A deep-sea exploration airgun array layout optimization method and system for improving wavelet quality

Abstract

A method and system for optimizing the arrangement of deep-sea exploration airgun arrays for improving wavelet quality relates to the field of deep-sea resource exploration. The method solves the problem of rapid optimization design of airgun arrays in existing projects. The method comprises: inputting initial information parameters of the airgun array, obtaining airgun bubble pressure wavelet data and sound pressure level spectrum data before optimization based on the mathematical and physical model of airgun bubble dynamics; establishing upper and lower limits of the airgun sinking depth and constraints on the airgun volume variation range; setting its optimization objective function and optimization control parameters; combining spherical bubble dynamics theory with particle swarm optimization algorithm to iteratively optimize the array; calculating the airgun array pressure wavelet and sound pressure level spectrum. If the calculation results meet the requirements, the airgun array information optimized based on the particle swarm algorithm is obtained, the pressure wavelet and sound pressure level spectrum are calculated, and the optimized airgun array information is output to achieve inverse optimization of the airgun array; if the requirements are not met, the array is iteratively optimized.

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 continues to grow, the energy generated by a single source is insufficient to directly detect seafloor resources at greater distances and depths. However, the firing frequency can be controlled by adjusting the capacity, depth, and combination of individual guns. This has led to the development of airgun arrays, which combine multiple airguns in a specific pattern and exploit the interaction between bubbles to improve the performance of airgun sources. The use of airgun arrays increases the source energy, enabling the generated wavelets to detect deeper seabeds. In practical seismic exploration, ensuring that the airgun array possesses the characteristics of low-frequency, broadband, and high energy is crucial. However, the array layout cannot be determined arbitrarily, and sometimes designing an array that meets the requirements can be time-consuming. Traditional array design often relies on the experience of engineers, gradually optimizing through repeated trials and adjustments. However, this approach is not only expensive but also relatively inefficient.

[0003] Current array design typically relies on engineers' experience, often requiring gradual optimization through multiple trials and adjustments, or by making minor modifications to known high-quality airgun arrays. This approach to array design is not only time-consuming, but also requires engineers to limit their energy and make it impossible to consider all optimization methods, potentially leading to the omission of excellent airgun arrays.

[0004] Therefore, how to quickly and efficiently design high-quality air gun 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 airgun 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 airgun volume variation range during the optimization process;

[0010] Step 3: setting an optimization objective function based on 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 realize 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 air gun array before optimization input in step one include the volume of each air gun, the arrangement position of each air gun, the excitation depth of each air gun, the time delay, the coherence coefficient, the air gun type, the air gun working pressure, the number of air guns, the total volume of the array and the number of spare air guns.

[0015] Furthermore, a preferred embodiment is provided, in which the mathematical and physical model of the 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 embodiment 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, the Through 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, respectively. 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] Furthermore, a preferred embodiment is provided, 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:

[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 embodiment 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 airgun 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 obtained by 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. 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, the optimization module is executed.

[0035] Solution 3: A computer device includes a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes any one of the methods described in Solution 1.

[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 present invention describes a method and system for optimizing the layout of deep-sea exploration airgun arrays for improving wavelet quality. This method combines airgun bubble dynamics theory with a particle swarm optimization algorithm, introduces constraints to make the optimization more applicable to actual engineering applications, and establishes an optimization objective function and optimized control variables based on high-quality pressure wavelets and spectra, achieving the goal of rapid optimization of the airgun array. This provides a theoretical basis and fundamental 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 airgun arrays for improving wavelet quality, as described in embodiment 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 the eleventh embodiment.

[0042] Figure 3 This is a schematic diagram of a spectrum curve used as a basis for establishing an optimization objective function according to the eleventh embodiment.

[0043] Figure 4 This is a comparison diagram of the pressure wavelet optimization before and after 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 inversion optimization of the deep-sea exploration air gun array 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 this application clearer, the technical solutions in the implementation methods of this application will be clearly and completely described below in combination with the drawings in the implementation methods of this application. Obviously, the described implementation methods are only part of the implementation methods of this application, not all of the implementation methods.

[0046] Embodiment 1: This embodiment provides a method for optimizing the arrangement of deep-sea exploration airgun 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 airgun volume variation range during the optimization process;

[0049] Step 3: setting an optimization objective function based on 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 realize 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 further limits 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 further limits the deep-sea exploration air gun array layout optimization method for improving wavelet quality described in implementation method one. The mathematical and physical model of air gun bubble dynamics described in step one is established based on the spherical bubble pulsation theory under a compressible flow field, and a correction term step is added.

[0055] Implementation 4: This implementation further defines the deep-sea exploration airgun array arrangement optimization method for improving wavelet quality described in Implementation 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, the Through 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, respectively. 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] Implementation 5. This implementation further defines the deep-sea exploration airgun array arrangement optimization method for improving wavelet quality described in Implementation 1. 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 as follows:

[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] Implementation method 6: This implementation method further limits the deep-sea exploration air gun array arrangement optimization method for improving wavelet quality described in implementation method 1. 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 further limits the deep-sea exploration air gun array layout optimization method for improving wavelet 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 airgun 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 obtained by 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. 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, the optimization module is executed.

[0074] Implementation method 9. This implementation method proposes a computer device including a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the method described in any one of implementation methods 1 to 7.

[0075] Implementation 10: This implementation 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 Implementation 1 to Implementation 7 are implemented.

[0076] Implementation 11: This implementation provides an example, which is used to explain the above implementations 1 to 8. Specifically, the 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 deep-sea exploration air gun array layout optimization method 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] In the second step, 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 3: Establish an optimization objective function based on the requirements of high-quality source wavelets and spectral characteristics.

[0081] In step 4, the optimized control variables were designed based on the airgun array information parameters.

[0082] Step five: Combining bubble dynamics theory with particle swarm optimization algorithm, and referring to the optimization objective function and optimization control variables, the air gun array is inversely optimized.

[0083] Step six: record and post-process the information parameters after inversion optimization of the air gun array.

[0084] Specifically, in step 1, the initial parameters of the airgun array before optimization are input, including the volume of each gun, the arrangement of each gun, the excitation depth of each gun, the time delay, the coherence coefficient, the airgun type, the airgun operating pressure, the number of airguns, the total array volume, and the number of spare guns. By modifying these array parameters within a certain range, the initial airgun array before optimization is set. Once the airgun array is obtained, the pre-optimization pressure wavelet and spectrum 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 Determine the physical quantities related to the airgun bubble pulsation. ξ1 and ξ2 are additional empirical coefficients that control 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 value 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 optimal range of the airgun volume. According to the setting of the airgun excitation depth in the actual project, the optimization constraint range of the airgun excitation depth is designed taking into account the exploration imaging accuracy.

[0098] Furthermore, in step 3, the optimization objective function that meets the requirements of high-quality pressure wavelets is selected. 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 to have 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 excessively large bubble pulses can interfere with the reception of the main pulse and reflected waves, resulting in confusing and inaccurate detection data. Therefore, 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, the 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 optimization objective is the same as that of the far-field pressure wavelet, which is to eliminate the influence of bubble pulses. However, the objective in the spectrum is to increase the effective bandwidth of the spectrum and improve its smoothness. The new optimization objective function is established based on the spectrum variance and aims to reduce the fluctuation of the spectrum curve in the range of 0-100Hz, 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 of the frequencies at each small increment, q(w i ) represents the weight.

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

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

[0109] Furthermore, in step four, the optimized control variables refer to the array parameters to be adjusted during the calculation to achieve better detection resolution for the pressure waves excited by the airguns. These primarily include: airgun volume, airgun operating pressure, airgun excitation depth, airgun spacing, array spacing, and delayed excitation time. During the optimization process, parameters of the subarray can be adjusted as a whole, or the parameters of each gun in the entire array can be adjusted, also known as "full-array adjustment." While adjusting individual gun parameters is slightly more complex than adjusting subarray parameters, the optimization effect is highly 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 flocks’ foraging behavior. It solves complex optimization problems by simulating the process of particle swarms moving in the search space and searching for the optimal solution. It is inspired by the simulation of bird flocks’ 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 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, recording the optimal position found by each particle so far, and at the same time moving towards the global optimal position p g,best Movement. The individual optimal position p of the kth particle 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 follows based on the local optimal and global optimal particles:

[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 velocity change and balance the global search and local search capabilities; c1 and c2 are the individual learning factor and the social learning factor, respectively, which represent 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 air gun array is inversely optimized with reference to the optimization objective function and the optimization control variables, and iterative calculation is performed to obtain the optimized air gun 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 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 flowcharts described in the flowcharts or otherwise described herein can be understood to represent a module, segment or portion of code including one or more executable instructions for implementing the steps of a custom logic function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed in a manner other than the order shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention relate. The logic and / or steps represented in the flowcharts or otherwise described herein illustrate the possible 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, program segment, or portion of code, which 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 boxes can also occur in an order different from that marked in the figures. For example, two boxes shown in succession can actually be executed substantially in parallel, or they can sometimes be executed in a reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams or flowcharts, and combinations of blocks in the block diagrams or flowcharts, 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 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 (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device).

[0125] Those skilled in the art will understand that the above description is only a preferred embodiment of the present invention, and the features described in the various embodiments and / or claims of the present disclosure may be combined or coupled in various ways, even if such a combination or coupling is 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 make equivalent substitutions for 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 scope of protection of the present invention.

[0126] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. 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, the present invention is intended to include such changes and modifications as fall within the scope of the claims and their equivalents.

Claims

1. A deep-sea exploration airgun array layout optimization method 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 airgun volume variation range during the optimization process; Step 3: setting an optimization objective function based on 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. 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: (4) (5) (6) in, is the fluid velocity at the measuring point, is the flow field pressure at the measuring point, is the distance from the bubble center to the measuring point, 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, the , through the known Calculate the physical quantities of the airgun bubble pulsation, is the speed of sound, and H is the enthalpy difference from the flow field at the bubble wall to infinity.

2. The deep-sea exploration airgun 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 are input, including 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 airgun 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 airgun array layout optimization method for improving wavelet quality according to claim 3, characterized in that: The correction term is calculated using the following method: (1) (2) (3) in, are the additional empirical coefficients for controlling the multi-period bubble energy decay problem, is the pressure on the bubble surface, They represent the mass, gas constant and gas temperature of the gas in the bubble respectively. The gas parameters are , is the volume of the bubble, is the hydrostatic pressure at the center of the bubble.

5. The deep-sea exploration airgun array layout optimization method for improving wavelet quality according to claim 1 is characterized in that In, Setting the optimization objective function in step three also includes the step of realizing it through spectrum characteristics.

6. The deep-sea exploration airgun array layout optimization method for improving wavelet quality according to claim 1 is characterized in that In, 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.

7. A deep-sea exploration airgun 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 obtained by the optimization control parameter setting module; The output module is used to calculate the pressure wavelet and sound pressure level spectrum of the air gun array. If the prediction accuracy of the calculation results meets the requirements, the air gun 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 achieve the inversion optimization of the air gun array. If the prediction accuracy of the calculation results does not meet the requirements, the optimization module is executed. The method for obtaining the airgun bubble pressure wavelet data before optimization based on the mathematical and physical model of airgun bubble dynamics in the input module is: (4) (5) (6) in, is the fluid velocity at the measuring point, is the flow field pressure at the measuring point, is the distance from the bubble center to the measuring point, 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, the , through the known Calculate the physical quantities of the airgun bubble pulsation, is the speed of sound, and H is the enthalpy difference from the flow field at the bubble wall to infinity.

8. 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 6.

9. 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 6 are implemented.

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