Transient electromagnetic inversion method and related equipment

Abstract

The invention discloses a transient electromagnetic inversion method and related equipment, relates to the technical field of geophysical exploration, and solves the problem of low accuracy of multi-layer terrain inversion in a general scheme. The stratum resistivity and thickness are inverted by adopting a teaching and learning algorithm, and Tent chaotic mapping and Cauchy-differential evolution mixed variation are introduced to improve the global optimization performance of the algorithm. Iteration parameters can be reduced through a teaching and learning algorithm, the individual iteration process in the iteration process is independent, the optimization process can be accelerated through parallel calculation, and the calculation efficiency is improved; and meanwhile, a Cauchy-differential evolution hybrid variation strategy is introduced, so that the risk that the algorithm falls into local optimum can be reduced, and the accuracy of inverting the multi-layer terrain parameters through the transient electromagnetic method is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geophysical exploration, and particularly relates to a transient electromagnetic inversion method and related equipment. Background Technique

[0002] The transient electromagnetic method is a geophysical exploration method based on the principle of electromagnetic induction. It emits a primary pulsed magnetic field into the ground, induces currents in the underground medium, and measures the decay characteristics of the resulting secondary electromagnetic field, thereby inferring the distribution of parameters such as the resistivity of the underground medium.

[0003] Traditional inversion methods based on the transient electromagnetic method include the smoke ring inversion method. Smoke ring inversion is an approximate analytical method based on the "smoke ring theory", which is fast but has poor accuracy and is difficult to accurately invert multi-layer complex geological bodies. In recent years, meta-heuristic optimization methods such as particle swarm optimization, genetic algorithm, differential evolution algorithm, etc., algorithms based on random search and heuristic strategies, have good effects in dealing with complex non-linear problems, so they are widely used in transient electromagnetic inversion problems. However, for the high-dimensional problem of multi-layer terrain inversion, it is easy to fall into local optimum during the solution process, resulting in inaccurate inversion results.

[0004] In view of this, a transient electromagnetic inversion method and related equipment are needed. Summary of the Invention

[0005] Aiming at the problem of low accuracy of multi-layer terrain inversion in the prior art, the present invention provides a transient electromagnetic inversion method and related equipment, which can improve the accuracy of inverting multi-layer terrain parameters by the transient electromagnetic method. The specific technical solutions are as follows:

[0006] In a first aspect, an embodiment of the present application provides a transient electromagnetic inversion method, including:

[0007] S1: Obtain the resistivity parameter range, layer thickness parameter range and induced voltage data of the formation to be measured. The induced voltage data is the induced voltage data obtained by the receiving coil of the transient electromagnetic device sensing the secondary electromagnetic field of the formation to be measured after the transmitting coil of the transient electromagnetic device emits a pulsed current above the formation to be measured; the secondary electromagnetic field is the electromagnetic field generated by the medium in the formation to be measured based on the pulsed current;

[0008] S2: Based on the Tent chaotic map, initialize the vectors of N individuals in the teaching and learning based optimization algorithm. The vectors include the resistivity parameter and layer thickness parameter of the formation to be measured. The resistivity parameter is within the resistivity parameter range, and the layer thickness parameter is within the layer thickness parameter range. N is a positive integer greater than or equal to 20;

[0009] S3: Based on the vectors of the N individuals and the induced voltage data, iteratively calculate the fitness of the N individuals through the teaching and learning optimization algorithm;

[0010] S4: When the fitness of the teacher individual among the N individuals has not improved in M consecutive iterations, update the vectors of the student individuals among the N individuals based on the Cauchy-difference hybrid mutation strategy, and return to execute step S3, where M is a positive integer greater than or equal to 3;

[0011] S5: When the number of iterations is equal to the preset number of times, output the vector of the teacher individual in the last iteration as the optimal inversion parameter result, where the preset number of times is greater than or equal to 50.

[0012] Preferably, step S3 includes:

[0013] S3.1: Based on the induced voltage data and the vectors of the N individuals, calculate the first fitness of each individual, and determine the teacher individual and student individuals among the N individuals based on the first fitness;

[0014] S3.2: Based on the average value of the vectors of all student individuals among the N individuals, the vector of the student individual, and the vector of the teacher individual, calculate the first value of the vector of each student individual;

[0015] S3.3: Based on the first value of the vector of the first student individual and the first value of the vector of the second student individual, calculate the second value of the vector of the first student individual, where the first student individual is any one of the student individuals, and the second student individual is any one of the student individuals other than the first student individual;

[0016] S3.4: Calculate the second fitness of each student individual based on the second value;

[0017] S3.5: For a student individual, when the second fitness is greater than the first fitness, update the vector of the student individual based on the second value; when the second fitness is less than or equal to the first fitness, keep the vector of the student individual unchanged;

[0018] S3.6: After updating or maintaining the vectors of all student individuals, return to execute step S3.1.

[0019] Preferably, the calculation formula of Tent chaotic mapping in step S2 includes:

[0020]

[0021] X i+1 =F min +(F max -F min )xi+1

[0022] Among them, x i and x i+1 are the i-th and (i + 1)-th chaotic variables respectively, X i+1 is the (i + 1)-th individual, where i = 1, 2,..., N - 1; Fmin is the vector composed of the minimum value of the resistivity parameter range and the minimum value of the layer thickness parameter range of the formation to be measured, Fmax is the vector composed of the maximum value of the resistivity parameter range and the maximum value of the layer thickness parameter range of the formation to be measured, and α is a random number between 0 and 1.

[0023] Preferably, the calculation formula for calculating the fitness f of this individual in step S3 includes:

[0024]

[0025] Among them, V obs is the induced voltage data, and V cal is the simulated voltage data obtained by forward modeling according to the vector of the individual.

[0026] Preferably, the calculation formula for calculating this first value in step S3.2 includes:

[0027]

[0028] Among them, X i is the i-th student individual, X teacher is the teacher individual, is the first value of the i-th student individual, r is a random number uniformly distributed between [0, 1], TF is a preset teaching factor, is the average value of the vectors of all student individuals among N individuals.

[0029] Preferably, the calculation formula for calculating this second value in step S3.3 includes:

[0030]

[0031] Among them, and are the first value of the first student individual and the first value of the second student individual respectively, is the second value of the first student individual, r is a random number uniformly distributed between [0, 1], and are the fitness corresponding to the first value of the first student individual and the fitness corresponding to the first value of the second student individual respectively.

[0032] Preferably, step S4 includes:

[0033] S4.1: Perform hybrid mutation on the i-th student individual. The calculation formula for hybrid mutation includes:

[0034]

[0035] where X DE represents the differential mutation vector, X CT represents the Cauchy mutation vector, X r1 , X r2 and X r3 are the vectors of three random student individuals excluding the teacher individual and the i-th student individual. F is the scaling factor, Cauchy(m) represents the m-dimensional Cauchy distribution, and λ1 and λ2 are adjustment factors. represents the vector of the i-th student individual after hybrid mutation, and X i represents the vector of the i-th student individual before hybrid mutation;

[0036] S4.2: Perform crossover calculation on the vector of the i-th student individual. The calculation formula for crossover calculation includes:

[0037]

[0038] where is the j-th element of the i-th student individual after crossover calculation, and a ij and are the values of the j-th elements of the vectors of the i-th student individual before and after hybrid mutation, respectively; CE is the preset crossover probability, and its value range is [0, 1]; rand is a random number between [0, 1]; randi(D) represents a randomly generated integer in the D-dimensional solution space, and D is the dimension number of the vector of the student individual.

[0039] S4.3: Update the vectors of the student individuals among the N individuals. The calculation formula for updating the vectors includes:

[0040]

[0041] where is the vector of the updated i-th student individual, is the vector of the i-th student individual after crossover calculation; and f(X i ) are the fitness values corresponding to and X i , respectively.

[0042] In a second aspect, an embodiment of the present application provides a transient electromagnetic inversion system, which is applied to the method described in the first aspect. The system includes:

[0043] An acquisition module, configured to acquire the resistivity parameter range, the layer thickness parameter range, and the induced voltage data of the formation to be measured. The induced voltage data is the induced voltage data obtained by the receiving coil of the transient electromagnetic device sensing the secondary electromagnetic field of the formation to be measured after a pulsed current is emitted above the formation to be measured by the transmitting coil of the transient electromagnetic device. The secondary electromagnetic field is the electromagnetic field generated by the medium in the formation to be measured based on the pulsed current.

[0044] An initialization module, configured to initialize the vectors of N individuals in the teaching and learning optimization algorithm based on the Tent chaotic map. The vectors include the resistivity parameter and the layer thickness parameter of the formation to be measured. The resistivity parameter is within the resistivity parameter range, and the layer thickness parameter is within the layer thickness parameter range. N is a positive integer greater than or equal to 20.

[0045] An iteration module, configured to iteratively calculate the fitness of the N individuals through the teaching and learning optimization algorithm based on the vectors of the N individuals and the induced voltage data.

[0046] A mutation module, configured to update the vectors of the student individuals among the N individuals based on the Cauchy-difference hybrid mutation strategy and return to execute step S3 when the fitness of the teacher individual among the N individuals has not improved in M consecutive iterations. M is a positive integer greater than or equal to 3.

[0047] An output module, configured to output the vector of the teacher individual in the last iteration as the optimal inversion parameter result when the number of iterations is equal to a preset number. The preset number is greater than or equal to 50.

[0048] In a third aspect, an embodiment of the present application provides a computing device, including: a memory, configured to store a program; and a processor, configured to load the program to execute the method described in the first aspect.

[0049] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium including a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the method described in the first aspect.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows: The teaching and learning algorithm is used to invert the formation resistivity and thickness, and the Tent chaotic map and the Cauchy-difference evolutionary hybrid mutation are introduced to improve the global optimization performance of the algorithm. The teaching and learning algorithm can reduce the iterative parameters, and the individual iterative processes are independent during the iteration process, and the optimization process can be accelerated through parallel computing to improve the computing efficiency. At the same time, the Cauchy-difference evolutionary hybrid mutation strategy is introduced, which can reduce the risk of the algorithm falling into a local optimum, thereby improving the accuracy of inverting multi-layer terrain parameters by the transient electromagnetic method. Description of the Drawings

[0051] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally denoted by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0052] Figure 1 It is a schematic flow chart of a transient electromagnetic inversion method provided by an embodiment of the present application;

[0053] Figure 2 It is a comparison diagram of an inversion model and a true model of formation parameters provided by an embodiment of the present application;

[0054] Figure 3 It is a schematic structural diagram of a transient electromagnetic inversion system provided by an embodiment of the present application;

[0055] Figure 4 It is a schematic structural diagram of a computing device provided by an embodiment of the present application. Specific Embodiments

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0057] It should be understood that when used in this specification and the appended claims, the terms "comprises" and "comprising" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0058] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0059] It should be further understood that the term " / and / " used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0060] To solve the problem of low accuracy in multi-layer terrain inversion in the prior art, the present invention provides a transient electromagnetic inversion method and related equipment, which can improve the accuracy of inverting multi-layer terrain parameters by the transient electromagnetic method.

[0061] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a transient electromagnetic inversion method provided by an embodiment of the present application. This method is applied to a computing device; as Figure 1 shown, this method includes:

[0062] S1: The computing device obtains the resistivity parameter range, layer thickness parameter range, and induced voltage data of the formation to be measured.

[0063] Among them, the induced voltage data is the induced voltage data obtained by the receiving coil of the transient electromagnetic device sensing the secondary electromagnetic field of the formation to be measured after the transmitting coil of the transient electromagnetic device emits a pulsed current above the formation to be measured; the secondary electromagnetic field is the electromagnetic field generated by the medium in the formation to be measured based on the pulsed current.

[0064] Among them, the resistivity parameter range and layer thickness parameter range can be data preset in the computing device, and the computing device can directly obtain them from the internal storage unit; or they can be data stored in a database outside the computing device, and the computing device can obtain them from the database through wired or wireless communication methods.

[0065] Among them, the induced voltage data can be obtained by the computing device from a system or device after being collected by a system or device including a transient electromagnetic device, or the computing device can emit a pulsed current to the formation to be measured through the transient electromagnetic device connected and controlled by it, and then obtain the induced voltage data in real time.

[0066] In an embodiment of the present invention, both the formation resistivity parameter ρ and the layer thickness parameter t are one-dimensional vectors, forming the parameter vector to be solved F = [ρ, t];

[0067] Specifically, in an embodiment of the present invention, the true model of the formation to be measured is a five-layer geoelectric model, with resistivity ρ_true = [20, 50, 300, 100, 50], unit Ω·m, and thickness t_true = [20, 60, 20, 100, inf], unit m. The thickness of the last layer is defaulted to infinity, so it does not need to be inverted.

[0068] In this embodiment, the parameter vector F to be solved is F = [ρ1, ρ2, ρ3, ρ4, ρ5, t1, t2, t3, t4], and the prior parameter range is Fmin = [10, 10, 200, 10, 10, 10, 10, 10, 10], Fmax = [200, 200, 500, 200, 200, 200, 200, 200, 200].

[0069] Specifically, in an embodiment of the present invention, the side length of the transmitting wire frame of the transient electromagnetic device is 100 m, the transmitting current is 10 A, the radius of the receiving coil is 1 m, and the induced voltage data is collected at 50 points within the time range of [10 -7 s, 10 -2 s] after the transmitting current is turned off.

[0070] S2: The computing device initializes the vectors of N individuals in the teaching and learning optimization algorithm based on the Tent chaotic map.

[0071] Among them, the vectors of the N individuals are the above-mentioned parameter vectors to be solved, including the resistivity parameters and layer thickness parameters of the formation to be measured. The resistivity parameters are within the resistivity parameter range, and the layer thickness parameters are within the layer thickness parameter range. N is a positive integer greater than or equal to 20.

[0072] Specifically, the computing device can first obtain N chaotic variables by using the chaotic map, and then map the N chaotic variables into the vectors of the N individuals to obtain the initial values of the vectors of the N individuals.

[0073] In some other possible implementations, the computing device can also perform Tent chaotic mapping based on the median of the prior parameter range to obtain the initial vectors of the N individuals.

[0074] Preferably, the calculation formula of the Tent chaotic map in step S2 includes:

[0075]

[0076] X i+1 = F min +(F max -F min )x i+1

[0077] Among them, x i and x i+1 are the i-th and (i + 1)-th chaotic variables respectively, and X i+1is the (i + 1)-th individual, where i = 1, 2, ..., N - 1; Fmin is the vector composed of the minimum value of the resistivity parameter range and the minimum value of the layer thickness parameter range of the formation to be measured, Fmax is the vector composed of the maximum value of the resistivity parameter range and the maximum value of the layer thickness parameter range of the formation to be measured, and α is a random number between 0 and 1.

[0078] Among them, when i = 0, x 0 can be a random number between 0 and 1.

[0079] That is to say, Fmin and Fmax constitute the prior parameter range [Fmin, Fmax].

[0080] Specifically, in an embodiment of the present invention, N = 60 individuals are selected for initialization using Tent chaotic mapping.

[0081] S3: The computing device iteratively calculates the fitness of the N individuals based on the vectors of the N individuals and the induced voltage data through the teaching and learning optimization algorithm.

[0082] Among them, step S3 includes:

[0083] S3.1: The computing device calculates the first fitness of each individual based on the induced voltage data and the vectors of the N individuals, and determines the teacher individual and the learner individuals among the N individuals based on the first fitness.

[0084] Among them, the computing device can calculate the fitness f of the individual through the following calculation formula:

[0085]

[0086] Among them, V obs is the induced voltage data, and V cal is the simulated voltage data obtained by forward modeling according to the vector of the individual.

[0087] Specifically, the computing device can calculate V cal .

[0088] Since the resistivity of each layer of the formation can be regarded as uniform, and the resistivity of adjacent formations usually has significant differences, the computing device can select the points with significant changes from the measured induced voltage data as V obs , and calculate the fitness f with the simulated voltage data obtained by calculating the parameters representing the corresponding formation in the vector of the individual.

[0089] Exemplarily, there are 5 layers in the formation to be measured. The computing device can select 5 groups of corresponding voltage data to calculate the fitness f, and then comprehensively calculate the comprehensive fitness f of this individual by taking the average value or other means.

[0090] S3.2: In the teaching stage, the computing device can calculate the first value of the vector of each student individual based on the vector of this student individual, the vector of this teacher individual, and the average value of the vectors of all student individuals among these N individuals.

[0091] Among them, each student individual learns according to the difference between the vector of the teacher individual and the average value of the vectors of the student individuals.

[0092] Specifically, the calculation formula for calculating this first value includes:

[0093]

[0094] Among them, X i is the i-th student individual, X teacher is the teacher individual, is the first value of the i-th student individual, r is a random number uniformly distributed between [0, 1], TF is a preset teaching factor, is the average value of the vectors of all student individuals among N individuals.

[0095] S3.3: In the learning stage, the computing device can calculate the second value of the vector of this first student individual based on the first value of the vector of the first student individual and the first value of the vector of the second student individual.

[0096] Among them, the first student individual is any one of these student individuals, and the second student individual is any one of these student individuals except the first student individual.

[0097] Among them, in the learning stage, each student individual (as the first student individual) randomly selects a learning object (the second student individual) and makes adjustments according to the difference between himself and the learning object.

[0098] Specifically, the calculation formula for calculating this second value includes:

[0099]

[0100] Among them, and are respectively the first value of the first student individual and the first value of the second student individual, is the second value of the first student individual, r is a random number uniformly distributed between [0, 1], and They are the fitness values corresponding to the first values of the first student individual and the second student individual, respectively.

[0101] S3.4: Calculate the second fitness of each of the student individuals based on the second value.

[0102] After calculating the second values of each student individual, the computing device can calculate the corresponding second fitness based on the second value. The implementation manner of calculating the second fitness is similar to the implementation manner of calculating the first fitness described above, and will not be elaborated here.

[0103] S3.5: When the second fitness is greater than the first fitness, update the vector corresponding to the student individual based on the second value; when the second fitness is less than or equal to the first fitness, keep the vector of the student individual unchanged.

[0104] Among them, after a student individual experiences the teaching and learning stage, if the second fitness calculated based on the second value is greater than the first fitness, it means that the simulated voltage data is closer to the measured induced voltage data, and the corresponding vector is closer to the true formation parameters. Therefore, its own vector can be updated to the second value. Otherwise, it still remains the original value unchanged, and the individual with the maximum fitness is reselected from the N individuals as the teacher individual for the next iteration.

[0105] It can be understood that when the fitness calculation formula is different, it is also possible that the smaller the fitness, the better. In this case, the individual with the smaller fitness should be used as the teacher individual, and the logic for updating the vector of the student individual should also be changed accordingly.

[0106] S3.6: After updating or maintaining the vector of the student individual, return to execute step S3.1.

[0107] It can be understood that iterative schemes obtained by making simple changes or replacing steps and parameters based on the basic logic of the above steps are all within the protection scope of the embodiments of the present application. The computing device can execute any scheme within the protection scope to iteratively calculate the fitness of the N individuals.

[0108] S4: When the fitness of the teacher individual among the N individuals has not increased in M consecutive iterations, the computing device updates the vectors of the student individuals among the N individuals based on the Cauchy-difference hybrid mutation strategy.

[0109] Among them, M is a positive integer greater than or equal to 3. Specifically, in an embodiment of the present invention, M = 3 is selected.

[0110] S4.1: Perform hybrid mutation on the i-th student individual. The calculation formula for hybrid mutation includes:

[0111]

[0112] Among them, X DE represents the differential mutation vector, and X CT represents the Cauchy mutation vector, and X r1 , X r2 and X r3 are vectors of three random student individuals excluding the teacher individual and the i-th student individual. F is the scaling factor, Cauchy(m) represents the Cauchy distribution in m dimensions, and λ1 and λ2 are adjustment factors. represents the vector of the i-th student individual after hybrid mutation, and X i represents the vector of the i-th student individual before hybrid mutation.

[0113] Exemplarily, the value range of F is [0.5, 2].

[0114] Specifically, the adjustment factors λ1 and λ2 are used to ensure that the proportion of Cauchy mutation is greater than that of differential mutation at the initial stage of iteration, and the proportion of differential mutation is greater than that of Cauchy mutation at the end stage of iteration.

[0115] S4.2: Perform crossover calculation on the vector of the i-th student individual. The calculation formula for crossover calculation includes:

[0116]

[0117] Among them, is the j-th element of the i-th student individual after crossover calculation, and a ij and are the values of the j-th elements of the vectors of the i-th student individual before and after hybrid mutation respectively; CE is the preset crossover probability, and its value range is [0, 1]; rand is a random number between [0, 1]; randi(D) represents a randomly generated integer in the D-dimensional solution space, and D is the dimensionality of the vector of the student individual.

[0118] Among them, the first line in the calculation formula means that when rand is less than or equal to CR, or j is equal to randi(D), as long as one of these two conditions is met, then the corresponding j-th element takes the value of the corresponding element in the hybrid mutation vector. When neither of these two conditions is met, this corresponding j-th element takes the value before hybrid mutation.

[0119] For example, if X i is a 9-dimensional vector, then j is an integer between 1 and 9. Assuming randi(D) = 3, then even if rand > CR, the third dimension of Xi will still mutate. It should be noted that for each rand will be recalculated to compare with CR each time, but randi(D) will only be calculated once, which ensures that at least one element corresponding to j = randi(D) mutates.

[0120] S4.3: Update the vectors of the student individuals among the N individuals. The calculation formula for updating the vectors includes:

[0121]

[0122] Among them, is the vector of the i-th student individual after update, is the vector of the i-th student individual after crossover calculation; and f(X i ) are respectively and the fitness corresponding to X i .

[0123] After updating the vectors of the student individuals among the N individuals, return to execute step S3.

[0124] S5: When the number of iterations is equal to the preset number, the computing device outputs the vector of the teacher individual in the last iteration as the optimal inversion parameter result.

[0125] Among them, the preset number is greater than or equal to 50. In an embodiment of the present invention, M = 50 is selected, and the comparison graph between the finally output result and the true electrical model is as Figure 2 shown.

[0126] It can be understood that it is difficult to find an absolute optimal solution for such problems, and only by infinite iteration can we approach it infinitely. After more than or equal to 50 iterations, the change of the iterated vector is very small, and at this time it can be regarded as convergence.

[0127] In the embodiment of the present application, the teaching and learning algorithm is used to invert the formation resistivity and thickness, and the Tent chaotic mapping and Cauchy-differential evolution hybrid mutation are introduced to improve the global optimization performance of the algorithm. The teaching and learning algorithm can reduce the iteration parameters, and the individual iteration processes are independent during the iteration process, and the optimization process can be accelerated through parallel computing to improve the computing efficiency; at the same time, the Cauchy-differential evolution hybrid mutation strategy is introduced, which can reduce the risk of the algorithm falling into the local optimum, thereby improving the accuracy of inverting multi-layer terrain parameters by the transient electromagnetic method.

[0128] Please refer to Figure 3 , Figure 3 which is the structural schematic diagram of a transient electromagnetic inversion system provided by the embodiment of the present application. As Figure 3 shown, the system 30 includes:

[0129] An acquisition module 301 is configured to acquire a resistivity parameter range, a layer thickness parameter range, and induction voltage data of a formation to be measured. The induction voltage data is obtained by a receiving coil of a transient electromagnetic device sensing a secondary electromagnetic field of the formation to be measured after a pulse current is emitted above the formation to be measured by a transmitting coil of the transient electromagnetic device. The secondary electromagnetic field is an electromagnetic field generated by a medium in the formation to be measured based on the pulse current.

[0130] An initialization module 302 is configured to initialize vectors of N individuals in a teaching and learning based optimization algorithm based on Tent chaotic mapping. The vectors include the resistivity parameter and the layer thickness parameter of the formation to be measured. The resistivity parameter is within the resistivity parameter range, and the layer thickness parameter is within the layer thickness parameter range. N is a positive integer greater than or equal to 20.

[0131] An iteration module 303 is configured to iteratively calculate fitnesses of the N individuals through the teaching and learning based optimization algorithm based on the vectors of the N individuals and the induction voltage data.

[0132] A mutation module 304 is configured to, in a case where the fitness of the teacher individual among the N individuals has not been improved in continuous M iterations, update vectors of student individuals among the N individuals based on a Cauchy-difference hybrid mutation strategy, and return to execute step S3. M is a positive integer greater than or equal to 3.

[0133] An output module 305 is configured to, in a case where the number of iterations is equal to a preset number, output the vector of the teacher individual in the last iteration as an optimal inversion parameter result. The preset number is greater than or equal to 50.

[0134] Preferably, the iteration module 303 is specifically configured to execute the following steps:

[0135] S3.1: Calculate a first fitness of each individual based on the induction voltage data and the vectors of the N individuals, and determine a teacher individual and student individuals among the N individuals based on the first fitness.

[0136] S3.2: Calculate a first value of the vector of each student individual based on an average value of vectors of all student individuals among the N individuals, the vector of the student individual, and the vector of the teacher individual.

[0137] S3.3: Calculate a second value of the vector of the first student individual based on the first value of the vector of the first student individual and the first value of the vector of the second student individual. The first student individual is any one of the student individuals, and the second student individual is any one of the student individuals other than the first student individual.

[0138] S3.4: Calculate a second fitness of each student individual based on the second value.

[0139] S3.5: For a particular individual student, when the second fitness is greater than the first fitness, update the vector of the individual student based on the second value; when the second fitness is less than or equal to the first fitness, keep the vector of the individual student unchanged.

[0140] S3.6: After updating or keeping the vectors of all individual students, return to execute step S3.1.

[0141] Preferably, the calculation formula of the Tent chaotic mapping in the initialization module 302 includes:

[0142]

[0143] X i+1 = F min +(F max -F min )x i+1

[0144] where, x i and x i+1 are the i-th and (i + 1)-th chaotic variables respectively, X i+1 is the (i + 1)-th individual, i = 1, 2,..., N - 1; Fmin is the vector composed of the minimum value of the resistivity parameter range and the minimum value of the layer thickness parameter range of the formation to be measured, Fmax is the vector composed of the maximum value of the resistivity parameter range and the maximum value of the layer thickness parameter range of the formation to be measured, and α is a random number between 0 and 1.

[0145] Preferably, the calculation formula for calculating the individual fitness f in the iteration module 303 includes:

[0146]

[0147] where, V obs is the induced voltage data, and V cal is the simulated voltage data obtained by forward modeling according to the vector of the individual.

[0148] Preferably, the calculation formula for calculating the first value in the iteration module 303 includes:

[0149]

[0150] where, X i is the i-th student individual, X teacher is the teacher individual, is the first value of the i-th student individual, r is a random number uniformly distributed between [0, 1], TF is a preset teaching factor, is the average value of the vectors of all individual students among N individuals.

[0151] Preferably, the calculation formula for calculating the second value in the iteration module 303 includes:

[0152]

[0153] Wherein, and are respectively the first value of the first student individual and the first value of the second student individual, is the second value of the first student individual, r is a random number uniformly distributed between [0, 1], and are respectively the fitness corresponding to the first value of the first student individual and the fitness corresponding to the first value of the second student individual.

[0154] Preferably, the mutation module 304 is specifically configured to perform the following steps:

[0155] S4.1: Perform hybrid mutation on the i-th student individual, and the calculation formula for hybrid mutation includes:

[0156]

[0157] Wherein, X DE represents the differential mutation vector, X CT represents the Cauchy mutation vector, X r1 , X r2 and X r3 are vectors of three random student individuals excluding the teacher individual and the i-th student individual, F is the scaling factor, Cauchy(m) represents the m-dimensional Cauchy distribution, and λ1 and λ2 are adjustment factors, represents the vector of the i-th student individual after hybrid mutation, and X i represents the vector of the i-th student individual before hybrid mutation;

[0158] S4.2: Perform crossover calculation on the vector of the i-th student individual, and the calculation formula for crossover calculation includes:

[0159]

[0160] Wherein, is the j-th element of the i-th student individual after crossover calculation, a ij and are respectively the values of the j-th element of the vectors of the i-th student individual before and after hybrid mutation; CE is a preset crossover probability, and its value range is [0, 1]; rand is a random number between [0, 1]; randi(D) represents a random integer generated in the D-dimensional solution space, and D is the dimensionality of the vector of the student individual;

[0161] S4.3: Update the vectors of the student individuals among the N individuals. The calculation formula for updating the vectors includes:

[0162]

[0163] where is the vector of the i-th student individual after update, is the vector of the i-th student individual after crossover calculation; and f(X i ) are respectively and X i corresponding fitness values.

[0164] The transient electromagnetic inversion system provided by the embodiments of the present application can be understood by referring to the corresponding content in the foregoing method embodiment section, and will not be repeated here.

[0165] As Figure 4 shown, Figure 4 is a possible schematic logical structure diagram of the computing device provided by the embodiments of the present application. The computing device 400 includes: a processor 401, a communication interface 402, a memory 403, and a bus 404. The processor 401, the communication interface 402, and the memory 403 are interconnected through the bus 404. In the embodiments of the present application, the processor 401 is used to control and manage the actions of the computing device 400. For example, the processor 401 is used to execute Figure 1 the steps in the embodiment and / or other processes for the technologies described herein. The communication interface 402 is used to support the computing device 400 to communicate. The memory 403 is used to store the program code and data of the computing device 400.

[0166] Among them, the processor 401 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in connection with the disclosure of the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and so on. The bus 404 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 only one thick line is shown in

[0167] In another embodiment of the present application, a computer-readable storage medium is further provided. The computer-readable storage medium includes instructions that, when running on a computer, cause the computer to execute the above-mentioned Figure 1 method described in some of the embodiments.

[0168] Those of ordinary skill in the art can realize that the units of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0169] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0170] In several embodiments provided in the embodiments of the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0171] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0172] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0173] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, read-only memories (ROM), random access memories (RAM), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0174] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of various embodiments of the present invention, and they should all be covered within the scope of the claims and the description of the present invention.

Claims

1. A transient electromagnetic inversion method, characterized in that: Including: S1: Obtain the resistivity parameter range, layer thickness parameter range, and induced voltage data of the formation to be measured. The induced voltage data is the induced voltage data obtained by the receiving coil of the transient electromagnetic device sensing the secondary electromagnetic field of the formation to be measured after a pulsed current is emitted above the formation to be measured by the transmitting coil of the transient electromagnetic device; The secondary electromagnetic field is the electromagnetic field generated by the medium in the formation to be measured based on the pulsed current; S2: Based on the Tent chaotic map, initialize the vectors of N individuals in the teaching and learning optimization algorithm. The vectors include the resistivity parameter and layer thickness parameter of the formation to be measured. The resistivity parameter is within the resistivity parameter range, and the layer thickness parameter is within the layer thickness parameter range. N is a positive integer greater than or equal to 20; S3: Through the teaching and learning optimization algorithm, based on the vectors of the N individuals and the induced voltage data, iteratively calculate the fitness of the N individuals; S4: In the case where the fitness of the teacher individual among the N individuals has not improved in M consecutive iterations, update the vectors of the student individuals among the N individuals based on the Cauchy-difference hybrid mutation strategy, and return to execute step S3. M is a positive integer greater than or equal to 3; S5: When the number of iterations is equal to the preset number, output the vector of the teacher individual in the last iteration as the optimal inversion parameter result. The preset number is greater than or equal to 50.

2. The method according to claim 1, characterized in that The fitness includes the first fitness and the second fitness; Step S3 includes: S3.1: Based on the induced voltage data and the vectors of the N individuals, calculate the first fitness of each individual, and determine the teacher individual and student individuals among the N individuals based on the first fitness; S3.2: Based on the average value of the vectors of all student individuals among the N individuals, the vector of the student individual, and the vector of the teacher individual, calculate the first value of the vector of each student individual; S3.3: Based on the first value of the vector of the first student individual and the first value of the vector of the second student individual, calculate the second value of the vector of the first student individual. The first student individual is any individual among the student individuals, and the second student individual is any individual among the student individuals other than the first student individual; S3.4: Calculate the second fitness of each student individual based on the second value; S3.5: For a student individual, if the second fitness is greater than the first fitness, update the vector of the student individual based on the second value; if the second fitness is less than or equal to the first fitness, keep the vector of the student individual unchanged; S3.6: After updating or maintaining the vectors of all student individuals, return to execute step S3.

1.

3. The method according to claim 1, characterized in that, The calculation formula of the Tent chaotic map in step S2 includes: X i+1 =F min +(F max -F min )x i+1 where x i and x i+1 are the i-th and (i + 1)-th chaotic variables respectively, X i+1 is the (i + 1)-th individual, i = 1, 2, ..., N - 1; F min is a vector composed of the minimum value of the resistivity parameter range and the minimum value of the layer thickness parameter range of the formation to be measured, F max is a vector composed of the maximum value of the resistivity parameter range and the maximum value of the layer thickness parameter range of the formation to be measured, and α is a random number between 0 and 1.

4. The method according to claim 1, characterized in that, The calculation formula for calculating the fitness f of the individual in step S3 includes: Among them, V obs is the induced voltage data, and V cal is the simulated voltage data obtained by forward vector calculation for the individual.

5. The method according to claim 2, wherein The calculation formula for calculating the first value in step S3.2 includes: Among them, X i is the i-th student individual, X teacher is the teacher individual, is the first value of the i-th student individual, r is a random number uniformly distributed between [0, 1], and TF is a preset teaching factor, is the average value of the vectors of all student individuals among N individuals.

6. The method according to claim 5, wherein The calculation formula for calculating the second value in step S3.3 includes: Among them, and are the first value of the first student individual and the first value of the second student individual respectively, is the second value of the first student individual, r is a random number uniformly distributed between [0, 1], and are the fitness corresponding to the first value of the first student individual and the fitness corresponding to the first value of the second student individual respectively.

7. The method according to any one of claims 1 to 6, characterized in that Step S4 includes: S4.1: Perform hybrid mutation on the individual student. The calculation formula for hybrid mutation includes: Among them, X DE represents the differential mutation vector, X CT represents the Cauchy mutation vector, X r1 , X r2 and X r3 are the vectors of three random student individuals other than the teacher individual and the i-th student individual. F is the scaling factor, Cauchy(m) represents the Cauchy distribution in m dimensions, and λ1 and λ2 are the adjustment factors. represents the vector of the i-th student individual after hybrid mutation, and X i represents the vector of the i-th student individual before hybrid mutation; S4.2: Perform crossover calculation on the vector of the individual student. The calculation formula for crossover calculation includes: in, is the jth element of the i-th student after cross calculation, a ij and are the values of the jth element of the vector of the i-th student individual before and after the mixed mutation; CE is the preset crossover probability, ranging from [0,1]; rand is a random number between [0,1]; randi(D) represents the random generation of integers in the D-dimensional solution space, where D is the number of dimensions of the student individual vector; S4.3: Update the vector of the individual student among the N individuals. The calculation formula for updating the vector includes: Among them, is the vector of the updated i-th student individual, is the vector of the i-th student individual after crossover calculation; and f(X i ) are respectively and X i corresponding fitness values.

8. A transient electromagnetic inversion system, characterized in that, Applied to the method according to any one of claims 1-7, the system includes: An acquisition module, configured to acquire the resistivity parameter range, layer thickness parameter range, and induced voltage data of the formation to be measured. The induced voltage data is the induced voltage data obtained by the receiving coil of the transient electromagnetic device sensing the secondary electromagnetic field of the formation to be measured after a pulsed current is emitted above the formation to be measured by the transmitting coil of the transient electromagnetic device; The secondary electromagnetic field is the electromagnetic field generated by the medium in the formation to be measured based on the pulsed current; An initialization module, configured to initialize the vectors of N individuals in the teaching and learning optimization algorithm based on the Tent chaotic map. The vectors include the resistivity parameter and layer thickness parameter of the formation to be measured. The resistivity parameter is within the resistivity parameter range, and the layer thickness parameter is within the layer thickness parameter range. N is a positive integer greater than or equal to 20; An iteration module, configured to iteratively calculate the fitness of the N individuals based on the vectors of the N individuals and the induced voltage data through the teaching and learning optimization algorithm; A mutation module, configured to update the vectors of the individual students among the N individuals based on the Cauchy-difference hybrid mutation strategy and return to execute step S3 when the fitness of the teacher individual among the N individuals has not improved in M consecutive iterations. M is a positive integer greater than or equal to 3; An output module, configured to output the vector of the teacher individual in the last iteration as the optimal inversion parameter result when the number of iterations is equal to the preset number. The preset number is greater than or equal to 50.

9. A computing device, characterized in that, Comprising: A memory, configured to store a program; A processor, configured to load the program to execute the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program runs, it controls the device where the computer-readable storage medium is located to execute the method according to any one of claims 1-7.

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