Multi-point seabed seismic oscillation generation method and related equipment

Abstract

The embodiment of the invention provides a multi-point submarine seismic oscillation generation method and related equipment. The time domain non-stationarity of seismic oscillation and the spatial variability of complex terrain can be considered when multi-point seismic oscillation is synthesized. The method comprises the following steps: generating one-dimensional seismic waveform data according to three-direction seismic waveform data, random noise and a target single-point random seismic oscillation model; splicing the one-dimensional seismic waveform data with random data satisfying Dirichlet distribution to obtain spliced multipoint seismic oscillation data; according to the size of the semi-space uniform isotropic geologic body, the position of the fault plane and the size of the fault plane, establishing a three-dimensional site model containing the fault; according to the fault plane parameters corresponding to different moment magnitudes and the three-dimensional site model, performing simulation analysis of seismic oscillation through a spectral element method to obtain multi-point seismic oscillation data of each measuring point in the measuring point set; and inputting the spliced multi-point seismic oscillation data and the multi-point seismic oscillation data into a multi-point seismic oscillation data generation model to obtain multi-point seismic oscillation.

Description

Technical Field

[0001] The present invention relates to the field of earthquake motion, and in particular to a method for generating multi-point seabed earthquake motion and related equipment. Background Art

[0002] Reasonable ground motion synthesis methods are of great significance for the evaluation of earthquake dynamic response of underground engineering. On the one hand, real ground motion records show obvious time-frequency non-stationarity, and the synthetic ground motion waveform should consider many constraints, such as frequency, amplitude, duration, response spectrum, etc. On the other hand, there are often unfavorable geological conditions such as submarine basins, valleys and faults in marine sites, and ground motion shows obvious spatial variability and inconsistency during propagation. Therefore, for large-span marine engineering (such as ultra-long cross-sea tunnels, cross-sea bridges, etc.), the multi-point ground motion synthesis method should simultaneously consider the time-domain non-stationarity and spatial variability of ground motion.

[0003] In view of the time-frequency non-stationarity of seismic motion, the methods currently proposed mainly include two categories: random finite fault method and engineering synthesis method. The parameter determination of random finite fault method is relatively complex and its applicability in actual engineering is relatively weak; the engineering synthesis method requires the mathematical model to be determined in advance based on a large number of seismic records, but the seismic records of most marine sites are not rich, and it is difficult to obtain a reliable mathematical statistical model of marine seismic motion. At the same time, both methods do not consider the duration and energy characteristics of seismic waves, and cannot take into account all the time-frequency domain constraints of seismic waves. Therefore, the methods currently proposed cannot accurately simulate the time-frequency non-stationarity of marine seismic motion. In view of the spatial variability of seismic motion, the methods currently proposed mainly establish power spectral density models and coherence models based on array recording data. Since the power spectral density model assumes that the local site is a uniform site, this method cannot consider complex marine sites. Therefore, the methods currently proposed are not suitable for simulating the spatial variability of seismic motion. Summary of the invention

[0004] The present invention provides a method and device for generating multi-point seabed seismic motions, which combines a spectral element numerical simulation method with a generative neural network, and takes into account the time domain non-stationarity of seismic motions and the spatial variability of complex terrain when synthesizing multi-point seismic motions.

[0005] A first aspect of the present invention provides a method for generating multi-point seabed earthquake motions, the method comprising:

[0006] Obtain target three-directional seismic waveform data corresponding to the target area;

[0007] Generate target one-dimensional seismic waveform data corresponding to the target three-directional seismic waveform data according to the target three-directional seismic waveform data, random noise and a target single-point random seismic motion model;

[0008] splicing the target one-dimensional seismic waveform data with random data satisfying Dirichlet distribution to obtain target spliced ​​multi-point seismic motion data;

[0009] Establishing a three-dimensional site model containing faults according to the size of the semi-space homogeneous isotropic geological body, the position of the fault plane and the size of the fault plane;

[0010] According to the fault plane parameters corresponding to different moment magnitudes and the three-dimensional site model, a spectral element method is used to simulate and analyze the seismic motion to obtain target multi-point seismic motion data of each measuring point in the set of measuring points arranged in the target area;

[0011] The target spliced ​​multi-point seismic data and the target multi-point seismic data are input into a target multi-point seismic data generation model to obtain the multi-point seismic data corresponding to the target area.

[0012] A second aspect of the present invention provides a device for generating multi-point seabed earthquake motions, comprising:

[0013] An acquisition module is used to acquire target three-directional seismic waveform data corresponding to the target area;

[0014] A generating module, used for generating target one-dimensional seismic waveform data corresponding to the target three-directional seismic waveform data according to the target three-directional seismic waveform data, random noise and a target single-point random seismic motion model;

[0015] A splicing module, used for splicing the target one-dimensional seismic waveform data with random data satisfying Dirichlet distribution to obtain target spliced ​​multi-point seismic motion data;

[0016] A construction module for establishing a three-dimensional site model containing faults according to the size of the semi-space uniform isotropic geological body, the position of the fault plane and the size of the fault plane;

[0017] A simulation analysis module, for performing simulation analysis of earthquake motions by using a spectral element method according to fault plane parameters corresponding to different moment magnitudes and the three-dimensional site model, to obtain target multi-point earthquake motion data of each measuring point in a set of measuring points arranged in the target area;

[0018] The synthesis module is used to input the target spliced ​​multi-point seismic data and the target multi-point seismic data into a target multi-point seismic data generation model to obtain the multi-point seismic motion corresponding to the target area.

[0019] A third aspect of an embodiment of the present invention provides an electronic device, including a memory and a processor, wherein the processor is used to implement the steps of the method for generating multi-point seabed seismic motions as described in the first aspect when executing a computer management program stored in the memory.

[0020] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer management program stored thereon, which, when executed by a processor, implements the steps of the method for generating multi-point seafloor seismic motions as described in the first aspect above.

[0021] To sum up, it can be seen that in the embodiment provided by the present invention, a single-point random seismic motion model and a multi-point seismic motion data generation model are pre-trained by a generative adversarial neural network, and at the same time, the spectral element method is introduced when determining the multi-point seismic motion data of the measuring point. Therefore, when synthesizing the sea area seismic motion according to the single-point random seismic motion model and the multi-point seismic motion data generation model, the spectral element method numerical simulation method and the generative neural network are combined, and the time domain non-stationarity of the seismic motion and the spatial variability of the complex terrain are considered at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of a flow chart of a method for generating multi-point seafloor seismic motions provided in an embodiment of the present application;

[0023] Figure 2 A schematic diagram of a process for generating real data of a seismic image provided by an embodiment of the present invention;

[0024] Figure 3 A schematic diagram of the structure of the generator and the discriminator in the generative neural network provided by the present invention;

[0025] Figure 4 A schematic diagram of a virtual structure of a device for generating multi-point seabed earthquake motion provided by an embodiment of the present invention;

[0026] Figure 5 A schematic diagram of the hardware structure of a device for generating multi-point seafloor earthquake motion provided by an embodiment of the present invention;

[0027] Figure 6 A schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention;

[0028] Figure 7 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0030] In the following description, specific embodiments of the present invention will be described with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be mentioned several times as being performed by a computer, and computer execution referred to herein includes operations by a computer processing unit of electronic signals representing data in a structured form. This operation converts the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise change the operation of the computer in a manner familiar to testers in the field. The data structure maintained by the data is a physical location in the memory, which has specific characteristics defined by the data format. However, the principles of the present invention are described in the above text, which does not represent a limitation, and testers in the field will understand that the various steps and operations described below can also be implemented in hardware.

[0031] The principles of the present invention operate with many other general or special purpose computing, communication environments or configurations. Examples of well-known computing systems, environments and configurations suitable for use with the present invention may include, but are not limited to, handheld phones, personal computers, servers, multiprocessor systems, microcomputer-based systems, mainframe computers, and distributed computing environments, including any of the above systems or devices.

[0032] The terms "first", "second", and "third" in the present invention are used to distinguish different objects rather than to describe a specific order. In addition, the terms "include", "have", and any variations thereof are intended to cover non-exclusive inclusions.

[0033] Numerical simulation has always been an important means for engineers to understand the propagation law of site seismic motion and earthquake response. By establishing a certain sea site model, rich spatial multi-point seismic motion data can be obtained. Common numerical simulation methods include spectral element method, finite element method, finite difference method, etc. Among them, the spectral element method has high accuracy and can be used for seismic motion simulation of large-scale and complex geological structure models. It has good applicability in the simulation of multi-point seismic motion in the sea area. At the same time, deep learning methods have also been widely used in various engineering fields, which can help process and generate various data samples, obtain the characteristics of the data itself and the inherent laws between the data, and can flexibly and conveniently process sea seismic motion data. It has considerable potential and advantages in the field of seismic motion synthesis method technology. The present invention combines the spectral element method with deep learning, wherein the spectral element method is used to generate a site seismic motion database covering a wide range of geological conditions; deep learning is used to generate single-point random seismic motion on the one hand, and on the other hand, the nonlinear mapping relationship between single-point seismic motion and multi-point seismic motion is learned according to the site seismic motion database, thereby generating multi-point random seismic motion that can reflect the distribution of seismic motion in the real field.

[0034] The following describes a method for generating multi-point seabed seismic motions from the perspective of a device for generating multi-point seabed seismic motions. The device for generating multi-point seabed seismic motions may be a server or a service unit in a server, and is not specifically limited.

[0035] See also Figure 1 , Figure 1 A schematic flow chart of a method for generating multi-point seafloor seismic motion provided by an embodiment of the present invention includes:

[0036] 101. Obtain target three-directional seismic waveform data corresponding to the target area.

[0037] In this embodiment, the device for generating multi-point seabed seismic motion can obtain target three-directional seismic waveform data corresponding to the target area, wherein the target area is a sea area with an adjustable range, and the target three-directional seismic waveform data includes n data points. The method for obtaining the target three-directional seismic waveform data is not specifically limited here.

[0038] 102. Generate target one-dimensional seismic waveform data corresponding to the target three-directional seismic waveform data according to the target three-directional seismic waveform data, random noise, and a target single-point random seismic motion model.

[0039] In this embodiment, the device for generating multi-point seabed seismic motion first trains the training data set through an adversarial generative network to obtain a target single-point random seismic motion model, and then generates target one-dimensional seismic waveform data corresponding to the target three-directional seismic waveform data based on the target three-directional seismic waveform data, random noise and the target single-point random seismic motion model, that is, firstly process the target three-directional seismic waveform data to obtain unidirectional seismic motion data, and generate seismic motion real image data X based on the unidirectional seismic motion data, and input X into the target single-point random seismic motion model to finally obtain the target one-dimensional seismic waveform data. The training method of the target single-point random seismic motion model is described below:

[0040] Step 1, acquiring m groups of three-directional seismic waveform data corresponding to each of the multiple regions according to the user's operation instruction;

[0041] In this step, the device for generating multi-point seabed seismic motions can manually select three-directional seismic waveform data for training and testing from the existing seismic wave data corresponding to each of the multiple areas, a total of m groups, wherein each seismic waveform data in the m groups of three-dimensional seismic waveform data includes n data points, wherein m and n are both integers greater than or equal to 1.

[0042] Step 2, constructing a seismic data matrix based on m groups of three-directional seismic waveform data;

[0043] In this step, the device for generating multi-point seafloor seismic motions can construct a seismic data matrix X based on m groups of three-directional seismic waveform data, wherein the seismic data matrix is ​​represented by the following formula:

[0044]

[0045] Among them, x ij is the i-th data point in the j-th group of three-directional seismic waveform data, i is any one of the n data points, and j is any one of the m groups of data.

[0046] Step 3, performing principal component analysis on the seismic data matrix to obtain one-dimensional seismic waveform data corresponding to each seismic waveform data in the m groups of three-directional seismic waveform data;

[0047] In this step, the device for generating multi-point seafloor seismic motion can first perform data standardization processing on each seismic waveform data in the target group of three-directional seismic waveform data, where the target group of three-directional seismic waveform data is any one of the m groups of three-directional seismic waveform data. Specifically, each seismic waveform data can be subjected to data standardization processing by the following formula:

[0048]

[0049] x′ ij For x ij After the standardized data, var(x j ) is the mean square error, is the mean,

[0050] Afterwards, the correlation coefficient matrix corresponding to each seismic waveform data in the three-directional seismic waveform data of the target group is calculated. Specifically, the correlation coefficient matrix can be expressed by the following formula:

[0051]

[0052] in,

[0053] Finally, the one-dimensional seismic waveform data corresponding to each seismic waveform data in the three-directional seismic waveform data of the target group is determined according to the singular value decomposition method (SVD) and the correlation coefficient matrix. Specifically,

[0054] R=UDV T

[0055] Among them, U corresponds to m m-dimensional standard orthogonal vectors, V T Corresponding to n n-dimensional standard orthogonal vectors, they correspond to RR T and RT The decomposed eigenvectors of R are used to compress the number of rows and columns respectively. D corresponds to a diagonal matrix of m rows and n columns, and the diagonal elements represent the square of the eigenvalues. Based on the eigenvectors and eigenvalues, the target one-dimensional seismic wave data can be obtained.

[0056] It should be noted that the target one-dimensional seismic wave data can be used as the input value of the parameterized generator combination, X m =X m×n V n , X m is the input data of the parameterized generator combination after dimension reduction, X m×n is the original sample data, V n V T The corresponding eigenvector in .

[0057] Step 4: Determine the frequency, amplitude and wavelet coefficients of the one-dimensional seismic waveform data.

[0058] In this step, the device for generating multi-point seafloor seismic motion can obtain three-dimensional data of frequency, time and wavelet coefficients based on one-dimensional seismic waveform data by wavelet transform, wherein the wavelet basis function is a Morlet wave function. The wavelet basis function is represented by the following formula:

[0059]

[0060] Among them, f(t) is the original seismic data, a is the wavelet transform scale, corresponding to the frequency (its inverse), controlling the expansion and contraction of the wavelet function, and b is the wavelet translation, corresponding to time, controlling the translation of the wavelet function. Figure 2 As shown, Figure 2 A schematic diagram of the process of generating real data of earthquake motion images provided by an embodiment of the present invention.

[0061] Step 5: Generate a seismic wave data image according to the frequency, amplitude and wavelet coefficient, and establish a seismic image database according to the seismic wave data image;

[0062] In this step, after obtaining the frequency, time and wavelet coefficient, the device for generating multi-point seafloor seismic motion can form a seismic waveform time-frequency diagram based on the frequency, time and wavelet coefficient, which is called a single-point real seismic image, whose abscissa is time, ordinate is frequency, and the color of each point represents the wavelet coefficient. Finally, a seismic image database is established based on the single-point real seismic image.

[0063] Step 6: Train the initial single-point random seismic motion model based on the seismic motion image database to obtain the target single-point random seismic motion model.

[0064] In this step, after the multi-point seabed seismic generation device has completed the construction of the seismic image database, it can train the initial single-point random seismic model based on the seismic image database to obtain the target single-point random seismic model. The training process of the model is described below:

[0065] The device for generating multi-point seabed seismic motion inputs random noise into the first generator of the initial single-point random seismic motion model to obtain seismic sample data, that is, the random noise is input into the first generator, and after being processed by the convolution layer, the deconvolution layer and the pooling layer, the single-point synthetic seismic motion image generated by the first generator is obtained;

[0066] and inputting the single-point synthetic seismic image and the image sample data in the seismic image database into the first discriminator of the single-point random seismic model to obtain a predicted value;

[0067] Then, the loss values ​​of the first generator and the first discriminator are calculated according to the predicted values ​​corresponding to the single-point synthetic seismic image and the single-point real seismic image, and the weight parameters of the first generator and the first discriminator are updated using the Adam optimizer. The loss value is calculated according to the loss function, which can be, for example, the root mean square difference, which is expressed by the following formula:

[0068]

[0069] in, They represent the response spectra of generated earthquake motion and real earthquake motion respectively, ε=0.01, z is the noise input to the generator, and S is the response spectrum of earthquake motion.

[0070] Finally, the above steps are iterated until the preset iteration termination condition is reached to obtain the target single-point random seismic motion model. That is, the device for generating multi-point seabed seismic motion can set physical constraints, and through cyclic iteration, when the generated single-point seismic motion data satisfies the requirements of the loss function and the physical constraints at the same time, the iteration is terminated to obtain the target single-point random seismic motion model.

[0071] See also Figure 3 , Figure 3 A schematic diagram of the structure of the generator and the discriminator in the generative adversarial neural network provided in an embodiment of the present invention, wherein the target single-point random seismic motion model adopts a generative adversarial neural network, and the generative adversarial neural network includes a generator and a discriminator; the length of the random noise is set to 4096 sampling points, and the seismic motion data X generally takes a sampling value of 120s in length. In the present invention, 4096 sampling points are set, and at the same time, considering that the amplitude of the seismic waveform varies greatly, the maximum absolute value normalization method is used to place the seismic motion data in the [-1,1] interval in advance to eliminate the influence of the amplitude difference, as shown in the following formula:

[0072]

[0073] in, is the normalized intercepted waveform, max(*) and abs(*) are the maximum value function and the absolute value function respectively.

[0074] For the generator G, its input is random noise z; then the convolution operation and the maximum pooling operation are used in sequence to extract and downsample the random noise features to obtain high-dimensional abstract features; then the deconvolution operation and the convolution operation are used in sequence to upsample and extract the high-dimensional abstract features to obtain the synthetic seismic image. Specifically, the input random noise is first subjected to the convolution operation to obtain the Gconv1-1 layer, and then the maximum pooling operation is performed to obtain the Gpool1 layer. The process is repeated to obtain the Gconv2-1, Gpool2, Gconv3-1, Gpool3, Gconv4-1, Gpool4 and Gconv5-1 layers respectively; then the deconvolution operation is used to upsample and the Gconv4-1 layer is spliced ​​in the layer dimension to obtain the Gup4 layer, and then the convolution operation is performed to obtain the Gconv4-2 layer. The process is repeated to obtain the Gup3, Gconv3-2, Gup2, Gconv2-2, Gup1 and Gconv1-2 layers respectively, and the Gconv1-2 layer is the synthetic seismic image. The convolution kernel size of each convolution operation in the generator is 5×5, the stride is 1×1, the pooling stride and receptive field of each pooling operation are 4×4, and the upsampling ratio of each deconvolution operation is 4×4. The number of layers of Gconv1-1, Gconv2-1, Gconv3-1, Gconv4-1 and Gconv5-1 are [4,8,16,32,64] respectively, and the number of layers of Gconv1-2, Gconv2-2, Gconv3-2 and Gconv4-2 are [1,8,16,32] respectively.

[0075] For the discriminator D, the input of the discriminator D is the seismic image; then the convolution layer and the pooling layer are used alternately to extract high-dimensional features; then, the multi-channel features of the fully connected layer are integrated and the sigmoid function is used to generate true and false prediction values ​​S. The step size of the convolution layers Dconv1~Dconv5 is 1×1, the convolution kernel size is 5×5, and the number of output channels is [4,8,16,32,64] respectively; the pooling receptive field and step size of the pooling layers Dpool1~Dpool4 are both 4×4; the number of output nodes of the fully connected layer Dfull is 1.

[0076] For the loss function, the loss function of the generator is expressed by the following formula:

[0077] LG=-∑lns fake

[0078] Among them, s fake is the predicted true or false probability of a single-point synthetic ground motion image;

[0079] The loss function of the discriminator is as follows: LD = -∑lns real -∑ln(1-s fake )

[0080] Among them, s real is the predicted true or false probability of a single point real earthquake image.

[0081] 103. The target one-dimensional seismic waveform data is spliced ​​with random data satisfying the Dirichlet distribution to obtain target spliced ​​multi-point seismic motion data.

[0082] In this embodiment, after determining the target one-dimensional seismic waveform data, the device for generating the multi-point seabed seismic motion may splice the target one-dimensional seismic waveform data with random data satisfying the Dirichlet distribution to obtain target spliced ​​multi-point seismic motion data.

[0083] 104. A three-dimensional site model containing faults is established based on the size of the semi-space uniform isotropic geological body, the position of the fault plane and the size of the fault plane.

[0084] In this embodiment, the device for generating multi-point seafloor seismic motions can determine the size of the semi-space uniform isotropic geological body corresponding to the target area, the position of the fault plane and the size of the fault plane, and establish a three-dimensional site model containing faults.

[0085] 105. According to the fault plane parameters corresponding to different moment magnitudes and the three-dimensional site model, a spectral element method is used to simulate and analyze the seismic motion to obtain target multi-point seismic motion data of each measuring point in the set of measuring points arranged in the target area.

[0086] In this embodiment, the moment magnitude is a physical quantity used to describe the mechanical strength when an earthquake occurs. It is based on the concept of seismic moment, and the seismic moment itself is obtained by multiplying the rupture area of ​​the seismic fault, the average displacement, and the shear modulus of the rock. The moment magnitude can reflect the energy released by the earthquake, and there is no saturation problem like other magnitude scales. The generating device of the multi-point seabed earthquake motion can arrange the measuring points at fixed intervals along the parallel fault direction and the vertical fault direction corresponding to the target area, and obtain the set of measuring points arranged in the target area. Since the three-dimensional site model containing the fault is constructed in advance, according to the three-dimensional site model and the set parameters, the SPECFEM3D spectral element method program is used to simulate and analyze the earthquake motion, and the earthquake motion data of each measuring point is obtained. Therefore, after the set of measuring points is arranged in the target area, different moment magnitudes can be input to the three-dimensional site model, and the fault surface parameters such as the formation parameters, friction coefficient, and initial stress of the target area can be changed, so that multiple groups of sea area multi-point earthquake motion data of each measuring point parallel to the fault direction and the vertical fault direction can be obtained, that is, the target multi-point earthquake motion data.

[0087] It should be noted that the target spliced ​​multi-point seismic motion data can be determined through steps 102 to 103, and the target multi-point seismic motion data can be determined through steps 104 to 105. However, there is no restriction on the execution order between steps 102 to 103 and steps 104 to 105. Steps 104 to 105 can be executed first, or steps 102 to 103 can be executed first, or they can be executed simultaneously, without specific limitation.

[0088] 106. Input the target spliced ​​multi-point seismic motion data and the target multi-point seismic motion data into a target multi-point seismic motion data generation model to obtain the multi-point seismic motion corresponding to the target area.

[0089] In this embodiment, the device for generating multi-point seabed seismic motion can pre-build a sea area multi-point seismic motion database, and train the initial multi-point random seismic motion data generation model based on the sea area multi-point seismic motion database to obtain a target multi-point seismic motion data generation model, and input the target spliced ​​multi-point seismic motion data and the target multi-point seismic motion data into the target multi-point seismic motion data generation model to obtain the multi-point seismic motion corresponding to the target area. The training process of the target multi-point seismic motion data generation model is described below:

[0090] Step 1: determine sample multi-point seismic data of each measuring point in a set of sample measuring points arranged in each of the multiple areas;

[0091] In this step, the device for generating multi-point seabed seismic motion can first determine the size of the semi-space uniform isotropic geological body and the position and size of the fault plane, establish a three-dimensional site model containing faults, and arrange sample measuring points at fixed intervals along the direction parallel to the fault and perpendicular to the fault to obtain a set of sample measuring points; then, according to the three-dimensional site model and set parameters, use the SPECFEM3D spectral element method program to simulate and analyze the seismic motion and obtain the seismic motion data of each sample measuring point; finally, input different moment magnitudes, change the formation parameters and the friction coefficient, initial stress and other fault plane parameters, and obtain the multi-point seismic motion data of each measuring point parallel to the fault direction and perpendicular to the fault direction. And establish a multi-point seismic motion database in the sea area based on the multi-point seismic motion data.

[0092] Step 2: splicing the one-dimensional seismic waveform data with the random data to obtain spliced ​​multi-point seismic motion data;

[0093] In this step, the device for generating multi-point seafloor earthquake motions may splice the one-dimensional seismic waveform data with the random data to obtain spliced ​​multi-point earthquake motion data, wherein the random data satisfies the Dirichlet distribution.

[0094] Step 3, inputting the spliced ​​multi-point earthquake motion data into the second generator of the initial multi-point earthquake motion data generation model to obtain generated data;

[0095] Step 4, inputting the sample multi-point seismic data and the generated data into the second discriminator of the initial multi-point seismic data generation model to obtain predicted data;

[0096] Step 5: Adjust the loss functions of the second generator and the second discriminator according to the predicted data and the generated data;

[0097] Step 6: Perform iterative training based on the second generator and the second discriminator after adjusting the loss function to obtain the target multi-point seismic data generation model.

[0098] That is, the device for generating multi-point seabed seismic motions splices single-point seismic motion data and random data (satisfying Dirichlet distribution) as input data of the multi-point seismic motion data generation model generator; inputs the real seismic motion data into the discriminator of the multi-point seismic motion data generation model to obtain predicted data; finally, the loss functions of the second generator and the second discriminator are adjusted according to the generated data and the predicted data, and training is performed iteratively to finally obtain a maturely trained target multi-point seismic motion data generation model, which can obtain multi-point seismic motions.

[0099] It should be noted that the composition, generator, discriminator and loss function of the generative neural network used in the target multi-point seismic data generation model are similar to those of the target single-point random seismic model. The only difference is that the output channel size of the generator Gconv1-2 is the number of measurement points of the multi-point seismic motion, which has been explained in detail above and will not be repeated here. It is worth noting that the generator weight parameters and discriminator weight parameters in the target multi-point seismic data generation model are independent of the generator and discriminator parameters in the target single-point random seismic model and are not shared.

[0100] To sum up, it can be seen that in the embodiment provided by the present invention, a single-point random seismic motion model and a multi-point seismic motion data generation model are pre-trained by a generative adversarial neural network, and the spectral element method is introduced when determining the multi-point seismic motion data of the measuring point. Therefore, when synthesizing the sea area seismic motion according to the single-point random seismic motion model and the multi-point seismic motion data generation model, the spectral element method numerical simulation method and the generative neural network are combined, and the time domain non-stationarity of the seismic motion and the spatial variability of the complex terrain are considered at the same time.

[0101] The above describes an embodiment of the present invention from the perspective of a method for generating multi-point seabed seismic motions. The following describes an embodiment of the present invention from the perspective of an apparatus for generating multi-point seabed seismic motions.

[0102] See also Figure 4 , a virtual structural diagram of a device for generating multi-point seabed earthquake motion according to an embodiment of the present invention, wherein the device for generating multi-point seabed earthquake motion 400 comprises:

[0103] An acquisition module 401 is used to acquire target three-directional seismic waveform data corresponding to a target area;

[0104] A generating module 402, configured to generate target one-dimensional seismic waveform data corresponding to the target three-directional seismic waveform data according to the target three-directional seismic waveform data, random noise and a target single-point random seismic motion model;

[0105] A splicing module 403 is used to splice the target one-dimensional seismic waveform data with random data satisfying Dirichlet distribution to obtain target spliced ​​multi-point seismic motion data;

[0106] A construction module 404 is used to establish a three-dimensional site model containing faults according to the size of the semi-space uniform isotropic geological body, the position of the fault plane and the size of the fault plane;

[0107] The simulation analysis module 405 is used to perform simulation analysis of ground motion by spectral element method according to the fault plane parameters corresponding to different moment magnitudes and the three-dimensional site model, so as to obtain target multi-point ground motion data of each measuring point in the set of measuring points arranged in the target area;

[0108] The synthesis module 406 is used to input the target spliced ​​multi-point seismic data and the target multi-point seismic data into a target multi-point seismic data generation model to obtain the multi-point seismic data corresponding to the target area.

[0109] In a possible design, the generating module 402 is further used for:

[0110] According to the user's operation instruction, m groups of three-dimensional seismic waveform data corresponding to each area of ​​the multiple areas are obtained, each piece of seismic waveform data in the m groups of three-dimensional seismic waveform data includes n data points, wherein m and n are both integers greater than or equal to 1;

[0111] Constructing a seismic data matrix based on the m groups of three-directional seismic waveform data;

[0112] Performing principal component analysis on the seismic data matrix to obtain one-dimensional seismic waveform data corresponding to each seismic waveform data in the m groups of three-directional seismic waveform data;

[0113] Determining the frequency, amplitude and wavelet coefficient of the one-dimensional seismic waveform data;

[0114] generating a seismic wave data image according to the frequency, the amplitude and the wavelet coefficient, and establishing a seismic image database according to the seismic wave data image;

[0115] The initial single-point random seismic motion model is trained based on the seismic motion image database to obtain the target single-point random seismic motion model.

[0116] In a possible design, the generating module 402 performs principal component analysis on the seismic data matrix to obtain one-dimensional seismic waveform data including:

[0117] Performing data standardization processing on each piece of seismic waveform data in the target group of three-directional seismic waveform data, wherein the target group of three-directional seismic waveform data is any one group of three-directional seismic waveform data among the m groups of three-directional seismic waveform data;

[0118] Calculating the correlation coefficient matrix corresponding to each piece of seismic waveform data in the target group of three-directional seismic waveform data;

[0119] The one-dimensional seismic waveform data corresponding to each piece of seismic waveform data in the target group of three-directional seismic waveform data is determined according to the singular value decomposition method and the correlation coefficient matrix.

[0120] In a possible design, the generating module 402 trains the initial single-point random seismic motion model based on the seismic motion image database to obtain the target single-point random seismic motion model, including:

[0121] Inputting random noise into the first generator of the initial single-point random ground motion model to obtain a single-point synthetic ground motion image;

[0122] Inputting the single-point synthetic seismic image and the image sample data in the seismic image database into the first discriminator of the single-point random seismic model to obtain a predicted value;

[0123] Calculating loss values ​​of the first generator and the first discriminator according to the prediction values ​​corresponding to the single-point synthetic ground motion image and the single-point real ground motion image, and updating weight parameters of the first generator and the first discriminator using an optimizer;

[0124] The above steps are iteratively executed until a preset iteration termination condition is reached to obtain the target single-point random earthquake motion model.

[0125] In a possible design, the synthesis module 406 is further used for:

[0126] Determine sample multi-point seismic data of each measuring point in a set of sample measuring points arranged in each of the multiple areas;

[0127] splicing the one-dimensional seismic waveform data with the random data to obtain spliced ​​multi-point seismic motion data;

[0128] Inputting the spliced ​​multi-point earthquake motion data into a second generator of an initial multi-point earthquake motion data generation model to obtain generated data;

[0129] Inputting the sample multi-point seismic data and the generated data into a second discriminator of the initial multi-point seismic data generation model to obtain predicted data;

[0130] Adjust the loss functions of the second generator and the second discriminator according to the predicted data and the generated data;

[0131] Iterative training is performed on the second generator and the second discriminator after the loss function is adjusted to obtain the target multi-point seismic data generation model.

[0132] In a possible design, the target single-point random seismic model and the target multi-point seismic data generation model both use a generative adversarial neural network, and the generative adversarial neural network includes a generator and a discriminator;

[0133] The dimension of the random noise is set to 12, the number of hidden units of the fully connected layer of the generator is 32, the upsampling ratio of the deconvolution layer of the generator is 2, the number of layers of the output layer of the generator is , and the lengths of the output layers are [64, 128, 256, 512, 1024, 2048, 4096] respectively;

[0134] The convolution kernel length of the convolution layer of the discriminator is 5, the compensation is 1, and batch normalization is used in the convolution layer to adjust the distribution of the feature map; the compensation of the convolution layer of the discriminator is 1, the convolution kernel lengths are [3, 3, 3, 5, 5], and the number of output channels are [4, 8, 16, 32, 64], respectively. The pooling receptive field and step size of the pooling layers Dpool1 and Dpool2 of the discriminator are both 2, the pooling receptive field and step size of the pooling layers Dpool3 to Dpool5 of the discriminator are both 4, and the number of hidden units of the fully connected layers Dfull1 to Dfull3 of the discriminator are [128, 4, 1], respectively.

[0135] In one possible design, the loss function of the generator is expressed by the following formula:

[0136] LG=-∑lns fake ;

[0137] The loss function of the discriminator is expressed by the following formula:

[0138] LD=-∑lns real -∑ln(1-s fake );

[0139] Among them, s fake is the predicted true or false probability of the single-point synthetic seismic image, s real is the predicted true or false probability of a single point real earthquake image.

[0140] above Figure 4 The device for generating multi-point seabed earthquake motion in the embodiment of the present invention is described from the perspective of modular functional entities. The device for generating multi-point seabed earthquake motion in the embodiment of the present invention is described in detail from the perspective of hardware processing. Figure 5 , a schematic diagram of an embodiment of a device 500 for generating multi-point seabed earthquake motions in an embodiment of the present invention, wherein the device 500 for generating multi-point seabed earthquake motions comprises:

[0141] An input device 501, an output device 502, a processor 503 and a memory 504 (the number of the processor 503 can be one or more, Figure 5In some embodiments of the present invention, the input device 501, the output device 502, the processor 503 and the memory 504 may be connected via a communication bus or other means, wherein: Figure 5 The communication bus connection is taken as an example.

[0142] Wherein, by calling the operation instruction stored in the memory 504, the processor 503 is used to perform the following steps:

[0143] Obtain target three-directional seismic waveform data corresponding to the target area;

[0144] Generate target one-dimensional seismic waveform data corresponding to the target three-directional seismic waveform data according to the target three-directional seismic waveform data, random noise and a target single-point random seismic motion model;

[0145] splicing the target one-dimensional seismic waveform data with random data satisfying Dirichlet distribution to obtain target spliced ​​multi-point seismic motion data;

[0146] Establishing a three-dimensional site model containing faults according to the size of the semi-space homogeneous isotropic geological body, the position of the fault plane and the size of the fault plane;

[0147] According to the fault plane parameters corresponding to different moment magnitudes and the three-dimensional site model, a spectral element method is used to simulate and analyze the seismic motion to obtain target multi-point seismic motion data of each measuring point in the set of measuring points arranged in the target area;

[0148] The target spliced ​​multi-point seismic data and the target multi-point seismic data are input into a target multi-point seismic data generation model to obtain the multi-point seismic data corresponding to the target area.

[0149] By calling the operation instructions stored in the memory 504, the processor 503 is also used to execute Figure 1 Any method in the corresponding embodiment.

[0150] See also Figure 6 , Figure 6 A schematic diagram of an electronic device according to an embodiment of the present invention.

[0151] like Figure 6 As shown, an embodiment of the present invention provides an electronic device, including a memory 610, a processor 620, and a computer program 611 stored in the memory 610 and executable on the processor 620. When the processor 620 executes the computer program 611, the following steps are implemented:

[0152] Obtain target three-directional seismic waveform data corresponding to the target area;

[0153] Generate target one-dimensional seismic waveform data corresponding to the target three-directional seismic waveform data according to the target three-directional seismic waveform data, random noise and a target single-point random seismic motion model;

[0154] splicing the target one-dimensional seismic waveform data with random data satisfying Dirichlet distribution to obtain target spliced ​​multi-point seismic motion data;

[0155] Establishing a three-dimensional site model containing faults according to the size of the semi-space homogeneous isotropic geological body, the position of the fault plane and the size of the fault plane;

[0156] According to the fault plane parameters corresponding to different moment magnitudes and the three-dimensional site model, a spectral element method is used to simulate and analyze the seismic motion to obtain target multi-point seismic motion data of each measuring point in the set of measuring points arranged in the target area;

[0157] The target spliced ​​multi-point seismic data and the target multi-point seismic data are input into a target multi-point seismic data generation model to obtain the multi-point seismic data corresponding to the target area.

[0158] In a specific implementation process, when the processor 620 executes the computer program 611, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0159] Since the electronic device introduced in this embodiment is a device used to implement a multi-point seabed seismic generation device in the embodiment of the present invention, based on the method introduced in the embodiment of the present invention, technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present invention will not be described in detail here. As long as the equipment adopted by technical personnel in this field to implement the method in the embodiment of the present invention is within the scope of protection of the present invention.

[0160] See also Figure 7 , Figure 7 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention.

[0161] like Figure 7 As shown, the embodiment of the present invention further provides a computer-readable storage medium 700, on which a computer program 711 is stored. When the computer program 711 is executed by a processor, the following steps are implemented:

[0162] Obtain target three-directional seismic waveform data corresponding to the target area;

[0163] Generate target one-dimensional seismic waveform data corresponding to the target three-directional seismic waveform data according to the target three-directional seismic waveform data, random noise and a target single-point random seismic motion model;

[0164] splicing the target one-dimensional seismic waveform data with random data satisfying Dirichlet distribution to obtain target spliced ​​multi-point seismic motion data;

[0165] Establishing a three-dimensional site model containing faults according to the size of the semi-space homogeneous isotropic geological body, the position of the fault plane and the size of the fault plane;

[0166] According to the fault plane parameters corresponding to different moment magnitudes and the three-dimensional site model, a spectral element method is used to simulate and analyze the seismic motion to obtain target multi-point seismic motion data of each measuring point in the set of measuring points arranged in the target area;

[0167] The target spliced ​​multi-point seismic data and the target multi-point seismic data are input into a target multi-point seismic data generation model to obtain the multi-point seismic data corresponding to the target area.

[0168] In the specific implementation process, the computer program 711 is executed by the processor to achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0169] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0170] The embodiment of the present invention also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes the following Figure 1 The process in the corresponding embodiment.

[0171] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is fully or partially generated.

[0172] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0173] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating multi-point seafloor earthquake motions, characterized in that: include: Obtain target three-directional seismic waveform data corresponding to the target area; Generate target one-dimensional seismic waveform data corresponding to the target three-directional seismic waveform data according to the target three-directional seismic waveform data, random noise and a target single-point random seismic motion model; splicing the target one-dimensional seismic waveform data with random data satisfying Dirichlet distribution to obtain target spliced ​​multi-point seismic motion data; Establishing a three-dimensional site model containing faults according to the size of the semi-space homogeneous isotropic geological body, the position of the fault plane and the size of the fault plane; According to the fault plane parameters corresponding to different moment magnitudes and the three-dimensional site model, a spectral element method is used to simulate and analyze the seismic motion to obtain target multi-point seismic motion data of each measuring point in the set of measuring points arranged in the target area; The target spliced ​​multi-point seismic data and the target multi-point seismic data are input into a target multi-point seismic data generation model to obtain the multi-point seismic data corresponding to the target area.

2. The method according to claim 1, characterized in that The method further comprises: According to the user's operation instruction, m groups of three-dimensional seismic waveform data corresponding to each area of ​​the multiple areas are obtained, each piece of seismic waveform data in the m groups of three-dimensional seismic waveform data includes n data points, wherein m and n are both integers greater than or equal to 1; Constructing a seismic data matrix based on the m groups of three-directional seismic waveform data; Performing principal component analysis on the seismic data matrix to obtain one-dimensional seismic waveform data corresponding to each seismic waveform data in the m groups of three-directional seismic waveform data; Determining the frequency, amplitude and wavelet coefficient of the one-dimensional seismic waveform data; generating a seismic wave data image according to the frequency, the amplitude and the wavelet coefficient, and establishing a seismic image database according to the seismic wave data image; The initial single-point random seismic motion model is trained based on the seismic motion image database to obtain the target single-point random seismic motion model.

3. The method according to claim 2, characterized in that The performing principal component analysis on the seismic data matrix to obtain one-dimensional seismic waveform data comprises: Performing data standardization processing on each piece of seismic waveform data in the target group of three-directional seismic waveform data, wherein the target group of three-directional seismic waveform data is any one group of three-directional seismic waveform data among the m groups of three-directional seismic waveform data; Calculating the correlation coefficient matrix corresponding to each piece of seismic waveform data in the target group of three-directional seismic waveform data; The one-dimensional seismic waveform data corresponding to each piece of seismic waveform data in the target group of three-directional seismic waveform data is determined according to the singular value decomposition method and the correlation coefficient matrix.

4. The method according to claim 2, characterized in that: The training of the initial single-point random seismic motion model based on the seismic motion image database to obtain the target single-point random seismic motion model includes: Inputting random noise into the first generator of the initial single-point random ground motion model to obtain a single-point synthetic ground motion image; Inputting the single-point synthetic seismic image and the image sample data in the seismic image database into the first discriminator of the single-point random seismic model to obtain a predicted value; Calculating loss values ​​of the first generator and the first discriminator according to the prediction values ​​corresponding to the single-point synthetic ground motion image and the single-point real ground motion image, and updating weight parameters of the first generator and the first discriminator using an optimizer; The above steps are iteratively executed until a preset iteration termination condition is reached to obtain the target single-point random earthquake motion model.

5. The method according to any one of claims 2 to 4, characterized in that The method further comprises: Determine sample multi-point seismic data of each measuring point in a set of sample measuring points arranged in each of the multiple areas; splicing the one-dimensional seismic waveform data with the random data to obtain spliced ​​multi-point seismic motion data; Inputting the spliced ​​multi-point earthquake motion data into a second generator of an initial multi-point earthquake motion data generation model to obtain generated data; Inputting the sample multi-point seismic data and the generated data into a second discriminator of the initial multi-point seismic data generation model to obtain predicted data; Adjust the loss functions of the second generator and the second discriminator according to the predicted data and the generated data; Iterative training is performed on the second generator and the second discriminator after the loss function is adjusted to obtain the target multi-point seismic data generation model.

6. The method according to any one of claims 1 to 4, characterized in that The target single-point random earthquake motion model and the target multi-point earthquake motion data generation model both adopt a generative adversarial neural network, and the generative adversarial neural network includes a generator and a discriminator; The dimension of the random noise is set to 12, the number of hidden units of the fully connected layer of the generator is 32, the upsampling ratio of the deconvolution layer of the generator is 2, the number of layers of the output layer of the generator is , and the lengths of the output layers are [64, 128, 256, 512, 1024, 2048, 4096] respectively; The convolution kernel length of the convolution layer of the discriminator is 5, the compensation is 1, and batch normalization is used in the convolution layer to adjust the distribution of the feature map; the compensation of the convolution layer of the discriminator is 1, the convolution kernel lengths are [3, 3, 3, 5, 5], and the number of output channels are [4, 8, 16, 32, 64], respectively. The pooling receptive field and step size of the pooling layers Dpool1 and Dpool2 of the discriminator are both 2, the pooling receptive field and step size of the pooling layers Dpool3 to Dpool5 of the discriminator are both 4, and the number of hidden units of the fully connected layers Dfull1 to Dfull3 of the discriminator are [128, 4, 1], respectively.

7. The method according to claim 6, characterized in that The loss function of the generator is expressed by the following formula: LG=-∑lns fake ; The loss function of the discriminator is expressed by the following formula: LD=-∑lns real -∑ln(1-s fake ); Among them, s fake is the predicted true or false probability of the single-point synthetic seismic image, s real is the predicted true or false probability of a single point real earthquake image.

8. A device for generating multi-point seafloor earthquake motions, characterized in that: include: An acquisition module is used to acquire target three-directional seismic waveform data corresponding to the target area; A generating module, used for generating target one-dimensional seismic waveform data corresponding to the target three-directional seismic waveform data according to the target three-directional seismic waveform data, random noise and a target single-point random seismic motion model; A splicing module, used for splicing the target one-dimensional seismic waveform data with random data satisfying Dirichlet distribution to obtain target spliced ​​multi-point seismic motion data; A construction module for establishing a three-dimensional site model containing faults according to the size of the semi-space uniform isotropic geological body, the position of the fault plane and the size of the fault plane; A simulation analysis module, for performing simulation analysis of earthquake motions by using a spectral element method according to fault plane parameters corresponding to different moment magnitudes and the three-dimensional site model, to obtain target multi-point earthquake motion data of each measuring point in a set of measuring points arranged in the target area; The synthesis module is used to input the target spliced ​​multi-point seismic data and the target multi-point seismic data into a target multi-point seismic data generation model to obtain the multi-point seismic motion corresponding to the target area.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the processor is used to implement the steps of the method for generating multi-point seafloor seismic motion according to any one of claims 1 to 7 when executing a computer management program stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer management program, and when the computer management program is executed by a processor, the steps of the method for generating multi-point seafloor seismic motion as described in any one of claims 1 to 7 are implemented.

Images