A deep-sea hydrological environment data fitting method and device and a computer medium

Abstract

This invention proposes a method, device, and computer medium for fitting deep-sea hydrological environment data. The method constructs a unique third-order interpolation function to calculate salinity and temperature data at unknown depths, and finally obtains the global underwater acoustic velocity distribution data of the test area through sound velocity fitting. The calculation results are more accurate, thereby further improving the accuracy of deep-sea acoustic velocity distribution data obtained by fitting sound velocity based on temperature and salinity data.

Description

Technical Field

[0001] This invention relates to the field of hydrological measurement technology, specifically to a method, apparatus, and computer medium for fitting deep-sea hydrological environment data. Background Technology

[0002] In current hydrological measurement technology, methods such as MVP, XBT, and XCTD are commonly used to acquire regional hydrological data and underwater acoustic field characteristics. However, these methods can only obtain hydrological environmental data at limited depths and cannot acquire hydrological environmental data for the entire ocean area. Therefore, it is necessary to fit the hydrological environmental data that cannot be directly measured. Currently, first-order interpolation methods are typically used to obtain temperature and salinity data, and then empirical formulas for sound velocity fitting are used to fit the global underwater acoustic velocity distribution.

[0003] However, as the depth increases, the salinity and temperature data in the deep sea are not completely identical. This leads to an increasing error between the salinity and temperature obtained by first-order interpolation and the measured values ​​of the corresponding parameters, which in turn results in a large error in the fitted underwater acoustic velocity distribution. Summary of the Invention

[0004] Purpose of the invention: This invention aims to address the technical shortcomings of existing hydrological measurement methods that lack accuracy in fitting underwater acoustic velocity data. It proposes a deep-sea hydrological environment data fitting method, device, and computer medium to improve the accuracy of underwater acoustic velocity fitting results.

[0005] Technical solution: To achieve the above objectives, the first aspect of this invention proposes a method for fitting deep-sea hydrological environment data, comprising the following steps:

[0006] S1. Collect hydrological and environmental data of the target area;

[0007] S2. Based on the collected hydrological environmental data, construct salinity calculation functions and temperature calculation functions:

[0008]

[0009]

[0010] Among them, S(h x ) represents the unknown depth h x The salinity at a given depth h0 is given by S0, where a1, a2, a3, and a4 are the undetermined coefficients of the third-order interpolation function; T(h x ) represents the unknown depth h x The temperature at a known depth h0 is T0, and b1, b2, b3, and b4 are the undetermined coefficients of the third-order interpolation function.

[0011] S3. Select sampling data from N consecutive sampling points in the hydrological environment data, and calculate the linearity σ0 of the salinity data segment and the linearity σ1 of the temperature data segment in the sampling data.

[0012] S4. Calculate the (N+1)th data point of the salinity data segment and the temperature data segment using the method described in step S2; and calculate the linearity σ2 of the N data points between the 2nd data point and the (N+1)th data point in the salinity data segment, and the linearity σ3 of the N data points between the 2nd data point and the (N+1)th data point in the temperature data segment.

[0013] S5. Construct relative deviation functions for salinity data and temperature data:

[0014]

[0015]

[0016] Where ε1 represents the relative deviation of salinity data and ε2 represents the relative deviation of temperature data;

[0017] S6. By adjusting the number of data points N, make ε1 and ε2 the minimum, and calculate a1, a2, a3, a4, b1, b2, b3, b4 when ε1 and ε2 take the minimum value; substitute the values ​​of a1, a2, a3, a4, b1, b2, b3, b4 into the salinity data calculation function and temperature data calculation function described in step S2;

[0018] S7. Using the salinity data calculation function and temperature data calculation function determined in step S6, calculate the salinity data and temperature data at unknown depths. Based on the calculated salinity data and temperature data, as well as the hydrological environment data collected in step S1, perform sound velocity fitting to obtain the global underwater sound velocity distribution of the area to be measured.

[0019] A second aspect of the present invention provides a storage medium on which one or more programs are stored, the programs being executable by one or more processors to cause the processors to perform the deep-sea hydrological environment data fitting method.

[0020] A third aspect of the present invention provides a deep-sea hydrological environment data fitting device, the device comprising a processor and a memory, the memory storing a computer program executable by the processor, the processor executing the computer program to implement the deep-sea hydrological environment data fitting method.

[0021] Beneficial effects: This invention uses a third-order interpolation empirical formula to calculate temperature and salinity data at unknown depths, resulting in more accurate calculations. This further improves the accuracy of deep-sea acoustic velocity distribution data obtained by fitting acoustic velocity data based on temperature and salinity data. Attached Figure Description

[0022] Figure 1 This is a flowchart of the deep-sea hydrological environment data fitting method involved in the embodiments of the present invention;

[0023] Figure 2 This is a comparison chart of salinity data calculated using the first-order interpolation algorithm and measured data.

[0024] Figure 3 This is a comparison chart of temperature data calculated using the first-order interpolation algorithm and measured data.

[0025] Figure 4 This is a comparison chart of the hydroacoustic velocity data fitted from temperature and salinity data calculated using a first-order interpolation algorithm and the hydroacoustic velocity data calculated from measured data.

[0026] Figure 5 This is a comparison chart of salinity data calculated using the method described in this embodiment and measured data;

[0027] Figure 6 This is a comparison chart of temperature data calculated using the method described in this embodiment and measured data;

[0028] Figure 7 This is a comparison chart of the underwater acoustic velocity data fitted from the temperature and salinity data calculated using the method described in this embodiment and the underwater acoustic velocity data calculated using measured data. Detailed Implementation

[0029] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments. However, it should be understood that the present invention can be implemented in various forms. The exemplary and non-limiting embodiments shown in the drawings and described below are not intended to limit the invention to the specific embodiments illustrated.

[0030] It should be understood that, where technically feasible, the technical features listed below for different embodiments can be combined with each other to form other embodiments within the scope of this invention. Furthermore, the specific examples and embodiments described in this invention are non-limiting, and corresponding modifications can be made to the structures, steps, and order described above without departing from the protection scope of this invention.

[0031] Existing technologies typically employ first-order interpolation methods to calculate salinity and temperature data at unknown depths. Taking marine environmental data acquired by the ARGO buoy as an example, basic first-order interpolation calculations are performed on hydrological data deeper than 2000 meters to obtain temperature, salinity, and fitted underwater acoustic velocity. The global underwater acoustic velocity distribution is then fitted using the Mackenzie empirical formula, as shown in the following formula:

[0032] c(D,S,T)=1448.96+4.591T-5.304×10 -2 T -2 +

[0033] 2.374×10 -4 T 2 +1.340(S-35)+1.630×10 -2 D+

[0034] 1.675×10 -7 D 2 -1.025×10 -2 T(S-35)-7.139×10 -13 TD 3

[0035] Where T represents temperature in °C; S represents salinity in psu; D represents depth in m; and c represents the speed of sound in water.

[0036] The above first-order interpolation fitting results for underwater acoustic velocity are compared with the actual results as follows: Figures 2 to 4 As shown, where, Figure 2 The comparison results between the first-order interpolated fitting values ​​of salinity and the measured salinity values ​​are shown; Figure 3 The comparison results between the first-order interpolated temperature values ​​and the measured temperature values ​​are shown; Figure 4 The comparison between the first-order interpolated fitting value and the measured underwater acoustic velocity is shown. (Reference) Figures 2 to 4 It can be seen that in the comparison results, after reaching a certain depth ( Figures 2 to 4 The salinity and temperature data in the deep sea (around 2000 meters) are not entirely consistent, causing the error between the salinity and temperature obtained by first-order interpolation and the measured values ​​of the corresponding parameters to increase with depth. This, in turn, leads to significant errors in the fitted underwater acoustic velocity distribution. Therefore, the accuracy of fitting salinity, temperature, and underwater acoustic velocity distribution data using the first-order interpolation method is relatively low.

[0037] To overcome this problem, this disclosure proposes a deep-sea hydrological environment data fitting method to improve the accuracy of underwater acoustic velocity fitting results. The specific process of this method is as follows: Figure 1 As shown, it includes the following steps:

[0038] S1. Collect hydrological and environmental data of the target area;

[0039] S2. Based on the collected hydrological environmental data, construct salinity calculation functions and temperature calculation functions:

[0040]

[0041]

[0042] Among them, S(h x ) represents the unknown depth h x The salinity at a given depth h0 is given by S0, where a1, a2, a3, and a4 are the undetermined coefficients of the third-order interpolation function; T(h x ) represents the unknown depth h x The temperature at a known depth h0 is T0, and b1, b2, b3, and b4 are the undetermined coefficients of the third-order interpolation function.

[0043] S3. Select sampling data from N consecutive sampling points in the hydrological environment data, and calculate the linearity σ0 of the salinity data segment and the linearity σ1 of the temperature data segment in the sampling data.

[0044] S4. Calculate the (N+1)th data point of the salinity data segment and the temperature data segment using the method described in step S2; and calculate the linearity σ2 of the N data points between the 2nd data point and the (N+1)th data point in the salinity data segment, and the linearity σ3 of the N data points between the 2nd data point and the (N+1)th data point in the temperature data segment.

[0045] S5. Construct relative deviation functions for salinity data and temperature data:

[0046]

[0047]

[0048] Where ε1 represents the relative deviation of salinity data and ε2 represents the relative deviation of temperature data;

[0049] S6. By adjusting the number of data points N, make ε1 and ε2 the minimum, and calculate a1, a2, a3, a4, b1, b2, b3, b4 when ε1 and ε2 take the minimum value; substitute the values ​​of a1, a2, a3, a4, b1, b2, b3, b4 into the salinity data calculation function and temperature data calculation function described in step S2;

[0050] S7. Using the salinity data calculation function and temperature data calculation function determined in step S6, calculate the salinity data and temperature data at unknown depths. Based on the calculated salinity data and temperature data, as well as the hydrological environment data collected in step S1, perform sound velocity fitting to obtain the global underwater sound velocity distribution of the area to be measured.

[0051] To verify the technical effectiveness of the method proposed in this disclosure, we applied this method to process the same set of marine environmental data acquired by the ARGO buoy, and obtained the following results: Figures 5 to 7 The processing results. Among them, Figure 5 This is a comparison chart of salinity data calculated using the method described in this embodiment and actual measured data. Figure 6 This is a comparison chart of the temperature data calculated using the method described in this embodiment and the measured data. Figure 7 This is a comparison chart of the underwater acoustic velocity data fitted from the temperature and salinity data calculated using the method described in this embodiment and the underwater acoustic velocity data calculated using measured data.

[0052] pass Figures 2 to 4 and Figures 5 to 7 As can be seen from the comparison, the deep-sea hydrological environment data fitting method provided in this embodiment can effectively improve the accuracy of salinity data, temperature data and the final fitted hydroacoustic velocity distribution data, and achieve good technical results.

[0053] Furthermore, this disclosure also discloses a deep-sea hydrological environment data fitting device, the device including a processor and a memory;

[0054] The memory is used to store computer programs;

[0055] The processor is used to execute computer programs to implement the steps of the deep-sea hydrological environment data fitting method.

[0056] The invention also discloses a storage medium, which is a computer storage medium, on which one or more programs are stored, the one or more programs being executable by one or more processors to cause the one or more processors to perform the steps of the deep-sea hydrological environment data fitting method.

[0057] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0058] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for fitting deep-sea hydrological environment data, characterized in that, Including the following steps: S1. Collect hydrological and environmental data of the target area; S2. Based on the collected hydrological environmental data, construct salinity calculation functions and temperature calculation functions: ； ； in, Indicates unknown depth The salinity at that location For known depth The salinity at that location , , , These are the undetermined coefficients of the third-order interpolation function; Indicates unknown depth The temperature at that location For known depth The temperature at that location , , , These are the undetermined coefficients of the third-order interpolation function; S3. Select sampling data from N consecutive sampling points in the hydrological environment data, and calculate the linearity of the salinity data segment in the sampling data. linearity of temperature data segments ; S4. Calculate the (N+1)th data point of the salinity data segment and the temperature data segment using the method described in step S2; and calculate the linearity of the N data points between the 2nd and (N+1)th data points in the salinity data segment. And the linearity of the N data points between the 2nd and N+1th data points in the temperature data segment. ; S5. Construct relative deviation functions for salinity data and temperature data: ； ； in, This indicates the relative deviation of the salinity data. This indicates the relative deviation of the temperature data; S6. By adjusting the number of data points N, so that and Minimum, and calculate and When taking the minimum value , , , , , , , ;Will , , , , , , , Substitute the values ​​into the salinity data calculation function and temperature data calculation function described in step S2; S7. Using the salinity data calculation function and temperature data calculation function determined in step S6, calculate the salinity data and temperature data at unknown depths. Based on the calculated salinity data and temperature data, as well as the hydrological environment data collected in step S1, perform sound velocity fitting to obtain the global underwater sound velocity distribution of the area to be measured.

2. A computer medium, characterized in that, The computer medium stores one or more programs, which can be executed by one or more processors to enable the one or more processors to perform the deep-sea hydrological environment data fitting method of claim 1.

3. A deep-sea hydrological environment data fitting device, characterized in that, The device includes a processor and a memory, the memory storing a computer program executable by the processor, the processor executing the computer program to implement the deep-sea hydrological environment data fitting method of claim 1.

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