Transient electromagnetic signal acquisition method and device in strong electromagnetic interference environment

Abstract

The application provides a transient electromagnetic signal acquisition method and equipment in a strong electromagnetic interference environment. The method first acquires an interference factor generated by an interference object on a transient electromagnetic instrument in a target state, and determines an upper limit value of a target magnetic moment. Then, a genetic algorithm is used to determine the upper limit value of the transmittable magnetic moment of the transient electromagnetic instrument according to the interference factor and a preset fitness function. The influence of the transient electromagnetic instrument on the interference object is evaluated to determine the final maximum transmittable magnetic moment. The signal transmission uses a specially designed coil assembly, including a first transmitting coil and a second transmitting coil with internal reverse current. The signal reception uses a first receiving coil located between the large second transmitting coil and a second receiving coil forming a differential signal. The structural design and adaptive magnetic moment control effectively improve the signal acquisition quality in a strong interference environment.

Description

Technical Field

[0001] This invention belongs to the field of electromagnetic signal acquisition technology, specifically relating to a method and device for acquiring transient electromagnetic signals under strong electromagnetic interference environment. Background Technology

[0002] Electromagnetic detection technology has wide applications in geological exploration, mineral resource surveys, archaeological excavations, and engineering surveys. In particular, transient electromagnetic methods, as an important geophysical exploration method, play a crucial role in resource exploration and environmental surveys due to their advantages such as large detection depth and high resolution. Traditional electromagnetic detection technology is mainly used in relatively ideal field environments, such as areas far from cities and industrial zones, where electromagnetic interference is minimal and the equipment can operate in a relatively clean electromagnetic environment.

[0003] With the acceleration of urbanization and the development of industrial technology, the application scenarios of electromagnetic detection have gradually expanded to complex environments such as urban areas, industrial parks, mining areas, airports, and ports. These areas contain a large number of power facilities, communication equipment, and industrial machinery, generating complex and variable electromagnetic interference. Traditional electromagnetic detection technologies are mostly based on the simple principle of electromagnetic induction, which works well in ideal environments, but performs poorly in complex electromagnetic environments. With the development of modern industrial and communication technologies, the sources of electromagnetic interference in the environment are becoming increasingly numerous and complex, including interference signals of various frequencies and intensities generated by power lines, communication equipment, and industrial machinery. These interference signals are mixed with the signals of the detection target, making it difficult for traditional technologies to effectively distinguish them. Summary of the Invention

[0004] This invention provides a method and device for acquiring transient electromagnetic signals under strong electromagnetic interference environment to solve the above-mentioned technical problems.

[0005] In a first aspect, the present invention provides a method for acquiring transient electromagnetic signals under strong electromagnetic interference environment, applied to a transient electromagnetic instrument including a transmitting coil assembly and a receiving coil assembly, the method comprising the following steps:

[0006] Obtain the interference factor generated by the interference object on the transient electromagnetic instrument under the adjusted target state, and determine the upper limit value of the target magnetic moment of the interference object under the target state;

[0007] A genetic algorithm is used, along with an interference factor and a preset fitness function, to determine the upper limit of the emittable magnetic moment of the transient electromagnetic instrument when measuring at the measured position.

[0008] If the transient electromagnetic instrument does not affect the state of the object being disturbed when it operates at the upper limit of the emittable magnetic moment, then the upper limit of the emittable magnetic moment is determined as the maximum emittable magnetic moment.

[0009] If the transient electromagnetic instrument affects the state of the object being disturbed when it operates at the upper limit of the emittable magnetic moment, then the minimum value between the upper limit of the emittable magnetic moment and the upper limit of the target magnetic moment is determined as the maximum emittable magnetic moment.

[0010] Based on the maximum transmitting magnetic moment and using the transmitting coil assembly to transmit electromagnetic signals, the transmitting coil assembly includes a first transmitting coil and a second transmitting coil, the second transmitting coil is disposed inside the first transmitting coil, and the current path of the second transmitting coil is opposite to the current path of the first transmitting coil;

[0011] After the electromagnetic signal is transmitted, the transient electromagnetic signal is received by the receiving coil assembly. The receiving coil assembly includes a first receiving coil and a second receiving coil. The first receiving coil is disposed between the first transmitting coil and the second transmitting coil, and the second receiving coil and the first receiving coil form a differential signal.

[0012] Optionally, obtaining the interference factor generated by the interference object on the transient electromagnetic instrument under the adjusted target state, and determining the upper limit of the target magnetic moment of the interference object in the target state, includes the following steps:

[0013] Collect the spectral characteristics, field strength distribution and temporal variation of the interfering objects within the target detection area, and construct a dynamic electromagnetic interference environment model;

[0014] Laboratory electromagnetic radiation effect tests were conducted on the interfering object to determine the sensitivity threshold of the interfering object at different frequencies and field strengths.

[0015] By combining the sensitivity threshold and the geometric relative position of the transient electromagnetic instrument and the interference object, and by performing electromagnetic simulation based on the dynamic electromagnetic interference environment model, the electromagnetic induction parameters of the interference object in the target state are calculated as the interference factor.

[0016] A functional relationship model is established between the interference factor, the geometric relative position, and the emitted magnetic moment of the transient electromagnetic instrument. The upper limit of the target magnetic moment of the interference object in the target state is determined by solving the functional relationship model.

[0017] Optionally, the step of using a genetic algorithm and based on an interference factor and a preset fitness function to determine the upper limit of the emittable magnetic moment of the transient electromagnetic instrument at the measured position includes the following steps:

[0018] The emission magnetic moment to be optimized is encoded into a chromosome population in a genetic algorithm;

[0019] Construct the fitness function for the genetic algorithm. The fitness function is specifically expressed as follows:

[0020]

[0021] Where M is the emitted magnetic moment, To predict the signal-to-noise ratio, For the kth interference factor, This is the penalty function when the interference factor exceeds the safety threshold. Hardware constraint penalty for the emission magnetic moment. These are the weighting coefficients;

[0022] The chromosome population is optimized based on the fitness function until the preset number of iterations or fitness value convergence condition is met.

[0023] The emitted magnetic moment corresponding to the chromosome with the highest fitness in the optimized chromosome population is used as the upper limit of the emitted magnetic moment.

[0024] Optionally, the following steps are included before transmitting the electromagnetic signal using the transmitting coil assembly:

[0025] The geometric parameters, number of turns, relative position and current ratio of the two transmitting coils in the transmitting coil assembly were modeled using three-dimensional electromagnetic simulation software to obtain the transmitting coil model;

[0026] The model parameters in the transmitting coil model are optimized by parameter simulation. The goal of parameter simulation is to minimize the rate of change of primary magnetic flux caused by the change of transmitting current in the transmitting coil assembly at the initial position of the receiving coil assembly.

[0027] The transmitting coil assembly was optimized and adjusted based on the optimized model parameters;

[0028] An alternating current is fed into the transmitting coil assembly, and the positions of the two transmitting coils are adjusted to minimize the amplitude of the induced voltage generated by the transmitting current at the initial position of the receiving coil assembly.

[0029] Optionally, after determining the maximum emission magnetic moment, the following steps may also be included:

[0030] The transient electromagnetic instrument uses an internal sensor array to collect in real time the noise characteristics of the ambient electromagnetic noise and the interference characteristics of the interfering object. The interference characteristics include the state parameters of the interfering object and the near-field electromagnetic leakage characteristics.

[0031] By combining noise and interference characteristics and using machine learning models, the state change trend of the interfering object in one or more future detection cycles is dynamically predicted, and the dynamic interference factor spectrum of the interfering object on the transient electromagnetic instrument is evaluated.

[0032] A dynamic electromagnetic susceptibility model of the interference object is constructed based on the interference characteristics, and the predicted interference characteristics of the interference object are obtained by using the dynamic electromagnetic susceptibility model.

[0033] The disturbance threshold of the external electromagnetic field is calculated based on the predicted interference characteristics. The optimal emission magnetic moment range is generated by combining the disturbance threshold with the currently determined maximum emission magnetic moment.

[0034] Based on the optimal launch magnetic moment range, a multi-objective optimization method is used to generate the optimal launch strategy for the transient electromagnetic instrument.

[0035] Optionally, the step of generating the optimal launch strategy for the transient electromagnetic instrument using a multi-objective optimization method based on the optimal launch magnetic moment range includes the following steps:

[0036] The optimization objectives are to maximize the signal-to-noise ratio of the expected transmitted signal of the transient electromagnetic instrument and minimize the impact of noise characteristics and predicted interference characteristics. A Pareto optimal solution set containing multiple transmission strategies is generated using a multi-objective optimization algorithm. Each transmission strategy corresponds to a set of transmission magnetic moments within an optimal transmission magnetic moment range, as well as preset waveform characteristics and transmission timing.

[0037] For each launch strategy in the Pareto optimal solution set, a risk quantification assessment is performed. The assessment includes the probability that the state parameters of the interference object exceed the safety boundary defined by the dynamic electromagnetic sensitivity model after the launch strategy is implemented, and the probability that the expected launch signal quality fails to meet the preset mission requirements.

[0038] The optimal launch strategy is selected from the Pareto optimal solution set based on the risk quantification assessment results.

[0039] Optionally, the process of transmitting electromagnetic signals using the transmitting coil assembly includes the following steps:

[0040] The control transmitting coil assembly uses a preset special code to transmit micro-disturbance pulses and monitors the instantaneous response characteristics of the interference object to the micro-disturbance pulses;

[0041] Adjust the launch parameters in the optimal launch strategy based on real-time response characteristic feedback;

[0042] The control coil assembly transmits electromagnetic signals according to the adjusted optimal transmission strategy.

[0043] In a second aspect, the present invention also provides a transient electromagnetic signal acquisition device under strong electromagnetic interference environment, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the transient electromagnetic signal acquisition method under strong electromagnetic interference environment as described in the first aspect.

[0044] Thirdly, the present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the transient electromagnetic signal acquisition method under strong electromagnetic interference environment as described in the first aspect.

[0045] The beneficial effects of this invention are:

[0046] This invention introduces an adaptive magnetic moment optimization mechanism based on a genetic algorithm, which intelligently adjusts the transmission power according to real-time interference characteristics. This ensures signal strength while avoiding adverse effects on surrounding sensitive equipment, achieving an optimal balance between detection performance and environmental friendliness. Particularly in terms of coil structure design, this invention employs an innovative combination of a large second transmitting coil with reverse current configuration and a secondary receiving coil with differential signal processing, forming a highly efficient interference suppression mechanism. This effectively eliminates environmental noise and self-excited interference, significantly improving the signal-to-noise ratio. This structure not only enhances the system's resistance to external interference but also optimizes the spatial distribution of transmitted energy, improving energy utilization efficiency. In practical applications, this invention can operate stably in highly interference environments such as industrial areas, power facility perimeters, and near communication base stations, acquiring high-quality detection data and expanding the application scope of transient electromagnetic detection technology. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating a transient electromagnetic signal acquisition method under strong electromagnetic interference environment in one embodiment of this application. Detailed Implementation

[0048] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0049] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0050] Figure 1 This is a flowchart illustrating a transient electromagnetic signal acquisition method under strong electromagnetic interference conditions in one embodiment. The transient electromagnetic signal acquisition method under strong electromagnetic interference conditions is applied to a transient electromagnetic instrument including a transmitting coil assembly and a receiving coil assembly. It should be understood that, although... Figure 1The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps. For example Figure 1 As shown, the transient electromagnetic signal acquisition method disclosed in this invention under strong electromagnetic interference environment specifically includes the following steps:

[0051] S101. Obtain the interference factor generated by the interference object on the transient electromagnetic instrument under the adjusted target state, and determine the upper limit value of the target magnetic moment of the interference object under the target state.

[0052] In environments with strong electromagnetic interference, a comprehensive analysis of the electromagnetic characteristics of the interfering object is first required. A spectrum analyzer is used to scan the target detection area, recording the radiation intensity of the interfering object at different frequencies; for example, the radiated power of a communication device in the 2.4GHz band is -30dBm. Simultaneously, a field strength meter is used to measure the spatial electromagnetic field distribution, establishing a three-dimensional field strength distribution map. Next, in a laboratory environment, electromagnetic irradiation tests are conducted on the interfering object, gradually increasing the irradiation intensity until the equipment malfunctions, recording the critical threshold. For example, if an electronic device begins to exhibit bit errors at a field strength of 10V / m, its sensitivity threshold is 10V / m. Combining geometric positional relationships, electromagnetic simulation software is used to calculate parameters such as the induced current and voltage of the interfering object under target conditions, serving as the interference factor. Finally, a functional relationship between the interference factor and the emitted magnetic moment is established. Where M is the magnetic moment, d is the distance, and k and n are coefficients. This is achieved by solving the inequalities. Obtain the upper limit of the target magnetic moment to ensure that the object being disturbed can function normally under this magnetic moment.

[0053] S102. Using a genetic algorithm and based on the interference factor and the preset fitness function, determine the upper limit of the emitted magnetic moment of the transient electromagnetic instrument when measuring at the measured position.

[0054] The genetic algorithm searches for the optimal solution by simulating biological evolution. First, the emitted magnetic moment is encoded as a binary chromosome; for example, the magnetic moment range of 0-100 A·m² is encoded as a 10-bit binary string. A population of 50 individuals is initialized, each representing a possible emitted magnetic moment value. A fitness function is constructed to evaluate the quality of each solution, considering both maximizing the signal-to-noise ratio (SNR) and minimizing interference. Specifically, the predicted SNR is calculated... Where S is the signal strength and N is the noise strength. When the interference factor exceeds the safety threshold, the penalty function... This will significantly reduce the fitness value. Population evolution is carried out through selection, crossover, and mutation operations. Selection retains individuals with high fitness, crossover exchanges gene segments between two individuals, and mutation randomly alters certain gene loci. After 100 generations of evolution, the magnetic moment value corresponding to the individual with the highest fitness is the upper limit of the emittable magnetic moment, which can usually minimize interference with surrounding equipment while ensuring measurement accuracy.

[0055] S103. If the transient electromagnetic instrument does not affect the state of the object being disturbed when it operates at the upper limit of the emittable magnetic moment, then the upper limit of the emittable magnetic moment shall be determined as the maximum emittable magnetic moment.

[0056] After obtaining the upper limit of the emittable magnetic moment, it is necessary to assess whether this magnetic moment will adversely affect the object being interfered with. Electromagnetic field simulation calculations are used to determine the electromagnetic field strength generated at the location of the object being interfered with when the transient electromagnetic instrument operates at the upper limit of the emittable magnetic moment. For example, if the calculated electric field strength at a distance of 50 meters is 5V / m, and the sensitivity threshold of the object being interfered with is 10V / m, then 5V / m < 10V / m, indicating no impact. In this case, the upper limit of the emittable magnetic moment is directly set as the maximum emittable magnetic moment. Conversely, if the calculated electric field strength is 15V / m > 10V / m, then it will affect the object being interfered with, and the smaller value between the upper limit of the emittable magnetic moment and the upper limit of the target magnetic moment needs to be selected as the maximum emittable magnetic moment. This dual constraint mechanism ensures that both measurement requirements are met and surrounding sensitive equipment is protected, achieving a balance between measurement performance and electromagnetic compatibility. Through this adaptive adjustment, the usable range of the maximum emittable magnetic moment is typically increased by 20-30% compared to the traditional fixed setting method.

[0057] S104. If the transient electromagnetic instrument affects the state of the object being disturbed when it operates at the upper limit of the emittable magnetic moment, then the minimum value between the upper limit of the emittable magnetic moment and the upper limit of the target magnetic moment shall be determined as the maximum emittable magnetic moment.

[0058] S105. Based on the maximum transmitting magnetic moment, electromagnetic signals are transmitted using the transmitting coil assembly.

[0059] The transmitting coil assembly employs an innovative dual-coil reverse current design to optimize the electromagnetic field distribution. The assembly includes a first transmitting coil and a second transmitting coil, with the second coil located inside the first, and its current path opposite to that of the first. The first transmitting coil typically has a diameter of 1-2 meters and 10-20 turns, carrying the main transmitting current. The second transmitting coil has a diameter approximately 0.6-0.8 times that of the first, with fewer turns, and is located concentrically inside the first transmitting coil. The key technology lies in the opposite current directions of the two coils; when a clockwise current is applied to the first transmitting coil, a counterclockwise current is applied to the second. This design is based on Ampere's circuital law, where the magnetic fields generated by the two coils superimpose in the near-field region and cancel each other out in the far-field region, thus achieving field optimization. The current ratio is typically set between 3:1 and 5:1 between the first and second transmitting coils. During transmission, a square wave or trapezoidal wave pulse current is fed into the coil assembly. The pulse width is typically 1-10 milliseconds, and the current amplitude is determined based on the maximum transmitting magnetic moment.

[0060] S106. After the electromagnetic signal is transmitted, the transient electromagnetic signal is received through the receiving coil assembly.

[0061] The receiving coil assembly employs a differential configuration to suppress common-mode interference and enhance the target signal. The receiving coil assembly includes a first receiving coil and a second receiving coil. The first receiving coil is positioned between the first and second transmitting coils, and the second receiving coil forms a differential signal with the first receiving coil. The first receiving coil is located in an annular region between the first and second transmitting coils, with a diameter between the two, typically 0.7-0.9 times the diameter of the first transmitting coil. The second receiving coil is positioned at a certain distance from the first receiving coil, forming a spatially separated differential pair. The working principle is based on the different attenuation characteristics of near-field and far-field signals: the secondary transient electromagnetic field from the underground target is a near-field signal, with a significant difference in signal strength at the two receiving coils; while the far-field electromagnetic interference signal has essentially the same strength at both coils. The differential amplifier circuit calculates the difference between the signals from the two coils. , where k is the correction coefficient. Through this differential processing, common-mode interference is effectively canceled, while the target signal is preserved and amplified.

[0062] In one embodiment, obtaining the interference factor generated by the interference object on the transient electromagnetic instrument under the adjusted target state, and determining the upper limit value of the target magnetic moment of the interference object in the target state, includes the following steps:

[0063] Collect the spectral characteristics, field strength distribution and temporal variation of the interfering objects within the target detection area, and construct a dynamic electromagnetic interference environment model;

[0064] Laboratory electromagnetic radiation effect tests were conducted on the interfering object to determine the sensitivity threshold of the interfering object at different frequencies and field strengths.

[0065] By combining the sensitivity threshold and the geometric relative position of the transient electromagnetic instrument and the interference object, and by performing electromagnetic simulation based on the dynamic electromagnetic interference environment model, the electromagnetic induction parameters of the interference object in the target state are calculated as the interference factor.

[0066] A functional relationship model is established between the interference factor, the geometric relative position, and the emitted magnetic moment of the transient electromagnetic instrument. The upper limit of the target magnetic moment of the interference object in the target state is determined by solving the functional relationship model.

[0067] In this embodiment, a broadband spectrum analyzer is used to perform a full-band scan of the target detection area, typically covering a frequency range of 10kHz to 18GHz, recording the power spectral density at each frequency point. For example, a WiFi signal of -40dBm was detected in the 2.4GHz band, and a mobile communication signal of -35dBm was detected in the 900MHz band. Simultaneously, multiple field strength probes are deployed to form a monitoring network, measuring the electric and magnetic field strengths at different locations to establish a three-dimensional field strength distribution map. The temporal variation pattern is obtained through continuous 24-hour monitoring, recording the periodic changes in signal strength, such as a 10-15dB increase in communication signal strength during operating hours. Based on the collected data, a dynamic model is established using electromagnetic field theory.

[0068]

[0069] in For time-varying amplitude, It is a spatial distribution function. ω is the angular frequency.

[0070] In a standard electromagnetic compatibility (EMC) laboratory, the interfering object is placed on a test bench in an anechoic chamber, and controlled electromagnetic radiation is generated using a signal generator and power amplifier. The testing process employs a stepwise increase method: starting with the lowest power, the radiation intensity is gradually increased in 1 dB increments, while simultaneously monitoring the operating status of the interfering object. For example, when testing an electronic device, if it operates normally at a frequency of 1 MHz and a field strength of 5 V / m, exhibits slight interference at 8 V / m, and a serious malfunction at 12 V / m, then the sensitivity threshold at that frequency is determined to be 8 V / m. This process is repeated for each key frequency point, and sensitivity threshold curves are plotted. The test also included two modes: pulse interference and continuous wave interference, with pulse widths varying from 1 μs to 1 ms. The sensitivity function was obtained by fitting a large amount of test data. , where a, b, and c are the fitting parameters.

[0071] Based on the obtained sensitivity threshold and geometric positional relationships, precise calculations were performed using finite element electromagnetic simulation software. First, a three-dimensional geometric model was established, including the detailed structure of the transient electromagnetic instrument's transmitting coil, the interfering object, and the surrounding environment. Material parameters were set as follows: the relative permittivity of air was 1, and the conductivity of the conductor was 5.8 × 10⁻⁶. 7 S / m. In the simulation, the excitation source is set as a time-domain pulse current, and the waveform is an exponentially decaying function. ,in This is the peak current. The time constant is used. The electromagnetic field distribution is solved using Maxwell's equations to calculate the induced electric field at the location of the interfering object. and induced current The interference factor is defined as the ratio of the actual sensing parameter to the sensitivity threshold. .

[0072] Based on simulation results, a mathematical model was established to demonstrate the relationship between the interference factor, geometric position, and emission magnetic moment. Through multiple regression analysis, the functional relationship was obtained: Where M is the emitted magnetic moment, r is the distance, θ is the angle, and α, β, and γ are fitting coefficients. This is the angle correction function.

[0073] For example, suppose the fit is obtained The correlation coefficient R² reached over 0.95. To determine the upper limit of the target magnetic moment, safety constraints were set. ,generally To allow for a safety margin. This is achieved by solving the inequalities. ,get When the distance r = 50m and the angle θ = 30°, the calculated upper limit of the target magnetic moment is 85 A·m².

[0074] In one embodiment, determining the upper limit of the emittable magnetic moment of the transient electromagnetic instrument at the measured position using a genetic algorithm and based on an interference factor and a preset fitness function includes the following steps:

[0075] The emission magnetic moment to be optimized is encoded into a chromosome population in a genetic algorithm;

[0076] Construct the fitness function for the genetic algorithm. The fitness function is specifically expressed as follows:

[0077]

[0078] Where M is the emitted magnetic moment, To predict the signal-to-noise ratio, For the kth interference factor, This is the penalty function when the interference factor exceeds the safety threshold. Hardware constraint penalty for the emission magnetic moment. These are the weighting coefficients;

[0079] The chromosome population is optimized based on the fitness function until the preset number of iterations or fitness value convergence condition is met.

[0080] The emitted magnetic moment corresponding to the chromosome with the highest fitness in the optimized chromosome population is used as the upper limit of the emitted magnetic moment.

[0081] In this embodiment, chromosome encoding in the genetic algorithm is the process of transforming a practical problem into a numerical string that can be processed by a computer. The range of the emitted magnetic moment is assumed to be 0-200 A·m. 2 It uses a binary encoding method, representing a magnetic moment value with a 16-bit binary number. The encoding precision is 200 / 2. 16 =0.003A·m 2 This meets engineering accuracy requirements. For example, a magnetic moment value of 100 A·m. 2 The corresponding binary encoding is as follows: First, calculate 100 / 200 × 65535 = 32767, which is converted to binary to get 10000000000000000. The initial population contains 100 individuals, each representing a possible emission magnetic moment solution. Population initialization uses a random generation method, using a pseudo-random number generator to generate 100 integers in the range [0, 65535], which are then converted into corresponding magnetic moment values. For example, the random number 32768 corresponds to a magnetic moment value of 100.0015 A·m. 2 The random number 16384 corresponds to a magnetic moment value of 50.0008 A·m. 2 This encoding method ensures complete coverage of the search space, with each chromosome uniquely corresponding to a specific magnetic moment value.

[0082] The fitness function is a mathematical expression that evaluates the quality of each solution, taking into account two key factors: signal quality and interference constraints. The fitness function is specifically expressed as:

[0083]

[0084] Where M is the emitted magnetic moment, To predict the signal-to-noise ratio, For the kth interference factor, This is the penalty function when the interference factor exceeds the safety threshold. Hardware constraint penalty for the emission magnetic moment. These are the weighting coefficients. The interference factor penalty function uses a piecewise function to ensure that the fitness drops sharply when the interference factor exceeds the safety threshold. Hardware constraint penalty. The magnetic moment is limited to no more than 200 A·m², which is the maximum capacity of the equipment.

[0085] Population optimization achieves evolution through three basic operations: selection, crossover, and mutation. In selection, the probability of an individual being selected is proportional to its fitness; individuals with higher fitness have a greater chance of passing on their genes to the next generation. Crossover randomly selects two parent individuals and exchanges genes at random positions on the chromosomes, with a crossover probability of 0.8. For example, if parent 1 is 10000000000000000 and parent 2 is 0100000000000000, after crossover at the 5th position, offspring 1 will be 1000000000000000 and offspring 2 will be 01000000000000000. Mutation randomly flips a position on the chromosome with a probability of 0.01, increasing population diversity and preventing premature convergence. After each generation, the average fitness and optimal fitness of the population are calculated. Convergence is considered achieved when the change in optimal fitness is less than 0.1% for 20 consecutive generations, or when the preset maximum number of iterations (500 generations) is reached.

[0086] After evolution is complete, the individual with the highest fitness is selected as the optimal solution from the final population. First, the fitness of all individuals is sorted, and the chromosome with the highest fitness value is selected. For example, the binary code of the optimal individual is 1100000000000000, corresponding to a decimal value of 49152, which translates to an actual magnetic moment of 49152 / 65535 × 200 = 150.0 A·m². To verify the reliability of the solution, it is also necessary to check whether the magnetic moment value satisfies all constraints: whether the signal-to-noise ratio meets the expected requirements, whether all interference factors are within a safe range, and whether it exceeds hardware limitations. If the optimal solution does not satisfy some constraints, a suboptimal solution is selected for testing until a feasible solution that satisfies all constraints is found.

[0087] In one embodiment, the following steps are included before transmitting the electromagnetic signal using the transmitting coil assembly:

[0088] The geometric parameters, number of turns, relative position and current ratio of the two transmitting coils in the transmitting coil assembly were modeled using three-dimensional electromagnetic simulation software to obtain the transmitting coil model;

[0089] The model parameters in the transmitting coil model are optimized by parameter simulation. The goal of parameter simulation is to minimize the rate of change of primary magnetic flux caused by the change of transmitting current in the transmitting coil assembly at the initial position of the receiving coil assembly.

[0090] The transmitting coil assembly was optimized and adjusted based on the optimized model parameters;

[0091] An alternating current is fed into the transmitting coil assembly, and the positions of the two transmitting coils are adjusted to minimize the amplitude of the induced voltage generated by the transmitting current at the initial position of the receiving coil assembly.

[0092] In this embodiment, a precise 3D model is established using professional electromagnetic simulation software such as ANSYS Maxwell or CST Studio Suite. The first transmitting coil is designed as a circular structure with a diameter of 2.0 meters, using multi-strand stranded copper wire with a diameter of 2.5 mm², 15 turns, and a coil thickness of 0.05 meters. The second transmitting coil is concentrically located inside the first transmitting coil, with a diameter of 1.2 meters, 8 turns, and using the same specification wire. The vertical distance between the two coils is set to 0.1 meters, with the second transmitting coil positioned above the first transmitting coil. In the material parameter settings, the conductivity of the copper wire is 5.8 × 10⁻⁶. 7 The magnetic permeability is 1, and the relative permittivity of air is 1. The current excitation is reversed: the first transmitting coil has a current amplitude of 100A and a phase of 0°; the second transmitting coil has a current amplitude of 60A and a phase of 180°, achieving a current ratio of 5:3. Boundary conditions are set as radiating boundaries, and the computational domain is extended to more than 5 times the coil size to ensure far-field accuracy. An adaptive tetrahedral mesh is used, with the mesh density near the coil conductors refined to 0.01 meters, and the mesh size widened to 0.2 meters in the far-field region. The core objective of parameter optimization is to reduce the direct coupling between the transmitting coil and the receiving coil. The receiving coil is positioned in a ring-shaped region between the large and second transmitting coils, 1.5 meters from the center.

[0093] The formula for calculating the rate of change of magnetic flux is: ,in The mutual inductance coefficient is used. Four key parameters are optimized through parametric scanning: the diameter D1 of the first transmitting coil varies from 1.8 to 2.2 meters; the diameter D2 of the second transmitting coil varies from 1.0 to 1.4 meters; the current ratio k varies from 0.4 to 0.8; and the vertical spacing h varies from 0.05 to 0.15 meters. The rate of change of magnetic flux at the receiving coil position is calculated for each parameter combination. The objective function is set as follows: A genetic algorithm was used for global optimization, and after 200 generations of evolution, the optimal parameter combination was obtained: D1=2.05m, D2=1.25m, k=0.62, h=0.08m. The optimized magnetic flux change rate was reduced from the initial design of -15mWb / s to -0.8mWb / s, a reduction of 94%. This significant improvement means a substantial reduction in direct interference to the receiving coil, improving the signal-to-noise ratio and detection accuracy of the measurement system.

[0094] Then, based on the simulation optimization results, the actual hardware was precisely adjusted. The diameter of the first transmitting coil was adjusted from the original design of 2.0 meters to 2.05 meters, achieved by rewinding or adjusting the support frame. The diameter of the second transmitting coil was adjusted from 1.2 meters to 1.25 meters, while the vertical spacing was precisely adjusted from 0.1 meters to 0.08 meters. The current ratio was adjusted by modifying the current distribution of the drive circuit. The current of the first transmitting coil remained unchanged at 100A, while the current of the second transmitting coil was adjusted from 60A to 62A, achieving the target ratio of k=0.62. The coil position was adjusted using a precision mechanical adjustment device, with positional accuracy controlled within ±2mm. Low-impedance welding technology was used for wire connections to ensure uniform current distribution. The support structure was made of non-magnetic materials such as aluminum alloy or fiberglass to avoid affecting the magnetic field distribution. During the adjustment process, a laser rangefinder and a level were used to ensure geometric accuracy, and all key dimensions were measured three times and the average value was taken.

[0095] A sinusoidal alternating current of 1 kHz was fed into the adjusted transmitting coil assembly for practical testing and verification. The first transmitting coil was fed a 100 A RMS current, and the second transmitting coil was fed a 62 A RMS reverse current. The current amplitude and phase were precisely controlled by a power amplifier and a current sensor. A high-precision induction coil with a diameter of 0.5 meters and 50 turns was placed at the initial position of the receiving coil assembly as a monitoring probe, connected to a high-input-impedance differential amplifier and a digital oscilloscope. The induced voltage caused by changes in the transmitting current was measured, with an initial value of 85 mV. Fine-tuning of the two transmitting coils was performed using a fine-tuning mechanism with an adjustment step of 1 mm, and the change in induced voltage was recorded after each adjustment. The position of the first transmitting coil remained fixed, and the radial and axial positions of the second transmitting coil were mainly adjusted. This significant improvement verified the correctness of the theoretical design and simulation optimization, minimized the direct coupling between the transmitting and receiving coils, and provided an excellent hardware foundation for high-precision transient electromagnetic measurements.

[0096] In one embodiment, the following steps are included before receiving the transient electromagnetic signal via the receiving coil assembly in the transient electromagnetic instrument:

[0097] Electromagnetic simulation optimization is performed based on the geometric parameters, number of turns, and initial position of the two receiving coils in the receiving coil assembly. The optimization goal of the electromagnetic simulation optimization is to make the two receiving coils have consistent response characteristics to common-mode interference signals that are uniformly distributed in the far field and in space, while having different response characteristics to target secondary transient electromagnetic field signals that are near field and originate from underground.

[0098] The output signal of the receiving coil assembly is connected to the differential amplifier front-end circuit and the analog-to-digital conversion processing circuit.

[0099] In this embodiment, the optimized design of the receiving coil assembly is based on the different propagation characteristics of near-field and far-field electromagnetic signals. The first receiving coil is located within the annular region of the transmitting coil, with a diameter of 1.6 meters and 20 turns; the second receiving coil is located 3 meters from the center, with a diameter of 0.8 meters and 10 turns. Two typical signal sources are established in the simulation: the far-field interference source is simulated as a plane wave at a distance of 1000 meters, with a frequency range of 1-100kHz and a field strength of 10V / m; the near-field target signal is simulated as a magnetic dipole source at a depth of 50 meters underground, with a magnetic moment of 1A·m². The optimized objective function is designed as follows:

[0100]

[0101] in These are the responses of the two coils to far-field signals, respectively. In response to near-field signals, The desired near-field response ratio is typically set to 2-3. Parameters such as coil diameter, number of turns, and relative position are optimized through parametric scanning. After 500 iterations, the optimal configuration is obtained: the first receiving coil has a diameter of 1.65 meters and 18 turns, the second receiving coil has a diameter of 0.75 meters and 12 turns, and the distance between the two coils is 2.8 meters. The output signal of the receiving coil assembly is processed by a specially designed differential amplifier circuit. The front end uses a high-input-impedance instrumentation amplifier AD8429, with an input impedance greater than 10¹²Ω. The signal from the first receiving coil is connected to the positive input terminal, and the signal from the second receiving coil is connected to the negative input terminal, realizing hardware differential operation. Where G is the adjustable gain (1-1000 times) and k is the balance coefficient (adjustable from 0.8-1.2). The circuit design includes low-noise power supply filtering, shielding grounding, and temperature compensation. After differential amplification, the signal enters a 24-bit high-precision ADC (such as ADS1274), with a sampling rate set to 1MHz and a dynamic range of 120dB. Digital signal processing uses an FPGA to implement real-time filtering and data preprocessing, including 50Hz power frequency notch filtering, high-pass filtering (cutoff frequency 100Hz), and low-pass filtering (cutoff frequency 50kHz). The processed digital signal is transmitted to a host computer for further analysis via USB 3.0 or Ethernet interface.

[0102] In one embodiment, after determining the maximum emission magnetic moment, the following steps are also included:

[0103] The transient electromagnetic instrument uses an internal sensor array to collect in real time the noise characteristics of the ambient electromagnetic noise and the interference characteristics of the interfering object. The interference characteristics include the state parameters of the interfering object and the near-field electromagnetic leakage characteristics.

[0104] By combining noise and interference characteristics and using machine learning models, the state change trend of the interfering object in one or more future detection cycles is dynamically predicted, and the dynamic interference factor spectrum of the interfering object on the transient electromagnetic instrument is evaluated.

[0105] A dynamic electromagnetic susceptibility model of the interference object is constructed based on the interference characteristics, and the predicted interference characteristics of the interference object are obtained by using the dynamic electromagnetic susceptibility model.

[0106] The disturbance threshold of the external electromagnetic field is calculated based on the predicted interference characteristics. The optimal emission magnetic moment range is generated by combining the disturbance threshold with the currently determined maximum emission magnetic moment.

[0107] Based on the optimal launch magnetic moment range, a multi-objective optimization method is used to generate the optimal launch strategy for the transient electromagnetic instrument.

[0108] In this embodiment, the transient electromagnetic instrument integrates multiple sensor arrays for environmental monitoring. Electromagnetic noise monitoring employs broadband electric field probes (10kHz-1GHz) and magnetic field probes (1Hz-100kHz), deployed at four locations around the device to collect background electromagnetic environment data in real time. Noise characteristic parameters include power spectral density, peak factor, and time-domain statistical characteristics. For example, in an industrial area, the 50Hz power frequency interference intensity was measured to be -20dBμV / m, and the broadband noise floor was -45dBμV / m. Interference target monitoring is achieved through a near-field probe array, consisting of eight small induction coils (10cm in diameter) arranged around the device to monitor electromagnetic leakage of electronic equipment within a range of 5-50 meters. Status parameter monitoring utilizes optical sensors, vibration sensors, and temperature sensors to acquire the operating status of interference targets in real time. For example, the transmission power of a communication base station was monitored to periodically change between 30-45dBm with a period of 10 minutes. Near-field electromagnetic leakage characteristics are extracted through spectrum analysis, recording the frequency, amplitude, and modulation characteristics of the leakage signal. The data acquisition frequency is 100Hz, generating a data vector containing 128 characteristic parameters per second. All sensor data is collected by the central processing unit via a CAN bus to establish a real-time environmental electromagnetic situation map.

[0109] A hybrid prediction model combining Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) is employed. The LSTM network captures long-term dependencies in time series data, with a network structure containing three hidden layers, each with 128 neurons, and an input window length of 60 time steps (corresponding to 10 minutes of historical data). The SVR model handles nonlinear mappings, employing a radial basis function kernel with a penalty parameter C=100 and a kernel parameter γ=0.01. The training dataset contains monitoring data from seven consecutive days, totaling one million sample points. During prediction, the feature vectors of the 60 sample points before the current time are input into the LSTM network, outputting coarse predictions for the next six time steps; these are then refined using the SVR model in conjunction with current environmental parameters. For example, predicting the transmit power change of a mobile communication base station within one hour: the current power is 35 dBm, the LSTM predicts 38 dBm after one hour, and the SVR-corrected power is 37.2 dBm. The dynamic interference factor spectrum is calculated using the predicted state parameters. ,in For power prediction, d represents distance, and G(θ) is the directionality function.

[0110] The dynamic electromagnetic susceptibility model is described using state-space equations: Where x(t) is the state vector, containing parameters such as internal equipment temperature, operating current, and signal processing load; u(t) is the external electromagnetic field input; and w(t) is the process noise. The system matrix A and input matrix B are obtained through system identification methods, and historical data are fitted using the least squares method. The sensitivity function is defined as: ,in Let E be the equipment error rate, E be the external field strength, and f be the frequency. The sensitivity curve was obtained by fitting a large amount of experimental data. The model also considers dynamic changes in equipment status; when the equipment is under high load, the sensitivity threshold decreases by 20-30%. Predicted interference characteristics are obtained through forward calculation of the model: given the predicted external electromagnetic environment, the response characteristics and possible anomalies of the equipment at future times are calculated, and then the safe disturbance threshold of the external electromagnetic field is calculated based on the predicted interference characteristics.

[0111] The disturbance threshold is defined as the maximum permissible electromagnetic field strength that allows the disturbed object to maintain normal operation. The calculation formula is as follows: ,in The basic sensitivity threshold is used, SF is the safety factor (usually set to 0.5-0.8), and CF is the state correction factor. The state correction factor is dynamically adjusted according to the current operating state of the equipment: CF=1.0 for normal state, CF=0.7 for high load state, and CF=1.3 for standby state. For example, if the sensitivity threshold of an industrial controller under normal operating conditions is 5V / m, and the safety factor is 0.6, then the disturbance threshold is 3V / m. This is combined with the electromagnetic field propagation model. Calculate the relationship between the emitted magnetic moment and the field strength, where M is the magnetic moment, ω is the angular frequency, and r is the distance. This is achieved by solving the inequalities. The upper limit of the magnetic moment is obtained, and the minimum value is taken when considering multiple interfering objects. The optimal transmission magnetic moment range is set to [0.3×M_max, M_max], with the lower limit ensuring sufficient signal strength and the upper limit guaranteeing electromagnetic compatibility.

[0112] Finally, a non-dominated sorting genetic algorithm (NSGA-II) is used for multi-objective optimization, simultaneously considering both maximizing signal quality and minimizing interference risk. The optimization yields a Pareto solution set containing multiple non-dominated solutions, each representing a transmission strategy. Decision-makers can select the most suitable strategy from the Pareto solution set based on specific mission requirements, achieving the optimal balance between signal quality and electromagnetic compatibility.

[0113] In one implementation, the optimal launch strategy for the transient electromagnetic instrument is generated using a multi-objective optimization method based on the optimal launch magnetic moment range, including the following steps:

[0114] The optimization objectives are to maximize the signal-to-noise ratio of the expected transmitted signal of the transient electromagnetic instrument and minimize the impact of noise characteristics and predicted interference characteristics. A Pareto optimal solution set containing multiple transmission strategies is generated using a multi-objective optimization algorithm. Each transmission strategy corresponds to a set of transmission magnetic moments within an optimal transmission magnetic moment range, as well as preset waveform characteristics and transmission timing.

[0115] For each launch strategy in the Pareto optimal solution set, a risk quantification assessment is performed. The assessment includes the probability that the state parameters of the interference object exceed the safety boundary defined by the dynamic electromagnetic sensitivity model after the launch strategy is implemented, and the probability that the expected launch signal quality fails to meet the preset mission requirements.

[0116] The optimal launch strategy is selected from the Pareto optimal solution set based on the risk quantification assessment results.

[0117] In this embodiment, an improved non-dominated sorting genetic algorithm, NSGA-III, is used for multi-objective optimization, simultaneously handling three conflicting objective functions. The first objective is to maximize the signal-to-noise ratio:

[0118]

[0119] Where M is the emitted magnetic moment, and G(τ,α) is the waveform gain function. This represents the total noise power.

[0120] The second objective is to minimize the impact of noise: ,in The degree of influence of the k-th type of noise, Weighting coefficients.

[0121] The third objective is to minimize the impact of interference: ,in Let be the interference factor for the j-th interference object. The importance weights for this object are defined. Optimization variables include the emission magnetic moment M∈[80,150]A·m², pulse width τ∈[2,8]ms, waveform parameters α∈[0.5,2.0], and emission frequency f∈[0.5,5]Hz. The algorithm is set with a population size of 200, 91 reference points, and runs for 300 generations. After optimization, a Pareto front containing 85 non-dominated solutions is obtained, each representing a unique combination of emission strategies. Next, a detailed risk quantification analysis is needed for each emission strategy in the Pareto solution set. The probability of exceeding the safety boundary is calculated using Monte Carlo simulation, considering random fluctuations in environmental parameters and measurement uncertainties. 10,000 random scenarios are generated in the simulation, each containing random perturbations to the state parameters of the interfering object; the perturbation amplitude is determined based on historical statistical data.

[0122] For each scenario, the response parameters of the interfering object are calculated after the transmission strategy is implemented to determine whether they exceed the safety threshold. Signal quality risk assessment is based on signal-to-noise ratio distribution statistics, considering the impact of factors such as changes in geological conditions and equipment aging on signal quality. The probability of quality failure is defined as: The probability density function is used for integration. Then, based on the risk quantification assessment results, a multi-criteria decision-making method is employed to select the optimal strategy from the Pareto solution set. First, safety risk thresholds and quality risk thresholds are set. Then, strategies exceeding these thresholds are eliminated, resulting in 62 feasible solutions selected from 85 solutions. Finally, a comprehensive evaluation index is constructed.

[0123]

[0124] in To normalize the signal-to-noise ratio, robustness is used as the robustness metric, with weighting coefficients set according to task priority. The robustness metric is calculated through parameter sensitivity analysis to measure the strategy's adaptability to environmental changes. After comprehensive scoring, this strategy ensures high signal quality while keeping various risks within acceptable limits and exhibits good environmental adaptability.

[0125] In one embodiment, transmitting an electromagnetic signal using a transmitting coil assembly includes the following steps:

[0126] The control transmitting coil assembly uses a preset special code to transmit micro-disturbance pulses and monitors the instantaneous response characteristics of the interference object to the micro-disturbance pulses;

[0127] Adjust the launch parameters in the optimal launch strategy based on real-time response characteristic feedback;

[0128] The control coil assembly transmits electromagnetic signals according to the adjusted optimal transmission strategy.

[0129] In this embodiment, the micro-perturbation pulse is encoded using a special pseudo-random binary sequence (PRBS) with a sequence length of 127 bits, a symbol width of 50 μs, and a total duration of 6.35 ms. The pulse amplitude is set to 10% of the predetermined transmitting magnetic moment to ensure a detectable but non-damaging effect on the object being interfered with. The transmitting coil generates a precise encoded pulse sequence through a high-speed switching circuit with a switching frequency of 20 kHz and a current rise time of less than 10 μs. Simultaneously, a multi-channel monitoring system is activated, including eight near-field electromagnetic probes that acquire changes in the electromagnetic field around the object being interfered with in real time, with a sampling frequency set to 1 MHz to ensure the capture of transient responses. An optical sensor monitors changes in the status indicator lights of the object being interfered with, with an image acquisition frequency of 1000 fps. A vibration sensor detects the mechanical response of the device, with an acceleration measurement range of ±50g and a frequency response range of DC-10 kHz. A temperature sensor monitors the thermal response of the device, with an accuracy of ±0.1°C and a response time of 1 second. All sensor data are synchronously recorded via a high-speed data acquisition card, with a time synchronization accuracy of 1 μs. Response characteristic parameters, including response delay time, peak amplitude, spectral characteristics, and decay time constant, are extracted through relevant analysis to establish a real-time response fingerprint of the interference object.

[0130] Response feature analysis employs a combination of wavelet transform and Hilbert-Huang transform to extract time-frequency domain feature vectors. Key feature parameters include:

[0131] Response strength index Response delay Spectrum centroid and quality factor The parameter adjustment rule is based on the fuzzy logic controller design: when RI > 0.8, it indicates that the interference object is highly sensitive, and the emission magnetic moment adjustment coefficient is adjusted accordingly. When RI < 0.3, the sensitivity is low. When 0.3 ≤ RI ≤ 0.8, Pulse width adjustment is based on response delay: ,in This is a reference delay time. The transmission frequency is adjusted according to the spectral centroid: when When the value deviates from the expected value, the transmission frequency should be adjusted appropriately to avoid sensitive frequency bands.

[0132] Based on the optimal transmission strategy adjusted by feedback, the control unit of the transmitting coil assembly executes the formal electromagnetic signal transmission. The transmission control system employs a high-precision digital signal processor (DSP) and a field-programmable gate array (FPGA) working together to achieve microsecond-level timing control accuracy. The transmission sequence is generated according to the adjusted parameters: a magnetic moment of 115 A·m² corresponds to a first transmitting coil current of 153 A and a second transmitting coil current of 95 A (current ratio 1.6:1), a pulse width of 4.2 ms, and a repetition period of 454 ms (corresponding to a frequency of 2.2 Hz). The waveform uses an optimized trapezoidal pulse with a rise time of 0.5 ms, a flat-top time of 3.2 ms, and a fall time of 0.5 ms to ensure the smooth establishment and dissipation of the electromagnetic field.

[0133] During transmission, the coil current, voltage, and temperature are monitored in real time, and closed-loop feedback control ensures the accuracy of transmission parameters. Current control accuracy reaches ±0.5%, and timing control accuracy is ±1μs. Immediately after transmission, the system switches to receive mode, and the receiving coil assembly begins acquiring transient electromagnetic response signals. The dead time of the entire transmit-receive cycle is controlled within 50μs to ensure effective signal acquisition in the early stages.

[0134] In one embodiment, the method may further include the following steps:

[0135] After each transmission and data acquisition is completed with the final maximum transmission parameter set, the complete parameters of this transmission, the actual signal quality indicators acquired, and the actual state change data of the monitored interference objects are automatically recorded.

[0136] The recorded transmission parameters, signal quality indicators, and data on changes in the state of the interfering object were used as training samples.

[0137] Using training samples, the machine learning model, dynamic interference factor spectrum model, dynamic electromagnetic susceptibility model, and surrogate models or parameters in the multi-objective optimization algorithm are iteratively updated through online or offline learning methods.

[0138] By using reinforcement learning or experience replay mechanisms, the strategy selection logic of the intelligent decision engine and the generation efficiency of the Pareto optimal solution set are continuously optimized.

[0139] This enables the entire transmission control and receiving system to achieve self-learning, self-adaptation, and continuous evolution capabilities when facing changing environments with strong electromagnetic interference.

[0140] In this implementation, after each transmission, the data recording system automatically collects and stores comprehensive information across multiple dimensions. Transmission parameter records include: actual transmitted magnetic moment, pulse width, transmission frequency, waveform parameters, current ratio of the second transmitting coil, and transmission timestamp. Signal quality indicators are obtained through real-time analysis, including: signal-to-noise ratio, effective signal bandwidth, signal strength in the early and late time periods, signal distortion, and phase consistency index. Interference target status monitoring data includes: electromagnetic field strength changes at each monitoring point, equipment status parameters such as CPU utilization, memory usage, communication error rate, and operating temperature, as well as abnormal indicators detected by optical and vibration sensors. All data is stored in a time-series database in a standardized format, with a data acquisition accuracy of 16 bits and a storage frequency of 1000 records per second. A data quality verification mechanism is established to ensure data reliability through outlier detection and consistency checks.

[0141] The recorded multi-source heterogeneous data was transformed into standardized machine learning training samples. Feature engineering methods were used to construct the sample feature vectors: transmission parameter features were normalized, signal quality indices underwent logarithmic transformation and standardization, and interference object state data was dimensionality-reduced using principal component analysis, retaining 95% of the variance information. Sample label design included multiple target variables: signal quality level (excellent / good / average / poor, encoded as 0-3), interference risk level (low / medium / high, encoded as 0-2), system stability index (continuous values ​​0-1), etc. Data preprocessing included missing value imputation, outlier handling, and time series alignment. Data augmentation techniques were used to expand the sample set, including noise injection and parameter perturbation, increasing the sample size to 3-5 times the original data, thereby improving the model's generalization ability and robustness.

[0142] A hybrid update strategy combining online and offline learning is employed. The machine learning prediction model uses incremental learning, progressively updating the LSTM network weights based on new samples. The dynamic disturbance factor spectrum model adjusts parameters through a Bayesian update mechanism: the posterior distribution is obtained by combining the prior distribution with new observation data. The dynamic electromagnetic susceptibility model uses Kalman filtering for state estimation updates, and the surrogate model in the multi-objective optimization algorithm uses Gaussian process regression for updates. The covariance matrix and mean function are updated after each new evaluation point. The model update frequency is set as follows: online updates are performed every 10 samples, and offline batch updates are performed every 1000 samples. An early stopping mechanism is used during the update process to prevent overfitting; training stops when the validation set loss does not decrease for five consecutive iterations.

[0143] A reinforcement learning framework based on Deep Q-Network (DQN) is constructed to optimize policy selection. The state space is defined as the electromagnetic feature vector S_t of the current environment, containing 128 dimensions of features such as noise level, interfering object state, and historical performance indicators. The action space A contains five types of decisions: firing policy selection (selected from the Pareto solution set), parameter fine-tuning magnitude, firing timing selection, risk threshold adjustment, and model update frequency control. The experience replay buffer stores the most recent 10,000 experience tuples, and 32 batches are randomly sampled for network updates during each training session. An ε-greedy policy balances exploration and exploitation, with the ε value linearly decreasing from 0.9 to 0.1. The DQN network structure contains three hidden layers, each with 256 neurons, using the ReLU activation function. The target network is updated every 100 steps, with a learning rate set to 0.0001. Finally, a complete self-evolving closed-loop system is established to achieve continuous optimization in complex electromagnetic environments. The self-learning mechanism is implemented through a meta-learning framework, learning how to quickly adapt to new environmental conditions. The meta-learner employs the Model-Independent Meta-Learning (MAML) algorithm to rapidly adjust model parameters on a small number of new samples. The adaptive mechanism includes an environmental change detection module that monitors the distribution changes of key indicators through statistical process control methods, automatically triggering model retraining when significant changes are detected.

[0144] The present invention also discloses a transient electromagnetic signal acquisition device under strong electromagnetic interference environment, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the transient electromagnetic signal acquisition method under strong electromagnetic interference environment as described in any of the above embodiments.

[0145] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it.

[0146] The memory can be an internal storage unit of a computer device, such as a hard disk or RAM, or an external storage device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) provided on the computer device. Furthermore, the memory can be a combination of internal storage units and external storage devices of a computer device. The memory is used to store computer programs and other programs and data required by the computer device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0147] The present invention also discloses a computer-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the transient electromagnetic signal acquisition method under strong electromagnetic interference environment described in any of the above embodiments.

[0148] The computer program can be stored in a machine-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The machine-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the machine-readable medium includes, but is not limited to, the above-mentioned components.

[0149] The transient electromagnetic signal acquisition method under strong electromagnetic interference environment described in the above embodiments is stored in the computer-readable storage medium and loaded and executed on the processor to facilitate the storage and application of the above method.

[0150] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of protection of this application is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this application as described above, which are not provided in detail for the sake of brevity.

[0151] One or more embodiments in this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments in this application should be included within the protection scope of this application.

Claims

1. A method for acquiring transient electromagnetic signals under strong electromagnetic interference environment, characterized in that, The method, applied to a transient electromagnetic instrument comprising a transmitting coil assembly and a receiving coil assembly, includes the following steps: Obtain the interference factor generated by the interference object on the transient electromagnetic instrument under the adjusted target state, and determine the upper limit value of the target magnetic moment of the interference object under the target state; A genetic algorithm is used, along with an interference factor and a preset fitness function, to determine the upper limit of the emittable magnetic moment of the transient electromagnetic instrument when measuring at the measured position. If the transient electromagnetic instrument does not affect the state of the object being disturbed when it operates at the upper limit of the emittable magnetic moment, then the upper limit of the emittable magnetic moment is determined as the maximum emittable magnetic moment. If the transient electromagnetic instrument affects the state of the object being disturbed when it operates at the upper limit of the emittable magnetic moment, then the minimum value between the upper limit of the emittable magnetic moment and the upper limit of the target magnetic moment is determined as the maximum emittable magnetic moment. The transient electromagnetic instrument uses an internal sensor array to collect in real time the noise characteristics of the ambient electromagnetic noise and the interference characteristics of the interfering object. The interference characteristics include the state parameters of the interfering object and the near-field electromagnetic leakage characteristics. By combining noise and interference characteristics and using machine learning models, the state change trend of the interfering object in one or more future detection cycles is dynamically predicted, and the dynamic interference factor spectrum of the interfering object on the transient electromagnetic instrument is evaluated. A dynamic electromagnetic susceptibility model of the interference object is constructed based on the interference characteristics, and the predicted interference characteristics of the interference object are obtained by using the dynamic electromagnetic susceptibility model. The disturbance threshold of the external electromagnetic field is calculated based on the predicted interference characteristics. The optimal emission magnetic moment range is generated by combining the disturbance threshold with the currently determined maximum emission magnetic moment. Based on the optimal launch magnetic moment range, a multi-objective optimization method is used to generate the optimal launch strategy for the transient electromagnetic instrument. Based on the optimal transmission strategy and using the transmission coil assembly to transmit electromagnetic signals, the transmission coil assembly includes a first transmission coil and a second transmission coil. The second transmission coil is disposed inside the first transmission coil, and the current path of the second transmission coil is opposite to the current path of the first transmission coil. After the electromagnetic signal is transmitted, the transient electromagnetic signal is received by the receiving coil assembly. The receiving coil assembly includes a first receiving coil and a second receiving coil. The first receiving coil is disposed between the first transmitting coil and the second transmitting coil, and the second receiving coil and the first receiving coil form a differential signal.

2. The method for acquiring transient electromagnetic signals under strong electromagnetic interference environment according to claim 1, characterized in that, The steps of obtaining the interference factor generated by the interference object on the transient electromagnetic instrument under the adjusted target state and determining the upper limit of the target magnetic moment of the interference object in the target state include the following: Collect the spectral characteristics, field strength distribution and temporal variation of the interfering objects within the target detection area, and construct a dynamic electromagnetic interference environment model; Laboratory electromagnetic radiation effect tests were conducted on the interfering object to determine the sensitivity threshold of the interfering object at different frequencies and field strengths. By combining the sensitivity threshold and the geometric relative position of the transient electromagnetic instrument and the interference object, and by performing electromagnetic simulation based on the dynamic electromagnetic interference environment model, the electromagnetic induction parameters of the interference object in the target state are calculated as the interference factor. A functional relationship model is established between the interference factor, the geometric relative position, and the emitted magnetic moment of the transient electromagnetic instrument. The upper limit of the target magnetic moment of the interference object in the target state is determined by solving the functional relationship model.

3. The method for acquiring transient electromagnetic signals under strong electromagnetic interference environment according to claim 1, characterized in that, The process of determining the upper limit of the emittable magnetic moment of the transient electromagnetic instrument at the measured position using a genetic algorithm and based on an interference factor and a preset fitness function includes the following steps: The emission magnetic moment to be optimized is encoded into a chromosome population in a genetic algorithm; Construct the fitness function for the genetic algorithm. The fitness function is specifically expressed as follows: ， Where M is the emitted magnetic moment, To predict the signal-to-noise ratio, For the kth interference factor, This is the penalty function when the interference factor exceeds the safety threshold. Hardware constraint penalty for the emission magnetic moment. These are the weighting coefficients; The chromosome population is optimized based on the fitness function until the preset number of iterations or fitness value convergence condition is met. The emitted magnetic moment corresponding to the chromosome with the highest fitness in the optimized chromosome population is used as the upper limit of the emitted magnetic moment.

4. The method for acquiring transient electromagnetic signals under strong electromagnetic interference environment according to claim 1, characterized in that, The following steps are included before transmitting electromagnetic signals using the transmitting coil assembly: The geometric parameters, number of turns, relative position and current ratio of the two transmitting coils in the transmitting coil assembly were modeled using three-dimensional electromagnetic simulation software to obtain the transmitting coil model; The model parameters in the transmitting coil model are optimized by parameter simulation. The goal of parameter simulation is to minimize the rate of change of primary magnetic flux caused by the change of transmitting current in the transmitting coil assembly at the initial position of the receiving coil assembly. The transmitting coil assembly was optimized and adjusted based on the optimized model parameters; An alternating current is fed into the transmitting coil assembly, and the positions of the two transmitting coils are adjusted to minimize the amplitude of the induced voltage generated by the transmitting current at the initial position of the receiving coil assembly.

5. The method for acquiring transient electromagnetic signals under strong electromagnetic interference environment according to claim 4, characterized in that, Before receiving transient electromagnetic signals through the receiving coil assembly in the transient electromagnetic instrument, the following steps are included: Electromagnetic simulation optimization is performed based on the geometric parameters, number of turns, and initial position of the two receiving coils in the receiving coil assembly. The optimization goal of the electromagnetic simulation optimization is to make the two receiving coils have consistent response characteristics to common-mode interference signals that are uniformly distributed in the far field and in space, while having different response characteristics to target secondary transient electromagnetic field signals that are near field and originate from underground. The output signal of the receiving coil assembly is connected to the differential amplifier front-end circuit and the analog-to-digital conversion processing circuit.

6. The method for acquiring transient electromagnetic signals under strong electromagnetic interference environment according to claim 1, characterized in that, The process of generating the optimal launch strategy for the transient electromagnetic instrument using a multi-objective optimization method based on the optimal launch magnetic moment range includes the following steps: The optimization objectives are to maximize the signal-to-noise ratio of the expected transmitted signal of the transient electromagnetic instrument and minimize the impact of noise characteristics and predicted interference characteristics. A Pareto optimal solution set containing multiple transmission strategies is generated using a multi-objective optimization algorithm. Each transmission strategy corresponds to a set of transmission magnetic moments within an optimal transmission magnetic moment range, as well as preset waveform characteristics and transmission timing. For each launch strategy in the Pareto optimal solution set, a risk quantification assessment is performed. The assessment includes the probability that the state parameters of the interference object exceed the safety boundary defined by the dynamic electromagnetic sensitivity model after the launch strategy is implemented, and the probability that the expected launch signal quality fails to meet the preset mission requirements. The optimal launch strategy is selected from the Pareto optimal solution set based on the risk quantification assessment results.

7. The method for acquiring transient electromagnetic signals under strong electromagnetic interference environment according to claim 6, characterized in that, The process of transmitting electromagnetic signals using the transmitting coil assembly includes the following steps: The control transmitting coil assembly uses a preset special code to transmit micro-disturbance pulses and monitors the instantaneous response characteristics of the interference object to the micro-disturbance pulses; Adjust the launch parameters in the optimal launch strategy based on real-time response characteristic feedback; The control coil assembly transmits electromagnetic signals according to the adjusted optimal transmission strategy.

8. A transient electromagnetic signal acquisition device under strong electromagnetic interference environment, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the transient electromagnetic signal acquisition method under strong electromagnetic interference environment as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the transient electromagnetic signal acquisition method under strong electromagnetic interference environment according to any one of claims 1 to 7.

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