13 results about "Sea-surface height" patented technology

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 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%.

This invention discloses a method, apparatus, equipment, and medium for sea surface height measurement based on dual-antenna GNSS, relating to the field of reflection measurement technology. The method includes: acquiring satellite observations of several satellites from a first GNSS-R antenna and a second GNSS-R antenna at the current epoch to construct a first observation equation; the first and second GNSS-R antennas are installed on the same vertical line; the first GNSS-R antenna is used to receive direct satellite signals; the second GNSS-R antenna is used to receive sea surface reflected signals; the eastward and northward components of the baseline vector are preset as zero vectors, and combined with the first observation equation, a second observation equation is constructed and solved in real time to obtain the baseline vector; the absolute position coordinates of any one of the antennas are obtained to calculate the sea surface height. This invention can suppress horizontal errors in baseline calculation by introducing horizontal constraints, thereby improving the accuracy of sea surface height measurement results.

The present application belongs to the field of marine surveying and geodetic surveying, and discloses a far-sea GNSS buoy height reference transmission method combined with satellite altimeter, aiming to solve the technical problems of difficult transmission of 1985 national height reference in far-sea area and large height measurement error of GNSS buoy antenna, the method establishes height reference conversion relationship through shore-based leveling joint measurement, carries out precise positioning and calculation on far-sea GNSS buoy observation data, and retains effective sea surface information through low-pass filtering processing; combines satellite altimeter data to carry out space-time matching, inverses and dynamically corrects the height of buoy antenna, converts the ellipsoidal height of buoy sea surface into 1985 national height through geodetic datum difference correction, and finally outputs the results through quality evaluation. The method realizes accurate transmission of far-sea height reference and dynamic correction of the height of buoy antenna, guarantees the reliability of far-sea sea surface height calculation, and provides accurate height data support for far-sea marine observation, marine engineering and other fields.

This invention discloses a Bayesian inversion extraction method for the equilibrium signal of wide-span altimeter data. The method involves acquiring and preprocessing sea surface height anomaly sequences from radar interferometers and nadir altimeters. A normalized sine square window function is applied for windowing, and multidimensional spatial averaging is used to estimate the one-dimensional wavenumber power spectrum. A piecewise power law-based equilibrium signal spectrum model and a noise spectrum model constrained by dynamic sea state are constructed, and the set of spectral parameters is extracted through logarithmic domain weighted least squares fitting. A set of spatial covariance matrices is constructed using cosine integral transform and Abelian forward and inverse transforms. A graphics processor is scheduled to perform batch matrix decomposition and singular fault-tolerant regularized inversion to solve for the posterior mean vector and posterior covariance matrix of the target equilibrium signal. Window fusion and index mapping are applied to fill the gaps in nadir observations. Geostrophic dynamics parameters are calculated, uncertainty quantification is performed based on the linear error propagation law, and the knowledge base is updated based on the exponential moving average algorithm. This invention achieves suppression of observation noise and physical filling of observation gaps, improving the adaptability of the inversion system to environmental changes while preserving non-Gaussian dynamic characteristics.

The application discloses a gravity anomaly calculation method, device, equipment, storage medium and product, relates to the satellite altimetry data processing technical field, and the gravity anomaly calculation method comprises: obtaining sea surface height data observed by an altimetry satellite; according to the sea surface height data, a plurality of residual geoid gradients in different directions are calculated; the plurality of residual geoid gradients in different directions are fused by a Bayesian triangular cap method, and an east-west direction component and a south-north direction component of the residual geoid gradient are obtained; and based on the east-west direction component and the south-north direction component of the residual geoid gradient, the marine gravity anomaly of a target region is calculated by a gravity anomaly inversion algorithm. The application solves the technical problems of low utilization rate of multi-direction gradient information of a single altimetry satellite and insufficient component extraction precision, realizes mining and optimization of multi-direction gradient information of a single satellite, and improves the inversion precision of the marine gravity anomaly.

The application discloses a method and system for inverting marine gravity anomaly based on physical information neural network, and particularly relates to the field of physical geodesy, and the method comprises the following steps: acquiring multi-source altimetry satellite sea surface height data, and calculating the north-south and east-west components of residual vertical deflection; utilizing the inverse Vening-Meinesz formula to invert gravity anomaly to construct a training set; constructing a physical information neural network, embedding the physical gradient relationship between the vertical deflection and the gravity anomaly into a loss function to perform constraint training, and establishing a high-precision nonlinear mapping model of the two; and finally inputting the data of a region to be measured to output inversion results. Through data driving and physical equation collaborative optimization, the application does not need data gridding and complex discretization processing, effectively fuses prior knowledge, and significantly improves the precision and physical consistency of marine gravity anomaly inversion.

The application discloses a simulation generation method and device for near-shore strong nonlinear waves and a terminal, and relates to the technical field of ocean engineering, in particular to a simulation generation method for near-shore strong nonlinear waves. The method comprises the following steps: acquiring sea surface height data of a tsunami wave at a certain position; inputting the sea surface height data into a wave speed variation and water depth variation relationship model, wherein the wave speed variation and water depth variation relationship model outputs wave flow speed; and obtaining a tsunami wave based on the sea surface height data and the wave flow speed. Compared with the prior art, the application can accurately generate strong nonlinear waves in a computational fluid dynamics model, fills the gap of the model method in this aspect, lays a foundation for the research on strong nonlinear waves, such as extreme storm surges and tsunami waves, and engineering effects thereof, and thus can more accurately understand, simulate and quantify the engineering effects of extreme wave disasters.

The invention discloses a sea surface height anomaly (SLA) multi-step space-time prediction method based on combination of self-attention Transform and a recurrent neural network, and the method comprises a data preprocessing module, a space-time feature extraction module, a prediction generation module and a space reconstruction module, and comprises the following steps: inputting SLA data obtained by satellite height measurement into the data preprocessing module; performing blocking and normalization processing to generate a standardized input sequence; inputting the standardized sequence into a spatio-temporal feature extraction module, extracting global spatial dependency characteristics by using a self-attention Transform module, and extracting dynamic change characteristics of time dimensions through a recurrent neural network; recursively generating SLA prediction data of a plurality of time steps by using a prediction generation module to form a multi-step prediction sequence; and finally, the multi-step prediction sequence is restored to the original spatial resolution through a spatial reconstruction module, and a final prediction result is obtained. According to the method, the self-attention Transform and the recurrent neural network are innovatively combined, the limitation of a traditional method in modeling of the complex spatial-temporal dynamic characteristics of the SLA is broken through, the multi-step prediction precision is remarkably improved, and effective technical support is provided for ocean mesoscale vortex monitoring and environment forecasting.

This invention discloses a parametric simulation method for ocean forecasting based on cross-density vertical mixing processes, comprising: Step 1, acquiring a three-dimensional multivariate marine environmental field, water depth data, vortex trajectory dataset, vortex kinetic energy data, and atmospheric driving field. The environmental field includes sea surface height, ocean temperature, ocean salinity, and meridional and zonal current velocities; the atmospheric driving field includes wind speed, sea surface pressure, 2-meter temperature, sea surface temperature, shortwave radiation, cloud component, precipitation, and relative humidity; Step 2, introducing vortex horizontal asymmetry, constructing a parametric ocean model based on cross-density vertical mixing processes, and calculating the vertical mixing coefficient; Step 3, constructing a regional ocean model based on the regional ocean numerical model ROMS, and verifying the vertical mixing coefficient; Step 4, performing ocean forecasting using the parametric ocean model based on cross-density vertical mixing processes. This invention links vertical mixing with the influence of vortex horizontal asymmetry, improving the accuracy of multi-element ocean simulation.

The application discloses a pelagic fishing ground fish population distribution prediction method and system, and belongs to the technical field of pelagic fishery informatization. The method fuses six marine environmental elements, i.e. sea surface temperature, sea surface salinity, chlorophyll a concentration, sea surface height anomaly, current field and mixed layer depth, learns fish population habitat preference by constructing positive and negative samples to train a gradient boosting classification model, calculates habitat suitability index point by point to identify high-probability fishing grounds, and predicts seasonal migration trajectories of the fishing grounds by using a long short-term memory network. The prediction results are sent to fishing vessels through Beidou short messages and feedback is received to drive model incremental updating.

The application discloses a sea surface height inversion method and system based on a gated memory network and GNSS-IR, and relates to the technical fields of ocean remote sensing and satellite navigation. The method comprises the following steps: acquiring GNSS-IR observation data, and extracting physical inversion process parameters therefrom; combining corresponding spatio-temporal matching meteorological parameters and measured sea surface height data of a tide station to construct a first multi-source feature data sequence; inputting the first multi-source feature data sequence into a gated memory network for training; the training process comprises grouping and independently pruning network weight gradients corresponding to different types of features in the first multi-source feature data sequence; and updating network weights related to meteorological parameters by using a differentiated learning rate strategy. GNSS-IR observation data to be inverted is constructed into a second multi-source feature data sequence, and the trained gated memory network is inputted, so that sea surface height inversion results are outputted. The second multi-source feature data sequence adopts the same feature structure and construction logic as the first multi-source feature data sequence.

The invention provides a sea surface height abnormal data fusion method and device, and relates to the technical field of ocean data processing. According to the method, flattening processing is carried out on wide-swath two-dimensional sea surface height abnormal data, so that the wide-swath two-dimensional sea surface height abnormal data can be fused with along-rail sea surface height abnormal data under a unified framework; the method comprises the following steps: flattening one-dimensional data, performing scale separation on the flattened one-dimensional data, fusing different scale components by adopting an optimal interpolation method based on space-time, compensating a fusion result corresponding to a second scale by utilizing a trained compensation model, and finally reconstructing fusion results obtained under different scales to obtain a fusion result. Therefore, large-scale background information and sub-mesoscale detail information can be processed separately and reconstructed cooperatively, and the problem that a small-scale structure is easy to be smoothed excessively due to mutual coupling of different-scale information in an existing unified fusion mode is solved. The obtained target sea surface height anomaly fusion data can well consider the stability of a background field and the recovery capability of a fine structure.