KR101885991B1 - SYSTEM FOR MONITORING SEA ENVIRONMENT USING Ocean Acoustic Tomography AND METHOD THEREOF. - Google Patents

Abstract

The present invention provides an acoustic tomography system to observe and monitor marine environments in real-time, comprising: a data measurement unit (P.1) including an acoustic data measurement unit and an environmental data measurement unit; a data transmission unit (P.2) connected to the data measurement unit (P.1) to transmit the measured acoustic and environmental data to a data processing unit (P.3); a first data processing unit (P.3) receiving the data from the data transmission unit (P.2) to calculate a flow speed distribution by each vertical layer; a second data processing unit (P.4) receiving the data from the data transmission unit (P.2) to calculate a sound speed distribution; and a data synchronization unit (P.5) using a result of the first and second data processing units (P.3, P.4) and a digital marine model to correct data and output a result. According to the present invention, seawater temperature and flow direction/speed changes of the sea are able to be monitored, thereby being used for marine product industries, such as a cultivating industry, fishery, and the like, and performing a role for predicting and preventing natural disasters, such as red tide, tsunami, and the like.

Description

TECHNICAL FIELD [0001] The present invention relates to an acoustic tomography system and a method thereof for monitoring a real-time marine environment.

The present invention relates to a marine acoustic tomography technique, and more particularly, to an acoustic tomography system for observing a marine environment by a vertical real-time single layer considering both a sound velocity distribution and a flow velocity distribution of a vertical single layer of the ocean, and a marine environment monitoring method using the same .

Marine acoustic tomography, or Ocean Acoustic Tomography (OAT), is a method designed to observe changes in the marine environment, such as sea water temperature or directional flow, in relation to global climate. The sound velocity in the sea is about 1500 / sec, but the sound velocity increases in the surface layer with high temperature and the sea floor under high pressure, and it decreases in the middle 1000 ~ 1500m water depth layer. This layer is called a sound channel. When a sound wave is emitted from this layer, the sound wave propagates in a sine curve in this layer, so that it does not reach the sea surface or the seabed so that the attenuation due to reflection propagates long distances do. Marine acoustic tomography uses this characteristic. By arranging a plurality of devices that surround a certain sea area and combining a sound source and a receiver and obtaining the difference between the theoretical value and the experimental value with respect to the time during which the sound waves are transmitted between the respective devices, The distribution of water temperature and flow can be investigated in three dimensions.

The Japan Marine Science and Technology Center (JAMSTEC) is developing a 20Hz low frequency sound source with 1000km propagation capability for the purpose of surveying a 1000km sea area. In the United States, it has received 57Hz sound from the Indian Ocean and received it in the Pacific and the Atlantic Ocean . In recent years, coastal acoustic tomography (CAT) research has been attracting attention in order to observe changes in water temperature and ocean currents in relatively shallow coastal areas.

Through the use of tomography techniques, it is possible to observe changes in water temperature and directional flow rate, which have a great influence on marine life, and thus can be used not only for global environmental monitoring but also for aquaculture such as aquaculture and fishery. Can predict and prevent natural disasters.

As a general method of ocean acoustic tomography, measurements of CTD sensor and ADCP should be made to observe the changes in sea water temperature and flow direction. In addition, estimation of water temperature in a wide area is possible by remote ocean exploration method through satellite, but this is actually limited to surface water temperature.

In the conventional tomography system, to calculate the water temperature distribution and the velocity distribution for each vertical fault, each other tomography system is used. In order to calculate the water temperature distribution, it is necessary to use one wave and water wave for each vertical wave. Using only the measured acoustic signal arrival information, the water temperature distribution for each vertical fault layer is calculated. In order to calculate the flow velocity distribution, at least one pair of transmitting and receiving duplexers is required, and the propagation time difference and the path information of the sound waves measured by the transceivers are inversely calculated using the propagated time, To be limited. Therefore, it is necessary to study the ocean acoustic tomography which can provide more accurate oceanographic information accurately by improving the technique of ocean acoustic tomography.

Korean Patent Laid-Open Publication No. 10-2003-0000681 discloses a method in which first and second acoustic tomograms each composed of a VLA portion in which a plurality of hydrophones are arranged at regular intervals (d) and an acoustic source portion that generates a detection sound wave, A step of arranging them so as to have a distance; Projecting one VLA portion having a three-dimensional slope of the first acoustic tomography to a contiguous plane axis having an angle of [alpha] [theta], causing the projected plane axis to come to an acoustical source portion of the second acoustic tomography; ; Wherein an angle between an acoustic source part of the first acoustic tomography and an axis perpendicular to an extension line of the projected VLA sub-center of the first acoustic tomography and the projected VLA is β and the angle of the received sound wave is Φ And determining a delay time for the beamforming in the case of the beamforming of the acoustic acoustic tomography. Korean Patent No. 10-1031875 discloses a method for evaluating the threat of intentions in the marine acoustic environment, comprising the steps of (a) selecting threat considerations to be considered in the threat evaluation, and (b) (C) calculating the threat considerations for the targets by numerical values in consideration of the current underwater environment, and (d) ) Calculating the integrated threat value per target by fusing the weight parameter of the step (b) to the calculated value of the step (c), and (e) calculating the final threat value by further correcting the operator's offset value to the integrated threat value And a method for evaluating a threat in consideration of a marine acoustic environment including a step of performing a threat assessment. Korean Patent No. 10-1502408 discloses a noise generator for generating a noise source such as engine noise of a ship, noise of a propeller, noise generated by a ship, etc., occurring in a marine environment; A transfer function generator for predicting the inherent sound information of the amplitude of the sound line, the time delay and the phase of the sound reaching the receiver from the noise source, and generating a transfer function corresponding to the predicted inherent sound information; A simulator for simulating a sonar reception signal by multiplying the noise source received by the receiver by the transfer function; Calculates a reception distance difference of the sound line based on a difference of arrival angles of the sound lines reaching a plurality of array receivers and approximates the simulated sonar reception signal in consideration of a transmission loss corresponding to the difference of the reception distances of the sound lines And a controller for generating the approximate received signal as a multi-channel receive acoustic signal, and a method for generating a multi-channel receive acoustic signal in a marine environment. However, the above-mentioned inventions differ from the configuration and effect of the tomography system for real-time marine environment observation of the present invention which more accurately provides ocean observation information.

In the conventional tomography system, each tomography system is used to calculate the water temperature distribution and the velocity distribution of vertical faults. Generally, one water wave and one water wave are required to calculate the water temperature distribution, The water temperature distribution of the vertical faults should be calculated using only the acoustic signal arrival information measured in the vertical direction. In order to calculate the flow velocity distribution, at least one pair of transmitting and receiving duplexers is required, and the propagation time and the path information of the sound waves measured by the transceivers are inversely calculated using the propagated time, The present invention provides a marine acoustic tomography system capable of accurately providing more extended marine observation information. SUMMARY OF THE INVENTION

In order to solve the above problems, the present invention provides a marine acoustic tomography system, comprising: a data measuring unit P.1 consisting of an acoustic data measuring unit and an environmental data measuring unit; (P.2) for transmitting sound data and environmental data to a data processing unit (P.3), a first transmission unit for receiving data from the data transmission unit (P.2) A second data processing unit P.4 for calculating a sound velocity distribution by receiving data from the data transfer unit P.2, And a data moving unit (P.5) for outputting a result of the data correction using the result of the second data processing unit (P.4) and the marine numerical model, and a tomography system for real-time marine environment observation .

By using the ocean acoustic tomography system and the environmental measurement method for observing the marine environment of the real time vertical single layer according to the present invention, it is possible to monitor the change of the water temperature and the directional flow of the ocean and can be used for the fisheries such as aquaculture and fishing, And a natural disaster such as a tsunami can be predicted and prevented.

1 is a schematic diagram of a tomography technique for observing a real-time marine environment according to an embodiment of the present invention.
FIG. 2 is a flowchart illustrating a tomography technique for observing a real-time marine environment according to an embodiment of the present invention.
3 is a detailed block diagram of a tomography technique measurement and transmission unit for real-time marine environment observation according to an embodiment of the present invention (P.1 to P.2).
4 is a detailed block diagram of the tomography technique data processing and animation unit for real-time marine environment observation according to the embodiment of the present invention (P.3 to P.5).
5 is an exemplary graph of SVP, CVP, and C-SVP of a vertical fault plane according to an embodiment of the present invention.
FIG. 6 illustrates a process of comparing data and measured data through a sound tracing model according to an embodiment of the present invention.
FIG. 7 is a diagram illustrating a process of identifying an acoustic line in time according to an acoustic line tracking model according to an embodiment of the present invention.
8 is a graph showing the results of water temperature distribution experiment for each vertical single layer according to an embodiment of the present invention.
FIG. 9 shows a real time flow velocity observation and a flow velocity correction result using mutual multiple models according to an embodiment of the present invention.

The present invention relates to an acoustic tomography system for observing a marine environment in a real-time vertical fault layer in consideration of a sound velocity distribution and a velocity distribution of a vertical single layer of the ocean, and a method for observing a marine environment using the system.

In the present invention, unlike the conventional ocean acoustic tomography technique, the water temperature and the flow velocity of each vertical fault layer are simultaneously considered, thereby providing improved accuracy over the existing tomography technique. To this end, in the present invention, a pair of transmitting and receiving duplex transceivers are basically used, and the signal arrival time and the sound path are analyzed in both transceivers to calculate a vertical current profile (CVP). The estimated CVP affects the delivery speed of the actual sound signal, that is, the sound vertical profile (SVP). Therefore, to minimize the effect of CVP on SVP, the accuracy of the obtained flow velocity and water temperature is improved by using the vertical sound velocity distribution considered together with the flow velocity, that is, C-SVP (Combined Sound Vertical Profile).

Also, in the present invention, the flow velocity and water temperature distribution of the vertical single layer finally obtained are corrected in real time using a data moving part, i.e., a marine model using existing cumulative measured marine information. In addition, we apply a mutual multiple model to cope with unexpected environmental changes in a stochastic manner, so that the reliability of the entire system can be improved and provided to users. A specific example will be described in detail below.

FIG. 1 is a schematic view of a tomography technique for observing a real-time marine environment according to an embodiment of the present invention, and FIG. 2 is a flowchart thereof. The tomography system for observing real-time marine environment (water temperature, flow velocity) is composed of a data measurement unit P.1, a data transfer unit P.2, a first data processing unit I (P.3), a second data processing unit P. 4) and a data moving unit (P.5).

The data measuring part (P.1) is divided into a part for measuring acoustic data and a part for measuring environmental data. First, the sound data measuring unit basically measures a bidirectional sound signal in real time using a transceiver for both transmission and reception.

The signal arrival time corresponding to the multipath is obtained by calculating the power delay profile (PDP) based on the measured sound signal. In addition, the depth information can be extracted through the pressure sensor attached to the transceiver. The acoustic data measuring unit is connected to the system or a part via a cable. And transmits the acoustic data information and the power delay profile (PDP) information obtained through bidirectional transmission and reception to the connected system, and the GPS information for calculating the distance of the transceiver and the depth information obtained from the pressure sensor. At this time, the system or transponders are synchronized with each other through a time synchronization device (eg GPS module). Environmental data measuring part is based on one-time measurement.

In addition, echosounders are used to measure local submarine topography information. And the Sound Velocity Profile (SVP) is measured by the CTD (Conductivity-Temperature-Depth sensor). At this time, the measured SVP can continuously update the water temperature and flow velocity data processed by the data processing unit in the future and provide information in real time.

The data transfer section (P.2) consists of a system (transceiver) or a buoy, and is connected to the transceiver through a cable. The data transmission unit transmits the acoustic data measured by the transceiver and the marine environment data to the external central processing unit, that is, the data processing unit through the wireless communication unit.

The data processing unit is an external central processing unit in the boat of the watercraft or in a building or place near the waterfront, which serves to process signals transmitted from the system or the buoy. In the present invention, the vertical single-layer flow velocity distribution (first data processing section) and sound velocity distribution (second data processing section) are divided.

The first data processing unit (P.3) measures the flow velocity distribution of each vertical single layer as follows. Basically, based on the SVP measured by the CTD, Ray tracing is performed in a given environment to predict the arrival path of the incoming sound. In this case, SVP (z) is the average sound velocity per vertical fault, that is, the average sound velocity per depth of the measured area. Also, based on the predicted arrival sound path, the process is performed to identify the sound line most similar to the actually measured sound data signal PDP.

The second data processing unit P.4 considers a sonic vertical distribution, ie, a pair of C-SVPs (Combined SVP) combined with a flow velocity distribution, in order to consider the effects of the calculated velocity on the sound wave propagation velocity. Of the pair of C-SVP, one of the ⁺ C- SVP (Z) is the speed of sound the sound signal toward a vertical single-walled area, the average delivered from songsupagi songsupagi 1 to 2, C- SVP other, ^from (Z) is songsupagi 2 SVP (Z) is the average sound velocity per vertical layer of the sound signal transmitted to the transceiver 1. In this case, the value of the C-SVP can be calculated as the number of transducers increases, The accuracy of the accuracy of the identified sound lines can be improved. Therefore, by using the improved sound recognition accuracy and the multi-path sound transmission characteristic, finally, the average sound velocity distribution C _r (z) and the average water temperature distribution T _ray (z) can be obtained.

The data moving part (P.5) aims to provide the marine reliable and real time by using the marine numerical model based on the result (water temperature and flow velocity distribution) of this data processing part and the statistical data collected over the previous season. There is a moving average filter as the most basic method for data correction. However, the moving average filter does not utilize known statistical information and does not reflect marine and climatic conditions because it only averages the measured values.

In order to solve the above problem, the present invention uses a marine model (water temperature and flow rate) based on cumulative observation statistical data (seasonal and date-specific) of the information to be provided. Based on these cumulative observational statistical data, the ocean model can be used to supplement the measured water temperature and flow velocity values or missing observational data measured by the tomography system.

In addition, in the present invention, in order to consider an unexpected change in the marine environment, the interaction of various models is used as a data assimilation technique. In other words, the predicted estimates and predicted covariances are calculated from the ocean model, and the velocity and temperature values are calibrated and updated using U _ray (z) T _ray (z) calculated in steps 8) and 14).

First, we constructed a marine model by using reasonably collected statistical data, predicted the change, and used the method of reducing the error of prediction by using the observations. Because the ordinary ocean model is represented as a nonlinear function, it is easy to calculate the predictive covariance and the correction gain by linearizing using the derivative.

Nonetheless, a single ocean model and data assimilation based on cumulative observational statistics can not represent all the various changes in the unexpected reality. For example, when irregular natural phenomena such as typhoons, tsunamis, and red tides occur, the characteristics of water temperature and velocity distributions change seriously, and it is very difficult to express these changes in a single model.

Therefore, we used mutual multiple models that complement each other with various models as an algorithm that can cope with these unexpected changes. To represent mutual multiple models, it is possible to change probabilistic marine numerical models and marine observational models based on Markov model. It can have different dimensions based on marine numerical models collected from various statistics and can be created through errors based on different statistical properties. The water temperature distribution and the flow velocity distribution obtained through the mutual multiple models and the dynamic processing method are updated in real time and used instead of the water temperature distribution and the flow velocity distribution measured initially by the marine data. The data updated in real-time as described above can be free from the inconvenience of continuously measuring the marine environment using CTD or ADCP. Hereinafter, the steps of each component of the present invention will be described in detail.

<P.1 Data Measuring part  - Environmental data>

Initial setting step) Water temperature measurement by vertical single layer and measurement of undersea topography information

3 is a detailed block diagram of a tomography technique measurement and transmission unit for real-time marine environment observation according to an embodiment of the present invention (P.1 to P.2). This environmental data measurement unit is an initial measurement step to simultaneously consider the temperature and flow rate of the real ocean environment, that is, the vertical fault. At this stage, at least two marine environmental data are generally measured. First, the temperature (T), salinity (S) and pressure (z) are measured by the CTD sensor and the sound velocity distribution, ie SVP, of the vertical fault layer is calculated by Mackenzie equation as follows.

[Equation 1]

The calculated C (T, S, z) is regarded as the sound velocity distribution SVP (z) for each vertical fault layer.

Secondly, the echosounder measures sea surface topography (bathymetry). The two marine environmental data above are initial information needed to calculate the sound velocity and flow rate using the line tracing model. In the future, the initial SVP or water temperature distribution is updated to the SVP or water temperature distribution calculated in step 16 ) , thereby calculating the sound velocity and the flow velocity in real time.

<P.1 Data Measuring part  - Acoustic data>

Step 1) Acoustic signal reception through the bidirectional transceiver's hydrophone

In this step, the sound signal transmitted from each transceiver is received.

Step 2) Measure the power delay profile (PDP) of received signals

과 2번 송수파기의 신호 도착 시간

를 측정할 수 있다. 여기서 i는 다중경로로 도달하는 음선들의 개수이며, 여기서는 수학식의 편의상 i=1로 생각하고 계산하고자 한다.In this step, the multi-path power delay profile (PDP) of the received signal is measured as shown in FIG. 7 (b) using the correlation between the received sound signal and the transmitted sound signal, that is, correlation. The measured PDP assists in selecting a valid sound line in comparison with the sound line model based power delay profile calculated in the future sound tracing model. Based on the PDP measured at each transceiver, the signal arrival time at the first transceiver

And the signal arrival time of the second transceiver

Can be measured. Here, i is the number of sound lines arriving at multiple paths. Here, i is assumed to be i = 1 for convenience of the mathematical expression.

Step 3) Transfer each measured power delay profile and transceiver location and depth information

)와 송수파기2(

)의 깊이는 각각의 압력센서를 통해 알 수 있다.
In this step, the measured PDP information and the position and depth information of the transceiver are transmitted to the system (or the buoy) connected to each transceiver. Here, the position of the transceiver can be grasped by GPS, and the distance between the two transceivers can be expressed by R. Also, the depth of the transceiver 1

) And transceiver 2 (

) Can be known through the respective pressure sensors.

<P.2 Data Transfer Section>

Step 4) Transferring Data to the Data Processing Unit

The SVP (or water temperature distribution) and the undersea topography information measured by the environmental data measuring unit, the PDP measured by the sound data measuring unit, the location and depth information of the transceiver, and the like are transmitted to the external central processing unit That is, the data processing unit.

<P.3 Data processing section I - Flow rate>

Step 5) SVP-based tracing model of arrival

4 is a detailed block diagram of the tomography technique data processing and animation unit for real-time marine environment observation according to the embodiment of the present invention (P.3 to P.5). In this step, based on the received SVP information as shown in FIG. 8, the arrival sound path is predicted through the sound-line tracking model (typically using the BELLHOP model). Here, FIG. 8 shows a predicted arrival sound path according to SVP measured over time.

&Quot; (2) &quot;

-> 송수파기간의 음선경로와 PDP 생성

-> Generate sound path and PDP between transceiver

Step 6) Ray identification process

In this step, in order to identify a valid sound line, as compared with the PDP obtained from the sound data received in the M-PDP (Modeled Power Delay Profile) calculated in the sound tracing model as shown in FIG. 7 and the second step And the tendency of the sound lines reached by the multi-path, and identifies the sound lines judged to be valid.

Step 7) Calculate the actual path length of the identified sound line

를 계산한다. 여기서 i는 식별된 다중경로 음선들의 개수이며, 다중경로 개수에 따라 수직 단층별 유속 및 음속 분포가 나오게 된다. 단 여기서는 수학식의 편의상 i=1로 생각하고 계산하고자 한다.
In this step, the length of the actual movement path of the sound lines identified as shown in FIG. 8

. Where i is the number of identified multi-path sound lines, and the flow velocity and sound velocity distribution for each vertical monolayer is given by the number of multipaths. However, here, we want to calculate i = 1 for convenience of mathematical expression.

Step 8) Calculation of vertical fault velocity along the sound path

과 평균유속

을 계산하기 위해, 2단계에서 송수파기1에서 측정된

과 송수파기2에서 측정된

을 사용한다.

과

는 다음과 같이 표현될 수 있다.

,

, (여기서

은 송수파기 간의 거리). 따라서 평균음속

, 평균유속

, 즉 송수파기1에서 송수파기2로 전달되는 평균유속

을 계산할 수 있다. 그러나

은 송수파기 간의 직선 거리를 사용함으로써, 유속오차를 발생하게 된다. 이 단계에서는 7단계)에서 계산된 식별된 음선의 실제 이동 경로 길이인

을 직선거리

대신 사용하게 된다. 즉 평균유속을 다음과 같이 계산할 수 있다.Basically,

And average flow rate

, In step 2, the measured

And measured at transponder 2

Lt; / RTI &gt;

and

Can be expressed as follows.

,

, (here

Distance between transceivers). Therefore,

, Average flow rate

That is, the average flow rate from the transceiver 1 to the transceiver 2

Can be calculated. But

By using the linear distance between the transducers, a flow velocity error is generated. In this step, the actual movement path length of the identified sound line calculated in step 7)

Straight line distance

Instead. That is, the average flow rate can be calculated as follows.

&Quot; (3) &quot;

들을 고려해주면, 본 단계에서 송수파기1에서 송수파기2 방향으로 흐르는 해류의 수직 단층별 유속

을 계산할 수 있게 된다.
At this time, the length of the sound path reaching the multi-path (

The flow rate of the vertical current flowing in the direction from the transceiver 1 to the transceiver 2 in this stage

Can be calculated.

<P.4 Data processing part II - Sound speed>

Step 9) By vertical fault And SVP  measured Pair of  C- SVP  produce

값을 사용해서, 도 6과 같이 한쌍의 C-SVP를 그리고자 한다. 여기서 C-SVP는 다음과 같이 한쌍이 나타날 수 있다.In this stage, in case of SVP measured by CTD in the initial stage of environmental data measurement, it is a value measured without reflecting the influence on actual ocean current. Therefore, the average flow rate per vertical single layer calculated in data processing section I

Using the values, We want to have a pair of C-SVPs as well. Here, a pair of C-SVPs can appear as follows.

&Quot; (4) &quot;

Step 10) Estimating the arrival path of the arrival of each C-SVP based on the line tracing model

와

를 이용해서 음선추적모델을 통한 도착 음선 경로 예측을 하도록 한다.In this step, a pair of sound velocity distributions C-SVP considering the distribution of vertical single-layer flow velocity,

Wow

To perform the arrival line path prediction through the line tracking model.

&Quot; (5) &quot;

-> 송수파기1에서 송수파기2로 전달되는 음선경로와 M-PDP1 생성

-> Create the sound path and M-PDP1 to be transmitted from transceiver 1 to transceiver 2

-> 송수파기2에서의 송수파기1로 전달되는 음선경로와 M-PDP2 생성

-> Create the sound path and M-PDP2 to the transceiver 1 in the transceiver 2

Step 11) For each case, the ray identification process

In this step, in order to identify a valid sound line, a valid sound line is discriminated by comparing M-PDP1 and M-PDP2 calculated by the sound line tracking model with actually measured PDP1 and PDP2 as in FIGS. 6 and 6) .

Step 12) Identified Acute  Actual travel path length L _{One , ray}  , L _{2 , ray} Calculation

In this step, the length of the actual movement path of the sound lines identified as in FIGS. 6 and 7), not the linear distance Calculate L _{1 , ray} , L _{2 , and ray} .

Step 13) Calculate sound speed per vertical layer according to sound path

,

을 통해 고정된 수심의 평균음속

와

를 계산한다. 그리고 식별된 다중경로(i)별로 수심별 평균음속을 계산하면,

,

을 계산할 수 있으며, 최종적으로 출력하고자 하는 수직단층별 음속분포인

를 구할 수 있다.In this step, the movement path lengths L _{1 , ray} , L _{2 , and ray} And each arrival time

,

The average sound velocity of the fixed depth through

Wow

. Then, by calculating the average sound velocity per depth by the identified multipath (i)

,

, And the distribution of the sound velocity of the vertical fault layer to be finally output

Can be obtained.

&Quot; (6) &quot;

,

,

&Quot; (7) &quot;

14) Calculation of water temperature by vertical fault

를 [수학식 1]의 Mackenzie equation을 사용하면, 도 9와 같이 수직단층별 수온분포

를 얻을 수 있다.
In this step, the sound velocity distributions

Using the Mackenzie equation of Equation (1), the vertical distribution of water temperature distribution

Can be obtained.

<P.5 Data Animation Section>

15 steps) Seasonal, hourly flow velocity and water temperature map prediction based on statistical data

과 유속

의 해양수치모델은 다음과 같이 나타낼 수 있다.This step is a process of predicting the flow rate, temperature map and covariance matrix of the next time from the ocean model based on season / time statistical data. Statistical data based on water temperature

And flow rate

Can be expressed as follows.

&Quot; (8) &quot;

Marine numerical model:

과

은 각 해양모델의 오차를 나타낸다. 유속과 수온의 해양모델 f 와 g은 현재 시간의 유속과 수온 값

,

과 시간

의 함수로써 다음 시간의 유속과 수온 값

,

을 계산하는 해양예측모델 함수이다. 또한 8단계)과 14단계)에서 계산된 수온

과 유속

은 실제값과 오차의 합으로 표현된다. 이를 해양관측모델이라고 부른다.Where f and g are oceanic models of flow velocity and water temperature respectively,

and

Represents the error of each ocean model. The oceanic models f and g of the flow velocity and water temperature show the flow rate of the current time and the water temperature value

,

And time

As a function of the velocity of the next time and the water temperature value

,

Is a marine forecasting model function. Also, the water temperature calculated in steps 8) and 14)

And flow rate

Is expressed by the sum of the actual value and the error. This is called a marine observational model.

&Quot; (9) &quot;

Ocean Observation Model:

과

은 해양관측에서 발생한 오차를 의미한다. 본 데이터 동화부에서는 해양수치모델 수학식 8을 바탕으로 다음시간의 해양정보를 예측하고, 해양관측모델 수식의 관계로 얻어진 8단계)과 14단계)의 결과

,

를 활용하여 유속값

과

을 보정 및 업데이트 하는 과정을 거친다. 그 중 예측 과정은 다음과 같다. (이 과정부터는 수온 분포 역시 유속 분포와 동일하게 계산이 가능함으로, 유속 분포에 대해서만 나타내고자 한다.)here

and

Is an error in ocean observations. The data moving part predicts the marine information of the next time on the basis of the numerical equation 8 of the marine numerical model and obtains the results of the steps 8) and 14) obtained by the relation of the marine observation model equation

,

The flow rate value

and

And corrects and updates them. The prediction process is as follows. (From this process, the water temperature distribution can also be calculated in the same way as the flow velocity distribution.

&Quot; (10) &quot;

Calculating Predicted Estimates:

&Quot; (11) &quot;

Compute the predicted estimate covariance:

,

는 n번째 추정치

의 공분산 행렬,

은

로부터 예측한

의 공분산 행렬,

은 통계자료에 기반한 모델의 오류인

의 공분산 행렬이다.here

,

Is the nth estimate

Of the covariance matrix,

silver

Predicted from

Of the covariance matrix,

Is an error in the model based on statistics

Lt; / RTI &gt;

Step 16) Step 8), Step 14) based on the flow rate and water temperature information based on the real-time correction and update

은 유속에 대한 해양관측오차

의 공분산이다.In this step, it is a step of correcting and updating the predicted estimate of step 15) based on the observed data of step 8). The calibration and update procedure is as follows.

Is the ocean observation error

.

&Quot; (11) &quot;

Calculation of optimized calibration gain:

&Quot; (12) &quot;

Calculation of Calculated Estimates:

&Quot; (13) &quot;

Calculation of the adjusted estimate covariance:

로 변화하는 해양수치모델과 해양관측모델을 다음과 같이 수정한다.The calibration technique described above is a description of a single ocean model. However, in the marine environment, natural disasters such as typhoons, tsunamis, and red tide phenomena, which are not often described as typical models, occur. These natural phenomena are difficult to express in a general model because the pattern changes rapidly. Therefore, multiple models are combined with calibration techniques to reflect different models reflecting different natural disasters. When existing marine and marine observational models are represented in various models, the model parameters

The oceanic numerical model and the oceanic observational model are changed as follows.

&Quot; (14) &quot;

Multiple Model Representation - Marine Numerical Model, Ocean Observation Model:

은 서로 다른 해양모델의 모드를 의미하고

는 초기 설정한 해양모델들의 집합이다. 서로 다른 해양모델들은 각각의 환경에 따라 유기적으로 변화하며

인 모델에서

인 모델로 변화하는 확률을 마르코브 모델을 이용하여 다음과 같이 정의한다.here

Means the mode of different ocean models

Is a set of ocean models initially set. Different ocean models vary organically according to their environment

In the model

The probability of changing to the in-model is defined as follows using the Markov model.

&Quot; (15) &quot;

는 해양모델변환 확률 분포 함수,

는 해양모델들의 집합을 나타낸다. 해양모델변환 확률분포함수

는 각기 다른 해양모델 사이의 유기적으로 변화하는 것을 표현한다.here

Is an ocean model conversion probability distribution function,

Represents a set of ocean models. Marine model conversion probability distribution function

Represent organically changing between different ocean models.

&Quot; (16) &quot;

&Quot; (17) &quot;

와

까지의 유속 관측치들에 조건부인 관측치

의 확률 밀도함수를 나타내고, 이를 이용하여 여태까지 관측치가 주어졌을 때

시점의 해양모델이

일 사후 확률을 수학식17과 같이 구한다. 위에서 정의한 해양모델의 사후확률

를 이용하여 최종적으로 상호다중모델을 고려한 유속 추정치와 공분산을 다음과 같이 계산한다.Equation (16)

Wow

Lt; RTI ID = 0.0 &gt; observations &lt; / RTI &

And the probability density function of the probability density function

The ocean model of the point

(17) &lt; / RTI &gt; The posterior probability of the ocean model defined above

, The flow velocity estimates and the covariances considering the mutual multiple models are finally calculated as follows.

&Quot; (18) &quot;

Calculation of Uncertain Flow Velocities Applying Mutual Multiple Model:

&Quot; (19) &quot;

Calculating the Covariance of Uncertain Flow Velocity Estimates Using Mutual Multiple Models:

와 수온 추정치

를 데이터 동화부의 최종 결과값으로 산출한다.
Estimates of flow velocity considering multiple ocean models using the above method

And water temperature estimates

As a final result value of the data moving unit.

<Real-time marine environment observation method using tomography system >

The present invention provides a real-time marine environment observation method using the tomography system for real-time marine environment observation as described above. That is, a data measuring step S1 for measuring the acoustic data and the environment data in the data measuring unit P.1, a data transmitting step S1 for transmitting the data measured in the data measuring step S1 to the first or second data processing unit, A data processing step S3 for calculating a flow velocity distribution or a sound velocity distribution from the transmitted data, a data processing step S3 for correcting data using the calculated data and a marine numerical model, And an ocean model correction step (S5) of correcting and updating multiple mutual models in real time as a result of the comparison.

By using the ocean acoustic tomography system and the environmental measurement method for observing the marine environment of the real time vertical single layer according to the present invention, it is possible to monitor the change of the water temperature and the directional velocity of the ocean and can be used for the fisheries such as aquaculture and fishing, And tsunamis, and it can be used industrially.

Claims (9)

In a marine acoustic tomography system,
A data measuring unit (P.1) comprising an acoustic data measuring unit and an environmental data measuring unit;
A data transmission unit P.2 connected to the data measurement unit P.1 for transmitting the measured sound data and the environment data data to the data processing unit P.3;
A first data processing unit (P.3) for receiving data from the data transfer unit (P.2) and calculating a flow velocity distribution for each vertical fault layer;
A second data processing unit (P.4) for receiving data from the data transfer unit (P.2) and calculating a sound velocity distribution; And a data moving unit (P.5) for outputting a result of data correction using the results of the first data processing unit (P.3) and the second data processing unit (P.4)
In order to take into consideration the effects of the calculated flow velocity on the sound wave propagation velocity, the second data processing section (P.4) calculates a sound velocity vertical distribution, i.e., a pair of C-SVP (Combined Sound Vertical Profile) ( C-SVP ⁺ (Z)) of the vertical direction of the sound signal transmitted from the transceiver 1 to the transceiver 2 and the vertical direction of the sound signal transmitted from the transceiver 2 to the transceiver 1 And the average sonic velocity ( C-SVP ^- (Z)) of each tomographic layer.
2. The method according to claim 1, wherein the acoustic data measures a two-directional acoustic signal in real time using a transceiver for both transmission and reception, and the environmental data measuring unit measures echo- sensor is used to measure the water velocity profile (SVP) of each vertical single layer.
The method as claimed in claim 1, wherein the first data processing unit (P.3) comprises the step of identifying a sound line most similar to the actually measured sound data signal (PDP) based on the predicted arrival sound path Tomography system for observing real - time marine environment.
The method according to claim 3, wherein the first data processing unit (P.3) calculates the actual travel path length of the identified sound line reached by the multipath, calculates a flow velocity average for each depth corresponding to the path, And a tomography system for observing real-time marine environment.
delete The method of claim 1, wherein the C-SVP increases the number of transducers to calculate a value for the flow direction to form the C-SVP (Z) , thereby improving the accuracy of the identified sound lines Tomography system for observing real - time marine environment.
The method according to claim 1, wherein the marine numerical model of the data moving unit (P.5) is a function of a marine numerical model according to Equation (1) based on cumulative observation statistics collected during each season Lt; / RTI &
Equation 1: Marine numerical model:

(Where, f : ocean model of flow velocity,

: Ocean model of water temperature,

: Error of flow velocity model,

: Error of water temperature ocean model,

: Flow rate of current time

: The temperature time of the current time, the flow rate of the next time as a function and the water temperature value

: The flow rate of the next time,

: Water temperature value of next time)
The measured water temperature and the flow velocity distribution value, or the missing observation data, and outputs the corrected data to the tomography system for observing the real-time marine environment.
The method of claim 7, wherein the moving image data unit (P.5) are related by a single-layer vertical flow rate of the currents obtained by the prediction information and the marine ocean observation model formula based on the marine numerical model, and the vertical U _ray tomography by the water temperature Distribution, and error from T- _ray are used to calculate the ocean observation model,
Equation 2: Ocean Observation Model:

(However, here

and

The error caused by ocean observations)
Wherein the data is corrected and the data is updated again to update the marine numerical model.
The method of claim 8, wherein the moving image data unit (P.5) marine value calculating a predicted estimate and covariance predictions from the model and the observation model marine and vertical fault-specific flow rate of the ocean current, U _ray and the vertical water temperature distribution by a single layer, T _ray is used to calibrate and update the flow velocity and temperature value of a tomography system for observing real-time marine environment.

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