Cloning selection-based mourhue surface modeling method and related equipment

Abstract

The invention discloses a clone selection-based mourhua surface modeling method and related equipment, and the method constructs an'antibody-parameter 'iterative optimization framework by simulating the clone selection principle of an immune system, achieves the automatic and intelligent global search of key parameters, and improves the modeling efficiency. The defect that parameters are set by depending on artificial experience in a traditional method can be effectively overcome, and then the objectivity and repeatability of the model are ensured; through loop iteration of a series of collaborative operations such as initialization, evaluation, cloning, variation, supplementation and reselection, convergence from a preset parameter space to an optimal solution can be efficiently achieved, when a precision threshold value is met, stopping and outputting an optimal antibody are achieved, and it can be ensured that a high-precision mourhua face model is obtained in limited computing resources. According to the method, full-process automation from parameter initialization to optimal model output is realized, manual participation can be remarkably reduced, the modeling efficiency is improved, the calculation process is more stable and robust, and the method can be widely applied to the technical field of data processing.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a Moho surface modeling method and related equipment based on cloning selection. Background Technology

[0002] The Parker-Oldenburg inversion method is a classic approach for calculating the Moho depth (the interface between the crust and mantle) using gravity anomaly data. Its accuracy and reliability are crucial for geophysical research, resource exploration, and understanding crustal structure. However, traditional implementation methods and existing improvements generally suffer from the following key drawbacks, limiting the method's practical application and automation level: First, there are simplifications in the physical basis of the model. Existing technical solutions typically simplify the relationship between density difference (the density difference between the crust and mantle) and the Moho depth to a linear relationship or treat it as a constant. However, in actual geological structures, density difference often exhibits complex variations with depth. This linear or constant assumption introduces significant model errors, resulting in an inaccurate Moho topography that fails to accurately reflect the details of the subsurface structure.

[0003] Secondly, the determination of key parameters relies too heavily on human experience. Due to the lack of automated parameter optimization mechanisms, existing methods typically require researchers to pre-determine density differences and average depths based on personal or regional experience. This subjectivity leads to vastly different inversion models from different researchers, even within the same study area, resulting in inconsistent model quality, difficulties in cross-validation, and challenges in unified management and application.

[0004] Finally, existing methods suffer from inefficiency and insufficient automation in their computational processes. Parameter search and model validation are often computationally intensive and lack a systematic and automated execution framework. This makes the entire inversion process highly dependent on manual intervention and trial and error, which is not only time-consuming and labor-intensive but also makes it difficult to guarantee the stability and accuracy of the computational results. Ultimately, the obtained Moho surface model is often coarser than the actual situation. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, electronic device, storage medium, and program product for Moho surface modeling based on cloning selection, aiming to solve at least one problem of the prior art.

[0006] To achieve the above objectives, one aspect of this invention proposes a Moho surface modeling method based on clonal selection, the method comprising: The antibody population is initialized based on random values ​​within a preset range, and the antibody population is used as an iterative population. The antibody population includes a first number of antibodies, and each antibody corresponds to a set of parameter information, including density difference and average depth. Based on iterative population, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model. Then, the deviation is evaluated by combining the preset seismic depth matrix to obtain the deviation value corresponding to each antibody. The deviation value is determined. If the deviation value of the antibody in the iterative population is less than the optimal threshold, the corresponding antibody is selected as the optimal antibody. Otherwise, the second number of antibodies are selected as cloning candidate populations based on the order of deviation value from smallest to largest. Perform a fixed-fold cloning operation on each antibody in the cloning candidate population to obtain the cloning antibody group corresponding to each antibody in the cloning candidate population; Based on the iteration progress, each antibody in the cloned antibody group is mutated to obtain the mutated antibody group; Based on the mutant antibody group, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model. Then, the deviation is evaluated by combining the preset seismic depth matrix to obtain the deviation value corresponding to each antibody. From each group of variant antibodies, the antibody with the smallest deviation value is selected and organized into a clonal variant population. A supplementary population is generated through supplementary operations. Based on the supplementary population, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model. Then, the deviation is evaluated by combining the preset seismic depth matrix to obtain the deviation value corresponding to each antibody. The cloned mutant population and the supplementary population are combined into an iterative population. The iteration count is incremented by 1, and the step of judging the deviation value is returned until the deviation value of the antibody in the iterative population is less than the optimal threshold for iteration or the iteration count reaches the maximum number of iterations. The antibody corresponding to the minimum deviation value in the iterative population is taken as the optimal antibody. The Moho modeling results are determined based on the Moho model corresponding to the optimal antibody.

[0007] In some embodiments, the antibody population is initialized based on random values ​​within a preset range, including the following steps: The first preset interval is determined based on the preset minimum and maximum density difference values, and the second preset interval is determined based on the preset minimum and maximum average depth values. The density difference is obtained by randomly sampling within a first preset interval; The average depth is obtained by randomly sampling within a second preset interval; Using density difference and average depth as antibodies, the process returns to the step of random sampling in a first preset interval until the number of antibodies reaches a first quantity, thus initializing the antibody population.

[0008] In some embodiments, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model, and then a deviation evaluation is performed in combination with a preset seismic depth matrix to obtain the deviation value corresponding to each antibody, including the following steps: By iterating through the parameter information corresponding to each antibody, inversion modeling is performed based on the Parker-Oldenburg inversion method to construct the Moho surface model corresponding to each antibody. Based on the preset earthquake depth matrix, the deviation of the Moho surface model is evaluated to obtain the deviation value corresponding to each antibody.

[0009] In some embodiments, the seismic depth matrix includes sampling point data of the area to be modeled, and the Moho model includes inversion data of all points in the area to be modeled. Based on the preset seismic depth matrix, the Moho model is subjected to bias evaluation to obtain the bias value corresponding to each antibody, including the following steps: Based on the location information corresponding to the sampling point data, inversion data with corresponding location information is extracted from the Moho model as evaluation data; The root mean square operation is performed on the difference between the data at each sampling point and its corresponding evaluation data to obtain the deviation value of the antibody corresponding to the Moho model.

[0010] In some embodiments, each antibody in the cloned antibody group is mutated based on the iteration progress to obtain a mutated antibody group, including the following steps: The mutation intensity is determined based on the maximum number of iterations and the number of iterations in the current iteration, combined with a preset mutation value. The mutation intensity was used to mutate each antibody in the cloned antibody group to obtain a mutated antibody group.

[0011] In some embodiments, the preset mutation value includes a minimum mutation strength and a maximum mutation strength. The mutation strength is determined based on the maximum number of iterations and the number of iterations in the current iteration, combined with the preset mutation value, including the following steps: The value of the first parameter is determined based on the difference between the maximum and minimum values ​​of the variation intensity. The value of the second parameter is determined based on the ratio of the current iteration number to the maximum iteration number; The value of the third parameter is determined based on the product of the first parameter value and the second parameter value; The mutation intensity is obtained by adding the minimum mutation intensity to the third parameter value.

[0012] In some embodiments, generating a supplementary population through a supplementary operation includes the following steps: Two different antibodies are randomly selected from the current iteration population as the first antibody and the second antibody. The numerical range between the density difference of the first antibody and the density difference of the second antibody is taken as the third preset range, and the numerical range between the average depth of the first antibody and the average depth of the second antibody is taken as the fourth preset range. The supplementary density difference is obtained by randomly sampling in the third preset interval; The supplementary average depth is obtained by randomly sampling in the fourth preset interval; The supplementary density difference and supplementary average depth are integrated as antibodies, and the process is returned to the step of random sampling in the third preset interval until the number of antibodies obtained based on the supplementary density difference and supplementary average depth reaches the third number, thus obtaining the supplementary population. The third quantity is determined based on the difference between the first and second quantities.

[0013] To achieve the above objectives, another aspect of the present invention provides a Moho surface modeling apparatus based on clonal selection, the apparatus comprising: The first module is used to initialize the antibody population based on random values ​​within a preset range, and to use the antibody population as an iterative population; wherein, the antibody population includes a first number of antibodies, and each antibody corresponds to a set of parameter information, including density difference and average depth; The second module is used to invert and model the corresponding Moho surface model by traversing the parameter information of each antibody based on the iterative population, and then combine it with the preset seismic depth matrix to evaluate the deviation and obtain the deviation value corresponding to each antibody. The third module is used to determine the deviation value. When the deviation value of the antibody in the iterative population is less than the optimal threshold of the iteration, the corresponding antibody is taken as the optimal antibody. Otherwise, the second number of antibodies are selected as cloning candidate populations based on the order of the deviation value from smallest to largest. The fourth module is used to perform a fixed-fold cloning operation on each antibody in the cloning candidate population to obtain the cloning antibody group corresponding to each antibody in the cloning candidate population. The fifth module is used to perform mutation operations on each antibody in the cloned antibody group based on the iteration progress, so as to obtain the mutated antibody group; The sixth module is used to invert and model the corresponding Moho surface model by traversing the parameter information of each antibody based on the mutant antibody group, and then combine it with the preset seismic depth matrix to evaluate the deviation and obtain the deviation value corresponding to each antibody. The seventh module is used to select the antibody with the smallest deviation value from each group of variant antibodies and organize them into a clonal variant population. The eighth module is used to generate a supplementary population through supplementary operations. Based on the supplementary population, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model. Then, the deviation is evaluated by combining the preset seismic depth matrix to obtain the deviation value corresponding to each antibody. The ninth module is used to combine the cloned mutant population and the supplementary population as the iterative population, increment the iteration count by 1, and return to execute the step of judging the deviation value until the deviation value of the antibody in the iterative population is less than the optimal iteration threshold or the iteration count reaches the maximum iteration count. The antibody corresponding to the minimum deviation value in the iterative population is taken as the optimal antibody. The tenth module is used to determine the Moho modeling result based on the Moho model corresponding to the optimal antibody.

[0014] To achieve the above objectives, another aspect of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method.

[0015] To achieve the above objectives, another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method.

[0016] To achieve the above objectives, another aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0017] The embodiments of the present invention include at least the following beneficial effects: The present invention provides a method, apparatus, electronic device, storage medium, and program product for Moho surface modeling based on clonal selection. This scheme initializes an antibody population based on random values ​​within a preset interval, using the antibody population as an iterative population. The antibody population includes a first number of antibodies, each antibody corresponding to a set of parameter information, including density difference and average depth. Based on the iterative population, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model. Then, a deviation evaluation is performed using a preset seismic depth matrix to obtain a deviation value for each antibody. The deviation value is judged; if the deviation value of an antibody in the iterative population is less than the optimal threshold, the corresponding antibody is selected as the optimal antibody; otherwise, a second number of antibodies are selected as cloning candidate populations based on the ascending order of deviation values. A fixed-fold cloning operation is performed on each antibody in the cloning candidate population to obtain a cloning antibody group corresponding to each antibody in the cloning candidate population. Based on iterative selection… The process involves mutating each antibody in the cloned antibody group to obtain a mutated antibody group. Based on the mutated antibody group, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model. Then, a deviation evaluation is performed using a preset seismic depth matrix to obtain the deviation value for each antibody. The antibody with the smallest deviation value is selected from each mutated antibody group and organized into a clonal variant population. A supplementary population is generated through a supplementary operation. Based on the supplementary population, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model. Then, a deviation evaluation is performed using a preset seismic depth matrix to obtain the deviation value for each antibody. The clonal variant population and the supplementary population are combined into an iterative population. The iteration count is incremented by 1, and the step of judging the deviation value is returned until the deviation value of an antibody in the iterative population is less than the optimal iteration threshold or the maximum number of iterations is reached. The antibody with the smallest deviation value in the iterative population is selected as the optimal antibody. The Moho surface modeling result is determined based on the Moho surface model corresponding to the optimal antibody. This invention, by simulating the clonal selection principle of the immune system, constructs an iterative optimization framework of "antibody-parameters," achieving automated and intelligent global search for key parameters (density difference, average depth). This effectively overcomes the drawbacks of traditional methods that rely on manual experience to set parameters, thus ensuring the objectivity and repeatability of the model. Specifically, through a series of coordinated iterative operations including initialization, evaluation, cloning, mutation, supplementation, and reselection, this invention efficiently converges from a preset parameter space to the optimal solution. When a precision threshold is met (deviation less than the iterative optimal threshold), the process stops and the optimal antibody is output, ensuring a high-precision Moho surface model within limited computational resources. In detail, the overall method of this invention defines a clear, unified, and closed computational execution framework, achieving full automation from parameter initialization to optimal model output. This significantly reduces manual intervention, improves modeling efficiency, and makes the computation process more stable and robust. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of an implementation environment for the Moho surface modeling method based on clonal selection provided in this embodiment of the invention; Figure 2 This is a flowchart illustrating a Moho surface modeling method based on cloning selection provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the process for initializing the antibody population according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the inversion modeling and deviation value evaluation process provided in the embodiments of the present invention; Figure 5 This is a schematic diagram of the deviation assessment process provided in the embodiments of the present invention; Figure 6 This is a schematic diagram of the unfolding process of step S500 provided in the embodiment of the present invention; Figure 7 This is a schematic diagram of the unfolding process of step S510 provided in the embodiment of the present invention; Figure 8 This is a schematic diagram of the overall process of the Moho surface modeling method based on cloning selection provided in the embodiments of the present invention; Figure 9 This is a schematic diagram of a Moho surface modeling device based on clonal selection provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0020] It is understood that the terms “first,” “second,” etc., used in this invention may be used herein to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are used only to distinguish one concept from another. For example, first information may also be referred to as second information without departing from the scope of embodiments of the invention, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to determination” as used herein may be interpreted as “when…” or “when…” or “in response to determination.”

[0021] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0023] To facilitate understanding of the technical solution of this invention, the technical skills that may be involved in the technical solution of this invention will first be explained: Mohorovi i The Moho (Discontinuity) is the boundary between the Earth's crust and mantle, first discovered in 1909 by Croatian seismologist Ivan Mohorovičić while analyzing seismic wave data. The most significant characteristic of this boundary is the abrupt change in the propagation velocity of seismic waves (especially P-waves), increasing dramatically from 6-7 km / s in the crust to over 8 km / s in the mantle, indicating a significant difference in the composition and physical state of the materials in the upper and lower layers. As one of the most critical global boundaries within the Earth, the Moho is an important source of information for studying the Earth's layered structure, plate tectonics, and crustal evolution.

[0024] Heuristic search, also known as informed search, uses heuristic information about the problem to guide the search, aiming to reduce the search scope and lower the problem's complexity. This search process utilizing heuristic information is called heuristic search. For example, the weed optimization algorithm is a classic heuristic search algorithm.

[0025] The Clone Selection Algorithm (CSA) is an important branch of artificial immune algorithms. It searches for the global optimum by simulating the selection, cloning, mutation, and updating behaviors of B cells in a living organism. It possesses excellent self-learning and adaptive capabilities, as well as strong robustness, giving it significant advantages in intelligent optimization algorithms. It is widely used in scheduling, fault diagnosis, and intrusion detection.

[0026] The Parker-Oldenburg inversion method is a frequency-domain density interface iterative inversion method proposed by Oldenburg based on Parker's formula. Due to its fast computational speed, its application has rapidly expanded. The Parker-Oldenburg inversion method is one of the important methods for calculating terrain and is also the core starting point of this invention. Currently, the input parameters (density difference, depth, etc.) of this method are designed as constants based on experience, but in fact, there is still room for optimization and improvement of these parameters.

[0027] In related technologies, current solutions all treat the parameters of the Parker-Oldenburg inversion as constants, which results in low solution accuracy.

[0028] In view of this, this invention provides a method and related equipment for Moho discontinuity modeling based on clonal selection. This method initializes an antibody population with random values ​​within a preset interval, using the antibody population as an iterative population. The antibody population includes a first number of antibodies, each corresponding to a set of parameter information, including density difference and average depth. Based on the iterative population, the parameter information of each antibody is traversed to invert and model the corresponding Moho discontinuity model. Then, a deviation evaluation is performed using a preset seismic depth matrix to obtain a deviation value for each antibody. The deviation value is judged; if the deviation value of an antibody in the iterative population is less than the optimal threshold, the corresponding antibody is selected as the optimal antibody; otherwise, a second number of antibodies are selected as cloning candidate populations based on the ascending order of deviation values. A fixed-fold cloning operation is performed on each antibody in the cloning candidate population to obtain a cloning antibody group corresponding to each antibody in the cloning candidate population. Based on the iteration progress, each antibody in the cloning antibody group... Antibodies undergo mutation to obtain mutated antibody groups. Based on these groups, the parameters of each antibody are traversed to perform inversion modeling, resulting in a corresponding Moho surface model. This model is then combined with a pre-defined seismic depth matrix for deviation evaluation, yielding a deviation value for each antibody. The antibody with the smallest deviation value from each mutated antibody group is selected and grouped into a clonal mutant population. A supplementary population is generated through a supplementary operation. Based on this population, the parameters of each antibody are traversed to perform inversion modeling, resulting in a corresponding Moho surface model. This model is then combined with a pre-defined seismic depth matrix for deviation evaluation, yielding a deviation value for each antibody. The clonal mutant population and the supplementary population are combined into an iterative population. The iteration count is incremented by 1, and the process of judging deviation values ​​is repeated until an antibody in the iterative population has a deviation value less than the optimal iteration threshold or the maximum iteration count is reached. The antibody with the smallest deviation value in the iterative population is then selected as the optimal antibody. The Moho surface modeling result is determined based on the Moho surface model corresponding to the optimal antibody. This invention, by simulating the clonal selection principle of the immune system, constructs an iterative optimization framework of "antibody-parameters," achieving automated and intelligent global search for key parameters (density difference, average depth). This effectively overcomes the drawbacks of traditional methods that rely on manual experience to set parameters, thus ensuring the objectivity and repeatability of the model. Specifically, through a series of coordinated iterative operations including initialization, evaluation, cloning, mutation, supplementation, and reselection, this invention efficiently converges from a preset parameter space to the optimal solution. When a precision threshold is met (deviation less than the iterative optimal threshold), the process stops and the optimal antibody is output, ensuring a high-precision Moho surface model within limited computational resources. In detail, the overall method of this invention defines a clear, unified, and closed computational execution framework, achieving full automation from parameter initialization to optimal model output. This significantly reduces manual intervention, improves modeling efficiency, and makes the computation process more stable and robust.

[0029] It is understood that the Moho surface modeling method based on clonal selection provided by this invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal can be a smartphone, tablet, laptop, or desktop computer, but it is not limited to these.

[0030] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided by an embodiment of the present invention. (Refer to...) 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 via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0031] Server 101 can be a standalone physical server, a server cluster or distributed system consisting 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 communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0032] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0033] Terminal 102 can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the invention does not impose any limitations.

[0034] For example, based on Figure 1 The implementation environment shown in this embodiment of the invention provides a Moho surface modeling method based on clone selection. The following description uses the application of this Moho surface modeling method based on clone selection in server 101 as an example. It can be understood that this Moho surface modeling method based on clone selection can also be applied to terminal 102.

[0035] Reference Figure 2 , Figure 2 This is an optional flowchart of the clone-selection-based Moho surface modeling method provided in the embodiments of the present invention. The executing entity of the clone-selection-based Moho surface modeling method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 The method may include, but is not limited to, steps S100 to S1000.

[0036] Step S100: Initialize the antibody population based on random values ​​within a preset range, and use the antibody population as the iterative population; The antibody population includes a first number of antibodies, each antibody corresponding to a set of parameter information, including density difference and average depth. It should be noted that in some embodiments, such as Figure 3 As shown, initializing an antibody population based on random values ​​within a preset interval may include the following steps: S110, determining a first preset interval based on preset minimum and maximum density difference values, and determining a second preset interval based on preset minimum and maximum average depth values; S120, obtaining the density difference by random sampling within the first preset interval; S130, obtaining the average depth by random sampling within the second preset interval; S140, using the density difference and average depth as antibodies, returning to the step of random sampling within the first preset interval until the number of antibodies reaches a first quantity, thus initializing and obtaining an antibody population.

[0037] For example, in some specific implementations, antibody population initialization can be achieved as follows: The antibody population consists of a large number of antibodies, each antibody having two parameters representing the density difference (contrast) and the average depth (depth). The core of population initialization is to generate maxS (i.e., the first number) antibodies, and initialize the corresponding parameters for each antibody, as shown in the following formula:

[0038]

[0039]

[0040]

[0041] in Represents the i-th density difference contrast. Let represent the i-th average depth. Performing this operation maxS times will yield a population consisting of maxS antibodies.

[0042] Specifically, by setting reasonable preset intervals for density difference and average depth and performing random sampling, the embodiments of the present invention can ensure the diversity and coverage of the antibody population at the beginning of the algorithm, thereby providing a good global search starting point for the subsequent clone selection optimization process. This helps to avoid the algorithm from getting trapped in local optima too early and increases the possibility of finding the globally optimal parameter combination.

[0043] Step S200: Based on the iterative population, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model, and then the deviation is evaluated by combining the preset seismic depth matrix to obtain the deviation value corresponding to each antibody. It should be noted that in some embodiments, such as Figure 4 As shown, the parameter information of each antibody is traversed to perform inversion modeling to obtain the corresponding Moho surface model. Then, the deviation is evaluated by combining the preset seismic depth matrix to obtain the deviation value corresponding to each antibody. This can include the following steps: Step T100: Traverse the parameter information corresponding to each antibody and perform inversion modeling based on the Parker-Oldenburg inversion method to construct the Moho surface model corresponding to each antibody; Step T200: Based on the preset seismic depth matrix, perform deviation evaluation on the Moho surface model to obtain the deviation value corresponding to each antibody.

[0044] Specifically, this embodiment of the invention combines the Parker-Oldenburg inversion method with the optimization algorithm proposed in this invention, and uses the external real or high-precision data of the seismic depth matrix as the evaluation standard to establish an objective and quantitative evaluation standard (deviation value) for each parameter combination (antibody), so that the optimization process has a clear goal orientation.

[0045] It should be noted that the seismic depth matrix includes sampled point data of the area to be modeled, and the Moho model includes inversion data of all points in the area to be modeled. In some embodiments, such as... Figure 5 As shown, based on the preset seismic depth matrix, the deviation assessment of the Moho model is performed to obtain the deviation value corresponding to each antibody. This can include the following steps: T210, based on the location information corresponding to the sampling point data, the inversion data of the corresponding location information is extracted from the Moho model as the assessment data; T220, the root mean square operation is performed on the difference between each sampling point data and its corresponding assessment data to obtain the deviation value of the antibody corresponding to the Moho model.

[0046] For example, in some specific implementations, firstly, a Moho surface model is constructed by iterating through each antibody: information for each antibody is iterated through one by one. Then, based on the Parker-Oldenburg inversion method, the corresponding terrain data is calculated. The terrain data is a matrix composed of (longitude, latitude, depth) triples, which is the three-dimensional model of the target area. Next, the RMS (based on the root mean square deviation) is calculated for each antibody: each antibody has its corresponding Moho surface data (Moho model), and then combined with the seismic depth matrix SemisMat, the corresponding RMS is calculated. Each antibody calculates its own RMS, ultimately resulting in an array of size maxS.

[0047] Specifically, the embodiments of the present invention use root mean square (RMS) error as a specific measure of deviation value. By comparing the inverted value with the reference value at the seismic data sampling points, it can comprehensively and effectively reflect the overall fitting accuracy of the entire Moho surface model at key points, making the evaluation of the "antibody" quality more scientific and reliable.

[0048] Step S300: Determine the deviation value. If the deviation value of the antibody in the iterative population is less than the optimal threshold of the iteration, the corresponding antibody is taken as the optimal antibody. Otherwise, the second number of antibodies are selected as cloning candidate populations based on the order of the deviation values ​​from smallest to largest. For example, in some specific implementations, all antibodies are traversed to determine if any antibody has an RMS value less than the iterative optimal threshold iterRMS. If such an antibody exists, it indicates that the optimal antibody has been found. Otherwise, subsequent steps are performed, and the Top N (i.e., the second number) excellent antibodies can be selected: all antibodies are sorted in ascending order according to their root mean square error (RMS) (the smaller the RMS, the higher the fitness), and then the top N excellent antibodies are selected for cloning. Here, N is the number of excellent antibodies, generally defined as 20%~50% of maxS, with a default of 30% (this can be adjusted according to actual needs). The core idea here is that the top N excellent antibodies undergo cloning, mutation, and other operations, forming a new temporary population with the remaining antibodies, and finally, the excess antibodies are eliminated through competition.

[0049] Step S400: Perform a fixed-fold cloning operation on each antibody in the cloning candidate population to obtain the cloning antibody group corresponding to each antibody in the cloning candidate population. For example, in some specific implementations, the cloning operation can be performed as follows: Iterate through the N excellent antibodies selected in the aforementioned steps, clone each antibody a fixed multiple of C times, generating a cloning population, where C is the number of antibody clones, at least 2, determined according to requirements, with a default of 10 (this can be adjusted according to actual needs). The core idea of ​​the cloning operation is to retain more excellent antibody genes; the more excellent the antibody, the higher the probability of retention, ultimately generating N×C cloned antibodies. :

[0050]

[0051] Step S500: Based on the iteration progress, perform mutation operations on each antibody in the cloned antibody group to obtain the mutated antibody group; It should be noted that in some embodiments, such as Figure 6 As shown, step S500 may include the following steps: S510, determining the mutation intensity based on the maximum number of iterations and the number of iterations of the current iteration, combined with a preset mutation value; S520, performing mutation operations on each antibody in the cloned antibody group using the mutation intensity to obtain a mutated antibody group.

[0052] Specifically, the embodiments of the present invention introduce an adaptive mutation mechanism. The mutation intensity is not fixed, but is related to the iteration progress (current iteration number / maximum iteration number). Specifically, using a larger mutation intensity in the early stage of iteration helps to explore new solutions globally, while using a smaller mutation intensity in the later stage of iteration helps to perform fine search near the optimal solution, thus balancing global search capability and local development capability.

[0053] It should be noted that the preset mutation values ​​include the minimum and maximum mutation strengths. In some embodiments, such as... Figure 7 As shown, step S510 may include the following steps: S511, determining a first parameter value based on the difference between the maximum and minimum mutation intensity; S512, determining a second parameter value based on the ratio of the current iteration number to the maximum iteration number; S513, determining a third parameter value based on the product of the first and second parameter values; S514, adding the third parameter value to the minimum mutation intensity to obtain the mutation intensity.

[0054] Specifically, this invention provides a linearly decreasing adaptive mutation intensity implementation. The method of this invention is simple and efficient, and can smoothly adjust the mutation intensity length according to the preset mutation intensity range (minimum and maximum values) combined with the iteration progress (the ratio of the current iteration number to the maximum iteration number), ensuring that the algorithm behavior is controllable and conforms to the optimization logic, and further improving the efficiency and stability of the algorithm convergence.

[0055] For example, in some specific embodiments, performing the mutation operation can be achieved as follows: traversing the N×C cloned antibodies generated by the cloning operation. A mutation formula is applied to each antibody. The mutation simulates immune mutation behavior, introducing random perturbations to enhance the exploration capability. , Let $G$ represent the minimum and maximum values ​​of the mutation intensity, and $i$ represent the current iteration number, with a minimum of 1 and a maximum of $MaxG$. MR represents the mutation intensity in the i-th iteration. As the iterations proceed, MR gradually increases. This indicates the new antibody obtained after mutation:

[0056]

[0057]

[0058]

[0059]

[0060] Among them, normal This represents a normally distributed random number with a mean of 0 and a standard deviation of MR; the superscript "new" indicates the parameter identifier after the mutation operation, and the superscript "old" indicates the parameter identifier before the mutation operation.

[0061] Step S600: Based on the mutant antibody group, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model, and then the deviation is evaluated by combining the preset seismic depth matrix to obtain the deviation value corresponding to each antibody. For example, in some specific implementations, for each antibody group after mutation Iterate through the information of each antibody one by one. Then, the corresponding terrain data is calculated based on the Parker-Oldenburg inversion method. The terrain data is a matrix composed of (longitude, latitude, depth) triplets. Then, combined with the seismic depth matrix SemisMat, the corresponding RMS is calculated. Each antibody will calculate its own RMS.

[0062] Step S700: Select the antibody with the smallest deviation value from each mutant antibody group and organize them into a clonal mutant population; For example, in some specific implementations, for each antibody clone after mutation Of the 10 antibodies selected, the one with the smallest RMS value was chosen to proceed to the next iteration. A total of 10 antibodies were selected. An antibody. For example, an antibody... After cloning, mutation, and other operations, C antibodies were obtained. From these C antibodies, the antibody with the smallest RMS is selected as the next-generation antibody, thus obtaining N antibodies of the next-generation antibody.

[0063] Step S800: A supplementary population is generated through the supplementary operation. Based on the supplementary population, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model. Then, the deviation is evaluated by combining the preset seismic depth matrix to obtain the deviation value corresponding to each antibody. It should be noted that, in some embodiments, generating a supplementary population through a supplementary operation may include the following steps: randomly selecting two different antibodies from the current iteration population as a first antibody and a second antibody; defining the numerical range between the density difference of the first antibody and the density difference of the second antibody as a third preset range, and defining the numerical range between the average depth of the first antibody and the average depth of the second antibody as a fourth preset range; obtaining a supplementary density difference by randomly sampling within the third preset range; obtaining a supplementary average depth by randomly sampling within the fourth preset range; integrating the supplementary density difference and the supplementary average depth as an antibody, and returning to execute the step of randomly sampling within the third preset range until the number of antibodies obtained based on the integration of the supplementary density difference and the supplementary average depth reaches a third quantity, thereby obtaining a supplementary population; wherein, the third quantity is determined based on the difference between the first quantity and the second quantity.

[0064] For example, in some specific implementations, the antibody set size after clonal mutation is N, but the initial population size is maxS. Therefore, new antibodies are randomly generated to replenish the population to maxS size, maintaining population diversity. The formula is as follows, where... and It is randomly selected from the existing MaxS antibodies, where Random(0,1) represents a random value in the range [0,1]. This indicates the number of new antibodies generated. A new antibody is obtained, at which point a new generation of antibody population (i.e., a supplementary population) is obtained.

[0065]

[0066]

[0067]

[0068]

[0069]

[0070] It is important to note that the RMS of the selected N antibodies (clonal variant populations) has already been calculated, while the supplementary data... Each antibody needs to have its RMS recalculated, and the two together form a new generation of N antibody populations. In addition, antibodies that have already had their Moho model and RMS calculated in step S200 will not undergo secondary calculation, thereby improving the running speed. Only the RMS of the supplemented antibodies will be calculated.

[0071] Step S900: Combine the cloned mutant population and the supplementary population as the iterative population, increment the iteration count by 1, and return to the step of judging the deviation value until the deviation value of the antibody in the iterative population is less than the optimal threshold for iteration or the iteration count reaches the maximum iteration count. The antibody corresponding to the minimum deviation value in the iterative population is taken as the optimal antibody. For example, in some specific implementations, steps S300 to S900 are executed iteratively. Each execution represents one iteration of the antibody population, with a maximum of maxG iterations. If maxG is exceeded, the antibody with the smallest deviation value in the iterative population is selected as the optimal antibody, and the entire search process ends.

[0072] Step S1000: Determine the Moho modeling result based on the Moho model corresponding to the optimal antibody; For example, in some specific implementations, reaching this step signifies the end of the entire search process. The solution obtained here falls into only two categories: the optimal vector satisfying the iterative optimal threshold (iterRMS), or the optimal vector obtained by searching the entire space. At this point, the entire search process concludes, and not only has the optimal solution been obtained, but the search has also been completed. They also obtained the corresponding terrain model. .

[0073] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0074] In view of the shortcomings of existing technologies, this invention provides a Moho surface modeling method based on clonal selection. By improving the clonal selection method to find optimal parameters, and then using the Parker-Oldenburg inversion method, a more accurate terrain is obtained. Figure 8 As shown, embodiments of the present invention can be implemented through the following process steps: Step 1: Parameter initialization. The parameters mainly include those based on the improved clone selection method and the Parker-Oldenburg inversion method.

[0075] The input parameters for the improved clone selection method are: The density difference `contrast` takes values ​​in the range [minC, maxC], where maxC represents the maximum value and minC represents the minimum value. The range is determined based on the average value of the target search area, generally empirically, and satisfies maxC - minC >= 0.4. `contrast` is a parameter used in target search.

[0076] The average depth ranges from [minD, maxD], where maxD represents the maximum value and minD represents the minimum value. The range is determined based on the average value of the target search area, generally based on experience, and is considered sufficient if maxD - minD >= 10. Depth is a parameter for target search.

[0077] The maximum number of antibodies in the population, maxS, is determined by the requirements and is set to 50 by default.

[0078] The maximum number of iterations for the population is maxG, which represents the maximum number of iterations for the antibody population. The value can be set according to the requirements, with a default value of 10.

[0079] The optimal iteration threshold is iterRMS. If an antibody less than or equal to iterRMS is found, it means that the optimal solution has been found, and the entire search process ends directly.

[0080] The main parameters of the Parker-Oldenburg inversion method are as follows: Density difference contrast, in g / cm3, is obtained by searching using an improved clonal selection method.

[0081] Average depth: in km, this variable is obtained by searching using the improved clonal selection method.

[0082] Seismic depth matrix SemisMat: Seismic data of the target area, a matrix composed of (longitude, latitude, depth) triplets, generally data observed by seismic surveys or other means, and is a constant in this invention.

[0083] Step 2: Antibody Population Initialization. The antibody population consists of a large number of antibodies. Each antibody has two parameters, representing the density difference (contrast) and the average depth (depth). The core of population initialization is to generate maxS antibodies and initialize the corresponding parameters for each antibody, using the following formula:

[0084]

[0085]

[0086]

[0087] in Indicates density difference contrast. This represents the average depth. Performing this process maxS times will yield a population of maxS antibodies; proceed to step 3.

[0088] Step 3: Construct a Moho surface model by traversing the current antibody population. For the current population, iterate through the information of each antibody one by one. Then, based on the Parker-Oldenburg inversion method, the corresponding terrain data is calculated. The terrain data is a matrix composed of (longitude, latitude, depth) triplets, which is also the three-dimensional model of the target area. Proceed to step 4.

[0089] Step 4: Iterate through the current antibody population and calculate the RMS. At this point, each antibody in the population has its corresponding Moho discontinuity data calculated. Then, combined with the seismic depth matrix SemisMat, the corresponding RMS is calculated. Each antibody will calculate its own RMS, ultimately resulting in an array of size maxS. Proceed to Step 5.

[0090] Step 5: Find the target antibody. Iterate through all antibodies and determine if there is an antibody whose RMS is less than the iterative optimal threshold iterRMS. If so, proceed to step 10, indicating that the optimal antibody has been found; otherwise, proceed to step 6.

[0091] Step 6: Select the Top N Excellent Antibodies. All antibodies are sorted in ascending order based on their root mean square error (RMS) (the smaller the RMS, the higher the fitness). Then, the top N excellent antibodies are selected for cloning. Here, N is the number of excellent antibodies, generally defined as 20%~50% of maxS, with a default of 30%. The core idea here is that the top N excellent antibodies undergo cloning, mutation, and other operations, forming a new temporary population with the remaining antibodies. Finally, competition eliminates the excess antibodies. Proceed to Step 7.

[0092]

[0093] Step 7: Perform the cloning operation. Iterate through the N superior antibodies selected in Step 6, cloning each antibody a fixed multiple of C times to generate a clone population, where C is the number of antibody clones, at least 2, determined according to requirements, with a default of 10. The core idea of ​​the cloning operation is to preserve more superior antibody genes; the more superior the antibody, the higher the probability of preservation. Proceed to Step 8.

[0094]

[0095]

[0096] Step 8: Perform the mutation operation. Iterate through the N×C cloned antibodies generated in Step 7. A mutation formula is applied to each antibody. The mutation simulates immune mutation behavior, introducing random perturbations to enhance the exploration capability. , Let $G$ represent the minimum and maximum values ​​of the mutation intensity, and $i$ represent the current iteration number, with a minimum of 1 and a maximum of $MaxG$. MR represents the mutation intensity in the i-th iteration, and it gradually increases as the iteration progresses. This indicates the new antibody obtained after mutation. Proceed to step 9.

[0097]

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[0102] Step 9: Perform the selection operation. For each antibody group after mutation in Step 8... Iterate through the information of each antibody one by one. Then, based on the Parker-Oldenburg inversion method, the corresponding terrain data is calculated. The terrain data is a matrix composed of (longitude, latitude, depth) triplets. Then, combined with the seismic depth matrix SemisMat, the corresponding RMS is calculated. Each antibody will have its own RMS calculated. For each antibody clone after mutation... Of the 10 antibodies selected, the one with the smallest RMS value was chosen to proceed to the next iteration. A total of 10 antibodies were selected. An antibody. For example, an antibody... After cloning, mutation, and other operations, C antibodies were obtained. Select the antibody with the smallest RMS from these C antibodies as the next-generation antibody, thus obtaining N antibodies for the next-generation antibody. Proceed to step 10.

[0103] Step 10: Perform the replenishment operation. After Step 9, the antibody set size is N, but the initial population size is maxS. Therefore, new antibodies are randomly generated to replenish the population to maxS size, maintaining population diversity. The formula is as follows, where... and It is randomly selected from the existing MaxS antibodies, where Random(0,1) represents a random value in the range [0,1]. This indicates the number of new antibodies generated. A new antibody is obtained, thus a new generation of antibody population is obtained, proceeding to step 3.

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] One important detail to mention is that the RMS values ​​of the N antibodies selected in step 9 have already been calculated, and step 10 is used to supplement them. Each antibody needs to have its RMS recalculated, and the two together form a new generation of N antibody populations. In steps 3 and 4, antibodies whose Moho model and RMS have already been calculated will not undergo secondary calculation, thus improving the running speed; only the RMS of the supplementary antibodies will be calculated.

[0110] Step 11: Iteration complete. Steps 3 to 10 are executed iteratively. Each execution represents one iteration of the antibody population, with a maximum of maxG iterations. If maxG is exceeded, proceed to step 12, and the entire search process ends; otherwise, return to step 3 to continue iterating.

[0111] Step 12: Find the optimal solution. Reaching this step signifies the end of the entire search process. The solution obtained here falls into only two categories: the optimal vector satisfying the iterative optimal threshold (iterRMS), or the optimal vector obtained by searching the entire space. At this point, the entire search process concludes, and not only has the optimal solution been found, but the search has also been completed. They also obtained the corresponding terrain model. .

[0112] In summary, this invention proposes a Moho surface modeling method based on clonal selection. By improving the clonal selection method to find optimal parameters, and then using the Parker-Oldenburg inversion method, a more accurate terrain is obtained. Compared with the prior art, this invention has at least the following beneficial effects: 1) An improved clone selection method is proposed to search for the optimal density difference contrast and depth, and an antibody adaptive iteration strategy is used to improve the accuracy of solving key parameters. 2) Proportional cloning based on fitness gives superior antibodies more opportunities to replicate, enhancing the algorithm's global exploration capabilities and improving search efficiency; 3) By combining the improved clone selection method and the Parker-Oldenburg inversion, an automated search process for optimal parameters and an optimal model solution process were designed, which improved the efficiency of automated data processing.

[0113] like Figure 9 As shown, this embodiment of the invention also provides a Moho surface modeling device 900 based on cloning selection, which can implement the above-described method. This device may include: The first module 901 is used to initialize the antibody population based on random values ​​within a preset range, and to use the antibody population as an iterative population; wherein, the antibody population includes a first number of antibodies, and each antibody corresponds to a set of parameter information, including density difference and average depth; The second module 902 is used to invert and model the corresponding Moho surface model by traversing the parameter information of each antibody based on the iterative population, and then combine it with the preset seismic depth matrix to evaluate the deviation and obtain the deviation value corresponding to each antibody. The third module 903 is used to determine the deviation value. When the deviation value of the antibody in the iterative population is less than the optimal threshold of the iteration, the corresponding antibody is taken as the optimal antibody. Otherwise, the second number of antibodies are selected as cloning candidate populations based on the order of the deviation values ​​from smallest to largest. The fourth module 904 is used to perform a fixed-fold cloning operation on each antibody in the cloning candidate population to obtain the cloning antibody group corresponding to each antibody in the cloning candidate population. The fifth module 905 is used to perform mutation operations on each antibody in the cloned antibody group based on the iteration progress to obtain the mutated antibody group; The sixth module 906 is used to invert and model the corresponding Moho surface model based on the parameter information of each antibody group, and then combine it with the preset seismic depth matrix to evaluate the deviation and obtain the deviation value corresponding to each antibody. Module 7, 907, is used to select the antibody with the smallest deviation value from each group of mutant antibodies and organize them into a clonal mutant population. Module 8, 908, is used to generate a supplementary population through supplementary operations. Based on the supplementary population, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model. Then, the deviation is evaluated by combining the preset seismic depth matrix to obtain the deviation value corresponding to each antibody. Module 909 is used to combine the cloned mutant population and the supplementary population as an iterative population, increment the iteration count by 1, and return to execute the step of judging the deviation value until the deviation value of the antibody in the iterative population is less than the optimal iteration threshold or the iteration count reaches the maximum iteration count. The antibody corresponding to the minimum deviation value in the iterative population is taken as the optimal antibody. Module 10, 910, is used to determine the Moho modeling result based on the Moho model corresponding to the optimal antibody.

[0114] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions 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.

[0115] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0116] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment 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.

[0117] like Figure 10 As shown, Figure 10 The hardware structure of an electronic device 1000 according to another embodiment is illustrated. The electronic device 1000 includes: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, 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. The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RaM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004); The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0118] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0120] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0121] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0122] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product 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.

[0123] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0124] The Moho discontinuity modeling method, apparatus, electronic device, storage medium, and program product based on cloning selection provided in this invention initializes an antibody population with random values ​​based on a preset interval, using the antibody population as an iterative population. The antibody population includes a first number of antibodies, each corresponding to a set of parameter information, including density difference and average depth. Based on the iterative population, the parameter information of each antibody is traversed to invert and model the corresponding Moho discontinuity model. Then, a deviation evaluation is performed using a preset seismic depth matrix to obtain a deviation value for each antibody. The deviation value is judged; if the deviation value of an antibody in the iterative population is less than the optimal threshold, the corresponding antibody is selected as the optimal antibody; otherwise, a second number of antibodies are selected as cloning candidate populations based on the ascending order of deviation values. A fixed-fold cloning operation is performed on each antibody in the cloning candidate population to obtain a cloning antibody group corresponding to each antibody in the cloning candidate population. The cloning antibody group is then processed based on the iteration progress. Each antibody undergoes a mutation operation to obtain a mutant antibody group. Based on the mutant antibody group, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model. Then, a deviation evaluation is performed using a preset seismic depth matrix to obtain the deviation value for each antibody. The antibody with the smallest deviation value from each mutant antibody group is selected and organized into a clonal mutant population. A supplementary population is generated through a supplementary operation. Based on the supplementary population, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model. Then, a deviation evaluation is performed using a preset seismic depth matrix to obtain the deviation value for each antibody. The clonal mutant population and the supplementary population are combined into an iterative population. The iteration count is incremented by 1, and the step of judging the deviation value is returned until the deviation value of an antibody in the iterative population is less than the optimal iteration threshold or the maximum number of iterations is reached. The antibody with the smallest deviation value in the iterative population is selected as the optimal antibody. The Moho surface modeling result is determined based on the Moho surface model corresponding to the optimal antibody. This invention, by simulating the clonal selection principle of the immune system, constructs an iterative optimization framework of "antibody-parameters," achieving automated and intelligent global search for key parameters (density difference, average depth). This effectively overcomes the drawbacks of traditional methods that rely on manual experience to set parameters, thus ensuring the objectivity and repeatability of the model. Specifically, through a series of coordinated iterative operations including initialization, evaluation, cloning, mutation, supplementation, and reselection, this invention efficiently converges from a preset parameter space to the optimal solution. When a precision threshold is met (deviation less than the iterative optimal threshold), the process stops and the optimal antibody is output, ensuring a high-precision Moho surface model within limited computational resources. In detail, the overall method of this invention defines a clear, unified, and closed computational execution framework, achieving full automation from parameter initialization to optimal model output. This significantly reduces manual intervention, improves modeling efficiency, and makes the computation process more stable and robust.

[0125] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0126] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present invention, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0127] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0129] The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present invention should be within the scope of the claims of the present invention.

Claims

1. A Moho surface modeling method based on clonal selection, characterized in that, The method includes the following steps: The antibody population is initialized based on random values ​​within a preset range, and the antibody population is used as an iterative population; wherein, the antibody population includes a first number of antibodies, each antibody corresponds to a set of parameter information, the parameter information including density difference and average depth; Based on the iterative population, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model, and then the deviation is evaluated by combining the preset seismic depth matrix to obtain the deviation value corresponding to each antibody. The deviation value is determined. If the deviation value of the antibody in the iterative population is less than the iterative optimal threshold, the corresponding antibody is selected as the optimal antibody. Otherwise, a second number of antibodies are selected as cloning candidate populations based on the deviation value in ascending order. Perform a fixed-fold cloning operation on each antibody in the cloning candidate population to obtain a cloning antibody group corresponding to each antibody in the cloning candidate population; Based on the iteration progress, each antibody in the cloned antibody group is mutated to obtain a mutated antibody group; Based on the mutant antibody group, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model, and then the deviation is evaluated by combining the preset seismic depth matrix to obtain the deviation value corresponding to each antibody. From each group of said variant antibodies, the antibody with the smallest deviation value is selected and organized into a clonal variant population; A supplementary population is generated through supplementary operations. Based on the supplementary population, the parameter information of each antibody is traversed to invert and model the corresponding Moho surface model. Then, the deviation is evaluated by combining the preset seismic depth matrix to obtain the deviation value corresponding to each antibody. The cloned mutant population and the supplementary population are combined to form the iterative population. The iteration number is incremented by 1, and the step of judging the deviation value is returned to be executed until the deviation value of the antibody in the iterative population is less than the optimal iteration threshold or the iteration number reaches the maximum iteration number. The antibody corresponding to the smallest deviation value in the iterative population is taken as the optimal antibody. The Moho modeling result is determined based on the Moho model corresponding to the optimal antibody.

2. The method according to claim 1, characterized in that, The initialization of the antibody population based on random values ​​within a preset range includes the following steps: The first preset interval is determined based on the preset minimum and maximum density difference values, and the second preset interval is determined based on the preset minimum and maximum average depth values. The density difference is obtained by randomly sampling within the first preset interval; The average depth is obtained by randomly sampling within the second preset interval; Using the density difference and the average depth as the antibody, the process returns to the step of randomly sampling in the first preset interval until the number of antibodies reaches the first quantity, thus initializing the antibody population.

3. The method according to claim 1, characterized in that, The process of inverting and modeling the parameter information of each antibody to obtain the corresponding Moho surface model, and then combining it with a preset seismic depth matrix to perform deviation evaluation, thereby obtaining the deviation value corresponding to each antibody, includes the following steps: By iterating through the parameter information corresponding to each antibody, inversion modeling is performed based on the Parker-Oldenburg inversion method to construct the Moho surface model corresponding to each antibody. Based on the preset earthquake depth matrix, the deviation assessment is performed on the Moho surface model to obtain the deviation value corresponding to each antibody.

4. The method according to claim 3, characterized in that, The seismic depth matrix includes sampling point data of the area to be modeled, and the Moho surface model includes inversion data of all points in the area to be modeled. The deviation evaluation of the Moho surface model based on the preset seismic depth matrix to obtain the deviation value corresponding to each antibody includes the following steps: Based on the location information corresponding to the sampling point data, inversion data corresponding to the location information is extracted from the Moho surface model as evaluation data; The root mean square operation is performed on the difference between the data at each sampling point and the corresponding evaluation data to obtain the deviation value of the antibody corresponding to the Moho surface model.

5. The method according to claim 1, characterized in that, The step of performing mutation operations on each antibody in the cloned antibody group based on the iteration progress to obtain a mutated antibody group includes the following steps: The mutation intensity is determined based on the maximum number of iterations and the number of iterations in the current iteration, combined with a preset mutation value; The mutation operation is performed on each antibody in the cloned antibody group using the mutation intensity to obtain the mutated antibody group.

6. The method according to claim 5, characterized in that, The preset mutation value includes a minimum mutation strength and a maximum mutation strength. Determining the mutation strength based on the maximum number of iterations and the number of iterations in the current iteration, combined with the preset mutation value, includes the following steps: The first parameter value is determined based on the difference between the maximum value of the variation intensity and the minimum value of the variation intensity; The value of the second parameter is determined based on the ratio of the number of iterations in the current iteration to the maximum number of iterations; The third parameter value is determined based on the product of the first parameter value and the second parameter value; The mutation intensity is obtained by adding the minimum value of the mutation intensity to the value of the third parameter.

7. The method according to claim 1, characterized in that, The process of generating a supplementary population through supplementation includes the following steps: Two different antibodies are randomly selected from the iterative population in the current iteration as the first antibody and the second antibody; The numerical range between the density difference of the first antibody and the density difference of the second antibody is taken as the third preset range, and the numerical range between the average depth of the first antibody and the average depth of the second antibody is taken as the fourth preset range. The supplementary density difference is obtained by randomly sampling within the third preset interval; The supplementary average depth is obtained by randomly sampling within the fourth preset interval; The supplementary density difference and the supplementary average depth are integrated as the antibody, and the step of randomly sampling in the third preset interval is returned to be executed until the number of the antibody obtained based on the supplementary density difference and the supplementary average depth reaches the third number, thereby obtaining the supplementary population; The third quantity is determined based on the difference between the first quantity and the second quantity.

8. A Moho surface modeling device based on cloning selection, characterized in that, The device includes: The first module is used to initialize an antibody population based on random values ​​within a preset range, and to use the antibody population as an iterative population; wherein, the antibody population includes a first number of antibodies, each antibody corresponds to a set of parameter information, the parameter information including density difference and average depth; The second module is used to invert and model the corresponding Moho surface model by traversing the parameter information of each antibody based on the iterative population, and then combine it with the preset seismic depth matrix to perform deviation evaluation and obtain the deviation value corresponding to each antibody. The third module is used to determine the deviation value. When the deviation value of the antibody in the iterative population is less than the iterative optimal threshold, the corresponding antibody is taken as the optimal antibody. Otherwise, a second number of antibodies are selected as cloning candidate populations based on the order of the deviation values ​​from smallest to largest. The fourth module is used to perform a fixed-fold cloning operation on each antibody in the cloning candidate population to obtain a cloning antibody group corresponding to each antibody in the cloning candidate population; The fifth module is used to perform mutation operations on each antibody in the cloned antibody group based on the iteration progress to obtain a mutated antibody group; The sixth module is used to invert and model the corresponding Moho surface model by traversing the parameter information of each antibody based on the mutant antibody group, and then perform deviation evaluation by combining the preset seismic depth matrix to obtain the deviation value corresponding to each antibody. The seventh module is used to select the antibody with the smallest deviation value from each of the mutant antibody groups and organize them into a clonal mutant population; The eighth module is used to generate a supplementary population through supplementary operations. Based on the supplementary population, the corresponding Moho surface model is obtained by inverting the parameter information of each antibody. Then, the deviation is evaluated by combining the preset seismic depth matrix to obtain the deviation value corresponding to each antibody. The ninth module is used to summarize the cloned mutant population and the supplementary population as the iterative population, increment the iteration number by 1, and return to execute the step of judging the deviation value until the deviation value of the antibody in the iterative population is less than the optimal iteration threshold or the iteration number reaches the maximum iteration number, and the antibody corresponding to the smallest deviation value in the iterative population is taken as the optimal antibody. The tenth module is used to determine the Moho modeling result based on the Moho model corresponding to the optimal antibody.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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