Method and system for quickly fitting Biot model parameter curve and electronic equipment

Abstract

The invention discloses a method and system for quickly fitting a Biot model parameter curve and electronic equipment, and the method comprises the steps: obtaining ocean sediment data, and determining an initial parameter value of each parameter of sediment based on the ocean sediment data; constructing a preliminary curve of a Biot model according to the initial parameter values; performing sensitivity analysis on the Biot model to obtain a weight coefficient of each parameter; based on a Biot model, piecewise fitting is carried out through actually measured data in combination with preset frequency piecewise; in the fitting process, a target fitting result of the Biot model is obtained by searching through a particle swarm optimization algorithm. According to the method, accurate fitting of the Biot model parameter curve is realized by combining sensitivity analysis and a segmentation fitting method and utilizing a particle swarm optimization algorithm, so that the fitting efficiency is improved, the characteristics of the seabed sediment can be reflected more accurately, powerful support is provided for marine geological exploration and resource development, and the method has a wide application prospect. The method can be widely applied to the technical field of data processing.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method, a system and an electronic device for quickly fitting a Biot model parameter curve. Background Art

[0002] In marine geological exploration, understanding the characteristics of marine sediments is of great significance for resource exploration, marine engineering construction, environmental protection, etc. Marine sediment data contains rich sediment information, such as parameters like grain size, density, porosity, water content, etc., and these parameters are crucial for evaluating the physical and mechanical properties of submarine sediments.

[0003] Traditional methods for obtaining marine sediment data mainly rely on sampling and analysis. However, this method is not only time-consuming and laborious, but also difficult to comprehensively reflect the spatial distribution characteristics of submarine sediments. In recent years, with the development of acoustic detection technology, using acoustic parameters (such as sound velocity and sound attenuation) to invert the characteristics of submarine sediments has become a new effective means.

[0004] The Biot model is a theoretical model that describes the acoustic wave propagation characteristics in porous media and can better simulate the acoustic wave propagation process in submarine sediments. However, there are some problems in directly applying the Biot model for fitting submarine sediment parameters, such as numerous model parameters and complex interactions between parameters, resulting in inaccurate fitting results. Summary of the Invention

[0005] The present invention aims to solve the problems of related technical limitations to at least a certain extent. For this purpose, the present invention provides a method, a system and an electronic device for quickly fitting a Biot model parameter curve, which can quickly and accurately fit the Biot model parameter curve.

[0006] On the one hand, an embodiment of the present invention provides a method for quickly fitting a Biot model parameter curve, including the following steps:

[0007] Obtain marine sediment data, and determine the initial parameter values of each parameter of the sediment based on the marine sediment data; construct a preliminary curve of the Biot model according to the initial parameter values; the preliminary curve includes a sound velocity curve and a sound attenuation curve;

[0008] Conduct a sensitivity analysis on the Biot model to obtain the weight coefficient of each parameter;

[0009] Based on the Biot model, perform segmented fitting by combining measured data with a preset frequency segmentation; wherein, the weight coefficient is used to guide the determination of the search range of each parameter in the fitting process of segmented fitting;

[0010] In the fitting process, search for the target fitting result of the Biot model through the particle swarm optimization algorithm.

[0011] Optionally, determining the initial parameter values of the sediment based on seafloor sediment data includes the following steps:

[0012] Determine the classification of the sediment and the physical range of each parameter according to the experimental data and the seafloor sediment research literature;

[0013] For the first type of parameters, determine the initial parameter values through measured values;

[0014] For the second type of parameters, determine the initial parameter values through representative values or intermediate values of the physical range.

[0015] Optionally, performing a sensitivity analysis on the Biot model to obtain the weight coefficient of each parameter includes the following steps:

[0016] Based on the preliminary curve, obtain the sensitivity index of each parameter through local sensitivity analysis;

[0017] Normalize the sensitivity index to obtain the first weight coefficient of each parameter;

[0018] Based on the Biot model, perform a global sensitivity analysis to obtain the total effect sensitivity index of each parameter;

[0019] Normalize the total effect sensitivity index to obtain the second weight coefficient of each parameter.

[0020] Optionally, obtaining the sensitivity index of each parameter through local sensitivity analysis based on the preliminary curve includes the following steps:

[0021] Based on a preset proportional increment, perturb the preliminary curve corresponding to each parameter to obtain the curve difference before and after perturbation;

[0022] Among them, the preliminary curve includes the sound velocity curve and the attenuation curve;

[0023] Perform an absolute value averaging process on the curve differences of the sound velocity curve and the attenuation curve corresponding to the same parameter to obtain the sensitivity index corresponding to the corresponding parameter.

[0024] Optionally, performing a global sensitivity analysis based on the Biot model to obtain the total effect sensitivity index of each parameter includes the following steps:

[0025] According to each input parameter on which the Biot model depends, decompose the total variance through the Sobol method to obtain the contribution of each parameter to the output of the Biot model and the interaction between parameters;

[0026] Process according to the contribution of each parameter and the interaction between parameters to obtain the total effect sensitivity index of each parameter.

[0027] Optionally, based on the Biot model, segmented fitting is performed by combining measured data with a preset frequency segmentation, including the following steps:

[0028] Obtain multiple frequency points and their corresponding measured acoustic values as measured data; the measured acoustic values include measured sound velocity values and measured sound attenuation values;

[0029] Split the measured data into multiple frequency segments based on the preset frequency segmentation;

[0030] For the measured data of each frequency segment, perform segmented fitting with the goal of minimizing a preset cost function;

[0031] Among them, the cost function is defined by the least squares error method.

[0032] Optionally, the target fitting result of the Biot model is obtained by searching through the particle swarm optimization algorithm, including the following steps:

[0033] Initialize the particle swarm; each particle in the particle swarm represents a set of model parameters of the Biot model;

[0034] Determine the particle fitness value of each particle in the particle swarm according to the cost function;

[0035] Update and iterate the velocity and position of each particle in the particle swarm, and re-determine the particle fitness value after each iteration until the particle fitness value meets the preset conditions, and output the target fitting result of the Biot model.

[0036] On the other hand, an embodiment of the present invention provides a system for quickly fitting the parameter curve of the Biot model, including:

[0037] The first module is used to obtain marine bottom sediment data, determine the initial parameter values of each parameter of the sediment based on the marine bottom sediment data; construct a preliminary curve of the Biot model according to the initial parameter values; the preliminary curve includes a sound velocity curve and a sound attenuation curve;

[0038] The second module is used to perform sensitivity analysis on the Biot model to obtain the weight coefficient of each parameter;

[0039] The third module is used to perform segmented fitting based on the Biot model by combining measured data with a preset frequency segmentation; among them, the weight coefficient is used to guide the determination of the search range of each parameter in the fitting process of the segmented fitting;

[0040] The fourth module is used to search for the target fitting result of the Biot model through the particle swarm optimization algorithm during the fitting process.

[0041] On the other hand, an embodiment of the present invention provides an electronic device, including: a processor and a memory; the memory is used for storing a program; the processor executes the program to implement the method for quickly fitting the Biot model parameter curve as described above.

[0042] On the other hand, an embodiment of the present invention provides a computer storage medium, in which there is a program executable by a processor, and the program executable by the processor is used to implement the method for quickly fitting the Biot model parameter curve as described above when executed by the processor.

[0043] In the embodiment of the present invention, by obtaining marine bottom sediment data, the initial parameter values of each parameter of the sediment are determined based on the marine bottom sediment data; a preliminary curve of the Biot model is constructed according to the initial parameter values; the preliminary curve includes a sound velocity curve and a sound attenuation curve; a sensitivity analysis is performed on the Biot model to obtain the weight coefficient of each parameter; based on the Biot model, segmented fitting is performed by combining measured data with a preset frequency segmentation; wherein, the weight coefficient is used to guide the determination of the search range of each parameter in the fitting process of the segmented fitting; in the fitting process, the target fitting result of the Biot model is obtained by searching through the particle swarm optimization algorithm. The present invention has the following beneficial effects:

[0044] 1. First, obtain marine bottom sediment data, and determine the initial parameter values of each parameter of the sediment based on these data. This step provides a basis for the subsequent inversion process.

[0045] 2. Then, use these initial parameter values to construct a preliminary curve of the Biot model, including a sound velocity curve and a sound attenuation curve. These curves can reflect the basic acoustic characteristics of the seabed sediment.

[0046] 3. Then, perform a sensitivity analysis on the Biot model to obtain the weight coefficient of each parameter. These weight coefficients reflect the influence degree of each parameter on the model output, and provide guidance for the subsequent segmented fitting process.

[0047] 4. In the segmented fitting process, combine measured data and a preset frequency segmentation, and use the weight coefficient to guide the determination of the search range of each parameter. This step can narrow the search space and improve the fitting efficiency.

[0048] 5. Finally, the target fitting result of the Biot model is obtained by searching through the particle swarm optimization algorithm within the specified search range. This method can globally search for the optimal solution and improve the accuracy of the Biot model parameter curve fitting.

[0049] In summary, the present invention combines sensitivity analysis and piecewise fitting methods, and uses the particle swarm optimization algorithm for global search, achieving accurate fitting of the Biot model parameter curve. This method not only improves the fitting efficiency, but also can more accurately reflect the characteristics of submarine sediments, providing strong support for marine geological exploration and resource development. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings are used to provide a further understanding of the technical solutions of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solutions of the present invention, and do not constitute a limitation to the technical solutions of the present invention.

[0051] Figure 1 FIG. is a schematic diagram of an implementation environment for quickly fitting the Biot model parameter curve provided by an embodiment of the present invention;

[0052] Figure 2 FIG. is a schematic flowchart of a method for quickly fitting the Biot model parameter curve provided by an embodiment of the present invention;

[0053] Figure 3 FIG. is a schematic diagram of a specific implementation process of a method for quickly fitting the Biot model parameter curve provided by an embodiment of the present invention;

[0054] Figure 4 FIG. is a schematic structural diagram of a system for quickly fitting the Biot model parameter curve provided by an embodiment of the present invention;

[0055] Figure 5 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0057] It should be noted that although functional module division is performed in the system schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the system or the flowchart in the flowchart. The terms "first / S100", "second / S200", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence.

[0058] References herein to "embodiments" mean that particular features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the invention. The phrase appears in various places in the specification and is not necessarily referring to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive of other embodiments. Those skilled in the art will understand explicitly and implicitly that the embodiments described herein can be combined with other embodiments.

[0059] It can be understood that the method for quickly fitting the Biot model parameter curve provided by the embodiments of the present invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various types of terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal is a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto.

[0060] As Figure 1 shown, it is a schematic diagram of an implementation environment provided by the embodiments of the present invention. Referring to Figure 1 , this implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be network-connected wirelessly or wiredly to complete data transmission and exchange.

[0061] The server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0062] In addition, the server 101 can also be a node server in a blockchain network. Among them, the blockchain is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithms.

[0063] The terminal 102 can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal 102 and the server 101 can be directly or indirectly connected through wired or wireless communication means, and the embodiments of the present invention do not limit this here.

[0064] Exemplarily based on Figure 1 the implementation environment shown, the embodiments of the present invention provide a method for quickly fitting the parameter curve of the Biot model. Taking the application of the method for quickly fitting the parameter curve of the Biot model to the server 101 as an example for illustration, it can be understood that the method for quickly fitting the parameter curve of the Biot model can also be applied to the terminal 102.

[0065] Referring to Figure 2 , Figure 2 is a flowchart of the method for quickly fitting the parameter curve of the Biot model applied to the server provided by the embodiments of the present invention. The execution subject of the method for quickly fitting the parameter curve of the Biot model can be any one of the foregoing computer devices (including the server or the terminal). Referring to Figure 2 ,the method includes the following steps:

[0066] S100. Obtain marine bottom sediment data, and determine the initial parameter values of each parameter of the sediment based on the marine bottom sediment data; construct a preliminary curve of the Biot model according to the initial parameter values;

[0067] wherein, the preliminary curve includes a sound velocity curve and a sound attenuation curve;

[0068] It should be noted that in some embodiments, determining the initial parameter values of the sediment based on the marine bottom sediment data may include the following steps: determining the classification of the sediment and the physical range of each parameter according to experimental data and marine bottom sediment research literature; for the first type of parameters, determining the initial parameter values through measured values; for the second type of parameters, determining the initial parameter values through representative values or intermediate values of the physical range.

[0069] Exemplarily, in some specific implementation manners, the classification of the sediment and the physical range of each parameter can be determined based on a large amount of experimental data and by collecting relevant marine bottom sediment research literature. Specifically, according to the classification of the sediment itself, the empirical ranges of its various physical parameters are determined, including: porosity, tortuosity, viscosity coefficient, pore water mass density, pore water bulk modulus, particle mass density, particle bulk modulus, permeability, pore size, shear modulus of the frame, and bulk modulus of the frame. Take a set of "representative" or "values in typical literature" within the range of each physical parameter as the initial parameter values, and use the Biot model to generate the initial sound velocity - frequency and sound attenuation - frequency curves to provide a reference for subsequent optimization.

[0070] S200. Perform a sensitivity analysis on the Biot model to obtain the weight coefficient of each parameter;

[0071] It should be noted that in some embodiments, step S200 may include the following steps: based on the preliminary curve, obtain the sensitivity index of each parameter through local sensitivity analysis; perform normalization processing on the sensitivity index to obtain the first weight coefficient of each parameter; based on the Biot model, perform global sensitivity analysis to obtain the total effect sensitivity index of each parameter; perform normalization processing on the total effect sensitivity index to obtain the second weight coefficient of each parameter.

[0072] Among them, in some embodiments, based on the preliminary curve, obtaining the sensitivity index of each parameter through local sensitivity analysis may include the following steps: perform perturbation processing on the preliminary curve corresponding to each parameter based on a preset proportional increment to obtain the curve difference before and after perturbation; wherein, the preliminary curve includes the sound velocity curve and the attenuation curve; perform absolute value averaging on the curve differences of the sound velocity curve and the attenuation curve corresponding to the same parameter to obtain the sensitivity index corresponding to the corresponding parameter.

[0073] Exemplarily, in some specific embodiments, the local sensitivity analysis (LSA) and weight calculation can be implemented as follows:

[0074] First, define a set of reference parameters and set the initial values of these parameters according to the type of sediment and the data in relevant literature. Then, perform a small perturbation (e.g., ±5%) on each parameter in turn while keeping other parameters unchanged. By calculating the difference in the fast wave sound velocity curve before and after the perturbation of each parameter, the influence of each parameter on the curve can be evaluated. The sensitivity index is defined by calculating the absolute value of the curve deviation at several frequency points, and finally, the relative weight coefficient of each parameter is obtained through normalization processing. These weight coefficients can guide the subsequent optimization process and help us reasonably adjust the search step size and order of the parameters during the fitting process.

[0075] Among them, in some embodiments, based on the Biot model, performing global sensitivity analysis to obtain the total effect sensitivity index of each parameter may include the following steps: according to each input parameter on which the Biot model depends, decompose the total variance through the Sobol method to obtain the contribution of each parameter to the output of the Biot model and the interaction between parameters; process according to the contribution of each parameter and the interaction between parameters to obtain the total effect sensitivity index of each parameter.

[0076] Exemplarily, in some specific embodiments, the global sensitivity analysis can be implemented as follows:

[0077] Perform global sensitivity analysis through the Sobol method to calculate the contribution of each parameter to the model outputs (i.e., sound speed and sound attenuation). The contribution of each parameter is normalized to a weight. According to the obtained parameter weights, adjust the search range of each parameter to make the optimization process more efficient. Narrow the search range for parameters with larger weights to make the optimization adjustment more refined. Expand the search range for parameters with smaller weights to avoid excessive restriction.

[0078] S300. Based on the Biot model, perform piecewise fitting by combining measured data with preset frequency segments.

[0079] Among them, the weight coefficient is used to guide the determination of the search range of each parameter in the fitting process of piecewise fitting.

[0080] It should be noted that in some embodiments, step S300 may include the following steps: Obtain multiple frequency points and their corresponding acoustic measured values as measured data; the acoustic measured values include measured sound speed values and measured sound attenuation values; based on the preset frequency segments, split the measured data into multiple frequency segments; for the measured data of each frequency segment, perform piecewise fitting with the goal of minimizing a preset cost function; among them, the cost function is defined by the least squares error method.

[0081] Exemplarily, in some specific embodiments, piecewise fitting can be achieved by inputting measured data and splitting frequency segments, and it can be specifically implemented as follows:

[0082] In the fitting process, divide the measured data into multiple frequency segments for piecewise fitting one by one. First, obtain the measured sound speed and sound attenuation data corresponding to multiple frequency points from the experiment, and these data points will be used as the target values of the optimization algorithm. Next, divide the frequency segments into several ranges. For example, the fast wave sound speed is processed in three intervals of 0 - 5 kHz, 5 - 100 kHz, and > 100 kHz, and the fast wave sound attenuation is also processed in multiple frequency bands. For the measured data of each frequency band, define the objective function by the least squares error method, calculate the error between the sound speed and sound attenuation values output by the model and the measured values, and the goal is to minimize this cost function.

[0083] S400. In the fitting process, search for the target fitting result of the Biot model through the particle swarm optimization algorithm.

[0084] It should be noted that in some embodiments, the target fitting result of the Biot model obtained by searching through the particle swarm optimization algorithm may include the following steps: initializing the particle swarm; each particle in the particle swarm represents a set of model parameters of the Biot model; determining the particle fitness value of each particle in the particle swarm according to the cost function; updating and iterating the velocity and position of each particle in the particle swarm, and re-determining the particle fitness value after each iteration until the particle fitness value meets the preset conditions, and outputting the target fitting result of the Biot model.

[0085] Exemplarily, in some specific embodiments, the parameter optimization based on the PSO (Particle Swarm Optimization) algorithm can be achieved as follows:

[0086] In the parameter optimization process, the particle swarm optimization algorithm (PSO) is used to search for the optimal solution, and the value of the cost function is used as the fitness value of the particle. In each iteration, the particle fitness is compared to update the individual optimal solution and the global optimal solution. The optimization result is evaluated through indicators such as the coefficient of determination (R 2 ) and the root mean square error (RMS) of the fitting result to ensure excellent fitting effect. And the robustness of the model is verified through cross-validation.

[0087] Meanwhile, in some specific application scenarios, a comparison graph of the actual measurement data and the output of the optimized model can also be drawn to intuitively display the sound velocity-frequency and sound attenuation-frequency curves.

[0088] To explain the principle of the technical solution of the present invention in detail, the overall process of the present invention will be described below in combination with some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0089] First of all, it should be noted that the Biot-Stoll model can be used to calculate the acoustic propagation characteristics (such as sound velocity, sound attenuation, etc.) of sound waves (fast waves, slow waves) in porous media (such as sediments like sandy silt). This model depends on 13 input physical property parameters, among which 11 parameters are more critical: {porosity beta, tortuosity alfa, viscosity eita, pore water mass density rouw, pore water bulk modulus Kw, grain mass density roug, grain bulk modulus Kg, permeability kai, pore size radia, shear modulus of the frame miu, bulk modulus of the frame Kf}. Among them, different parameters have different effects on the "sound velocity-frequency, sound attenuation-frequency" curves. Some mainly affect the "up and down" movement (amplitude), and some mainly affect the "left and right" movement (frequency-related or phase-related effects). How to inversely deduce the corresponding parameters of the Biot-Stoll model based on the existing measured data is the starting point of the design of the method of the present invention.

[0090] The Biot-Stoll model can be used to calculate the acoustic propagation characteristics (such as sound velocity, sound attenuation, etc.) of sound waves (fast waves, slow waves) in porous media (such as sediments like sandy silt). Aiming at the problem that it is difficult to accurately determine the parameters of the Biot model, the purpose of the present invention is to provide a method for quickly fitting the parameter curve of the Biot model. As Figure 3 shown, the method of the present invention can achieve the following:

[0091] Step 1: Parameter initialization and initial condition setting:

[0092] Based on a large amount of experimental data and collecting relevant marine sediment research literature, determine the classification of sediments and the physical ranges of each parameter. Among them, for parameters that are easy to actually measure, such as porosity, pore water mass density, etc., their measured values can be directly used; for the remaining physical parameters that are not easy to measure, including tortuosity, viscosity coefficient, pore water mass density, pore water bulk modulus, particle mass density, particle bulk modulus, permeability, pore size, shear modulus of the frame, bulk modulus of the frame, etc., representative values or intermediate values within their physical parameter ranges are selected as the initial parameter values. According to these initial values, the preliminary sound velocity and sound attenuation curves of the Biot model are calculated.

[0093] Step 2: Local Sensitivity Analysis (LSA) and weight calculation:

[0094] According to a specific sediment type, define a set of reference parameters:

[0095] θ 0 ={alfa 0 ,eita 0 ,Kw 0 ,roug 0 ,Kg 0 ,kai 0 ,radia 0 ,miu 0 ,Kf 0}

[0096] Successively make a small perturbation (or a small change) (such as ±5% or a very small increment Δp i ) to each parameter p i , and keep the other parameters unchanged. Calculate the differences in the fast wave sound velocity curves before and after the perturbation respectively:

[0097]

[0098] Then define a sensitivity index. For the sound velocity curve, the absolute value of the average deviation can be defined at several frequency points; do a similar treatment for the sound attenuation curve. Then, obtain a total deviation amount to characterize the influence of parameter p i :

[0099]

[0100] To make the sensitivity indices of the remaining nine parameters comparable, normalization is required:

[0101]

[0102] The obtained W i is the relative weight coefficient of parameter p i , ranging from [0, 1], and ∑ i W i = 1. Through the above steps, a set of weight coefficients representing local sensitivity can be obtained. Subsequently, during fitting, these weights can be used to guide the search or adjust the search step size.

[0103] Step 3: Global sensitivity analysis:

[0104] Global sensitivity analysis is performed by the Sobol method. For a model that depends on n input parameters, the output is a random variable and can be expressed as:

[0105] Y = f(X 1 , X 2 , K, X n )

[0106] The Sobol method decomposes the total variance into the contribution of individual parameters to the output and the interaction effects between parameters:

[0107]

[0108] where: V(Y) is the total variance of the model output; V i is the main effect variance contribution of the individual variable X i ; Vij is the interaction effect variance contribution of variables X i and X j ; S i is the first-order sensitivity index, which measures the contribution ratio of variable X i to the output variability; S Ti is the total effect sensitivity index, which measures the contribution ratio of variable X i and all its possible interaction effects to the output variability.

[0109] Normalize the total effect sensitivity index S Ti of each parameter into a weight. The calculation formula is:

[0110]

[0111] After obtaining the weights of each parameter, these weights can be applied to adjust the search range in the optimization process. Parameters with larger weights will have a smaller search range to ensure that these parameters are adjusted more precisely; for parameters with smaller weights, a wider search range is allowed to avoid excessive restrictions. The specific formula is:

[0112] new_range = [L i + α·w i (U i - L i ), U i - α·w i (U i - L i )]

[0113] where L i and U i are the lower and upper limits of the initial range of parameter X i , w i is the weight of parameter X i , and α is a control constant.

[0114] Step 4: Input the measured data and split the frequency bands for piecewise fitting:

[0115] Take the measured data of sound speed and sound attenuation corresponding to multiple frequency points obtained from the experiment as input. These data points serve as the target values of the optimization algorithm, and the frequency bands are split into:

[0116] Fast wave sound speed: 0–5 kHz, 5–100 kHz, >100 kHz;

[0117] Fast wave sound attenuation: 0.1–1 kHz, 1–20 kHz, >20 kHz.

[0118] Similar processing is also done for the slow wave. The objective function is defined in the way of least square error and is defined for each frequency band respectively: for the j-th frequency band, there are corresponding measured data {(fi, C exp (fi))}:

[0119]

[0120] Calculate the error between the sound speed and sound attenuation values output by the model and the measured values, and the goal is to minimize this cost function.

[0121] Similarly for sound attenuation:

[0122]

[0123] Step 5: Parameter optimization based on the PSO algorithm and display of fitting effect:

[0124] During the parameter optimization process, the Particle Swarm Optimization (PSO) algorithm is used to search for the optimal parameters of the Biot-Stoll model to minimize the difference between the fast wave speed or sound attenuation curve calculated by the model and the measured data. First, initialize the particle swarm, where each particle represents a set of parameters θ of the Biot-Stoll model i . The velocity and position of the particles are randomly initialized according to the upper and lower limits of the parameters. Then, calculate the fitness value of each particle, which is the value of the objective function MSE. MSE represents the average of the squared differences between the sound speed calculated by the model and the measured data:

[0125]

[0126] This value reflects the quality of the current solution of the particle. The smaller the fitness, the better the fitting effect. The Particle Swarm Optimization algorithm continuously improves the solution by updating the velocity and position of the particles. The update formula for the particle velocity is:

[0127] V i (t + 1) = w·v i (t) + c 1 ·r 1 ·(p best - x i (t)) + c 2 ·r 2 ·(g best - x i (t))

[0128] Where: V i (t) is the velocity of the i-th particle at time t; X i (t) is the position of the i-th particle at time t, that is, the current parameter value θ i ; w is the inertia weight; C 1 , C 2 are acceleration factors; r 1 , r 2 are random numbers in the interval [0, 1]; p best is the personal best solution of the i-th particle; g best is the global best solution of the particle swarm

[0129] The update formula for the position (parameters) of the particle is:

[0130] x i (t + 1) = x i (t) + v i (t + 1)

[0131] After each update, the new fitness value is calculated and compared with the particle's personal best solution. If the current solution is better, the particle's personal best solution is updated. Meanwhile, the global best solution of the entire population is updated according to the fitness values of all particles. The update process of velocity and position can continuously adjust the position of the particle (i.e., the model parameters), making the cost function (MSE) gradually decrease, so as to find the optimal parameter combination.

[0132] After multiple iterations, the particle swarm optimization algorithm will return a global best solution, that is, a set of optimal parameters θ best , which minimizes the difference between the model output and the measured data. To evaluate the fitting effect, the coefficient of determination R 2 and the root mean square error (RMS) are used as evaluation indicators. The coefficient of determination measures the goodness of fit between the fitting curve and the measured data. The closer the value is to 1, the better the fitting effect. The root mean square error calculates the average error between the model output and the measured data during the fitting process. The smaller the value, the more accurate the fitting. Finally, the MATLAB plotting function is used to plot the sound velocity-frequency curve and the sound attenuation-frequency curve to visualize the fitting effect.

[0133] Step 6: Model application:

[0134] In practical applications, the model uses local sensitivity analysis and global sensitivity analysis to obtain parameter weights and adjust the search range, avoiding ineffective searches in unnecessary parameter spaces, improving the search efficiency of the optimization algorithm, and shortening the time to find the optimal parameter combination. Adjusting and optimizing the 11 parameters of the Biot model can more effectively reduce the error between the model output and the actual measurement data, enabling the model to more accurately describe the propagation characteristics of sound waves in sediments and improving the application accuracy of the model in related fields. Through multiple optimization and verification steps, including cross-validation and sensitivity analysis, the robustness of the model can be effectively verified, avoiding overfitting or underfitting phenomena.

[0135] In summary, the present invention provides a method for quickly fitting the parameter curve of the Biot model, which improves the application accuracy of the porous medium wave propagation model based on the Biot theory in the fields of marine acoustics, geophysical exploration, etc. by optimizing the model parameters.

[0136] On the other hand, as Figure 4 shown, an embodiment of the present invention provides a system 900 for quickly fitting the parameter curve of the Biot model, which may include:

[0137] The first module 901 is used to obtain marine bottom sediment data, determine the initial parameter values of each parameter of the sediment based on the marine bottom sediment data; construct a preliminary curve of the Biot model according to the initial parameter values; the preliminary curve includes a sound velocity curve and a sound attenuation curve;

[0138] The second module 902 is configured to perform sensitivity analysis on the Biot model to obtain the weight coefficient of each parameter;

[0139] The third module 903 is configured to perform piecewise fitting based on the Biot model by combining measured data with a preset frequency segmentation; wherein, the weight coefficient is used to guide the determination of the search range of each parameter in the fitting process of the piecewise fitting;

[0140] The fourth module 904 is configured to search for the target fitting result of the Biot model through a particle swarm optimization algorithm during the fitting process.

[0141] The content of the method embodiment of the present invention is applicable to the system embodiment of the present invention. The functions specifically implemented by the system embodiment of the present invention are the same as those of the above method embodiment, and the beneficial effects achieved are also the same as those of the above method.

[0142] On the other hand, an embodiment of the present invention further provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the method for quickly fitting the Biot model parameter curve. The electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0143] It can be understood that the content in the above method embodiment is applicable to the device embodiment of the present invention. The functions specifically implemented by the device embodiment of the present invention are the same as those of the above method embodiment, and the beneficial effects achieved are also the same as those of the above method embodiment.

[0144] As Figure 5 shown, Figure 5 schematically shows the hardware structure of an electronic device 1000 according to another embodiment. The electronic device 1000 includes:

[0145] A processor 1001, which can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is configured to execute relevant programs to implement the technical solution provided by the embodiment of the present invention;

[0146] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 1002 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1002 and are called by the processor 1001 to execute the network node population optimization method of the embodiments of the present invention;

[0147] The input / output interface 1003 is used to implement information input and output;

[0148] The communication interface 1004 is used to implement communication interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0149] The bus 1005 transmits information between various components of the device (such as the processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004);

[0150] Among them, the processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004 achieve communication connections with each other inside the device through the bus 1005.

[0151] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solutions of this embodiment.

[0152] The content of the method embodiments of the present invention is applicable to the electronic device embodiments of the present invention. The functions specifically implemented by the electronic device embodiments of the present invention are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method.

[0153] Another aspect of the embodiments of the present invention also provides a computer-readable storage medium. The storage medium stores a program, and the program is executed by a processor to implement the previous method.

[0154] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present invention, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0155] The content of the method embodiments of the present invention is applicable to this computer-readable storage medium embodiment. The functions specifically implemented by this computer-readable storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method.

[0156] The embodiments of the present invention also disclose a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and these computer instructions are stored in a computer-readable storage medium. The processor of the computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the foregoing method.

[0157] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and combinations of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0158] It should be noted that although several modules of devices for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present invention, the features and functions of two or more of the above-described modules or units can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0159] From the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present invention.

[0160] In some alternative embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two consecutive blocks shown may actually be executed substantially simultaneously or the blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowcharts of the present invention are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated where the order of various operations is changed and where sub-operations described as part of a larger operation are executed independently.

[0161] In addition, although the present invention has been described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It should also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. Rather, given the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Thus, those skilled in the art can implement the present invention as set forth in the claims without undue experimentation. It should also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0162] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0163] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution device, apparatus, or equipment (such as a computer-based device, a device including a processor, or other devices that can fetch instructions from the instruction execution device, apparatus, or equipment and execute the instructions), or in combination with these instruction execution devices, apparatuses, or equipment. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution device, apparatus, or equipment.

[0164] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which a program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0165] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution device. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well-known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0166] In the description of this specification, the descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0167] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0168] The above has specifically described the preferred embodiments of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of the present invention.

Claims

1. A method for quickly fitting a Biot model parameter curve, characterized in that: The following steps are involved: Acquire ocean bottom sediment data, and determine the initial parameter value of each sediment parameter based on the ocean bottom sediment data; Constructing a preliminary curve of the Biot model according to the initial parameter values; the preliminary curve includes a sound velocity curve and a sound attenuation curve; Performing sensitivity analysis on the Biot model to obtain a weight coefficient for each parameter; Based on the Biot model, segmented fitting is performed by combining the measured data with the preset frequency segmentation; wherein the weight coefficient is used to guide the search range of each parameter in the fitting process of the segmented fitting; In the fitting process, the target fitting result of the Biot model is obtained by searching through a particle swarm optimization algorithm.

2. The method for fast fitting Biot model parameter curve according to claim 1, characterized in that: The method of determining the initial parameter value of the sediment based on the ocean bottom data comprises the following steps: Determine the classification of sediments and the physical range of each of the stated parameters based on experimental data and marine sediment research literature; For the parameters of the first type, determining the initial parameter values ​​through measured values; For the parameters of the second type, the initial parameter value is determined by a representative value or a middle value of the physical range.

3. The method for fast fitting Biot model parameter curve according to claim 1, characterized in that: The sensitivity analysis of the Biot model is performed to obtain the weight coefficient of each parameter, comprising the following steps: Based on the preliminary curve, obtaining a sensitivity index of each of the parameters through local sensitivity analysis; Normalizing the sensitivity index to obtain a first weight coefficient for each parameter; Perform global sensitivity analysis based on the Biot model to obtain the total effect sensitivity index of each parameter; The total effect sensitivity index is normalized to obtain a second weight coefficient of each parameter.

4. The method for fast fitting Biot model parameter curve according to claim 3, characterized in that: The step of obtaining a sensitivity index of each parameter through local sensitivity analysis based on the preliminary curve comprises the following steps: Performing a disturbance process on the preliminary curve corresponding to each parameter based on a preset proportional increment to obtain a difference between the curves before and after the disturbance; Wherein, the preliminary curve includes a sound velocity curve and an attenuation curve; The absolute value averaging process is performed on the curve differences between the sound velocity curve and the attenuation curve corresponding to the same parameter to obtain the sensitivity index corresponding to the corresponding parameter.

5. The method for fast fitting Biot model parameter curve according to claim 3, characterized in that: The global sensitivity analysis based on the Biot model is performed to obtain the total effect sensitivity index of each parameter, comprising the following steps: According to each input parameter that the Biot model depends on, the total variance is decomposed by the Sobol method to obtain the contribution of each parameter to the output of the Biot model and the interaction between the parameters; The total effect sensitivity index of each parameter is obtained according to the contribution of each parameter and the interaction between the parameters.

6. The method for fast fitting Biot model parameter curve according to claim 1, characterized in that: The Biot model is based on the measured data and the preset frequency segmentation to perform segmented fitting, including the following steps: Acquiring multiple frequency points and their corresponding acoustic measured values ​​as the measured data; the acoustic measured values ​​include sound velocity measured values ​​and sound attenuation measured values; Splitting the measured data into a plurality of frequency segments based on the preset frequency segmentation; For the measured data of each frequency band, performing piecewise fitting with minimizing a preset cost function as a fitting target; The cost function is defined by the least squares error method.

7. The method for fast fitting Biot model parameter curve according to claim 6, characterized in that: The method of obtaining the target fitting result of the Biot model by searching through the particle swarm optimization algorithm comprises the following steps: Initializing a particle swarm; each particle in the particle swarm represents a set of model parameters of the Biot model; Determine a particle fitness value of each particle in the particle swarm according to the cost function; The speed and position of each particle in the particle swarm are updated and iterated, and the particle fitness value after each iteration is re-determined until the particle fitness value meets the preset conditions, and the target fitting result of the Biot model is output.

8. A system for quickly fitting the parameter curve of the Biot model, characterized in that: include: The first module is used to obtain ocean bottom sediment data and determine the initial parameter value of each sediment parameter based on the ocean bottom sediment data; Constructing a preliminary curve of the Biot model according to the initial parameter values; the preliminary curve includes a sound velocity curve and a sound attenuation curve; The second module is used to perform sensitivity analysis on the Biot model to obtain the weight coefficient of each parameter; The third module is used to perform segmented fitting based on the Biot model by combining the measured data with the preset frequency segmentation; wherein the weight coefficient is used to guide the determination of the search range of each parameter in the fitting process of the segmented fitting; The fourth module is used to search and obtain the target fitting result of the Biot model through a particle swarm optimization algorithm during the fitting process.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 7 when executed by the processor.

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