A method for detecting internal waves in the ocean based on an autonomous underwater vehicle

Abstract

Aiming at the specific marine physical phenomenon of ocean internal wave, the application provides a method for directly detecting ocean internal wave by an autonomous underwater vehicle, and realizes sensing and observation of the ocean internal wave. The autonomous underwater vehicle ocean internal wave observation method and system constructed by the technical scheme of the application can directly measure the ocean internal wave, and continuously optimize the water temperature profile regression equation through the process of floating and sinking, and continuously optimize the water wave background data through depth navigation, so as to enhance the detection accuracy. Compared with the temperature chain detection of internal wave, the technical scheme of the application is more simple and flexible, the autonomous underwater vehicle is movable, can flexibly switch the detection route for tracking, has a large detection range, a lower detection cost, simple data processing, and can be applied to observation of natural ocean internal wave and observation of ocean internal wave caused by a marine navigation body, and has wide application prospect and scientific value.

Description

Technical Field

[0001] This invention relates to the field of ocean internal wave technology, and in particular to a method for detecting ocean internal waves based on an autonomous underwater vehicle. Background Technology

[0002] Internal ocean waves are a ubiquitous phenomenon in the ocean, occurring within the density-stabilized stratified seawater. The amplitude of internal waves is generally larger than that of surface waves, ranging from several meters to tens or even hundreds of meters. Atmospheric pressure fluctuations, seabed topography, and moving objects can all induce the generation of internal waves. The generation and mixing of internal ocean waves play a crucial role in the transport of mass, momentum, and energy in the ocean, as well as in global climate change. Achieving fully autonomous real-time internal wave detection is timely and necessary.

[0003] Techniques for monitoring internal waves in the ocean include visible light or SAR satellite remote sensing based on sea surface changes, internal wave observation based on acoustic field interferometry, and time-series internal wave observation based on moored temperature chains. In applications of internal waves, some researchers have also conducted numerical forecasting of internal waves based on ocean dynamic models in areas prone to them. The limitations of satellite remote sensing for internal waves lie in its inability to accurately reflect subsurface environmental characteristics, requiring the use of other methods to provide underwater internal wave parameters. Furthermore, the retrieved internal wave parameters cannot be communicated in real-time with underwater platforms, limiting their timeliness. The method of retrieving internal wave parameters based on the influence of acoustic field interferometry has relatively high system complexity, especially in normal mode separation, requiring data from a full-depth vertical receiving array and accurate knowledge of the array's orientation. The moored temperature chain method for internal wave observation suffers from drawbacks such as fixed-point detection, complex data, high deployment costs, and significant safety risks. Numerical prediction methods based on ocean dynamic models can provide global internal wave activity processes through numerical calculations, but obtaining accurate local parameters requires prior information such as observational data. Therefore, it is particularly necessary to develop a new system and method that can easily and accurately detect ocean internal waves. Summary of the Invention

[0004] This invention proposes a method for directly detecting internal ocean waves based on an autonomous underwater vehicle (AUV), enabling the perception and observation of internal ocean waves. The autonomous underwater vehicle (AUV) navigates in the observed sea area, loading a marine background environment database and combining the temperature and depth sensors and signal processors onboard the AUV to measure temperature and depth, obtaining a depth-temperature regression equation for the water body's temperature and depth structure background. It obtains water temperature time series through constant-depth navigation, and uses the depth-temperature regression equation to invert and obtain a water wave amplitude time series. Navigation observations with small water wave amplitudes are used as water wave background data. The temperature time series from the constant-depth navigation observations is inverted into a water wave time series in real time. Comparing this with the water wave background data, if the wave amplitude is significantly higher than the average amplitude of the background field wave, it is considered that internal ocean waves exist; otherwise, it is considered that internal ocean waves do not exist. Temperature profile measurement data during the AUV's ascent and descent are continuously added to the temperature structure background database, and the depth-temperature regression equation is iterated and optimized in real time. Navigation data (wave amplitude time series) without internal ocean waves are added to the water wave background field data for further optimization and iteration, used for the next internal wave determination.

[0005] This invention provides a method for detecting internal ocean waves using an autonomous underwater vehicle, comprising the following steps:

[0006] S1—Water Temperature and Depth Structure Background Loading Observation: Load the marine background environment database of the observation area, and measure the water temperature profile structure of the observation area multiple times by integrating temperature and depth sensors and signal processors on an autonomous underwater vehicle. After data assimilation, the average temperature profile curve is obtained, and the initial depth-temperature regression equation is obtained by fitting.

[0007] S2 – Water Wave Background Observation: The autonomous underwater vehicle (AUV) equipped with a temperature and depth sensor dives to a certain depth in the observation area and navigates at a certain speed to measure the temperature of the water body in real time. After a period of time, it surfaces and transmits the temperature observation data back. The water wave amplitude-time curve is obtained by inverting the optimized depth-temperature regression equation. If the water wave amplitude is less than a certain value, the water wave amplitude-time curve of this section of navigation is used as the water wave background data. In addition, the depth-temperature data measured by the AUV during surfacing and diving in S2 is added to the water temperature profile observation database in S1 to enrich the water temperature structure background observation data and obtain the optimized depth-temperature regression equation.

[0008] S3 – Depth-Controlled Navigation Data Detection: The autonomous underwater vehicle (AUV) equipped with a temperature sensor descends to the S2 depth-controlled navigation depth in the observation area and observes the real-time temperature of the water body at a certain speed. After a period of time, it surfaces and substitutes the temperature observation time series into the optimized depth-temperature regression equation to obtain the real-time water body fluctuation amplitude time series. If the water body fluctuation amplitude is greater than a certain value, the internal wave comparison and decision are performed in S4. Otherwise, the navigation observation data is added to the water body fluctuation background field data as a new water body fluctuation background. At the same time, the depth-temperature data measured by the AUV during surfacing and diving in S3 are added to the depth-temperature data matrix in S2 to enrich the water body temperature structure background observation data and update the depth-temperature regression equation.

[0009] S4 – Internal Wave Comparison and Judgment: Compare and analyze the real-time water wave amplitude observation data during navigation with the background water wave data. If the real-time wave amplitude value during navigation is significantly higher than the average amplitude of the background field wave, it is considered that there is an internal ocean wave here; otherwise, it is considered that there is no internal ocean wave here.

[0010] Preferably, the speed of the underwater autonomous vehicle is 3-12 knots, more preferably 4-10 knots.

[0011] Preferably, the depth of the underwater autonomous vehicle at which it travels at a constant depth is 30 to 500 meters above the sea surface, more preferably 50 to 300 meters.

[0012] Preferably, the temperature depth sensor is a CTD sensor.

[0013] Preferably, when the water wave amplitude is less than 2 meters in step S2, the water wave amplitude-time curve of that section of the voyage is used as the water wave background data.

[0014] Preferably, if the amplitude of water wave fluctuation is greater than 2 meters in step S3, then S4 internal wave comparison and judgment are performed.

[0015] Preferably, the ocean internal waves include natural ocean internal waves and ocean-sourced internal waves.

[0016] This invention also discloses an ocean internal wave detection system, comprising: an underwater autonomous vehicle (1), a temperature and depth testing device (1-1), a data storage device (1-2), a depth-fixed navigation control device (1-3), a signal transmitting device (1-4), a signal receiving device (2-1), and a data processing terminal device (2-2); the temperature and depth testing device (1-1), the data storage device (1-2), the depth-fixed navigation control device (1-3), and the signal transmitting device (1-4) are integrated and mounted on the underwater autonomous vehicle (1); the temperature and depth data measured by the temperature and depth testing device (1-1) during the ascent and descent of the underwater autonomous vehicle (1) are stored in the data storage device (1-2), and after ascent, the data is transmitted to the signal receiving device (1-4) via the signal transmitting device (1-4). The signal receiving device (2-1) can transmit data information to the data processing terminal (2-2) for data processing, obtain and continuously update and iterate to generate the depth-temperature regression equation; the underwater autonomous vehicle (1) navigates at a constant depth under the control of the constant depth navigation control device (1-3). During the navigation, the temperature data measured by the temperature depth testing device (1-1) is stored in the data storage device (1-2). After surfacing, the data is sent to the signal receiving device (2-1) through the signal transmitting device (1-4). The signal receiving device (2-1) can transmit data information to the data processing terminal (2-2) for data processing, generate the water wave amplitude-time curve as the water wave background, perform internal wave comparison and judgment, and continuously update and iterate to generate a new water wave background.

[0017] The beneficial effects of the present invention are as follows: The autonomous underwater vehicle ocean internal wave observation method and system constructed using the technical solution of the present invention can directly measure ocean internal waves, and continuously optimize the depth-temperature regression equation through the ascent and descent process, and continuously optimize the water wave background data through constant depth navigation, thereby enhancing the detection accuracy.

[0018] Compared to the drawbacks of temperature chain detection of internal waves, such as fixed-point detection, complex data, high deployment costs, and significant safety hazards, the technical solution of this invention is simpler and more flexible. Furthermore, the autonomous underwater vehicle is mobile, can flexibly switch detection routes for tracking, has a large detection range, lower detection costs, and simpler data processing. It can be applied to the observation of natural internal waves in the ocean as well as the observation of internal waves caused by ocean vehicles, and has broad application prospects and scientific value. Attached Figure Description

[0019] Figure 1 Schematic diagram of autonomous underwater vehicle detecting internal ocean waves

[0020] Figure 2 Observational sea area temperature-depth variation curve

[0021] Figure 3AUV Internal Wave Measurement Flowchart

[0022] Figure 4 Background field temperature variation curve observed by AUV during mobile observation.

[0023] Figure 5 Background field AUV observation wave inversion results

[0024] Figure 6 Temperature variation curves observed by an AUV during ocean internal wave navigation.

[0025] Figure 7 Ocean internal wave AUV observation wave inversion results

[0026] Figure 8 Ocean internal wave generation process Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The principle behind this method is as follows: the vertical distribution of seawater temperature follows this pattern: it decreases with increasing depth; from the surface to 1000 meters, the water temperature decreases rapidly with increasing depth, while below 1000 meters, the temperature decrease slows down. Because the temperature variation is large below 1000 meters, exhibiting a functional relationship, it can be measured using marine observation instruments. Figure 1 A schematic diagram illustrating the detection of internal ocean waves by an autonomous underwater vehicle.

[0029] Example 1. Ocean Internal Wave Detection by Autonomous Underwater Vehicle

[0030] S1 water temperature structure background observation

[0031] A marine background environment database for the observation area was loaded. This database contains pre-measured seawater depth and temperature data for the observation area. By integrating a CTD sensor and signal processor onto an autonomous underwater vehicle, multiple measurements of the water temperature profile structure in the observation area were performed to obtain depth-temperature data pairs. These data were then added to the marine background environment database. After data assimilation, an average temperature profile curve was obtained, and an initial depth-temperature regression equation was fitted. Figure 2 This is the depth-temperature regression curve.

[0032] S2 Water Body Fluctuation Background Observation

[0033] An autonomous underwater vehicle (AUV) equipped with a CTD sensor dives to 50m in the observation area and maintains a constant depth at 4 knots, measuring the temperature of the water body in real time. After a period of time, it surfaces and transmits the temperature observation data back. The water wave amplitude-time curve is obtained by inverting the initial depth-temperature regression equation in S1. If no water wave phenomenon of more than 2 meters occurs, the water wave amplitude-time curve of this section of the journey is used as the water wave background data. In addition, the depth-temperature data measured by the AUV during surfacing and diving in S2 are added to the depth-temperature database in S1 to enrich the background observation data of water temperature structure and obtain an optimized depth-temperature regression equation.

[0034] S3 Depth-Stabilized Navigation Data Detection

[0035] An autonomous underwater vehicle (AUV) equipped with a CTD sensor dives to 50m in the observation area and travels at a constant depth along a preset route at a speed of 4 knots. It observes the real-time temperature of the water body it is in. After a period of time, it surfaces and substitutes the temperature observation data into the optimized depth-temperature regression equation to obtain the real-time water wave amplitude-time curve. If water waves with an amplitude of more than 2 meters are observed, internal wave comparison and judgment are performed in S4. Otherwise, the observation data of this journey is added to the background field data to obtain the optimized water wave amplitude-time curve as a new water wave background. At the same time, the depth-temperature data measured by the AUV during surfacing and diving in S3 are added to the depth-temperature data matrix in S2 to enrich the background observation data of water temperature structure and update the depth-temperature regression equation.

[0036] Comparison and Judgment of S4 Internal Waves

[0037] By comparing and analyzing the real-time water wave amplitude-time curve during navigation with the background water wave data, if the real-time wave amplitude value is 20% or more higher than the average amplitude of the background wave field, it is considered that there are internal ocean waves at this location; otherwise, it is considered that there are no internal ocean waves at this location.

[0038] The detection process is as follows: Figure 3 As shown, by loading the marine background environment database of the observed sea area and combining it with the background observation of water temperature structure, an initial depth-temperature regression equation is obtained; by observing the background of water waves, the amplitude-time curve of water waves is obtained by inversion as the background of water waves, and the depth-temperature regression equation is iteratively optimized; by obtaining real-time water temperature data through constant-depth navigation data detection, the amplitude-time curve of water waves during navigation is obtained by inversion, and the amplitude-time curve of water waves during navigation is iteratively optimized as the new background of water waves, and the depth-temperature regression equation is iteratively optimized; by comparing and judging internal waves, it is determined whether it is an internal wave, and the amplitude-time curve of water waves is iteratively optimized as the new background of water waves.

[0039] like Figure 4 As shown, the temperature change curve measured by the autonomous underwater vehicle during its journey at a constant depth of 50 meters above sea level in a background field is used to invert the temperature change curve into a real-time water ripple curve during the journey. Figure 5 As shown.

[0040] like Figure 6 As shown, the temperature change curve measured during the autonomous underwater vehicle's constant-depth navigation at 50 meters above sea level was converted into a real-time water ripple curve during navigation. Figure 7 As shown. By using with Figure 5 compared to, Figure 7 The maximum amplitude in the depth variation curve is approximately 25 meters, far exceeding... Figure 5 The amplitude is 20% of the average amplitude, therefore it is determined that there are internal ocean waves here. Figure 8 The generation process of internal waves observed during a single detection process once again verifies that the technical solution of this invention can be used for internal wave detection.

Claims

1. A method for detecting internal ocean waves using an autonomous underwater vehicle, characterized in that, The autonomous underwater vehicle (AUV) navigates in the observed sea area and, by loading marine background environmental data and combining it with the temperature and depth sensors and signal processors on the AUV, performs temperature and depth measurements to obtain the depth-temperature regression equation of the water body temperature and depth structure background. Water temperature time series were obtained by fixed-depth navigation, and water wave amplitude time series were obtained by inversion through depth-temperature regression equation. Water wave observation data with amplitude less than 2 meters were used as water wave background data. The temperature time series observed during constant-depth navigation is inverted into a water wave time series in real time. Compared with the water wave background data, if the wave amplitude is 20% or more higher than the average background wave amplitude, it is considered that there is an internal ocean wave; otherwise, it is considered that there is no internal ocean wave. Temperature profile measurement data during the ascent and descent of the autonomous underwater vehicle are continuously added to the depth-temperature regression equation, and the depth-temperature regression equation is optimized and iterated in real time. Navigation observation data that are not internal ocean waves are added to the water wave background data for optimization and iteration of the water wave background, which is used for the next internal wave judgment.

2. The method for detecting ocean internal waves by an autonomous underwater vehicle as described in claim 1, wherein the ocean internal waves include natural ocean internal waves and ocean-sourced internal waves.

3. The method for detecting internal ocean waves by an autonomous underwater vehicle as described in claim 1, wherein the temperature-depth sensor is a CTD sensor.

4. A marine internal wave detection system, comprising: The underwater autonomous vehicle (1), temperature and depth testing device (1-1), data storage device (1-2), depth-fixed navigation control device (1-3), signal transmitting device (1-4), signal receiving device (2-1), and data processing terminal device (2-2) are integrated on the underwater autonomous vehicle (1). During the ascent and descent process, the temperature and depth data measured by the temperature and depth testing device (1-1) of the underwater autonomous vehicle (1) are stored in the data storage device (1-2). After ascent, the data is sent to the signal receiving device (2-1) through the signal transmitting device (1-4). The signal receiving device (2-1) can transmit the data information to the data processing terminal device (2-2) for data processing, obtain and continuously update and iterate to generate the depth-temperature regression equation. The underwater autonomous vehicle (1) navigates at a constant depth under the control of the constant depth navigation control device (1-3). During the navigation, the temperature data measured by the temperature depth testing device (1-1) is stored in the data storage device (1-2). After surfacing, the data is sent to the signal receiving device (2-1) through the signal transmitting device (1-4). The signal receiving device (2-1) can transmit the data information to the data processing terminal device (2-2) for data processing, generate a water wave amplitude time series as the water wave background, perform internal wave comparison and judgment, and continuously update and iterate to generate a new water wave background.

5. The ocean internal wave detection system as described in claim 4, wherein the temperature and depth testing device (1-1) is a CTD sensor.

Images