High-temperature superconducting transient electromagnetic nonlinear inversion method and system

Abstract

The invention discloses a high-temperature superconducting transient electromagnetic nonlinear inversion method and system. The method comprises the following steps: acquiring actual transient electromagnetic data of the earth surface; constructing a layered resistivity model according to the actual earth surface electromagnetic data and preset theoretical earth surface electromagnetic data, wherein model parameters in the layered resistivity model comprise resistivity and layer thickness; electromagnetic data inversion is conducted on the layered resistivity model through a preset improved bat algorithm, stratum parameters of the exploration area are obtained, and the stratum parameters are the resistivity and the layer thickness of each horizontal layered medium. The global and local search of the algorithm in the evolutionary process is balanced through the spiral flight strategy, the solving precision of the algorithm is improved, the algorithm is prevented from falling into local optimum too early, and the calculation efficiency and the inversion precision are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electromagnetic detection data processing, and particularly relates to a high-temperature superconducting transient electromagnetic nonlinear inversion method and system. Background Art

[0002] In transient electromagnetic method detection, the system transmits the primary electromagnetic field to the ground and measures the secondary electromagnetic field generated by the underground eddy current. Then, by solving the nonlinear inverse problem (inversion), geoelectric information can be obtained from the measured data. To explain the collected measured data, various linear or quasi-linear inversion techniques have been proposed. However, TEM inversion is a complex nonlinear inversion problem, and traditional inversion methods such as Gauss-Newton or conjugate gradient are mostly local optimization techniques, which require the calculation of derivatives and appropriate initial guesses. Therefore, there is an urgent need to find a better and alternative nonlinear inversion method that takes into account both the nonlinearity and multimodality of the inversion problem and provides an appropriate inversion solution for TEM data.

[0003] With the rapid development of inversion technology, the OCCAM method has been widely recognized by geophysical researchers. However, the traditional OCCAM inversion algorithm can only reflect the approximate shape of the formation model, with low accuracy and long calculation time, which cannot meet the requirements of high measurement efficiency, and even easily causes misjudgment of the inversion results and misunderstanding of geological understanding. Summary of the Invention

[0004] The present invention provides a high-temperature superconducting transient electromagnetic nonlinear inversion method and system, which are used to solve the technical problems that the traditional OCCAM inversion algorithm can only reflect the approximate shape of the formation model, with low accuracy and long calculation time, which cannot meet the requirements of high measurement efficiency, and even easily causes misjudgment of the inversion results and misunderstanding of geological understanding.

[0005] In a first aspect, the present invention provides a high-temperature superconducting transient electromagnetic nonlinear inversion method, including:

[0006] Obtaining actual surface transient electromagnetic data;

[0007] Constructing a layered resistivity model according to the actual surface electromagnetic data and the preset theoretical surface electromagnetic data, where the model parameters in the layered resistivity model include resistivity and layer thickness;

[0008] Using a preset improved bat algorithm to perform electromagnetic data inversion on the layered resistivity model to obtain formation parameters of the exploration area, where the formation parameters are the resistivity and layer thickness of each horizontal layered medium. Among them, the improved bat algorithm includes logistic chaotic mapping and a spiral flight strategy, and the expression of the spiral flight strategy is:

[0009]

[0010] where k is the spiral flight step size, iter is the number of iterations, maxgen is the maximum number of iterations, L is a uniformly distributed random number in [-1, 1], z is a spiral parameter that varies with the number of iterations, is the position of the i-th bat in the search space in the t-th generation, x * is the current global optimal solution, is the position of the i-th bat in the search space in the (t - 1)-th generation, and e is the natural constant.

[0011] In a second aspect, the present invention provides a high-temperature superconducting transient electromagnetic nonlinear inversion system, including:

[0012] An acquisition module configured to acquire actual surface transient electromagnetic data;

[0013] A construction module configured to construct a layered resistivity model based on the actual surface electromagnetic data and preset theoretical surface electromagnetic data, and the model parameters in the layered resistivity model include resistivity and layer thickness;

[0014] An inversion module configured to perform electromagnetic data inversion on the layered resistivity model by using a preset improved bat algorithm to obtain formation parameters of the exploration area, where the formation parameters are the resistivity and layer thickness of each horizontal layered medium. Among them, the improved bat algorithm includes a logistic chaotic mapping and a spiral flight strategy, and the expression of the spiral flight strategy is:

[0015]

[0016] where k is the spiral flight step size, iter is the number of iterations, maxgen is the maximum number of iterations, L is a uniformly distributed random number in [-1, 1], z is a spiral parameter that varies with the number of iterations, is the position of the i-th bat in the search space in the t-th generation, x * is the current global optimal solution, is the position of the i-th bat in the search space in the (t - 1)-th generation, and e is the natural constant.

[0017] In a third aspect, an electronic device is provided, which includes: at least one processor, and a memory communicatively connected to the at least one processor, where the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of the high-temperature superconducting transient electromagnetic nonlinear inversion method according to any embodiment of the present invention.

[0018] Fourthly, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the program instructions are executed by a processor, the processor is caused to execute the steps of the high-temperature superconducting transient electromagnetic nonlinear inversion method according to any embodiment of the present invention.

[0019] For the high-temperature superconducting transient electromagnetic nonlinear inversion method and system of the present application, a nonlinear novel inversion method using an improved bat algorithm is adopted. This method is different from the traditional inversion methods that have the problem of local extrema. It does not need to repeatedly iterate for gradient learning, and only needs to adaptively find the optimal solution through swarm intelligence. In addition, this method uses the logistic chaotic mapping to improve the initial population distribution to enhance the global search ability of the algorithm, and adopts a spiral flight strategy to balance the global and local searches of the algorithm during the evolution process. It not only improves the solution accuracy of the algorithm, but also avoids the algorithm from falling into local optimality prematurely, greatly improving the calculation efficiency and inversion accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is a flowchart of a high-temperature superconducting transient electromagnetic nonlinear inversion method provided by an embodiment of the present invention;

[0022] Figure 2 It is a schematic diagram of a layered earth for a specific embodiment provided by an embodiment of the present invention;

[0023] Figure 3 It is a schematic diagram of the inversion result of an H-type geoelectric model for a specific embodiment provided by an embodiment of the present invention;

[0024] Figure 4 It is a schematic diagram of the inversion result of the magnetic field intensity of a noisy H-type geoelectric model for a specific embodiment provided by an embodiment of the present invention;

[0025] Figure 5 It is a schematic diagram of the inversion results of three algorithms for a specific embodiment provided by an embodiment of the present invention;

[0026] Figure 6 It is a structural block diagram of a high-temperature superconducting transient electromagnetic nonlinear inversion system provided by an embodiment of the present invention;

[0027] Figure 7 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, 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.

[0029] Please refer to Figure 1 , which shows a flowchart of a high-temperature superconducting transient electromagnetic nonlinear inversion method of the present application.

[0030] As Figure 1 shown, the high-temperature superconducting transient electromagnetic nonlinear inversion method specifically includes the following steps:

[0031] Step S101, obtaining actual surface transient electromagnetic data.

[0032] Specifically, actual surface transient electromagnetic data is obtained by using observation elements arranged in the exploration area.

[0033] Step S102, constructing a layered resistivity model according to the actual surface electromagnetic data and preset theoretical surface electromagnetic data, where the model parameters in the layered resistivity model include resistivity and layer thickness.

[0034] The forward algorithm of the transient electromagnetic method (TEM) is specifically as follows: starting from the frequency-domain response expression of the central loop source in a horizontally layered medium, the vertical magnetic field intensity value in the receiving loop is calculated through Hankel transform, Gaussian integral and Gaver-Stehfest transform.

[0035] Assume that in the x direction, at the origin O(0,0,0) of the Cartesian coordinate system, there is a circular ring with a radius of r on the surface of the layered medium, and the medium has a total of N layers. The resistivity and thickness of each layer are ρ i and h i (i = 1,2,…,N), as Figure 2 shown.

[0036] According to the Maxwell equation expression with a current source:

[0037]

[0038] In the formula, is the curl of the vector field cross-multiplied with it, E is the electric field intensity, H is the magnetic field intensity, ω is the angular frequency of the electromagnetic wave, μ is the magnetic permeability, ε is the permittivity, J is the current density, and j is the imaginary symbol;

[0039] The vertical magnetic field excited by the surface horizontal central loop source can be obtained:

[0040]

[0041] In the formula, I is the magnitude of the transmitting loop current, ρ is the distance from the receiving point to the transmitting source, J1 is the first-order Bessel function; m is the integration variable, k1 is the conduction current, μ1 and σ1 respectively represent the magnetic permeability and conductivity of the homogeneous half-space medium, and ω is the angular frequency of the electromagnetic wave, is the input impedance of the first layer.

[0042] Since there is no analytical solution for the time-domain response of the layered earth, only numerical calculation methods can be used to solve it. For the Bessel function, the Hankel transform is used for solution, and then the Gaver-Stehfest transform is used to realize the frequency-time domain conversion, and the time-domain response can be obtained as:

[0043]

[0044] In the formula, S n =(ln2 / t)×n, K n is the coefficient, and generally N = 12 is taken.

[0045] In this step, since most difficult explorations require finding the optimal model parameters through inversion, the demand for the ability to quickly invert the obtained data is increasing. Obviously, to start inversion, correct forward calculation is required. In the measurement modeling of the high-temperature superconducting transient electromagnetic method studied in this paper, a one-dimensional planar layered model is adopted, and the formation thickness h and resistivity ρ describe the geoelectric properties of the earth. In the forward simulation, assuming that the geoelectric parameters are known, the time-domain receiving field can be calculated through the secondary magnetic field response H z In inversion, the formation thickness h and resistivity ρ will be reconstructed based on the field measured data. The process of transient electromagnetic data inversion modeling is as follows:

[0046] Generally, the more layers in the inversion model, the closer it is to the actual geological conditions, but it will increase the inversion time and reduce the inversion stability. Therefore, the parameter settings must be combined with the actual situation. Adopting a typical three-layer H-type geoelectric model (the number of inverted layers is 15, and the thickness of each layer is fixed at 20m), the upper and lower intervals of the resistivity parameter search of the inversion model are 50% of the resistivity parameters of the theoretical model. Invert the theoretical model, generate a layered resistivity model according to the value range of resistivity and layer thickness; conduct forward simulation calculations on the layered geoelectric model based on the observation system parameters of the transient electromagnetic method to obtain transient electromagnetic response data. The distribution of model parameters is as follows:

[0047] Model parameters:

[0048]

[0049] where ρ1 - ρ 15 are the resistivity of each layer of the theoretical model, and h1 - h 14 are the layer thicknesses of each layer of the theoretical model respectively;

[0050] Given the minimum model parameters:

[0051]

[0052] Given the maximum model parameters:

[0053]

[0054] A three - layer H - type geoelectric model was selected for inversion. The model parameters include resistivity and layer thickness, and its initial data was randomly generated within the range of the minimum and maximum model reference values. Among them, the computer simulation running environment is as follows: the CPU is Core(TM)i7 - 12700F, the memory is 16GB, the operating system is Window 10, and the Matlab program was developed in 2022b.

[0055] A comprehensive example of using the improved bat algorithm for transient electromagnetic data inversion will be presented to verify the effectiveness and feasibility of the proposed method. The theoretical parameter settings of the central loop transient electromagnetic method are as follows: the current intensity I = 1A, the transmitter radius r = 100m, the turn - off time T = 1μs (ramp turn - off), and the receiving point is located at the center of the receiver coil. Simulate the Hz component of the transient electromagnetic response data from 10 -6 to 10 -4 s, which is equally logarithmically spaced at 31 time sampling points.

[0056] The forward response of the vertical component of the magnetic field intensity in horizontally layered media can be solved by Hankel transform, sine - cosine transform or G - S inverse Laplace transform. The fitness objective function is constructed as:

[0057]

[0058] where f is the fitness objective function of the layered resistivity model, N is the number of time channels, h sn is the measured data response of the vertical magnetic field for the nth time channel, and h cn is the calculated response of the vertical magnetic field for the nth time channel. The error is defined as the fitness function, and solving the optimal solution is to find the resistivity value when the fitness objective function f is minimized. The smaller the fitness objective function, that is, the smaller the error, the closer the forward calculation response is to the measured data response.

[0059] Step S103: Use a preset improved bat algorithm to perform electromagnetic data inversion on the layered resistivity model to obtain formation parameters of the exploration area. The formation parameters are the resistivity and layer thickness of each horizontal layered medium.

[0060] In this step, initialize the population. The mathematical model of the improved bat algorithm is described as follows. Let the size of the bat population be P, and the initial population X(0) = {X1, X2,..., X P}, and the dimension of the feasible solution space be d. Then the i-th bat in the solution space is represented as X i = [x i1 , x i2 , …, x id . The initial population of the improved bat algorithm is randomly generated within a certain search range and evenly distributed in the solution space.

[0061] Randomly forming the initial population means: Introduce the improved bat algorithm to perform electromagnetic data inversion to obtain formation parameters of the exploration area. The formation parameters are the resistivity and layer thickness of each horizontal layered medium. Among them, the position of the bat in the improved bat algorithm represents the formation parameters, that is, the position of each bat in the bat population represents a set of formation parameters. Within a certain search parameter range, use a random method to determine each bat in the bat population to form the initial population.

[0062] In transient electromagnetic inversion, the standard bat algorithm balances the global and local search performance of the algorithm well, which is conducive to the effective search for the global optimal solution and the improvement of the overall execution efficiency. However, the initialization of the standard BA algorithm randomly generates the positions of bat individuals within the upper and lower bounds. Such initialization may cause the bats to be unevenly distributed in space, resulting in premature convergence of the algorithm and falling into a local optimal solution. To solve this problem, this paper proposes an initialization form based on the logistic chaotic sequence.

[0063] Due to the randomness, ergodicity and other properties of chaotic mapping, it can be used for the initialization of the population of swarm intelligence algorithms. The principle is to use the sequence generated by chaotic mapping between [0, 1], and then initialize the bat population according to the chaotic factor. This can control the initial distribution of bats to be more reasonable and avoid premature convergence. The mathematical model of the logistic mapping is described as:

[0064] x n+1 = axn(1 - x n ),

[0065] where X n ∈ [0, 1] is the chaotic variable of the n-th generation, a ∈ [0, 4] is the logistic parameter, and x n+1is the chaotic variable of the (n + 1)-th generation. Through the logistic chaotic mapping method, a large number of initial underlying parameters are generated within a certain range of model parameter search. The present invention improves the construction of the initial population by using the logistic chaotic mapping, avoiding the influence of uneven distribution of the initial population in the standard bat algorithm.

[0066] The BA algorithm based on the logistic chaotic mapping reduces the probability of premature convergence, but its optimal bat self-learning ability still needs to be improved. The closer the bat is to the extreme point, the more restricted the position update becomes, resulting in the bat population being able to search only in the local area and its neighborhood, which is an important factor affecting the optimization accuracy of the algorithm. To solve this problem, a spiral flying position update strategy is introduced. Its main idea is: introducing multiple parameters to control the radial and angular changes of the flight, and dynamically adjusting the spiral shape of the bat search. The algorithm improved by the spiral flying strategy is abbreviated as LSBA. The basic formula of the spiral flying position update strategy is as follows:

[0067]

[0068]

[0069] where k is the spiral flying step size, iter is the iteration number, maxgen is the maximum iteration number, L is a uniformly distributed random number in [-1, 1], z is the spiral parameter that changes with different iteration numbers, is the position of the i-th bat in the search space at the t-th generation, x * is the current global optimal solution, is the position of the i-th bat in the search space at the (t - 1)-th generation, and e is the natural constant.

[0070] Based on the logistic chaotic mapping, a spiral flying position update strategy is introduced to help the bat jump out of the local optimum. The spiral flying strategy uses the properties of the cosine function to dynamically adjust the size and amplitude of the spiral, so that the bat searches at a certain rotation angle, maximizing the avoidance of the generation of duplicate individuals, and effectively balancing the local and global searches of the algorithm.

[0071] Specifically, the improved bat algorithm (LSBA) based on the logistic chaotic mapping and the spiral flying strategy is applied to transient electromagnetic inversion, including:

[0072] Initializing the population: Given the maximum iteration number maxgen, the bat population size P, the pulse loudness A0, the pulse emission rate r0, the minimum value f min of the frequency emitted by the bat, and the maximum value f max, and within the search range of the lower and upper bounds of the given minimum and maximum model parameters, randomly generate an initial population using the logistic chaotic map;

[0073] Calculate the fitness objective function: Based on the obtained initial population, call the fitness objective function to find the bat individual with the lowest global fitness. Its global optimal bat is denoted as X g ;

[0074] Start the iterative loop: Enter the loop with the condition that the iteration number t is less than the maximum iteration number maxgen or the stopping criterion has not been reached. Update the pulse loudness A0 and pulse emission rate r0 of each bat;

[0075] Traverse each bat: Use a specific equation to generate the position of each bat; If the generated random number is less than the current bat's pulse emission rate r0, update the position of the current bat and update the global optimal bat X g ;

[0076] Spiral flight strategy: Adopt the spiral flight strategy to generate a new temporary position for each bat. If the fitness of the new temporary position is lower, update the position of the current bat and update the global optimal bat X at the same time g .

[0077] Condition judgment: Judge whether the current termination condition is satisfied. If so, save the current global optimal solution; otherwise, continue to execute the step of traversing each bat.

[0078] Result output: Output the global optimal solution, which is the inversion result of the final exploration area. The global optimal solution is the formation parameters corresponding to the global optimal bat X after the iteration is completed g corresponding formation parameters.

[0079] In summary, an improved bat algorithm-based non-linear novel inversion method is proposed. This method is different from the traditional inversion methods that have local extreme value problems. It does not need to repeatedly iterate for gradient learning, but only needs to adaptively find the optimal solution through swarm intelligence. In addition, this method uses the logistic chaotic map to improve the initial population distribution to enhance the global search ability of the algorithm, and uses the spiral flight strategy to balance the global and local searches of the algorithm during the evolution process, not only improving the solution accuracy of the algorithm, but also avoiding the algorithm from falling into local optimality prematurely, greatly improving the calculation efficiency and inversion accuracy.

[0080] In a specific embodiment, in order to verify the effect of the method of the present invention, a transient electromagnetic forward calculation is performed on a layered medium geoelectric model, and a traditional BA algorithm and the LSBA algorithm of the present invention are used for transient electromagnetic inversion calculation. The inversion parameters are set as follows: the number of bat populations is 50, and the maximum number of iterations is 60. To avoid the contingency of a single inversion, 10 independent inversions are performed for each model, and the average value of these 10 inversions is used as the final inversion result.

[0081] A: Inversion of one-dimensional layered geoelectric model.

[0082] To evaluate the inversion performance of the LSBA algorithm, taking the three-layer H-type model as an example, an inversion trial calculation is carried out, and the observation parameters are set as follows: the resistivity of the H-type model is set to [100, 50, 100], the thickness is [100, 100], the number of inverted layers is 15, and the thickness of each layer is fixed at 20 m. The fitting curve of the magnetic field intensity, the inversion resistivity result, and the optimization curve are as Figure 3 shown.

[0083] From Figure 3 (a)- Figure 3 (b), it can be seen that the resistivity result inverted by the LSBA algorithm is basically consistent with the variation law of the theoretical geoelectric model, and the inversion effect is better than that of the traditional BA algorithm. The forward magnetic field response of the inversion result of the LSBA algorithm has a good fitting effect with the forward magnetic field response of the theoretical model. From Figure 3 (c), it can be seen that the convergence speed of the BA algorithm is slow, and the convergence stagnates in the later stage, falling into the local optimum. However, the LSBA algorithm has a faster convergence speed in the early stage, and its optimization ability in the later stage is better than that of the BA algorithm, and it is not easy to fall into the local optimum. At the same iteration number, the iteration error of the LSBA inversion algorithm is smaller than that of the BA inversion algorithm. The results show that the LSBA algorithm has a higher inversion accuracy when applied to TEM data processing.

[0084] In summary, the above results show that compared with the traditional BA method, LSBA has greater advantages in TEM data inversion (optimal inversion model, minimum response curve fitting error, and faster convergence speed), and can effectively reduce the inversion error caused by nonlinear characteristics. The main reason is that the spiral flight strategy enables the algorithm to update the position of the bat at a certain rotation angle, which can effectively avoid it falling into the local optimum and has a strong global search ability. At the same time, an initialization population strategy based on logistic chaotic mapping is proposed to improve the global search ability in the early stage and the inversion stability in the later stage. However, due to the concentrated distribution of bats and the lack of randomness in position changes in the BA algorithm, its inversion error is relatively large. In short, the LSBA algorithm can significantly improve the inversion accuracy of thickness and resistivity and increase the inversion speed, so as to obtain better detection results, which has an important role in prospecting for minerals or groundwater.

[0085] B: Anti-noise performance test.

[0086] In actual work, the collected signals may contain various noise signals, which can lead to unstable processing results and large errors in the resistivity of the inversion results compared to the actual model. Therefore, it is necessary to analyze the anti-noise ability of the algorithm. We selected a typical H-type geoelectric model for experiments and inverted the data with 10% (10 dB) and 20% (7 dB) Gaussian random noise. Then, BA and LSBA inversions were performed on the noise-free TEM data and the data with 10% and 20% Gaussian random noise added, with the parameter settings unchanged. The inversion results of BA and LSBA were compared. The inversion results of the magnetic field intensity of the three-layer H-type geoelectric model with 10% and 20% noise are as Figure 4 shown.

[0087] As can be seen from the figure, when the induced magnetic field intensity values with 10% and 20% Gaussian random noise are added to the induced magnetic field intensity values of the theoretical model, the inversion results of the LSBA algorithm fit better with the curve of the induced magnetic field intensity of the theoretical model, and the inversion error of LSBA is smaller than that of BA. It can be seen that regardless of the type of random noise, the error of the LSBA algorithm is generally smaller than that of the BA algorithm, indicating that the anti-noise ability of the LSBA algorithm is better than that of the BA algorithm.

[0088] C: Examples of transient electromagnetic inversion.

[0089] To further study the applicability of the algorithm, we selected a field data set from a mining area in Anhui Province for inversion verification. Considering the low inversion accuracy of traditional transient electromagnetic data processing methods, this study used traditional PSO, BA, and LSBA algorithms to invert and interpret the TEM measurement data of the large loop source in the coal mine. In the transient electromagnetic vertical magnetic field (Hz), two pipelines in the mining area were selected at 380 - 1380 meters at 11 measuring points. The electromagnetic sounding data was collected through the GDP-32II TEM data acquisition system. The original data was preprocessed by the Datpro software related to the GDP-32II system provided by Zong. The total population numbers of the three algorithms were set to 50, and the maximum number of iterations was 60 times. Figure 5 (a)-(b) provide the inversion fitting response curve and inversion fitting error measured at 380 m. It can be seen that the inversion data of PSO, BA, and LSBA are consistent with the observed results. After 60 iterations, it was found that the inversion of the measurement data by the LSBA algorithm has the best fitness level and the smallest relative error.

[0090] Please refer to Figure 6 , which shows the structural block diagram of a high-temperature superconducting transient electromagnetic nonlinear inversion system of the present application.

[0091] As Figure 6As shown in the figure, the high-temperature superconducting transient electromagnetic nonlinear inversion system 200 includes an acquisition module 210, a construction module 220, and an inversion module 230.

[0092] Among them, the acquisition module 210 is configured to acquire actual surface transient electromagnetic data;

[0093] The construction module 220 is configured to construct a layered resistivity model according to the actual surface electromagnetic data and the preset theoretical surface electromagnetic data, and the model parameters in the layered resistivity model include resistivity and layer thickness;

[0094] The inversion module 230 is configured to perform electromagnetic data inversion on the layered resistivity model by using a preset improved bat algorithm to obtain formation parameters of the exploration area, where the formation parameters are the resistivity and layer thickness of each horizontal layered medium. Among them, the improved bat algorithm includes a logistic chaotic map and a spiral flight strategy, and the expression of the spiral flight strategy is:

[0095]

[0096] In the formula, k is the spiral flight step size, iter is the number of iterations, maxgen is the maximum number of iterations, L is a uniformly distributed random number in [-1, 1], Z is a spiral parameter that changes with different numbers of iterations, is the position of the i-th bat in the search space in the t-th generation, x * is the current global optimal solution, is the position of the i-th bat in the search space in the (t - 1)-th generation, and e is the natural constant.

[0097] It should be understood that Figure 6 The modules described in Figure 1 correspond to the respective steps in the method described in the reference Figure 6 . Therefore, the operations, features, and corresponding technical effects described above for the method also apply to the

[0098] In some other embodiments, the embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored. When the program instructions are executed by a processor, the processor is caused to execute the high-temperature superconducting transient electromagnetic nonlinear inversion method in any of the above method embodiments;

[0099] As an implementation manner, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are set as:

[0100] Acquire actual surface transient electromagnetic data;

[0101] Construct a layered resistivity model based on the actual surface electromagnetic data and the preset theoretical surface electromagnetic data. The model parameters in the layered resistivity model include resistivity and layer thickness;

[0102] Use a preset improved bat algorithm to perform electromagnetic data inversion on the layered resistivity model to obtain the formation parameters of the exploration area. The formation parameters are the resistivity and layer thickness of each horizontal layered medium. Among them, the improved bat algorithm includes logistic chaotic mapping and a spiral flight strategy. The expression of the spiral flight strategy is:

[0103]

[0104] In the formula, k is the spiral flight step size, iter is the number of iterations, maxgen is the maximum number of iterations, L is a uniformly distributed random number in [-1, 1], z is a spiral parameter that changes with different numbers of iterations, is the position of the i-th bat in the search space in the t-th generation, x * is the current global optimal solution, is the position of the i-th bat in the search space in the (t - 1)-th generation, and e is the natural constant.

[0105] A computer-readable storage medium may include a storage program area and a storage data area. Among them, the storage program area can store an operating system and application programs required for at least one function; the storage data area can store data created according to the use of the high-temperature superconducting transient electromagnetic nonlinear inversion system, etc. In addition, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the computer-readable storage medium may optionally include a memory remotely provided relative to the processor, and these remote memories can be connected to the high-temperature superconducting transient electromagnetic nonlinear inversion system through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0106] Figure 7 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. As Figure 7 shown, the device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 can be connected through a bus or other means, Figure 7Take the bus connection as an example. The memory 320 is the aforementioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 320, that is, to implement the high-temperature superconducting transient electromagnetic nonlinear inversion method in the above method embodiment. The input device 330 can receive input digital or character information, and generate key signal inputs related to the user settings and function controls of the high-temperature superconducting transient electromagnetic nonlinear inversion system. The output device 340 can include display devices such as a display screen.

[0107] The above electronic device can execute the method provided by the embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. For the technical details not described in detail in this embodiment, reference can be made to the method provided by the embodiment of the present invention.

[0108] As an implementation manner, the above electronic device is applied to a high-temperature superconducting transient electromagnetic nonlinear inversion system and is used for a client, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0109] Obtain the actual surface transient electromagnetic data;

[0110] Construct a layered resistivity model based on the actual surface electromagnetic data and the preset theoretical surface electromagnetic data, and the model parameters in the layered resistivity model include resistivity and layer thickness;

[0111] Use a preset improved bat algorithm to perform electromagnetic data inversion on the layered resistivity model to obtain the formation parameters of the exploration area, where the formation parameters are the resistivity and layer thickness of each horizontal layered medium. Among them, the improved bat algorithm includes logistic chaotic mapping and spiral flight strategy, and the expression of the spiral flight strategy is:

[0112]

[0113] In the formula, k is the spiral flight step size, iter is the number of iterations, maxgen is the maximum number of iterations, L is a uniformly distributed random number in [-1, 1], z is a spiral parameter that changes with the number of iterations, is the position of the i-th bat in the search space in the t-th generation, x * is the current global optimal solution, is the position of the i-th bat in the search space in the (t - 1)-th generation, and e is the natural constant.

[0114] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiments.

[0115] 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 recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A high-temperature superconducting transient electromagnetic nonlinear inversion method, characterized in that, Including: Obtaining actual surface transient electromagnetic data; Constructing a layered resistivity model based on the actual surface electromagnetic data and the preset theoretical surface electromagnetic data, where the model parameters in the layered resistivity model include resistivity and layer thickness; Using a preset improved bat algorithm to perform electromagnetic data inversion on the layered resistivity model to obtain formation parameters of the exploration area, where the formation parameters are the resistivity and layer thickness of each horizontal layered medium. Among them, the improved bat algorithm includes a logistic chaotic map and a spiral flight strategy, and the expression of the spiral flight strategy is: where k is the step size of the spiral flight, iter is the number of iterations, maxgen is the maximum number of iterations, L is a uniformly distributed random number in [-1, 1], z is a spiral parameter that varies with the number of iterations, is the position of the i-th bat in the search space in the t-th generation, x * is the current global optimal solution, is the position of the i-th bat in the search space in the (t - 1)-th generation, and e is the natural constant.

2. A high-temperature superconducting transient electromagnetic nonlinear inversion method according to claim 1, characterized in that, The expression of the logistic chaotic map is: x n+1 = ax n (1 - x n ) where X n ∈ [0, 1] is the chaotic variable of the n-th generation, a ∈ [0, 4] is the logistic parameter, and x n+1 is the chaotic variable of the (n + 1)-th generation.

3. A high-temperature superconducting transient electromagnetic nonlinear inversion method according to claim 1, characterized in that The expression of the fitness objective function of the layered resistivity model is: where f is the fitness objective function of the layered resistivity model, N is the number of time channels, h sn is the measured response of the vertical magnetic field for the nth time channel in the forward modeling, h cn is the calculated response of the vertical magnetic field for the nth time channel in the forward modeling.

4. A high-temperature superconducting transient electromagnetic nonlinear inversion method according to claim 1, characterized in that The step of using a preset improved bat algorithm to perform electromagnetic data inversion on the layered resistivity model to obtain formation parameters of the exploration area includes: Initialize the population: Given the maximum number of iterations maxgen, the bat population size P, the pulse loudness A0, the pulse emission rate r0, the minimum frequency fmin of the frequencies emitted by bats min and the maximum frequency fmax of the frequencies emitted by bats max , and randomly generate the initial population using the logistic chaotic map within the search range of the lower and upper bounds of the given minimum and maximum model parameters; Calculate the fitness objective function: According to the obtained initial population, call the fitness objective function to find the bat individual with the lowest global fitness. Its global optimal bat is denoted as X g ; Starting an iterative loop: Entering the loop, with the condition that the iteration number t is less than the maximum iteration number maxgen or the stopping criterion is not reached, and updating the pulse loudness A0 and pulse emission rate r0 of each bat; Traverse each bat: Use a specific equation to generate the position of each bat; if the generated random number is less than the pulse emission rate r0 of the current bat, update the position of the current bat and update the global optimal bat X g ; Spiral flight strategy: Adopt the spiral flight strategy to generate a new temporary position for each bat. If the fitness of the new temporary position is lower, update the current bat position and simultaneously update the global optimal bat X g . Condition judgment: Judging whether the current termination condition is satisfied. If so, saving the current global optimal solution; otherwise, continuing to execute the step of traversing each bat. Result output: Output the global optimal solution, which is the inversion result of the final exploration area. The global optimal solution is the global optimal bat X after the iteration is completed. g The corresponding formation parameters.

5. A high-temperature superconducting transient electromagnetic nonlinear inversion system, characterized in that, Including: An acquisition module configured to obtain actual surface transient electromagnetic data; A construction module configured to construct a layered resistivity model based on the actual surface electromagnetic data and the preset theoretical surface electromagnetic data, where the model parameters in the layered resistivity model include resistivity and layer thickness; An inversion module configured to use a preset improved bat algorithm to perform electromagnetic data inversion on the layered resistivity model to obtain formation parameters of the exploration area, where the formation parameters are the resistivity and layer thickness of each horizontal layered medium. Among them, the improved bat algorithm includes a logistic chaotic map and a spiral flight strategy, and the expression of the spiral flight strategy is: where k is the spiral flight step size, iter is the number of iterations, maxgen is the maximum number of iterations, L is a uniformly distributed random number in [-1, 1], z is a spiral parameter that varies with the number of iterations, is the position of the i-th bat in the search space in the t-th generation, x * is the current global optimal solution, is the position of the i-th bat in the search space in the (t - 1)-th generation, and e is the natural constant.

6. An electronic device, characterized in that, Including: At least one processor, and a memory communicatively connected to the at least one processor, where the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute 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 program is executed by the processor, it implements the method according to any one of claims 1 to 4.

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