CN115422746A - Internal solitary wave parameter extraction algorithm based on underwater glider - Google Patents

Internal solitary wave parameter extraction algorithm based on underwater glider Download PDF

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CN115422746A
CN115422746A CN202211064070.4A CN202211064070A CN115422746A CN 115422746 A CN115422746 A CN 115422746A CN 202211064070 A CN202211064070 A CN 202211064070A CN 115422746 A CN115422746 A CN 115422746A
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王智峰
崔俊男
夏浩峰
过武宏
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PLA Navy Submarine College
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Abstract

The utility model provides an interior solitary wave parameter extraction algorithm based on glider under water, belongs to solitary wave observation technical field in the ocean, and the extraction algorithm adopts the glider matrix to survey, includes following step: step 1, extracting space-time data observed by an underwater glider, and analyzing the space-time data by adopting a hierarchical clustering analysis method; step 2, performing equal-depth interval interpolation on data in the simultaneous empty data set, calculating Euclidean distance, and judging; step 3, extracting suspicious sequences with the maximum depth difference of the iso-surface larger than 25m and the depth of the maximum depth difference larger than 20m, and performing data and calculation on machine numbers, section numbers and average densities of the suspicious sequences to obtain ocean layering conditions, floating frequencies and inner isolated wave amplitudes; and 4, selecting a proper internal solitary wave theory according to the optimal application range of the internal solitary wave theory to invert the influence of the internal solitary wave theory on the isosurface, and outputting a density profile containing the information of the internal solitary wave. The method can obviously improve the accuracy of the inversion result of the internal solitary wave.

Description

Internal solitary wave parameter extraction algorithm based on underwater glider
Technical Field
The invention belongs to the technical field of ocean interior solitary wave observation, and particularly relates to an interior solitary wave parameter extraction algorithm based on an underwater glider.
Background
The traditional method for observing the internal solitary wave mainly comprises submerged buoy pavement, shipborne instrument observation, argo buoy and the like, the field observation can obtain valuable first-hand data, the ocean phenomenon can be reflected most truly, but the defects are obvious: the cost of putting instruments is huge, the manpower and time cost of the sailing type observation are too high, large-area observation is difficult to realize, and the like. Meanwhile, observation can be carried out based on a satellite remote sensing mode, information such as the space position and the propagation direction of the internal isolated wave can be accurately obtained by the method, and the amplitude of the internal isolated wave and the influence of the amplitude on ocean thermohalites and density jump layers cannot be obtained.
Along with unmanned observation platform matures gradually, glider shows obvious advantage under water, specifically embodies: the instrument is simple and easy to collect and release, the data time is continuous, the position area is flexible, and the labor cost is reduced. Meanwhile, the use efficiency of observation data of the glider can be effectively improved by utilizing the research on internal wave characteristic parameters and three-dimensional temperature salt of the underwater glider. The glider is used for ocean observation, the internal wave characteristics are extracted, the sea area temperature and salt fine structure is analyzed and researched, the underwater sound field calculation is facilitated, and the implementation of underwater engineering or military operation is guaranteed.
Because the motion of an underwater glider in sea water is mainly driven by gravity and buoyancy, the underwater glider is easily influenced by the flow field change generated by ocean phenomena. By utilizing the characteristic of the glider, some students can invert the characteristic parameters of the internal waves encountered by the glider through the motion condition of the underwater glider. Rudnick et al (2013) performed seven glider observation experiments on the igni strait from april 2007 to july 2008, and observed the occurrence of high-frequency internal waves, and the occurrence period of the high-frequency internal waves is smaller than the acquisition period of the glider on seawater profile information (3-6 hours). They estimate the vertical velocity caused by the internal waves through the pressure measured by the glider and the glider attitude, and provide an algorithm for calculating the vertical velocity through the glider equation of motion and estimating the vertical velocity through a high-pass filter. During the period, the maximum vertical flow velocity observed by the thermal shock layer is over 0.2m/s, and the maximum amplitude of the thermal shock layer vertical direction is close to 200m. Palmer et al (2015) conducted ocean microstructure studies using underwater gliders in Kaltet's sea in 2012 summer. They found that the movement inside the sea is mainly dependent on widely distributed ocean internal waves and that the maximum internal wave amplitude observed is over 50m, corresponding to 1/3 of the total water depth. The density jump layer mixing caused by the action of the internal waves is the main reason of the density reduction of the seawater bottom layer, which means that the movement of the density jump layer is an important mechanism for regulating seawater exchange in continental shelf areas and deep sea areas. Ma et al (2018) observed the occurrence of internal solitary waves in October 2017 in south China sea using an underwater glider. And the combination of the satellite marine image with the mesoscale resolution proves that the motion of the thermocline of the south sea mainly depends on the influence of the internal wave. They estimate the period and amplitude of the isolated waves except the inner isolated waves by the vertical speed and the motion characteristics of the glider, which are 24 minutes and 127 meters respectively.
However, the current research is in the initial stage, various theories and methods are not mature, and a large amount of improvement space still exists. In the aspect of observing the internal solitary wave by using the underwater glider, the theoretical basis of the internal solitary wave is lacked, the internal solitary wave parameters are difficult to accurately invert, and the influence of random factors on the movement of the glider is difficult to overcome by adopting a single glider for observation in the traditional method, so that the observation precision is influenced, and the information such as the amplitude of the internal solitary wave cannot be accurately determined.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention discloses an internal solitary wave parameter extraction algorithm based on an underwater glider, which aims to extract internal solitary wave characteristic parameters by means of matrix observation of the underwater glider and data analysis by combining a space-time analysis method of ocean data, an ocean vertical velocity model and an internal solitary wave theoretical model, and invert the influence of the internal solitary wave on ocean thermohaline and ocean density, thereby solving the problems of poor maneuverability, high observation cost and low data precision of the traditional observation means.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an internal solitary wave parameter extraction algorithm based on an underwater glider is characterized in that a glider matrix is adopted for observation, and the glider matrix refers to the following steps: the method comprises the following steps of putting a plurality of underwater gliders in different places at the same time in a space matrix mode, and observing the underwater gliders simultaneously through the underwater gliders, wherein the method comprises the following steps:
step 1, extracting space-time data observed by an underwater glider, wherein the space-time data comprises a glider machine number, an observation section number, a measuring point longitude and latitude, observation time, temperature, salinity, density and water depth, and analyzing the space-time data by adopting a hierarchical clustering analysis method to obtain a simultaneous space data set;
step 2, performing equal-depth interval interpolation on data in the simultaneous empty data set to obtain average data with the same depth, then comparing single-section data with the average data with the same depth, calculating Euclidean distance, and judging; the single-section data refers to: when each group of data obtained by hierarchical clustering analysis contains a plurality of profile data of different gliders at different time, single profile information in the same group is single profile data;
step 3, extracting suspicious sequences with the maximum depth difference of the iso-surface being more than 25m and the depth of the maximum depth difference being more than 20m, and performing data and calculation on machine numbers, section numbers and average density of the suspicious sequences to obtain ocean layering conditions, floating frequencies and inner isolated wave amplitudes;
and 4, selecting a proper internal solitary wave theory according to the ocean layering situation and the amplitude of the internal solitary wave and the optimal application range of the internal solitary wave theory to invert the influence of the internal solitary wave on the isosurface, combining the influence with the original profile, and finally outputting a density profile containing the information of the internal solitary wave.
Preferably, in the step 1, a hierarchical clustering analysis method from bottom to top (geometric) is adopted, when the distance is calculated, firstly, the Euclidean distance (Euclidean distance) between the samples is calculated, and after the cluster is formed, the distance between the clusters is calculated; the distance calculation method from class to class is the shortest distance method (single link) or the longest distance method (complete link) or the class average distance method (average link) or the central distance method (centroid link).
Preferably, in step 1, all the sections observed by all the gliders in the same mission are extracted, and the machine number n corresponding to each observed section is extracted M Section number n P Longitude and latitude lon and lat of water entry point and average time parameter of profile
Figure BDA0003827081940000031
And the time is subjected to dimensionless processing, as shown in formula (1), so that the time meets the order requirement of space-time clustering analysis:
Figure BDA0003827081940000032
in the formula (1), the reaction mixture is,
Figure BDA0003827081940000033
is the average of the observation times of a single section in seconds, r t Is a cluster analysis spatial threshold c S And a time threshold c T Is a ratio of (i.e. r) t ＝c S /c T ；
Then, a three-dimensional matrix of spatio-temporal data is constructed, having:
Figure BDA0003827081940000034
suppose that the three-dimensional matrix is divided into m disjoint classes C according to the clustering criterion 1 ，C 2 ，...，C m Of which two classes C α And C β The distance of (d) is expressed as:
Figure BDA0003827081940000035
in the formula (3), Δ is a distance operator, Δ a，b Representing the euclidean distance from sample a to sample b;
Figure BDA0003827081940000036
represents class C α Longitude and latitude and average time parameters of the ith sample;
in the calculation, the shortest distance method is expressed as:
Figure BDA0003827081940000037
the longest distance method is expressed as:
Figure BDA0003827081940000038
the mean distance-like method is expressed as:
Figure BDA0003827081940000039
the center distance method is expressed as:
Figure BDA00038270819400000310
in the formula (7), the reaction mixture is,
Figure BDA00038270819400000311
and
Figure BDA00038270819400000312
are respectively indicated as class C α And C β At the center of (i.e. C) α And C β Average of the samples in (1).
Preferably, in the step 1, the time-space data is analyzed by selecting a shortest distance method, and during the analysis, the original time-space data is divided into M groups of data according to the standards that the longitude and latitude and the average time parameter distance are smaller than respective threshold values, wherein each group comprisesm pieces of profile information; when the same group of time-space data is time-space consistent data, the time-space data of the gliders in the group have similar observation results; after grouping the data, outputting the group number N of the group G Machine number N M Section number N P : as shown in equation (8), equation (8) is the output data of step one and the input data of step two:
Data 1 ＝[N G ，N M ，N P ] (8)。
preferably, in the step 2, according to the obtained group number, the machine number and the section number, the operation time t, the depth dp, the temperature tp, the salinity st and the density ds of all the machine numbers and the section numbers in the same group are extracted for analysis; extracting the maximum submergence depth and the minimum surfacing depth in different section observations from the same group of data as an interpolation depth interval of the group of data; as shown in the following formula:
Figure BDA0003827081940000041
in formula (9):
Figure BDA0003827081940000042
a depth interval representing the set of data interpolations; dp down Representing a sequence of submerged depths, dp up Representing a floating depth sequence;
and then performing equal-depth interpolation on different section data in the same group of data to obtain interpolation results of temperature, salinity and density of each section in the same depth interval, wherein the interpolation results are shown as the following formula:
Figure BDA0003827081940000043
and averaging the temperature and salinity density results of the sections with the same depth to obtain an average sequence, which is shown as the following formula:
Figure BDA0003827081940000044
in the formula (11), j represents a cross-section number in the same group, and i represents a data number in the same cross-section.
Preferably, in step 3, the euclidean distance between each profile data and the average data is compared in the same depth interval, and the profile with the largest euclidean distance is selected as the suspicious sequence for further analysis, as shown in the following formula:
Figure BDA0003827081940000045
in formula (12): d Eucl (A, B) represents the calculation of Euclidean distance of the sequences A and B, and the specific calculation formula is shown in the right side of the equation;
Figure BDA0003827081940000051
the density sequence of the j-th section is calculated by a Newton interpolation method according to the measured density sequence by the formula (10),
Figure BDA0003827081940000052
representing the average density sequence of the group;
analyzing the suspicious section to find the maximum value diff of the difference diff between the equal-density surface and the average equal-density surface max And its corresponding depth dp md Finally, only extracting profile data with the difference value of the iso-surface being more than 25m and the maximum depth being more than 20m for further analysis, and outputting the machine number N of the suspicious sequence M Section number N P The maximum difference diff between the iso-surface and the average iso-surface max Maximum differential depth dp md Floating frequency N, average depth interval
Figure BDA0003827081940000053
Equal depth average density
Figure BDA0003827081940000054
Upper and lower layer thickness h 1 、h 2 Upper and lower layer density ρ 1 、ρ 2 ；
Output data based on suspicious sequencesAnd carrying out the next analysis on the suspicious sequence: firstly, according to the space coordinate of the suspicious section plane finding out the local water depth data, then according to the measured density data calculating buoyancy frequency (A)
Figure BDA0003827081940000057
Frequency) as shown in the following equation:
Figure BDA0003827081940000055
in formula (13): g represents the gravitational acceleration, and rho represents the density of the seawater;
finding the depth with the maximum buoyancy frequency as the depth d of the density jump layer pyc The maximum value diff of the density difference in the previous analysis is determined max And corresponding depth d md Substituting into equation (14), estimating the maximum internal isolated wave amplitude at the density jump layer by linear interpolation or other interpolation methods:
Figure BDA0003827081940000056
in formula (14): a represents the amplitude, diff max Denotes the maximum value of the density difference, d md Indicating the depth of occurrence of the maximum of the density difference, d tot Indicating the total water depth, d pyc Indicating the depth of the density jump.
Preferably, in the step 4, a suitable internal solitary wave theory is selected according to the thicknesses of the upper and lower layers in the ground and the amplitude of the internal solitary wave to calculate the characteristic parameters of the internal solitary wave (Cui et al, 2021), a suitable internal solitary wave theory is selected to reconstruct a temperature-salt profile of the internal solitary wave at the occurrence time, wherein the amplitude change form along the depth is calculated in a linear fitting manner or an exponential fitting manner according to the actual situation to form an isodensity surface change Δ P caused by the internal solitary wave, and the isodensity surface change Δ P is compared with the initial density profile P 0 And combining to obtain density profile data which is finally output and contains the influence of the internal solitary wave on the isosurface, wherein the density profile data is shown as the following formula:
P＝P 0 +ΔP (15)
in formula (15): p denotes the output density profile, P 0 The original density profile is shown, and Δ P represents the change in the iso-surface due to the internal solitary wave calculated according to the algorithm.
The internal solitary wave parameter extraction algorithm based on the underwater glider has the beneficial effects that:
1. the algorithm for extracting the characteristic parameters of the internal solitary waves from the glider matrix observation data greatly improves the maneuverability of internal solitary wave observation, improves the probability of capturing the data of the internal solitary waves, and simultaneously improves the accuracy of inverting the characteristic parameters of the internal solitary waves.
2. The invention adopts matrix type observation data, can more accurately measure the space-time distribution of the local temperature salt, and is more beneficial to the extraction of the characteristic parameters of the internal solitary wave.
3. The invention adopts hierarchical clustering analysis and considers the space-time factor at the same time, and the grouping method of the data is closer to the actual situation.
4. The characteristic parameters of the method are inverted by adopting the internal solitary wave theory, and the inversion result is more accurate.
Drawings
FIG. 1 is a block flow diagram of the present invention;
fig. 2 is a diagram of the optimal application range of the internal soliton theory.
Detailed Description
The following description is only exemplary of the present invention and should not be construed as limiting the scope of the present invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention should be included in the scope of the present invention.
In an initial embodiment, the invention relates to an internal solitary wave parameter extraction algorithm based on an underwater glider, wherein the extraction algorithm adopts a glider matrix for observation, and the glider matrix refers to the following steps: a plurality of underwater gliders (namely a plurality of underwater gliders are arranged in a space matrix form) are thrown at different places at the same time in a space matrix form, and the data of the temperature and salt space-time change of a target sea area can be captured more accurately through simultaneous observation of the plurality of underwater gliders, so that the characteristic parameters of the inner solitary wave can be estimated more accurately. As shown in fig. 1 and 2, the method comprises the following steps:
step 1, extracting space-time data observed by an underwater glider, wherein the space-time data comprises a glider machine number, an observation section number, a measuring point longitude and latitude, observation time, temperature, salinity, density and water depth, and analyzing the space-time data by adopting a hierarchical clustering analysis method to obtain a simultaneous space data set;
step 2, performing equal-depth interval interpolation on data in the simultaneous space data set to obtain equal-depth average data, then comparing single-section data with the equal-depth average data, calculating Euclidean distance, and judging; the single-section data refers to: when each group of data obtained by hierarchical clustering analysis contains a plurality of profile data of different gliders at different time, single profile information in the same group is single profile data; comparing the single profile with the average data in the same group to reflect the difference between the single profile and the average data;
step 3, extracting suspicious sequences with the maximum depth difference of the iso-surface larger than 25m and the depth of the maximum depth difference larger than 20m, and performing data and calculation on machine numbers, section numbers and average densities of the suspicious sequences to obtain ocean layering conditions, floating frequencies and inner isolated wave amplitudes;
and 4, selecting a proper internal solitary wave theory according to the ocean layering situation and the amplitude of the internal solitary wave and the optimal application range of the internal solitary wave theory to invert the influence of the internal solitary wave on the isosurface, combining the influence with the original profile, and finally outputting a density profile containing the information of the internal solitary wave.
In a further embodiment, as shown in fig. 1 and 2, in step 1, a bottom-up (hierarchical clustering) hierarchical clustering analysis method is adopted, which is based on the basic principle that each sample in the data is first regarded as a class, then the same class is searched according to the distance, the samples are combined step by step upwards, and finally a total cluster is formed; the distance calculation method between classes is a shortest distance method (single distance) or a longest distance method (complete distance) or an average distance method (average distance) or a central distance method (centroid distance).
In a further embodiment, as shown in fig. 1 and 2, in step 1, all the sections observed by all gliders in the same mission are first extracted, and the machine number n corresponding to each observed section is extracted respectively M Section number n P Longitude and latitude lon and lat of entry point, and section average time parameter
Figure BDA0003827081940000071
And the time is subjected to dimensionless processing, as shown in formula (1), so that the time meets the order requirement of space-time clustering analysis:
Figure BDA0003827081940000072
in the formula (1), the reaction mixture is,
Figure BDA0003827081940000073
is the average value of the observation time of a single section, with the unit of second, r t Is a cluster analysis spatial threshold c S And a time threshold c T Ratio of (i.e. r) t ＝c S /c T ；
Then, a three-dimensional matrix of spatio-temporal data is constructed, having:
Figure BDA0003827081940000074
assume that the clustering criterion (here, the clustering criterion refers to a threshold value of cluster analysis time and spatial distance, i.e., c mentioned above, set manually by a data analyst S And c T ) The three-dimensional matrix is divided into m disjoint classes C 1 ，C 2 ，...，C m Then two kinds of C α And C β The distance of (d) is expressed as:
Figure BDA0003827081940000081
in the formula (3), Δ is a distance operator, Δ a，b Representing the euclidean distance from sample a to sample b;
Figure BDA0003827081940000082
represents class C α Longitude and latitude and average time parameters of the ith sample;
in the calculation, the shortest distance method is expressed as:
Figure BDA0003827081940000083
the longest distance method is expressed as:
Figure BDA0003827081940000084
the class average distance method is expressed as:
Figure BDA0003827081940000085
the center distance method is expressed as:
Figure BDA0003827081940000086
in the formula (7), the reaction mixture is,
Figure BDA0003827081940000087
and
Figure BDA0003827081940000088
are respectively like C α And C β At the center of (i.e. C) α And C β Average of the samples in (1).
In a further embodiment, as shown in fig. 1 and 2, in step 1, the time-space data is analyzed by selecting the shortest distance method, and during the analysis, the original time-space data is referred to according to longitude and latitude and average timeDividing the distance into M groups of data according to the standard that the distance is less than the threshold value of each group, wherein each group comprises M section information; when the same group of time-space data is time-space consistent data, the time-space data of gliders in the group have similar observation results; after grouping the data, outputting the group number N of the group G Machine number N M Section number N P : as shown in equation (8), equation (8) is the output data of step one and the input data of step two:
Data 1 ＝[N G ，N M ，N P ] (8)。
in a further embodiment, as shown in fig. 1 and 2, in step 2, according to the obtained group number, machine number and section number, the operation time t, depth dp, temperature tp, salinity st and density ds of all machine numbers and section numbers in the same group are extracted for analysis; extracting the maximum submergence depth and the minimum surfacing depth in different section observations from the same group of data as an interpolation depth interval of the group of data; as shown in the following formula:
Figure BDA0003827081940000089
in formula (9):
Figure BDA00038270819400000810
a depth interval representing the set of data interpolations; dp down Representing a sequence of submerged depths, dp up Representing a floating depth sequence;
and then performing equal-depth interpolation on different section data in the same group of data to obtain interpolation results of temperature, salinity and density of each section in the same depth interval, wherein the interpolation results are shown as the following formula:
Figure BDA0003827081940000091
and averaging the temperature and salinity density results of the sections with the same depth to obtain an average sequence, which is shown as the following formula:
Figure BDA0003827081940000092
in the formula (11), j represents a cross-section number in the same group, and i represents a data number in the same cross-section.
In a further embodiment, as shown in fig. 1 and 2, in step 3, the euclidean distance between each section data and the average data is compared in the same depth interval, and the section with the largest euclidean distance is selected as the suspicious sequence for further analysis, as shown in the following formula:
Figure BDA0003827081940000093
in formula (12): d Eucl (A, B) represents the calculation of Euclidean distance of the sequences A and B, and the specific calculation formula is not shown on the right side of the equation;
Figure BDA0003827081940000094
the density sequence representing the jth section is calculated by a Newton interpolation method according to the measured density sequence by the formula (10),
Figure BDA0003827081940000095
representing the mean density series of the group;
analyzing the suspicious section to find the maximum value diff of the difference diff between the equal-density surface and the average equal-density surface max And its corresponding depth dp md Finally, only extracting the profile data with the difference value of the iso-dense surface being more than 25m and the maximum depth difference being more than 20m for further analysis, and outputting the machine number N of the suspicious sequence M Section number N P The maximum difference diff between the iso-surface and the average iso-surface max Maximum differential depth dp md Floating frequency N, mean depth interval
Figure BDA0003827081940000096
Equal depth average density
Figure BDA0003827081940000097
Upper and lower layer thicknessh 1 、h 2 Upper and lower layer density ρ 1 、ρ 2 ；
And based on the output data of the suspicious sequence, carrying out the next analysis on the suspicious sequence: firstly, local water depth data is found according to the space coordinates of the suspicious section, and then buoyancy frequency is calculated according to the measured density data (
Figure BDA0003827081940000098
Frequency) as shown in the following equation:
Figure BDA0003827081940000101
in formula (13): g represents the gravity acceleration, and rho represents the density of the seawater;
finding the depth with the maximum buoyancy frequency as the depth d of the density jump layer pyc The maximum value diff of the density difference in the previous analysis is determined max And corresponding depth d md Substituting into equation (14), estimating the maximum internal isolated wave amplitude at the density jump layer by linear interpolation or other interpolation methods:
Figure BDA0003827081940000102
in formula (14): a represents the amplitude, diff max Denotes the maximum value of the density difference, d md Indicating the depth of occurrence of the density difference maximum, d tot Indicating the total water depth, d pyc Indicating the depth of the density jump layer.
In a further embodiment, as shown in fig. 1 and 2, in step 4, an appropriate internal solitary wave theory is selected according to the thicknesses of the upper and lower local layers and the amplitude of the internal solitary wave, so as to calculate the characteristic parameters of the internal solitary wave (Cui et al, 2021): as shown in the following table:
theoretical optimum range of solitary waves in Table 1
Figure BDA0003827081940000103
Selecting a proper internal solitary wave theory to reconstruct a temperature-salt profile at the time of the internal solitary wave, wherein the amplitude change form along the depth is calculated in a linear fitting mode or an exponential fitting mode according to the actual situation to form an isosurface change delta P caused by the internal solitary wave, and the isosurface change delta P is combined with an initial density profile P 0 And combining to obtain density profile data which is finally output and contains the influence of the internal solitary wave on the isosurface, wherein the density profile data is shown as the following formula:
P＝P 0 +ΔP (15)
in formula (15): p denotes the density profile of the output, P 0 The original density profile is shown, and Δ P represents the change in the iso-surface due to the internal solitary wave calculated according to the algorithm.
Appendix: the expression of the internal soliton theory is exemplified as follows:
a) The wave front of the KdV theory can be expressed as:
Figure BDA0003827081940000111
b) Inner solitary wave surface under eKdV theory:
Figure BDA0003827081940000112
c) Wavefront under mKdV theory:
Figure BDA0003827081940000113
d) Wave surface of inner solitary wave under MCC theory:
Figure BDA0003827081940000114

Claims (7)

1. an internal solitary wave parameter extraction algorithm based on an underwater glider is characterized in that: the extraction algorithm adopts a glider matrix for observation, wherein the glider matrix refers to the following steps: throwing a plurality of underwater gliders at different places in the form of a space matrix at the same time, and simultaneously observing the underwater gliders by the plurality of underwater gliders, wherein the method comprises the following steps:
step 1, extracting space-time data observed by an underwater glider, wherein the space-time data comprises a glider machine number, an observation section number, a measuring point longitude and latitude, observation time, temperature, salinity, density and water depth, and analyzing the space-time data by adopting a hierarchical clustering analysis method to obtain a simultaneous space data set;
step 2, performing equal-depth interval interpolation on data in the simultaneous empty data set to obtain average data with the same depth, then comparing single-section data with the average data with the same depth, calculating Euclidean distance, and judging; (ii) a The single section data refers to: when each group of data obtained by hierarchical clustering analysis contains a plurality of profile data of different gliders at different time, single profile information in the same group is single profile data;
step 3, extracting suspicious sequences with the maximum depth difference of the iso-surface being more than 25m and the depth of the maximum depth difference being more than 20m, and performing data and calculation on machine numbers, section numbers and average density of the suspicious sequences to obtain ocean layering conditions, floating frequencies and inner isolated wave amplitudes;
and 4, selecting a proper internal solitary wave theory according to the ocean layering condition and the amplitude of the internal solitary wave and the optimal application range of the internal solitary wave theory to invert the influence of the internal solitary wave on the isosurface, combining the internal solitary wave with the original profile, and finally outputting a density profile containing the information of the internal solitary wave.
2. The underwater glider-based internal soliton wave parameter extraction algorithm according to claim 1, characterized in that: in the step 1, a bottom-up hierarchical clustering analysis method is adopted, when the distance is calculated, firstly, the Euclidean distance between samples is calculated, and after a class group is formed, the distance between classes is calculated; the distance calculation method of the class is a shortest distance method, a longest distance method, a class average distance method or a center distance method.
3. The underwater glider-based internal soliton wave parameter extraction algorithm as claimed in claim 2, wherein: in the step 1, firstly, all the sections observed by all gliders in the same task are extracted, and the machine number n corresponding to each observation section is respectively extracted M Section number n P Longitude and latitude lon and lat of entry point, and section average time parameter
Figure FDA0003827081930000011
And the time is subjected to dimensionless processing, as shown in formula (1), so that the time meets the order requirement of space-time clustering analysis:
Figure FDA0003827081930000012
in the formula (1), the reaction mixture is,
Figure FDA0003827081930000013
is the average value of the observation time of a single section, with the unit of second, r t Is a cluster analysis spatial threshold c s And a time threshold c T Ratio of (i.e. r) t ＝c s /c T ；
Then, a three-dimensional matrix of spatio-temporal data is constructed, having:
Figure FDA0003827081930000021
suppose that the three-dimensional matrix is divided into m disjoint classes C according to the clustering criterion 1 ，C 2 ，...，C m Then two kinds of C α And C β Is expressed as:
Figure FDA0003827081930000022
in the formula (3), Δ is a distance operator, Δ a，b Representing a sampleThe Euclidean distance from a to sample b;
Figure FDA0003827081930000023
represents class C α Longitude and latitude and average time parameters of the ith sample;
in the calculation, the shortest distance method is expressed as:
Figure FDA0003827081930000024
the longest distance method is expressed as:
Figure FDA0003827081930000025
the class average distance method is expressed as:
Figure FDA0003827081930000026
the center distance method is expressed as:
Figure FDA0003827081930000027
in the formula (7), the reaction mixture is,
Figure FDA0003827081930000028
and
Figure FDA0003827081930000029
are respectively indicated as class C α And C β In the centre, i.e. C α And C β Average of the samples in (1).
4. The underwater glider-based internal soliton wave parameter extraction algorithm according to claim 3, characterized in that: in the step 1, the shortest distance method is selected to analyze the time-space data,during analysis, dividing the original spatiotemporal data into M groups of data according to the standards that the longitude and latitude and the average time parameter distance are smaller than respective threshold values, wherein each group comprises M pieces of profile information; when the same group of time-space data is time-space consistent data, the time-space data of gliders in the group have similar observation results; after grouping the data, outputting the group number N of the group G Machine number N M Section number N P : as shown in equation (8):
Data 1 ＝[N G ，N M ，N P ] (8)。
5. the underwater glider-based internal soliton wave parameter extraction algorithm as claimed in claim 4, wherein: in the step 2, extracting the operation time t, the depth dp, the temperature tp, the salinity st and the density ds of all the machine numbers and the section numbers in the same group for analysis according to the obtained group number, the machine number and the section number; extracting the maximum submergence depth and the minimum surfacing depth in different section observations from the same group of data as an interpolation depth interval of the group of data; as shown in the following formula:
Figure FDA0003827081930000031
in formula (9):
Figure FDA0003827081930000032
a depth interval representing the set of data interpolations; dp down Representing a sequence of submerged depths, dp up Representing a floating depth sequence;
and then performing equal-depth interpolation on different section data in the same group of data to obtain interpolation results of temperature, salinity and density of each section in the same depth interval, wherein the interpolation results are shown as the following formula:
Figure FDA0003827081930000033
and averaging the temperature, salinity and density results of different sections with the same depth to obtain an average sequence as shown in the following formula:
Figure FDA0003827081930000034
in the formula (11), j represents a cross-section number in the same group, and i represents a data number in the same cross-section.
6. The underwater glider-based internal soliton wave parameter extraction algorithm according to claim 5, wherein: in the step 3, the euclidean distance between each section data and the average data is compared in the same depth interval, and the section with the largest euclidean distance is selected as a suspicious sequence for further analysis, as shown in the following formula:
Figure FDA0003827081930000035
in formula (12): d Eucl (A, B) represents the calculation of Euclidean distance of the sequences A and B, and the specific calculation formula is not shown on the right side of the equation;
Figure FDA0003827081930000036
the density sequence representing the jth section is calculated by a Newton interpolation method according to the measured density sequence by the formula (10),
Figure FDA0003827081930000037
representing the mean density series of the group;
analyzing the suspicious section to find the maximum value diff of the difference diff between the equal-density surface and the average equal-density surface max And its corresponding depth dp md Finally, only extracting the profile data with the difference value of the iso-dense surface being more than 25m and the maximum depth difference being more than 20m for further analysis, and outputting the machine number N of the suspicious sequence M Section number N P The maximum difference diff between the iso-surface and the average iso-surface max Maximum difference depth dp md Float frequency N, mean depthInterval(s)
Figure FDA0003827081930000038
Equal depth average density
Figure FDA0003827081930000039
Upper and lower layer thickness h 1 、h 2 Upper and lower layer densities ρ 1 、ρ 2 ；
And based on the output data of the suspicious sequence, carrying out the next analysis on the suspicious sequence: firstly, local water depth data is found according to the space coordinates of the suspicious section, and then the buoyancy frequency is calculated according to the measured density data, as shown in the following formula:
Figure FDA0003827081930000041
in formula (13): g represents the gravity acceleration, and rho represents the density of the seawater;
finding the depth with the maximum buoyancy frequency as the depth d of the density jump layer pyc The maximum value diff of the density difference appearing in the previous analysis max And corresponding depth d md Substituting into equation (14), estimating the maximum internal isolated wave amplitude at the density jump layer by linear interpolation or other interpolation methods:
Figure FDA0003827081930000042
in formula (14): a represents the amplitude, diff max Denotes the maximum value of the density difference, d md Indicating the depth of occurrence of the density difference maximum, d tot Indicating the total water depth, d pyc Indicating the depth of the density jump.
7. The underwater glider-based internal soliton wave parameter extraction algorithm according to claim 6, wherein: in the step 4, according to the thicknesses of the upper and lower layers in the ground and the amplitude of the internal solitary wave, a proper internal solitary wave theory is selected for internal solitary wave characteristic parameterCalculating numbers, selecting a proper inner solitary wave theory to reconstruct a temperature-salt profile at the occurrence moment of the inner solitary wave, wherein the amplitude variation form along the depth is calculated in a linear fitting mode or an exponential fitting mode according to the actual situation to form an isosurface variation delta P caused by the inner solitary wave, and the isosurface variation delta P is compared with the initial density profile P 0 And combining to obtain density profile data which is finally output and contains the influence of the internal solitary wave on the isosurface, wherein the density profile data is shown as the following formula:
P＝P 0 +ΔP (15)
in formula (15): p denotes the density profile of the output, P 0 The original density profile is shown, and Δ P represents the change in the iso-surface due to the internal solitary wave calculated according to the algorithm.
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