Quantitative evaluation method for influence of marine environment forecast product error on sound field forecast
By constructing a quantitative link between marine environment forecasting error and acoustic field forecasting error, the problem of insufficient acoustic field forecasting accuracy is solved, and quantitative assessment and reliability improvement of acoustic field forecasting are achieved. This approach is applicable to different marine regions and acoustic models and supports the optimization of sonar systems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NAT MARINE DATA & INFORMATION SERVICE
- Filing Date
- 2026-01-21
- Publication Date
- 2026-05-12
AI Technical Summary
In existing technologies, errors in marine environmental forecast products lead to insufficient accuracy in acoustic field forecasts, and the lack of systematic quantitative evaluation methods affects the performance and application reliability of underwater sonar systems.
By constructing a quantitative link between marine environmental forecast product error and sound field forecast error, cubic spline interpolation, normal distribution model and parabolic equation model are used to calculate the root mean square error of sound velocity error and sound field convergence zone distance, generate error spatial distribution map, and quantify sound field forecast error.
It enables quantitative assessment of acoustic field prediction errors, improves the credibility and reliability of acoustic field prediction, provides information support for the performance optimization of sonar systems, and is applicable to different ocean regions and acoustic models.
Smart Images
Figure CN122020372A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of marine engineering technology, and more specifically, to a quantitative assessment method for the impact of errors in marine environmental forecast products on acoustic field forecasts. Background Technology
[0002] As the core physical carrier for underwater target detection, communication, and navigation, the underwater acoustic field's prediction accuracy directly determines the performance ceiling of sonar systems and has a decisive impact on applications such as marine resource development and underwater safety management. Currently, acoustic field prediction mainly follows the technical paradigm of "marine environment prediction driving acoustic computation," which uses environmental prediction elements such as temperature, salinity, and current output from marine numerical models such as HYCOM and ROMS as input, and uses underwater acoustic propagation models such as Bellhop, RAM, and KRAKEN to achieve numerical simulation and prediction of acoustic field parameters.
[0003] However, this technological chain suffers from a fundamental bottleneck: the marine environmental forecasting products themselves contain non-negligible systematic errors. These errors primarily stem from the incompleteness and uncertainty of the initial field information in the marine environmental forecasting system, and inherent defects in the parameterization schemes of physical processes, such as the simplified handling of ocean mixing processes and boundary layer effects. These environmental errors, as upstream input disturbances, are further amplified or modulated through the nonlinear transmission mechanism in the acoustic propagation model after being transmitted to the acoustic calculation stage, ultimately leading to significant deviations in the acoustic field forecast. In practical applications, this can result in serious consequences such as target misjudgment and communication link interruptions. Therefore, systematically quantifying the impact mechanism of marine environmental forecasting errors on the accuracy of acoustic field forecasts has become a key scientific issue for improving the reliability of underwater acoustic forecasts and promoting the transformation of forecasting capabilities from "qualitative experience" to "quantitative controllability."
[0004] In current underwater acoustic forecasting research, those skilled in the art generally recognize that marine environmental forecasting errors affect the accuracy of acoustic field forecasts. However, this understanding is mostly qualitative, such as vaguely stating that "temperature forecasting deviations cause distortion of the sound velocity profile, which in turn leads to errors in acoustic field calculations." While such qualitative judgments have some guiding significance, a systematic analytical method to quantify the impact of marine environmental forecasting errors on acoustic field forecasts has yet to be developed. Therefore, developing an assessment method that can quantitatively reflect the correlation between marine environmental forecasting errors and acoustic field forecasting accuracy has become a crucial issue that urgently needs to be addressed to improve underwater acoustic forecasting capabilities. Summary of the Invention
[0005] To address the shortcomings of existing technologies, the present invention aims to provide a quantitative assessment method for the impact of marine environmental forecast product errors on acoustic field forecasts. By constructing a complete quantitative link of "environmental forecast error - acoustic field forecast error", the method describes the amplitude and spatial distribution characteristics of underwater acoustic field forecast errors caused by uncertainties in marine environmental forecasts, providing a basis and scientific support for determining the credibility and improving the reliability of marine acoustic forecasts.
[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for quantitatively assessing the impact of marine environmental forecast product errors on acoustic field forecasting, comprising the following steps:
[0007] S1. Use the seawater sound speed profile data measured on-site in the target sea area as the true sound speed. The ocean sound velocity profile data output by the marine environmental forecast product within the corresponding spatiotemporal range is used as the forecast sound velocity. ;
[0008] S2. Data Spatiotemporal Alignment and Calculation of Sound Velocity Error in Ocean Forecasting
[0009] The predicted sound speed is obtained by using cubic spline interpolation. Compared with the actual sound speed measured on site Aligning on a spatiotemporal grid, the difference between the predicted and measured sound speeds is calculated at each spatiotemporal sampling point to obtain the seawater sound speed prediction error. And according to the depth of analysis of its statistical characteristics, its mean is calculated. and standard deviation ;
[0010] S3. Generate a spatial distribution map of sound velocity error.
[0011] Based on the average sound velocity prediction error within the grid spatial cell A two-dimensional spatial distribution matrix of sound velocity error is generated and visualized.
[0012] S4. Construct a sound velocity perturbation probability model and a sound velocity perturbation sample set based on the sound velocity prediction error; calculate the sound field propagation loss using a parabolic equation model to obtain the reference sound propagation loss. Harmony field propagation loss perturbation sample ;
[0013] S5, from the reference sound propagation loss Extract the baseline first convergence region distance Distance from the second convergence zone of the benchmark From the sound field propagation loss perturbation sample Extracting the first convergence region distance of the perturbation sample Distance between the second convergence region and the perturbation sample And calculate the distance of the first convergence region of the perturbation sample. relative to the first convergence zone distance root mean square error and the distance of the second convergence region of the perturbation sample Relative to the second convergence zone distance root mean square error .
[0014] S6. Based on the root mean square error of the distance between the first convergence zone of the disturbance samples within each grid spatial cell. Root mean square error of the distance to the second convergence region of the perturbation sample Generate a spatial distribution map of the distance error in the sound field convergence zone.
[0015] Furthermore, the seawater sound speed prediction error in step S2 Where x and y are horizontal coordinates, z is depth coordinates, and t is time.
[0016] Furthermore, in step S3, the specific steps for generating the spatial distribution map of sound velocity error include:
[0017] (1) Divide the target sea area into spatial units using a 1°×1° grid;
[0018] (2) Calculate the average value of the sound velocity prediction error within each grid spatial cell. Thus, the average sound velocity error of the grid cell is obtained, and the average sound velocity errors of all grids are arranged in geographical order to form a two-dimensional spatial distribution matrix of sound velocity error.
[0019] (3) Visualize the spatial distribution matrix of sound speed error using drawing tools, use color to represent the magnitude of sound speed error, and overlay land outlines to generate a spatial distribution map of sound speed error.
[0020] Furthermore, in step S4, the sound speed perturbation probability model adopts a normal distribution model, which follows a normal distribution. .
[0021] Furthermore, in step S4, M independent sets of data satisfying the normal distribution are generated based on the normal distribution model. random numbers ,Will As a sound speed perturbation factor, a sound speed perturbation sample set is constructed:
[0022] .
[0023] Furthermore, we set M≥1000 to ensure the stability of the statistical results.
[0024] Furthermore, in step S4, The reference sound propagation loss is obtained by inputting the parabolic equation model. Each sound speed perturbation sample The corresponding sound field propagation loss perturbation samples are obtained by inputting the parabolic equation model respectively. .
[0025] Furthermore, in step S5, the method for extracting the convergence region distance is as follows:
[0026] By performing depth integration and horizontal smoothing averaging on the sound field propagation loss, the horizontal distance-propagation loss curve is obtained. ;
[0027] From the propagation loss curve The minimum point is selected as the center of the convergence region.
[0028] Furthermore, the selection of minimum points meets the following conditions:
[0029] (1) The propagation loss at this point is more than 5 dB lower than the surrounding background;
[0030] (2) The horizontal distances exhibit a periodic distribution with approximately equal intervals.
[0031] Furthermore, in step S5,
[0032] Distance of the first convergence region of the perturbation sample relative to the first convergence zone distance root mean square error for:
[0033] ;
[0034] Distance of the second convergence region of the perturbation sample Relative to the second convergence zone distance root mean square error for:
[0035] .
[0036] Furthermore, the method for generating the spatial distribution map of the distance error in the sound field convergence zone is as follows:
[0037] (1) Divide the target sea area into spatial units using a 1°×1° grid;
[0038] (2) Calculate the root mean square error of the distance to the first convergence zone of the perturbation samples within each grid spatial cell. Root mean square error of distance to the second convergence region of the perturbation sample Generate the spatial distribution matrix of the distance error in the sound field convergence zone;
[0039] (3) Use drawing tools to visualize the spatial distribution matrix of the convergence zone distance error, use color to represent the magnitude of the convergence zone distance error, and overlay the land outline to generate the spatial distribution map of the sound field convergence zone distance error.
[0040] In summary, the present invention has the following beneficial effects:
[0041] The method of this invention maps marine environmental forecasting errors into quantitative errors in sound field forecasting by constructing a technical chain of "marine environmental forecasting—sound velocity disturbance sample construction—sound propagation calculation—convergence zone distance extraction—sound field error statistics." This method is independent of specific marine environmental forecasting products and marine acoustic propagation models, possessing good versatility and can be widely applied to acoustic forecasting and assessment tasks in different marine environments. Targeting the convergence zone—a core physical phenomenon in sonar detection—the method uses the convergence zone distance as a quantitative parameter for sound field error, directly providing information support for the application and performance optimization of sonar systems. Attached Figure Description
[0042] Figure 1 This is a spatial distribution diagram of the sound velocity error in the method of the present invention;
[0043] Figure 2 This is the reference sound propagation loss of the present invention. picture;
[0044] Figure 3 This is a perturbation sample of the sound field propagation loss of the present invention. picture;
[0045] Figure 4 This is a diagram showing the sound field convergence zone distance extraction results of the present invention;
[0046] Figure 5 This is a distance error distribution diagram of the first convergence zone of the sound field according to the present invention;
[0047] Figure 6 This is a distance error distribution diagram of the second convergence zone of the sound field according to the present invention. Detailed Implementation
[0048] The present invention will be further described in detail below with reference to the embodiments.
[0049] This embodiment focuses on a region in the Northwest Pacific Ocean (130°E-148°E, 12°N-40°N) to quantitatively assess the impact of marine environmental forecast product errors on acoustic field forecasts during the period from January 1, 2022 to December 31, 2022.
[0050] S1. The seawater sound speed profile data measured by Argo buoys in the target sea area (130°E-148°E, 12°N-40°N) is used as the true sound speed. (Also known as in-situ measured sound speed), using the seawater sound speed profile data output by the HYCOM ocean numerical prediction system for the corresponding spatiotemporal range as the predicted sound speed. .
[0051] Among them, marine environmental forecast products can be marine numerical model forecast products such as HYCOM, ROMS, and POM, or marine intelligent model forecast products.
[0052] S2. Data Spatiotemporal Alignment and Calculation of Sound Velocity Error in Ocean Forecasting
[0053] The predicted sound speed is obtained by using cubic spline interpolation. With on-site measurement of sound velocity Aligning on a spatiotemporal grid, the difference between the predicted sound velocity and the on-site measured sound velocity is calculated at each spatiotemporal sampling point to obtain the seawater sound velocity prediction error. Based on the depth analysis of its statistical characteristics, the mean value of the seawater sound speed prediction error is calculated. and standard deviation .
[0054] Among them, the seawater sound speed prediction error In the formula, x and y are the horizontal coordinates, z is the depth coordinate, and t is the time.
[0055] S3. Generate a spatial distribution map of sound velocity error.
[0056] Based on the average sound velocity prediction error within the grid spatial cell A two-dimensional spatial distribution matrix of sound velocity error is generated and visualized.
[0057] The specific steps for generating the spatial distribution map of sound velocity error include:
[0058] (1) Divide the target sea area into spatial units using a 1°×1° grid;
[0059] (2) Calculate the average value of the sound velocity prediction error within each grid spatial cell. Thus, the average sound velocity error of the grid cell is obtained, and the average sound velocity errors of all grids are arranged in geographical order to form a two-dimensional spatial distribution matrix of sound velocity error.
[0060] (3) The spatial distribution matrix of sound speed error is visualized using the plotting tool MATLAB. Colors represent the magnitude of sound speed error, and land contour lines are superimposed to generate a spatial distribution map of sound speed error, such as... Figure 1 As shown.
[0061] S4. Construct a sound velocity perturbation probability model and a sound velocity perturbation sample set based on the sound velocity prediction error; calculate the sound field propagation loss using a parabolic equation model to obtain the reference sound propagation loss. Harmony field propagation loss perturbation sample ,like Figure 2-3 As shown.
[0062] This embodiment selects the parabolic equation model to calculate the sound field propagation loss, but different types of sound propagation models such as RAM, Bellhop, and KRAKEN can also be selected.
[0063] The sound speed perturbation probability model adopts a normal distribution model and follows a normal distribution. The mean of a normal distribution and standard deviation The differences in sound velocity error characteristics at different depths were taken into account.
[0064] Generate M independent sets of data that satisfy the normal distribution model. random numbers ,Will As a sound speed perturbation factor, a sound speed perturbation sample set is constructed:
[0065] .
[0066] To ensure the stability of the statistical results, M is set to ≥ 1000.
[0067] Will The reference sound propagation loss is obtained by inputting the parabolic equation model (RAM). Each sound speed perturbation sample Inputting the parabolic equation model (RAM) into each model yields the corresponding sound field propagation loss perturbation samples. .
[0068] The above settings can adapt to the horizontally changing marine environment and accurately simulate the sound propagation effect in the actual marine environment; moreover, they have high computational efficiency and are suitable for large-scale parallel computing.
[0069] S5, from the reference sound propagation loss Extract the baseline first convergence region distance Distance from the second convergence zone of the benchmark From the sound field propagation loss perturbation sample Extracting the first convergence region distance of the perturbation sample Distance between the second convergence region and the perturbation sample And calculate the distance of the first convergence region of the perturbation sample. relative to the first convergence zone distance root mean square error Distance between the second convergence region and the perturbation sample Relative to the second convergence zone distance root mean square error .
[0070]
[0071]
[0072] In the formula, The distance to the first convergence region of the perturbation sample. The distance to the second convergence region of the perturbation sample. Based on the distance of the first convergence zone, M is the distance to the second convergence zone, which is the baseline, and M is the number of samples.
[0073] The method for extracting the convergence distance is as follows:
[0074] loss of reference sound propagation Sound field propagation loss perturbation sample By performing depth integration and horizontal smoothing averaging, the horizontal distance-propagation loss curve is obtained. From the propagation loss curve The minimum point is selected as the center of the convergence region.
[0075] The selection of minimum points meets the following conditions:
[0076] (1) The propagation loss at this point is more than 5 dB lower than the surrounding background;
[0077] (2) The horizontal distances exhibit a periodic distribution with approximately equal intervals.
[0078] like Figure 4 The image shows the convergence zone distance extraction results, corresponding to the baseline acoustic propagation loss. The propagation loss curve is represented by the red line. The red arrows correspond to the distances of the first and second benchmark convergence zones, respectively, and represent the propagation loss of the perturbation sample. The propagation loss curve is represented by the blue line, and the blue arrows correspond to the distances of the first and second convergence regions of the perturbation samples.
[0079] S6. Based on the root mean square error of the distance between the first convergence zone of the disturbance samples within each grid spatial cell. Root mean square error of the distance to the second convergence region of the perturbation sample Generate a spatial distribution map of the distance error in the sound field convergence zone.
[0080] Specifically:
[0081] (1) Divide the target sea area into spatial units using a 1°×1° grid;
[0082] (2) Calculate the root mean square error of the distance between the first convergence zone and the disturbance sample in the convergence zone within each grid spatial cell. Root mean square error of distance to the second convergence region of the perturbation sample The spatial distribution matrix of the distance error in the first convergence zone and the spatial distribution matrix of the distance error in the second convergence zone are obtained respectively.
[0083] (3) The spatial distribution matrix of the convergence zone distance error is visualized using the plotting tool MATLAB. Color is used to represent the magnitude of the convergence zone distance error, and land contour lines are superimposed to generate the spatial distribution maps of the first and second sound field convergence zone distance errors, as shown below. Figure 5 and Figure 6 As shown.
[0084] Combination Figure 1 , Figure 5-6 The sound velocity error distribution and the convergence zone distance error distribution of marine environmental forecast products show a significant spatial consistency. That is, in the sea area where the former error is large, the latter error also increases synchronously. This strong correlation verifies the rationality of the quantitative evaluation method of the present invention.
[0085] This invention constructs a technical chain of "marine environment forecasting—sound velocity disturbance sample construction—sound propagation calculation—convergence zone distance extraction—sound field error statistics," mapping the errors of marine environment forecasting products to the quantitative errors of sound field forecasting. Furthermore, this invention has good versatility, applicable not only to marine numerical model forecasting products such as HYCOM, ROMS, and POM, as well as marine intelligent model forecasting products, but also to different types of sound propagation models such as RAM, Bellhop, and Kraken, enabling its wide application in the quantitative assessment of acoustic forecasts in various marine environments. In addition, by using the convergence zone distance as the quantitative parameter for sound field error, this invention can directly provide information support for the tactical application and performance optimization of sonar systems.
[0086] This specific embodiment is merely an explanation of the present invention and is not intended to limit the invention. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of the claims of the present invention.
Claims
1. A quantitative assessment method for the impact of marine environmental forecast product errors on acoustic field forecasting, characterized in that, Includes the following steps: S1. Use the seawater sound speed profile data measured on-site in the target sea area as the true sound speed. The ocean sound velocity profile data output by the marine environmental forecast product within the corresponding spatiotemporal range is used as the forecast sound velocity. ; S2. Data Spatiotemporal Alignment and Calculation of Sound Velocity Error in Ocean Forecasting The predicted speed of sound is obtained by using cubic spline interpolation. Compared with the actual sound speed measured on site Aligning on a spatiotemporal grid, the difference between the predicted and measured sound speeds is calculated at each spatiotemporal sampling point to obtain the seawater sound speed prediction error. And calculate its mean by analyzing its statistical characteristics in depth. and standard deviation ; S3. Generate a spatial distribution map of sound velocity error. Based on the average sound velocity prediction error within the grid spatial cell A two-dimensional spatial distribution matrix of sound velocity error is generated and visualized. S4. Construct a sound velocity perturbation probability model and a sound velocity perturbation sample set based on the sound velocity prediction error; calculate the sound field propagation loss using a parabolic equation model to obtain the reference sound propagation loss. Harmony field propagation loss perturbation sample ; S5, from the reference sound propagation loss Extract the baseline first convergence region distance Distance from the second convergence zone of the benchmark From the sound field propagation loss perturbation sample Extracting the first convergence region distance of the perturbation sample Distance between the second convergence region and the perturbation sample ; And calculate the distance of the first convergence region of the perturbation sample. Relative to the first convergence zone distance root mean square error Distance between the second convergence region and the perturbation sample Relative to the second convergence zone distance root mean square error ; S6. Based on the distance error of the first convergence zone within each grid spatial cell. Second convergence zone distance error Generate a spatial distribution map of the distance error in the sound field convergence zone.
2. The method according to claim 1, characterized in that, Seawater sound speed prediction error in step S2 Where x and y are horizontal coordinates, z is depth coordinates, and t is time.
3. The method according to claim 1, characterized in that, In step S3, the specific steps for generating the spatial distribution map of sound velocity error include: (1) Divide the target sea area into spatial units using a 1°×1° grid; (2) Calculate the average value of the sound velocity prediction error within each grid spatial cell. Thus, the average sound velocity error of the grid cell is obtained, and the average sound velocity errors of all grids are arranged in geographical order to form a two-dimensional spatial distribution matrix of sound velocity error. (3) Visualize the spatial distribution matrix of sound speed error using drawing tools, use color to represent the magnitude of sound speed error, and overlay land outlines to generate a spatial distribution map of sound speed error.
4. The method according to claim 1, characterized in that, In step S4, the sound speed perturbation probability model adopts a normal distribution model, which follows a normal distribution. .
5. The method according to claim 4, characterized in that, In step S4, M independent sets of data satisfying the normal distribution are generated based on the normal distribution model. random numbers ,Will As a sound speed perturbation factor, a sound speed perturbation sample set is constructed: 。 6. The method according to claim 1, characterized in that, In step S4, The reference sound propagation loss is obtained by inputting the parabolic equation model. Each sound speed perturbation sample The corresponding sound field propagation loss perturbation samples are obtained by inputting the parabolic equation model respectively. .
7. The method according to any one of claims 1-6, characterized in that, The method for extracting the convergence distance is as follows: By performing depth integration and horizontal smoothing averaging on the sound field propagation loss, the horizontal distance-propagation loss curve is obtained. ; From the propagation loss curve The minimum point is selected as the center of the convergence region.
8. The method according to claim 7, characterized in that, The selection of minimum points meets the following conditions: (1) The propagation loss at this point is more than 5 dB lower than the surrounding background; (2) The horizontal distances exhibit a periodic distribution with approximately equal intervals.
9. The method according to claim 1, characterized in that, In step S5, the distance of the first convergence region of the perturbation sample is... Relative to the first convergence zone distance root mean square error for: ； Distance of the second convergence region of the perturbation sample Relative to the second convergence zone distance root mean square error for: 。 10. The method according to claim 1, characterized in that, The method for generating the spatial distribution map of the distance error in the sound field convergence zone is as follows: (1) Divide the target sea area into spatial units using a 1°×1° grid; (2) Calculate the root mean square error of the distance between the first convergence zone and the disturbance sample in the convergence zone within each grid spatial cell. Root mean square error of distance to the second convergence region of the perturbation sample The spatial distribution matrix of the distance error in the first convergence zone and the spatial distribution matrix of the distance error in the second convergence zone are obtained respectively. (3) Use drawing tools to visualize the spatial distribution matrix of the convergence zone distance error, use color to represent the magnitude of the convergence zone distance error, and overlay the land outline to generate a spatial distribution map of the sound field convergence zone distance error.