A wide-area electromagnetic method multi-parameter synchronous inversion method, device, equipment and medium

Abstract

The application relates to a wide-area electromagnetic method multi-parameter synchronous inversion method, device, equipment and medium. The method comprises the following steps: constructing a geology-geophysical model, taking the tangential component of a horizontal electric field as an observation quantity, constructing a target function by taking the square minimum of an error vector between observation data and a forward result as an objective, determining an initial population according to the range of resistivity and excitation polarization parameters of each stratum, solving the target function by using a reverse learning self-adaptive differential optimization algorithm, obtaining an optimization result of a model vector parameter, including resistivity, polarization rate, time constant and frequency correlation coefficient and other parameters, and realizing multi-parameter synchronous inversion of a geological abnormal body. The application realizes wide-area electromagnetic method multi-parameter synchronous inversion by using a reverse learning self-adaptive differential evolution algorithm, provides as many wide-area electromagnetic method parameters as possible for geological interpretation, and can improve the geological interpretation precision and accuracy.

Description

Technical Field

[0001] This application relates to the field of geophysical controlled-source electromagnetic exploration technology, and in particular to a wide-area electromagnetic method, multi-parameter synchronous inversion method, apparatus, equipment, and medium. Background Technology

[0002] Wide-area electromagnetic (WEMA) exploration can evaluate the electrical and polarization properties of subsurface geological anomalies, typically by extracting the resistivity and induced polarization parameters of the anomalies. Research by Peleton et al. confirms that the complex resistivity of rocks and ores generally satisfies the Cole-Cole model, meaning that the polarization information of geological anomalies is controlled by three parameters: polarizability, time constant, and frequency correlation coefficient. Each parameter reflects a specific aspect of the anomaly, and while they are interrelated, they are not equivalent. Currently, WEMA inversion mainly focuses on resistivity parameter inversion, with induced polarization parameter processing primarily qualitative. While some inversions have achieved polarizability, simultaneous inversion of multiple parameters is not yet effectively implemented. This leads to insufficient parameters in the subsequent interpretation of geological anomalies, affecting the accuracy and precision of the interpretation. Therefore, existing technologies suffer from poor adaptability. Summary of the Invention

[0003] Therefore, it is necessary to provide a wide-area electromagnetic method for multi-parameter synchronous inversion, an apparatus, a computer device, and a storage medium that can improve the interpretation accuracy and precision of the above-mentioned technical problems.

[0004] A wide-area electromagnetic multi-parameter synchronous inversion method, the method comprising:

[0005] Based on known geological exploration information, a geological-geophysical model is constructed, and the range of resistivity and intensified polarization parameters of each stratum is determined;

[0006] Based on the geological-geophysical model, an objective function is constructed with the tangential component of the horizontal electric field as the observation and minimizing the square of the error vector between the observation data and the forward modeling results as the objective. The objective function uses model vector parameters as independent variables, including resistivity, polarizability, time constant, and frequency correlation coefficient.

[0007] The initial population is determined based on the resistivity and polarization parameter range of each stratum. The objective function is solved by the back-learning adaptive differential optimization algorithm to obtain the optimized model vector parameters.

[0008] The multi-parameter synchronous inversion results of the geological anomaly are obtained based on the optimization results of the model vector parameters.

[0009] In one embodiment, the method further includes: based on the geological-geophysical model, using the tangential component of the horizontal electric field as the observation, and aiming to minimize the square of the error vector between the observation data and the forward modeling results, constructing an objective function as follows:

[0010]

[0011]

[0012]

[0013] in, Here, d represents the model vector parameters, d0 represents the one-dimensional observation data vector, and d0 = G(h,ρ,m,τ,c), where G is the known forward modeling operator, h is the formation depth vector, ρ is the formation resistivity vector, m is the polarizability vector, τ is the time constant vector, and c is the frequency correlation coefficient vector. This represents a function constructed from the squares of the error vector between the observed data and the forward modeling results. Let λ be the model constraint function, λ be the regularization factor, and R be the rough matrix.

[0014] In one embodiment, the method further includes: determining an initial positive population based on the range of resistivity and polarization parameters of each stratum.

[0015]

[0016] The initial reverse population is determined as follows:

[0017]

[0018] Among them, X i (0), X i '(0) represents the i-th individual in the first generation population and the reverse population, respectively, j represents the j-th dimension, each dimension represents a parameter to be solved, and D is the total number of parameters to be solved. rand(0,1) represents a random number in the range [0,1]. These represent the minimum and maximum values ​​of the j-th dimension, respectively, and are determined by the ranges of the resistivity and excitation polarization parameters of each stratum. NP represents the number of individuals in the population and the reverse population.

[0019] In one embodiment, the method further includes setting k=0, k=0.5, and k=1 to obtain three initial reverse populations.

[0020] In one embodiment, the method further includes: solving the objective function using a reverse learning adaptive differential optimization algorithm; wherein, during the population mutation process, a double mutation operator is used alternately to achieve individual mutation; the double mutation operator is a DE / current-to-best / 1 and a DE / rand / 1 mutation operator.

[0021] In one embodiment, the method further includes: solving the objective function using a reverse learning adaptive differential optimization algorithm; wherein the mutated individual and the current evolved individual in the population are cross-crossed in a discrete manner to generate offspring individuals.

[0022] In one embodiment, the method further includes: constructing a geological-geophysical model based on well logging data from the survey line.

[0023] A wide-area electromagnetic method multi-parameter synchronous inversion device, the device comprising:

[0024] The geological-geophysical model building module is used to build geological-geophysical models based on known geological exploration information and determine the range of resistivity and polarization parameters of each stratum.

[0025] The objective function construction module is used to construct an objective function based on the geological-geophysical model, taking the tangential component of the horizontal electric field as the observation and minimizing the square of the error vector between the observation data and the forward modeling results as the objective. The objective function uses model vector parameters as independent variables. The model vector parameters include resistivity, polarizability, time constant, and frequency correlation coefficient.

[0026] The objective function solving module is used to determine the initial population based on the resistivity and polarization parameters of each stratum, and solve the objective function through a back-learning adaptive differential optimization algorithm to obtain the optimized results of the model vector parameters.

[0027] The multi-parameter synchronous inversion module is used to obtain the multi-parameter synchronous inversion results of geological anomalies based on the optimization results of the model vector parameters.

[0028] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0029] Based on known geological exploration information, a geological-geophysical model is constructed, and the range of resistivity and intensified polarization parameters of each stratum is determined;

[0030] Based on the geological-geophysical model, an objective function is constructed with the tangential component of the horizontal electric field as the observation and minimizing the square of the error vector between the observation data and the forward modeling results as the objective. The objective function uses model vector parameters as independent variables, including resistivity, polarizability, time constant, and frequency correlation coefficient.

[0031] The initial population is determined based on the resistivity and polarization parameter range of each stratum. The objective function is solved by the back-learning adaptive differential optimization algorithm to obtain the optimized model vector parameters.

[0032] The multi-parameter synchronous inversion results of the geological anomaly are obtained based on the optimization results of the model vector parameters.

[0033] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0034] Based on known geological exploration information, a geological-geophysical model is constructed, and the range of resistivity and intensified polarization parameters of each stratum is determined;

[0035] Based on the geological-geophysical model, an objective function is constructed with the tangential component of the horizontal electric field as the observation and minimizing the square of the error vector between the observation data and the forward modeling results as the objective. The objective function uses model vector parameters as independent variables, including resistivity, polarizability, time constant, and frequency correlation coefficient.

[0036] The initial population is determined based on the resistivity and polarization parameter range of each stratum. The objective function is solved by the back-learning adaptive differential optimization algorithm to obtain the optimized model vector parameters.

[0037] The multi-parameter synchronous inversion results of the geological anomaly are obtained based on the optimization results of the model vector parameters.

[0038] The aforementioned wide-area electromagnetic method (WEEM) multi-parameter synchronous inversion method, apparatus, computer equipment, and storage medium construct a geological-geophysical model. Using the tangential component of the horizontal electric field as the observation, and minimizing the square of the error vector between the observed data and the forward modeling results as the objective function, an initial population is determined based on the pre-defined ranges of resistivity and polarization parameters of each stratum. The objective function is solved using a back-learning adaptive differential optimization algorithm to obtain optimized model vector parameters, including resistivity, polarizability, time constant, and frequency correlation coefficient, thus achieving multi-parameter synchronous inversion of geological anomalies. This invention utilizes a back-learning adaptive differential evolution algorithm to achieve wide-area electromagnetic method multi-parameter synchronous inversion, providing as many wide-area electromagnetic parameters as possible for geological interpretation, thereby improving the accuracy and precision of geological interpretation. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating a multi-parameter synchronous inversion method using the wide-area electromagnetic method in one embodiment.

[0040] Figure 2 This is a structural block diagram of a multi-parameter synchronous inversion device for the wide-area electromagnetic method in one embodiment;

[0041] Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0043] In one embodiment, such as Figure 1 As shown, a multi-parameter synchronous inversion method using a wide-area electromagnetic method is provided, comprising the following steps:

[0044] Step 102: Construct a geological-geophysical model based on known geological exploration information, and determine the range of resistivity and intensified polarization parameters for each stratum.

[0045] Known geological exploration information includes existing geological, geophysical, and well logging data. In this embodiment, using well logging data that traverses the survey line is optimal for model construction. If no well logging curves traverse the survey line are available, the well logging data closest to the survey line can be used. Selecting well logging data closest to the survey line to construct the geological-geophysical model allows for the establishment of a more accurate geoelectric model, thereby improving inversion efficiency and effectiveness.

[0046] Rock property testing was used to determine the resistivity and induced polarization parameters of each major formation, providing a range of parameter values ​​for constructing the initial population when solving the objective function. Well logging core samples are the best source for rock property testing.

[0047] Step 104: Based on the geological-geophysical model, construct an objective function with the tangential component of the horizontal electric field as the observation and minimizing the square of the error vector between the observation data and the forward modeling results as the objective.

[0048] The objective function uses the model vector parameters as independent variables; the model vector parameters include resistivity, polarizability, time constant, and frequency correlation coefficient.

[0049] Specifically, the objective function is constructed as follows:

[0050]

[0051]

[0052]

[0053] in, Here, d represents the model vector parameters, d0 represents the one-dimensional observation data vector, and d0 = G(h,ρ,m,τ,c), where G is the known forward modeling operator, h is the formation depth vector, ρ is the formation resistivity vector, m is the polarizability vector, τ is the time constant vector, and c is the frequency correlation coefficient vector. This represents a function constructed from the squares of the error vector between the observed data and the forward modeling results. Let λ be the model constraint function, λ be the regularization factor, and R be the rough matrix.

[0054] Assuming the number of stratigraphic positions N, then the model vector parameters A 5N-1 dimensional vector (h1, h2, ..., h N-1 ,ρ1,ρ2,...,ρ N ,m1,m2,...,m N ,τ1,τ2,...,τ N c1, c2, ..., c N ) T Assuming the number of observation frequency points is K, then the observation vector d or the forward modeling vector d0 is a 2×K dimensional vector (ρ1, ρ2, ..., ρ...). K ,θ1,θ2,...,θ K ) T , where ρ i θ i These are the observed or simulated values ​​of apparent resistivity and phase, respectively, for the i-th frequency point.

[0055] Step 106: Determine the initial population based on the resistivity and polarization parameters of each stratum, and solve the objective function using the back-learning adaptive differential optimization algorithm to obtain the optimized model vector parameters.

[0056] Specifically:

[0057] Step 1: Determine the initial positive population as follows:

[0058]

[0059] The initial reverse population is determined as follows:

[0060]

[0061] Among them, X i (0), X i'(0) represents the i-th individual in the first generation population and the reverse population, respectively, j represents the j-th dimension, each dimension represents a parameter to be solved, and D is the total number of parameters to be solved. rand(0,1) represents a random number in the range [0,1]. Let represent the minimum and maximum values ​​of the j-th dimension, respectively, which are determined by the range of resistivity and excitation polarization parameters of each stratum. NP represents the number of individuals in the population and the reverse population.

[0062] Step 2: Select individuals from both the forward and reverse populations to form an optimized initial population. The selection strategy involves screening corresponding individuals between the forward and reverse populations as follows:

[0063] If the individual fitness F(x) i )>F(x i '),but Otherwise X i (1) = X i '(0), so a new initial population is generated:

[0064] {X i (1)|X1(1),X2(1),...,X NP (1); i = 1, 2, ..., NP}.

[0065] The third step is to sort the population, calculate the fitness of each individual, and select the optimal individual. Then, determine if the conditions are met. If not, proceed to the next step; otherwise, output the result and end the iteration.

[0066] The fourth step involves using a double mutation operator to mutate individuals. The DE / current-to-best / 1 and DE / rand / 1 mutation operators are selected to form a double mutation operator, which is used alternately during the mutation process. An adaptive method is applied to the mutation factor F: three randomly selected individuals from the mutation operators are ranked from best to worst to obtain X. b X m and X w Corresponding fitness f b f m and f w The value of F adaptively changes based on the two individuals that generate the difference vector:

[0067]

[0068] Among them, F l =0.1, F u =0.9

[0069] Step 5, the mutated individual Ui (g+1) and the current evolved individual X in the population i (g) Perform crossover operations in a discrete manner to generate offspring individuals V. i (g+1), then V i The j-th component of (g+1) is represented as:

[0070]

[0071] Where g is the current iteration number; rand(0,1) is a random number uniformly distributed between [0,1]; rand(1,D) is a random integer between [1,D]; CR is the crossover operator, which uses a crossover probability operator that increases exponentially with the iteration number:

[0072]

[0073] Where a = 50; b = 5; G max CR is the maximum number of iterations. min =0.1; CR max =0.6.

[0074] Step 6: Compare the current individual X based on fitness. i and its offspring V i The population is then updated using the better individuals from both groups.

[0075] Step 7, g = g + 1, proceed to the next iteration, and go to step 4.

[0076] Step 108: Obtain the multi-parameter synchronous inversion results of the geological anomaly based on the optimization results of the model vector parameters.

[0077] After solving the objective function to obtain the final optimization result, the model vector parameters are obtained based on the individual information in the positive population, realizing the multi-parameter synchronous inversion of geological anomalies, including numerical solutions for resistivity, polarizability, time constant, and frequency correlation coefficient, etc., using the Cole-Cole model:

[0078]

[0079] This allows us to obtain information on the intensification of geological anomalies, providing as many parameters as possible for geological interpretation based on the wide-area electromagnetic method, thereby improving the accuracy of the interpretation.

[0080] In the aforementioned wide-area electromagnetic method for multi-parameter synchronous inversion, a geological-geophysical model is constructed. The tangential component of the horizontal electric field is used as the observation, and the objective function is built with the goal of minimizing the square of the error vector between the observed data and the forward modeling results. An initial population is determined based on the pre-defined ranges of resistivity and polarization parameters for each stratum. The objective function is solved using a back-learning adaptive differential optimization algorithm to obtain optimized model vector parameters, including resistivity, polarizability, time constant, and frequency correlation coefficient, thus achieving multi-parameter synchronous inversion of geological anomalies. This invention utilizes a back-learning adaptive differential evolution algorithm to achieve multi-parameter synchronous inversion using the wide-area electromagnetic method, providing as many wide-area electromagnetic parameters as possible for geological interpretation, thereby improving the accuracy and precision of geological interpretation.

[0081] In one embodiment, the method further includes: based on a geological-geophysical model, using the tangential component of the horizontal electric field as the observation, and aiming to minimize the square of the error vector between the observation data and the forward modeling results, constructing an objective function as follows:

[0082]

[0083]

[0084]

[0085] in, Here, d represents the model vector parameters, d0 represents the one-dimensional observation data vector, and d0 = G(h,ρ,m,τ,c), where G is the known forward modeling operator, h is the formation depth vector, ρ is the formation resistivity vector, m is the polarizability vector, τ is the time constant vector, and c is the frequency correlation coefficient vector. This represents a function constructed from the squares of the error vector between the observed data and the forward modeling results. Let λ be the model constraint function, λ be the regularization factor, and R be the rough matrix.

[0086] In one embodiment, the method further includes: determining an initial positive population based on the resistivity and polarization parameters of each stratum.

[0087]

[0088] The initial reverse population is determined as follows:

[0089]

[0090] Among them, X i (0), X i '(0) represents the i-th individual in the first generation population and the reverse population, respectively, j represents the j-th dimension, each dimension represents a parameter to be solved, and D is the total number of parameters to be solved. rand(0,1) represents a random number in the range [0,1]. Let represent the minimum and maximum values ​​of the j-th dimension, respectively, which are determined by the range of resistivity and excitation polarization parameters of each stratum. NP represents the number of individuals in the population and the reverse population.

[0091] In one embodiment, the method further includes setting k=0, k=0.5, and k=1 to obtain three initial reverse populations.

[0092] In one embodiment, the method further includes: solving the objective function using a reverse learning adaptive differential optimization algorithm; wherein, during the population mutation process, a double mutation operator is used alternately to achieve individual mutation; the double mutation operator is the DE / current-to-best / 1 and DE / rand / 1 mutation operator.

[0093] In one embodiment, the method further includes: solving the objective function using a reverse learning adaptive differential optimization algorithm; wherein the mutated individual and the current evolved individual in the population are cross-crossed in a discrete manner to generate offspring individuals.

[0094] In one embodiment, the method further includes: constructing a geological-geophysical model based on well logging data from the survey line.

[0095] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0096] In one embodiment, such as Figure 2 As shown, a wide-area electromagnetic method multi-parameter synchronous inversion device is provided, including: a geological-geophysical model construction module 202, an objective function construction module 204, an objective function solution module 206, and a multi-parameter synchronous inversion module 208, wherein:

[0097] The geological-geophysical model construction module 202 is used to construct a geological-geophysical model based on known geological exploration information and to determine the range of resistivity and intensified polarization parameters of each stratum.

[0098] The objective function construction module 204 is used to construct an objective function based on the geological-geophysical model, with the tangential component of the horizontal electric field as the observation and the goal of minimizing the square of the error vector between the observation data and the forward modeling results. The objective function uses model vector parameters as independent variables. The model vector parameters include resistivity, polarizability, time constant, and frequency correlation coefficient.

[0099] The objective function solving module 206 is used to determine the initial population based on the resistivity and polarization parameters of each stratum, and solve the objective function through a back-learning adaptive differential optimization algorithm to obtain the optimized results of the model vector parameters.

[0100] The multi-parameter synchronous inversion module 208 is used to obtain the multi-parameter synchronous inversion results of geological anomalies based on the optimization results of the model vector parameters.

[0101] The geological-geophysical model building module 202 is also used to build a geological-geophysical model based on well logging data from the survey line.

[0102] Objective function construction module 204 is also used to construct an objective function based on a geological-geophysical model, using the tangential component of the horizontal electric field as the observation and minimizing the square of the error vector between the observation data and the forward modeling results as the objective:

[0103]

[0104]

[0105]

[0106] in, Here, d represents the model vector parameters, d0 represents the one-dimensional observation data vector, and d0 = G(h,ρ,m,τ,c), where G is the known forward modeling operator, h is the formation depth vector, ρ is the formation resistivity vector, m is the polarizability vector, τ is the time constant vector, and c is the frequency correlation coefficient vector. This represents a function constructed from the squares of the error vector between the observed data and the forward modeling results. Let λ be the model constraint function, λ be the regularization factor, and R be the rough matrix.

[0107] The objective function solving module 206 is also used to solve the objective function through a reverse learning adaptive difference optimization algorithm; wherein, during the population mutation process, the double mutation operator is used alternately to realize individual mutation; the double mutation operator is the DE / current-to-best / 1 and DE / rand / 1 mutation operator.

[0108] The objective function solving module 206 is also used to solve the objective function through a reverse learning adaptive differential optimization algorithm; wherein, the mutated individual and the current evolved individual in the population are cross-crossed in a discrete manner to generate offspring individuals.

[0109] Specific limitations regarding the wide-area electromagnetic method multi-parameter synchronous inversion device can be found in the limitations of the wide-area electromagnetic method multi-parameter synchronous inversion method described above, and will not be repeated here. Each module in the aforementioned wide-area electromagnetic method multi-parameter synchronous inversion device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in a computer device, or stored in software in the memory of a computer device, so that the processor can call and execute the corresponding operations of each module.

[0110] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When executed by the processor, the computer program implements a wide-area electromagnetic multi-parameter synchronous inversion method. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0111] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0112] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps in the above method embodiment.

[0113] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0114] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0115] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0116] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A multi-parameter synchronous inversion method using a wide-area electromagnetic method, characterized in that, The method includes: Based on known geological exploration information, a geological-geophysical model is constructed, and the range of resistivity and intensified polarization parameters of each stratum is determined; Based on the geological-geophysical model, an objective function is constructed with the tangential component of the horizontal electric field as the observation and minimizing the square of the error vector between the observation data and the forward modeling results as the objective. The objective function uses model vector parameters as independent variables, including resistivity, polarizability, time constant, and frequency correlation coefficient. The initial population is determined based on the resistivity and polarization parameters of each stratum, including: Based on the resistivity and polarization parameter ranges of the various strata, the initial positive population is determined as follows: The initial reverse population is determined as follows: in, , These represent the first generation population and the reverse population, respectively. Individual, Indicates the first Dimension, each dimension represents a parameter to be solved. The total number of parameters to be solved. , express random numbers, They represent the first The minimum and maximum values ​​of the dimension are determined by the range of resistivity and excitation polarization parameters of each stratum. , , The number of individuals in the population and the reverse population; The objective function is solved using a back-learning adaptive differential optimization algorithm to obtain the optimized model vector parameters; wherein, the initial population includes an initial forward population and three initial backward populations, the three initial backward populations respectively corresponding to , and Individuals are selected from the positive population and the three negative populations based on fitness to form an initial population; The objective function is solved using a back-learning adaptive differential optimization algorithm; wherein, during the population mutation process, a double mutation operator is alternately used to achieve individual mutation; the double mutation operator is... and Mutation operators; Variants The value of adapts to the two individuals that generate the difference vector: ； In the formula, , and These are the fitness values ​​of the three individuals, sorted from best to worst fitness. In the crossover operation, the mutated individual and the current evolved individual in the population undergo a discrete crossover process to generate offspring individuals, with crossover probability... Exponentially increasing with the number of iterations: ； In the formula, The maximum number of iterations, , , This represents the current iteration number; The multi-parameter synchronous inversion results of the geological anomaly are obtained based on the optimization results of the model vector parameters.

2. The method according to claim 1, characterized in that, Based on the aforementioned geological-geophysical model, using the tangential component of the horizontal electric field as the observation, and aiming to minimize the square of the error vector between the observed data and the forward modeling results, an objective function is constructed, including: Based on the aforementioned geological-geophysical model, taking the tangential component of the horizontal electric field as the observation, and aiming to minimize the square of the error vector between the observed data and the forward modeling results, the objective function is constructed as follows: in, For model vector parameters, A one-dimensional observation data vector. Forward data vector, , For known forward operators, This is the burial depth vector. This represents the formation resistivity vector. The polarizability vector, A time constant vector, This is a vector of frequency correlation coefficients. This represents a function constructed from the squares of the error vector between the observed data and the forward modeling results. For model constraint functions, As a regularization factor, It is a rough matrix.

3. The method according to claim 1, characterized in that, The step of solving the objective function using a reverse learning adaptive differential optimization algorithm includes: The objective function is solved by a reverse learning adaptive differential optimization algorithm; wherein, the mutated individual and the current evolving individual in the population are cross-crossed in a discrete manner to generate offspring individuals.

4. The method according to any one of claims 1 to 3, characterized in that, Based on known geological exploration information, a geological-geophysical model is constructed, including: A geological-geophysical model is constructed based on well logging data from the survey lines.

5. A wide-area electromagnetic method multi-parameter synchronous inversion device, characterized in that, The apparatus comprising the method according to any one of claims 1 to 4, wherein the apparatus includes: The geological-geophysical model building module is used to build geological-geophysical models based on known geological exploration information and determine the range of resistivity and polarization parameters of each stratum. The objective function construction module is used to construct an objective function based on the geological-geophysical model, taking the tangential component of the horizontal electric field as the observation and minimizing the square of the error vector between the observation data and the forward modeling results as the objective. The objective function uses model vector parameters as independent variables. The model vector parameters include resistivity, polarizability, time constant, and frequency correlation coefficient. The objective function solving module is used to determine the initial population based on the resistivity and polarization parameters of each stratum, and solve the objective function through a back-learning adaptive differential optimization algorithm to obtain the optimized results of the model vector parameters. The multi-parameter synchronous inversion module is used to obtain the multi-parameter synchronous inversion results of geological anomalies based on the optimization results of the model vector parameters.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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