A method for extracting jump layers from multiple types of sound velocity profiles

Abstract

The present invention provides a method for extracting jump layers of multiple types of sound velocity profiles, belonging to the field of marine environmental survey. Based on the traditional optimal segmentation method, the present invention simplifies the sound velocity profile into a four-layer structure; according to the principle of the number of sound channel axes that meet the gradient threshold, the accurate identification of multiple jump layers is realized; taking the gradient mutation point as the starting point, the upper limit of the sound velocity jump layer is obtained through repeated iterative calculation; in the case of a single sound channel axis, the optimal segmentation algorithm is used to determine the lower limit of the jump layer; in the case of multiple sound channel axes, the optimal three-segmentation method is used to determine the upper and lower limits of the remaining jump layers. The method of the present application can effectively and accurately identify the multi-jump layer structure, and can also realize the feature extraction of multiple types of sound velocity profile jump layers, which is helpful to improve the reliability of underwater communication and the concealment performance of submarines.

Description

Technical Field

[0001] The invention relates to the field of marine environment survey, and in particular to a method for extracting jump layers of multiple types of sound velocity profiles. Background Art

[0002] The traditional method of extracting sonic jumps is usually to find a minimum value of the sound velocity gradient, and define the water layer that exceeds this gradient standard value as the sonic jump. This method of calculating sonic jumps is adopted by my country's "Ocean Survey Specifications" (2007). "Ocean Survey Specifications Part 7: Ocean Survey Data Exchange" stipulates that the threshold of sonic jumps is 0.5 / s in shallow waters and 0.2 / s in deep waters, and a depth of less than 200m is considered shallow water. However, this method only generally proposes a general algorithm for calculating sonic jumps, and there are problems in extracting jumps in complex acoustic environments in different sea areas.

[0003] Domestic and foreign scholars have proposed some thermocline calculation methods based on the above specifications. For example, the ST method defines the depth that is a fixed value lower than the sea surface element as the upper boundary of the thermocline. However, the fixed value selection of this method is more difficult to adapt than the threshold selection due to the influence of solar radiation, ocean currents and various water bodies, and this method can only find the upper boundary of the thermocline but cannot determine the lower boundary of the thermocline.

[0004] Some scholars in my country have analyzed the characteristics of the sound speed between the first and second island chains in the northwest Pacific Ocean and found that the threshold value in this area can be selected as 0.05 / s, which is more in line with the intuitive feeling of the jump layer in this area. Although it solves the problem of mismatch in transitional sea areas compared to the traditional vertical gradient method, some sound speed profiles are still not applicable to this threshold and are not universal.

[0005] Someone introduced the vertical gradient method and the optimal segmentation method to determine the upper and lower boundaries of the jump layer, and found that both methods are effective for typical profile data. However, for the case where the maximum vertical gradient is lower than the minimum standard, and the shallow sea and deep sea standards cannot match in the transition zone, these two methods cannot obtain satisfactory results. For example, the optimal segmentation method has the defect of selecting points that are too deep. Summary of the invention

[0006] In view of the problems that the traditional optimal segmentation algorithm has the problems of inconsistent criteria for distinguishing deep sea and shallow sea, too discrete extraction of sound velocity jump layers, and inaccurate description of deep sea sound velocity jump layers when extracting sound velocity jump layers, the present invention provides a method for extracting jump layers of multiple types of sound velocity profiles.

[0007] The technical solution of the present invention is:

[0008] An algorithm for extracting jump layers of multiple types of sound velocity profiles, which can be used as an evaluation index to optimize any wave energy device or construct a wave energy device array based on the wave conditions in the target sea area;

[0009] The sound channel axis is extracted from different types of ocean sound velocity profiles, and then several optimal segmentation algorithms are used to extract the sound velocity jump layers based on the principle that the number of sound channel axes is the number of jump layers;

[0010] The extraction of the sound channel axis from different ocean sound speed profiles means that several sound speed profiles in the ocean can be simplified and classified into a type where there is no sound channel axis, a single sound channel axis, and a multi-sound channel axis type;

[0011] The use of the optimal segmentation algorithm several times means that the jump layer result is no longer obtained by calculating the segmentation result only once, but multiple optimal segmentation results are calculated by continuously changing the segmentation end point, and a continuous sequence in the result is extracted as the jump layer upper boundary; the jump layer lower boundary is extracted from the jump layer upper boundary to the channel axis using the optimal segmentation method, so that the jump layer recognition result is more continuous and accurate;

[0012] The number of channel axes is based on the principle of the number of jump layers, which means that in a typical deep-sea sound velocity profile, the channel axis is used as a parameter to participate in the division of the layered structure. When there are several channel axes that meet the minimum gradient requirement, it is considered that there are several complete sound velocity jump layers, thereby achieving a clear definition of the number of jump layers.

[0013] Furthermore,

[0014] Specific steps:

[0015] 1) Calculate the ocean vertical sound velocity profile data based on the temperature, salinity and corresponding depth data;

[0016] 2) performing data interpolation and resampling on the ocean vertical sound velocity profile data obtained in step 1);

[0017] 3) Use the difference method to calculate the sound velocity gradient;

[0018] 4) Find the vocal tract axis based on the sound velocity gradient and gradient threshold;

[0019] 5) Introduce the sound channel axis as a parameter for determining the jump layer structure, extract the sound velocity jump layer, and obtain the upper and lower bounds of the jump layer;

[0020] 6) Calculate and record the jump-layer related attributes and construct the jump-layer related data set.

[0021] Furthermore,

[0022] In step 1), the ocean is densely divided into grid points, and the temperature, salinity and corresponding depth data are respectively substituted into the empirical sound speed formula to obtain the ocean vertical sound speed profile data. The sound speed profile of each grid point is calculated using multi-threading technology to construct the global ocean sound speed field.

[0023] In step 2), the Akima interpolation method is used to interpolate the sound speed data points to obtain a set of continuous sound speed values; Akima interpolation can produce a smoother interpolation curve when processing irregular data point distribution; the interpolated sound speed data points are approximated by a cubic polynomial to obtain a smoother sound speed profile curve. Finally, the median filter is used to filter out the subtle burrs generated after Akima interpolation.

[0024] In step 3), the difference method calculation formula is

[0025] (1)

[0026] Where g is the sound velocity gradient; is the sound velocity difference between adjacent depth points; is the depth difference between adjacent points.

[0027] In step 4), determine whether the maximum gradient of the entire sound velocity profile curve is less than or equal to 0.01 / s, and whether the gradient range is less than or equal to 0.02 / s. If it is satisfied, it means that there is no jump layer and there is no need to find the channel axis; the second step is to determine whether it is an inverse jump layer type. There is no channel axis in the inverse jump layer; find the typical deep-sea channel axis, and choose to find the minimum value of the sound velocity as the channel axis; after obtaining the minimum value of the sound velocity, the original channel axis set can be obtained, and the preliminary channel axis result sequence is expanded upward and downward by xm respectively. Within the range of 2x m, the point found needs to be the minimum sound velocity, otherwise it is discarded; set the sea depth to be less than 200m, x is 10, and the sea depth is greater than 200m, then x is 100, and finally the set of channel axes is obtained;

[0028] After the first step of screening, for the multi-channel axis type sound velocity profile, some channel axes have extremely small gradients that do not meet the conditions for generating jump layers. For this type of sound velocity profile, it is necessary to screen whether the maximum value of the gradient between each channel axis is less than 0.005 / s. If it is satisfied, it means that the gradient between the channel axes is extremely small. For this type of channel axis, the channel axis with a smaller sound velocity is retained, and those that do not meet the conditions are eliminated.

[0029] In step 5), the method used to calculate the upper limit of the jump layer is to iteratively calculate the sound velocity gradient mutation point, and use the optimal binary division to calculate the upper limit of the sound velocity jump layer to the sound channel axis or the seabed to obtain the lower limit of the sound velocity jump layer;

[0030] The judgment of the lower boundary of the jump layer is divided into three cases:

[0031] For the sound velocity profile with a single-channel axis type, the complete upper and lower boundary position information of the sound velocity jump layer is provided; for the sound velocity profile type with a dual-channel axis or a multi-channel axis, the corresponding number of sound velocity jump layers are provided; for the sound channel axis that is not formed, the lower boundary of the jump layer is not provided because the depth of the sound channel axis is unknown and the end position of the jump layer is unknown.

[0032] Step 5) includes the following steps:

[0033] 5-1) Take the point 1m underwater as the segmentation starting point, take the gradient mutation point H as the end point of the first optimal segmentation for the first segmentation, and then move the mutation point H downward until the iteration end point E to obtain a sequence of segmentation results;

[0034] 5-2) Find the point H where the gradient suddenly changes as the starting point of the iteration, and then use the sound channel axis or the seabed or 400m as the iteration end point E, and perform an iteration every nm from H, n>0; calculate the optimal binary segmentation result from the water depth of 1m to the current position in each iteration, and record the segmentation result;

[0035] 5-3) After the iteration is completed, if the difference between the segmentation results of two adjacent points is less than n / 2, it is considered to be a continuous value. When the number of continuous values ​​exceeds k, the average value of this continuous segment is calculated as the upper bound of the jump layer;

[0036] 5-4) Find the gradient mutation point, the three cases are different; the sound velocity jump layer can be divided into three cases: reverse jump layer, jump layer with sound channel axis, jump layer without sound channel axis; in the case of reverse jump layer, the point H of gradient mutation is the depth of the maximum gradient, while in the other two cases, the point where the sound velocity gradient changes from positive to negative or the gradient minimum is the smaller value of the two as the gradient mutation point H; in addition, the values ​​of each place in the step should also be set according to the situation; in the case of reverse jump layer, the sound velocity profile n is 3, k is 4, the sound velocity profile n without sound channel axis is 5, k is 3, the sound velocity profile with sound channel axis is 10, k is 5;

[0037] 5-5) For the sound velocity profile that forms the sound channel axis, the optimal segmentation method is used to set the segmentation starting point as the calculated upper boundary of the jump layer, and the end point as the sound channel axis to obtain the lower boundary of the jump layer; finally, determine whether it is a multi-channel axis. If it is a multi-channel axis and meets the minimum gradient requirement, the optimal three-segmentation method is used to obtain the jump layers between the sound channel axes.

[0038] In step 6), the thickness and intensity of the sonic jump layer are calculated from the upper and lower boundaries of the sonic jump layer generated above, and the relevant attribute information is recorded in a file to form a relevant data set; the thickness of the sonic jump layer is the difference between the upper and lower boundaries of the sonic jump layer, and the intensity of the sonic jump layer is the ratio of the sound speed at the upper and lower boundaries of the jump layer to the thickness of the sonic jump layer.

[0039] The beneficial effects of the present invention are:

[0040] The technical solution of the present application involves the identification of sonic jump layers in the ocean acoustic environment and the extraction of their characteristic parameters. It mainly aims at the problems of too deep extraction range, too scattered spatial distribution and inability to accurately extract in the case of multiple sonic jump layers when the traditional optimal segmentation algorithm is used to extract sonic jump layers. A method for extracting sonic jump layers that is adaptive to multiple sonic profiles and multiple jump layers is proposed - the AOPM method. Based on the traditional optimal segmentation method, this method uses the SSP gradient change and related environmental parameters to adaptively obtain the threshold, so as to obtain the number of sound channel axes that meet the gradient threshold requirements, and finally realize the identification of multiple jump layers. According to the sensitivity of the gradient change, the characteristic parameters of the sonic jump layer are accurately extracted. The algorithm can effectively and accurately identify the multi-jump layer structure, and can also realize the feature extraction of various types of sonic jump layers in the sonic profile.

[0041] In order to overcome the defects of the existing jump layer calculation method, such as inaccurate extraction of jump layer upper and lower boundaries, unreasonable threshold setting, and too deep extraction range, which leads to large jump layer extraction error, low efficiency, and poor applicability, this algorithm improves the traditional sound velocity jump layer calculation method in the following ways:

[0042] The parameter of the number of channel axes is introduced to determine the number of sound velocity jump layers. The gradient threshold is used to search for the channel axis and determine the number of channel axes. The number of jump layers is determined according to the principle that the number of channel axes determines the jump layer structure, thereby avoiding the problem that the multi-jump layer structure cannot be extracted.

[0043] The adaptive threshold is set by using the change of sound velocity gradient to realize the extraction of jump layers in different sound field environments. A typical sound velocity profile data set is constructed to verify the applicability of this algorithm under different sound velocity profile types in different regions. The results show that this algorithm is applicable to deep sea, shallow sea and transitional sound velocity profiles.

[0044] The upper boundary of the jump layer is regarded as the starting point of the seasonal jump layer and the permanent jump layer, which simplifies the jump layer structure and improves the accuracy of sound speed jump layer detection, thus avoiding the problem of extracting the jump layer too deep by traditional methods.

[0045] The average value of multiple optimal segmentation results is used to replace the single optimal segmentation result. Based on the improved calculation of the single optimal segmentation result, it is further proposed to calculate the multiple optimal segmentation results. Instead of simply using the single optimal segmentation algorithm, multiple optimal segmentations are used by changing the segmentation end point to obtain a continuous result sequence, calculate its average value, and determine the upper boundary of the jump layer. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the multi-target tracking process;

[0047] Figure 2The non-proportional vertical coordinate sound velocity profile and jump layer diagram; from top to bottom, they are the upper boundary, lower boundary position and channel axis position of the sound jump layer, T, D and V represent the upper boundary, lower boundary and channel axis depth of the sound jump layer, respectively; (a) is the (130°E, 16.5°N) success profile and jump layer boundary; (b) is the (14°W, 40°N) sound velocity profile and jump layer boundary;

[0048] Figure 3 This is a comparison of the sound speed profiles and jump layer methods of B1-B3 in the North Atlantic; (a) is the traditional optimal segmentation algorithm on April 1; (b) is the traditional optimal segmentation algorithm on July 1; (c) is the improved optimal segmentation algorithm on April 1; (d) is the improved optimal segmentation algorithm on July 1; B1-B3 are three different types of sound speed profiles respectively;

[0049] Figure 4 This is a comparison chart of the sound speed profiles and jump layer methods of B4-B6 in the North Atlantic; among them, (e) is the traditional optimal segmentation algorithm on October 1; (f) is the traditional optimal segmentation algorithm on January 1; (g) is the improved optimal segmentation algorithm on October 1; (h) is the improved optimal segmentation algorithm on January 1; B4-B6 are three different types of sound speed profiles. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0051] The present invention proposes an algorithm for extracting jump layers of multiple types of sound velocity profiles, which can be used as an evaluation index to optimize any wave energy device or construct an array of wave energy devices based on the wave conditions of the target sea area.

[0052] Technical solution:

[0053] 1) Calculate the ocean vertical sound velocity profile data based on the temperature, salinity and corresponding depth data;

[0054] 2) performing data interpolation and resampling on the ocean vertical sound velocity profile data obtained in step 1);

[0055] 3) Use the difference method to calculate the sound velocity gradient;

[0056] 4) Find the vocal tract axis based on the sound velocity gradient and gradient threshold;

[0057] 5) Introduce the sound channel axis as a parameter for determining the jump layer structure, extract the sound velocity jump layer, and obtain the upper and lower bounds of the jump layer;

[0058] 6) Calculate the relevant attributes of the jump layer and record the relevant attribute information of the jump layer to construct the jump layer related data set.

[0059] The above algorithm for extracting multiple types of sound speed profile jump layers is optimized. In step 1), the ocean is densely divided into grid points, and the temperature, salinity and corresponding depth data are respectively introduced into the sound speed empirical formula to obtain the ocean vertical sound speed profile data, and the sound speed profile of each grid point is calculated using multi-threading technology to construct the global ocean sound speed field;

[0060] Optimized, the above algorithm for extracting jump layers of multiple types of sound velocity profiles, in step 2), uses the Akima interpolation method to interpolate the sound velocity data points to obtain a set of continuous sound velocity values. Akima interpolation can produce a smoother interpolation curve when processing irregular data point distribution. The interpolated sound velocity data points are approximated by a cubic polynomial to obtain a smoother sound velocity profile curve. Finally, a median filter is used to filter out the subtle burrs generated after Akima interpolation.

[0061] Optimized, the above algorithm for extracting jump layers of multiple types of sound velocity profiles, in step 3), the difference method calculation formula is

[0062] (1)

[0063] Where g is the sound velocity gradient; is the sound velocity difference between adjacent depth points; is the depth difference between adjacent points.

[0064] Optimized, the above algorithm for extracting jump layers of multi-type sound velocity profiles, in step 4), determines whether the maximum gradient of the entire sound velocity profile curve is less than or equal to 0.01 / s, and whether the extreme difference of the gradient is less than or equal to 0.02 / s. If it is satisfied, it means that the sound velocity gradient is small, there is no jump layer, and there is no need to find the channel axis; the next second step is to determine whether it is an inverse jump layer type. The inverse jump layer means that the sound velocity increases with the increase of depth. In contrast to the positive jump layer, its gradient is positive, but the difference is negative. There is no channel axis for the inverse jump layer, so it needs to be considered separately; then find the typical deep-sea channel axis. The channel axis is generally the minimum sound velocity, but for the case where the depth is too shallow or there are dual channel axes, the minimum sound velocity is the channel axis and it is not applicable. Therefore, this application chooses to find the minimum sound velocity as the channel axis; after obtaining the minimum sound velocity, the original channel axis set can be obtained, but there will be some fine structures in the data, which are not considered as channel axes, and the first step of screening is required. The preliminary sound channel axis result sequence is expanded upward and downward by xm respectively. Within the range of 2x m, the point found needs to be the minimum sound speed, otherwise it is discarded. In this application, when the sea depth is less than 200m, x is 10, and when the sea depth is greater than 200m, x is 100, and finally the set of sound channel axes is obtained.

[0065] After the first step of screening, for the multi-channel axis type sound velocity profile, some channel axes have extremely small gradients that do not meet the conditions for generating jump layers. For this type of sound velocity profile, it is necessary to screen whether the maximum gradient between each channel axis is less than 0.005 / s (much less than the standard for the gradient of the speed jump layer). If it is satisfied, it means that the gradient between the channel axes is extremely small. For this type of channel axis, the channel axis with a smaller sound velocity is retained and the channel axis with a larger sound velocity is eliminated.

[0066] Optimized, in the above algorithm for extracting jump layers of multiple types of sound velocity profiles, in step 5), the method used for calculating the upper limit of the jump layer is to iteratively calculate the sound velocity gradient mutation point, and to use the optimal binary division to calculate the upper limit of the sound velocity jump layer to the sound channel axis or the seabed to obtain the lower limit of the sound velocity jump layer;

[0067] The judgment of the lower boundary of the jump layer is divided into three cases:

[0068] For the sound velocity profile with a single channel axis, the complete upper and lower bounds of the sound velocity jump layer are provided. For the sound velocity profile with a dual channel axis or a multi-channel axis, the corresponding number of sound velocity jump layers are provided. For the sound channel axis that is not formed, the lower bound of the jump layer is not provided because the depth of the channel axis is unknown and the end position of the jump layer is unknown.

[0069] Step 5) includes the following steps:

[0070] 5-1) Take the point 1m underwater as the segmentation starting point, take the gradient mutation point H as the end point of the first optimal segmentation for the first segmentation, and then move the mutation point H downward until the iteration end point E to obtain a sequence of segmentation results.

[0071] 5-2) Due to the particularity of the jump layer, find the point H where the gradient suddenly changes as the starting point of the iteration, and then use the sound channel axis or the seabed or 400m as the iteration end point E (take the smaller value of the three to prevent the iteration from being too deep. At the same time, the upper limit of the jump layer generally does not exceed 400m). Starting from H, an iteration is performed every nm. Each iteration calculates the optimal binary segmentation result from the water depth of 1m to the current position, and records the segmentation result.

[0072] 5-3) After the iteration is completed, if the difference between the segmentation results of two adjacent points is less than n / 2, it is considered to be a continuous value. When the number of continuous values ​​exceeds k, the average value of this continuous segment is calculated as the upper bound of the jump layer.

[0073] 5-4) Find the gradient mutation point, the three situations are different. The sound velocity jump layer in this application can be divided into three situations: reverse jump layer, jump layer with sound channel axis, and jump layer without sound channel axis. In the case of reverse jump layer, the point H of gradient mutation takes the depth of the maximum gradient, while in the other two cases, the point where the sound velocity gradient changes from positive to negative or the gradient minimum point (the smaller of the two) is taken as the gradient mutation point H. In addition, the values ​​of each place in the step should also be set according to the situation. In the case of reverse jump layer, the sound velocity profile n is 3 and k is 4. The sound velocity profile n without sound channel axis is 5 and k is 3. The sound velocity profile with sound channel axis is 10 and k is 5.

[0074] 5-5) For the sound velocity profile that forms the channel axis, the optimal segmentation method is used to set the segmentation starting point as the calculated upper boundary of the jump layer, and the end point as the channel axis to obtain the lower boundary of the jump layer. Finally, it is determined whether it is a multi-channel axis. If it is a multi-channel axis and meets the minimum gradient requirement, the optimal three-segmentation method is used to obtain the jump layers between the channel axes.

[0075] The above algorithm for extracting jump layers of multiple types of sound velocity profiles is optimized. In step 6), the upper and lower boundaries of the sound velocity jump layer generated above are used to calculate the thickness and strength of the sound velocity jump layer and record the relevant attribute information to the file to form a relevant data set. The thickness of the sound velocity jump layer is the difference between the upper and lower boundaries of the sound velocity jump layer, and the strength of the sound velocity jump layer is the ratio of the sound speed at the upper and lower boundaries of the jump layer to the thickness of the sound velocity jump layer.

[0076] The above description is only a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, and is not used to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A method for extracting multiple types of acoustic velocity profile jump layers, characterized in that: The sound channel axis is extracted from different types of ocean sound velocity profiles, and then several optimal segmentation algorithms are used to extract the sound velocity jump layers based on the principle that the number of sound channel axes is the number of jump layers; The extraction of acoustic channel axes from different types of ocean sound velocity profiles means that several types of sound velocity profiles in the ocean can be simplified and classified into types without acoustic channel axis, single acoustic channel axis, and multiple acoustic channel axis. The method of using the optimal segmentation algorithm several times is to calculate the optimal segmentation results several times by continuously changing the segmentation end point, extract the mean of the continuous sequence in the result as the upper boundary of the jump layer; and extract the lower boundary of the jump layer by using the optimal segmentation method from the upper boundary of the jump layer to the channel axis, so that the jump layer recognition result is more continuous and accurate. The number of acoustic channel axes is the number of jump layers as a principle, which means that in a typical deep-sea sound velocity profile, the acoustic channel axis is used as a parameter to participate in the division of the hierarchical structure. When there are several acoustic channel axes that meet the minimum gradient requirement, it is considered that there are several complete sound velocity jump layers, thus realizing the definition of the number of jump layers. The specific steps include: 1) Calculate the ocean vertical sound velocity profile data based on the temperature, salinity and corresponding depth data; 2) performing data interpolation and resampling on the ocean vertical sound velocity profile data obtained in step 1); 3) Use the difference method to calculate the sound velocity gradient; 4) Find the vocal tract axis based on the sound velocity gradient and gradient threshold; 5) Introduce the sound channel axis as a parameter for determining the jump layer structure, extract the sound velocity jump layer, and obtain the upper and lower bounds of the jump layer; 6) Calculate and record the relevant attributes of the jump layer and construct the jump layer related data set; In step 1), the ocean is densely divided into grid points, and the temperature, salinity and corresponding depth data are respectively substituted into the sound speed empirical formula to obtain the ocean vertical sound speed profile data. The sound speed profile of each grid point is calculated using multi-threading technology to construct the global ocean sound speed field. In step 3), the difference method calculation formula is （1） in, is the sound velocity gradient; is the sound velocity difference between adjacent depth points; is the depth difference between adjacent points; In step 4), determine whether the maximum gradient of the entire sound velocity profile curve is less than or equal to 0.01 / s, and whether the gradient range is less than or equal to 0.02 / s. If it is satisfied, it means that there is no jump layer and there is no need to find the channel axis; the second step is to determine whether it is an inverse jump layer type. There is no channel axis in the inverse jump layer; find the typical deep-sea channel axis, and choose to find the minimum value of the sound velocity as the channel axis; after obtaining the minimum value of the sound velocity, the original channel axis set can be obtained, and the preliminary channel axis result sequence is expanded upward and downward by xm respectively. Within the range of 2x m, the point found needs to be the minimum sound velocity, otherwise it is discarded; set the sea depth to be less than 200m, x is 10, and the sea depth is greater than 200m, then x is 100, and finally the set of channel axes is obtained; After the first step of screening, for the multi-channel axis sound velocity profile, some channel axes have extremely small gradients that do not meet the conditions for generating jump layers. For this type of sound velocity profile, it is necessary to screen whether the maximum value of the gradient between each channel axis is less than 0.005 / s. If it is satisfied, it means that the gradient between the channel axes is extremely small. For this type of channel axis, the channel axis with a smaller sound velocity is retained, and those that do not meet the conditions are eliminated. In step 5), the method used to calculate the upper limit of the jump layer is to iteratively calculate the sound velocity gradient mutation point, and use the optimal binary division to calculate the upper limit of the sound velocity jump layer to the sound channel axis or the seabed to obtain the lower limit of the sound velocity jump layer; The judgment of the lower boundary of the jump layer is divided into three cases: For the sound velocity profile with a single channel axis, the complete upper and lower bounds of the sound velocity jump layer are provided; for the sound velocity profile with multiple channel axes, the corresponding number of sound velocity jump layers are provided; for the sound channel axis that is not formed, the lower bound of the jump layer is not provided because the channel axis depth is unknown and the jump layer end position is unknown; In step 6), the thickness and intensity of the sonic jump layer are calculated from the upper and lower boundaries of the sonic jump layer generated above, and the relevant attribute information is recorded in a file to form a relevant data set.

2. The method according to claim 1, characterized in that In step 2), the Akima interpolation method is used to interpolate the sound speed data points to obtain a set of continuous sound speed values; Akima interpolation can produce a smoother interpolation curve when processing irregular data point distribution; the interpolated sound speed data points are approximated by a cubic polynomial to obtain a smoother sound speed profile curve; finally, median filtering is used to filter out the subtle burrs generated after Akima interpolation.

3. The method according to claim 2, characterized in that Step 5) includes the following steps: 5-1) Take the point 1m underwater as the segmentation starting point, take the gradient mutation point H as the end point of the first optimal segmentation for the first segmentation, and then move the mutation point H downward until the iteration end point E to obtain a sequence of segmentation results; 5-2) Find the point H where the gradient suddenly changes as the starting point of the iteration, then use the sound channel axis or the seabed or 400m as the iteration end point E, and perform an iteration every nm from H; calculate the optimal binary segmentation result from the water depth of 1m to the current position in each iteration, and record the segmentation result; 5-3) After the iteration is completed, if the difference between the segmentation results of two adjacent points is less than n / 2, it is considered to be a continuous value. When the number of continuous values ​​exceeds k, the average value of this continuous segment is calculated as the upper bound of the jump layer; 5-4) Find the gradient mutation point, the three cases are different; the sound velocity jump layer can be divided into three cases: reverse jump layer, jump layer with sound channel axis, jump layer without sound channel axis; in the case of reverse jump layer, the point H of gradient mutation is the depth of the maximum gradient, while in the other two cases, the sound velocity gradient changes from positive to negative or the gradient minimum point, and the smaller value of the two is taken as the result as the gradient mutation point H; in addition, the values ​​of each place in the step should also be set according to the situation; in the case of reverse jump layer, the sound velocity profile n is 3, k is 4, the sound velocity profile n without sound channel axis is 5, k is 3, and the sound velocity profile with sound channel axis is 10, k is 5; 5-5) For the sound velocity profile that forms the sound channel axis, the optimal segmentation method is used to set the segmentation starting point as the calculated upper boundary of the jump layer, and the end point as the sound channel axis to obtain the lower boundary of the jump layer; finally, determine whether it is a multi-channel axis. If it is a multi-channel axis and meets the minimum gradient requirement, the optimal three-segmentation method is used to obtain the jump layers between the sound channel axes.

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