阅读说明：本技术 气枪节流作用系数和气体释放效率参数获取方法及系统 (Method and system for acquiring throttling action coefficient and gas release efficiency parameters of air gun ) 是由 王建花 张金淼 王艳冬 卢双疆 刘志鹏 牛聪 凌云 王清振 于 2021-08-23 设计创作，主要内容包括：本发明涉及一种气枪节流作用系数和气体释放效率参数获取方法及系统,其包括：测量海水及气枪装置的基本参数,根据基本参数获得实测气枪远场子波,基本参数包括：海水温度、海水密度、气枪的工作压强、气枪的容积、气枪的沉放深度和海水中的声波速度；根据基本参数,构建模拟气枪远场子波；通过模拟远场子波与实测远场子波的能量相对误差,建立粒子群优化算法适应度函数,获取最优的节流常数、节流指数和气体释放效率。本发明能计算并调整气枪节流作用系数和气体释放效率,准确模拟气枪激发子波。本发明可广泛应用于油气地球物理勘探中的地震资料采集领域。(The invention relates to a method and a system for acquiring parameters of throttling action coefficient and gas release efficiency of an air gun, which comprises the following steps: measuring basic parameters of seawater and an air gun device, and obtaining an actually measured far-field wavelet of the air gun according to the basic parameters, wherein the basic parameters comprise: sea water temperature, sea water density, working pressure of the air gun, volume of the air gun, sinking depth of the air gun and sound wave velocity in sea water; constructing a simulated air gun far-field wavelet according to the basic parameters; and establishing a particle swarm optimization algorithm fitness function by simulating the energy relative error of the far-field wavelet and the actually-measured far-field wavelet, and obtaining the optimal throttling constant, throttling index and gas release efficiency. The invention can calculate and adjust the air gun throttling action coefficient and the gas release efficiency, and accurately simulate the air gun excitation wavelet. The invention can be widely applied to the field of seismic data acquisition in oil-gas geophysical exploration.)

1. A method for acquiring parameters of throttling action coefficient and gas release efficiency of an air gun is characterized by comprising the following steps:

measuring basic parameters of seawater and an air gun device, and obtaining an actually measured far-field wavelet of the air gun according to the basic parameters; the basic parameters include: sea water temperature, sea water density, working pressure of the air gun, volume of the air gun, sinking depth of the air gun and sound wave velocity in sea water;

constructing a simulated air gun far-field wavelet according to the basic parameters;

and establishing a particle swarm optimization algorithm fitness function according to the energy relative error of the simulated far-field wavelet and the actually-measured far-field wavelet, and obtaining the optimal throttling constant, throttling index and gas release efficiency.

2. The parameter acquisition method of claim 1, wherein said obtaining a measured airgun far-field wavelet from said base parameters comprises: the method comprises the steps of placing a hydrophone at a preset position away from an air gun, measuring a near-field wavelet of the air gun, obtaining an imaginary reflection generated on the surface of sea water according to the measured sinking depth of the air gun and the measured sound wave velocity in the sea water based on the near-field wavelet, and superposing the near-field wavelet and the imaginary reflection generated on the surface of the sea water to obtain the actually-measured far-field wavelet of the air gun.

3. The parameter acquisition method of claim 1 wherein constructing a simulated airgun far-field wavelet comprises:

obtaining the throttling action coefficient of the gun body according to the volume of the air gun, the throttling constant and the throttling index;

acquiring the initial temperature of the air gun body according to the seawater temperature and the working pressure of the air gun;

acquiring the molar mass of bubbles generated by the air gun in the equilibrium state according to the gas release efficiency;

obtaining the radius of the bubble, the movement speed of the bubble wall and the acceleration of the bubble wall which change along with time according to the throttling action coefficient, the initial temperature and the molar mass and a bubble vibration theoretical model of Ziolkowski;

obtaining an intermediate function from the bubble radius and the movement speed of the bubble wall;

obtaining pressure waves and virtual reflection thereof generated by bubbles according to the intermediate function, the seawater density, the air gun sinking depth and the sound wave velocity in the seawater;

and superposing the pressure wave and the ghost reflection to synthesize the far-field wavelet of the air gun.

4. The parameter acquisition method according to claim 1, wherein the particle swarm optimization algorithm fitness function is established by the relative energy error of the simulated far-field wavelet and the measured far-field wavelet:

in the formula, F_fitnessThe fitness function represents the energy relative error between the far-field wavelet simulated by the air gun and the actually measured far-field wavelet of the air gun, tau₀Denotes a throttling constant, beta denotes a throttling index, eta denotes a gas release efficiency, S (tau)₀β, η, j Δ t) represents the simulated far-field wavelet, W (j Δ t) represents the measured far-field wavelet, the units of the far-field wavelet are bar.m, j is 1,2, … …, N represents the number of sampling points, Δ t is the time sampling interval, and the unit is s.

5. The parameter acquisition method according to claim 1, wherein the throttling constant, the throttling index, and the gas release efficiency are defined as positions of particles in a population, and the acquiring of the optimal throttling constant, throttling index, and gas release efficiency comprises:

initializing a particle swarm;

updating the particle speed;

obtaining an updated particle position from the updated particle velocity;

calculating each updated particle fitness value from each of said updated particle positions, respectively;

comparing the updated fitness value of each particle with the optimal value of the particle of the previous iteration: if the current fitness value is smaller than the optimal particle value of the last iteration, updating the optimal particle value to be the current fitness value, and updating the position corresponding to the optimal particle value to be the current position corresponding to the particle; otherwise, the optimal value of the particles is not updated and iteration is carried out again;

comparing the updated optimal fitness value of the particle swarm with the optimal fitness value of the last iteration particle swarm: if the current optimal fitness value of the particle swarm is smaller than the optimal value of the last iteration particle swarm, updating the optimal value of the particle swarm to be the current fitness value of the particle swarm, and updating the position corresponding to the optimal value of the particle swarm to be the current corresponding position of the particle swarm; otherwise, the optimal value of the particle swarm is not updated, and iteration is carried out again;

ending until the optimal fitness value of all particles of the particle swarm reaches the preset precision or the iteration frequency reaches the maximum; and the three parameters corresponding to the currently obtained optimal particle position of the particle swarm are the optimal throttling constant, throttling index and gas release efficiency.

6. The parameter acquisition method of claim 5, wherein the particle swarm initialization comprises: initializing the position, initializing the speed and initializing the historical optimal position of each particle and the optimal positions of all particles.

7. The parameter acquisition method of claim 6, wherein the initializing the velocity and initializing the historical optimal position of each particle and the optimal positions of all particles comprises:

simulating far-field wavelets by the position of each particle, calculating the fitness function of the particles, and taking the obtained fitness value and the obtained position as the optimal value and the historical optimal position of the particle; and respectively taking the optimal fitness values of all the initialized particles and the corresponding particle positions thereof as the optimal value and the optimal position of the particle swarm.

8. An air gun throttling action coefficient and gas release efficiency parameter acquisition system, comprising: the device comprises an actual measurement module, a simulation module and an optimization module;

the actual measurement module is used for measuring the basic parameters of the seawater and the air gun device and obtaining an actual measurement air gun far-field wavelet according to the basic parameters; the basic parameters include: sea water temperature, sea water density, working pressure of the air gun, volume of the air gun, sinking depth of the air gun and sound wave velocity in sea water,

the simulation module constructs a simulation air gun far-field wavelet according to the basic parameters;

and the optimization module establishes a particle swarm optimization algorithm fitness function through the energy relative error of the simulated far-field wavelet and the actually-measured far-field wavelet to obtain the optimal throttling constant, throttling index and gas release efficiency.

9. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.

10. A computing device, comprising: one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-7.

Technical Field

The invention relates to the field of seismic data acquisition in oil-gas geophysical exploration, in particular to a method and a system for acquiring parameters of throttling action coefficients and gas release efficiency of an air gun.

Background

The offshore seismic exploration uses an air gun as a main seismic source, and the air gun is excited at a certain depth under the sea surface according to a certain mode to generate seismic wavelets, so that the structural condition of a stratum under the sea water is detected.

In order to increase the energy of seismic waves generated by air gun excitation and improve the signal-to-noise ratio, multiple air guns are generally subjected to combined excitation in marine seismic exploration, namely, an air gun array. The generated seismic wavelets are different in different air gun array combination forms; in order to obtain a high quality air gun array wavelet, such as a high peak-to-peak value and a high bubble ratio, wavelet simulation is performed on different air gun array design schemes, and simulation of the array wavelet is based on accurate simulation of a single air gun wavelet.

Ziolkowski (1970) proposes a classical mathematical model of airgun wavelet simulation, but the simulated wavelet has overlarge main pulse peak value and slow bubble pulse attenuation, so that the difference with the actually measured airgun wavelet is large, and the method cannot be applied to field production work of offshore seismic exploration. MacGillivray (2000) improves the accuracy of airgun wavelet simulations by varying the heat transfer coefficient, throttling constant, throttling index and gas release efficiency. In Li Guofa et al (2010), a Ziolkowski model is taken as a basis, the muzzle throttling effect, the heat transfer effect of bubbles and surrounding fluid and the influence of fluid viscosity and bubble vibration are comprehensively considered, Ziolkowski simulation is improved, and the simulated wavelets are higher in consistency with measured wavelets.

When the air gun is excited, high-pressure gas in the cavity is not instantaneously released into surrounding fluid, the release speed of the gas is influenced by the throttling action of the gun mouth, the influence is represented by the throttling action coefficient of the air gun and is calculated by two parameters, namely a throttling constant and a throttling index. After the air gun is excited, all the high-pressure gas is not completely released, part of the gas remains in the cavity, and the ratio of the gas released by the air gun to the total gas in the air gun before excitation is called gas release efficiency. The throttling action coefficient and the gas release efficiency of the air gun are two very important parameters in an air gun wavelet simulation model, are determined by the mechanical structure of the air gun, and have obvious influence on the air gun wavelet form.

In the current production, the simulation of air gun wavelets and the design of an air gun array are usually completed based on foreign air gun wavelet simulation software, and the air gun wavelets simulated by the software represent wavelets excited by a newly produced air gun in a still water environment (a calm lake). However, in the practical production and use of the air gun, due to repeated rapid inflation and high-pressure gas release, the abrasion and aging of the internal mechanical structure of the air gun are accelerated, so that the throttling action coefficient and the gas release efficiency in the air gun wavelet simulation are changed, and the form of the air gun wavelet is further influenced. In order to simulate the airgun wavelet more accurately, the throttling action coefficient and the gas release efficiency need to be adjusted and updated frequently during the use process of the airgun.

Disclosure of Invention

In view of the above problems, an object of the present invention is to provide a method and a system for obtaining parameters of an air gun throttling action coefficient and a gas release efficiency, which can calculate and adjust the air gun throttling action coefficient and the gas release efficiency, and accurately simulate an air gun excitation wavelet.

In order to achieve the purpose, the invention adopts the following technical scheme: an air gun throttling action coefficient and gas release efficiency parameter obtaining method comprises the following steps:

measuring basic parameters of seawater and an air gun device, and obtaining an actually measured far-field wavelet of the air gun according to the basic parameters; the basic parameters include: sea water temperature, sea water density, working pressure of the air gun, volume of the air gun, sinking depth of the air gun and sound wave velocity in sea water;

constructing a simulated air gun far-field wavelet according to the basic parameters;

and establishing a particle swarm optimization algorithm fitness function according to the energy relative error of the simulated far-field wavelet and the actually-measured far-field wavelet, and obtaining the optimal throttling constant, throttling index and gas release efficiency.

Further, obtaining a measured airgun far-field wavelet according to the basic parameters, comprising: the method comprises the steps of placing a hydrophone at a preset position away from an air gun, measuring a near-field wavelet of the air gun, obtaining an imaginary reflection generated on the surface of sea water according to the measured sinking depth of the air gun and the measured sound wave velocity in the sea water based on the near-field wavelet, and superposing the near-field wavelet and the imaginary reflection generated on the surface of the sea water to obtain the actually-measured far-field wavelet of the air gun.

Further, the constructing of the simulated airgun far-field wavelet comprises:

obtaining the throttling action coefficient of the gun body according to the volume of the air gun, the throttling constant and the throttling index;

acquiring the initial temperature of the air gun body according to the seawater temperature and the working pressure of the air gun;

acquiring the molar mass of bubbles generated by the air gun in the equilibrium state according to the gas release efficiency;

obtaining the radius of the bubble, the movement speed of the bubble wall and the acceleration of the bubble wall which change along with time according to the throttling action coefficient, the initial temperature and the molar mass and a bubble vibration theoretical model of Ziolkowski;

obtaining an intermediate function from the bubble radius and the movement speed of the bubble wall;

obtaining pressure waves and virtual reflection thereof generated by bubbles according to the intermediate function, the seawater density, the air gun sinking depth and the sound wave velocity in the seawater;

and superposing the pressure wave and the ghost reflection to synthesize the far-field wavelet of the air gun.

Further, establishing a particle swarm optimization algorithm fitness function by simulating the energy relative error of the far-field wavelet and the actually-measured far-field wavelet:

in the formula, F_fitnessThe fitness function represents the energy relative error between the far-field wavelet simulated by the air gun and the actually measured far-field wavelet of the air gun, tau₀Denotes a throttling constant, beta denotes a throttling index, eta denotes a gas release efficiency, S (tau)₀β, η, j Δ t) represents the simulated far-field wavelet, W (j Δ t) represents the measured far-field wavelet, the units of the far-field wavelet are bar.m, j is 1,2, … …, N represents the number of sampling points, Δ t is the time sampling interval, and the unit is s.

Further, defining the throttling constant, the throttling index and the gas release efficiency as the positions of the particles in the population, and acquiring the optimal throttling constant, the throttling index and the gas release efficiency, wherein the method comprises the following steps:

initializing a particle swarm;

updating the particle speed;

obtaining an updated particle position from the updated particle velocity;

calculating each updated particle fitness value from each of said updated particle positions, respectively;

comparing the updated fitness value of each particle with the optimal value of the particle of the previous iteration: if the current fitness value is smaller than the optimal particle value of the last iteration, updating the optimal particle value to be the current fitness value, and updating the position corresponding to the optimal particle value to be the current position corresponding to the particle; otherwise, the optimal value of the particles is not updated and iteration is carried out again;

comparing the updated optimal fitness value of the particle swarm with the optimal fitness value of the last iteration particle swarm: if the current optimal fitness value of the particle swarm is smaller than the optimal value of the last iteration particle swarm, updating the optimal value of the particle swarm to be the current fitness value of the particle swarm, and updating the position corresponding to the optimal value of the particle swarm to be the current corresponding position of the particle swarm; otherwise, the optimal value of the particle swarm is not updated, and iteration is carried out again;

ending until the optimal fitness value of all particles of the particle swarm reaches the preset precision or the iteration frequency reaches the maximum; and the three parameters corresponding to the currently obtained optimal particle position of the particle swarm are the optimal throttling constant, throttling index and gas release efficiency.

Further, the particle swarm initialization comprises: initializing the position, initializing the speed and initializing the historical optimal position of each particle and the optimal positions of all particles.

Further, the initializing the speed and initializing the historical optimal position of each particle and the optimal positions of all the particles includes:

simulating far-field wavelets by the position of each particle, calculating the fitness function of the particles, and taking the obtained fitness value and the obtained position as the optimal value and the historical optimal position of the particle; and respectively taking the optimal fitness values of all the initialized particles and the corresponding particle positions thereof as the optimal value and the optimal position of the particle swarm.

An air gun throttling action coefficient and gas release efficiency parameter acquisition system, comprising: the device comprises an actual measurement module, a simulation module and an optimization module;

the actual measurement module is used for measuring the basic parameters of the seawater and the air gun device and obtaining an actual measurement air gun far-field wavelet according to the basic parameters; the basic parameters include: sea water temperature, sea water density, working pressure of the air gun, volume of the air gun, sinking depth of the air gun and sound wave velocity in sea water,

the simulation module constructs a simulation air gun far-field wavelet according to the basic parameters;

and the optimization module establishes a particle swarm optimization algorithm fitness function through the energy relative error of the simulated far-field wavelet and the actually-measured far-field wavelet to obtain the optimal throttling constant, throttling index and gas release efficiency.

A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods as described above.

A computing device, comprising: one or more processors, memory, and one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs including instructions for performing any of the methods described above.

Due to the adoption of the technical scheme, the invention has the following advantages:

1. the particle swarm optimization algorithm has the characteristics of global optimization and high convergence speed, and can more accurately optimize three simulation parameters related to the air gun structure, so that the wavelet simulated by the optimized parameters has higher consistency with the wavelet actually measured in the use of the air gun.

2. The air gun wavelet simulated by the commercialized technology deviates from the wavelet excited by the air gun in actual production due to abrasion and aging of internal mechanical structures caused by repeated rapid inflation and high-pressure gas release of the air gun during operation. The invention utilizes far-field wavelets measured in the using process of the air gun, and three wavelet simulation parameters related to the mechanical structure of the air gun are adjusted and updated: the throttling constant, the throttling index and the gas release efficiency make the simulated wavelet more accord with the actually excited wavelet of the air gun, and have important significance for the simulation design of the offshore exploration air gun array.

3. According to the using condition of the air gun, two important mechanical parameters in the air gun wavelet simulation are calculated and adjusted: the throttling coefficient and the gas release efficiency to accurately simulate the wavelet excited by an air gun which is worn and aged in production and use.

Drawings

FIG. 1 is a schematic overall flow chart of a method for obtaining parameters of throttling effect coefficient and gas release efficiency of an air gun according to an embodiment of the present invention;

FIG. 2 is a diagram of airgun far-field wavelets in an embodiment of the present invention;

FIG. 3 is a schematic flow chart of a particle swarm optimization algorithm in an embodiment of the present invention;

FIG. 4 is a schematic diagram illustrating a comparison between a wavelet simulated by initial parameters and a wavelet actually measured according to an embodiment of the present invention;

FIG. 5 is a schematic diagram illustrating a comparison of simulated wavelets and measured wavelets for optimal parameters in an embodiment of the invention;

FIG. 6 is a schematic diagram of a computing device in an embodiment of the invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.

In an embodiment of the present invention, as shown in fig. 1, a method for obtaining an air gun throttling effect coefficient and a gas release efficiency parameter is provided, and this embodiment is illustrated by applying the method to a terminal, it is to be understood that the method may also be applied to a server, and may also be applied to a system including the terminal and the server, and is implemented by interaction between the terminal and the server. The method for acquiring the air gun throttling action coefficient and the gas release efficiency parameter provided by the embodiment can be used for acquiring the air gun throttling action coefficient and the gas release efficiency parameter, and can also be applied to acquiring methods of other parameters in other fields. In this embodiment, the method includes the steps of:

step 1, measuring basic parameters of seawater and an air gun device, and obtaining an actually measured far-field wavelet of the air gun according to the basic parameters; the basic parameters include: sea water temperature, sea water density, working pressure of the air gun, volume of the air gun, sinking depth of the air gun and sound wave velocity in sea water;

step 2, constructing a simulated air gun far-field wavelet according to the basic parameters;

and 3, establishing a particle swarm optimization algorithm fitness function by simulating the energy relative error of the far-field wavelet and the actually-measured far-field wavelet, and obtaining the optimal throttling constant, throttling index and gas release efficiency.

In the step 1, the basic parameters of the seawater and air gun device are as follows:

(1) temperature T of seawater_w＝300K；

(2) Sea water density rho 1000Kg/m³；

(3) Working pressure p of air gun_g＝2000psi；

(4) Volume V of air gun_g＝100in³；

(5) Setting the depth h of the air gun to be 8 m;

(6) the sound wave speed c in the seawater is 1500 m/s.

In the step 1, obtaining the actually measured far-field wavelet of the air gun according to the basic parameters includes: a hydrophone is placed at a preset position away from the air gun, wherein the distance is set to be 1m, and the near-field wavelet of the air gun is measured; based on the near-field wavelet, obtaining the virtual reflection generated on the surface of the seawater according to the measured sinking depth of the air gun and the sound wave velocity in the seawater, and superposing the near-field wavelet and the virtual reflection generated on the surface of the seawater to obtain the actually measured far-field wavelet of the air gun.

In this embodiment, a hydrophone may be placed at a distance of 1m from the air gun, and a far-field wavelet as shown in fig. 2 is synthesized from the measured sinking depth h of the air gun and the acoustic velocity c in the sea water by measuring the near-field wavelet of the air gun, and the synthesis formula is as follows:

W(t)＝w(t)+Rw_g(t-Δτ)

wherein W (t) represents far-field wavelet of air gun, w (t) represents near-field wavelet measured by hydrophone, R represents reflection coefficient of sea water surface, and R E [ -1, -0.9]Usually, R is-1, dimensionless, w_gThe term (t- Δ τ) represents an ghost occurring on the surface of the seawater, Δ τ represents a delay time of the ghost, and Δ τ is 2h/c is 0.0107 s.

As shown in fig. 2, in step 2, a simulated airgun far-field wavelet is constructed according to the basic parameters, which includes the following steps:

step 2.1, obtaining the throttling action coefficient of the gun body according to the volume of the air gun, the throttling constant and the throttling index;

in particular, the volume V of the air gun is obtained by measurement_gRandomly derived throttling constant τ_0iAnd throttle index beta_iCalculating the throttle coefficient tau of the gun body_iDimensionless, the formula is:

in the formula, τ_0iDenotes the throttle constant, beta_iRepresenting a throttle index;

step 2.2, acquiring the initial temperature of the air gun body according to the seawater temperature and the working pressure of the air gun;

the method specifically comprises the following steps: from measured sea water temperature T_wAnd working pressure p of air gun_gCalculating the initial temperature T of the air gun body_gIn units of K, the formula is:

in the formula, a constant p_c＝139MPa；

Step 2.3, acquiring the molar mass of the bubbles generated by the air gun in the equilibrium state according to the gas release efficiency;

the method specifically comprises the following steps: from the initial temperature T of the air gun body_gAnd randomly generated gas release efficiency eta_iCalculating the molar mass m of the bubble generated by the air gun in the equilibrium state_gThe unit is mol, and the formula is:

in the formula, R_GIs a proportionality constant equal to 8.2;

step 2.4, obtaining the radius of the bubble, the movement speed of the bubble wall and the acceleration of the bubble wall which change along with time according to the throttling action coefficient, the initial temperature and the molar mass and a bubble vibration theoretical model of Ziolkowski;

the method specifically comprises the following steps: according to the calculation result of the steps, adopting a Ziolkowski bubble vibration theory model to obtain:

radius of bubble R (τ)_0i,β_i,η_i,jΔt)；

Speed of movement of bubble wall；

Acceleration of bubble wall，j＝1,2,···,N；

Step 2.5, the radius R of the bubble and the moving speed of the bubble wallObtaining an intermediate function f, specifically:

from the bubble radius R (τ)_0i,β_i,η_iJ Δ t) and the moving speed of the bubble wallObtain the intermediate function f (tau)_0i,β_i,η_iJ Δ t), j ═ 1,2, ·, N, the formula:

step 2.6, obtaining pressure waves and virtual reflections thereof generated by bubbles according to the intermediate function, the density of the seawater, the sinking depth of the air gun and the sound wave speed in the seawater;

the method specifically comprises the following steps:

from the function f (τ)_0i,β_i,η_iJ Δ t) and the measured sea water density ρ, the air gun sinking depth h and the sound wave velocity c in the sea water, the pressure wave p (τ) generated by the bubble is calculated_0i,β_i,η_iJ Δ t) and its ghost reflection

Wherein the pressure wave generated by the bubble is:

wherein, the ghost that the bubble produced is:

step 2.7 pressure wave p (τ) generated by bubble_0i,β_i,η_iJ Δ t) and ghost reflectionSuperposition of far-field wavelets S (tau) of synthetic gas gun_0i,β_i,η_iJ Δ t), j ═ 1,2, ·, N, in Bar · m.

In the step 3, a particle swarm optimization algorithm fitness function is established by simulating the energy relative error of the far-field wavelet and the actually-measured far-field wavelet, and the formula is as follows:

in the formula, fitness function F_fitnessRepresenting the energy relative error, tau, of the far-field wavelet simulated by the airgun and the actually measured far-field wavelet of the airgun₀Denotes a throttling constant, beta denotes a throttling index, eta denotes a gas release efficiency, S (tau)₀β, η, j Δ t) represents the simulated far-field wavelet, W (j Δ t) represents the measured far-field wavelet, the units of the far-field wavelet are bar.m, j is 1,2, … …, N represents the number of sampling points, Δ t is the time sampling interval, and the unit is s.

In this embodiment, the energy relative error between the simulated far-field wavelet and the actually-measured far-field wavelet is used as a fitness function of the particle swarm optimization algorithm to optimize the throttling constant, the throttling index and the gas release efficiency of the studied air gun.

As shown in fig. 3, in step 3, the particle swarm optimization algorithm defines the throttling constant, the throttling index and the gas release efficiency as the positions of the particles in the swarm, and obtains the optimal throttling constant, the throttling index and the gas release efficiency, which includes the following steps:

step 3.1, particle swarm initialization, comprising: initializing the position, initializing the speed, initializing the historical optimal position of each particle and the optimal positions of all the particles, specifically:

as shown in fig. 4, the initialization position: let 20 particles in the particle group, each particle is 3-dimensional x_id＝(τ_0i,β_i,η_i) D is the dimension, known from the air gun excitation principle, the throttle constant τ₀The throttle index β and the gas release efficiency η i are in the range (0,1) 1,2, 20, so that the throttle constant τ is_0iThrottle index beta_iAnd gas release efficiency eta_iRandom numbers between 0 and 1;

initialization speed: velocity v corresponding to each particle_id＝(v_i1,v_i2,v_i3) Wherein v is_i1、v_i2And v_i3Random numbers between 0 and 1;

initializing the historical optimal position of each particle and the optimal positions of all the particles, specifically:

from the position x of each particle_id＝(τ_0i,β_i,η_i) Medium throttle constant tau_0iThrottle index beta_iAnd gas release efficiency eta_iSimulating the far-field wavelet S (tau)_0i,β_i,η_iJ Δ t), j ═ 1,2, ·, N, and the fitness function F of the particle is calculated_fitness(τ_0i,β_i,η_i) The fitness value F is set_fitness(τ_0i,β_i,η_i) And position x_id＝(τ_0i,β_i,η_i) As the optimum value pbest of the particle_iAnd historical optimal position p_id＝(p_i1,p_i2,p_i3)；

Respectively taking the optimal fitness values of all the initialized particles and the corresponding particle positions thereof as the optimal value gbest and the optimal position g of the particle swarm_d＝(g₁,g₂,g₃)。

The position of the 1 st particle is initialized to x_1d(0.15,0.62,0.70), i.e. the throttle constant τ₀₁0.15, throttle index beta₁0.62 and gas release efficiency η₁0.70, far-field wavelet S (τ) modeled by the set of random parameters₀₁,β₁,η₁J Δ t), j ═ 1,2,. cndot, N, the fitness function F of the particle, as compared to the measured wavelet_fitness(τ₀₁,β₁,η₁)＝0.1823。

Step 3.2, updating the particle speed;

the update particle velocity formula adopted in this embodiment is:

in the formula (I), the compound is shown in the specification,the velocity of the (k + 1) th iteration of the ith particle,for the velocity of the kth iteration of the particle,andrespectively the position of the kth iteration of the particle, the self historical optimal position and the optimal positions of all the particles; w is an inertia weight coefficient, generally set to 1; c. C₁、c₂Respectively tracking the weight coefficients of the historical optimal values of the particles and the optimal values of all the particles, wherein the weight coefficients are generally set to be 2; alpha and beta are random numbers uniformly distributed in (0,1), and k is the iteration number.

Step 3.3, calculating an updated particle position according to the updated particle speed;

the updated particle position formula in this embodiment is:

in the formula (I), the compound is shown in the specification,for the position of the (k + 1) th iteration of the ith particle,is the position of the kth iteration of the particle; r is a velocity constraint factor.

Step 3.4, calculating each updated particle fitness value according to each updated particle position;

and 3.5, comparing the updated fitness value of each particle with the optimal value of the particle of the previous iteration: if the current fitness value is smaller than the optimal particle value of the previous iteration, updating the optimal particle value to be the current fitness value, and updating the position corresponding to the optimal particle value to be the current position corresponding to the particle; otherwise, returning to the step 3.2 for next iteration;

for example, the updated fitness value for each particle is compared to the pbest of the last iteration_iThe size of (2): if the current fitness value is less than pbest_iThen pbest_iUpdated to the current fitness value, pbest_iUpdating the position of the particle to be the current position of the particle; otherwise pbest_iAnd not updated.

Step 3.6, comparing the updated optimal fitness value of the particle swarm with the optimal fitness value of the last iteration particle swarm: if the current optimal fitness value of the particle swarm is smaller than the optimal value of the particle swarm iterated last time, updating the optimal value of the particle swarm to be the current fitness value of the particle swarm, and updating the position corresponding to the optimal value of the particle swarm to be the current corresponding position of the particle swarm; otherwise, returning to the step 3.2 for next iteration;

for example, the updated particle swarm optimal fitness value is compared with the size of the previous iteration gbest: if the current optimal fitness value of the particle swarm is smaller than the gbest, updating the gbest to the current optimal fitness value, and updating the position of the gbest to the current position of the optimal particle; otherwise gbest is not updated.

And 3.7, ending the three parameters corresponding to the currently obtained optimal particle position of the particle swarm until the optimal fitness value of all particles of the particle swarm reaches the preset precision or the iteration frequency reaches the maximum, wherein the three parameters are the optimal throttling constant, the optimal throttling index and the optimal gas release efficiency. The three optimal parameters obtained in this embodiment are τ₀The simulated wavelet is compared to the wavelet actually measured in the use of the airgun as shown in fig. 5, where 0.12, 0.52, and 0.84.

In one embodiment of the present invention, there is provided an air gun throttle effect coefficient and gas release efficiency parameter acquisition system, comprising: the device comprises an actual measurement module, a simulation module and an optimization module;

the actual measurement module is used for measuring the basic parameters of the seawater and the air gun device and obtaining an actual measurement air gun far-field wavelet according to the basic parameters; the basic parameters include: sea water temperature, sea water density, working pressure of the air gun, volume of the air gun, sinking depth of the air gun and sound wave velocity in sea water;

the simulation module is used for constructing a simulation air gun far-field wavelet according to the basic parameters;

and the optimization module establishes a particle swarm optimization algorithm fitness function by simulating the energy relative error of the far-field wavelet and the actually-measured far-field wavelet, and obtains the optimal throttling constant, throttling index and gas release efficiency.

The system provided in this embodiment is used for executing the above method embodiments, and for details of the process and the details, reference is made to the above embodiments, which are not described herein again.

As shown in fig. 6, which is a schematic structural diagram of a computing device provided in an embodiment of the present invention, the computing device may be a terminal, and may include: a processor (processor), a communication Interface (communication Interface), a memory (memory), a display screen and an input device. The processor, the communication interface and the memory are communicated with each other through a communication bus. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium, an internal memory, the non-volatile storage medium storing an operating system and a computer program that when executed by a processor implements an acquisition method; the internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a manager network, NFC (near field communication) or other technologies. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computing equipment, an external keyboard, a touch pad or a mouse and the like. The processor may call logic instructions in memory to perform the following method:

measuring basic parameters of seawater and an air gun device, and obtaining an actually measured far-field wavelet of the air gun according to the basic parameters, wherein the basic parameters comprise: sea water temperature, sea water density, working pressure of the air gun, volume of the air gun, sinking depth of the air gun and sound wave velocity in sea water; constructing a simulated air gun far-field wavelet according to the basic parameters; and establishing a particle swarm optimization algorithm fitness function by simulating the energy relative error of the far-field wavelet and the actually-measured far-field wavelet, and obtaining the optimal throttling constant, throttling index and gas release efficiency.

In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment of the invention, a computer program product is provided, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions that, when executed by a computer, enable the computer to perform the methods provided by the above-described method embodiments, for example, comprising: measuring basic parameters of seawater and an air gun device, and obtaining an actually measured far-field wavelet of the air gun according to the basic parameters, wherein the basic parameters comprise: sea water temperature, sea water density, working pressure of the air gun, volume of the air gun, sinking depth of the air gun and sound wave velocity in sea water; constructing a simulated air gun far-field wavelet according to the basic parameters; and establishing a particle swarm optimization algorithm fitness function by simulating the energy relative error of the far-field wavelet and the actually-measured far-field wavelet, and obtaining the optimal throttling constant, throttling index and gas release efficiency.

In one embodiment of the invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided by the above embodiments, for example, including: measuring basic parameters of seawater and an air gun device, and obtaining an actually measured far-field wavelet of the air gun according to the basic parameters, wherein the basic parameters comprise: sea water temperature, sea water density, working pressure of the air gun, volume of the air gun, sinking depth of the air gun and sound wave velocity in sea water; constructing a simulated air gun far-field wavelet according to the basic parameters; and establishing a particle swarm optimization algorithm fitness function by simulating the energy relative error of the far-field wavelet and the actually-measured far-field wavelet, and obtaining the optimal throttling constant, throttling index and gas release efficiency.

The implementation principle and technical effect of the computer-readable storage medium provided by the above embodiments are similar to those of the above method embodiments, and are not described herein again.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.