Acoustic model recommendation method based on ocean topographic features

Abstract

The invention discloses an acoustic model recommendation method based on marine topographic features, and the method comprises the steps: obtaining the marine topographic data of a researched sea area, extracting a central region of a preset size from the researched sea area, and generating grid points; in combination with the ocean topographic data, for each grid point in a central area, extracting topographic features on a preset length path in a 360-degree direction at preset angle intervals from the center of the grid point, so as to obtain height difference and gradient statistics; in combination with the working frequency range of the acoustic model, calculating the acoustic model recommended by each grid point and direction angle under each working frequency according to a preset hierarchical decision logic, and storing a recommendation result in a four-dimensional array manner; integrating the four-digit array in a man-machine interaction platform, displaying acoustic model recommendation results on all direction angles within a 360-degree range associated with a grid point after a user interactively queries a center coordinate, a direction angle and a working frequency of the specified grid point, and visually displaying terrain attributes.

Description

Technical Field

[0001] This invention relates to the fields of marine acoustics and marine engineering, and specifically to a method for recommending underwater acoustic models based on seabed topographic features. Background Technology

[0002] In modern maritime warfare and sonar detection, the complexity of seabed topography significantly influences the selection of underwater acoustic models, with different models exhibiting marked performance variations under varying terrain conditions. Therefore, efficiently and accurately selecting appropriate underwater acoustic models based on sea surface characteristics and sound source frequencies has become a key research focus.

[0003] The applicability analysis conclusions of existing numerical analysis models are rather simplistic and are not applicable to some complex terrains. The specific analysis is as follows: Existing acoustic models exhibit a simplistic approach when evaluating usage conditions across different frequencies and terrains, providing only rough conclusions regarding model performance at various frequencies. Particularly when dealing with complex terrain, current technology fails to comprehensively and accurately define the boundary conditions for acoustic models to adapt to diverse complex environments at different frequencies, remaining at a simplified level of judgment. This simplification and limitation often leads to insufficient computational accuracy when dealing with complex terrain, thus affecting the model's performance in real-world marine environments.

[0004] In summary, existing technologies suffer from insufficient selection accuracy in recommending acoustic models based on marine topographic features. Summary of the Invention

[0005] The purpose of this invention is to provide an acoustic model recommendation method based on marine topographic features, which improves the accuracy, computational efficiency and environmental adaptability of model selection, thereby providing an efficient and reliable solution for marine work scenarios such as submarine stealth optimization and sonar detection.

[0006] To achieve the above objectives, the present invention employs the following technical solution: An acoustic model recommendation method based on ocean topography features includes: Acquire marine topographic data of the studied sea area, extract a central region of a preset size from the studied sea area, and generate grid points in the central region at a preset size; Combining the aforementioned marine topographic data, for each grid point in the central region, topographic features along a preset length path are extracted at preset angular intervals in the 360° direction, based on the center of that grid point, thereby obtaining height difference and slope statistics; Based on the extracted terrain features and combined with the operating frequency range of the acoustic model, the recommended acoustic model for each grid point and orientation angle at each operating frequency is calculated according to the preset hierarchical decision logic, and the recommendation results are stored in a four-dimensional array. The dimensions of the four-dimensional array are represented as the x-coordinate of the grid point center × the y-coordinate of the grid point center × the orientation angle × the frequency range. The four-bit array is integrated into the human-computer interaction platform. After the user queries the center coordinates, direction angle and working frequency of a specified grid point, the platform displays the acoustic model recommendation results for all direction angles within a 360° range associated with that grid point; or after further specifying the direction angle, the platform displays the acoustic model recommendation results for a single direction angle; at the same time, the terrain attributes of the recommendation results in each direction are visualized.

[0007] Furthermore, the preset dimensions are 100km×100km; the preset size is 100m×100m; the preset angle interval is 1°; and the preset length is 20km.

[0008] Furthermore, for the center of each grid point, depth data is extracted along a path of a preset length starting from a preset initial orientation angle, generating a sequence of depth data sampling points; during path sampling, the coordinates of the sampling points on the path are calculated using the following formula: ; in, This indicates starting from the initial direction angle and moving along the d-th direction angle. Above, the coordinates at a distance of t kilometers from the center of the grid point. t represents the coordinates of the center of the grid point; t represents the path sampling interval; d is the direction angle index, ranging from 1 degree to 360 degrees.

[0009] Furthermore, based on the sampling sequence of depth data acquired along a preset length path at each orientation angle of the grid points, the following terrain features are calculated: Height difference: Calculates the difference between the maximum and minimum depths along a path of preset length; The slope between any two adjacent sampling points along a path of a preset length is calculated using the following formula: ; in This represents the slope between the k-th and (k+1)-th sampling points. This represents the height value of the k-th sampling point on a path of preset length, where inteval represents the sampling interval. This is a function that converts radians to degrees. Average slope: Where N represents the number of slopes.

[0010] Furthermore, the hierarchical decision-making logic is as follows: The terrain is categorized into four types: deep-sea flat, shallow-sea flat, steep slope, and gentle slope. Based on steep slopes, the terrain is further subdivided into three complex types: seamounts, mid-ocean ridges, and trenches. The recommendation logic for terrain and corresponding acoustic models is as follows: Shallow and flat sea: For operating frequencies less than 200Hz, the RAM model is recommended; for operating frequencies greater than 200Hz, the BELLHOP model is recommended. For deep-sea flat / slightly sloping areas: When the operating frequency is less than 100Hz, the RAM model is recommended; when the operating frequency is greater than 100Hz, the BELLHOP model is recommended. steep slope: (1) When the operating frequency is less than 100Hz: For sloping terrain with a gradient greater than 6.5°, the BELLHOP model is recommended; otherwise, the RAM model is recommended. For seamount terrain with an elevation difference greater than 1500m; when there is a significant peak and the peak width is less than 10km, the BELLHOP model is recommended; otherwise, the RAM model is recommended. For mid-ocean ridge topography, the elevation difference is greater than 2800m; when there is a significant peak and the peak width is greater than 10km, the BELLHOP model is recommended; otherwise, the RAM model is recommended. For trench topography with a width greater than 45 km, the KRAKEN model is recommended when there are significant valleys; otherwise, the RAM model is recommended.

[0011] (2) When the operating frequency is greater than 100Hz: The BELLHOP model is recommended for all types of complex terrain.

[0012] Furthermore, the deep sea is flat: the sea depth is greater than 2000m, and the average slope of the terrain is [missing information]. Less than 1°; Shallow and flat sea: maximum sea depth less than 200m, depth standard deviation less than 10m and depth variation range less than 2m; Gentle slope: The average slope of the terrain is less than 6.5°; Large slope: The average slope of the terrain is greater than 6.5°, and includes complex terrain such as seamounts, mid-ocean ridges, and trenches; the specific type of complex terrain is calculated by the height difference.

[0013] Furthermore, for each grid point, acoustic model recommendations are calculated and performed at each directional angle in the 360° direction at its center. Each element in the four-digit array represents the acoustic model recommendation result at the directional angle and operating frequency corresponding to the center of a certain grid point, and the height difference, slope statistics, and terrain profile are used as the terrain attributes of the recommendation result.

[0014] A terminal device includes a processor, a memory, and a computer program stored in the memory; when the processor executes the computer program, it implements the acoustic model recommendation method based on marine topographic features.

[0015] A computer-readable storage medium storing a computer program; when executed by a processor, the computer program implements the acoustic model recommendation method based on ocean topographic features.

[0016] Compared with the prior art, the present invention has the following technical features: 1. This invention utilizes high-resolution mesh generation and multi-directional terrain feature analysis to accurately model the impact of ocean topography on sound wave propagation. Compared to traditional methods that rely on a single terrain parameter or low-resolution analysis, this invention extracts a high-resolution mesh generated within a 100km×100km area and at 100m intervals, and analyzes terrain features along a 20km path in a 360° direction. This allows for a comprehensive capture of the sound wave propagation characteristics of complex terrain, thereby significantly improving the accuracy and reliability of acoustic model recommendations.

[0017] 2. This invention employs hierarchical decision logic combined with sound source frequency range to achieve automated recommendation of acoustic models, enabling efficient handling of diverse marine topographic conditions. Compared to traditional methods relying on human experience or complex numerical simulations, this invention pre-calculates and rapidly adapts acoustic models to terrain and frequency ranges through hierarchical decision logic based on elevation differences, slope statistical characteristics, and sound source frequency ranges. This method boasts higher computational efficiency and environmental adaptability, making it suitable for various scenarios including deep sea, shallow sea, and rugged seabed.

[0018] 3. This invention significantly enhances the practicality of acoustic model recommendation results through interactive visualization. Traditional methods typically only provide static recommendation results and lack intuitive analysis tools. Furthermore, they do not incorporate frequency analysis. This invention stores the recommendation results in a four-dimensional array (grid point center x-coordinate × grid point center y-coordinate × direction angle × frequency range), and by combining frequency and terrain data, it displays the recommended model for each grid point along 360 directions in a radiation map. It supports interactive user queries for recommended models, terrain profiles, and slope statistics for specific grid points or directions, including depth distribution maps and slope variation maps, further enhancing the intuitiveness and practicality of the analysis. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the framework of the method of the present invention; Figure 2 This is a topographic map of a sea area in one embodiment of the present invention; wherein (a) is a selected sea area; and (b) is a 100km×100km topographic analysis area. Figure 3The following are the model error analysis results under different terrains in one embodiment of the present invention; where (a) is the case with a terrain slope of 0.76 degrees and (b) is the case with a terrain slope of 5.71 degrees. Figure 4 This is a terrain analysis map of the area analyzed in one embodiment of the present invention, with the grid point (4,5) at a 210-degree angle. Figure 5 (a) and (b) are recommended 360-degree terrain model diagrams of grid points (4,5) in the analysis area at frequencies of 25Hz and 300Hz in one embodiment of the present invention. Figure 6 This is a recommended topographic model of a marine area analyzed at a frequency of 25Hz in one embodiment of the present invention. Detailed Implementation

[0020] To address the problems in existing technologies, this invention provides an acoustic model recommendation method based on marine topographic features. Within the studied sea area, the method divides the selected area into a high-resolution grid, calculates different topographic features in multiple directions from the center of each grid point, and comprehensively analyzes the statistical characteristics of topographic height differences and slope, as well as the frequency range of the model, to recommend optimal acoustic models (such as Kraken, Bellhop, and RAM) for different directions. This invention, by introducing high-resolution grid generation technology and combining it with seabed depth, slope, and frequency range-based model recommendation, provides a reliable and optimized solution for acoustic model selection in complex marine environments.

[0021] This invention provides an acoustic model recommendation method based on marine topographic features, comprising: Step 1: Reading marine topographic data and dividing it into high-resolution grids.

[0022] Download the ocean topography data in NetCDF format for the area under study from the GEBCO website; extract a central region of a preset size from the selected area; generate grid points within this central region at a preset size, thereby forming a high-resolution grid in the central region to ensure that subsequent analysis can capture subtle changes in the topography.

[0023] The preset size can be, for example, 100km × 100km; the preset size can be, for example, a grid of 100m × 100m points; these two parameters can be set according to actual needs.

[0024] Step 2: Extraction and analysis of multi-directional terrain features.

[0025] Based on the aforementioned marine topographic data, for each grid point in the central region, topographic features along a predetermined length path in a 360° direction are extracted at predetermined angular intervals from the center of that grid point to obtain elevation differences and slope statistics, providing data support for model recommendations. The predetermined angular interval is, for example, 1°, corresponding to 360 directional angles (including the initial directional angle); the predetermined length is, for example, 20km. The specific process of this step is as follows: Step 2.1, path sampling.

[0026] For the center of each grid point, starting from the preset initial orientation angle, depth data is extracted along a 20km path at the current orientation angle to generate a sequence of depth data sampling points; the path sampling interval during depth data extraction is 100m; in the 360° direction, the corresponding sampling point sequence is extracted every 1° from the initial angle.

[0027] When sampling along a path, the formula for calculating the coordinates of sampling points on the path is: ; in, This indicates starting from the initial direction angle and moving along the d-th direction angle. Above, the coordinates at a distance of t kilometers from the center of the grid point. t represents the coordinates of the center of the grid point; d represents the path sampling interval; d is the direction angle index, ranging from 1 degree to 360 degrees; for example, if the initial direction angle is 0°, and the interval is 1°, then there are a total of 360 direction angles.

[0028] Step 2.2, Calculation of terrain features.

[0029] Based on the sampling sequence of depth data obtained along a path of preset length for each orientation angle of the grid points, the following terrain features are calculated; where the preset length can be set to, for example, 20km.

[0030] (1) Height difference: Calculate the difference between the maximum and minimum depths on the preset length path.

[0031] (2) Slope statistics, including average slope, maximum slope, steep slope ratio and slope change ratio.

[0032] First, calculate the slope between every two adjacent sampling points on the preset length path, using the following formula: ; in Indicates the first k The and the first k Slope between +1 sampling points Indicates the first [number]th ... k The height value of each sampling point, where inteval represents the sampling interval. This is a function to convert radians to degrees; thus, the following indices are derived: Average slope: ;in N This represents the number of slopes.

[0033] Maximum slope: ; where max represents the operation of finding the maximum value.

[0034] Steep slope ratio: ;in This indicates a slope greater than 15°. This indicates a length calculation operation.

[0035] Slope change ratio: ;in This indicates the calculation of the difference between adjacent slopes.

[0036] In addition to the features mentioned above, the following terrain features can be further calculated: Average depth: .

[0037] Depth standard deviation: .

[0038] Maximum depth: .

[0039] Minimum depth: .

[0040] Peak width: ; in, ; Y This is the distance sampling array.

[0041] Valley width:

[0042] in ;in Y This is the distance sampling array.

[0043] Step 3: Hierarchical decision-making logic and model recommendation.

[0044] Based on the extracted terrain features and the operating frequency range of the acoustic model, the optimal acoustic model for each grid point and orientation angle at each operating frequency is calculated and stored; the hierarchical decision-making logic is as follows: First, the terrain is roughly divided into four categories: flat deep sea, flat shallow sea, steep slope, and gentle slope. Based on the steep slope, the terrain is further subdivided into three complex types according to the height difference: seamount, mid-ocean ridge, and trench.

[0045] Regarding the combined impact of these four terrain types on work frequency and terrain, the recommended logic is as follows: Flat deep sea: The sea is deeper than 2000m and the average slope of the terrain is... Less than 1°.

[0046] Shallow and flat sea: maximum sea depth less than 200m, depth standard deviation less than 10m, and depth variation range less than 2m; thus ensuring that flat shallow water areas are not misidentified as seamounts.

[0047] Slope: The average slope of the terrain Less than 6.5°.

[0048] Steep slope: the average slope of the terrain The elevation is greater than 6.5° and includes complex terrain such as seamounts, mid-ocean ridges, and trenches. The specific type of complex terrain is calculated by the elevation difference. For example, if the elevation difference on a path of a preset length is negative, it is considered a trench. Seamounts and mid-ocean ridges correspond to different ranges of positive elevation differences.

[0049] (1) Shallow sea is flat.

[0050] For operating frequencies below 200Hz, the RAM model is recommended; for operating frequencies above 200Hz, the BELLHOP model is recommended.

[0051] (2) The deep sea is flat / slightly sloping.

[0052] When the operating frequency is less than 100Hz, the RAM model is recommended; when the operating frequency is greater than 100Hz, the BELLHOP model is recommended.

[0053] (3) steep slope.

[0054] When the operating frequency is less than 100Hz: For slope For slopes greater than 6.5°, the BELLHOP model is recommended; slope For temperatures less than 6.5 degrees, the RAM model is recommended. For seamount topography, the height difference is greater than 1500m; when there is a significant peak, i.e. If the peak width is less than 10km, the BELLHOP model is recommended; otherwise, the RAM model is recommended. For mid-ocean ridge topography, the elevation difference is greater than 2800m; when a significant peak exists... If the peak width is greater than 10km, the BELLHOP model is recommended; otherwise, the RAM model is recommended. For trench topography with a width greater than 45 km, when there is a significant valley value, i.e. If necessary, the KRAKEN model is recommended; otherwise, the RAM model is recommended.

[0055] When the operating frequency is greater than 100Hz, the BELLHOP model is recommended for all complex terrains.

[0056] For each grid point, the terrain features along a preset length path are calculated at each directional angle in the 360° direction at its center, and an acoustic model is recommended according to the hierarchical decision logic. The recommendation results are stored in a four-dimensional array (grid point center x-coordinate × grid point center y-coordinate × directional angle × frequency range). That is, each element in the four-dimensional array represents the acoustic model recommendation result at the directional angle and working frequency corresponding to the center of a certain grid point, and the height difference, slope statistics (average slope, maximum slope, steep slope ratio and slope change ratio), and terrain profile are used as the terrain attributes of the recommendation result.

[0057] The four-bit array is integrated into the human-computer interaction platform. After users query the center coordinates, direction angle and working frequency of a specified grid point, the platform automatically displays the recommended acoustic models for all direction angles within a 360° range associated with that grid point; or, after further specifying the direction angle, it displays the recommended acoustic models for a single direction angle; at the same time, the terrain attributes of the recommended results in each direction are visualized, improving analysis efficiency and intuitiveness.

[0058] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0059] Figure 1 The framework flowchart of the recommendation method of this invention is given; the implementation process is divided into three steps: (1) Extract a 100km×100km central area from the seabed topography file downloaded from the GEBCO website, and generate grid points (1001×1001 points in total) at 100m intervals within the area to form a high-resolution grid. (2) Extract terrain features from different grid center points in different directions at an angle of 1°, including slope, terrain profile, etc. (3) Recommend the model in different directions through the recommendation logic obtained from a large number of simulations, and visualize the terrain features and model recommendation results in different directions.

[0060] Figure 2 The results of the marine topographic maps downloaded from GEBCO are shown, where (a) is the topographic map of a selected marine area, and (b) is the topographic map of a 100km×100km key analysis area; the high-precision topographic grid division can better analyze the topographic change trend in different directions.

[0061] Figure 3The model error analysis results are presented for different terrains: (a) for a terrain slope of 0.76 degrees and (b) for a terrain slope of 5.71 degrees. The model error analysis results can be used to better obtain model recommendation results for different terrains and frequencies.

[0062] Figure 4 The image shows the terrain features of the selected grid point (4,5) and the direction 210°. The first image shows the terrain profile in this direction, the second image shows the slope variation in this direction, and the third image shows the slope statistics of this area. Through statistical analysis of terrain features, high-precision data can be provided for regional model recommendations.

[0063] Figure 5 (a) and (b) respectively present the recommended laser images of the model in the 360° direction at grid point (4,5) in the analysis area at frequencies of 25Hz and 300Hz; the grid point analysis provides data for the overall analysis of the selected sea area.

[0064] Figure 6 A partial grid recommendation map of the selected sea area at a frequency of 25Hz is provided; the actual center-to-center distance of each grid point is only 100m, which is considered high-precision grid division. Only a portion of the recommended grid map is shown in the figure.

[0065] This invention has achieved significant results in complex marine topographic conditions. By analyzing high-resolution marine topographic data and combining it with precise decision-making logic based on topographic features and sound source frequencies, it effectively improves the accuracy and efficiency of sound field simulation at different frequencies. Based on a high-resolution grid, this method extracts depth data along a 20km path in a 360° direction. Combined with topographic features and layered pre-calculation of frequency ranges, it achieves high-precision recommendation of acoustic models under diverse topographic and frequency conditions. Compared to traditional methods, this invention has significant advantages in model selection accuracy, computational efficiency, and environmental adaptability. It is applicable to various complex topographic environments, including deep sea, shallow sea, and rugged seabed, providing a reliable, simple, and efficient solution for marine acoustic modeling.

[0066] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for recommending an acoustic model based on ocean topographic features, the method comprising: The method comprises the following steps: Obtain the marine topographic data of the studied sea area, and extract a center area of a preset size from the studied sea area, and generate grid points in the center area with a preset size; Based on the marine topographic data, for each grid point in the center area, extract the topographic features on the path of a preset length from the center of the grid point at every preset angular interval in the 360° direction, thereby obtaining the height difference and slope statistics; Based on the extracted topographic features, in combination with the working frequency range of the acoustic model, calculate the recommended acoustic model for each grid point and direction angle at each working frequency according to a preset hierarchical decision logic, and store the recommended results in the form of a four-dimensional array; the dimensions of the four-dimensional array are represented as the horizontal coordinate of the grid point center × the vertical coordinate of the grid point center × the direction angle × the frequency range; Integrate the four-dimensional array into a human-computer interaction platform, and after the user specifies the grid point center coordinate, direction angle and working frequency through interaction query, display the acoustic model recommendation results for all direction angles within the 360° range associated with the grid point; or further specify the direction angle, and display the acoustic model recommendation results for a single direction angle; at the same time, visually display the topographic properties of the recommended results in each direction. 2.The method of claim 1, wherein, The preset size is 100 km × 100 km; the preset size is 100 m × 100 m; the preset angular interval is 1°; and the preset length is 20 km. 3.The method of claim 1, wherein, For the center of each grid point, start from a preset initial direction angle and extract the depth data on the path of a preset length in the current direction angle by sampling to generate a sequence of sampling points of the depth data; when sampling the path, the coordinate calculation formula of the sampling points on the path is: ； wherein, represents the coordinate of the grid point center t kilometers away from the initial direction angle along the dth direction angle above, the coordinate of the grid point center t kilometers away from the initial direction angle along the dth direction angle is the coordinate of the grid point center; t represents the path sampling interval; d is the direction angle index, ranging from 1 degree to 360 degrees. 4.The method of claim 1, wherein, Based on the sampling sequence of the depth data obtained on the path of a preset length in each direction angle of the grid point, calculate the following topographic features: Height difference: calculate the difference between the maximum depth and the minimum depth on the path of a preset length; Slope statistics, including average slope, maximum slope, steep slope proportion and slope change proportion; Calculate the slope between each adjacent two sampling points on the path of a preset length, and the formula is: ； wherein represents the slope between the kth and the k+1th sampling points, represents the height value of the kth sampling point on the preset length path, and inteval represents a sampling interval, is a function for converting radians to degrees; Average slope: ; where N represents the number of slopes. 5.The method of claim 1, wherein, The hierarchical decision logic is: Divide the terrain into four types of terrain, i.e. deep sea flat, shallow sea flat, large slope and small slope; further subdivide the terrain into three types of complex terrain, i.e. seamount, ridge and trench, based on the large slope; and the recommendation logic of the terrain and the corresponding acoustic model is as follows: Shallow sea flat: for the case where the working frequency is less than 200 Hz, recommend using the RAM model; when the working frequency is greater than 200 Hz, recommend using the BELLHOP model; Deep sea flat / small slope: when the working frequency is less than 100 Hz, recommend using the RAM model; when the working frequency is greater than 100 Hz, recommend using the BELLHOP model; Large slope: (1) When the working frequency is less than 100 Hz: For the slope terrain with a slope greater than 6.5°, recommend using the BELLHOP model; otherwise, recommend using the RAM model; For the seamount terrain, when the height difference is greater than 1500 m, and there is a significant peak and the peak width is less than 10 km, recommend using the BELLHOP model, otherwise, recommend using the RAM model; For seamounts, the height difference is greater than 2800m; when there is a significant peak and the peak width is greater than 10km, the BELLHOP model is recommended, otherwise, the RAM model is recommended; For trench topography with a width greater than 45km, when there is a significant valley, the KRAKEN model is recommended, otherwise, the RAM model is recommended. (2) When the operating frequency is greater than 100Hz: All types of complex topography recommend the BELLHOP model.

6. The method of claim 5, wherein, ABYSSAL FLAT: An area of the sea floor with an average slope of less than 1° and a depth greater than 2000 m. less than 1°; Shallow sea flat: the maximum sea depth is less than 200m, the depth standard deviation is less than 10m, and the depth variation range is less than 2m; Small slope: the average slope of the terrain is less than 6.5°; Large slope: the average slope of the terrain is greater than 6.5°, and contains seamounts, seamounts, trenches and other complex topography; the specific type of complex topography is calculated by height difference. 7.The method of claim 1, wherein, For each grid point, the acoustic model recommendation is calculated and performed in each direction angle of 360° direction at the center of each grid point; each element in the four-bit array represents the acoustic model recommendation result of a certain grid point center corresponding to the direction angle and the operating frequency, and the height difference, slope statistics, and topographic profile are the topographic attributes of the recommendation result.

8. A terminal device comprising a processor, a memory, and a computer program stored in the memory; characterized in that, The processor executes the computer program to implement the acoustic model recommendation method based on the marine topographic features according to any one of claims 1-7.

9. A computer readable storage medium having stored therein a computer program; characterized in that, The computer program is executed by the processor to implement the acoustic model recommendation method based on the marine topographic features according to any one of claims 1-7.

Images