Seabed sediment acoustic characteristic prediction method and system based on physical neural network

Abstract

The invention discloses a seabed sediment acoustic characteristic prediction method and system based on a physical neural network. The method comprises the following steps: acquiring a seabed sample of a target sea area; obtaining a seabed sample data set according to seabed sample measurement; training a pre-constructed neural network architecture based on the seabed sample data set, and further performing optimization construction by using a loss function to obtain a physical information neural network; wherein the constraint of the loss function comprises information of a wave equation; and based on the physical parameters and the environmental parameters of the to-be-predicted seabed sediment, utilizing a physical information neural network to predict and obtain acoustic characteristic parameters of the to-be-predicted seabed sediment. The method has the beneficial effects that the wave equation of the acoustic characteristics of the substrate is substituted into the physical information neural network, and the seabed substrate acoustic characteristic prediction method based on the physical neural network is constructed in combination with the constraint conditions of the seabed substrate, so that the acoustic characteristics of the substrate can be accurately predicted, and the prediction efficiency is improved. The method can be widely applied to the technical field of marine acoustics.

Description

Technical Field

[0001] The present invention relates to the field of marine acoustic technology, and in particular to a method and system for predicting the acoustic characteristics of seabed sediments based on a physical neural network. Background Art

[0002] The seabed is the lower interface of ocean sound propagation. The acoustic characteristics of the seabed are the ocean sound field and the essential input parameters for the evaluation of sonar detection effectiveness. How to accurately obtain and predict the acoustic characteristics of the seabed is the key difficulty of current research. Traditional bottom prediction methods include empirical equations and theoretical models. The empirical equation is constructed based on a single or two physical parameters. Its input parameters are difficult to obtain, and a single or two parameters cannot fully represent the acoustic characteristics of the bottom, so the prediction accuracy is limited; the theoretical model has 13 input parameters, and some parameters, such as tortuosity and pore size, are difficult to measure and obtain, making the application of the theoretical model difficult and unable to meet application requirements. Machine learning algorithm models, such as random forests and support vector machines, input training data and then directly use the algorithm for training. The output is the predicted value of the acoustic characteristic parameters of a single frequency, such as sound speed and sound attenuation. The acoustic characteristic parameters here are only the predicted values ​​of a single frequency, and the acoustic characteristic parameters at different frequencies cannot be given at the same time. This is because the training data cannot simultaneously input the training data of multiple frequencies. Therefore, how to solve the prediction of acoustic characteristics of multiple frequencies is the difficulty of current research. Furthermore, traditional methods and common machine learning algorithms are prone to overfitting or large errors. Summary of the invention

[0003] The present invention aims to solve the problem of related technical limitations at least to a certain extent. To this end, the present invention proposes a method and system for predicting the acoustic characteristics of seabed sediments based on physical neural networks, which can accurately predict the acoustic characteristics of seabed sediments.

[0004] On the one hand, an embodiment of the present invention provides a method for predicting the acoustic characteristics of seabed sediments based on a physical neural network, comprising the following steps:

[0005] Obtain seabed samples from the target sea area;

[0006] A seabed sample data set is obtained according to seabed sample measurements; the seabed sample data set includes acoustic characteristic parameters, physical parameters and environmental parameters corresponding to each seabed sample;

[0007] The pre-built neural network architecture is trained based on the seabed sample data set, and then the physical information neural network is constructed by optimizing the loss function; the constraints of the loss function include information about the wave equation;

[0008] Based on the physical parameters and environmental parameters of the seabed sediment to be predicted, the acoustic characteristic parameters of the seabed sediment to be predicted are predicted using the physical information neural network.

[0009] Optionally, obtaining a seabed sample from a target sea area includes the following steps:

[0010] A plurality of seabed sediment column samples are collected from the target sea area using seabed sampling equipment as seabed samples.

[0011] Optionally, the seabed sample data set includes a training set, a validation set, and a test set; obtaining the seabed sample data set according to seabed sample measurements includes the following steps:

[0012] Based on the preset experimental parameters, acoustic measurement experiments and physical measurement experiments are carried out on all seabed samples;

[0013] Based on the acoustic measurement experiment, the acoustic characteristic parameters of each seabed sample at different frequencies are obtained;

[0014] Based on physical measurement experiments, the physical and environmental parameters corresponding to each seabed sample are obtained;

[0015] The experimental data corresponding to all seabed samples are normalized and then divided into training set, validation set and test set based on the preset ratio;

[0016] Among them, the experimental data include acoustic characteristic parameters, physical parameters and environmental parameters.

[0017] Optionally, the seabed sample data set includes a training set, a validation set, and a test set; training a pre-constructed neural network architecture based on the seabed sample data set, and then optimizing the construction using a loss function to obtain a physical information neural network, including the following steps:

[0018] The physical parameters and environmental parameters corresponding to each seabed sample in the training set are used as input data into the neural network architecture to obtain the predicted values ​​of acoustic characteristics;

[0019] The neural network architecture is pre-built based on a multi-layer fully connected neural network, which includes an input layer, a multi-layer hidden layer, and an output layer connected in sequence; the number of neurons in the input layer is determined based on the data dimensions of the physical parameters and the environmental parameters, and the number of neurons in each hidden layer in the multi-layer hidden layer is gradually reduced according to a preset ratio, and the multi-layer hidden layer is also provided with an activation function;

[0020] Based on the predicted value of the acoustic characteristic, the acoustic characteristic value of the wave equation solution is obtained by solving the wave equation;

[0021] Constructing physical loss based on acoustic characteristic prediction values ​​and their corresponding wave equation solutions; constructing data loss based on acoustic characteristic prediction values ​​and their corresponding acoustic characteristic values;

[0022] The loss function is constructed by weighted summing of data loss and physical loss;

[0023] According to the loss function, the model parameters of the neural network architecture are optimized and adjusted through the back propagation algorithm and the preset optimizer;

[0024] Based on the optimized and adjusted neural network architecture, the validation set loss is obtained through the validation set;

[0025] The number of iterations is increased by 1, and the step of inputting the physical parameters and environmental parameters corresponding to each seabed sample in the training set as input data into the neural network architecture for processing to obtain the predicted values ​​of the acoustic characteristics is returned to the execution step until the validation set loss of consecutive preset rounds meets the preset conditions, or the number of iterations reaches the preset maximum training round, and the neural network architecture after the last optimization adjustment is used as the physical information neural network.

[0026] Optionally, the method comprises the following steps:

[0027] Determining constraint boundary conditions based on preset statistical range values ​​of physical parameters and environmental parameters;

[0028] The data in the seabed sample dataset are screened and processed based on constraint boundary conditions.

[0029] Optionally, the method further comprises the following steps:

[0030] Determine the pore fluid pressure involved in the constitutive relationship based on the constitutive equation of the seabed sediment;

[0031] Based on the pore fluid pressure, the wave equation is obtained by coupling the solid wave equation and the fluid wave equation.

[0032] Optionally, the method further comprises the following steps:

[0033] The test set is used to perform model evaluation operations on the physical information neural network to obtain the model evaluation results.

[0034] On the other hand, an embodiment of the present invention provides a system for predicting acoustic characteristics of seabed sediments based on a physical neural network, comprising:

[0035] The first module is used to obtain seabed samples in the target sea area;

[0036] The second module is used to obtain a seabed sample data set based on seabed sample measurements; the seabed sample data set includes acoustic characteristic parameters, physical parameters and environmental parameters corresponding to each seabed sample;

[0037] The third module is used to train the pre-built neural network architecture based on the seabed sample data set, and then use the loss function to optimize the physical information neural network; wherein the constraints of the loss function include information about the wave equation;

[0038] The fourth module is used to predict the acoustic characteristic parameters of the seabed sediment to be predicted based on the physical parameters and environmental parameters of the seabed sediment to be predicted using the physical information neural network.

[0039] Optionally, the system further comprises:

[0040] A fifth module is used to determine constraint boundary conditions according to preset statistical range values ​​of physical parameters and environmental parameters;

[0041] The sixth module is used to filter and process the data in the seabed sample data set based on constraint boundary conditions.

[0042] Optionally, the system further comprises:

[0043] The seventh module is used to determine the pore fluid pressure involved in the constitutive relationship based on the constitutive equation of the seabed sediment;

[0044] The eighth module is used to obtain the wave equation based on the pore fluid pressure by coupling the solid wave equation and the fluid wave equation.

[0045] Optionally, the system further comprises:

[0046] The ninth module is used to use the test set to perform model evaluation operations on the physical information neural network to obtain the model evaluation results.

[0047] 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 programs; the processor executes the program to implement the above-mentioned method for predicting the acoustic characteristics of the seabed sediment based on the physical neural network.

[0048] On the other hand, an embodiment of the present invention provides a computer storage medium, which stores a program executable by a processor. When the program executable by the processor is executed by the processor, it is used to implement the above-mentioned method for predicting the acoustic characteristics of the seabed sediment based on the physical neural network.

[0049] The embodiment of the present invention obtains seabed samples from the target sea area; obtains a seabed sample data set based on the seabed sample measurements; the seabed sample data set includes acoustic characteristic parameters, physical parameters and environmental parameters corresponding to each seabed sample; trains a pre-constructed neural network architecture based on the seabed sample data set, and then optimizes and constructs a physical information neural network using a loss function; wherein the constraints of the loss function include information of the wave equation; based on the physical parameters and environmental parameters of the seabed sediment to be predicted, the acoustic characteristic parameters of the seabed sediment to be predicted are predicted using the physical information neural network. The present invention includes beneficial effects: the present invention constructs a seabed sediment acoustic characteristic prediction method based on a physical neural network by substituting the wave equation of the sediment acoustic characteristic into the physical information neural network, and combining the constraints of the seabed sediment, thereby being able to accurately predict the sediment acoustic characteristic. 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 on the technical solution of the present invention.

[0051] Figure 1 It is a schematic diagram of an implementation environment for predicting the acoustic characteristics of seabed sediments based on a physical neural network provided by an embodiment of the present invention;

[0052] Figure 2 It is a schematic flow chart of a method for predicting acoustic characteristics of seabed sediments based on a physical neural network provided by an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of an example process of a method for predicting acoustic characteristics of seabed sediments based on a physical neural network provided in an embodiment of the present invention;

[0054] Figure 4 A schematic diagram of the structure of a system for predicting acoustic characteristics of seabed sediments based on a physical neural network provided in an embodiment of the present invention;

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

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 used to limit the present invention.

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

[0058] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood 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 predicting the acoustic characteristics of the seabed sediment based on the physical neural network 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 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 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, content distribution network), and big data and artificial intelligence platforms. Optionally, the terminal is a smart phone, a tablet computer, a laptop computer, and a desktop computer, 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 a network wirelessly or wired 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, the server 101 can also be a node server in the 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, 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 may be directly or indirectly connected via wired or wireless communication, which is not limited in the 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 predicting the acoustic characteristics of seabed sediments based on a physical neural network. The following is explained using the example of applying the method for predicting the acoustic characteristics of seabed sediments based on a physical neural network to a server 101. It can be understood that the method for predicting the acoustic characteristics of seabed sediments based on a physical neural network can also be applied to a terminal 102.

[0065] Reference Figure 2 , Figure 2 The flowchart of the method for predicting the acoustic characteristics of the seabed sediment based on a physical neural network applied to a server provided in an embodiment of the present invention, the execution subject of the method for predicting the acoustic characteristics of the seabed sediment based on a physical neural network can be any of the aforementioned computer devices (including a server or a terminal). Figure 2 , the method comprises the following steps:

[0066] S100, obtaining seabed samples from the target sea area;

[0067] It should be noted that, in some embodiments, step S100 may include the following steps: using a seabed sampling device to collect a plurality of seabed sediment columnar samples from the target sea area as seabed samples.

[0068] For example, in some specific embodiments, seabed sampling equipment such as gravity samplers and box samplers can be used to collect multiple columnar samples of seabed sediments from the target sea area.

[0069] S200, obtaining a seabed sample data set according to seabed sample measurements;

[0070] The seabed sample data set includes the acoustic characteristic parameters, physical parameters and environmental parameters corresponding to each seabed sample; the seabed sample data set includes a training set, a validation set and a test set;

[0071] It should be noted that, in some embodiments, step S200 may include the following steps: based on preset experimental parameters, performing acoustic measurement experiments and physical measurement experiments on all seabed samples; based on the acoustic measurement experiments, obtaining the acoustic characteristic parameters of each seabed sample at different frequencies; based on the physical measurement experiments, obtaining the physical parameters and environmental parameters corresponding to each seabed sample; normalizing the experimental data corresponding to all seabed samples, and then dividing them into training sets, validation sets and test sets based on preset ratios; wherein the experimental data include acoustic characteristic parameters, physical parameters and environmental parameters.

[0072] For example, in some specific implementations, acoustic measurement experiments are carried out on samples in a laboratory environment to obtain acoustic characteristic data such as sound velocity and sound attenuation coefficient of different samples at different frequencies; at the same time, the physical parameters of the samples are measured, such as porosity, density, particle size distribution, etc. In addition, environmental parameters such as offshore distance, sea depth, water temperature, and primary productivity data are recorded. The various types of collected data are normalized and mapped to the [0,1] interval to facilitate subsequent neural network training. The data set is randomly divided into training set, validation set, and test set according to the ratio of 70%, 15%, and 15% (the ratio can be adjusted accordingly according to the actual application conditions).

[0073] S300, training a pre-built neural network architecture based on a seabed sample data set, and then optimizing the physical information neural network using a loss function;

[0074] The constraints of the loss function include information of the wave equation; the seabed sample data set includes a training set, a validation set, and a test set;

[0075] It should be noted that, in some embodiments, step S300 may include the following steps: the physical parameters and environmental parameters corresponding to each seabed sample in the training set are input as input data into the neural network architecture for processing to obtain acoustic characteristic prediction values; wherein the neural network architecture is pre-constructed based on a multi-layer fully connected neural network, and the multi-layer fully connected neural network includes an input layer, a multi-layer hidden layer and an output layer connected in sequence; the number of neurons in the input layer is determined based on the data dimension of the physical parameters and the environmental parameters, and the number of neurons in each hidden layer in the multi-layer hidden layer gradually decreases according to a preset ratio, and the multi-layer hidden layer is also provided with an activation function; based on the acoustic characteristic prediction value, the wave equation is solved to obtain the acoustic characteristic value of the wave equation solution; according to the acoustic characteristic prediction value and the corresponding wave equation solution, the acoustic characteristic The physical loss is constructed based on the value; the data loss is constructed based on the acoustic characteristic prediction value and its corresponding acoustic characteristic value; the loss function is constructed by weighted summing of the data loss and the physical loss; according to the loss function, the model parameters of the neural network architecture are optimized and adjusted through the back propagation algorithm and the preset optimizer; based on the optimized and adjusted neural network architecture, the validation set loss is obtained through the validation set; the number of iterations is increased by 1, and the step of inputting the physical parameters and environmental parameters corresponding to each seabed sample in the training set as input data into the neural network architecture to obtain the acoustic characteristic prediction value is returned to the step, until the validation set loss of the consecutive preset rounds meets the preset conditions, or the number of iterations reaches the preset maximum training round, and the neural network architecture after the last optimization adjustment is used as the physical information neural network. Specifically, the number of iterations is initially 0.

[0076] For example, in some specific implementations, in view of the nonlinear correlation between the acoustic characteristics of the seabed and many complex factors, a multi-layer fully connected neural network is built. The number of neurons in the input layer is determined based on the input physical parameters and environmental parameter dimensions, and includes parameters such as porosity, density, key characteristic values ​​of particle size distribution, offshore distance, water depth, water temperature, salinity, frequency, and primary productivity data. After statistical analysis, the number of neurons in the input layer is determined to be n. 3-4 hidden layers are set in the middle, and the number of neurons in each layer gradually decreases according to a certain ratio, and the ReLU activation function is enabled; the output layer has 2 neurons, which correspond to the prediction of sound speed and sound attenuation coefficient at different frequencies.

[0077] The main idea of ​​PINN (Physics-Informed Neural Networks) is to use the constitutive equation and wave equation information as constraints and encode them into the neural network loss function for training. The loss function here contains two, one is data loss and the other is physical loss.

[0078] Data loss is the error obtained by subtracting the actual measured acoustic characteristic value from the predicted value of the model algorithm, and physical loss is the error obtained by subtracting the acoustic characteristic value obtained by solving the wave equation from the predicted value of the model algorithm. Therefore, the total loss function is the weighted sum of data loss and physical information loss. This total loss function guides the training direction of the model so that the model can satisfy the laws of physics while fitting the actual data.

[0079] Specifically, during the training process, the total loss function is minimized through the back propagation algorithm and the Adam+L-BFGS, SGD, and RMSprop optimizers. At the same time, in each iteration, the model parameters are updated according to the gradient of the loss function. It should be noted here that since the acoustic characteristic values ​​include two parameters, sound velocity and sound attenuation, they need to simultaneously satisfy the total sound velocity loss function at different frequencies and minimize the total sound attenuation loss function during the iteration process. The two will constrain each other, thereby improving the prediction accuracy.

[0080] Ultimately, through multiple iterations and feedback, physical losses and data losses are reduced, so that the total loss is minimized while satisfying the laws of physics, thereby ensuring that the acoustic characteristic predictions given by the model are suitable for practical applications.

[0081] In some embodiments, the method may further include the following steps: determining constraint boundary conditions according to preset statistical range values ​​of physical parameters and environmental parameters; and screening and processing the data in the seabed sample data set based on the constraint boundary conditions.

[0082] Exemplarily, in some specific embodiments, the statistical range values ​​of the porosity, density, median particle size, and average particle size parameters corresponding to different sediment types can be given according to different sediment types, and letter functions are used here to replace them, f_range(porosity, type), f_range(Mz, type), f_range(Md, type), f_range(density, type), and the above four functions are boundary constraints; the frequency variation range is given, f_range(f).

[0083] Specifically, when collecting training data, we ensure that the parameters such as porosity, particle size, density, and frequency values ​​in the data are within the range defined by their corresponding functions. This makes the data received by the input layer naturally conform to the laws of physics, and the acoustic properties predicted by the output layer are more realistic, making the neural network architecture follow physical boundaries from the root.

[0084] In some embodiments, the method may further include the following steps: determining the pore fluid pressure involved in the constitutive relationship based on the constitutive equation of the seabed sediment; and obtaining the wave equation by coupling the solid wave equation and the fluid wave equation based on the pore fluid pressure.

[0085] For example, in some specific implementations, the seabed substrate is regarded as a sediment composed of a solid skeleton and pore fluid, which has both skeleton elastic properties and fluid properties. For an isotropic elastic medium, its constitutive equation only requires two elastic moduli, and for an isotropic pore elastic medium, its constitutive equation relationship expression is as follows:

[0086]

[0087] In the formula,

[0088] w=β(uU)

[0089] The second constitutive relation involves the pore fluid pressure P f , which is expressed as follows:

[0090]

[0091] The frequency-dependent complex elastic modulus M appears in the above formula.

[0092] When sound waves propagate in the seabed, their wave equation is formed by the coupling of the solid wave equation and the fluid wave equation, so their wave equation is:

[0093]

[0094] The parameters G, β, and ρ represent the shear modulus of the solid framework, the porosity of the bottom sediment, and the overall density of the sediment, respectively. The parameter w is the displacement vector of the interstitial fluid relative to the solid framework. Other parameters include permeability κ, dynamic viscosity η, and the frequency-dependent viscous repair factor F.

[0095] In some embodiments, the method may further include the following steps: using a test set to perform a model evaluation operation on the physical information neural network to obtain a model evaluation result.

[0096] For example, in some specific implementations, after stopping training, the test set can be used to evaluate the final model's ability to predict the acoustic properties of the seabed sediment, output the prediction results, and conduct an in-depth analysis of the model's performance in different samples and different frequency bands.

[0097] S400: Based on the physical parameters and environmental parameters of the seabed sediment to be predicted, the acoustic characteristic parameters of the seabed sediment to be predicted are predicted using a physical information neural network.

[0098] For example, in some specific implementations, in actual application, the processed data can be sequentially input into the trained PINN model to ensure that each parameter is input in the order and format required by the model. The model prediction program is started, and the model quickly operates on the input data based on the complex physical relationships and data rules learned during training, and outputs the corresponding sound speed and sound attenuation prediction values ​​at different frequencies. Since the PINN model incorporates the constraints of physical equations, the prediction results are not only simple data fitting, but also physically reasonable.

[0099] 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.

[0100] First of all, it should be explained that, in response to the relevant problems of the prior art, the present invention proposes a dispersion physical mechanism for analyzing the acoustic characteristics of the seabed sediment, combines it with the physical neural network PINN algorithm, substitutes the wave equation and constitutive equation of the acoustic characteristics of the seabed sediment into the PINN algorithm, and combines it with the boundary conditions of the seabed sediment to construct a seabed sediment acoustic characteristics prediction method based on a physical neural network, thereby realizing the prediction of multi-band sediment acoustic characteristics.

[0101] In some specific implementations, the technical principle of a method for predicting the acoustic characteristics of seabed sediments based on a physical neural network provided by the present invention can be implemented through the following process steps:

[0102] S1. Analysis of the constitutive equation and wave equation of the acoustic characteristics of the seabed:

[0103] S1-1 The seafloor is regarded as a sediment composed of a solid skeleton and pore fluid, which has both skeleton elastic properties and fluid properties. For an isotropic elastic medium, its constitutive equation only requires two elastic moduli, while for an isotropic pore elastic medium, its constitutive equation relationship expression is as follows:

[0104]

[0105] In the formula,

[0106] w=β(uU)

[0107] The second constitutive relation involves the pore fluid pressure P f , which is expressed as follows:

[0108]

[0109] The frequency-dependent complex elastic modulus M appears in the above formula.

[0110] When sound waves propagate in the seabed, their wave equation is formed by the coupling of the solid wave equation and the fluid wave equation, so their wave equation is:

[0111]

[0112] The parameters G, β, and ρ represent the shear modulus of the solid framework, the porosity of the bottom sediment, and the overall density of the sediment, respectively. The parameter w is the displacement vector of the interstitial fluid relative to the solid framework. Other parameters include permeability κ, dynamic viscosity η, and the frequency-dependent viscous repair factor F.

[0113] S1-2 gives the constraint boundary conditions:

[0114] According to different sediment types, the statistical range values ​​of porosity, density, median particle size and average particle size parameters corresponding to the sediment types are given. Letter functions are used here to replace them, f_range(porosity, type), f_range(Mz, type), f_range(Md, type), f_range(density, type). The above four functions are boundary constraints; the frequency variation range is given, f_range(f).

[0115] S2. Construct the physical neural network PINN algorithm model:

[0116] The main idea of ​​S2-1PINN is to use the constitutive equation and wave equation information as constraints and encode them into the neural network loss function for training. The loss function here contains two, one is data loss and the other is physical loss.

[0117] Data loss is the error obtained by subtracting the actual measured acoustic characteristic value from the predicted value of the model algorithm, and physical loss is the error obtained by subtracting the acoustic characteristic value obtained by solving the wave equation from the predicted value of the model algorithm. Therefore, the total loss function is the weighted sum of data loss and physical information loss. This total loss function guides the training direction of the model so that the model can satisfy the laws of physics while fitting the actual data.

[0118] S2-2 When constructing the PINN algorithm model, it is necessary to bring the boundary constraints into the physical loss. Based on the boundary condition input range, the acoustic characteristic value range of the wave equation solution is given, and then it is subtracted from the acoustic characteristic value predicted by the physical model to obtain the physical loss; since the wave equation contains the frequency-related viscous repair factor F and gives the frequency variation range, f_range(f), the acoustic characteristic parameter value given by the solution of the wave equation here is a value that changes with frequency, such as the sound speed and the sound attenuation. Therefore, in model training, the input label data should also be the measured sound speed and sound attenuation values ​​at different frequencies, and the input training data is the digitized data of different factors affecting the acoustic characteristics, including offshore distance, particle size multi-parameters, physical parameters, water depth, frequency, and primary productivity data, and the predicted value given by the model training is also multi-frequency point data that changes with frequency.

[0119] During the training process of S2-3, the total loss function is minimized through the back propagation algorithm and the Adam+L-BFGS, SGD, and RMSprop optimizers. At the same time, in each iteration, the model parameters are updated according to the gradient of the loss function. It should be noted here that since the acoustic characteristic values ​​include two parameters, sound velocity and sound attenuation, they need to simultaneously satisfy the total sound velocity loss function at different frequencies and minimize the total sound attenuation loss function during the iteration process. The two will constrain each other, thereby improving the prediction accuracy.

[0120] Through multiple iterations and feedback, physical loss and data loss are reduced, so that the total loss is minimized while satisfying the laws of physics, thus ensuring that the acoustic characteristic prediction values ​​given by the model are suitable for practical applications.

[0121] S3. Model Validation:

[0122] S3-1 uses measured data that are not involved in model training, including sound velocity and sound attenuation data at different frequencies, to verify the trained model. If the verification data cannot meet the prediction accuracy, repeat the above steps to adjust the parameters again and iterate the model training until the model can meet the prediction accuracy when making predictions on data that has never been verified.

[0123] In some specific application scenarios, such as Figure 3 As shown, the method for predicting the acoustic characteristics of seabed sediments based on physical neural network provided by the present invention can be implemented by the following process:

[0124] Step 1: Seabed sample data collection:

[0125] Using seabed sampling equipment such as gravity samplers and box samplers, multiple columnar samples of seabed sediments are collected from the target sea area. In a laboratory environment, acoustic measurement experiments are carried out on the samples to obtain acoustic characteristic data such as sound velocity and sound attenuation coefficient of different samples at different frequencies; at the same time, the physical parameters of the samples are measured, such as porosity, density, particle size distribution, etc. In addition, environmental parameters such as offshore distance, sea depth, water temperature, and primary productivity data are recorded. The various types of data collected are normalized and mapped to the [0,1] interval to facilitate subsequent neural network training. The data set is randomly divided into training set, validation set, and test set according to the ratio of 70%, 15%, and 15%.

[0126] Step 2: Neural network architecture construction:

[0127] In view of the nonlinear correlation between the acoustic characteristics of the seabed and many complex factors, a multi-layer fully connected neural network is built. The number of neurons in the input layer is determined based on the input physical parameters and environmental parameter dimensions, and includes parameters such as porosity, density, key characteristic values ​​of particle size distribution, offshore distance, water depth, water temperature, salinity, frequency, and primary productivity data. After statistical analysis, the number of neurons in the input layer is determined to be n. 3-4 hidden layers are set in the middle, and the number of neurons in each layer gradually decreases according to a certain proportion, and the ReLU activation function is enabled; the output layer has 2 neurons, which correspond to the prediction of sound speed and sound attenuation coefficient at different frequencies.

[0128] According to different sediment types, the statistical range values ​​of porosity, density, median particle size, and average particle size parameters corresponding to the sediment type are given. f_range(porosity, type) gives the statistical range value of porosity corresponding to the sediment type, f_range(Mz, type) and f_range(Md, type) define the range of median particle size and average particle size respectively, and f_range(density, type) clarifies the density range. In addition, f_range(f) determines the range of frequency variation. When collecting training data, ensure that the parameters such as porosity, particle size, density, and frequency values ​​in the data fall within the range defined by their corresponding functions. The data received by the input layer naturally conforms to the laws of physics, and the acoustic characteristics predicted by the output layer are more realistic, so that the neural network architecture follows the physical boundaries from the root.

[0129] When sound waves propagate in the seabed, their wave equation is formed by the coupling of the solid wave equation and the fluid wave equation, so their wave equation is:

[0130]

[0131] The parameters G, β, and ρ represent the shear modulus of the solid framework, the porosity of the bottom sediment, and the overall density of the sediment, respectively. The parameter w is the displacement vector of the interstitial fluid relative to the solid framework. Other parameters include permeability κ, dynamic viscosity η, and the frequency-dependent viscous repair factor F.

[0132] The staggered grid finite difference method is used to discretize the partial derivatives of the wave field variables in space, and the second-order precision time integration format is used in the time dimension to rewrite the continuous wave equation into an iterative relationship at discrete space-time points. The wave field variable values ​​predicted by the neural network are input, and the difference between the two sides of the equation is calculated based on the discretized wave equation to obtain the residual vector of the wave equation. The elements of the residual vector of the wave equation are squared and accumulated to construct the wave equation loss term L wave ,Right now Where N is the number of discrete space-time points involved in the calculation, i is the residual of the wave equation at the ith discrete point.

[0133] For each frequency point and each set of data, the difference between the acoustic characteristic value predicted by the model and the actual measured acoustic characteristic value is calculated one by one. The differences corresponding to all frequency points and all sample data are squared and then summed up to construct the data loss. The commonly used calculation method is the mean square error (MSE), which is expressed as Where N is the total number of training samples, F is the number of frequency points considered, and predicted ij is the predicted acoustic characteristic value of the i-th sample at the j-th frequency point, and the actual ij The corresponding actual measured value.

[0134] Integrate data-driven mean square error loss L data and the wave equation loss L wave , construct the final loss function L = ω1L data +ω2L wave . Set appropriate weight values ​​through preliminary test adjustments.

[0135] Step 3: Neural network training optimization:

[0136] Neural network training uses optimizers such as Adam+L-BFGS, SGD, and RMSprop. In the early stage, the Adam optimizer is used to accelerate convergence using its adaptive learning rate, and in the later stage, L-BFGS is used to find the global optimum with the help of the second-order derivative. SGD and RMSprop assist in comparing the effects. The model parameters are updated according to the gradient of the loss function at each iteration.

[0137] At the end of each round of training, the model performance is comprehensively evaluated on the validation set, and the loss function value and prediction accuracy indicators are closely monitored. Special attention is paid to the prediction errors of sound velocity and sound attenuation. Since the two are mutually constrained, if one side shows abnormal fluctuations or continues to fail to converge, the model hyperparameters must be immediately backtracked. When the validation set loss does not decrease for five consecutive rounds, or reaches the preset maximum number of training rounds, stop training, use the test set to evaluate the final model's ability to predict the acoustic characteristics of the seabed, output the prediction results, and deeply analyze the performance of the model in different samples and different frequency bands.

[0138] Step 4: Sound velocity and sound attenuation prediction:

[0139] In actual application, the processed data is input into the trained PINN model in sequence, ensuring that all parameters are input in the order and format required by the model. The model prediction program is started, and the model quickly calculates the input data based on the complex physical relationships and data rules learned during training, and outputs the corresponding sound speed and sound attenuation prediction values ​​at different frequencies. Since the PINN model incorporates the constraints of physical equations, the prediction results are not only simple data fitting, but also physically reasonable.

[0140] Step 5: Model application:

[0141] This model can output the predicted values ​​of sound velocity and sound attenuation at different frequencies, and can provide sound velocity and sound attenuation values ​​at different frequencies according to the application scenario. For example, in marine acoustic detection, the sound wave frequency of active sonar is around 5kHz. At this time, the sound velocity and sound attenuation value of 5kHz can be provided for sonar detection according to demand and input into the sonar detection model to improve the sonar detection efficiency;

[0142] Other application scenarios, such as shallow layer profiling, multi-beam detection, and scanning sonar, can provide the sound velocity and sound attenuation values ​​of the required frequency band according to the required frequency band and detection accuracy requirements, thereby improving the detection accuracy and range.

[0143] In some feasible application scenarios, the working frequency band of the marine shallow stratum profile is generally in the range of several hundred Hz to several thousand Hz. The specific frequency band may vary depending on different geological exploration purposes and technical means. The common ones are the following frequency bands:

[0144] 1. Low frequency band (tens of Hz to hundreds of Hz): used to detect deeper strata and suitable for large-scale geological surveys. It can penetrate thicker sedimentary layers, but has lower resolution.

[0145] 2. Medium frequency band (several hundred Hz to several thousand Hz): suitable for medium-depth strata detection, can provide clearer profiles, and is often used in shallow geological exploration and resource exploration.

[0146] 3. High frequency band (several thousand hertz to more than ten kilohertz): suitable for detailed detection of shallow strata, able to provide high-resolution geological structure information, but with weak penetration ability. It is usually used to study thinner sedimentary layers or to detect specific objects, such as buried objects, pipelines, etc.

[0147] Different frequency selections should be determined according to specific survey requirements, geological conditions and target depths, and these detection frequency bands all require the input of sound velocity and sound attenuation values ​​of seabed sediments for data analysis. Therefore, the prediction model of the present invention can give the sound velocity and sound attenuation values ​​of sediments in different frequency bands, thereby improving the quality of data analysis and reducing data analysis work time.

[0148] In summary, the present invention uses the physical neural network PINN algorithm to measure acoustic characteristics at different frequencies, including sound velocity and sound attenuation, and on the basis of data, combines the acoustic wave equation of the seabed sediment and boundary conditions to construct a total loss function including data loss and physical loss, and minimizes the total loss function through the back propagation algorithm and Adam+L-BFGS, SGD, and RMSprop optimizers. At the same time, since the sound velocity and sound attenuation values ​​at different frequencies are mutually constrained, the prediction model can meet the measured data fitting on the basis of conforming to the laws of physics, improve the prediction accuracy requirements, and thus enable the constructed prediction model to be able to carry out practical applications. The constructed prediction model can simultaneously give the predicted values ​​of sound velocity and sound attenuation at different frequencies, so as to meet the frequency requirements for the acoustic characteristics of the sediment in different application scenarios, and expand the applicability of the method model. The present invention is widely used in technical fields such as marine science, marine acoustics, and marine resource detection.

[0149] On the other hand, Figure 4 As shown, the embodiment of the present invention provides a seabed bottom acoustic characteristic prediction system 900 based on a physical neural network, which may include:

[0150] The first module 901 is used to obtain seabed samples in the target sea area;

[0151] The second module 902 is used to obtain a seabed sample data set according to the seabed sample measurement; the seabed sample data set includes acoustic characteristic parameters, physical parameters and environmental parameters corresponding to each seabed sample;

[0152] The third module 903 is used to train the pre-constructed neural network architecture based on the seabed sample data set, and then use the loss function to optimize the construction to obtain the physical information neural network; wherein the constraints of the loss function include information of the wave equation;

[0153] The fourth module 904 is used to predict the acoustic characteristic parameters of the seabed sediment to be predicted based on the physical parameters and environmental parameters of the seabed sediment to be predicted using the physical information neural network.

[0154] In some embodiments, the system may further include:

[0155] A fifth module is used to determine constraint boundary conditions according to preset statistical range values ​​of physical parameters and environmental parameters;

[0156] The sixth module is used to filter and process the data in the seabed sample data set based on constraint boundary conditions.

[0157] In some embodiments, the system may further include:

[0158] The seventh module is used to determine the pore fluid pressure involved in the constitutive relationship based on the constitutive equation of the seabed sediment;

[0159] The eighth module is used to obtain the wave equation based on the pore fluid pressure by coupling the solid wave equation and the fluid wave equation.

[0160] In some embodiments, the system may further include:

[0161] The ninth module is used to use the test set to perform model evaluation operations on the physical information neural network to obtain the model evaluation results.

[0162] 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 method.

[0163] On the other hand, an embodiment of the present invention further provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned method for predicting the acoustic characteristics of seabed sediments based on a physical neural network when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0164] It can be understood that the contents of the above method embodiments are all 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.

[0165] like Figure 5 As shown, Figure 5 The hardware structure of an electronic device 1000 of another embodiment is illustrated. The electronic device 1000 includes:

[0166] The processor 1001 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (application-specific integrated circuit, aSIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention;

[0167] 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 solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1002, and the processor 1001 calls and executes the network node population optimization method of the embodiment of the present invention;

[0168] Input / output interface 1003, used to implement information input and output;

[0169] The communication interface 1004 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0170] A bus 1005 , which transmits information between various components of the device (e.g., the processor 1001 , the memory 1002 , the input / output interface 1003 , and the communication interface 1004 );

[0171] 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 .

[0172] The electronic device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0173] 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.

[0174] Another aspect of an embodiment of the present invention further provides a computer-readable storage medium, the storage medium stores a program, and the program is executed by a processor to implement the above method.

[0175] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. 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 disk read-only memory (Compact Disc Read to Only Memory, CD to ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0176] 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.

[0177] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the above method.

[0178] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order 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 flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0179] 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 embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0180] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation 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, 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 implementation of the present invention.

[0181] In some selectable 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 by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0182] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also 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.

[0183] 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, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform 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, etc. Various media that can store program codes.

[0184] The logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by an instruction execution device, apparatus or device (such as a computer-based device, a device including a processor, or other device that can fetch instructions from an instruction execution device, apparatus or device and execute the instructions), or in conjunction with such instruction execution device, apparatus or device. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution device, apparatus or device, or in conjunction with such instruction execution device, apparatus or device.

[0185] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), 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 disk read-only memory (CDROM). In addition, the computer-readable medium may even be a 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, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0186] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of 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, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0187] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0188] Although the 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 present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0189] 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 predicting the acoustic characteristics of seabed sediments based on physical neural networks, characterized in that: The following steps are involved: Obtain seabed samples from the target sea area; A seabed sample data set is obtained according to the seabed sample measurement; the seabed sample data set includes acoustic characteristic parameters, physical parameters and environmental parameters corresponding to each of the seabed samples; The pre-constructed neural network architecture is trained based on the seabed sample data set, and then a physical information neural network is constructed by optimizing the loss function; wherein the constraints of the loss function include information of the wave equation; Based on the physical parameters and environmental parameters of the seabed sediment to be predicted, the physical information neural network is used to predict the acoustic characteristic parameters of the seabed sediment to be predicted.

2. The method for predicting the acoustic characteristics of seabed sediments based on a physical neural network according to claim 1, characterized in that: The method of obtaining seabed samples in the target sea area comprises the following steps: A plurality of columnar samples of seafloor sediments are collected from the target sea area using seafloor sampling equipment as the seafloor samples.

3. The method for predicting the acoustic characteristics of seabed sediments based on physical neural network according to claim 1, characterized in that: The seabed sample data set includes a training set, a validation set and a test set; the step of obtaining the seabed sample data set according to the seabed sample measurement includes the following steps: Based on preset experimental parameters, acoustic measurement experiments and physical measurement experiments are performed on all the seabed samples; Obtaining the acoustic characteristic parameters of each seabed sample at different frequencies based on the acoustic measurement experiment; Based on the physical measurement experiment, the physical parameters and the environmental parameters corresponding to each of the seabed samples are obtained; Normalizing the experimental data corresponding to all the seabed samples, and then dividing them into a training set, a validation set, and a test set based on a preset ratio; Wherein, the experimental data includes the acoustic characteristic parameters, the physical parameters and the environmental parameters.

4. The method for predicting the acoustic characteristics of seabed sediments based on physical neural network according to claim 1, characterized in that: The seabed sample data set includes a training set, a validation set and a test set; the pre-constructed neural network architecture is trained based on the seabed sample data set, and then a physical information neural network is constructed by optimizing the loss function, including the following steps: Input the physical parameters and the environmental parameters corresponding to each seabed sample in the training set as input data into the neural network architecture for processing to obtain acoustic characteristic prediction values; The neural network architecture is pre-built based on a multi-layer fully connected neural network, and the multi-layer fully connected neural network includes an input layer, a multi-layer hidden layer and an output layer connected in sequence; the number of neurons in the input layer is determined based on the data dimensions of the physical parameters and the environmental parameters, and the number of neurons in each hidden layer in the multi-layer hidden layer is gradually reduced according to a preset ratio, and the multi-layer hidden layer is also provided with an activation function; Based on the predicted acoustic characteristic value, obtaining an acoustic characteristic value of a wave equation solution by solving the wave equation; Constructing a physical loss based on the acoustic characteristic prediction value and the acoustic characteristic value corresponding to it by solving the wave equation; constructing a data loss based on the acoustic characteristic prediction value and the acoustic characteristic value corresponding to it; The loss function is constructed by performing weighted summation on the data loss and the physical loss; According to the loss function, the model parameters of the neural network architecture are optimized and adjusted through a back propagation algorithm and a preset optimizer; Based on the optimized and adjusted neural network architecture, obtaining a validation set loss through the validation set; The number of iterations is increased by 1, and the step of inputting the physical parameters and the environmental parameters corresponding to each seabed sample in the training set as input data into the neural network architecture for processing to obtain acoustic characteristic prediction values ​​is returned to the step, until the validation set loss of consecutive preset rounds meets the preset conditions, or the number of iterations reaches the preset maximum training round, and the neural network architecture after the last optimization adjustment is used as the physical information neural network.

5. The method for predicting the acoustic characteristics of seabed sediments based on physical neural networks according to claim 1 or 4, characterized in that: The method comprises the following steps: Determining constraint boundary conditions according to preset statistical range values ​​of the physical parameter and the environmental parameter; The data in the seabed sample data set is screened based on the constraint boundary conditions.

6. The method for predicting the acoustic characteristics of seabed sediments based on physical neural network according to claim 1 or 4, characterized in that: The method further comprises the following steps: Determine the pore fluid pressure involved in the constitutive relationship based on the constitutive equation of the seabed sediment; Based on the pore fluid pressure, the wave equation is obtained by coupling the solid wave equation and the fluid wave equation.

7. The method for predicting the acoustic characteristics of seabed sediments based on physical neural network according to claim 4, characterized in that: The method further comprises the following steps: The test set is used to perform a model evaluation operation on the physical information neural network to obtain a model evaluation result.

8. A prediction system for seabed acoustic characteristics based on physical neural network, characterized in that: include: The first module is used to obtain seabed samples in the target sea area; The second module is used to obtain a seabed sample data set according to the seabed sample measurement; the seabed sample data set includes acoustic characteristic parameters, physical parameters and environmental parameters corresponding to each of the seabed samples; The third module is used to train the pre-constructed neural network architecture based on the seabed sample data set, and then optimize the physical information neural network by using the loss function; wherein the constraints of the loss function include information of the wave equation; The fourth module is used to predict the acoustic characteristic parameters of the seabed sediment to be predicted based on the physical parameters and environmental parameters of the seabed sediment to be predicted using the physical information neural network.

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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