A method, system and electronic device for quickly fitting Biot model parameter curve

Abstract

The present invention discloses a method, system, and electronic device for quickly fitting a Biot model parameter curve. The method comprises: obtaining ocean bottom sediment data, determining an initial parameter value for each sediment parameter based on the ocean bottom sediment data; constructing a preliminary curve of the Biot model based on the initial parameter values; performing a sensitivity analysis on the Biot model to obtain a weight coefficient for each parameter; performing a piecewise fitting based on the Biot model by combining measured data with a preset frequency segment; and, during the fitting process, searching for a target fitting result of the Biot model using a particle swarm optimization algorithm. The present invention achieves accurate fitting of the Biot model parameter curve by combining the sensitivity analysis and piecewise fitting methods and utilizing the particle swarm optimization algorithm, thereby not only improving fitting efficiency but also more accurately reflecting the characteristics of seabed sediments. This provides strong support for marine geological exploration and resource development, and can be widely applied in the field of data processing technology.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, system and electronic equipment for quickly fitting a Biot model parameter curve. Background Art

[0002] In marine geological exploration, understanding the characteristics of the ocean floor is crucial for resource exploration, marine engineering construction, and environmental protection. Ocean floor data contains a wealth of sediment information, such as particle size, density, porosity, and water content, which are crucial for assessing the physical and mechanical properties of seafloor sediments.

[0003] Traditional methods for acquiring ocean floor data rely primarily on sampling and analysis, but this approach is not only time-consuming and labor-intensive, but also fails to fully reflect the spatial distribution characteristics of seafloor sediments. In recent years, with the advancement of acoustic detection technology, the use of acoustic parameters (such as sound velocity and attenuation) to invert seafloor sediment characteristics has become a new and effective method.

[0004] The Biot model is a theoretical model that describes the propagation characteristics of sound waves in porous media. It can effectively simulate the propagation of sound waves in seafloor sediments. However, directly applying the Biot model to fit seafloor sediment parameters has some problems, such as the large number of model parameters and the complex interactions between the parameters, which often lead to inaccurate fitting results. Summary of the Invention

[0005] The present invention aims to at least partially address the limitations of the related art. To this end, the present invention provides a method, system, and electronic device for quickly fitting a Biot model parameter curve, which can quickly and accurately fit the Biot model parameter curve.

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

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

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

[0009] Based on the Biot model, piecewise fitting is performed by combining measured data with preset frequency segments. The weight coefficient is used to guide the search range of each parameter in the piecewise fitting process.

[0010] During the fitting process, the target fitting result of the Biot model is obtained by searching through the particle swarm optimization algorithm.

[0011] Optionally, determining initial parameter values ​​of sediment based on ocean bottom data comprises the following steps:

[0012] Determine the sediment classification and the physical range of each parameter based on experimental data and marine sediment research literature;

[0013] For the first type of parameters, the initial parameter values ​​are determined by measured values;

[0014] For parameters of the second type, the initial parameter value is determined by a representative value or a middle value of the physical range.

[0015] Optionally, a sensitivity analysis is performed on the Biot model to obtain a weight coefficient for each parameter, including the following steps:

[0016] Based on the preliminary curves, the sensitivity index of each parameter was obtained through local sensitivity analysis;

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

[0018] A global sensitivity analysis was performed based on the Biot model to obtain the total effect sensitivity index of each parameter;

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

[0020] Optionally, based on the preliminary curve, a sensitivity index of each parameter is obtained by local sensitivity analysis, comprising the following steps:

[0021] Perform perturbation on the preliminary curve corresponding to each parameter based on a preset proportional increment to obtain the difference between the curves before and after the perturbation;

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

[0023] The absolute value of the difference between the sound velocity curve and the attenuation curve corresponding to the same parameter is averaged to obtain the sensitivity index corresponding to the corresponding parameter.

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

[0025] 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;

[0026] The total effect sensitivity index of each parameter was obtained according to the contribution of each parameter and the interaction between parameters.

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

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

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

[0030] For the measured data of each frequency band, segmented fitting is performed with the goal of minimizing the preset cost function;

[0031] The cost function is defined by the least squares error method.

[0032] Optionally, a target fitting result of the Biot model is obtained by searching using a 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] 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.

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

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

[0038] The second module is used to perform sensitivity analysis on the Biot model and 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 preset frequency segments; the weight coefficient is used to guide the search range of each parameter in the segmented fitting process;

[0040] The fourth module is used to obtain 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 to store a program; the processor executes the program to implement the above-mentioned method of quickly fitting the Biot model parameter curve.

[0042] On the other hand, an embodiment of the present invention provides a computer storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to implement the above-mentioned method for quickly fitting the parameter curve of the Biot model.

[0043] The embodiment of the present invention obtains ocean bottom sediment data and determines the initial parameter value of each sediment parameter based on the ocean bottom sediment data; constructs a preliminary curve of the Biot model based on the initial parameter value; the preliminary curve includes a sound velocity curve and a sound attenuation curve; performs a sensitivity analysis on the Biot model to obtain a weight coefficient for each parameter; based on the Biot model, a piecewise fitting is performed by combining measured data with preset frequency segments; wherein the weight coefficient is used to guide the search range of each parameter in the piecewise fitting process; during the fitting process, the target fitting result of the Biot model is obtained by searching using a particle swarm optimization algorithm. The present invention has the following beneficial effects:

[0044] 1. First, obtain ocean floor data and use this data to determine the initial parameter values ​​for each sediment parameter. This step provides the basis for the subsequent inversion process.

[0045] 2. Next, these initial parameter values ​​are used to construct preliminary curves for the Biot model, including sound velocity and attenuation curves. These curves can reflect the basic acoustic properties of seafloor sediments.

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

[0047] 4. During the segmented fitting process, the measured data and the preset frequency segments are combined, and the weight coefficients are used to guide the determination of the search range for each parameter. This step can narrow the search space and improve fitting efficiency.

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

[0049] In summary, this paper achieves accurate fitting of the Biot model parameter curve by combining sensitivity analysis with a piecewise fitting method and using a particle swarm optimization algorithm for global search. This method not only improves fitting efficiency but also more accurately reflects the characteristics of seafloor sediments, providing strong support for marine geological exploration and resource development. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the technical solution 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 solution of the present invention and do not constitute a limitation to the technical solution of the present invention.

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

[0052] Figure 2 1 is a flow chart of a method for quickly fitting a Biot model parameter curve provided by an embodiment of the present invention;

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

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

[0055] Figure 5 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

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

[0057] It should be noted that although the system diagrams illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the system or the sequence in the flowcharts. The terms "first / S100" and "second / S200" in the specification, claims, and drawings are used to distinguish similar objects and are not necessarily used to describe a specific sequence or precedence.

[0058] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0059] It is understandable that the method for quickly fitting the parameter curve of the Biot model provided in the embodiment of the present invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various 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 that provides 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, tablet computer, laptop computer, desktop computer, etc., but is not limited to this.

[0060] like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the present invention. Figure 1 , the implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected to the network in a wireless or wired manner to complete data transmission and exchange.

[0061] Server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides 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), as well as big data and artificial intelligence platforms.

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

[0063] The terminal 102 may be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited thereto. The terminal 102 and the server 101 may be connected directly or indirectly via wired or wireless communication, which is not limited in this embodiment of the present invention.

[0064] Based on the example Figure 1 In the implementation environment shown, an embodiment of the present invention provides a method for quickly fitting a Biot model parameter curve. The following is an example of applying the method for quickly fitting a Biot model parameter curve to the server 101. It can be understood that the method for quickly fitting a Biot model parameter curve can also be applied to the terminal 102.

[0065] Reference Figure 2 , Figure 2 The flowchart of the method for quickly fitting the parameter curve of the Biot model applied to the server provided by the embodiment of the present invention, the execution subject of the method for quickly fitting the parameter curve of the Biot model can be any of the aforementioned computer devices (including servers or terminals). Figure 2 , the method comprises the following steps:

[0066] S100, obtaining ocean bottom sediment data, determining initial parameter values ​​for each sediment parameter based on the ocean bottom sediment data; constructing a preliminary curve of the Biot model based on the initial parameter values;

[0067] Among them, the preliminary curves include sound velocity curve and sound attenuation curve;

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

[0069] For example, in some specific implementations, the classification of sediments and the physical range of each parameter can be determined based on a large amount of experimental data and the collection of relevant marine sediment research literature. Specifically, based on the classification of the sediments themselves, the empirical range of each physical parameter is determined, including: porosity, tortuosity, viscosity, pore water mass density, pore water bulk modulus, particle mass density, particle bulk modulus, permeability, pore size, framework shear modulus, and framework bulk modulus. Within the range of each physical parameter, a set of "representative" or "typical literature values" is taken as the initial parameter value, and the Biot model is used to generate the initial sound velocity-frequency and sound attenuation-frequency curves to provide a reference for subsequent optimization.

[0070] S200, perform 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, obtaining the sensitivity index of each parameter through local sensitivity analysis; normalizing the sensitivity index to obtain the first weight coefficient of each parameter; performing global sensitivity analysis based on the Biot model to obtain the total effect sensitivity index of each parameter; normalizing the total effect sensitivity index to obtain the second weight coefficient of each parameter.

[0072] In some embodiments, the sensitivity index of each parameter is obtained through local sensitivity analysis based on the preliminary curve, which may include the following steps: the preliminary curve corresponding to each parameter is disturbed based on a preset proportional increment to obtain the difference between the curves before and after the disturbance; wherein the preliminary curve includes a sound speed curve and an attenuation curve; the absolute value of the curve difference between the sound speed curve and the attenuation curve corresponding to the same parameter is averaged to obtain the sensitivity index corresponding to the corresponding parameter.

[0073] For example, in some specific implementations, local sensitivity analysis (LSA) and weight calculation can be implemented as follows:

[0074] First, a set of baseline parameters are defined, and the initial values ​​of these parameters are set according to the type of sediment and data from relevant literature. Next, each parameter is slightly perturbed (for example, ±5%) in turn, while keeping other parameters unchanged. By calculating the difference in the fast wave sound velocity curve before and after each parameter perturbation, the impact 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. 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] In some embodiments, performing a global sensitivity analysis based on the Biot model to obtain the total effect sensitivity index of each parameter may include the following steps: decomposing the total variance by the Sobol method according to each input parameter on which the Biot model depends, to obtain the contribution of each parameter to the output of the Biot model and the interaction between the parameters; and obtaining the total effect sensitivity index of each parameter according to the contribution of each parameter and the interaction between the parameters.

[0076] For example, in some specific implementations, global sensitivity analysis can be implemented as follows:

[0077] A global sensitivity analysis is performed using the Sobol method to calculate the contribution of each parameter to the model output (i.e., sound velocity and attenuation). Each parameter contribution is normalized into a weight. Based on the obtained parameter weights, the search range for each parameter is adjusted to make the optimization process more efficient. For parameters with larger weights, the search range is narrowed, resulting in more refined optimization adjustments. For parameters with smaller weights, the search range is expanded to avoid over-restriction.

[0078] S300, based on the Biot model, performs segmented fitting by combining measured data with preset frequency segments;

[0079] Among them, the weight coefficient is used to guide 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: obtaining multiple frequency points and their corresponding acoustic measured values ​​as measured data; the acoustic measured values ​​include sound speed measured values ​​and sound attenuation measured values; splitting the measured data into multiple frequency segments based on preset frequency segments; for the measured data of each frequency segment, performing segmented fitting with the goal of minimizing a preset cost function; wherein the cost function is defined by the least squares error method.

[0081] For example, in some specific implementations, measured data may be input and divided into frequency segments for segmented fitting, which may be implemented as follows:

[0082] During the fitting process, the measured data is divided into multiple frequency segments so that they can be fitted segment by segment. First, the measured data of sound velocity and sound attenuation corresponding to multiple frequency points are obtained from the experiment. These data points will serve as the target values ​​of the optimization algorithm. Next, the frequency segment is divided into several ranges. For example, the fast wave sound velocity is processed in three intervals of 0-5kHz, 5-100kHz, and >100kHz, and the fast wave sound attenuation is also processed in multiple frequency bands. For the measured data of each frequency band, the least squares error method is used to define the objective function, and the error between the sound velocity and sound attenuation values ​​output by the model and the measured values ​​is calculated. The goal is to minimize the cost function.

[0083] S400: During the fitting process, a target fitting result of the Biot model is obtained by searching through a particle swarm optimization algorithm.

[0084] It should be noted that, in some embodiments, obtaining the target fitting result of the Biot model through searching by 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; iteratively updating the speed 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] For example, in some specific implementations, parameter optimization based on the PSO (Particle Swarm Optimization) algorithm can be implemented as follows:

[0086] During parameter optimization, a particle swarm optimization (PSO) algorithm is used to search for the optimal solution. The value of the cost function is used as the fitness value of the particles. The fitness of the particles is compared at each iteration, and the individual and global optimal solutions are updated. The optimization results are evaluated using metrics such as the coefficient of determination (R²) and root mean square error (RMS) of the fitting results to ensure a good fit. Cross-validation is also used to verify the robustness of the model.

[0087] At the same time, in some specific application scenarios, a comparison chart can be drawn between the actual measurement data and the optimized model output to intuitively display the sound speed-frequency and sound attenuation-frequency curves.

[0088] In order to explain the principle of the technical solution of the present invention in detail, the overall process of the present invention is 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 cannot 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 sound propagation characteristics (sound velocity, sound attenuation, etc.) of sound waves (fast waves, slow waves) in porous media (such as sandy silt and other sediments). The model relies on 13 input physical property parameters, of which 11 parameters are more critical: {porosity beta, tortuosity alfa, viscosity coefficient eita, pore water mass density rouw, pore water bulk modulus Kw, particle mass density roug, particle bulk modulus Kg, permeability kai, pore size radia, framework shear modulus miu, framework bulk modulus 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 infer the corresponding parameters of the Biot-Stoll model based on 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 sound propagation characteristics (sound velocity, sound attenuation, etc.) of sound waves (fast waves, slow waves) in porous media (such as sandy silt and other sediments). In view of the problem that the parameters of the Biot model are difficult to accurately determine, the purpose of this invention is to provide a method for quickly fitting the parameter curve of the Biot model. Figure 3 As shown, the method of the present invention can be implemented as follows:

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

[0092] Based on extensive experimental data and relevant marine sediment research literature, the sediment classification and physical ranges of various parameters were determined. For readily measurable parameters such as porosity and pore water mass density, measured values ​​were used directly. For other less easily measurable physical parameters, including tortuosity, viscosity, pore water mass density, pore water bulk modulus, particle mass density, particle bulk modulus, permeability, pore size, framework shear modulus, and framework bulk modulus, representative values ​​or intermediate values ​​within their physical parameter ranges were used as initial parameter values. Preliminary sound velocity and attenuation curves for the Biot model were calculated based on these initial values.

[0093] Step 2: Local Sensitivity Analysis (LSA) and Weight Calculation:

[0094] Based on the specific sediment type, a set of benchmark parameters is defined:

[0095] θ0={alfa0,eita0,Kw0,roug0,Kg0,kai0,radia0,miu0,Kf0}

[0096] For each parameter p i Make a small perturbation (or small change) (such as ±5% or a very small increment Δp i ), and the other parameters remain unchanged. Calculate the difference in the fast wave speed curve before and after the disturbance:

[0097]

[0098] Next, we define the sensitivity index. For the sound velocity curve, we can define the absolute value of the average deviation at several frequency points; we do the same for the sound attenuation curve. Then, we get a total deviation to characterize the parameter p. i Impact:

[0099]

[0100] In order to make the sensitivity indicators of the remaining nine parameters comparable, normalization processing is required:

[0101]

[0102] The obtained W i That is the parameter p i The relative weight coefficient of is between [0,1], and ∑ i W i = 1. Through the above steps, a set of weight coefficients representing local sensitivity can be obtained. Later, 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 using the Sobol method. For a model that depends on n input parameters, the output is a random variable that can be expressed as:

[0105] Y=f(X1,X2,K,X n )

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

[0107]

[0108] Where: V(Y) is the total variance of the model output; V i is a single variable X i The main effect variance contribution of Vij is the variable X. i and X j The variance contribution of the interaction effect of S i is the first-order sensitivity index, measuring the variable X i The contribution ratio to the output variability; S Ti is the total effect sensitivity index, which measures the effect of variable X i and the proportion of all their possible interactions that contribute to the variability in the output.

[0109] The total effect sensitivity index S of each parameter Ti Normalized to weight. The calculation formula is:

[0110]

[0111] After obtaining the weight of each parameter, these weights can be applied to the search range adjustment during the optimization process. Parameters with larger weights will have smaller search ranges to ensure that these parameters are adjusted more finely; for parameters with smaller weights, a wider search range is allowed to avoid over-restriction. The specific formula is:

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

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

[0114] Step 4: Input the measured data and split the frequency segments for segmented fitting:

[0115] The sound velocity and sound attenuation data corresponding to multiple frequency points obtained from the experiment are used as input. These data points are used as the target values ​​of the optimization algorithm, and the frequency segments are divided into:

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

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

[0118] The slow wave is also processed similarly. The objective function is defined by the least square error method, and each frequency band is defined as follows: For the jth frequency band, there are corresponding measured data {(fi, C exp (fi))}:

[0119]

[0120] The error between the sound velocity and sound attenuation values ​​output by the calculation model and the measured values ​​is calculated, with the goal of minimizing the cost function.

[0121] The same is true for sound attenuation:

[0122]

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

[0124] In the parameter optimization process, the particle swarm optimization (PSO) algorithm is used to search for the optimal parameters of the Biot-Stoll model so that the difference between the fast wave sound speed or sound attenuation curve calculated by the model and the measured data is minimized. First, the particle swarm is initialized, where each particle represents a set of parameters θ of the Biot-Stoll model. iThe speed and position of the particles are randomly initialized according to the upper and lower limits of the parameters. Then, the fitness value of each particle is calculated. This fitness value is the value of the objective function MSE, which represents the average value of the square difference between the sound speed calculated by the model and the measured data:

[0125]

[0126] This value reflects the quality of the particle's current solution. The smaller the fitness, the better the fitting effect. The particle swarm optimization algorithm continuously improves the solution by updating the particle's speed and position. The update formula of the particle speed is:

[0127] V i (t+1)=w·v i (t)+c1·r1·(p best -x i (t))+c2·r2·(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; C1 and C2 are acceleration factors; r1 and r2 are random numbers in the interval [0,1]; p best is the individual optimal solution of the i-th particle; g best is the global optimal solution of the particle swarm

[0129] The particle position (parameter) update formula is:

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

[0131] After each update, a new fitness value is calculated and compared with the particle's individual optimal solution. If the current solution is better, the particle's individual optimal solution is updated. Simultaneously, the global optimal solution for the entire swarm is updated based on the fitness values ​​of all particles. The velocity and position update process continuously adjusts the particle's position (i.e., model parameters) to gradually reduce the cost function (MSE), thereby finding the optimal parameter combination.

[0132] After multiple iterations, the particle swarm optimization algorithm returns a global optimal solution, that is, a set of optimal parameters θ best , this set of parameters minimizes the difference between the model output and the measured data. In order to evaluate the fitting effect, the coefficient of determination R 2The coefficient of determination (CDR) measures the goodness of fit between the fitted curve and the measured data; values ​​closer to 1 indicate a better fit. The RMS error (RMS) calculates the average error between the model output and the measured data during the fitting process; smaller values ​​indicate a more accurate fit. Finally, MATLAB plotting functions are used to plot the sound velocity vs. frequency and the sound attenuation vs. frequency curves to visualize the fitting results.

[0133] Step 6: Model Application:

[0134] In practical application, the model utilizes local and global sensitivity analyses to determine parameter weights and adjust the search range, avoiding ineffective searches in unnecessary parameter space, improving the optimization algorithm's search efficiency, and shortening the time required to find the optimal parameter combination. Adjusting and optimizing the Biot model's 11 parameters effectively reduces the error between the model output and actual measured data, enabling the model to more accurately describe the propagation characteristics of sound waves in sediments and improving the model's accuracy in related applications. Multiple optimization and validation steps, including cross-validation and sensitivity analysis, effectively verify the model's robustness and avoid overfitting or underfitting.

[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 Biot theory in the fields of ocean acoustics, geophysical exploration, etc. by optimizing the model parameters.

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

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

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

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

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

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

[0142] In another aspect, an embodiment of the present invention further provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method for rapidly fitting a Biot model parameter curve. The electronic device can be any intelligent terminal, such as a tablet computer or an in-vehicle computer.

[0143] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

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

[0145] The processor 1001 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided by the embodiments 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). The memory 1002 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called by the processor 1001 to execute the network node population optimization method of the embodiment of the present invention.

[0147] Input / output interface 1003, used to implement information input and output;

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

[0149] Bus 1005 , which transmits information between various components of the device (e.g., processor 1001 , memory 1002 , input / output interface 1003 , and communication interface 1004 );

[0150] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via 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 separate, i.e., they may be located in one location or distributed across multiple network units. Some or all of these modules may be selected based on actual needs to achieve the objectives of this embodiment.

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

[0153] Another aspect of an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the above method.

[0154] It should be noted that the computer-readable medium shown in the embodiments of the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of computer-readable storage media may 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 thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

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

[0156] The present invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the above method.

[0157] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

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

[0159] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution 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 to ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of 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 optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0161] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into 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 will also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art will be able to implement the present invention as set forth in the claims using ordinary skill without undue experimentation. It will 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 the 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, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0163] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution apparatus, device, or apparatus (e.g., a computer-based apparatus, a device including a processor, or other apparatus that can fetch instructions from and execute instructions on an instruction execution apparatus, device, or apparatus). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution apparatus, device, or apparatus.

[0164] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0165] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution device. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0166] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0167] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0168] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in 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: Acquiring ocean bottom sediment data, and determining an 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, piecewise fitting is performed by combining measured data with preset frequency segments; wherein the weight coefficient is used to guide the search range of each parameter in the fitting process of the piecewise fitting; 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 segment, performing segmented fitting with minimizing a preset cost function as a fitting goal; Wherein, the cost function is defined by the least square error method; In the fitting process, the target fitting result of the Biot model is obtained by searching through the particle swarm optimization algorithm.

2. The method for fast fitting Biot model parameter curve according to claim 1, wherein 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 parameters described 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, wherein 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, wherein The step of obtaining a sensitivity index of each parameter by local sensitivity analysis based on the preliminary curve comprises the following steps: Performing a perturbation 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 perturbation; Wherein, the preliminary curve includes a sound velocity curve and an attenuation curve; Absolute value averaging processing 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, wherein 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: Decomposing the total variance according to each input parameter on which the Biot model depends 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, wherein The method of obtaining the target fitting result of the Biot model by searching with a 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; Determining 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.

7. A system for rapidly fitting a Biot model parameter curve, characterized in that: include: The first module is used to obtain ocean sediment data and determine the initial parameter value of each sediment parameter based on the ocean 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 segments; wherein the weight coefficient is used to guide the determination of the search range of each parameter in the segmented fitting process; 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 segment, performing segmented fitting with minimizing a preset cost function as a fitting goal; Wherein, the cost function is defined by the least square error method; The fourth module is used to search and obtain the target fitting result of the Biot model through the particle swarm optimization algorithm during the fitting process.

8. 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 6.

9. 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 6 when executed by the processor.

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