Magnetotelluric data screening method and device based on multiple targets, equipment and medium

Abstract

The invention provides a magnetotelluric data screening method and device based on multiple targets, equipment and a medium, and relates to the technical field of magnetotelluric data processing. The problems that in the prior art, an existing magnetotelluric power spectrum data screening method is low in efficiency and high in subjectivity, multiple electromagnetic targets cannot be optimized at the same time, and the screening effect is poor when the noise proportion is high are solved. Performing segmented windowing and fast Fourier transform on the magnetotelluric data to obtain original power spectrum data; performing preliminary screening on the original power spectrum data by adopting a mahalanobis distance to obtain an original transmission function estimation value and a plurality of objective functions; and screening the original power spectrum data again based on a non-dominated sorting genetic algorithm II and a plurality of target functions to obtain target power spectrum data, so as to automatically and efficiently screen high-quality power spectrum data, thereby improving the estimation precision of the transmission function and the reliability of data analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of magnetotelluric data processing, and in particular to a magnetotelluric data screening method, device, equipment and medium based on multiple targets. Background Art

[0002] Magnetotelluric (MT) is an important geophysical exploration method that uses natural electromagnetic fields as field sources to study the electrical structure of the earth's interior. In the traditional MT impedance tensor estimation method, the quality of the transmission function estimation can be improved by removing the power spectrum data of the noisy period before the transmission function estimation, which is called power spectrum screening.

[0003] Common methods for screening magnetotelluric power spectrum data include manual screening methods and automatic screening methods based on statistical characteristics. The manual screening method has the disadvantages of strong subjectivity, inconsistent results, low efficiency, easy omission of important information and inability to quantify and optimize multi-objective requirements. The data screening method based on Mahalanobis distance has the technical problems of being easily affected by data with a high proportion of noise, single-target requirements, the need to meet statistical assumptions and insufficient robustness. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a multi-target based magnetotelluric data screening method, device, equipment and medium to alleviate the problems existing in the prior art of magnetotelluric power spectrum data screening methods, such as low efficiency, strong subjectivity, inability to simultaneously optimize multiple electromagnetic targets, and poor screening effect when the noise ratio is high.

[0005] In a first aspect, the present invention provides a multi-objective magnetotelluric data screening method, comprising: Get the original power spectrum data; The original power spectrum data is preliminarily screened using Mahalanobis distance to obtain the estimated value of the transfer function corresponding to the original power spectrum data; According to the estimated value of the transfer function, multiple objective functions corresponding to the original power spectrum data are determined; wherein the objective functions include the deviation of the transfer function from the expected value, the deviation of the apparent resistivity from the expected value, the dispersion of the electromagnetic field parameters, the correlation of the electromagnetic field and the deviation of the earth's electromagnetic impedance.

[0006] Optionally, the original power spectrum data is screened based on the non-dominated sorting genetic algorithm II and multiple objective functions to obtain target power spectrum data, including: Randomly generate an initial population; each individual in the population is a subset of the original power spectrum data; An iterative screening operation is performed based on the population until it is determined that the first iterative optimization termination condition is met, and the power spectrum data output when the iterative screening operation is performed for the last time is used as the target power spectrum data; wherein the iterative screening operation includes: Using the fitness function, calculate the fitness value of each individual in the population; Based on the values ​​of multiple objective functions, the individuals in the population are non-dominatedly sorted to obtain multiple levels, and the crowding distance of each individual in each level is calculated based on the Mahalanobis distance; Based on multiple levels and crowding distances, the population is updated using parent selection, crossover, and mutation.

[0007] Optionally, the original power spectrum data is screened based on the non-dominated sorting genetic algorithm II and multiple objective functions to obtain target power spectrum data, further comprising: Obtain multiple excellent individuals in the new population that are located in the Pareto frontier solution set; Based on the introduction mechanism, multiple excellent individuals are introduced into the current population.

[0008] Optionally, obtaining multiple excellent individuals in the new population in the Pareto front solution set also includes: If the number of multiple excellent individuals exceeds the threshold, multiple excellent individuals are screened according to non-dominated sorting and crowding distance.

[0009] Optionally, the original power spectrum data is screened based on the non-dominated sorting genetic algorithm II and multiple objective functions to obtain target power spectrum data, further comprising: Obtaining a frequency data set corresponding to the original power spectrum data; wherein the frequency data set includes multiple frequencies; Based on the frequency data set, an iterative single frequency point screening operation is performed on the original power spectrum until it is determined that the second iteration termination condition is met, and the power spectrum data output when the iterative single frequency point screening operation is performed for the last time is determined as the target power spectrum data; wherein the iterative single frequency point screening operation includes: Selecting a current frequency from a frequency data set; Based on the current frequency and the original power spectrum data, the non-dominated sorting genetic algorithm II is used to screen the original power spectrum data to obtain the target power spectrum data.

[0010] Optionally, the first iteration termination condition is: reaching a preset number of iterations or the change range of the Pareto front solution set of the population is lower than a set threshold; The second iteration termination condition is: reaching the preset number of iterations.

[0011] Optionally, obtaining original power spectrum data also includes: Obtain raw magnetotelluric data; The original magnetotelluric data are segmented, windowed, fast Fourier transformed and combined to obtain the original power spectrum data corresponding to the original magnetotelluric data at each frequency.

[0012] In a second aspect, the present invention provides a multi-objective magnetotelluric data screening device comprising: A data acquisition unit, used for acquiring original power spectrum data; A preliminary screening unit, used for performing preliminary screening on the original power spectrum data by using Mahalanobis distance to obtain a transfer function estimation value corresponding to the original power spectrum data; Determine the objective function unit, which is used to determine multiple objective functions corresponding to the original power spectrum data according to the estimated value of the transmission function; wherein the objective function includes the deviation of the transmission function from the expected value, the deviation of the apparent resistivity from the expected value, the dispersion of the electromagnetic field parameters, the correlation of the electromagnetic field and the deviation of the earth electromagnetic impedance; The data screening unit is used to screen the original power spectrum data based on the non-dominated sorting genetic algorithm II and multiple objective functions to obtain target power spectrum data.

[0013] In a third aspect, the present invention further provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements a multi-objective-based magnetotelluric data screening method when executing the computer program.

[0014] In a fourth aspect, the present invention further provides a computer-readable storage medium storing computer instructions, which implement a multi-objective magnetotelluric data screening method when executed by a processor.

[0015] The embodiments of the present invention provide a multi-objective magnetotelluric data screening method, device, equipment and medium, which obtain original power spectrum data; use Mahalanobis distance to preliminarily screen the original power spectrum data to obtain a transfer function estimate; determine multiple objective functions based on the transfer function estimate; and screen the original power spectrum data based on a non-dominated sorting genetic algorithm II and multiple objective functions to obtain target power spectrum data. Compared with the magnetotelluric power spectrum data screening method in the prior art, the multi-objective optimization technology is used to automatically and efficiently screen high-quality power spectrum data, thereby improving the accuracy of transfer function estimation and the reliability of data analysis.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A schematic flow chart of a multi-objective magnetotelluric data screening method provided by an embodiment of the present invention is shown; Figure 2 A flow chart of screening original power spectrum data based on a non-dominated sorting genetic algorithm II (NSGA-II) provided in an embodiment of the present invention is shown; Figure 3 A schematic diagram of a process flow based on the non-dominated sorting genetic algorithm II (NSGA-II) provided in an embodiment of the present invention is shown; Figure 4 The figure shows the apparent resistivity and phase curve of a certain measured data processing result by the data screening method based on Mahalanobis distance provided by an embodiment of the present invention; Figure 5 The apparent resistivity and phase curves of certain measured data processing results of the multi-objective magnetotelluric data screening method provided by the embodiment of the present invention are shown; Figure 6 A schematic structural diagram of a multi-objective magnetotelluric data screening device provided by an embodiment of the present invention is shown; Figure 7 A schematic structural diagram of an electronic device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0019] In order to make the purpose, 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 in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0020] Figure 1 A schematic flow chart of a multi-objective magnetotelluric data screening method provided by an embodiment of the present invention is as follows: Figure 1As shown, the method comprises at least the following steps: Step S110: Obtain original power spectrum data.

[0021] In the embodiment of the present application, the original power spectrum data is obtained by the following method: Obtain raw magnetotelluric data; The original magnetotelluric data are segmented, windowed, fast Fourier transformed and combined to obtain the original power spectrum data corresponding to the original magnetotelluric data at each frequency.

[0022] Specifically, the original magnetotelluric data are obtained and divided into A segments according to time periods to facilitate the subsequent analysis of the magnetotelluric data; for each segment of the divided time series data (i.e., the original magnetotelluric data), the fast Fourier transform (FFT) is applied to convert the original magnetotelluric data into frequency domain data, thereby decomposing the original magnetotelluric data into different frequency components; and the original auto / cross power spectrum data corresponding to the original geodata at each frequency are calculated.

[0023] In the embodiment of the present application, the original magnetotelluric data of the five channels (Ex, Ey, Hx, Hy, Hz) are respectively subjected to FFT conversion, so that five groups of original spectrum data can be obtained. 25 groups of different self / cross power spectra can be calculated to form a 5×5 power spectrum matrix, which can be calculated using the following formula:

[0024] In the formula, It is The FFT result of the channel, It is The complex conjugate of the FFT result of the channel, if i≠j, is the cross power spectrum. If i=j, then is the autopower spectrum.

[0025] Furthermore, after obtaining A power spectrum matrices, they are divided into B groups and superimposed to obtain B superimposed power spectrum matrices as original power spectrum data.

[0026] Step S120: use Mahalanobis distance to preliminarily screen the original power spectrum data to obtain the estimated value of the transfer function corresponding to the original power spectrum data.

[0027] In order to improve the robustness of the screening, the original power spectrum data is preliminarily screened using the Mahalanobis distance data of the minimum determinant to obtain the estimated value of the transfer function, which can be done by the following method: Based on B superimposed power spectrum matrices, the real and imaginary parts of the transfer functions of group B are calculated, and their Mahalanobis distance covariance matrix and Mahalanobis distance are calculated. Some power spectra with smaller Mahalanobis distances are retained, and the covariance matrix is ​​updated. This step is repeated until the determinant of the covariance matrix no longer decreases. The power spectrum matrices finally retained are superimposed to calculate the estimated value of the transfer function.

[0028] Step S130, determining multiple objective functions corresponding to the original power spectrum data according to the estimated value of the transmission function; wherein the objective functions include the deviation of the transmission function from the expected value, the deviation of the apparent resistivity from the expected value, the dispersion of the electromagnetic field parameters, the correlation of the electromagnetic field and the deviation of the earth's electromagnetic impedance.

[0029] In an embodiment of the present application, electromagnetic field parameters are calculated based on the original power spectrum data, and optimization targets involved in subsequent data screening are determined, wherein the optimization targets include the real and imaginary parts of the transfer function, the electromagnetic field self-power spectrum density, and the phase tensor.

[0030] Further, the real and imaginary parts of the transfer function: , , , , , , , , , , and ; Electromagnetic field self-power spectrum density: , , , ; The phase tensor is a derivative parameter of the impedance tensor. The impedance tensor is divided into real and imaginary parts, and the impedance tensor is: , where is the real part of the impedance tensor, is the imaginary part of the impedance tensor, so the phase tensor can be defined as: ,in, for The four elements in .

[0031] In the embodiment of the present application, the setting of the objective function can be increased or adjusted according to the actual application requirements. In the present application, the objective function includes the deviation of the transmission function from the expected value, the deviation of the apparent resistivity from the expected value, the dispersion of the electromagnetic field parameters, the correlation of the electromagnetic field and the deviation of the magnetotelluric impedance. The deviation of the transmission function from the expected value is used to measure the difference between the impedance tensor estimate and the expected value, and is used to evaluate the reliability of the power spectrum data. The expected value of the transfer function is predicted by the continuity of a single transfer function component and the correlation between the transfer function components. The prediction method can be an artificial intelligence method or a curve fitting method. The expected values ​​of apparent resistivity and phase can be calculated by the expected value of the transfer function or obtained using the one-dimensional inversion method Rhoplus; The dispersion of electromagnetic field parameters is used to measure the consistency of power spectrum data distribution to reflect the coordination between data points. The dispersion can use one or more of the range, variance, standard deviation, interquartile range, coefficient of variation or mean absolute deviation according to different electromagnetic parameters and application conditions. The median of the data used to calculate the dispersion can be replaced by the expected value of the corresponding parameter to make the objective function more robust. Among them, the electromagnetic field parameters include phase tensor, transfer function and electromagnetic field autopower spectrum density; Coherence is used to measure the correlation between the electric and magnetic field signals corresponding to the power spectrum data, and is one of the indicators of the strength of the signal and noise. The magnetotelluric impedance deviation includes one-dimensional deviation and two-dimensional deviation, which is one of the indicators of the noise level of the data.

[0032] Step S140 , screening the original power spectrum data based on the non-dominated sorting genetic algorithm II and multiple objective functions to obtain target power spectrum data.

[0033] In the embodiment of the present application, a multi-objective optimization screening method based on a non-dominated sorting genetic algorithm II (NSGA-II) is used to encode the original magnetotelluric power spectrum data into binary individuals, and the population is iteratively optimized through a genetic algorithm. The deviation of the transmission function from the expected value, the deviation of the apparent resistivity and phase from the expected value, the dispersion of the electromagnetic field parameters, the electromagnetic field coherence and other objective functions are comprehensively considered to achieve intelligent screening of the original power spectrum data and obtain the target power spectrum data, thereby improving the estimation accuracy of the magnetotelluric transmission function and the reliability of subsequent geophysical analysis.

[0034] The following is a detailed description of the process of screening the original power spectrum data based on the non-dominated sorting genetic algorithm II (NSGA-II) provided in the embodiment of the present application. Figure 2 As shown, the process of screening the original power spectrum data based on the non-dominated sorting genetic algorithm II (NSGA-II) provided in the embodiment of the present application is as follows: Step S210: Obtain a frequency data set corresponding to the original power spectrum data; wherein the frequency data set includes multiple frequencies.

[0035] In the embodiment of the present application, when obtaining the frequency data set, the following methods may be used but are not limited to: First, the original magnetotelluric data are obtained and divided according to time periods; Then, for each segment of the original magnetotelluric data, the fast Fourier transform (FFT) is applied to convert the original magnetotelluric data into frequency domain data, and the original power spectrum data corresponding to the original magnetotelluric data at each frequency is calculated; Finally, based on the original power spectrum data and frequency domain data, a frequency data set is obtained.

[0036] Step S220: Select the current frequency from the frequency data set.

[0037] Step S230: Based on the current frequency and the original power spectrum data, the original power spectrum data is screened using a non-dominated sorting genetic algorithm II to obtain target power spectrum data.

[0038] Step S240, determine whether the second iteration termination condition is met; if so, execute step S250; if not, return to step S220; wherein, the second iteration termination condition is that the number of iterations is not less than the predicted number of iterations.

[0039] Step S250: Determine the target power spectrum data based on the power spectrum data output when the iterative single frequency point screening operation is performed for the last time.

[0040] In this application, by using the non-dominated sorting genetic algorithm II (NSGA-II) to screen the original power spectrum data at different frequencies, it is possible to effectively remove noise and improve the signal-to-noise ratio, so that the target power spectrum data can better reflect the actual signal characteristics and have high accuracy at multiple key frequency points.

[0041] The following is a detailed description of the operation process of the non-dominated sorting genetic algorithm II (NSGA-II) provided in the embodiment of the present application. Figure 3 As shown, the operation process of the non-dominated sorting genetic algorithm II (NSGA-II) provided in the embodiment of the present application is as follows: Step S310: randomly generate an initialization population; wherein each individual in the population is a subset of the original power spectrum data.

[0042] In the embodiment of the present application, whether each original power spectrum data point participates in the calculation is encoded as a binary variable, 0 indicates that the original power spectrum data point does not participate in the calculation, and 1 indicates that the original power spectrum data point participates in the calculation; An initial population is randomly generated, where each individual in the population consists of a set of binary codes, the code length is equal to the number of original power spectrum data points, and the population size N is a preset parameter that controls the number of individuals participating in the screening.

[0043] Step S320: Use a fitness function to calculate the fitness value of each individual in the population; wherein the fitness function may be, but is not limited to, the sum of squares of prediction errors, etc.

[0044] Step S330: performing non-dominated sorting on the individuals in the population based on the values ​​of multiple objective functions to obtain multiple levels, and calculating the crowding distance of each individual in each level based on the Mahalanobis distance.

[0045] In an embodiment of the present application, individuals in a population are non-dominated and sorted according to multiple objective function values. If an individual is not worse than another individual in all objective functions and performs better in at least one objective function, the individual is said to "dominate" the other individual; all individuals that are not dominated by other individuals are divided into the first layer of Pareto frontier; after removing the first layer from the population, the above process is repeated to determine the second layer, the third layer, and the second of the fourth layer; and the non-dominated sorting level (Rank) of each individual is marked, the lower the level value, the better the individual.

[0046] Furthermore, for individuals in each layer, the crowding distance is calculated based on the Mahalanobis distance according to each objective function value, and the crowding distance is used to measure the density of solutions around the individual; for each objective function, the individuals are sorted from small to large according to the crowding distance value; and the distance between adjacent individuals within the objective function value range is calculated, where the crowding distance is the comprehensive distance on all objective functions, and the larger the crowding distance value, the more dispersed the solutions around the individual.

[0047] Step S340: Based on multiple levels and crowding distances, the population is updated by selecting parents, crossover and mutation.

[0048] In an embodiment of the present application, based on non-dominated sorting and crowding distance, N individuals are selected from the combined population of the current population and the previous generation population, among which individuals with lower hierarchy values ​​are preferentially selected; in the same hierarchy layer, individuals with larger crowding distance are preferentially selected to maintain the diversity of the population; using the simulated binary crossover (SBX) method, two individuals are randomly selected from the selected individuals, and their partial codes are exchanged to generate two new individuals; wherein the crossover probability is a preset parameter, usually set to 0.8-0.9; for each newly generated individual, a binary bit flip is performed (0 becomes 1, 1 becomes 0) according to a preset mutation probability, wherein the mutation probability is usually set to 3 / code length to increase the diversity of the population.

[0049] Step S350: Acquire multiple excellent individuals in the Pareto front solution set in the new population; and introduce the multiple excellent individuals into the current population based on the introduction mechanism.

[0050] In the embodiment of the present application, after each generation of iteration, a portion of individuals (i.e., excellent individuals) on the Pareto frontier in the new generation population are randomly selected and added to the external archive; based on the introduction mechanism, individuals in the external archive are introduced into the current population with a certain probability to enhance the diversity of the population and prevent the solution from falling into the local optimum. Among them, if the number of individuals in the external archive exceeds the set threshold, the individuals are screened according to the non-dominated sorting and crowding distance, and the inferior solutions are deleted.

[0051] Step S360, determine whether the first iteration termination condition is met; if so, execute step S370; if not, return to step S320; wherein, the first iteration termination condition is that the number of iterations is not less than the predicted number of iterations or the change amplitude of the Pareto front solution set of the population is less than any one of the set thresholds.

[0052] Step S370: Based on the Pareto front of the last generation of the population, the Mahalanobis distance determinant of each individual is calculated, and the individual with the smallest determinant is used as the target power spectrum data.

[0053] In the embodiment of the present application, multiple excellent solution sets are screened out from the original power spectrum data through multi-objective optimization screening based on non-dominated sorting genetic algorithm II (NSGA-II), forming a Pareto front, thereby improving the diversity and optimization of the screening results; by introducing excellent solution sets and supplementing external archives, convergence to the local optimum is avoided, and the optimal solution can be found in limited iterations.

[0054] The following is a comparison of the data processing results in combination with the processing results of the measured data and the multi-objective magnetotelluric data screening method provided by the present invention: the measured data comes from a magnetotelluric measuring point in a certain place. After the original time series is segmented, 1000 power spectrum data are calculated, and the power spectrum is screened using the Mahalanobis distance method and the multi-objective magnetotelluric data screening method of the present invention. Among them, the apparent resistivity phase curves of multiple angles of the target function selected in the method of the present invention are the deviations from the expected value, and the apparent resistivity phase curves are rotated to 6 different angles (0°, 15°, 30°, 45°, 60° and 75°), and the one-dimensional inversion method Rhoplus is used for inversion fitting to obtain the corresponding fitting apparent resistivity and phase as the expected value. During the operation of the NSGA-II algorithm provided by the present application, the target fitness of each individual is the apparent resistivity and phase difference (a total of 24 targets) of 6 angles and 2 polarization directions (TE mode and TM mode), and the final processing result is obtained after the NSGA-II data screening is performed on each frequency point in turn.

[0055] Figure 4 The apparent resistivity of a measured data processing result is obtained by the data screening method based on the Mahalanobis distance. and Phase curve; Figure 5 The apparent resistivity of a certain measured data processing result is obtained by the multi-objective magnetotelluric data screening method of the present invention. and Phase It can be seen from the curve that the quality of the processing result curve of the method of the present invention is better than that of the data screening method based on Mahalanobis distance.

[0056] The multi-objective magnetotelluric data screening method provided by the present invention has the following beneficial effects: 1. The non-dominated sorting genetic algorithm II (NSGA-II) is used to automatically screen magnetotelluric power spectrum data, replacing the traditional manual screening and single-objective screening methods; through multi-objective optimization and intelligent iteration, the efficiency of data processing is significantly improved, which is particularly suitable for large-scale, multi-frequency magnetotelluric data sets.

[0057] 2. Comprehensively optimize multiple electromagnetic targets to enhance the objectivity and consistency of screening: By introducing multi-objective optimization, the screening process is based on scientifically defined objective functions such as transfer function deviation, apparent resistivity phase deviation, electromagnetic field parameter dispersion, etc., to achieve all-round balanced optimization of complex data requirements. It is completely driven by algorithms, avoiding the subjectivity and inconsistency of manual judgment, and ensuring the reliability and repeatability of screening results.

[0058] 3. When the noise ratio is high or the signal quality is poor, the population iteration, external archive maintenance and Pareto frontier solution set update of the genetic algorithm can gradually eliminate noise points and retain high-quality data, significantly improving the robustness and anti-interference ability of the screening process.

[0059] 4. The objective function can be flexibly defined according to actual data and application requirements, such as the deviation between the transmission function and the expected value, the stability of the electromagnetic field parameters, the physical rationality of the apparent resistivity and phase, etc. The optimization target can be adjusted for different scenarios to ensure the adaptability and versatility of the algorithm.

[0060] 5. The minimum determinant method of the Mahalanobis distance covariance matrix is ​​used to select the solution from the Pareto frontier, avoiding the subjectivity of manually selecting the optimal solution from the Pareto frontier. The determinant of the Mahalanobis distance covariance matrix represents the consistency of the data in each parameter dimension. The minimum determinant solution is selected to ensure the quality of the solution.

[0061] 6. The screening of magnetotelluric power spectrum data is a process of gradually improving the quality of transmission function estimation and gradually approaching the true solution. The reasonable initial value of the iteration is set through the Mahalanobis distance method, and then the expected value of the transmission function and electromagnetic field parameters is gradually optimized through multiple rounds of iterations, and then the objective function is optimized, so that the processing results of each round are improved compared with the previous ones.

[0062] Figure 6 The present invention provides a schematic diagram of the structure of a multi-objective magnetotelluric data screening device, such as Figure 6 As shown, the device comprises: A data acquisition unit 410 is used to acquire original power spectrum data; A preliminary screening unit 420 is used to perform preliminary screening on the original power spectrum data using the Mahalanobis distance to obtain a transfer function estimation value corresponding to the original power spectrum data; The objective function determination unit 430 is used to determine multiple objective functions corresponding to the original power spectrum data according to the estimated value of the transmission function; wherein the objective function includes the deviation of the transmission function from the expected value, the deviation of the apparent resistivity from the expected value, the dispersion of the electromagnetic field parameters, the correlation of the electromagnetic field and the deviation of the earth electromagnetic impedance; The data screening unit 440 is used to screen the original power spectrum data based on the non-dominated sorting genetic algorithm II and multiple objective functions to obtain target power spectrum data.

[0063] In an optional embodiment, the data screening unit 440 is further configured to: Randomly generate an initial population; each individual in the population is a subset of the original power spectrum data; An iterative screening operation is performed based on the population until it is determined that the first iterative optimization termination condition is met, and the power spectrum data output when the iterative screening operation is performed for the last time is combined into the target power spectrum data; wherein the iterative screening operation includes: Using the fitness function, calculate the fitness value of each individual in the population; Based on the values ​​of multiple objective functions, the individuals in the population are non-dominatedly sorted to obtain multiple levels, and the crowding distance of each individual in each level is calculated based on the Mahalanobis distance; Based on multiple levels and crowding distances, the population is updated using parent selection, crossover, and mutation.

[0064] In an optional embodiment, the data screening unit 440 is further configured to: Obtain multiple excellent individuals in the new population that are located in the Pareto frontier solution set; Based on the introduction mechanism, multiple excellent individuals are introduced into the current population.

[0065] Optionally, obtaining multiple excellent individuals in the new population in the Pareto front solution set also includes: If the number of multiple excellent individuals exceeds the threshold, multiple excellent individuals are screened according to non-dominated sorting and crowding distance.

[0066] In an optional embodiment, the data screening unit 440 is further configured to: Obtaining a frequency data set corresponding to the original power spectrum data; wherein the frequency data set includes multiple frequencies; Based on the frequency data set, an iterative single frequency point screening operation is performed on the original power spectrum until it is determined that the second iteration termination condition is met, and the power spectrum data output when the iterative single frequency point screening operation is performed for the last time is determined as the target power spectrum data; wherein the iterative single frequency point screening operation includes: Selecting a current frequency from a frequency data set; Based on the current frequency and the original power spectrum data, the non-dominated sorting genetic algorithm II is used to screen the original power spectrum data to obtain the target power spectrum data.

[0067] In an optional embodiment, the first iteration termination condition is: reaching a preset number of iterations or the change range of the Pareto front solution set of the population is lower than a set threshold; The second iteration termination condition is: reaching the preset number of iterations.

[0068] In an optional embodiment, the data acquisition unit 410 is further configured to: Get the original power spectrum data, including: Obtain raw magnetotelluric data; The original magnetotelluric data are segmented, windowed, fast Fourier transformed and combined to obtain the original power spectrum data corresponding to the original magnetotelluric data at each frequency.

[0069] The implementation principle and technical effects of the device provided in the embodiments of the present application are the same as those of the aforementioned method embodiments. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiments.

[0070] like Figure 7 As shown, an electronic device 600 provided in an embodiment of the present application includes: a processor 601, a memory 602 and a bus, the memory 602 stores machine-readable instructions executable by the processor 601, when the electronic device is running, the processor 601 and the memory 602 communicate through the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the above-mentioned multi-target magnetotelluric data screening method.

[0071] Specifically, the memory 602 and the processor 601 can be general-purpose memories and processors, which are not specifically limited here. When the processor 601 runs the computer program stored in the memory 602, the multi-target magnetotelluric data screening method can be executed.

[0072] The processor 601 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 601. The above processor 601 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 602, and the processor 601 reads the information in the memory 602 and completes the steps of the above method in combination with its hardware.

[0073] Corresponding to the above-mentioned multi-objective magnetotelluric data screening method, an embodiment of the present application also provides a computer-readable storage medium, which stores machine-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the steps of the above-mentioned multi-objective magnetotelluric data screening method.

[0074] The multi-objective magnetotelluric data screening device provided in the embodiment of the present application can be specific hardware on the device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in the embodiment of the present application are the same as those of the aforementioned method embodiment. For the sake of brief description, the parts not mentioned in the device embodiment can refer to the corresponding contents in the aforementioned method embodiment. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0075] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0076] For another example, the flowchart and block diagram in the accompanying drawings show the possible architecture, functions and operations of the device, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and a part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0077] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0079] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable an electronic device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0080] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0081] Finally, it should be noted that the above embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the aforementioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the aforementioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application. They should all be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A multi-objective magnetotelluric data screening method, characterized in that: include: Get the original power spectrum data; The original power spectrum data is preliminarily screened using Mahalanobis distance to obtain a transfer function estimation value corresponding to the original power spectrum data; Determine multiple objective functions corresponding to the original power spectrum data according to the estimated value of the transmission function; wherein the objective functions include the deviation of the transmission function from the expected value, the deviation of the apparent resistivity from the expected value, the dispersion of electromagnetic field parameters, the correlation of the electromagnetic field and the deviation of the magnetotelluric impedance; The original power spectrum data is screened based on the non-dominated sorting genetic algorithm II and the multiple objective functions to obtain target power spectrum data.

2. The multi-objective magnetotelluric data screening method according to claim 1 is characterized in that: The original power spectrum data is screened based on the non-dominated sorting genetic algorithm II and the multiple objective functions to obtain target power spectrum data, including: Randomly generate an initialization population; wherein each individual in the population is a subset of the original power spectrum data; An iterative screening operation is performed based on the population until it is determined that the first iterative optimization termination condition is met, and the power spectrum data output when the iterative screening operation is performed for the last time is combined into the target power spectrum data; wherein the iterative screening operation includes: Using a fitness function, calculating the fitness value of each individual in the population; Based on the values ​​of the multiple objective functions, the individuals in the population are non-dominatedly sorted to obtain multiple levels, and the crowding distance of each individual in each level is calculated based on the Mahalanobis distance; Based on the multiple levels and the crowding distance, the population is updated by selecting parents, crossover and mutation.

3. The multi-objective magnetotelluric data screening method according to claim 2 is characterized in that: The original power spectrum data is screened based on the non-dominated sorting genetic algorithm II and the multiple objective functions to obtain target power spectrum data, further comprising: Obtain multiple excellent individuals in the new population that are located in the Pareto front solution set; Based on the introduction mechanism, a plurality of excellent individuals are introduced into the current population.

4. The multi-objective magnetotelluric data screening method according to claim 3 is characterized in that: Obtain multiple excellent individuals in the Pareto front solution set in the new population, including: If the number of multiple excellent individuals exceeds a threshold, the multiple excellent individuals are screened according to non-dominated sorting and crowding distance.

5. The multi-objective magnetotelluric data screening method according to claim 2, characterized in that: The original power spectrum data is screened based on the non-dominated sorting genetic algorithm II and the multiple objective functions to obtain target power spectrum data, further comprising: Acquire a frequency data set corresponding to the original power spectrum data; wherein the frequency data set includes multiple frequencies; Based on the frequency data set, an iterative single frequency point screening operation is performed on the original power spectrum until it is determined that a second iteration termination condition is met, and the power spectrum data output when the iterative single frequency point screening operation is performed for the last time is determined as the target power spectrum data; wherein the iterative single frequency point screening operation includes: Selecting a current frequency from the frequency data set; Based on the current frequency and the original power spectrum data, a non-dominated sorting genetic algorithm II is used to screen the original power spectrum data to obtain the target power spectrum data.

6. The multi-objective magnetotelluric data screening method according to claim 5, characterized in that: include: The first iteration termination condition is: reaching a preset number of iterations or the change range of the Pareto front solution set of the population is lower than a set threshold; The second iteration termination condition is: reaching a preset number of iterations.

7. The multi-objective magnetotelluric data screening method according to claim 1, characterized in that: Before obtaining the original power spectrum data, it also includes: Obtain raw magnetotelluric data; The original magnetotelluric data are segmented, windowed, fast Fourier transformed and combined to obtain original power spectrum data corresponding to the original magnetotelluric data at each frequency.

8. A multi-objective magnetotelluric data screening device, characterized in that: include: A data acquisition unit, used for acquiring original power spectrum data; A preliminary screening unit, used for performing preliminary screening on the original power spectrum data by using Mahalanobis distance to obtain a transfer function estimation value corresponding to the original power spectrum data; A target function determination unit is used to determine a plurality of target functions corresponding to the original power spectrum data according to the estimated value of the transmission function; wherein the target functions include the deviation of the transmission function from the expected value, the deviation of the apparent resistivity from the expected value, the dispersion of the electromagnetic field parameters, the correlation of the electromagnetic field and the deviation of the magnetotelluric impedance; The data screening unit is used to screen the original power spectrum data based on the non-dominated sorting genetic algorithm II and the multiple objective functions to obtain target power spectrum data.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the multi-objective magnetotelluric data screening method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the multi-objective-based magnetotelluric data screening method according to any one of claims 1 to 7 is implemented.

Images