is the predicted value,/> is the real value, n is the number of a set of temperature fields:
评判标准用于评估反演效果,验证实验结果;采用R2、MAE以及explained_variance_score进行比较,其中R2用于衡量对预测数据的拟合程度,取值介于[0,1]之间,其公式可表示为:The evaluation criteria are used to evaluate the inversion effect and verify the experimental results; R2, MAE and explained_variance_score are used for comparison. R2 is used to measure the degree of fit to the predicted data. The value is between [0,1], and its formula can be Expressed as:
其中,f为估计值,y为真实值,为观测数据的平均值。Among them, f is the estimated value, y is the real value, is the average of the observed data.
explained_variance为解释方差,用于衡量模型对数据集波动的解释程度,可计算真实值与预测值的可解释方差,取值介于[0,1]之间,其公式如下,其中var为方差,y为真实值,为预测值。explained_variance is the explained variance, which is used to measure the degree of explanation of the fluctuation of the data set by the model. It can calculate the explained variance of the true value and the predicted value. The value is between [0,1]. The formula is as follows, where var is the variance, y is the real value, is the predicted value.
步骤五、得到训练后模型,输入测试数据,输出次表层温度结构Step 5: Obtain the trained model, input test data, and output the subsurface temperature structure
迭代优化训练后的模型作为推理模型,输入测试数据中的海表面信息(SSH、SST)至推理模型中,得到反演出的中尺度涡不同深度下的温度结果,并利用matplotlib库进行数据可视化。The model after iterative optimization training is used as an inference model. The sea surface information (SSH, SST) in the test data is input into the inference model to obtain the inverted temperature results of mesoscale eddies at different depths, and the matplotlib library is used for data visualization.
实施例二Embodiment 2
本公开的一种实施例中提供了基于双流模型的中尺度涡三维次表层结构反演系统,包括数据采集模块、温度反演模块:An embodiment of the present disclosure provides a mesoscale eddy three-dimensional subsurface structure inversion system based on a two-flow model, including a data acquisition module and a temperature inversion module:
数据采集模块,被配置为:通过卫星采集待反演的中尺度涡海表面信息,包括海表面温度和海表面高度;The data collection module is configured to: collect mesoscale eddy sea surface information to be inverted through satellites, including sea surface temperature and sea surface height;
温度反演模块,被配置为:将中尺度涡海表面信息输入到训练好的双流模型中,反演出中尺度涡不同深度下的温度结果,得到中尺度涡次表层温度剖图;The temperature inversion module is configured to: input the mesoscale eddy sea surface information into the trained two-flow model, invert the temperature results at different depths of the mesoscale eddy, and obtain the mesoscale eddy subsurface temperature profile;
其中,所述双流模型引入Triplet attention注意力机制,采用三分支结构融合通道注意力和空间注意力进行跨维度交互,第一分支建立H维度与W维度之间的特征交互,第二分支建立C维度和W维度的特征交互,第三分支建立H维度和C维度的特征交互。Among them, the dual-stream model introduces a triplet attention mechanism, and uses a three-branch structure to fuse channel attention and spatial attention for cross-dimensional interaction. The first branch establishes the feature interaction between the H dimension and the W dimension, and the second branch establishes the C The feature interaction between dimension and W dimension, and the third branch establishes the feature interaction between H dimension and C dimension.
实施例三Embodiment 3
本实施例的目的是提供计算机可读存储介质。The purpose of this embodiment is to provide a computer-readable storage medium.
计算机可读存储介质,其上存储有计算机程序,该程序被处理器执行时实现如本公开实施例一所述的基于双流模型的中尺度涡三维次表层结构反演方法中的步骤。A computer-readable storage medium has a computer program stored thereon. When the program is executed by a processor, the steps in the mesoscale vortex three-dimensional subsurface structure inversion method based on the two-flow model described in Embodiment 1 of the present disclosure are implemented.
实施例四Embodiment 4
本实施例的目的是提供电子设备。The purpose of this embodiment is to provide electronic equipment.
电子设备,包括存储器、处理器及存储在存储器上并可在处理器上运行的程序,所述处理器执行所述程序时实现如本公开实施例一所述的基于双流模型的中尺度涡三维次表层结构反演方法中的步骤。Electronic equipment, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the mesoscale vortex three-dimensional model based on the two-flow model is implemented as described in Embodiment 1 of the present disclosure. Steps in the subsurface structure inversion method.
以上所述仅为本发明的优选实施例而已,并不用于限制本发明,对于本领域的技术人员来说,本发明可以有各种更改和变化。凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。The above descriptions are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.
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