CN114707327B - Parallel marine environment underwater sound characteristic diagnosis method, storage medium and device - Google Patents
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Abstract
The invention relates to a parallel marine environment underwater sound characteristic diagnosis method, a storage medium and a device, belonging to the field of fusion of physical marine and marine environment underwater sound characteristics. The gradient of the whole depth range is given by using a vertical gradient method, positive and negative gradient turning points are determined, all sound velocity minimum value points are searched and determined, and finally parameter detection results such as surface sound channel depth, cut-off frequency, half sound channel conditions, deep sea sound channel axial depth, shallow sea sound channel axial depth and the like are given. The invention also provides an operation module integrated by using the method and a system integrated with the operation module. The whole system realizes geographic space parallelization, provides the diagnosis of the underwater acoustic environment characteristic parameters of the global sea area, and has significance for improving the sonar technology and establishing a deep sea early warning system by forecasting the parameters.
Description
Technical Field
The invention belongs to the field of fusion of physical ocean and marine environment underwater acoustic characteristics, and relates to a method, a storage medium and a device for diagnosing marine underwater acoustic characteristic parameters.
Background
In recent decades, with the service of nuclear submarines, various countries put higher requirements on the research of underwater sound equipment, underwater sound theory, marine environment and the like. One of the important approaches for the cross-over development of the sonar technology is to deeply excavate the underwater acoustic characteristics of the marine environment. Therefore, the method has great significance for improving the anti-submergence and detection capabilities and establishing a deep sea early warning system by researching the distribution of the marine acoustic velocity field, knowing the time and space distribution change rule of the acoustic velocity profile, mastering the classification characteristics of the acoustic channels and exploring the geographical distribution condition of deep and shallow sea acoustic channel characteristic quantities.
The deep sea typical sound velocity profile can be divided into three layers: surface isothermal layer, thermocline and deep sea isothermal layer. The deep sea is characterized by unique ocean stratification and different sound propagation modes generated by the ocean stratification, and the sound propagation modes are closely related to the working principle of sonar. The sound channel formed when the sound velocity profile at the sea water surface is a positive gradient is called a surface sound channel. If the positive gradient of the speed of sound extends all the way to the seafloor, it is called a half-vocal tract. The depth at which the sound velocity minima lie is called the vocal tract axis. The vocal tract axes are divided into two categories: shallow and deep sea vocal tract axes. The shallow sea vocal tract axis occurs at the main jump, usually associated with frontal and vortex. The deep sea vocal tract axis is a stable feature of the deep sea, located below the main jump layer. The water acoustics shallow sea is generally dozens of meters to hundreds of meters in depth, and is characterized in that a fixed deep sea sound channel is not arranged.
The prior art does not have a method for diagnosing marine environment underwater sound characteristics.
Disclosure of Invention
The invention provides a parallel marine environment underwater sound characteristic diagnosis method, a module and a system, which are used for exploring the geographic distribution condition of marine sound channel characteristic quantities and mastering the classification characteristics of sound channels so as to improve sonar technology.
The system provides environmental parameters such as temperature, salinity and corresponding depth by using a wave-tide-circulation coupling numerical model of the natural resource part, calculates a sound velocity profile according to the environmental parameters, and diagnoses the sound velocity profile by using a vertical gradient method.
The invention is realized by the following technical scheme:
a parallel marine environment underwater sound characteristic diagnosis method comprises the following specific steps:
firstly, forecasting marine environment parameters including temperature, salinity and corresponding depth of a global sea area by using a marine model, solving a sound velocity profile of a corresponding grid point according to the marine environment parameters, wherein the sound velocity profile is solved by using a Mackenzie formula:
c＝1448.96+4.591T-5.304×10 -2 T 2 +2.374×10 -4 T 3 +1.340(S-35) +1.630×10 -2 D+1.675×10 -7 D 2 -1.025×10 -2 T(S-35) -7.139×10 -13 TD 3 ，
wherein c is a sound velocity profile, T is temperature (DEG C), S is salinity (per thousand), and D is depth (m);
secondly, solving the sound velocity gradient from the sea surface to the seabed by using a gradient function, and determining the depth of the positive and negative transition zero crossing point of the sound velocity gradient through positive and negative analysis of the sound velocity gradient;
thirdly, analyzing and determining the depth SLD of the surface sound channel, calculating the cut-off frequency SFD of the surface sound channel, judging the HAF condition of the half sound channel, starting from the sea surface, and if the initial sound velocity gradient is negative, the depth of the surface sound channel does not exist; if the initial sound velocity gradient is positive, searching and confirming the first depth from the sea surface to the zero crossing point from the positive sound velocity gradient to the negative sound velocity gradient, preliminarily judging that the depth is the surface sound channel depth SLD, and calculating the surface sound channel cut-off frequency:
if the positive sound velocity gradient extends from the sea surface to the seabed, confirming that the position meets half-track condition HAF;
fourthly, analyzing and determining the shallow sea vocal tract axis depth SSX and the deep sea vocal tract axis depth DSC; determining the depths of all minimum value points of the sound velocity profile by using a vertical gradient method, namely the depths of zero crossing points of the negative sound velocity gradient and the positive sound velocity gradient, and if only one sound velocity minimum value point exists, judging whether the sound velocity minimum value point is the shallow sea sound channel axis depth SSX or the deep sea sound channel axis depth DSC according to the minimum value point depth Z; if two or more sound velocity profile minima points exist, firstly combining the minima points with the depth interval within 5 meters, and then determining the shallow sea sound channel axial depth SSX and the deep sea sound channel axial depth DSC according to the boundary of the shallow sea sound channel axial depth and the deep sea sound channel axial depth.
Further, the boundary line is 350m.
The invention also provides an operation module integrated by using the method.
The invention also provides a system integrated with the operation module.
Compared with the prior art, the invention has the beneficial effects that:
the method combines a dynamic ocean model with an underwater acoustic environment characteristic diagnosis method, quickly realizes the underwater environment characteristic diagnosis and prediction, and the prediction parameters comprise: the surface sound channel depth and cut-off frequency, the half sound channel condition, the deep sea sound channel axial depth and the shallow sea sound channel axial depth have significance for improving the sonar technology and establishing a deep sea early warning system.
Drawings
FIG. 1 is a block diagram of a marine environmental underwater sound feature diagnosis and prognosis system;
FIG. 2 is a flow chart of a marine environmental underwater sound feature diagnosis and prognosis system;
FIG. 3 is a flow chart of a method for diagnosing characteristic parameters of an underwater acoustic environment;
FIG. 4 is a graph of sound velocity and sound velocity vertical gradient;
FIG. 5 is a representation of the characteristics of different sound velocity profiles, including surface channel, half channel, deep sea channel axis, shallow sea channel axis, etc.;
FIG. 6 is a diagnosis of the depth of surface vocal tract and cut-off frequency of the global sea area (months 2 and 8);
FIG. 7 is a global sea area binaural condition diagnosis (months 2 and 8);
FIG. 8 is a global sea area deep sea vocal tract axis depth diagnosis (months 2 and 8);
fig. 9 shows the results of the global sea area shallow sea vocal tract axis depth diagnosis (months 2 and 8).
Detailed Description
The technical solution of the present invention is further explained by the following embodiments with reference to the attached drawings, but the scope of the present invention is not limited in any way by the embodiments.
Example 1
A parallel marine environment underwater sound characteristic diagnosis method comprises the following specific steps:
firstly, forecasting marine environment parameters including temperature, salinity and corresponding depth of a global sea area by using a marine model, solving a sound velocity profile of a corresponding grid point according to the marine environment parameters, wherein the sound velocity profile is solved by using a Mackenzie formula:
c＝1448.96+4.591T-5.304×10 -2 T 2 +2.374×10 -4 T 3 +1.340(S-35) +1.630×10 -2 D+1.675×10 -7 D 2 -1.025×10 -2 T(S-35) -7.139×10 -13 TD 3 ，
wherein c is a sound velocity profile, T is temperature (DEG C), S is salinity (per thousand), and D is depth (m);
secondly, solving the sound velocity gradient from the sea surface to the seabed by using a gradient function, and determining the depth of the positive-negative transition zero crossing point of the sound velocity gradient through positive-negative analysis of the sound velocity gradient;
thirdly, analyzing and determining the depth SLD of the surface sound channel, calculating the cut-off frequency SFD of the surface sound channel, judging the HAF of the half sound channel, starting from the sea surface, and if the initial sound velocity gradient is negative, not having the depth of the surface sound channel; if the initial sound velocity gradient is positive, the search is confirmedConsidering the first depth from the positive sound velocity gradient to the negative sound velocity gradient zero crossing point from the white sea surface, preliminarily judging the depth to be the depth SLD of the surface sound channel, and calculating the cut-off frequency of the surface sound channel:
if the positive sound velocity gradient extends from the sea surface to the seabed, confirming that the position meets half-track condition HAF;
fourthly, analyzing and determining the depth SSX of the shallow sea vocal tract axis and the depth DSC of the deep sea vocal tract axis; determining the depths of all minimum value points of the sound velocity profile by using a vertical gradient method, namely the depths of zero crossing points of the negative sound velocity gradient and the positive sound velocity gradient, and if only one sound velocity minimum value point exists, judging whether the sound velocity minimum value point is the shallow sea sound channel axis depth SSX or the deep sea sound channel axis depth DSC according to the minimum value point depth Z; if two or more sound velocity profile minima points exist, firstly combining the minima points with the depth interval within 5 meters, and then determining the shallow sea sound channel axial depth SSX and the deep sea sound channel axial depth DSC according to the boundary of the shallow sea sound channel axial depth and the deep sea sound channel axial depth.
The dividing line used in this embodiment is 350m.
The method is realized by adopting the modularization of FORTRAN-90 programming language, and the whole design framework is shown as figure 1. The whole system is realized aiming at the geographic space parallelization, and the system can finally provide the forecast of 5 parameters, namely the surface sound channel depth and the cut-off frequency, the half-sound channel condition, the deep-sea sound channel axial depth and the shallow-sea sound channel axial depth.
The ocean model is developed by the first ocean research institute of the sea wave-tide-circulation coupling numerical model natural resource department, and is applied to the ocean power system numerical mode system and the sea wave-circulation coupling theory, namely the leading edge science 2007.3.
The specific calculation flow in the system execution process is shown in fig. 2:
firstly, system input initialization information is obtained from an ocean model, and each sub-process reads water depth and ocean environment parameters according to the geographic space range distributed by the main process.
Secondly, each subprocess calculates the sound velocity profile of each grid point of the distribution area according to the temperature, salinity and depth parameters.
Then, each subprocess performs an underwater acoustic feature diagnosis for the sound velocity profile in the self-allocated region, and the flowchart of the underwater acoustic feature parameter diagnosis is shown in fig. 3: (1) and extracting the sound velocity profile of the grid points, and calculating the vertical gradient of the sound velocity from the sea surface to the sea bottom. (2) Judging the surface sound velocity gradient, if the surface sound velocity gradient is a negative sound velocity gradient, the grid point does not have a surface sound channel and does not accord with a half sound channel condition; if the sound velocity is positive sound velocity gradient, further diagnosing and searching the position of a zero-crossing point of the sound velocity from the positive sound velocity gradient to the negative sound velocity gradient, namely a sound velocity maximum value point, confirming that the corresponding depth of the sound velocity is the surface sound channel depth SLD, and determining the depth according to a formula
Further calculating a surface channel cut-off frequency SFD; if the positive acoustic velocity gradient extends from the sea surface all the way to the seafloor, the grid point satisfies the half track condition HAF. (3) And judging all minimum value points of the sound velocity from the sea surface to the seabed by utilizing the sound velocity vertical gradient, and combining the minimum value points if the depth distance of two or more minimum value points is not more than 5m. And determining the deep sea sound channel axis depth DSC and the shallow sea sound channel axis depth SSX according to the finally determined minimum value point depth. In the present system, a depth of 350m is set as a boundary between the deep-sea vocal tract axis depth DSC and the shallow-sea vocal tract axis depth SSX. Judging whether the depth of the minimum value point z is the shallow sea vocal tract axial depth SSX (z is less than or equal to 350) or the deep sea vocal tract axial depth DSC (z is less than or equal to 350)>350)。
And finally, diagnosing and manufacturing the geospatial distribution of the marine environment underwater sound characteristic parameters in the global sea area and displaying a forecast result.
The present invention will be described in detail with reference to the following embodiments.
Taking the point a (157.2 ° E,32.8 ° N) as an example, firstly, a prediction result of marine environmental parameters at a certain time at the point a is given by using a marine model, and mainly includes a prediction result of temperature, salinity, corresponding depth and the like, and an acoustic velocity profile is calculated according to the temperature, the salinity and the depth, as shown in fig. 4 (a), and further, an acoustic velocity vertical gradient from the sea surface to the sea bottom is calculated, as shown in fig. 4 (b). Then, the sound velocity vertical gradient at the sea surface is judged, in this case, the sound velocity gradient at the depth of 0m is positive, so that the surface sound channel is preliminarily judged to exist; continuing down the depth, finding the depth of the zero-crossing point of the positive gradient to the negative gradient, which is 150m in this example, determining the depth as the depth of the surface channel, and calculating the corresponding cut-off frequency of the surface channel by using the cut-off frequency formula of the surface channel, wherein the result is 102Hz. If the speed of sound gradient is always a positive gradient from the sea surface to the sea bottom, it can be confirmed that the position satisfies the half-channel condition, and obviously the point a in this example does not satisfy the half-channel condition. And then, continuing to downwards along the depth, and searching the depth of the positive and negative gradient transition zero-crossing point, wherein the depth of the positive and negative gradient transition zero-crossing point is the depth of the sound velocity minimum point. In this example, a sound velocity minimum point is found at a depth of 900m, and therefore, it can be confirmed that the depth of 900m at point a is the depth of the deep-sea vocal tract axis, and the shallow-sea vocal tract axis does not exist at point a. By comparing with the sound speed profile characteristic shown in fig. 4 (a), it can be seen that the diagnostic result of the system coincides with the actual underwater acoustic environment characteristic. And then, performing underwater sound characteristic diagnosis on sound velocity profiles of 4 grid points by using the system again, and displaying the diagnosis result: FIG. 5 (a) corresponds to a surface channel depth of 165.1m, a surface channel cut-off frequency of 88.3854Hz, and a deep sea channel axial depth of 1005m; FIG. 5 (b) satisfies the half channel condition; FIG. 5 (c) there are three vocal tract axes, including two shallow sea vocal tract axes, 85.05m and 214.9m deep, respectively, and one deep sea vocal tract axis, 1362m deep; FIG. 5 (d) shows a shallow sea vocal tract axis of depth 174.9m, a deep sea vocal tract of insignificant depth, and a vocal tract axis depth of about 1005m. By comparison with the actual sound speed profile, it can be seen that the diagnostic results of the system coincide with the actual characteristics of the sound speed profile. Therefore, the system can accurately give the diagnosis and forecast results of a plurality of underwater sound characteristic parameters. Finally, diagnosis results for the characteristic parameters of the underwater acoustic environment in the global sea area are given by using a forecasting system, wherein fig. 6 shows the diagnosis forecasting results (2 months and 8 months) of the surface channel depth SLD and the surface channel cut-off frequency SFD of the global sea area; fig. 7 is a diagnostic forecast result (months 2 and 8) of the semivocal condition HAF in the global sea area, and the black area satisfies the semivocal condition; fig. 8 is a diagnostic forecast of deep-sea vocal tract axis depth DSCs in the global sea area (months 2 and 8); fig. 9 is a diagnostic prediction result (months 2 and 8) of shallow sea vocal tract axis depth SSX in the global sea area. The forecasting results have great significance for improving the sonar technology and establishing a deep sea early warning system.
Claims (4)
1. A parallel marine environment underwater sound characteristic diagnosis method is characterized by comprising the following specific steps:
firstly, forecasting marine environment parameters including temperature, salinity and corresponding depth of a global sea area by using a marine model, solving a sound velocity profile of a corresponding grid point according to the marine environment parameters, wherein the sound velocity profile is solved by using a Mackenzie formula:
wherein c is a sound velocity profile, T is temperature (DEG C), S is salinity (thousandths), and D is depth (m);
secondly, solving the sound velocity gradient from the sea surface to the seabed by using a gradient function, and determining the depth of the positive-negative transition zero crossing point of the sound velocity gradient through positive-negative analysis of the sound velocity gradient;
thirdly, analyzing and determining the depth SLD of the surface sound channel, calculating the cut-off frequency SFD of the surface sound channel, judging the HAF of the half sound channel, starting from the sea surface, and if the initial sound velocity gradient is negative, not having the depth of the surface sound channel; if the initial sound velocity gradient is positive, searching and confirming the first depth from the sea surface to the zero crossing point from the positive sound velocity gradient to the negative sound velocity gradient, preliminarily judging that the depth is the surface sound channel depth SLD, and calculating the surface sound channel cut-off frequency:
；
if the positive sound velocity gradient extends from the sea surface to the seabed, confirming that the grid points meet a half-channel condition HAF;
fourthly, analyzing and determining the depth SSX of the shallow sea vocal tract axis and the depth DSC of the deep sea vocal tract axis; determining the depths of all minimum value points of the sound velocity profile by using a vertical gradient method, namely the depths of zero crossing points of the negative sound velocity gradient and the positive sound velocity gradient, and if only one sound velocity minimum value point exists, judging whether the sound velocity minimum value point is the shallow sea sound channel axis depth SSX or the deep sea sound channel axis depth DSC according to the minimum value point depth Z; if two or more sound velocity profile minima points exist, firstly combining the minima points with the depth interval within 5 meters, and then determining the shallow sea sound channel axial depth SSX and the deep sea sound channel axial depth DSC according to the boundary of the shallow sea sound channel axial depth and the deep sea sound channel axial depth.
2. The parallel marine environmental hydroacoustic characterization method of claim 1, wherein the dividing line is 350m.
3. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program adapted to be loaded by a processor and to execute a parallel marine environment hydroacoustic feature diagnosis method of claim 1.
4. A parallel marine environmental underwater sound feature diagnosis apparatus that operates a parallel marine environmental underwater sound feature diagnosis method according to claim 1.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2015129806A (en) * | 2015-07-20 | 2017-01-23 | Федеральное государственное казенное военное образовательное учреждение высшего образования "Военный учебно-научный центр Военно-Морского Флота "Военно-морская академия имени Адмирала флота Советского Союза Н.Г. Кузнецова" | The method of completing the measured part of the profile of the vertical distribution of the speed of sound in water to the surface and to the bottom |
| CN109725053A (en) * | 2019-01-22 | 2019-05-07 | 中国人民解放军国防科技大学 | Method for acquiring water sound field characteristic data based on high-resolution ocean re-analysis product |
| CN112526590A (en) * | 2020-11-16 | 2021-03-19 | 哈尔滨工程大学 | Target detection method in deep sea complete sound channel |
| CN112526589A (en) * | 2020-11-16 | 2021-03-19 | 哈尔滨工程大学 | Target detection method in incomplete sound channel in deep sea |
| CN113218493A (en) * | 2021-04-08 | 2021-08-06 | 中国人民解放军国防科技大学 | Sound velocity profile inversion method based on empirical orthogonal function method |
| CN113259034A (en) * | 2021-07-08 | 2021-08-13 | 自然资源部第一海洋研究所 | Parallel coupled marine acoustic forecasting system and operation method |
-
2022
- 2022-04-01 CN CN202210339567.6A patent/CN114707327B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2015129806A (en) * | 2015-07-20 | 2017-01-23 | Федеральное государственное казенное военное образовательное учреждение высшего образования "Военный учебно-научный центр Военно-Морского Флота "Военно-морская академия имени Адмирала флота Советского Союза Н.Г. Кузнецова" | The method of completing the measured part of the profile of the vertical distribution of the speed of sound in water to the surface and to the bottom |
| CN109725053A (en) * | 2019-01-22 | 2019-05-07 | 中国人民解放军国防科技大学 | Method for acquiring water sound field characteristic data based on high-resolution ocean re-analysis product |
| CN112526590A (en) * | 2020-11-16 | 2021-03-19 | 哈尔滨工程大学 | Target detection method in deep sea complete sound channel |
| CN112526589A (en) * | 2020-11-16 | 2021-03-19 | 哈尔滨工程大学 | Target detection method in incomplete sound channel in deep sea |
| CN113218493A (en) * | 2021-04-08 | 2021-08-06 | 中国人民解放军国防科技大学 | Sound velocity profile inversion method based on empirical orthogonal function method |
| CN113259034A (en) * | 2021-07-08 | 2021-08-13 | 自然资源部第一海洋研究所 | Parallel coupled marine acoustic forecasting system and operation method |
Non-Patent Citations (6)
| Title |
|---|
| 台风"天秤"对两种深海声道下声传播的影响;马志康等;《海洋学研究》;20190915(第03期);全文 * |
| 声源深度对印度洋声传播的影响分析;曹震卿等;《科学技术与工程》;20180828(第24期);全文 * |
| 声速剖面主导的浅海声传播最佳深度规律研究;窦雨芮等;《中国舰船研究》;20200531(第05期);全文 * |
| 水声环境快速评估建模与仿真;李玉阳等;《系统仿真学报》;20120608(第06期);全文 * |
| 深海水体速度建模方法研究;魏脯力;《中国博士学位论文全文数据库 (基础科学辑)》;20220131;A011-140 * |
| 深海环境水声传播及声源定位方法研究;段睿;《中国博士学位论文全文数据库 (工程科技Ⅱ辑)》;20170831;C028-13 * |
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