85 results about "Sea-surface height" patented technology

The invention discloses an ocean three-dimensional temperature and salt field prediction method based on remote sensing data driving, and the method comprises the steps: firstly constructing a satellite remote sensing sea surface temperature data set, a sea surface height anomaly data set and an ocean three-dimensional reanalysis data set which are needed by an experiment; then, on the basis of a convolutional neural network and a Transform module, a basic framework of the model is constructed according to the temporal and spatial variation characteristics of the remote sensing data and the temporal and spatial variation characteristics of the three-dimensional temperature and salt field; the model adopts an end-to-end design mode, and adapts to planar spatial-temporal variation of historical remote sensing data through a two-dimensional convolutional coding structure; the three-dimensional spatial-temporal change of a future three-dimensional temperature and salt field is concerned through a three-dimensional convolution decoding structure; an attention mechanism and a feature evolution module are designed according to the spatial-temporal features of the two kinds of data, and the change relation of the remote sensing data and the temperature and salt field data under different spatial-temporal dimensions is learned. And finally, inputting a mode of combining satellite remote sensing temperature of historical n days and sea surface height anomaly into the trained model to obtain three-dimensional temperature and salt field data of future n days.

The invention discloses a marine environment forecasting method based on combination of machine learning and numerical forecasting, and particularly relates to the field of marine environment forecasting, which comprises the following steps: extracting marginal region features based on real-time observation data, constructing a forecasting field through a double-loss function, extracting extreme event features through space-time decoupling, and constructing a forecasting field through a double-loss function; and outputting a forecasting result of the target sea area. According to the marine environment forecasting method based on the combination of machine learning and numerical forecasting, the problem of initial field optimization failure caused by covariance matrix estimation deviation when marginal sea area data are sparse is relieved by embedding vorticity conservation equation residual calculation in a hidden layer; the dependence on the number of extreme event samples is reduced by realizing the targeted extraction of the extreme ocean event features; through a physical hard constraint layer technology of a residual error correction network, an initial field error of a data sparse region is corrected in combination with sea surface height and a flow velocity field constraint residual error feature map, and the problem of a large prediction error of an edge sea area is relieved.

The invention relates to the technical field of satellite altimetry, in particular to a height measurement method based on a feature-enhanced multi-base integrated re-tracking model, which is characterized in that the feature-enhanced multi-base integrated re-tracking model FMERM is constructed to optimize the position of a reflection waveform re-tracking point, and the FMERM model executes the following processing processes: 1, extracting input variable features by using PCA, and extracting the input variable features by using PCA; combining with an original input variable to form an enhanced feature set; secondly, optimizing the hyper-parameters of the XGBoost model by using a grid search method, so that the hyper-parameters of the XGBoost model are optimal; thirdly, utilizing the trained model to accurately calculate a re-tracking point normalization power value of the reflected signal waveform; fourthly, the sea surface height is inverted through the geometrical relationship and direct reflection signal time delay and atmospheric correction; an effective means is provided for solving the problem that a reflection waveform re-tracking method is inaccurate, and powerful support is provided for high-precision satellite-borne GNSS-R sea surface height measurement.

The invention relates to an internal solitary wave identification and amplitude inversion method in mode data based on machine learning, and belongs to the technical field of ocean observation. In combination with a Mask-RCNN and a variational auto-encoder, firstly, sea surface height data is preprocessed and imaged, texture features are extracted through a local binary pattern algorithm, a labeled data set is constructed, model training is performed, and internal solitary wave stripes and positions thereof are identified from the labeled data set; through extraction and processing of temperature field data, the temperature field at the water depth is inversed by using a variational auto-encoder; and extracting the amplitude of the internal solitary wave in the temperature field based on the internal wave identification result. According to the method, large-scale and refined ocean mode data can be efficiently and accurately processed, and the efficiency and accuracy of data processing are remarkably improved. The method has a wide application prospect, and especially in marine disaster early warning, marine scientific research and internal wave mechanics research, the automatic identification and amplitude extraction capability of internal waves can be greatly improved.

The invention relates to the technical field of satellite altimetry, in particular to an altimetry method based on a residual network multi-mode deep learning model, which can remarkably improve the robustness of SSH inversion under sea condition noise interference, and is characterized in that a fusion self-attention mechanism and a residual network multi-mode deep learning model ViTResNetMDL is established, and the model ViTResNetMDL is applied to improve the robustness of the SSH inversion under sea condition noise interference. A satellite-borne GNSS-R sea surface height inversion geometric physical model is established, a ViTResNetMDL model is composed of ResNet50, ViT and MHL-NN, the first part adopts a ResNet50 convolution structure to extract effective features from an original DDM, the second part adopts a ViT layer which captures global features in an effective scattering area DDM based on a self-attention mechanism in a Transform module, the third part is composed of linear layers, and the fourth part is composed of linear layers. And using the MHL-NN to retrieve the sea surface height.

The invention relates to the technical field of marine surveying and mapping, and discloses a sea level change trend accurate calculation method based on multi-source data fusion, which comprises the following steps: acquiring multi-source satellite height measurement data, performing data grouping, establishing a plurality of average sea level models, establishing a time-varying average sea level model through the average sea level models, and calculating the sea level change trend according to the time-varying average sea level model. And performing linear regression fitting on the average sea surface height time sequence at each grid point, constructing a grid average sea level change model, and calculating a change trend at each grid point. The method can improve the separation capability of signals with different time scales in sea level changes, is not affected by extreme climate events, natural environment changes and physical environment correction, and can greatly improve the prediction accuracy of the sea level trend.

The invention provides a method for cooperatively regulating and controlling chlorophyll-a concentration variability through vortex-upwelling, and relates to the technical field of ocean remote sensing and environmental monitoring. A multi-source satellite and reanalysis data set is obtained, and a stationary time sequence is constructed; identifying a dominant variation period in chlorophyll-a concentration, sea surface temperature and sea surface height anomalies, and extracting a monthly abnormal value sequence; performing complex empirical orthogonal function analysis on the sea surface height anomaly, extracting a dominant spatial variation mode of the sea surface height anomaly, and identifying a vortex distribution position and form; classifying and screening target vortexes according to vortex types and seasons, carrying out normalization processing on the chlorophyll-a concentration, aligning to vortex centers, and analyzing a spatial response relationship between the vortexes and the chlorophyll-a concentration; and drawing a density time-depth profile map and a CHL-SLA composite map by combining surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo buoy data, and analyzing an influence mechanism of vortex-upwelling on chlorophyll-a horizontal distribution.

The invention belongs to the technical field of physical ocean and remote sensing application, and particularly relates to a sub-mesoscale signal extraction method based on Ku / Ka dual-frequency SAR (Synthetic Aperture Radar) height measurement, which comprises the following steps of: acquiring a Ku / Ka dual-frequency sea surface height observation original data set and IMU (Inertial Measurement Unit) attitude and trajectory data; registering the average sea surface and tide height to a space-time sampling position of Ku / Ka synchronous observation and deducting from the original height to obtain an initial sea surface height anomaly sequence of the two channels, and processing the initial sea surface height anomaly data of the two channels to obtain a fused height anomaly sequence; performing attitude coupling mode extraction on the fused height anomaly sequence to obtain a height anomaly sequence after platform error elimination; meanwhile, smoothing processing is carried out to obtain a smoothed sequence; and interpolating the smoothed sequence into a two-dimensional regular grid, performing small-scale secondary smoothing denoising on grid data, interpolating a mesoscale background field at the same time into the same grid, and deducting the mesoscale background field to obtain a sea surface height anomaly data system with sub-mesoscale as a main part.

The invention relates to the technical field of crossing of satellite altimetry, electronic signaling and the like, in particular to a CYGNSS satellite intermediate frequency signal processing and sea surface altimetry method capable of improving altimetry precision, which comprises the following steps of: processing direct intermediate frequency signals, calculating specular reflection points, calculating bistatic delay, establishing an open-loop tracking model to obtain reflected signal parameters; the reflected intermediate frequency is processed, and direct intermediate frequency signal processing is achieved through open source GNSS receiver software so as to achieve estimation of the position, the speed and the time; according to the satellite-borne GNSS-R reflected intermediate frequency signal processing method, the SGRISP one-dimensional time delay correlation result is compared with the processing result provided by the Spanish Space Science Institute ICE-CSIC, and it is indicated that the SGRISP has high reliability in processing the satellite-borne GNSS-R reflected intermediate frequency signal.

The invention relates to the technical field of ocean dynamic environment intelligent forecasting, and provides a three-dimensional ocean dynamic environment intelligent forecasting method and device and electronic equipment, and the forecasting method comprises the steps: S1000, preparing 1 / 30-degree high-resolution ocean dynamic environment element data (three-dimensional temperature and salt flow and two-dimensional water level), and carrying out the normalization; s2000, selecting a corresponding model input sequence from the training set according to a forecasting strategy of the model; s3000, training a hierarchical feature projection network; and S4000: performing reverse normalization and generating future prediction data: performing reverse normalization on the feature-enhanced output data according to the maximum value and the minimum value of the ocean data, and finally generating a day-by-day prediction product of the sea surface height of the C sea, the three-dimensional sea temperature, the salinity, the latitudinal flow velocity and the longitude flow velocity with 1 / 30-degree high resolution in the future day, namely, the product is obtained by predicting the sea surface height of the C sea, the three-dimensional sea temperature, the salinity, the latitudinal flow velocity and the longitude flow velocity.

The invention discloses a real-time sea clutter simulation method based on a physical neural network, belongs to the field of sea clutter simulation, and particularly relates to a real-time sea clutter simulation method. The invention aims to solve the problems of poor real-time performance and long time consumption of the existing sea clutter simulation method. The method comprises the following steps: S1, initializing parameters and simulation; s2, numerical simulation of sea surface dynamics is realized according to a shallow water equation, and the obtained sea surface height field changing along with time, the flow velocity of the sea surface height field in the axial direction and the flow velocity of the sea surface height field in the axial direction are periodically stored; s3, constructing a physical neural network; s4, obtaining a trained physical neural network; s5, inputting the space coordinate and the time coordinate of the real-time sea clutter to be measured into the trained physical neural network, and outputting the sum; generating a real-time file in an STL format; and S6, obtaining sea clutter data by using the open source library.

The invention discloses a physical constraint-based ocean vortex three-dimensional temperature-salinity density reconstruction method and system, and the method comprises the steps: constructing a vertical distribution model of vortex density through integrating climate state temperature-salinity data, sea surface height, temperature, salinity observation data and other multi-source information, and employing a physical constraint condition and a vertical modal structure of vortex, thereby achieving the reconstruction of the ocean vortex three-dimensional temperature-salinity density. And further calculating the temperature-salt vertical structure of the vortex, and correcting a reconstruction result in combination with sea surface observation data, thereby realizing reconstruction of the vortex three-dimensional temperature-salt density structure. According to the method, vortex feature recognition and vortex high-precision three-dimensional temperature-salinity density reconstruction can be quickly realized by only observing sea surface height, temperature and salinity data in combination with historical data.

The invention relates to the technical field of ocean remote sensing image processing, and discloses an internal solitary wave amplitude inversion method based on SWOT observation data, which comprises the following steps of: acquiring sea surface height abnormal data of an SWOT satellite Ka-band radar interferometer L3, performing preprocessing, and performing adaptive noise filtering, maximum between-class variance method enhancement and morphological edge detection respectively; overlapping analysis is carried out, and pixel points which are all judged to be effective are reserved to serve as sea surface height abnormal data of internal solitary wave disturbance; inverting the first-order amplitude of the internal solitary wave; introducing a non-linear term in the extended KdV equation, and carrying out non-linear correction on the vertical modal function; and performing inversion to obtain the high-order amplitude of the internal solitary wave. According to the method disclosed by the invention, real and effective internal solitary wave disturbance information can be extracted as an input source of amplitude inversion, so that accurate inversion of the large-amplitude internal solitary wave is realized, and the technical potential of an SWOT satellite in quantitative research and practical application of the internal solitary wave is expanded.

The invention discloses an ocean mesoscale vortex intelligent identification method and system based on flow field geometric features and a satellite altimeter, and the method comprises the steps: obtaining the synchronous SSHA data of at least three satellites, and carrying out the calibration according to the actual measurement data of a buoy; on the basis of the calibrated SSHA data, utilizing a ground-to-balance equation to calculate weft-direction and warp-direction ground-to-balance flow velocity components; generating a flow field geometric feature VG vortex center criterion; dynamically adjusting VG vortex core criterion parameters according to the ocean dynamic environment; converting the SSHA and geostationary flow field data into a grayscale image, marking vortex polarity and a bounding box, and adding a salt and pepper noise expansion sample through random rotation; resNet-50 is adopted as a backbone network, and a bidirectional feature pyramid network BiFPN is added to optimize multi-scale feature fusion; and inputting the VG preliminary identification result into the trained ResNet-BiFPN model, and outputting the vortex position, radius and polarity. The technical defects of a traditional method in weak vortex recognition, background noise interference and calculation timeliness are overcome.

The invention discloses a satellite height measurement data optimal interpolation fusion method based on cross calibration and spectrum analysis, and the method comprises the steps: carrying out the sea surface height anomaly calculation and power spectrum analysis, and calculating the sea surface height anomaly of a satellite altimeter according to the precise orbit determination coordinates of a satellite and the distance measurement value of the altimeter; the related scale of adjacent observation points of the to-be-fused satellite altimeter is obtained through the power spectrum analysis of the sea surface height anomaly of the to-be-fused satellite altimeter; cross calibration: performing cross calibration on sea surface height anomaly of the fusion satellite altimeters by using the sea surface height anomaly of the reference satellite altimeter to obtain system deviation in the to-be-fused satellite altimeters; performing self-crossing calibration by using sea surface height anomaly of the satellite altimeter to be fused to obtain a diagonal element of an observation error covariance matrix; constructing a covariance matrix, wherein a non-diagonal element of the observation error covariance matrix is a product of a standard deviation at the two measurement points and a correlation function of the two measurement points; and fusing the fused satellite altimeter and the background field data by using an optimal interpolation method.

The present application relates to the technical field of satellite altimetry and ocean surveying and mapping, and in particular to a GNSS-R sea surface height measurement method based on variable step line segment intermediate frequency data processing, which can effectively improve the measurement accuracy and efficiency, comprising obtaining satellite-borne GNSS-R original intermediate frequency data, calculating mirror reflection points through a new variable step line segment (NVSLSM), generating a delay Doppler map (DDM), and performing sea surface height inversion. Compared with the prior art, the NVSLSM is used to process the mirror reflection points, the calculation time is increased by about 2.04 times and 1.47 times compared with the line segment bisection method and the QH method, and on the premise of ensuring the convergence accuracy, the stable solution can be reached through fewer iteration steps, thereby reducing the calculation cost per sample; in the delay Doppler map generation link, the overall calculation efficiency is increased by more than about 90%.

The application provides a method for vortex-uplift flow coordinated regulation chlorophyll-a concentration variability, relates to the field of ocean remote sensing and environmental monitoring technology, obtains multi-source satellite and reanalysis data set, constructs smooth time series; identifies dominant variability period in chlorophyll-a concentration, sea surface temperature and sea surface height anomaly, extracts monthly anomaly value sequence; applies complex empirical orthogonal function analysis to sea surface height anomaly, extracts its dominant spatial variability mode, identifies vortex distribution position and shape; according to vortex type and season, classifies and filters target vortex, carries out normalization processing to chlorophyll-a concentration and aligns to vortex center, analyzes spatial response relationship between vortex and chlorophyll-a concentration; in combination with surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo buoy data, density time-depth profile graph and CHL-SLA composite graph are drawn, and the influence mechanism of vortex-uplift flow on chlorophyll-a horizontal distribution is analyzed.

The invention provides a distributed InSAR sea surface two-dimensional flow velocity and elevation measurement method and device, and belongs to the technical field of ocean microwave remote sensing. According to the method, a dual-beam BiDi mode is introduced on a distributed InSAR platform, sea surface echoes are obtained at the same time through forward and backward squint angle beams, a multi-squint angle interference pair is constructed through beam separation and sub-aperture processing, and the sea surface two-dimensional flow velocity and elevation measurement is achieved. Image registration, interference, flat ground effect removal, phase unwrapping, interference suppression and Bragg wave phase velocity removal are sequentially completed, a multi-baseline phase equation set is finally established, and decoupling and synchronous inversion of the two-dimensional flow velocity and the elevation phase are achieved. According to the method, an additional antenna or a cooperative target is not needed, the sea surface flow velocity vector and the sea surface elevation can be obtained with high precision in one-time flight, and the ocean observation efficiency and the measurement precision of the distributed InSAR are remarkably improved.

The present invention discloses a fishery sea area monitoring system based on satellite remote sensing, which belongs to the field of fishery monitoring technology and specifically comprises: a remote sensing data acquisition module that acquires and pre-processes abnormal data on sea surface temperature, chlorophyll concentration and sea level height; a buoy data acquisition module that synchronously acquires temperature and salinity profile data of an Argo buoy array, and whose deployment density is dynamically adjusted according to historical fish catch and ocean front strength; a spatiotemporal alignment module that divides a time window by orbital period, corrects spatial offset in combination with buoy drift trajectory, sets a time buffer interval based on geostrophic velocity and satellite image resolution, and generates a spatiotemporally unified three-dimensional ocean environment data set; a fishery suitability calculation module that constructs a biophysical coupling model, associates surface chlorophyll with subsurface temperature and salinity structure, and calculates the suitability index of each water layer; a fishing ground division module that intelligently demarcates core areas and edge areas based on the three-dimensional distribution characteristics of the index; the present invention realizes accurate identification of fishing grounds based on multi-source data fusion.

The present invention belongs to the field of marine engineering technology and discloses a method, equipment, medium, and terminal for synchronous observation of waves, tides, and currents over the entire ocean area. The method comprises collecting GNSS phase, pseudorange, Doppler shift observations, and broadcast ephemeris using a GNSS receiver or board and antenna carried by a wave buoy on the ocean surface. The three-dimensional velocity of the buoy is obtained by epoch-by-epoch differential analysis of the phase observations, thereby obtaining the directional spectrum and frequency spectrum of the waves, as well as information on wave height, period, and direction. The sampling elements recorded by a shore-based station are sent to a GNSS receiver on the ocean surface via the Beidou short message service (BDS). Low-frequency sea surface height fluctuations are calculated in real time using the PPP method. Combined with the low-frequency velocity obtained by filtering, high-frequency tidal fluctuations are interpolated. The present invention expands the operating range of GNSS-based ocean wave and tide measurements and has practical application value.

The present application relates to the technical field of satellite altimetry and ocean surveying and mapping, and in particular to a GNSS-R sea surface height measurement method based on variable step line segment intermediate frequency data processing, which can effectively improve the measurement accuracy and efficiency, comprising obtaining satellite-borne GNSS-R original intermediate frequency data, calculating mirror reflection points through a new variable step line segment (NVSLSM), generating a delay Doppler map (DDM), and performing sea surface height inversion. Compared with the prior art, the NVSLSM is used to process the mirror reflection points, the calculation time is increased by about 2.04 times and 1.47 times compared with the line segment bisection method and the QH method, and on the premise of ensuring the convergence accuracy, the stable solution can be reached through fewer iteration steps, thereby reducing the calculation cost per sample; in the delay Doppler map generation link, the overall calculation efficiency is increased by more than about 90%.

The present invention discloses a dual-beam SAR system and method for measuring sea surface height and ocean current velocity. The system comprises three sets of identical X-band antennas Xa, Xb and Xc, three sets of X-band receivers, a digital stand-alone unit, a reference source, an excitation source, an internal calibrator, two servos, two wave controllers, and two sets of position and attitude measurement devices. Along-track and cross-track interferometric baselines are formed, the lengths of which are calculated respectively, and a timing sequence for time-sharing calibration of the three antenna arrays is designed, which is used for calibration of an in-cabin equipment link, a receiving link, and a transmitting link respectively. The dual-beam pointing of the antennas is controlled so that the three arrays collect data from the same target area. Cross-track interferometric data of the front channel or rear channel of the Xa and Xc antennas are collected to calculate the sea surface height of the target area. Along-track interferometric data of the front and rear channels of the Xa and Xb antennas are collected to calculate the ocean current velocity of the target area.

The invention discloses a sea surface height inspection equipment layout method based on an interference imaging altimeter, which belongs to the technical field of altimeter inspection, is used for sea surface height inspection equipment layout, and comprises the following steps: obtaining position data of a Glider buoy and temperature-salinity-depth data of a profile observed by the Glider buoy, putting the Glider buoy and a GNSS buoy in the same area at the same time before a satellite passes, and putting the GNSS buoy in the same area at the same time; the synchronous observation of the GNSS and the Glider buoy is realized; and carrying out position positioning when the Glider buoy descends and ascends to outcrop, carrying out matching with a grid position in a swath range observed by a satellite, and carrying out sea surface height inversion according to temperature-salinity-depth data of a section obtained through observation of the Glider buoy. The method adapts to data characteristics of different resolutions of the interference imaging altimeter, sea surface height synchronous observation of satellite data with different spatial resolutions under satellite transit is realized, and calibration inspection of the altimeter satellite is accurately completed.

Provided is a detection device 1 that detects an upwelling region, wherein the detection device 1 comprises: a data integration unit 14 that integrates sea surface temperature data in a predetermined region, chlorophyll-a data, sea surface altitude data, and sea bottom altitude data; and a classification unit 15 that identifies an upwelling region from the integrated data by using a classification algorithm.

The application discloses a sea surface height medium and long term prediction method and device, system and storage medium, comprising: obtaining spatial modes MEOFs, time series PCs and explained variances according to sea surface height anomaly SLA data and sea surface temperature SST data; selecting time series PCs corresponding to n spatial modes MEOFs with total explained variances greater than 95% for VMD decomposition to obtain VMD sub-modes corresponding to the PCs; using iTransformer training according to the VMD sub-modes corresponding to the PCs to obtain a sea surface height medium and long term prediction model; inputting a test set into the sea surface height medium and long term prediction model to obtain predicted sub-modes; synthesizing the predicted sub-modes into a new time series, and meanwhile, reconstructing the new time series together with the MEOFs to obtain SLA prediction values. The technical scheme of the application can realize higher-accuracy sea surface height medium and long term prediction in a selected region.

The invention provides an ocean vortex detection and identification method based on spatial-temporal characteristics, and belongs to the technical field of ocean vortex detection.The ocean vortex detection and identification method comprises the steps that sea surface height data is obtained and normalized into a two-dimensional matrix, and a mask matrix and a label matrix are generated based on vortex labeling information; the method comprises the following steps: constructing an encoder-decoder neural network model based on a shift window mechanism to perform hierarchical feature extraction and decoding, optimizing model parameters by using a training set through a cross entropy loss function, adjusting hyper-parameters on a verification set and evaluating performance on a test set, and finally realizing vortex automatic detection and identification of real-time sea surface height data. The technical problem of insufficient ocean vortex detection and identification precision is solved.

The application belongs to the technical field of circulation mixing coefficient estimation, and discloses a global estimation method, system, device and terminal of a wave-induced turbulent mixing model, the global estimation method of the wave-induced turbulent mixing model comprising: estimating the global three-dimensional structure of the internal tide-induced turbulent mixing coefficient based on the wave-induced turbulent mixing coefficient, the internal tide information extracted by the satellite altimeter and the WOA13 temperature and salinity data; determining the structure of the global internal wave spectrum; and estimating the distribution characteristics of the global annual average internal wave-induced turbulent mixing coefficient BVIW. The internal tide information extraction comprises: obtaining, processing and filtering satellite along-track sea surface height anomaly data, performing tidal harmonic analysis on the filtered data, and obtaining the internal tide harmonic constant of the global ocean tidal frequency M2. According to the derived mixing coefficient, new mixing is not created, but a mixing process that is not available under the existing framework and is rich in physical connotation is created, and the important significance of wave mixing to the circulation is given.

The invention discloses a sea surface height medium and long term prediction method, device and system and a storage medium, and the method comprises the steps: obtaining a spatial mode MEOFs, a time sequence PCs and an interpretation variance according to sea surface height anomaly SLA data and sea surface temperature SST data; selecting time sequences PCs corresponding to n spatial modals MEOFs with the total explanation variance greater than 95%, and performing VMD decomposition to obtain VMD sub-modals corresponding to the PCs; according to the VMD sub-mode corresponding to the PCs, using iTransform training to obtain a sea surface height medium and long term prediction model; inputting the test set into a sea surface height medium and long term prediction model to obtain a predicted sub-mode; and synthesizing the predicted sub-modes into a new time sequence, and reconstructing the new time sequence and the MEOFs together to obtain an SLA predicted value. By adopting the technical scheme of the invention, medium and long term prediction of the sea surface height with relatively high accuracy in the selected area can be realized.

The present invention relates to a method for identifying and inverting internal solitary waves in pattern data based on machine learning, and belongs to the field of ocean observation technology. The present invention combines Mask-RCNN and variational autoencoders, first preprocesses and images the sea surface height data, extracts texture features through a local binary pattern algorithm, constructs a labeled data set and performs model training, and identifies internal solitary wave stripes and their positions therefrom; then, through the extraction and processing of temperature field data, uses a variational autoencoder to invert the temperature field at depth; and extracts the amplitude of the internal solitary wave in the temperature field based on the internal wave identification result. The present invention can efficiently and accurately process large-scale, refined ocean pattern data, significantly improving the efficiency and accuracy of data processing. The present invention has broad application prospects, especially in marine disaster warning, marine scientific research, and internal wave dynamics research, and can greatly improve the automatic identification and amplitude extraction capabilities of internal waves.

The invention discloses a sea surface height continuous expression and reconstruction method and system for sparse satellite orbit observation, and the method comprises the steps: forming an input-output data pair according to three-dimensional time-space coordinates of an observation point of a satellite altimeter and an original along-orbit sea surface height observation value of the satellite altimeter; the three-dimensional time-space coordinates are converted into meshless continuous input coordinates; constructing a space-time continuous expression model based on implicit neural representation on the basis of a network structure of a sine cycle activation function, introducing gradient field consistency constraint and total variation regularization, and constructing comprehensive loss; and according to global sea surface height estimation output by the model, reconstructing a regular grid, calculating a sea surface height gradient, gradient change, local curvature and power agent quantity, completing continuous expression and reconstruction of the sea surface height, and realizing synchronous and high-quality reconstruction of a fine structure and an overall scene of a sea surface height field. And the defects of non-uniform sampling, high-gradient recovery and micro-analysis capability in the prior art can be effectively overcome.