Deep sea exploration air gun array arrangement optimization method and system for improving wavelet quality

Abstract

The invention discloses a deep-sea exploration air gun array arrangement optimization method and system for improving wavelet quality, and relates to the field of deep-sea resource exploration. The problem of rapid optimization design of the air gun array in the existing engineering is solved. The method comprises the following steps: inputting initial information parameters of an air gun array, and obtaining air gun bubble pressure wavelet data and sound pressure level spectrum data before optimization based on an air gun bubble dynamics mathematical physical model; establishing upper and lower limits of the air gun sinking depth and constraint conditions of an air gun volume change range; setting an optimization objective function and optimization control parameters; performing iterative optimization on the array in combination with a spherical bubble dynamics theory and a particle swarm optimization algorithm; calculating an air gun array pressure wavelet and a sound pressure level frequency spectrum, if a calculation result meets a requirement, obtaining air gun array information optimized based on a particle swarm algorithm, calculating the pressure wavelet and the sound pressure level frequency spectrum, outputting the optimized air gun array information, and realizing air gun array inversion optimization; and if the requirements are not met, continuously carrying out iterative optimization on the array.

Description

Technical Field

[0001] The present invention relates to the technical field of deep sea resource exploration, and in particular to a deep sea exploration air gun array arrangement optimization method and system for improving wavelet quality. Background Art

[0002] As the demand for high-quality seismic wave data in marine exploration gradually increases, the energy generated by a single source excitation is insufficient and cannot be directly used to detect seabed resources at a greater distance and depth. However, the emission frequency can be controlled by adjusting the capacity, depth, and combination of a single gun. As a result, an airgun array was born, which combines multiple airguns in a certain way, and uses the interaction between bubbles to improve the performance of the airgun source. The use of airgun arrays increases the source energy, allowing the generated source wavelets to detect deeper seabeds. In actual seismic exploration work, it is crucial to ensure that the airgun array has the characteristics of low frequency, broadband, and high energy. However, the layout of the array cannot be determined arbitrarily, and sometimes, it may take a lot of time to design an array that meets the requirements. Traditional array design usually relies on the experience of engineers and is gradually optimized through multiple tests and continuous adjustments. However, this method is not only expensive, but also relatively inefficient.

[0003] Current array design usually relies on the experience of engineers, and is gradually optimized through multiple experiments and continuous adjustments, or is a small modification based on a known high-quality airgun array. This form of array design is not only time-consuming, but also engineers have limited energy and cannot consider all optimization methods, which is likely to result in the omission of excellent airgun arrays.

[0004] Therefore, how to quickly and efficiently design high-quality airgun arrays has become a major technical challenge in the engineering field. Summary of the invention

[0005] The present invention aims to solve the problem of how to quickly and efficiently design a high-quality air gun array, which has become a major technical challenge in the engineering field.

[0006] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0007] Solution 1: The present invention proposes a method for optimizing the arrangement of deep-sea exploration air gun arrays for improving wavelet quality, the method comprising the following steps:

[0008] Step 1: input the initial information parameters of the airgun array before optimization, and obtain the airgun bubble pressure wavelet data and sound pressure level spectrum data before optimization based on the mathematical and physical model of airgun bubble dynamics;

[0009] Step 2: Establish the upper and lower limits of the airgun sinking depth and the constraint conditions of the airgun volume variation range during the optimization process;

[0010] Step 3, setting the optimization objective function according to the airgun bubble pressure wavelet data and sound pressure level spectrum data before optimization obtained in step 1 and the constraint conditions obtained in step 2;

[0011] Step 4: setting the optimization control parameters based on the optimization objective function set in step 3;

[0012] Step 5: Combining the spherical bubble dynamics theory with the particle swarm optimization algorithm, the array is iteratively optimized based on the optimization objective function obtained in step 3 and the optimization control parameters in step 4;

[0013] Step 6: Calculate the pressure wavelet and sound pressure level spectrum of the airgun array. If the prediction accuracy of the calculation results meets the requirements, the airgun array information optimized based on the particle swarm algorithm is obtained. The optimized pressure wavelet and sound pressure level spectrum are calculated and output to achieve the inversion optimization of the airgun array. If the prediction accuracy of the calculation results does not meet the requirements, execute step 5.

[0014] Furthermore, a preferred embodiment is provided, in which the initial information parameters of the airgun array before optimization input in step one include the volume of each airgun, the arrangement position of each airgun, the excitation depth of each airgun, the time delay, the coherence coefficient, the airgun type, the airgun working pressure, the number of airguns, the total volume of the array and the number of spare airguns.

[0015] Furthermore, a preferred embodiment is provided, in which the mathematical and physical model of airgun bubble dynamics described in step one is established based on the spherical bubble pulsation theory under a compressible flow field, and a correction term is added.

[0016] Furthermore, a preferred implementation is provided, wherein the correction term is calculated using the following method:

[0017]

[0018]

[0019]

[0020] Where C is the speed of sound, R is the radius of the airgun bubble, They represent the first-order derivative and the second-order derivative of the radius respectively. The equation is solved by the Runge-Kutta method, that is, Through the known Calculate the physical quantities of the airgun bubble pulsation. ξ1 and ξ2 are the additional empirical coefficients for controlling the multi-period bubble energy attenuation problem. P is the pressure on the bubble surface. m, R, and T represent the mass, gas constant, and gas temperature of the gas in the bubble. The gas parameter is 287 J / (kg×K). V is the volume of the bubble. P ∞ is the hydrostatic pressure at the center of the bubble.

[0021] Further, a preferred embodiment is provided, in which the method for obtaining the airgun bubble pressure wavelet data before optimization based on the airgun bubble dynamics mathematical and physical model in step 1 is:

[0022]

[0023]

[0024]

[0025] Where u is the fluid velocity at the measuring point, P field is the flow field pressure at the measuring point, and r is the distance from the bubble center to the measuring point.

[0026] Furthermore, a preferred implementation is provided, wherein setting the optimization objective function in step three also includes a step of implementing it through spectral features.

[0027] Furthermore, a preferred embodiment is provided, in step four, based on the optimization objective function set in step three, the optimization control parameters include airgun volume, airgun working pressure, airgun excitation depth, airgun spacing, array spacing, and delayed excitation time.

[0028] Solution 2: A deep-sea exploration air gun array layout optimization system for improving wavelet quality, the system comprising:

[0029] An input module is used to input the initial information parameters of the airgun array before optimization, and obtain the airgun bubble pressure wavelet data and sound pressure level spectrum data before optimization based on the mathematical and physical model of airgun bubble dynamics;

[0030] The constraint module is used to establish the upper and lower limits of the airgun sinking depth and the airgun volume variation range during the optimization process;

[0031] An optimization objective function setting module sets the optimization objective function according to the airgun bubble pressure wavelet data and sound pressure level spectrum data before optimization obtained by the input module and the constraint conditions obtained by the constraint module;

[0032] An optimization control parameter setting module, used for setting optimization control parameters based on the optimization objective function set by the optimization objective function setting module;

[0033] An optimization module is used to combine the spherical bubble dynamics theory with the particle swarm optimization algorithm to iteratively optimize the array based on the optimization objective function obtained by the optimization objective function setting module and the optimization control parameters of the optimization control parameter setting module;

[0034] The output module is used to calculate the pressure wavelet and sound pressure level spectrum of the airgun array. If the prediction accuracy of the calculation results meets the requirements, the airgun array information optimized based on the particle swarm algorithm is obtained, and the optimized pressure wavelet and sound pressure level spectrum are calculated and output to realize the inversion optimization of the airgun array; if the prediction accuracy of the calculation results does not meet the requirements, the optimization module is executed.

[0035] Solution three: A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes any one of the methods described in Solution one.

[0036] Solution 4: A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of Solution 1.

[0037] The present invention is beneficial in that:

[0038] The deep-sea exploration air gun array layout optimization method and system for improving wavelet quality described in the present invention combine the air gun bubble dynamics theory with the particle swarm algorithm, and introduce constraints to make the optimization closer to actual engineering applications. The optimization objective function and optimization control variables established based on high-quality pressure wavelets and spectrum achieve the purpose of rapid optimization of the air gun array, providing a theoretical basis and basic technical support for engineering construction.

[0039] The present invention is also applicable to the field of practical engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of a method for optimizing the arrangement of deep-sea exploration air gun arrays for improving wavelet quality as described in Implementation Example 1.

[0041] Figure 2 This is a schematic diagram of the far-field pressure wavelet curve used as a basis for establishing the optimization objective function described in Implementation Example 11.

[0042] Figure 3 This is a schematic diagram of a spectrum curve used as a basis for establishing an optimization objective function as described in Implementation Method 11.

[0043] Figure 4 This is a comparison diagram of the effects before and after the pressure wavelet optimization for the inversion optimization of the deep-sea exploration air gun array based on the particle swarm algorithm as described in the eleventh embodiment.

[0044] Figure 5 This is a comparison diagram of the spectrum before and after optimization of the deep-sea exploration air gun array inversion optimization based on the particle swarm algorithm as described in the eleventh embodiment. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the implementation methods of the present application clearer, the technical solutions in the implementation methods of the present application will be clearly and completely described below in conjunction with the drawings in the implementation methods of the present application. Obviously, the described implementation methods are only part of the implementation methods of the present application, not all of the implementation methods.

[0046] Embodiment 1: This embodiment provides a method for optimizing the arrangement of deep-sea exploration air gun arrays for improving wavelet quality, the method comprising the following steps:

[0047] Step 1: input the initial information parameters of the airgun array before optimization, and obtain the airgun bubble pressure wavelet data and sound pressure level spectrum data before optimization based on the mathematical and physical model of airgun bubble dynamics;

[0048] Step 2: Establish the upper and lower limits of the airgun sinking depth and the constraint conditions of the airgun volume variation range during the optimization process;

[0049] Step 3, setting the optimization objective function according to the airgun bubble pressure wavelet data and sound pressure level spectrum data before optimization obtained in step 1 and the constraint conditions obtained in step 2;

[0050] Step 4: setting the optimization control parameters based on the optimization objective function set in step 3;

[0051] Step 5: Combining the spherical bubble dynamics theory with the particle swarm optimization algorithm, the array is iteratively optimized based on the optimization objective function obtained in step 3 and the optimization control parameters in step 4;

[0052] Step 6: Calculate the pressure wavelet and sound pressure level spectrum of the airgun array. If the prediction accuracy of the calculation results meets the requirements, the airgun array information optimized based on the particle swarm algorithm is obtained. The optimized pressure wavelet and sound pressure level spectrum are calculated and output to achieve the inversion optimization of the airgun array. If the prediction accuracy of the calculation results does not meet the requirements, execute step 5.

[0053] Implementation method 2. This implementation method is a further limitation of the cabin flooding flow field prediction method based on adaptive physical neural network described in implementation method 1. In step 1, the initial information parameters of the air gun array before optimization are input, including the volume of each gun, the arrangement position of each air gun, the excitation depth of each air gun, the time delay, the coherence coefficient, the type of air gun, the working pressure of the air gun, the number of air guns, the total volume of the array and the number of spare air guns.

[0054] Implementation method three: This implementation method is a further limitation of the deep-sea exploration air gun array layout optimization method for improving sub-wave quality described in implementation method one. The mathematical and physical model of air gun bubble dynamics described in step one is based on the spherical bubble pulsation theory under a compressible flow field, and a correction term step is added.

[0055] Embodiment 4: This embodiment further limits the deep-sea exploration air gun array arrangement optimization method for improving wavelet quality described in embodiment 3. The correction term is calculated using the following method:

[0056]

[0057]

[0058]

[0059] Where C is the speed of sound, R is the radius of the airgun bubble, They represent the first-order derivative and the second-order derivative of the radius respectively. The equation is solved by the Runge-Kutta method, that is, Through the known Calculate the physical quantities of the airgun bubble pulsation. ξ1 and ξ2 are the additional empirical coefficients for controlling the multi-period bubble energy attenuation problem. P is the pressure on the bubble surface. m, R, and T represent the mass, gas constant, and gas temperature of the gas in the bubble. The gas parameter is 287 J / (kg×K). V is the volume of the bubble. P ∞ is the hydrostatic pressure at the center of the bubble.

[0060] Embodiment 5: This embodiment further limits the deep-sea exploration airgun array arrangement optimization method for improving wavelet quality described in embodiment 1. The method for obtaining the airgun bubble pressure wavelet data before optimization based on the mathematical and physical model of airgun bubble dynamics in step 1 is:

[0061]

[0062]

[0063]

[0064] Where u is the fluid velocity at the measuring point, P field is the flow field pressure at the measuring point, and r is the distance from the bubble center to the measuring point.

[0065] Embodiment 6: This embodiment further limits the deep-sea exploration air gun array arrangement optimization method for improving wavelet quality described in embodiment 1, and setting the optimization objective function in step 3 also includes a step of achieving it through spectral characteristics.

[0066] Implementation method seven. This implementation method is a further limitation of the deep-sea exploration air gun array layout optimization method for improving sub-wave quality described in implementation method one. In step four, based on the optimization objective function set in step three, the optimization control parameters are set, including air gun volume, air gun working pressure, air gun excitation depth, air gun spacing, array spacing, and delayed excitation time.

[0067] Embodiment 8: This embodiment proposes a deep-sea exploration air gun array layout optimization system for improving wavelet quality, the system comprising:

[0068] An input module is used to input the initial information parameters of the airgun array before optimization, and obtain the airgun bubble pressure wavelet data and sound pressure level spectrum data before optimization based on the mathematical and physical model of airgun bubble dynamics;

[0069] The constraint module is used to establish the upper and lower limits of the airgun sinking depth and the airgun volume variation range during the optimization process;

[0070] An optimization objective function setting module sets the optimization objective function according to the airgun bubble pressure wavelet data and sound pressure level spectrum data before optimization obtained by the input module and the constraint conditions obtained by the constraint module;

[0071] An optimization control parameter setting module, used for setting optimization control parameters based on the optimization objective function set by the optimization objective function setting module;

[0072] An optimization module is used to combine the spherical bubble dynamics theory with the particle swarm optimization algorithm to iteratively optimize the array based on the optimization objective function obtained by the optimization objective function setting module and the optimization control parameters of the optimization control parameter setting module;

[0073] The output module is used to calculate the pressure wavelet and sound pressure level spectrum of the airgun array. If the prediction accuracy of the calculation results meets the requirements, the airgun array information optimized based on the particle swarm algorithm is obtained, and the optimized pressure wavelet and sound pressure level spectrum are calculated and output to realize the inversion optimization of the airgun array; if the prediction accuracy of the calculation results does not meet the requirements, the optimization module is executed.

[0074] Embodiment 9. This embodiment proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the method described in any one of embodiments 1 to 7.

[0075] Embodiment 10: This embodiment proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method described in any one of embodiments 1 to 7 are implemented.

[0076] Implementation eleven: This implementation provides an example, which is used to explain the above implementations one to eight. The specific example is as follows:

[0077] See also Figures 1 to 5 This embodiment is described according to Figure 1 As shown, the present invention provides a method for optimizing the arrangement of deep-sea exploration air gun arrays for improving wavelet quality, comprising the following steps:

[0078] Step 1: input the airgun array information before optimization, and obtain the pressure wavelet and spectrum before optimization based on the mathematical and physical model of airgun bubble dynamics.

[0079] Step 2: Based on the limitations in actual engineering applications, the constraints of the volume range of each gun in the airgun array and the upper and lower limits of the excitation depth of each gun in the optimization process were established.

[0080] Step three, establish the optimization objective function according to the requirements of high-quality source wavelets and spectral characteristics.

[0081] Step 4: Based on the airgun array information parameters, the optimized control variables are designed.

[0082] Step five, combining bubble dynamics theory with particle swarm optimization algorithm, and performing inverse optimization of the airgun array with reference to the optimization objective function and optimization control variables.

[0083] Step six, record and post-process the information parameters after inversion optimization of the airgun array.

[0084] Specifically, in step 1, the initial information parameters of the airgun array before optimization are input, including the volume of each gun, the arrangement position of each gun, the excitation depth of each gun, the time delay, the coherence coefficient, the type of airgun, the working pressure of the airgun, the number of airguns, the total volume of the array, and the number of spare guns, etc. By modifying the above array information parameters within a certain range, the initial airgun array before optimization can be set. After obtaining the airgun array, the pressure wavelet and spectrum before optimization are calculated based on the mathematical and physical model of airgun bubble dynamics.

[0085] Furthermore, the mathematical and physical model of airgun bubble dynamics described in step 1 is established based on the spherical bubble pulsation theory under a compressible flow field, and a correction term is added therein:

[0086]

[0087] Where C is the speed of sound, which is related to salinity, temperature and the hydrostatic pressure at the center of the bubble; R is the radius of the airgun bubble, They represent the first-order derivative and the second-order derivative of the radius respectively. By solving the equation by the Runge-Kutta method, we can get Then you can use the known Calculate the physical quantities related to the airgun bubble pulsation. ξ1, ξ2 are the additional empirical coefficients for controlling the multi-period bubble energy decay problem.

[0088] Furthermore, H in formula (1) is the enthalpy difference from the flow field at the bubble wall to infinity, Its first-order derivative can be calculated according to the following formula:

[0089]

[0090]

[0091] Where P is the pressure on the bubble surface, m, R, and T represent the mass, gas constant, and gas temperature of the gas in the bubble, respectively. The gas parameter is 287 J / (kg×K), V is the volume of the bubble, and P ∞ is the hydrostatic pressure at the center of the bubble.

[0092] Furthermore, the pressure wavelet is calculated by the following formula:

[0093]

[0094]

[0095]

[0096] Where u is the fluid velocity at the measuring point, P field is the flow field pressure at the measuring point, and r is the distance from the bubble center to the measuring point.

[0097] Furthermore, in step 2, optimization constraints are set based on the airguns actually used in the project to obtain the optimization range of the airgun volume. The optimization constraint range of the airgun excitation depth is designed based on the setting of the airgun excitation depth in the actual project and the exploration imaging accuracy.

[0098] Furthermore, in step 3, the optimization objective function that meets the particle swarm algorithm is selected according to the requirements of high-quality pressure wavelets. In seabed resource exploration, since the main pulse can provide a stronger energy signal, in order to penetrate the underground layer more clearly and improve the resolution of the reflected wave signal, the pressure wavelet needs a main pulse as large as possible. The characteristics of the pressure wavelet are as follows: Figure 2As shown. We use f(x) to represent the optimization objective function, x is the candidate particle in the search space, and the optimization objective function of the large peak-to-peak value can be expressed by the following formula:

[0099] f(x i )=|P primary -P primary_ghost | (7)

[0100] Secondary waves such as bubble pulses and ghost waves can easily confuse reflected signals, and excessive bubble pulses may interfere with the reception of main pulses and reflected waves, resulting in confusing and inaccurate detection data. Therefore, in order to reduce this unnecessary noise interference and improve the accuracy of reflection data, the pressure wavelet is required to have a larger initial bubble ratio and smaller ghost reflections. The corresponding optimization objective function is as follows:

[0101] f(x i )=-P primary / P bubble (8)

[0102] f(x i )=|P primary_ghost | (9)

[0103] Furthermore, through the pressure wavelet, objective functions that can be obtained include a large initial bubble ratio, a small ghost reflection, and a large peak-to-peak value.

[0104] Furthermore, in step 3, the optimization objective function can be established by using the spectrum characteristics. The spectrum characteristics are as follows: Figure 3 As shown in the figure, the goal is the same as the optimization of the far-field pressure wavelet, which is to eliminate the influence of the bubble pulse, but in terms of the spectrum, it is reflected in increasing the effective bandwidth of the spectrum and improving its smoothness. The new optimization objective function is established based on the spectrum variance, aiming to reduce the fluctuation of the spectrum curve in the range of 0 to 100 Hz, as shown in the following formula:

[0105]

[0106] Among them, S(w i ) is the function value corresponding to the discrete point on the spectrum curve (10 <w i <100), E(w i ) is S(w i ) is the arithmetic mean at each small frequency increment, q(w i ) represents the weight.

[0107] Furthermore, in order to enhance the amplitude strength of the spectrum, a new optimization objective function is proposed, which is based on the spectrum envelope instead of using the arithmetic mean. The envelope is calculated by Hilbert transform, which decomposes the signal in the real domain, and its real and imaginary parts satisfy the Cauchy-Riemann equation. By calculating the geometric mean of the real and imaginary parts, the envelope of the original signal can be obtained.

[0108] Furthermore, through the pressure wavelet, objective functions that can be obtained include smooth spectrum, upper spectrum envelope, spectrum average and lower spectrum envelope.

[0109] Furthermore, in step 4, the optimized control variables refer to the array information parameters to be adjusted in the calculation so that the pressure wave excited by the airgun has a better detection resolution, mainly including: airgun volume, airgun working pressure, airgun excitation depth, airgun spacing, array spacing, delayed excitation time, etc. During the optimization process, the subarray can be used as the optimization object for overall parameter adjustment, or the parameters of each gun in the whole array can be adjusted, which can also be called "whole array adjustment". Compared with the subarray adjustment, the adjustment of single gun parameters is slightly more complicated to implement, but the optimization effect is very significant.

[0110] Furthermore, in step five, the bubble dynamics theory is combined with the particle swarm algorithm, where the particle swarm algorithm is a global optimization algorithm based on swarm intelligence, which is inspired by the simulation of bird flock foraging behavior. It solves complex optimization problems by simulating the process of particle swarms moving in the search space and finding the optimal solution, which is inspired by the simulation of bird flock foraging behavior.

[0111] Furthermore, an initial particle swarm X consisting of N particles is set, where each particle is an E-dimensional vector, and each particle has an initial position and a search speed. By continuously updating and iterating the position and search speed, the particles gradually approach the objective function. k and speed v k It can be expressed by the following formula:

[0112] x k =(x k1 ,x k2 ···x ke ···x kE ) (11)

[0113] v k =(v k1 ,v k2 ···v ke ···v kE ) (12)

[0114] Furthermore, the search speed of each particle in the calculation is affected by the individual optimal position pi,best Influence, that is, record the optimal position found by each particle so far, and move towards the global optimal position p at the same time g,best Movement. The individual optimal position of the kth particle is p k,best And the global optimal position p g,best , specifically expressed as follows:

[0115] p k,best =(p k1 ,p k2 ···p ke ···p kE ) (13)

[0116] p g,best =(p g1 ,p g2 ···p ge ···p gE ) (14)

[0117] Furthermore, the forward search speed of each particle in the search space can be expressed as:

[0118] v kd (t+1)=ω·v ke (t)+c1r1(p ke (t)-x ke (t))+c2r2(p ge (t)-x kd (t)) (15)

[0119] Among them, ω is the inertia weight used to control the amplitude of speed change and balance the global search and local search capabilities; c1 and c2 are the individual learning factor and social learning factor, respectively, indicating the degree to which the particle approaches the individual optimal position and the global optimal position; r1 and r2 are random numbers between [0,1].

[0120] Furthermore, once the particle velocity is determined, the position of the particle at each time step can be calculated:

[0121] x ke (t+1)=x ke (t)+v ke (t+1) k=1,2,…N; e=1,2,…E (16)

[0122] Furthermore, the airgun array is inversely optimized with reference to the optimization objective function and the optimization control variables, and iterative calculation is performed to obtain optimized airgun array information parameters.

[0123] Furthermore, in step 6, the optimized airgun array is post-processed. The post-processing is based on the mathematical and physical model of airgun bubble dynamics in step 1, and the pressure wavelet and spectrum of the optimized airgun array are calculated to achieve fast and convenient inversion optimization of the airgun array. The optimization objective function is selected as the large initial bubble ratio, and the optimization control variable is selected as the pressure wavelet and spectrum curve comparison before and after optimization of changing the airgun volume. Figure 4 and Figure 5 As shown, Figure 4 In the figure, the blue dotted line is the pressure wavelet curve before optimization, and the red solid line is the expected pressure wavelet curve after optimization. Figure 5 The blue dotted line is the spectrum curve before optimization, and the red solid line is the expected spectrum curve after optimization.

[0124] Figure 1 Any process or method description in the flowchart described in or otherwise described herein can be understood as representing a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a custom logic function or process, and the scope of the preferred embodiment of the present invention includes other implementations, in which the functions may be performed in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by a person skilled in the art of the present invention. The logic and / or steps represented in the flowchart or otherwise described herein illustrate the possible implementation architecture, functions and operations of the apparatus and methods according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logic function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It is also noted that each block in the block diagram or flowchart, and combinations of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions. For example, what may be considered as an ordered list of executable instructions for implementing a logical function may be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch instructions from and execute instructions in an instruction execution system, apparatus, or device).

[0125] Those skilled in the art will appreciate that the above are only preferred embodiments of the present invention, and the various embodiments of the present disclosure and / or the features described in the claims may be combined or combined in various ways, even if such combinations or combinations are not explicitly described in the present disclosure. It is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may still modify the technical solutions described in the aforementioned embodiments, or perform equivalent substitutions on some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0126] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing the arrangement of deep-sea exploration airgun arrays for improving wavelet quality, characterized in that: The method comprises the following steps: Step 1: input the initial information parameters of the airgun array before optimization, and obtain the airgun bubble pressure wavelet data and sound pressure level spectrum data before optimization based on the mathematical and physical model of airgun bubble dynamics; Step 2: Establish the upper and lower limits of the airgun sinking depth and the constraint conditions of the airgun volume variation range during the optimization process; Step 3, setting the optimization objective function according to the airgun bubble pressure wavelet data and sound pressure level spectrum data before optimization obtained in step 1 and the constraint conditions obtained in step 2; Step 4: setting the optimization control parameters based on the optimization objective function set in step 3; Step 5: Combining the spherical bubble dynamics theory with the particle swarm optimization algorithm, the array is iteratively optimized based on the optimization objective function obtained in step 3 and the optimization control parameters in step 4; Step 6: Calculate the pressure wavelet and sound pressure level spectrum of the airgun array. If the prediction accuracy of the calculation results meets the requirements, the airgun array information optimized based on the particle swarm algorithm is obtained. The optimized pressure wavelet and sound pressure level spectrum are calculated and output to achieve the inversion optimization of the airgun array. If the prediction accuracy of the calculation results does not meet the requirements, execute step 5.

2. The deep sea exploration air gun array layout optimization method for improving wavelet quality according to claim 1, characterized in that: In step 1, the initial information parameters of the airgun array before optimization include the volume of each airgun, the arrangement position of each airgun, the excitation depth of each airgun, the time delay, the coherence coefficient, the airgun type, the airgun working pressure, the number of airguns, the total volume of the array and the number of spare airguns.

3. The deep sea exploration air gun array layout optimization method for improving wavelet quality according to claim 1, characterized in that: The mathematical and physical model of airgun bubble dynamics described in step 1 is established based on the spherical bubble pulsation theory under a compressible flow field, and a correction term is added.

4. The deep sea exploration air gun array layout optimization method for improving wavelet quality according to claim 3, characterized in that: The correction term is calculated using the following method: Where C is the speed of sound, R is the radius of the airgun bubble, They represent the first-order derivative and the second-order derivative of the radius respectively. The equation is solved by the Runge-Kutta method, that is, Through the known Calculate the physical quantities of the airgun bubble pulsation. ξ1 and ξ2 are the additional empirical coefficients for controlling the multi-period bubble energy attenuation problem. P is the pressure on the bubble surface. m, R, and T represent the mass, gas constant, and gas temperature of the gas in the bubble. The gas parameter is 287 J / (kg×K). V is the volume of the bubble. P ∞ is the hydrostatic pressure at the center of the bubble.

5. The deep sea exploration air gun array layout optimization method for improving wavelet quality according to claim 1, characterized in that: In step 1, the method for obtaining the airgun bubble pressure wavelet data before optimization based on the mathematical and physical model of airgun bubble dynamics is: Where u is the fluid velocity at the measuring point, P field is the flow field pressure at the measuring point, and r is the distance from the bubble center to the measuring point.

6. The deep sea exploration air gun array layout optimization method for improving wavelet quality according to claim 1, characterized in that: Setting the optimization objective function in step three also includes a step of implementing it through spectrum features.

7. The deep sea exploration air gun array layout optimization method for improving wavelet quality according to claim 1, characterized in that: In step 4, based on the optimization objective function set in step 3, the optimization control parameters are set, including airgun volume, airgun working pressure, airgun excitation depth, airgun spacing, array spacing, and delayed excitation time.

8. A deep sea exploration air gun array layout optimization system for improving wavelet quality, characterized in that: The system comprises: An input module is used to input the initial information parameters of the airgun array before optimization, and obtain the airgun bubble pressure wavelet data and sound pressure level spectrum data before optimization based on the mathematical and physical model of airgun bubble dynamics; The constraint module is used to establish the upper and lower limits of the airgun sinking depth and the airgun volume variation range during the optimization process; An optimization objective function setting module sets the optimization objective function according to the airgun bubble pressure wavelet data and sound pressure level spectrum data before optimization obtained by the input module and the constraint conditions obtained by the constraint module; An optimization control parameter setting module, used for setting optimization control parameters based on the optimization objective function set by the optimization objective function setting module; An optimization module is used to combine the spherical bubble dynamics theory with the particle swarm optimization algorithm to iteratively optimize the array based on the optimization objective function obtained by the optimization objective function setting module and the optimization control parameters of the optimization control parameter setting module; The output module is used to calculate the pressure wavelet and sound pressure level spectrum of the airgun array. If the prediction accuracy of the calculation results meets the requirements, the airgun array information optimized based on the particle swarm algorithm is obtained, and the optimized pressure wavelet and sound pressure level spectrum are calculated and output to realize the inversion optimization of the airgun array; if the prediction accuracy of the calculation results does not meet the requirements, the optimization module is executed.

9. A computer device comprising a memory and a processor, characterized in that A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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