Seabed inversion method, device, equipment and medium based on genetic clonal selection

Abstract

The present invention discloses a seafloor inversion method, apparatus, device, and medium based on genetic clonal selection. The method comprises: initializing a first population and using the first population as a target population; performing inversion and evaluation based on inversion parameters of the target population to obtain the root mean square value corresponding to each individual; setting random parameters based on the minimum root mean square value and the number of population iterations; updating the population by improving a genetic algorithm based on the random parameters; when an individual in the updated population has a root mean square value that meets an optimal iteration threshold, using the corresponding individual as the target individual; otherwise, cyclically updating the population until the target individual is obtained or the number of population iterations equals a preset maximum number of population iterations; and finally determining the seafloor inversion result of the area to be inverted based on the seafloor topography data corresponding to the target individual. The present invention can accurately achieve seafloor inversion and can be widely applied in the field of data processing technology.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a seabed inversion method, device, equipment and medium based on genetic cloning selection. Background Art

[0002] With the continuous advancement of geophysical exploration technology, the demand for accurate inversion of crustal structure, particularly the Moho depth, is becoming increasingly urgent. The Parker-Oldenburg method, a classic in the field of interface inversion, has been widely used in the industry due to its efficient computational performance based on the Fast Fourier Transform (FFT). However, existing technical solutions generally treat the inversion parameters as fixed constants when applying this method, which is inadequate when faced with complex and changing underground geological conditions. Specifically, the relationship between the density difference and depth of the underground medium is not a simple linear relationship, but is instead influenced by a combination of geological factors. Therefore, fixing the parameters inevitably leads to a loss in the accuracy of the inversion results. Furthermore, existing solutions also have shortcomings in computational efficiency and automation, such as a large overall computational workload and a lack of a unified execution framework, which adversely affect the accuracy and practicality of the inversion results. Summary of the Invention

[0003] The present invention aims to at least partially address the limitations of the related art. To this end, the present invention provides a method, apparatus, device, and medium for seafloor inversion based on genetic clonal selection, which can accurately perform seafloor inversion.

[0004] In one aspect, an embodiment of the present invention provides a seafloor inversion method based on genetic clonal selection, comprising:

[0005] Randomly generate a first number of inversion parameters within a first interval, and initialize a first population based on the inversion parameters; use the first population as a target population; initialize the population with a number of iterations of 1; wherein the inversion parameters correspond to individuals in the target population; and the inversion parameters include density difference and seafloor depth;

[0006] Obtaining first seabed topography data of the area to be inverted based on the inversion parameters of each individual in the target population; obtaining a first root mean square value corresponding to each individual in the target population based on the first seabed topography data and the seismic depth matrix of the area to be inverted; and using the first root mean square value as the target root mean square value;

[0007] Obtain the minimum RMS value among the target RMS values ​​corresponding to all individuals in the target population, and set random parameters based on the minimum RMS value and the number of population iterations;

[0008] Obtaining individuals corresponding to the target root mean square value of each stage range from the target population and mixing them to obtain a second number of crossover individuals; the crossover individuals include two individuals;

[0009] Based on the preset crossover ratio, a weighted crossover operation is performed on the crossover individual to obtain the first sub-individual;

[0010] Obtain a set of individuals to be mutated from the target population based on probability, perform a mutation operation on each individual in the set of individuals based on a preset mutation random number, and obtain the corresponding second child individual;

[0011] A second population is obtained by sorting all first sub-individuals, all second sub-individuals, and the target population; second seabed topography data of the area to be inverted is obtained based on the inversion parameters corresponding to each individual in the second population; a second root mean square value corresponding to each individual in the second population is obtained based on the second seabed topography data and the seismic depth matrix;

[0012] When the second RMS value in the second population is less than or equal to the iterative optimal threshold, the corresponding individual is taken as the target individual; otherwise,

[0013] A third number of individuals are selected from the second population based on probability to obtain a third population, the third population is used as the target population, the second RMS value is used as the target RMS value, the number of population iterations is increased by 1, and the step of obtaining the minimum RMS value among the target RMS values ​​corresponding to all individuals in the target population is returned to execute until the target individual is obtained, or the number of population iterations equals a preset maximum number of population iterations;

[0014] When the number of population iterations is equal to the maximum number of population iterations, the target individual is output according to the target population obtained in the last cycle;

[0015] The second seafloor topography data corresponding to the target individual is used as the seafloor inversion result of the area to be inverted.

[0016] Optionally, setting random parameters based on the minimum root mean square value and the number of population iterations includes the following steps:

[0017] Based on the population iteration number and the minimum root mean square value, the random parameters are obtained in combination with the preset genetic parameter settings;

[0018] Among them, the genetic parameters include the iterative optimal threshold, the maximum number of population iterations, and the probability adjustment factor; the expression of the random parameter is:

[0019] ;

[0020] Where sigma represents a random parameter; minIterDelt represents the iterative optimal threshold; e represents a natural constant; iter represents the number of population iterations; maxGenCnt represents the maximum number of population iterations; F1 represents the probability adjustment factor; and curMinRMS represents the minimum root mean square value among the target root mean square values.

[0021] Optionally, obtaining individuals corresponding to the target root mean square value of each stage range from the target population and mixing them to obtain a second number of crossover individuals includes the following steps:

[0022] Traverse the target RMS values ​​of all individuals in the target population, sort all individuals in the target population in ascending order according to the target RMS values, and obtain the first sequence population;

[0023] All individuals in the first sequence population are equally divided based on the arrangement order to obtain multiple individual groups, and all pairing situations of any two individual groups are determined;

[0024] From each pairing situation, obtain the corresponding number of crossover individuals in the group;

[0025] The number of groups is determined based on the ratio of the second number to the total number of pairing situations, and the two individuals of the crossover individuals are respectively taken from the two individual groups of the corresponding pairing situations.

[0026] Optionally, the crossover individual includes a first individual and a second individual; performing a weighted crossover operation on the crossover individual based on a preset crossover ratio to obtain a first sub-individual includes the following steps:

[0027] Based on the random parameters, the crossover ratio is obtained in combination with the preset crossover operator ratio setting;

[0028] The expression of the cross ratio is:

[0029] ;

[0030] Where, β represents the crossover operator; crossoverAlpha represents the crossover operator ratio; crossoverAlpha is a constant; sigma represents random parameters; Indicates that 0 is the mean, sigma is a random value with standard deviation;

[0031] Based on the crossover ratio, the inversion parameters corresponding to the first individual and the second individual are weightedly added to obtain the inversion parameters of the first sub-individual;

[0032] Among them, the expression of the inversion parameter of the first sub-individual is:

[0033] A3=A1* β +A2*(1- β );

[0034] Where A3 represents the inversion parameters of the first sub-individual; A1 represents the inversion parameters of the first individual; and A2 represents the inversion parameters of the second individual.

[0035] Optionally, obtaining a set of individuals to be mutated from a target population based on probability includes the following steps:

[0036] Traverse each individual in the target population and set the mutation probability of each individual in turn based on the target RMS value combined with the random parameters;

[0037] Among them, the expression of mutation probability is:

[0038] ;

[0039] Where prob represents the probability of mutation; sigma represents random parameters; RMS represents the target RMS value of the corresponding individual;

[0040] The first individual of the target population is used as the individual to be mutated;

[0041] Generate a random number in the second interval as a mutation parameter;

[0042] When the mutation parameter is less than the mutation probability corresponding to the individual to be mutated, the individual to be mutated is sorted into the individual set; otherwise, the next individual of the individual to be mutated in the target population is used as the individual to be mutated, and the process returns to the step of generating a random number as the mutation parameter within the second interval until all individuals in the target population are traversed and the individual set is output.

[0043] Optionally, performing a mutation operation on each individual in the individual set based on a preset mutation random number to obtain a corresponding second child individual includes the following steps:

[0044] The first individual in the individual set is used as the mutant individual;

[0045] Generate mutant random numbers with 0 as mean and random parameter as standard deviation;

[0046] Perform mutation assignment on the inversion parameter corresponding to the mutated individual based on the product of the mutated random number and the interval range difference of the first interval, and obtain the inversion parameter of the second sub-individual;

[0047] Among them, the expression of the inversion parameter of the second sub-individual is:

[0048] A4=A1+(max-mix)*normal(0, sigma );

[0049] Where A4 represents the inversion parameter of the second child individual; A1 represents the inversion parameter of the variant individual; max represents the maximum value of the first interval; min represents the minimum value of the first interval; normal(0, sigma ) represents a mutated random number.

[0050] Optionally, selecting a third number of individuals from the second population based on probability to obtain a third population comprises the following steps:

[0051] Get the population size of the second population;

[0052] Based on the random parameters and the population size, a third size is obtained in combination with a preset population size adjustment factor setting;

[0053] The expression of the third quantity is:

[0054] ;

[0055] Where newSizePop represents the third quantity; curPopSize represents the population size; F2 represents the population size adjustment factor; sigma represents the random parameter; Indicates a random value with 0 as mean and sigma as standard deviation; max(X, Y) means taking the maximum value of X and Y;

[0056] Traversing the second root mean square values ​​of all individuals in the second population, sorting all individuals in the second population in ascending order according to the second root mean square values, and obtaining a second sequence population;

[0057] Selecting a fourth number of individuals from the pre-sequence of the second sequence population as the first candidate individual set; the fourth number is half of the third number;

[0058] Randomly selecting a fourth number of individuals from the second sequence population except the first candidate individual set as the second candidate individual set;

[0059] The third population is obtained based on the first set of candidate individuals and the second set of candidate individuals.

[0060] On the other hand, an embodiment of the present invention provides a seafloor inversion device based on genetic cloning selection, comprising:

[0061] A first module is configured to randomly generate a first number of inversion parameters within a first interval, initialize a first population based on the inversion parameters, use the first population as a target population, and initialize the population with a number of iterations of 1; wherein the inversion parameters correspond to individuals in the target population; and the inversion parameters include density difference and seafloor depth;

[0062] The second module is configured to obtain first seabed topography data of the area to be inverted based on the inversion parameters of each individual in the target population; obtain a first root mean square value corresponding to each individual in the target population based on the first seabed topography data and the seismic depth matrix of the area to be inverted; and use the first root mean square value as the target root mean square value;

[0063] The third module is used to obtain the minimum root mean square value among the target root mean square values ​​corresponding to all individuals in the target population, and set random parameters based on the minimum root mean square value and the number of population iterations;

[0064] The fourth module is used to obtain individuals corresponding to the target root mean square value of each stage range from the target population and mix them to obtain a second number of crossover individuals; the crossover individuals include two individuals;

[0065] A fifth module is configured to perform a weighted crossover operation on the crossover individuals based on a preset crossover ratio to obtain a first sub-individual;

[0066] The sixth module is used to obtain a set of individuals to be mutated from the target population based on probability, and perform a mutation operation on each individual in the set of individuals based on a preset mutation random number to obtain a corresponding second child individual;

[0067] The seventh module is used to obtain a second population based on all the first sub-individuals, all the second sub-individuals and the target population; invert the second seabed topography data of the area to be inverted based on the inversion parameters corresponding to each individual in the second population; and obtain a second root mean square value corresponding to each individual in the second population based on the second seabed topography data and the seismic depth matrix;

[0068] The eighth module is used to select the corresponding individual as the target individual when the second root mean square value in the second population is less than or equal to the iterative optimal threshold; otherwise,

[0069] A ninth module is configured to select a third number of individuals from the second population based on probability to obtain a third population, use the third population as the target population, use the second RMS value as the target RMS value, increment the number of population iterations by 1, and return to execute the third module until the target individual is obtained or the number of population iterations equals a preset maximum number of population iterations;

[0070] The tenth module is used to output the target individual according to the target population obtained in the last cycle when the number of population iterations is equal to the maximum number of population iterations;

[0071] The eleventh module is used to use the second seabed topography data corresponding to the target individual as the seabed inversion result of the area to be inverted.

[0072] On the other hand, an embodiment of the present invention provides an electronic device, including: a processor and a memory; the memory is used to store programs; the processor executes the program to implement the above-mentioned seabed inversion method based on genetic clonal selection.

[0073] On the other hand, an embodiment of the present invention provides a computer storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to implement the above-mentioned seabed inversion method based on genetic clonal selection.

[0074] The embodiment of the present invention randomly generates a first number of inversion parameters within a first interval, and obtains a first population based on initialization of the inversion parameters; uses the first population as a target population; initializes the population iteration number to 1; wherein the inversion parameters correspond to individuals in the target population; the inversion parameters include density difference and seabed depth; obtains first seabed topography data of the area to be inverted based on the inversion parameters of each individual in the target population; obtains a first root mean square value corresponding to each individual in the target population based on the first seabed topography data and the seismic depth matrix of the area to be inverted; uses the first root mean square value as the target root mean square value; obtains the minimum root mean square value among the target root mean square values ​​corresponding to all individuals in the target population, and sets random parameters based on the minimum root mean square value and the number of population iterations; obtains individuals corresponding to the target root mean square values ​​of each stage range from the target population and mixes them to obtain a second number of crossover individuals; the crossover individuals include two individuals; performs a weighted crossover operation on the crossover individuals based on a preset crossover ratio to obtain a first child individual; obtains a set of individuals to be mutated from the target population based on probability, and performs a mutation operation on each individual in the set of individuals based on a preset mutation random number. A mutation operation is performed to obtain a corresponding second sub-individual; a second population is obtained based on all first sub-individuals, all second sub-individuals, and a target population; second seafloor topography data of the area to be inverted is inverted based on the inversion parameters corresponding to each individual in the second population; a second root mean square value corresponding to each individual in the second population is obtained based on the second seafloor topography data and the seismic depth matrix; when a second root mean square value is less than or equal to the iterative optimal threshold in the second population, the corresponding individual is used as the target individual; otherwise, a third number of individuals are selected from the second population based on probability to obtain a third population, the third population is used as the target population, the second root mean square value is used as the target root mean square value, the number of population iterations is increased by 1, and the step of obtaining the minimum root mean square value among the target root mean square values ​​corresponding to all individuals in the target population is returned to execute until the target individual is obtained, or the number of population iterations is equal to the preset maximum number of population iterations; when the number of population iterations is equal to the maximum number of population iterations, the target individual is output according to the target population obtained in the last cycle; and the second seafloor topography data corresponding to the target individual is used as the seafloor inversion result of the area to be inverted. The embodiments of the present invention, based on clonal selection combined with related genetic operations, can ensure a more efficient genetic algorithm search process and enhance the accuracy and efficiency of data solution. Furthermore, the embodiments of the present invention first continuously optimize the population and its corresponding parameters through cyclic iterations based on genetic operations, effectively improving the accuracy of seafloor inversion. During the cyclic iteration process, random parameters are continuously set based on the minimum root mean square value of each round, and then feedback is applied to the processing of related genetic operations to further optimize the iteration accuracy. The embodiments of the present invention can accurately achieve seafloor inversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation to the technical solution of the present invention.

[0076] Figure 1 A schematic diagram of an implementation environment for performing seafloor inversion based on genetic clonal selection provided by an embodiment of the present invention;

[0077] Figure 2 A schematic flow chart of a seafloor inversion method based on genetic clonal selection provided by an embodiment of the present invention;

[0078] Figure 3 A schematic diagram of the expanded process of obtaining cross-individuals provided in an embodiment of the present invention;

[0079] Figure 4 A schematic diagram of the expansion process of the crossover operation provided in an embodiment of the present invention;

[0080] Figure 5 A schematic diagram of the expanded process of obtaining a set of individuals to be mutated provided in an embodiment of the present invention;

[0081] Figure 6 A schematic diagram of the expansion process of the mutation operation provided in an embodiment of the present invention;

[0082] Figure 7 A schematic diagram of the process of organizing the third population according to an embodiment of the present invention;

[0083] Figure 8 A schematic diagram of the overall process of the seafloor inversion method based on genetic clonal selection provided by an embodiment of the present invention;

[0084] Figure 9 A schematic structural diagram of a seafloor inversion device based on genetic cloning selection provided by an embodiment of the present invention;

[0085] Figure 10 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0086] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0087] It should be noted that although the system diagrams illustrate functional module divisions and the flowcharts illustrate a logical sequence, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the system or the sequence in the flowcharts. The terms "first / S100," "second / S200," and the like in the specification, claims, and drawings are used to distinguish similar objects and are not necessarily intended to describe a specific sequence or precedence.

[0088] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0089] It is understandable that the seabed inversion method based on genetic clone selection provided in the embodiment of the present invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal is a smart phone, tablet computer, laptop computer, desktop computer, etc., but is not limited to this.

[0090] To facilitate understanding of the technical solutions of the present invention, the following are the technical terms that may appear in the embodiments of the present invention:

[0091] Seafloor topography refers to the shape of the solid Earth's surface beneath water. Due to the obscuration of seawater, the undulations of seafloor topography are difficult to observe directly. Early plumb bob depth sounding methods were time-consuming and inaccurate. Since the 1920s, ships have used echo sounders during navigation, enabling rapid measurements of seafloor depth. Combined with precise positioning, these echo sounders reveal the true nature of seafloor topography. In this context, seafloor topography refers to the depth of water at various latitudes and longitudes of the ocean.

[0092] Heuristic search, also known as informed search, uses heuristic information about a problem to guide the search, reducing the search scope and complexity. This process of using heuristic information is called heuristic search. For example, the genetic algorithm is a classic heuristic search algorithm.

[0093] A genetic algorithm (GA) is a computational model that simulates biological evolution, leveraging mechanisms such as natural selection and genetic recombination to search and optimize complex problems. It is a heuristic search algorithm whose fundamental concept is to create a population of artificial individuals by simulating the mechanisms of genetics and evolution in nature to optimize the solution. In a GA, each individual represents a potential solution and has its own fitness evaluation criteria. By performing operations such as selection, crossover, and mutation on these individuals, the fitness of the current population is gradually improved, and the potential solution space is explored in the hope of finding the optimal solution. GAs are characterized by their wide application range, strong global search capabilities, and ease of parallel computing. They are suitable for solving a wide range of optimization problems, including combinatorial optimization, continuous optimization, and multi-objective optimization. Compared to other algorithms, GAs are highly efficient in finding the global optimal solution and are widely used in various fields, including but not limited to engineering optimization design, artificial intelligence tasks, and financial analysis and forecasting.

[0094] The Parker-Oldenburg inversion method is a frequency domain density interface iterative inversion method proposed by Oldenburg based on the Parker formula. Due to the advantage of fast calculation, this method has been rapidly developed in its application. However, there is a downward extension factor in the formula, which causes the formula to have oscillation of high-frequency signals during the inversion iteration process, and as the inversion depth increases, this phenomenon becomes more obvious, seriously affecting the convergence of the inversion results. To solve this problem, a low-pass filter is usually applied in the inversion process to eliminate the high-frequency oscillation phenomenon. The Parker-Oldenburg inversion method is one of the important methods for calculating the seabed topography and is also the core starting point of this invention. At present, the input parameters of this method (filter-related parameters, density difference, depth), etc. are designed according to constants based on experience, but in fact, there is still room for optimization of these parameters.

[0095] RMS: The abbreviation of Root Mean Square. The result of taking the square root of the sum of the squares of N items divided by N is the root mean square result.

[0096] like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the invention. Figure 1, the implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected to the network in a wireless or wired manner to complete data transmission and exchange.

[0097] Server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0098] In addition, server 101 can also be a node server in a blockchain network. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0099] The terminal 102 may be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited thereto. The terminal 102 and the server 101 may be connected directly or indirectly via wired or wireless communication, which is not limited in this embodiment of the present invention.

[0100] Based on the example Figure 1 In the implementation environment shown, an embodiment of the present invention provides a seabed inversion method based on genetic clone selection. The following is explained using the application of the seabed inversion method based on genetic clone selection in the server 101 as an example. It can be understood that the seabed inversion method based on genetic clone selection can also be applied to the terminal 102.

[0101] Reference Figure 2 , Figure 2 The flowchart of the seabed inversion method based on genetic clonal selection applied to a server provided in an embodiment of the present invention is provided. The execution subject of the seabed inversion method based on genetic clonal selection can be any of the aforementioned computer devices (including servers or terminals). Figure 2 , the method comprises the following steps:

[0102] S100, randomly generating a first number of inversion parameters within a first interval, and initializing a first population based on the inversion parameters; using the first population as a target population; and initializing the population to have a number of iterations of 1;

[0103] First, it should be noted that the present invention may also include a data preparation step to initialize various variables, parameters, and thresholds that may be applied. Specifically, the parameters that may be involved in the present invention are explained below:

[0104] The parameters mainly include two parts: the parameters of the improved genetic algorithm and the parameters of the Parker-Oldenburg inversion method, etc.

[0105] There are a total of 2 variables searched by the improved genetic algorithm, namely contrast (the physical meaning is: density difference, unit g / cm

[0112] ), and depth (the physical meaning is depth, unit km). Then, the parameters of the improved genetic algorithm are respectively:

[0106] The maximum density difference maxContrast: the maximum value of contrast, which is determined by the average value of the target search area, generally obtained according to experience, and satisfies minContrast < maxContrast. In most cases, it is sufficient to satisfy maxContrast - minContrast >= 0.4, and it can be set according to the actual situation.

[0107] The minimum density difference minContrast: the minimum value of contrast, which is determined by the average value of the target search area, generally obtained according to experience, and satisfies minContrast < maxContrast. In most cases, it is sufficient to satisfy maxContrast - minContrast >= 0.4, and it can be set according to the actual situation.

[0108] The maximum depth maxDepth: the maximum value of depth, which is determined by the average value of the target search area, generally obtained according to experience, and satisfies maxDepth < minDepth. In most cases, it is sufficient to satisfy maxDepth - minDepth >= 10, and it can be set according to the actual situation.

[0109] The minimum depth minDepth: the minimum value of depth, which is determined by the average value of the target search area, generally obtained according to experience, and satisfies maxDepth < minDepth. In most cases, it is sufficient to satisfy maxDepth - minDepth >= 10, and it can be set according to the actual situation.

[0110] The number of individuals in the population sizePop, the individual scale of the genetic algorithm, is taken according to requirements, and the default is 100.

[0111] The maximum number of iterations of the population maxGenCnt, the number of generations of the population in the genetic algorithm, that is, the number of iterations of the genetic algorithm, is taken according to requirements, and the default is 200.

[0112] Probability adjustment factor F1: Dynamically adjust sigma, the value range is (0,1], and the default value is 0.5.

[0113] The maximum number of crossover individuals maxCorssNum: controls the maximum number of crossover individuals. The value is not less than 3 and the default is 30, which means that each population has 30 crossover behaviors.

[0114] CrossoverAlpha is a parameter used by the crossover operator. Its value range is [0,1]. It is determined based on the requirements. The default value is 0.5.

[0115] Population adjustment factor F2: The adjustment factor for dynamically controlling the population size, with a value range of (0,1] and a default value of 0.5.

[0116] Iterate the optimal threshold minIterDelt. If an individual less than or equal to minIterDelt is found, it means that the optimal solution has been found and the entire search process ends directly.

[0117] The main parameters of the Parker-Oldenburg inversion method are as follows:

[0118] Density difference contrast, unit g / cm 3 , which is one of the variables searched with the help of the improved genetic algorithm, that is, the variable is obtained by searching with the improved genetic algorithm, and is one of the two search directions mentioned in the present invention.

[0119] Depth: unit is km. This is one of the variables searched by means of the improved genetic algorithm, that is, this variable is obtained by searching the improved genetic algorithm and is one of the two search directions mentioned in the present invention.

[0120] GravityMat: It is a matrix consisting of (longitude, latitude, gravity outlier) triplets, generally obtained from seismic measurements, satellite measurements, or other observations, and is a constant in this invention.

[0121] Seismic depth matrix SemisMat: It is a matrix composed of (longitude, latitude, depth) triplets, generally obtained from seismic measurements, satellite measurements or other observations, and is a constant in the present invention.

[0122] The iteration threshold criterio, i.e., the iteration rms threshold of the Parker-Oldenburg inversion method, is generally a constant, with a default value of 0.1, and is a constant in the present invention.

[0123] Frequency threshold WH: low-pass filter threshold, the initial value is obtained based on experience, the default value is 0.1, and it is a constant in the present invention.

[0124] Frequency threshold SH: high-pass filter threshold, the initial value is obtained based on experience, the default value is 0.12, and it is a constant in the present invention.

[0125] Window function threshold truncation: It is a signal sampling weighting function in signal processing. The initial value is obtained based on experience and is 0.1 by default. It is a constant in the present invention.

[0126] The inversion parameters correspond to individuals in the target population; the inversion parameters include density difference and seabed depth; the first interval includes a density difference interval (determined based on a preset maximum density difference value and a preset minimum density difference value) and a seabed depth interval (determined based on a preset maximum depth value and a preset minimum depth value);

[0127] For example, in some specific implementations, initializing the population may be achieved as follows:

[0128] The population consists of a large number of individuals, each of which has two genes, representing the density difference contrast and the average seafloor depth depth. The core of population initialization is to generate sizePop individuals and initialize the corresponding genes for each individual. This paper uses random generation of contrast and depth within the search interval to complete individual initialization. The corresponding generation method is as follows:

[0129] contrast=minContrast+(maxContrast -minContrast)*random();

[0130] depth=minDepth+(maxDepth-minDepth)*random();

[0131] random() represents the generation of random values ​​in [0,1]; normal(0,1) represents a normal distribution with a mean of 0 and a standard deviation of 1. The resulting random numbers satisfy the normal distribution, thus ensuring the fairness of gene generation.

[0132] S200, obtaining first seafloor topography data of the area to be inverted based on the inversion parameters of each individual in the target population; obtaining a first root mean square value corresponding to each individual in the target population based on the first seafloor topography data and the seismic depth matrix of the area to be inverted; and using the first root mean square value as the target root mean square value;

[0133] For example, in some specific implementations, after initialization of the individuals, the genetic contrast and depth of each individual are traversed one by one, and then the corresponding seafloor topography data is calculated based on the Parker-Oldenburg inversion method. At this point, each individual in the population has calculated the corresponding seafloor topography data, and then combined with the seismic depth matrix SemisMat, the corresponding RMS is calculated. Each individual will calculate its own RMS.

[0134] S300, obtaining the minimum root mean square value among the target root mean square values ​​corresponding to all individuals in the target population, and setting a random parameter based on the minimum root mean square value and the number of population iterations;

[0135] It should be noted that, in some embodiments, setting random parameters based on the minimum root mean square value and the number of population iterations may include the following steps: obtaining random parameters based on the number of population iterations and the minimum root mean square value in combination with preset genetic parameter settings; wherein the genetic parameters include an optimal iteration threshold, a maximum number of population iterations, and a probability adjustment factor; the expression of the random parameters is:

[0136] ;

[0137] Where sigma represents a random parameter; minIterDelt represents the iterative optimal threshold; e represents a natural constant; iter represents the number of population iterations; maxGenCnt represents the maximum number of population iterations; F1 represents the probability adjustment factor; and curMinRMS represents the minimum root mean square value among the target root mean square values.

[0138] For example, in some specific implementations, the calculation formula for Sigma (i.e., sigma / random parameter) is as follows:

[0139] ;

[0140] Where iter represents the iterth population iteration, with an initial value of 1 and a maximum value of maxGenCnt. curMinRMS represents the minimum RMS in the current population. In step 2, after completing population initialization, the RMS of each individual is calculated. The population is then traversed to find the individual with the minimum RMS, which is the corresponding RMS curMinRMS.

[0141] S400, obtaining individuals corresponding to the target root mean square value of each stage range from the target population and mixing them to obtain a second number of crossover individuals;

[0142] Among them, the crossover individual includes two individuals;

[0143] It should be noted that, in some embodiments, Figure 3As shown, step S400 may include the following steps: S401, traversing the target root mean square values ​​of all individuals in the target population, sorting all individuals of the target population from small to large according to the target root mean square values, and obtaining a first sequence population; S402, dividing all individuals of the first sequence population equally based on the arrangement order to obtain multiple individual groups, and determining all pairing situations of any two individual groups; S403, obtaining cross-individuals corresponding to the number of groups from each pairing situation; wherein the number of groups is determined based on the ratio of the second number to the total number of pairing situations, and the two individuals of the cross-individual are respectively taken from the two individual groups of the corresponding pairing situation.

[0144] For example, in some specific implementations, selecting a set of individuals to be crossed by traversing the population can be implemented as follows:

[0145] Sort all individuals by RMS from small to large (sort by individual performance from high to low). For example, if the individuals are divided into three groups, the minRMS, middleRMS, and maxRMS groups are divided equally. The minRMS group is the best performing group, while the maxRMS group is the worst performing group. The crossover is divided into the following situations:

[0146] 1) minRMS+middleRMS: select the best and worst individuals for crossover.

[0147] 2) minRMS+maxRMS: Cross-adjust the best and average individuals.

[0148] 3) middleRMS+maxRMS: adjust the worst and average individuals for crossover.

[0149] 4) minRMS+minRMS: Select the best internal crossover.

[0150] Faced with the above four groups of situations, the number of crossover individuals selected in each group is Here's a quick explanation. Taking the first group as an example, assuming maxCrossNum is 40, the first group needs to select 10 crossover individuals. One individual is randomly selected from minRMS, and then one is randomly selected from middleRMS to form a crossover individual. This is repeated 10 times. Together, these four groups produce maxCrossNum crossover individuals.

[0151] S500: performing a weighted crossover operation on the crossover individual based on a preset crossover ratio to obtain a first sub-individual;

[0152] It should be noted that the cross individual includes the first individual and the second individual; in some embodiments, such as Figure 4As shown, step S500 may include the following steps: S501, based on the random parameters, combined with the preset crossover operator ratio setting, a crossover ratio is obtained; wherein the expression of the crossover ratio is:

[0153] ;

[0154] Where, β represents the crossover operator; crossoverAlpha represents the crossover operator ratio; crossoverAlpha is a constant; sigma represents random parameters; Indicates that 0 is the mean, sigma is a random value with standard deviation;

[0155] S502. Based on the crossover ratio, perform weighted addition on the inversion parameters corresponding to the first individual and the second individual to obtain the inversion parameters of the first sub-individual; wherein the inversion parameters of the first sub-individual are expressed as:

[0156] A3=A1* β +A2*(1- β );

[0157] Where A3 represents the inversion parameters of the first sub-individual; A1 represents the inversion parameters of the first individual; and A2 represents the inversion parameters of the second individual.

[0158] For example, in some specific implementations, a crossover operator may be applied to individuals based on probability to select individuals for the next generation, which may be implemented as follows:

[0159] At this point, maxCrossNum crossover individuals have been selected, and each crossover individual is traversed to complete the crossover operation, as follows.

[0160] First, dynamically calculate the cross ratio β , the formula is as follows, where Indicates a random value with 0 as mean and sigma as standard deviation; crossoverAlpha indicates the crossover operator ratio.

[0161] ;

[0162] Then, the crossover operation is performed for density difference and depth. Assuming that individuals A1 and A2 are crossed, the formula is as follows, where 、 represent the density difference and depth of individual A1 respectively, 、 Represent the density difference and depth of individual A2 respectively. 、 They represent the density difference and degree of individual A3 obtained by crossover, respectively. The superscripts After and Before are used to indicate the parameter states before and after crossover.

[0163] ;

[0164] ;

[0165] S600: Obtain a set of individuals to be mutated from a target population based on probability, perform a mutation operation on each individual in the set of individuals based on a preset mutation random number, and obtain a corresponding second child individual;

[0166] It should be noted that, in some embodiments, Figure 5 As shown, obtaining a set of individuals to be mutated from a target population based on probability may include the following steps: S601, traversing each individual in the target population, and setting the mutation probability of each individual in turn based on the target root mean square value combined with the random parameter; wherein the expression of the mutation probability is:

[0167] ;

[0168] Where prob represents the probability of mutation; sigma represents random parameters; RMS represents the target RMS value of the corresponding individual;

[0169] S602, taking the first individual of the target population as the individual to be mutated;

[0170] S603, generating a random number in the second interval as a mutation parameter;

[0171] S604. When the mutation parameter is less than the mutation probability corresponding to the individual to be mutated, the individual to be mutated is sorted into the individual set; otherwise, the next individual after the individual to be mutated in the target population is used as the individual to be mutated, and the process returns to the step of generating a random number as the mutation parameter within the second interval until all individuals in the target population are traversed and the individual set is output.

[0172] For example, in some specific implementations, the set of individuals to be mutated can be selected by traversing the population, which can be specifically implemented as follows:

[0173] Traverse each individual in the population (excluding newly generated offspring individuals) and determine whether they need to be mutated one by one, as follows:

[0174] For each individual, the mutation probability prob is calculated using the following formula: RMS is the root mean square of the current individual. The larger the RMS, the worse the individual performance and the greater the mutation probability, and vice versa.

[0175] ;

[0176] Then calculate the random number R1 in [0,1]. If R1 is less than prob, the individual participates in the mutation, otherwise it does not participate in the mutation.

[0177] In general, individuals with larger RMS have a greater chance of mutation and are more likely to participate in mutation. Individuals with smaller RMS have a smaller chance of mutation and are less likely to participate in mutation. This is done to allow individuals with poor performance to participate in the next generation of population reproduction through mutation opportunities.

[0178] By traversing the individuals in the population one by one, we finally get the set of individuals to be mutated.

[0179] Among them, in some embodiments, such as Figure 6 As shown, performing a mutation operation on each individual in the individual set based on a preset mutation random number to obtain a corresponding second sub-individual may include the following steps: S605, taking the first individual in the individual set as the mutation individual; S606, generating a mutation random number with 0 as the mean and a random parameter as the standard deviation; S607, performing mutation assignment on the inversion parameter corresponding to the mutation individual based on the product of the difference between the mutation random number and the interval range of the first interval, to obtain the inversion parameter of the second sub-individual; wherein the expression of the inversion parameter of the second sub-individual is:

[0180] A4=A1+(max-mix)*normal(0, sigma );

[0181] Where A4 represents the inversion parameter of the second child individual; A1 represents the inversion parameter of the variant individual; max represents the maximum value of the first interval; min represents the minimum value of the first interval; normal(0, sigma ) represents a mutated random number.

[0182] For example, in some specific implementations, a mutation operator may be applied to individuals based on probability to select individuals for the next generation, which may be implemented as follows:

[0183] At this point, several individuals to be mutated have been selected, and the mutation operation has been completed by traversing the individuals. Assuming that individual A1 is mutated, the formula is as follows 、 Represent the density difference and depth of individual A1 respectively. 、 Respectively represent the density difference and degree of individual A4 obtained by mutation. It represents a random value with 0 as mean and sigma as standard deviation.

[0184] ;

[0185] ;

[0186] S700: obtaining a second population based on all first sub-individuals, all second sub-individuals, and the target population; inverting second seafloor topography data of the area to be inverted based on the inversion parameters corresponding to each individual in the second population; and obtaining a second root mean square value corresponding to each individual in the second population based on the second seafloor topography data and the seismic depth matrix.

[0187] For example, in some specific implementations, first, the seabed topography is calculated based on the current population: at this time, the individuals that make up the population are composed of parent individuals and child individuals, the parent individuals are individuals that participate in crossover mutation, and the child individuals are new individuals obtained through crossover and mutation. The gene contrast and depth of all child individuals are traversed, and then the corresponding seabed topography data are calculated based on the Parker-Oldenburg inversion method. Then, the fitness function value of each individual is evaluated: at this time, the seabed topography data of all child individuals are traversed, and then combined with the seismic depth matrix SemisMat, the corresponding RMS is calculated, and each individual will calculate its own RMS. At this point, the RMS of all individuals (parent individuals and child individuals) has been calculated.

[0188] S800: When there is a second RMS value in the second population that is less than or equal to the iterative optimal threshold, the corresponding individual is taken as the target individual and the process jumps to step S1100; otherwise, the process proceeds to the subsequent steps.

[0189] For example, in some specific implementations, all individuals in the population are traversed to determine whether there is an RMS less than the iterative optimal threshold minIterDelt. If so, go to step S1100, indicating that the optimal individual (optimal solution) has been found. Otherwise, execute subsequent steps.

[0190] S900: Probably select a third number of individuals from the second population to obtain a third population, use the third population as the target population, use the second RMS value as the target RMS value, increment the number of population iterations by 1, and return to the step of obtaining the minimum RMS value among the target RMS values ​​corresponding to all individuals in the target population until the target individual is obtained, or the number of population iterations equals the preset maximum number of population iterations.

[0191] It should be noted that, in some embodiments, Figure 7 As shown, selecting a third number of individuals from the second population based on probability to obtain a third population may include the following steps: S901, obtaining the population size of the second population; S902, obtaining a third number based on the random parameter and the population size in combination with a preset population size adjustment factor; wherein the expression of the third number is:

[0192] newSizePop=max(curPopSize*F2,curPopSize+F2*curPopSize*normal(0,sigma));

[0193] Where newSizePop represents the third quantity; curPopSize represents the population size; F2 represents the population size adjustment factor; sigma represents the random parameter; Indicates a random value with 0 as mean and sigma as standard deviation; max(X, Y) means taking the maximum value of X and Y;

[0194] S903. Traverse the second root mean square values ​​of all individuals in the second population, sort all individuals in the second population from small to large according to the second root mean square values, and obtain a second sequence population; S904. Select a fourth number of individuals from the pre-order of the second sequence population as the first alternative individual set; the fourth number is half of the third number; S905. Randomly select a fourth number of individuals from the other individuals in the second sequence population except the first alternative individual set as the second alternative individual set; S906. Arrange the first alternative individual set and the second alternative individual set to obtain a third population.

[0195] For example, in some specific implementations, a selection operator may be applied to individuals based on probability, which may be implemented as follows:

[0196] The new population size is calculated based on probability. The calculation formula is as follows, where curPopSize represents the current population size (the number of parent and child individuals), and newSizePop represents the number of individuals after selection. Indicates a random value with 0 as mean and sigma as standard deviation. curPopSize has a minimum value of .

[0197] newSizePop=max(curPopSize*F2,curPopSize+F2*curPopSize*normal(0,sigma))

[0198] After completing the calculation of newSizePop, you can start to eliminate redundant individuals and sort all individuals in ascending order according to RMS. Individuals directly enter the next generation population. Individuals, randomly selected based on probability Finally, the selection of newSizePop individuals is completed to form the next generation group.

[0199] To summarize, the core idea of ​​the selection operator is that the top 50% of individuals are directly selected into the next generation, while the remaining individuals are selected based on probability. As the population iterates and reproduces, the best individuals gradually gather, and the probabilistic selection also retains individuals with high potential.

[0200] It should be noted that after the operations of steps S300 to S900, a population reproduction cycle has been completed, resulting in a new population. However, no individuals that meet the expectations have been found, and the next round of population reproduction is required. Steps S300 to S900 are executed iteratively, and each execution represents one iteration of population reproduction, until the maximum number of population iterations, maxGenCnt, is reached.

[0201] S1000, when the number of population iterations is equal to the maximum number of population iterations, output the target individual according to the target population obtained in the last cycle;

[0202] In some embodiments, outputting a target individual based on the target population obtained in the last cycle may include the following steps: obtaining a minimum root mean square value among target root mean square values ​​corresponding to all individuals in the target population obtained in the last cycle, and outputting the individual corresponding to the minimum root mean square value as the target individual;

[0203] S1100 , using the second seafloor topography data corresponding to the target individual as the seafloor inversion result of the area to be inverted.

[0204] For example, in some specific implementations, finding the optimal solution (i.e., the target individual) can be achieved in only two ways: by finding the optimal vector that satisfies the iterative optimality threshold minIterDelt, or by finding the optimal vector obtained by searching the entire space (i.e., the individual with the smallest RMS value in the final population). At this point, the entire search process ends, and not only the optimal solution is found, but also the corresponding seafloor topography data.

[0205] In order to explain the principle of the technical solution of the present invention in detail, the overall process of the present invention is described below in combination with some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and cannot be regarded as a limitation of the present invention.

[0206] First, it should be noted that in current technical strategies, the parameters of the Parker-Oldenburg inversion are treated as constant, which limits the accuracy of the solution. In addition, the existing technology also faces several significant drawbacks:

[0207] 1. The current method simplifies the relationship between density difference and depth and assumes that they are linear, which leads to insufficient accuracy of the seabed topography data obtained by Parker-Oldenburg inversion.

[0208] 2. Despite extensive testing, the crossover and mutation mechanisms in existing genetic algorithms performed poorly when exploring the optimal parameters of the Parker-Oldenburg inversion method, manifested in slow solution speed and large fluctuations in average accuracy.

[0209] 3. Due to the huge overall computational workload and the lack of a unified execution framework, the automation process of terrain inversion faces challenges, which in turn affects the accuracy of the final result, making it appear rougher than the actual situation.

[0210] In view of this, the present invention proposes a method for solving seabed topography based on an improved genetic algorithm and Parker-Oldenburg inversion based on clonal selection. By finding the optimal parameters through the improved genetic algorithm, a more accurate seabed topography is obtained with the help of the Parker-Oldenburg inversion method. Figure 8 As shown, the present invention may include the following process steps:

[0211] Step 1: Parameter initialization. The parameters mainly include the improved genetic algorithm parameters and the Parker-Oldenburg inversion method parameters (for details, please refer to the parameter description above and will not be repeated here).

[0212] Step 2: Initialize the population. The population consists of a large number of individuals, each of which has two genes, representing the density difference contrast and the average seafloor depth depth. The core of population initialization is to generate sizePop individuals and initialize the corresponding genes for each individual. This paper uses random generation of contrast and depth within the search interval to complete individual initialization. The corresponding generation method is as follows:

[0213] contrast=minContrast+(maxContrast -minContrast)*random();

[0214] depth=minDepth+(maxDepth-minDepth)*random();

[0215] Random() generates a random value in the range [0,1]. Normal(0,1) represents a normal distribution with a mean of 0 and a standard deviation of 1. The resulting random numbers adhere to a normal distribution, ensuring fairness in gene generation. After initializing the individuals, the genetic contrast and depth of each individual are traversed one by one. The corresponding seafloor topography data is then calculated using the Parker-Oldenburg inversion method. At this point, each individual in the population has its own calculated seafloor topography data. This data is then combined with the seismic depth matrix SemisMat to calculate the corresponding RMS. Each individual calculates its own RMS, and then proceeds to step 3.

[0216] Step 3: Update the Sigma value. The calculation formula of Sigma (i.e. sigma / random parameter) is as follows:

[0217] ;

[0218] Where iter represents the iterth population iteration, with an initial value of 1 and a maximum value of maxGenCnt. curMinRMS represents the minimum RMS in the current population. In step 2, after completing population initialization, the RMS of each individual is calculated. The population is then traversed to find the individual with the minimum RMS, which is the corresponding RMS curMinRMS.

[0219] Step 4: Traverse the population and select the set of individuals to be crossovered. Sort all individuals by RMS (from smallest to largest) (sort by individual performance from highest to lowest). For example, if the individuals are divided equally into three groups, the minRMS, middleRMS, and maxRMS groups are divided equally. The minRMS group is the best performing group, while the maxRMS group is the worst performing group. Crossover can be divided into the following situations:

[0220] 1) minRMS+middleRMS: select the best and worst individuals for crossover.

[0221] 2) minRMS+maxRMS: Cross-adjust the best and average individuals.

[0222] 3) middleRMS+maxRMS: adjust the worst and average individuals for crossover.

[0223] 4) minRMS+minRMS: Select the best internal crossover.

[0224] Faced with the above four groups of situations, the number of crossover individuals selected in each group is Here's a quick explanation. Taking the first group as an example, assuming maxCrossNum is 40, the first group needs to select 10 crossover individuals. One individual is randomly selected from minRMS, and then another is randomly selected from middleRMS to form a crossover individual. This process is repeated 10 times. Together, these four groups produce maxCrossNum crossover individuals. Go to step 5.

[0225] Step 5: Apply the crossover operator to the individuals based on the probability to select the next generation of individuals. At this point, maxCrossNum crossover individuals have been selected. Each crossover individual is traversed to complete the crossover operation, as follows.

[0226] First, dynamically calculate the cross ratio β , the formula is as follows, where Indicates a random value with 0 as mean and sigma as standard deviation; crossoverAlpha indicates the crossover operator ratio.

[0227] ;

[0228] Then, the crossover operation is performed for density difference and depth. Assuming that individuals A1 and A2 are crossed, the formula is as follows, where 、 represent the density difference and depth of individual A1 respectively, 、 Represent the density difference and depth of individual A2 respectively. 、 They represent the density difference and degree of individual A3 obtained by crossover, respectively. The superscripts After and Before are used to indicate the parameter states before and after crossover.

[0229] ;

[0230] ;

[0231] Here is a specific example. Taking the intersection of A1 and A2 as an example, suppose the density difference of individual A1 is , the depth of individual A1 is ; The density difference of individual A2 is , the depth of individual A2 ,and is 0.8, then the individual A3 is calculated by the formula

[0232]

[0233]

[0234] In general, through the crossover operations in steps 4 and 5, we obtain maxCrossNum individuals in the next generation. Go to step 6.

[0235] Step 6: Traverse the population and select the set of individuals to be mutated. Traverse each individual in the population (excluding newly generated offspring individuals) and determine whether they need to be mutated one by one, as follows:

[0236] For each individual, the mutation probability prob is calculated using the following formula: RMS is the root mean square of the current individual. The larger the RMS, the worse the individual performance and the greater the mutation probability, and vice versa.

[0237] ;

[0238] Then calculate the random number R1 in [0,1]. If R1 is less than prob, the individual participates in the mutation, otherwise it does not participate in the mutation.

[0239] In general, individuals with larger RMS have a greater chance of mutation and are more likely to participate in mutation. Individuals with smaller RMS have a smaller chance of mutation and are less likely to participate in mutation. This is done to allow individuals with poor performance to participate in the next generation of population reproduction through mutation opportunities.

[0240] Traverse the individuals in the population one by one, and finally obtain the set of individuals to be mutated, and go to step 7.

[0241] Step 7: Apply the mutation operator to individuals based on probability and select the next generation of individuals. At this step, several individuals to be mutated have been selected, and the individuals have been traversed to complete the mutation operation. Assuming that individual A1 is mutated, the formula is as follows 、 Represent the density difference and depth of individual A1 respectively. 、 Respectively represent the density difference and degree of individual A4 obtained by mutation. It represents a random value with 0 as mean and sigma as standard deviation.

[0242] ;

[0243] ;

[0244] In general, through the mutation operations in steps 6 and 7, several offspring individuals are obtained. Go to step 8.

[0245] Step 8: Calculate the seafloor topography for each individual in the current population. The individuals that make up the population are composed of parent individuals and offspring individuals. Parent individuals are those that participated in crossover and mutation, while offspring individuals are the new individuals obtained through crossover and mutation. Traverse the genetic contrast and depth of all offspring individuals, and then calculate the corresponding seafloor topography data using the Parker-Oldenburg inversion method. Proceed to Step 9.

[0246] Step 9: Evaluate the fitness function for each individual. At this point, traverse the seafloor topography data for all descendant individuals and, combined with the seismic depth matrix SemisMat, calculate the corresponding RMS. Each individual will calculate its own RMS. Once the RMS calculations for all individuals (parent and descendant) are complete, proceed to Step 10.

[0247] Step 10: There is an individual with an RMS smaller than the threshold. Traverse all individuals in the population and determine whether there is an individual with an RMS smaller than the iterative optimal threshold minIterDelt. If so, go to step 14, indicating that the optimal individual (optimal solution) has been found. Otherwise, go to step 11.

[0248] Step 11: Apply the selection operator to the individuals based on the probability. The new population size is calculated based on the probability. The calculation formula is as follows, where curPopSize represents the current population size (the number of parent and child individuals), newSizePop represents the number of individuals after selection, Indicates a random value with 0 as mean and sigma as standard deviation. curPopSize has a minimum value of .

[0249] newSizePop=max(curPopSize*F2,curPopSize+F2*curPopSize*normal(0,sigma))

[0250] After completing the calculation of newSizePop, you can start to eliminate redundant individuals and sort all individuals in ascending order according to RMS. Individuals directly enter the next generation population. Individuals, randomly selected based on probability Finally, the selection of newSizePop individuals is completed to form the next generation group.

[0251] To summarize, the core idea of ​​the selection operator is that the top 50% of individuals are directly selected into the next generation, while the remaining individuals are selected based on probability. As the population iterates and reproduces, the best individuals gradually gather, and the probabilistic selection also retains individuals with high potential.

[0252] Step 12: Obtain the next generation of population. After steps 3 to 11, the population has been reproduced once and a new population has been obtained. However, no individuals that meet the expectations have been found yet, and the next round of population reproduction is required. Go to step 13.

[0253] Step 13: The maximum number of population iterations is met. Steps 3 through 12 are executed in an iterative loop, with each iteration representing one population iteration, until the maximum number of population iterations, maxGenCnt, is reached. If not, proceed to step 3; otherwise, proceed to step 14.

[0254] Step 14: Find the optimal solution. This step yields the optimal solution for the entire search process. There are only two possible solutions: an optimal vector that satisfies the iterative optimality threshold, minIterDelt, or an optimal vector obtained by searching the entire space. At this point, the search process is complete, and not only has the optimal solution been found, but also the corresponding seafloor topography data.

[0255] In summary, the present invention proposes a method for solving seafloor topography based on an improved genetic algorithm using clonal selection and Parker-Oldenburg inversion. By finding optimal parameters using a genetic algorithm based on an improved operator and then using the Parker-Oldenburg inversion method, a more accurate seafloor topography is obtained. Compared to existing technologies, the present invention has at least the following advantages:

[0256] 1) An improved genetic algorithm process based on clonal selection is proposed, which optimizes the calculation process of genetic operators, prioritizes selection and mutation operators, and then selects operators to ensure that each individual attempts evolution in different directions, thereby increasing the diversity of gene expression of individual populations and improving the search efficiency of the genetic algorithm.

[0257] 2) Optimized crossover, mutation, and selection operators are proposed to improve the computational complexity of the genetic algorithm, ensure that the improved genetic algorithm search process is more efficient, and improve the accuracy and efficiency of data solution;

[0258] 3) The improved genetic algorithm based on clonal selection and Parker-Oldenburg inversion are combined to ensure the accuracy of the seabed topography and improve the computational efficiency.

[0259] It should be emphasized that the present invention proposes a method for searching for the optimal density difference contrast and depth based on an improved genetic algorithm, and combines it with the Parker-Oldenburg inversion method to solve a more accurate seabed topography.

[0260] On the other hand, Figure 9As shown, an embodiment of the present invention provides a seafloor inversion device 900 based on genetic cloning selection, which may include:

[0261] The first module 901 is configured to randomly generate a first number of inversion parameters within a first interval, initialize a first population based on the inversion parameters, use the first population as a target population, and initialize the population with a number of iterations of 1; wherein the inversion parameters correspond to individuals in the target population; and the inversion parameters include density difference and seafloor depth.

[0262] The second module 902 is configured to obtain first seafloor topography data of the area to be inverted based on the inversion parameters of each individual in the target population; obtain a first root mean square value corresponding to each individual in the target population based on the first seafloor topography data and the seismic depth matrix of the area to be inverted; and use the first root mean square value as the target root mean square value;

[0263] The third module 903 is used to obtain the minimum root mean square value among the target root mean square values ​​corresponding to all individuals in the target population, and set random parameters based on the minimum root mean square value and the number of population iterations;

[0264] The fourth module 904 is configured to obtain individuals corresponding to the target root mean square value of each stage range from the target population and mix them to obtain a second number of crossover individuals; the crossover individuals include two individuals;

[0265] The fifth module 905 is configured to perform a weighted crossover operation on the crossover individual based on a preset crossover ratio to obtain a first sub-individual;

[0266] The sixth module 906 is configured to obtain a set of individuals to be mutated from the target population based on probability, and perform a mutation operation on each individual in the set of individuals based on a preset mutation random number to obtain a corresponding second child individual;

[0267] The seventh module 907 is configured to obtain a second population based on all first sub-individuals, all second sub-individuals, and the target population; obtain second seafloor topography data of the area to be inverted based on the inversion parameters corresponding to each individual in the second population; and obtain a second root mean square value corresponding to each individual in the second population based on the second seafloor topography data and the seismic depth matrix.

[0268] The eighth module 908 is configured to select an individual as a target individual when the second root mean square value in the second population is less than or equal to the iterative optimal threshold; otherwise,

[0269] A ninth module 909 is configured to select a third number of individuals from the second population based on probability to obtain a third population, set the third population as the target population, set the second RMS value as the target RMS value, increment the number of population iterations by 1, and return to execute the third module until the target individual is obtained or the number of population iterations equals the preset maximum number of population iterations.

[0270] The tenth module 910 is used to output a target individual according to the target population obtained in the last cycle when the number of population iterations is equal to the maximum number of population iterations;

[0271] The eleventh module 911 is used to use the second seabed topography data corresponding to the target individual as the seabed inversion result of the area to be inverted.

[0272] The contents of the method embodiments of the present invention are all applicable to the device embodiments. The functions specifically implemented by the 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.

[0273] In another aspect, an embodiment of the present invention further provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor implements the above-described method for predicting the bottom boundary of the hydrate stability region when executing the computer program. The electronic device can be any smart terminal, including a tablet computer and an in-vehicle computer.

[0274] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0275] like Figure 10 As shown, Figure 10 The hardware structure of an electronic device 1000 according to another embodiment is shown. The electronic device 1000 includes:

[0276] The processor 1001 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided by the embodiments of the present invention.

[0277] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called by the processor 1001 to execute the network node population optimization method of the embodiment of the present invention.

[0278] Input / output interface 1003, used to implement information input and output;

[0279] Communication interface 1004, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0280] Bus 1005 , which transmits information between various components of the device (e.g., processor 1001 , memory 1002 , input / output interface 1003 , and communication interface 1004 );

[0281] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via a bus 1005 .

[0282] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one location or distributed across multiple network units. Some or all of these modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0283] The contents of the method embodiments of the present invention are all applicable to the electronic device embodiments. The functions specifically implemented by the electronic device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0284] Another aspect of an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the above method.

[0285] The contents of the method embodiments of the present invention are all applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0286] The present invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the above method.

[0287] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0288] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A seafloor inversion method based on genetic clonal selection, characterized in that: The following steps are involved: Randomly generate a first number of inversion parameters within a first interval, and initialize a first population based on the inversion parameters; use the first population as a target population; and initialize the population with a number of iterations of 1; wherein the inversion parameters correspond to individuals in the target population; and the inversion parameters include density difference and seafloor depth; Obtaining first seabed topography data of the area to be inverted based on the inversion parameters of each individual in the target population; obtaining a first root mean square value corresponding to each individual in the target population based on the first seabed topography data and a seismic depth matrix of the area to be inverted; and using the first root mean square value as a target root mean square value; Obtaining a minimum root mean square value among the target root mean square values ​​corresponding to all the individuals in the target population, and setting a random parameter based on the minimum root mean square value and the number of population iterations; wherein the random parameter is obtained based on the number of population iterations and the minimum root mean square value in combination with preset genetic parameter settings; Obtaining the individuals corresponding to the target root mean square value of each stage range from the target population and mixing them to obtain a second number of crossover individuals; the crossover individuals include two of the individuals; Based on a preset crossover ratio, performing a weighted crossover operation on the crossover individual to obtain a first sub-individual; Obtaining a set of individuals to be mutated from the target population based on probability, performing a mutation operation on each individual in the set of individuals based on a preset mutation random number to obtain a corresponding second child individual; Arranging all the first sub-individuals, all the second sub-individuals, and the target population to obtain a second population; inverting the second seabed topography data of the area to be inverted based on the inversion parameters corresponding to each of the individuals in the second population; and obtaining a second root mean square value corresponding to each of the individuals in the second population based on the second seabed topography data and the seismic depth matrix; When the second RMS value in the second population is less than or equal to the iterative optimal threshold, the corresponding individual is taken as the target individual; otherwise, Selecting a third number of individuals from the second population based on probability to obtain a third population, taking the third population as the target population, taking the second RMS value as the target RMS value, increasing the number of population iterations by 1, and returning to the step of obtaining the minimum RMS value among the target RMS values ​​corresponding to all the individuals in the target population until the target individual is obtained, or the number of population iterations equals a preset maximum number of population iterations; When the population iteration number is equal to the population maximum iteration number, outputting the target individual according to the target population obtained in the last cycle; The second seabed topography data corresponding to the target individual is used as the seabed inversion result of the area to be inverted.

2. The seafloor inversion method based on genetic clonal selection according to claim 1, characterized in that: The step of setting random parameters based on the minimum root mean square value and the number of population iterations comprises the following steps: Based on the population iteration number and the minimum root mean square value, the random parameter is obtained in combination with a preset genetic parameter setting; The genetic parameters include the iterative optimal threshold, the maximum number of iterations of the population, and the probability adjustment factor; the expression of the random parameter is: ; Where sigma represents a random parameter; minIterDelt represents the iterative optimal threshold; e represents a natural constant; iter represents the number of population iterations; maxGenCnt represents the maximum number of population iterations; F1 represents the probability adjustment factor; and curMinRMS represents the minimum root mean square value among the target root mean square values.

3. The seafloor inversion method based on genetic clonal selection according to claim 1, characterized in that: The step of obtaining, from the target population, the individuals corresponding to the target root mean square values ​​in each stage range and mixing them to obtain a second number of crossover individuals comprises the following steps: Traversing the target root mean square values ​​of all the individuals in the target population, and sorting all the individuals in the target population in ascending order according to the target root mean square values ​​to obtain a first sequence population; Dividing all the individuals of the first sequence population equally based on the arrangement order to obtain a plurality of individual groups, and determining all pairing situations of any two of the individual groups; Obtain the crossover individuals of the corresponding group number from each pairing situation; The number of the groups is determined based on the ratio of the second number to the total number of the pairing situations, and the two individuals of the crossover individuals are respectively taken from the two individual groups corresponding to the pairing situations.

4. The seafloor inversion method based on genetic clonal selection according to claim 1, characterized in that: The crossover individual includes a first individual and a second individual; performing a weighted crossover operation on the crossover individual based on a preset crossover ratio to obtain a first sub-individual includes the following steps: Based on the random parameters, the crossover ratio is obtained in combination with a preset crossover operator ratio setting; The expression of the crossover ratio is: ; Where, β represents the crossover operator; crossoverAlpha represents the crossover operator ratio; crossoverAlpha is a constant; sigma represents random parameters; Indicates that 0 is the mean, sigma is a random value with standard deviation; Based on the crossover ratio, performing weighted addition on the inversion parameters corresponding to the first individual and the second individual to obtain the inversion parameter of the first sub-individual; The expression of the inversion parameter of the first sub-individual is: <h2 style=";text-align:left;direction:ltr">A3=A1*<h2 style=";text-align:left;direction:ltr"> β <h2 style=";text-align:left;direction:ltr"> +A2*(1-<h2 style=";text-align:left;direction:ltr"> β <h2 style=";text-align:left;direction:ltr"> ); Where A3 represents the inversion parameters of the first sub-individual; A1 represents the inversion parameters of the first individual; and A2 represents the inversion parameters of the second individual.

5. The seafloor inversion method based on genetic clonal selection according to claim 1, characterized in that: The method of obtaining a set of individuals to be mutated from the target population based on probability includes the following steps: Traversing each individual in the target population, and sequentially setting the mutation probability of each individual based on the target root mean square value and the random parameter; The expression of the mutation probability is: ; Where prob represents the probability of mutation; sigma represents random parameters; RMS represents the target RMS value of the corresponding individual; Taking the first individual of the target population as the individual to be mutated; Generate a random number in the second interval as a mutation parameter; When the mutation parameter is less than the mutation probability corresponding to the individual to be mutated, the individual to be mutated is sorted into the individual set; otherwise, the next individual of the individual to be mutated in the target population is used as the individual to be mutated, and the process returns to executing the step of generating a random number as the mutation parameter within the second interval until all the individuals in the target population are traversed and the individual set is output.

6. The seafloor inversion method based on genetic clonal selection according to claim 1, characterized in that: The step of performing a mutation operation on each individual in the individual set based on a preset mutation random number to obtain a corresponding second child individual includes the following steps: Taking the first individual in the individual set as a mutant individual; Generate the variant random number with 0 as mean and the random parameter as standard deviation; Performing mutation assignment on the inversion parameter corresponding to the mutated individual based on the product of the mutated random number and the interval range difference of the first interval to obtain the inversion parameter of the second sub-individual; The expression of the inversion parameter of the second sub-individual is: A4=A1+(max-mix)*normal(0, sigma ); Where A4 represents the inversion parameter of the second child individual; A1 represents the inversion parameter of the variant individual; max represents the maximum value of the first interval; min represents the minimum value of the first interval; normal(0, sigma ) represents a mutated random number.

7. The seafloor inversion method based on genetic clonal selection according to claim 1, characterized in that: The step of selecting a third number of individuals from the second population based on probability to obtain a third population comprises the following steps: Obtaining the population size of the second population; The third number is obtained based on the random parameter and the population size in combination with a preset population size adjustment factor; Wherein, the expression of the third quantity is: newSizePop=max(curPopSize*F2,curPopSize+F2*curPopSize*normal(0,sigma)); Where newSizePop represents the third quantity; curPopSize represents the population size; F2 represents the population size adjustment factor; sigma represents the random parameter; Indicates a random value with 0 as mean and sigma as standard deviation; max(X, Y) means taking the maximum value of X and Y; Traversing the second root mean square values ​​of all the individuals in the second population, and sorting all the individuals in the second population in ascending order according to the second root mean square values ​​to obtain a second sequence population; Selecting a fourth number of the individuals from the pre-sequence of the second sequence population as a first candidate individual set; the fourth number is half of the third number; Randomly selecting the fourth number of individuals from the individuals other than the first candidate individual set in the second sequence population as a second candidate individual set; The third population is obtained by sorting the first candidate individual set and the second candidate individual set.

8. A seafloor inversion device based on genetic clonal selection, characterized in that: include: A first module is configured to randomly generate a first number of inversion parameters within a first interval, and initialize a first population based on the inversion parameters; taking the first population as the target population; The number of iterations of the initialization population is 1; wherein the inversion parameters correspond to individuals in the target population; the inversion parameters include density difference and seabed depth; The second module is configured to obtain first seabed topography data of the area to be inverted based on the inversion parameters of each individual in the target population; obtain a first root mean square value corresponding to each individual in the target population based on the first seabed topography data and the seismic depth matrix of the area to be inverted; and use the first root mean square value as the target root mean square value; A third module is configured to obtain a minimum root mean square value among the target root mean square values ​​corresponding to all individuals in the target population, and to set a random parameter based on the minimum root mean square value and the number of population iterations; wherein the random parameter is obtained based on the number of population iterations and the minimum root mean square value in combination with a preset genetic parameter setting; A fourth module is configured to obtain from the target population the individuals corresponding to the target root mean square value in each stage range and mix them to obtain a second number of crossover individuals; the crossover individuals include two of the individuals; A fifth module is configured to perform a weighted crossover operation on the crossover individual based on a preset crossover ratio to obtain a first sub-individual; A sixth module is configured to obtain a set of individuals to be mutated from the target population based on probability, and perform a mutation operation on each individual in the set of individuals based on a preset mutation random number to obtain a corresponding second child individual; A seventh module is configured to obtain a second population based on all the first sub-individuals, all the second sub-individuals, and the target population; obtain second seabed topography data of the area to be inverted based on the inversion parameters corresponding to each of the individuals in the second population; and obtain a second root mean square value corresponding to each of the individuals in the second population based on the second seabed topography data and the seismic depth matrix. The eighth module is configured to, when there is an individual in the second population whose second root mean square value is less than or equal to the iterative optimal threshold, take the corresponding individual as the target individual; otherwise, a ninth module, configured to select a third number of the individuals from the second population based on probability to obtain a third population, use the third population as the target population, use the second RMS value as the target RMS value, increment the number of population iterations by 1, and return to execute the third module until the target individual is obtained, or the number of population iterations equals a preset maximum number of population iterations; A tenth module is configured to output the target individual according to the target population obtained in the last cycle when the population iteration number is equal to the maximum population iteration number; An eleventh module is configured to use the second seabed topography data corresponding to the target individual as the seabed inversion result of the area to be inverted.

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

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

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