Earth electromagnetic interference detection method and device

Abstract

The invention provides a ground electromagnetic interference detection method and device, and belongs to the technical field of geological reconnaissance, and the method comprises the steps: obtaining the air electromagnetic environment parameters of a target region; inputting the air electromagnetic environment parameters of the target area into a mapping model to obtain magnetotelluric environment parameters of the target area; the mapping model is a model obtained by training a neural network model based on historical data, and the historical data comprises historical data of air electromagnetic environment parameters and historical data of corresponding magnetotelluric environment parameters of the same detection area. According to the earth electromagnetic interference detection method and device provided by the invention, the detection efficiency of a field geophysical electrical method can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of geological exploration, and more specifically, relates to a magnetotelluric interference detection method and device. Background Art

[0002] In the field of geological exploration, field geophysical electrical methods can be used to detect the distribution range and boundaries of different geological bodies underground, such as determining the positions and trends of geological structures such as faults and folds, and then inferring the properties and structural differences of underground rocks, providing an important basis for the study of geological structures.

[0003] In plateau areas, the electromagnetic environment is complex and there are various electromagnetic interference sources, resulting in poor quality of data collected by field geophysical electrical methods. It is necessary to reselect measurement points or adjust the measurement scheme, which leads to repetitive rework and reduces the detection efficiency. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a magnetotelluric interference detection method and device to improve the detection efficiency of field geophysical electrical methods.

[0005] In the first aspect of the embodiments of the present disclosure, a magnetotelluric interference detection method is provided, including: Obtaining the air electromagnetic environment parameters of the target area; Inputting the air electromagnetic environment parameters of the target area into a mapping model to obtain the magnetotelluric environment parameters of the target area; the mapping model is a model obtained by training a neural network model based on historical data, and the historical data includes historical data of air electromagnetic environment parameters and historical data of corresponding magnetotelluric environment parameters of the same detection area.

[0006] In the second aspect of the embodiments of the present disclosure, a magnetotelluric interference detection device is provided, including: A data acquisition module for obtaining the air electromagnetic environment parameters of the target area; A data mapping module for inputting the air electromagnetic environment parameters of the target area into a mapping model to obtain the magnetotelluric environment parameters of the target area; the mapping model is a model obtained by training a neural network model based on historical data, and the historical data includes historical data of air electromagnetic environment parameters and historical data of corresponding magnetotelluric environment parameters of the same detection area.

[0007] In the third aspect of the embodiments of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned magnetotelluric interference detection method are implemented.

[0008] In a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-described magnetotelluric interference detection method are implemented.

[0009] The beneficial effects of the magnetotelluric interference detection method and device provided by the embodiments of the present disclosure are as follows: The embodiments of the present disclosure consider that the magnetotelluric environment and the air electromagnetic environment in the target area do not exist in isolation, but have an internal connection. For example, the current in the earth generates a magnetic field, and this magnetic field generates an induced electric field in the air; at the same time, the electric field in the air may also affect the charge distribution on the earth's surface through capacitive coupling and other means, thereby affecting the earth's electric field. Therefore, the embodiments of the present disclosure indirectly obtain the magnetotelluric environment parameters by pre-constructing a mapping model between the magnetotelluric environment parameters and the air electromagnetic environment parameters, and detecting the air electromagnetic environment parameters.

[0010] In practical applications, the direct measurement of magnetotelluric environment parameters is often difficult and costly, while the acquisition of air electromagnetic environment parameters is relatively easier. Therefore, the method for indirectly obtaining magnetotelluric environment parameters in the embodiments of the present disclosure can more efficiently detect the magnetotelluric environment, preferably select a site for field geophysical electrical methods with small electromagnetic interference, avoid repetitive rework, and improve the detection efficiency of field geophysical electrical methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0012] Figure 1 It is a schematic flow chart of a magnetotelluric interference detection method provided by an embodiment of the present disclosure; Figure 2 It is a structural block diagram of a magnetotelluric interference detection device provided by an embodiment of the present disclosure; Figure 3 It is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] In the following description, specific details such as specific system architectures, technologies, etc. are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present disclosure. However, those skilled in the art should clearly understand that the present disclosure can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present disclosure.

[0014] To make the objectives, technical solutions, and advantages of the present disclosure clearer, the following will be described through specific embodiments in conjunction with the accompanying drawings.

[0015] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of the magnetotelluric interference detection method provided for an embodiment of the present disclosure. The method includes: S101: Obtain the air electromagnetic environment parameters of the target area.

[0016] In this embodiment, the air electromagnetic environment parameters may include the air electric field intensity, air magnetic field intensity, etc. By using specialized electric field and magnetic field measurement devices, such as electric field probes, magnetic field sensors, etc., the air electric field intensity and air magnetic field intensity of the target area can be obtained.

[0017] The air electric field intensity reflects the strength of the electric field in the air, and its change may be affected by factors such as the space charge distribution and external electromagnetic interference; the air magnetic field intensity reflects the strength of the magnetic field in the air, and its change may be affected by factors such as the magnetic field effect generated by current, the induction of magnetic substances, and external electromagnetic interference.

[0018] S102: Input the air electromagnetic environment parameters of the target area into the mapping model to obtain the magnetotelluric environment parameters of the target area; the mapping model is a model obtained by training a neural network model based on historical data, and the historical data includes historical data of air electromagnetic environment parameters and historical data of corresponding magnetotelluric environment parameters of the same detection area.

[0019] In this embodiment, considering that the magnetotelluric environment and the air electromagnetic environment in the target area do not exist in isolation, but have an internal connection. For example, the current in the earth will generate a magnetic field, and this magnetic field will generate an induced electric field in the air; at the same time, the electric field in the air may also affect the charge distribution on the earth's surface through capacitive coupling and other means, thereby affecting the earth's electric field. Therefore, the embodiments of the present disclosure use the air electromagnetic environment parameters in the historical data and the corresponding magnetotelluric environment parameters of the same detection area to train the neural network. The neural network has a powerful non-linear mapping ability and can learn the complex relationship between the air electromagnetic environment parameters and the magnetotelluric environment parameters. During the training process, the neural network continuously adjusts its own weights and biases, so that the magnetotelluric environment parameters predicted based on the air electromagnetic environment parameters are as close as possible to the true values in the historical data, thereby constructing a mapping model between the air electromagnetic environment parameters and the magnetotelluric environment parameters.

[0020] Based on the obtained mapping model, by inputting the air electromagnetic environment parameters of the target area into the mapping model, the magnetotelluric environment parameters can be obtained, thereby realizing the indirect detection of the magnetotelluric environment parameters.

[0021] It can be concluded from the above that this embodiment considers that the magnetotelluric environment and the air electromagnetic environment in the target area do not exist in isolation, but have an internal connection. For example, the current in the earth will generate a magnetic field, and this magnetic field will generate an induced electric field in the air; at the same time, the electric field in the air may also affect the charge distribution on the earth's surface through capacitive coupling and other means, thereby affecting the earth's electric field. Therefore, in this embodiment, by pre-constructing a mapping model between the magnetotelluric environment parameters and the air electromagnetic environment parameters, the magnetotelluric environment parameters can be indirectly obtained by detecting the air electromagnetic environment parameters.

[0022] In practical applications, the direct measurement of magnetotelluric environment parameters is often difficult and costly, while the acquisition of air electromagnetic environment parameters is relatively easier. Therefore, the method for indirectly obtaining magnetotelluric environment parameters in the embodiments of the present disclosure can detect the magnetotelluric environment more efficiently, preferably arrange the sites of field geophysical electrical methods at measuring points with less electromagnetic interference, avoid repetitive rework, and improve the detection efficiency of field geophysical electrical methods.

[0023] In an embodiment of the present disclosure, inputting the air electromagnetic environment parameters of the target area into the mapping model to obtain the magnetotelluric environment parameters of the target area includes: Determining the area category of the target area based on the temperature and humidity of the target area; Selecting a target mapping model from multiple preset mapping models based on the area category of the target area; Inputting the air electromagnetic environment parameters of the target area into the target mapping model to obtain the magnetotelluric environment parameters of the target area.

[0024] In this embodiment, considering that temperature and humidity have a significant impact on electromagnetic characteristics, different temperatures and humidities will change the electric and magnetic field properties of air and the earth, thereby affecting the mapping relationship between the geomagnetic environment parameters and the air electromagnetic environment parameters. For example, an increase in humidity may change the conductivity of air and the earth, and a change in temperature may affect the molecular thermal motion and thus act on the propagation of electromagnetic signals.

[0025] Therefore, the detection area can be classified according to different temperatures and humidities, and then a mapping model corresponding to each area category can be pre-constructed in advance. Different mapping models have better adaptability and fitting ability for the electromagnetic data of the corresponding area categories.

[0026] In actual use, the area category of the target area can be determined first based on the temperature and humidity of the target area, and then the corresponding mapping model, that is, the target mapping model, can be selected based on the area category of the target area. By inputting the air electromagnetic environment parameters of the target area into the target mapping model, more accurate geomagnetic environment data can be obtained.

[0027] From the above, it can be concluded that this embodiment determines the area category of the target area based on temperature and humidity and selects the corresponding mapping model, which can more accurately reflect the true relationship between the geomagnetic environment parameters and the air electromagnetic environment parameters under different environmental conditions, thereby improving the prediction accuracy of the geomagnetic environment parameters.

[0028] In an embodiment of the present disclosure, the geomagnetic interference detection method further includes: Classifying the detection area based on temperature and humidity to obtain multiple area categories; Training a neural network model based on the historical data corresponding to each area category to obtain multiple preset mapping models.

[0029] In this embodiment, a specific implementation manner of constructing multiple preset mapping models is given. Multiple detection areas can be classified according to different temperatures and humidities to obtain multiple area categories. For each area category, the historical data corresponding to it is collected. These data include the air electromagnetic environment parameters and the geomagnetic environment parameters under this area category. By inputting these data into the neural network model for training, the mapping model corresponding to this partition classification can be obtained.

[0030] Using the same method, multiple mapping models corresponding to multiple area categories can be obtained, and the multiple mapping models corresponding to multiple area categories are used as multiple preset mapping models.

[0031] It can be concluded from the above that this embodiment classifies the detection area based on temperature and humidity, and trains a dedicated mapping model for each area category, which can more accurately capture the relationship between magnetotelluric interference and air electromagnetic environment parameters in different environments, making the detection of magnetotelluric interference in different areas more accurate in actual detection, reducing errors caused by environmental differences, and improving the reliability of detection results.

[0032] In an embodiment of the present disclosure, the neural network model is a multi-layer perceptron model. Training the neural network model based on the historical data corresponding to each area category includes: If the area category is the first type of area, set the number of hidden layers of the multi-layer perceptron model to the first number; the first type of area is an area where the corresponding temperature is less than the first temperature threshold and the humidity is less than the first humidity threshold; If the area category is the second type of area, set the number of hidden layers of the multi-layer perceptron model to the second number; the second type of area is an area where the corresponding temperature is greater than the second temperature threshold and the humidity is greater than the second humidity threshold, the second temperature threshold is greater than the first temperature threshold, and the second humidity threshold is greater than the second humidity threshold; If the area category is the third type of area, set the number of hidden layers of the multi-layer perceptron model to the third number; the third type of area is an area other than the first type of area and the second type of area; The first number, the third number, and the second number increase in sequence.

[0033] In this embodiment, the neural network model can specifically adopt a multi-layer perceptron. The structure of the multi-layer perceptron has great flexibility and can adjust the number of hidden layers and the number of neurons in each layer according to the needs of actual problems. When constructing the mapping relationship between air electromagnetic environment parameters and magnetotelluric environment parameters, the model structure can be optimized according to the actual needs of each area category.

[0034] Specifically, the first temperature threshold, the second temperature threshold, the first humidity threshold, and the second humidity threshold can be preset, and the detection area is divided into the first type of area, the second type of area, and the third type of area. Among them, the first type of area is a low-temperature and low-humidity area, such as some dry areas in the polar regions, the second type of area is a high-temperature and high-humidity area, such as tropical rainforest areas, and the third type of area is a low-temperature and high-humidity area or a high-temperature and low-humidity area, such as arid areas in temperate zones.

[0035] For the first type of area, the electromagnetic characteristics are relatively stable and simple. Correspondingly, the relationship between air electromagnetic environment parameters and magnetotelluric environment parameters is relatively simple, and a relatively small number of hidden layers (such as 1-2 layers) can be set to learn this relationship. A relatively small number of hidden layers can reduce the complexity of the model, reduce the amount of calculation and training time, and avoid overfitting at the same time, enabling the model to better capture the main features in this environment.

[0036] For the second type of region, considering that the increase in humidity will change the conductivity of air and the earth, and the increase in temperature may affect molecular thermal motion, etc., these factors combined lead to a more complex relationship between the air electromagnetic environment parameters and the earth electromagnetic environment parameters, showing more non-linear characteristics. Therefore, more hidden layers (such as 3 - 5 layers) are required to learn and fit this complex relationship, increasing the expressive power of the model to accurately predict the earth electromagnetic environment parameters.

[0037] For the third type of region, the complexity of its electromagnetic characteristics is between that of the first type of region and the second type of region. Therefore, setting a moderate number of hidden layers (such as 2 - 3 layers) can learn the complexity of the electromagnetic parameter relationship in this region while avoiding waste of computing resources caused by an overly complex model.

[0038] It can be concluded from the above that in this embodiment, according to the complexity of the electromagnetic characteristics of different region categories, the number of hidden layers of the multi-layer perceptron model is adjusted specifically, enabling the model to better adapt to the relationship between the air electromagnetic environment parameters and the earth electromagnetic environment parameters under different environmental conditions, which is beneficial to improving the accuracy of the model in detecting earth electromagnetic interference in different regions.

[0039] In an embodiment of the present disclosure, obtaining the air electromagnetic environment parameters of the target region includes: Obtaining the air electric field intensity and air magnetic field intensity of the target region; Performing spectral analysis on the air electric field intensity of the target region to obtain the air electric field intensity in multiple frequency bands, and performing spectral analysis on the air magnetic field intensity of the target region to obtain the air magnetic field intensity in multiple frequency bands; Determining the air electric field intensity in multiple frequency bands and the air magnetic field intensity in multiple frequency bands as the air electromagnetic environment parameters of the target region.

[0040] In this embodiment, considering that electromagnetic signals with different frequencies have different characteristics during propagation and their interaction methods with the surrounding environment are also different. For example, high-frequency signals are more susceptible to local interference, while low-frequency signals may carry information about more macroscopic electromagnetic phenomena. Therefore, by decomposing the air electric field intensity into the air electric field intensity in multiple frequency bands, decomposing the air magnetic field intensity into the air magnetic field intensity in multiple frequency bands, and determining the air electric field intensity in multiple frequency bands and the air magnetic field intensity in multiple frequency bands as the air electromagnetic environment parameters of the target region.

[0041] Correspondingly, in the training stage of the mapping model, the same method can be used to decompose the air electric field intensity in historical data into air electric field intensities of multiple frequency bands, and decompose the air magnetic field intensity in historical data into air magnetic field intensities of multiple frequency bands, train the mapping model, adjust different network structures and parameters for different frequency bands, so that the model can more effectively learn the mapping law between the air and the geoelectric environment parameters at each frequency.

[0042] Specifically, the air electric field intensity of the target area can be spectrally analyzed by the Fourier transform method to obtain air electric field intensities of multiple frequency bands, and the air magnetic field intensity of the target area can be spectrally analyzed to obtain air magnetic field intensities of multiple frequency bands.

[0043] It can be concluded from the above that in this embodiment, the air electric field intensity signal and the air magnetic field intensity signal are decomposed by frequency, and the mapping relationship with each frequency component of the earth is established, which can enable the mapping model to more finely capture the complex relationship between the two at different frequencies and improve the accuracy of the mapping model.

[0044] In an embodiment of the present disclosure, spectrally analyzing the air electric field intensity of the target area to obtain air electric field intensities of multiple frequency bands includes: Spectrally analyze the air electric field intensity of the target area to obtain the air electric field intensity spectrogram of the target area; Determine the area category of the target area based on the temperature and humidity of the target area; Determine the corresponding first frequency threshold and second frequency threshold based on the area category of the target area; the first frequency threshold is less than the second frequency threshold; Segment the air electric field intensity spectrogram of the target area based on the first frequency threshold and the second frequency threshold to obtain air electric field intensities of multiple frequency bands.

[0045] In this embodiment, the air electric field intensity signal can be spectrally analyzed by the Fourier transform method to clearly show the relative intensity of each frequency component in the original signal. Finally, the generated air electric field intensity spectrogram takes frequency as the horizontal axis and the air electric field intensity amplitude as the vertical axis, intuitively presenting the distribution of the air electric field intensity at different frequencies. Similarly, the air magnetic field intensity signal is spectrally analyzed by the Fourier transform method to obtain the air magnetic field intensity spectrogram.

[0046] Considering different regional categories, the frequency distributions of their electromagnetic characteristics are different. Therefore, the corresponding frequency band division method can be determined according to the regional category of the target area. For example, in the first type of area (low-temperature and low-humidity area), the low-frequency signal is less affected by the environment, and the low-frequency threshold can be relatively high, such as set to 10 Hz. In the high-temperature and high-humidity area, since the increase in humidity may change characteristics such as the conductivity of the earth, causing changes in the propagation depth and characteristics of the low-frequency signal, a lower-frequency signal is required to reflect the deep electromagnetic characteristics, and the low-frequency threshold can be reduced to 0.1 Hz.

[0047] Taking the air electric field intensity as an example, a first mapping relationship between the regional category and the first frequency threshold and the second frequency threshold can be pre-constructed. When actually used, according to the regional category of the target area, the above first mapping relationship is searched to determine the corresponding first frequency threshold and the second frequency threshold. Segmenting the air electric field intensity spectrum diagram according to the first frequency threshold and the second frequency threshold, the first frequency band (low-frequency band), the second frequency band (medium-frequency band), and the third frequency band (high-frequency band) can be obtained. Calculate the average value of the electric field intensity in each frequency band to obtain the air electric field intensity of multiple frequency bands.

[0048] It can be concluded from the above that in this embodiment, the regional category is determined according to the temperature and humidity, and accordingly the first frequency threshold and the second frequency threshold are determined. Based on the first frequency threshold and the second frequency threshold, the air electric field intensity spectrum diagram (and the air magnetic field intensity spectrum diagram) is segmented, which can make the frequency band division more in line with the actual situation of the electromagnetic characteristics of the area, so as to more effectively extract the key information of the air electric field intensity and the air magnetic field intensity in different frequency ranges in the target area, providing a guarantee for the subsequent accurate analysis of the electromagnetic environment.

[0049] In an embodiment of the present disclosure, the multiple frequency bands include a first frequency band, a second frequency band, and a third frequency band. The magnetotelluric interference detection method further includes: Determining the corresponding sub-frequency band threshold based on the regional category of the target area; Based on the corresponding sub-frequency band threshold, segmenting the air electric field intensity spectrum diagram of the second frequency band and / or the air electric field intensity spectrum diagram of the third frequency band into multiple sub-frequency bands to obtain the air electric field intensity of multiple sub-frequency bands; Determining the air electric field intensity of multiple sub-frequency bands as the air electromagnetic environment parameters of the target area.

[0050] In this embodiment, considering that in the second type of area and the third type of area, the electromagnetic environment is more complex than that of the first type of area. In order to cope with the complex and changeable situation of the medium-frequency signal and the high-frequency signal in the complex environment, the second frequency band and the third frequency band can be further subdivided.

[0051] Specifically, the first mapping relationship in the above embodiments may include sub-band thresholds corresponding to the second frequency band and the third frequency band. During actual use, by looking up the first mapping relationship according to the region category of the target area, the corresponding sub-band thresholds can be determined. The second frequency band and / or the third frequency band are divided into multiple sub-bands according to the sub-band thresholds, so as to further subdivide the second frequency band and / or the third frequency band.

[0052] Taking the air electric field intensity as an example, in the third type of area, the medium frequency band can be set to 10Hz - 2000Hz. This range can cover the changes in the electromagnetic responses of different depth geological structures caused by high temperature. The corresponding sub-band threshold is empty, and this frequency band does not need to be subdivided. The high frequency band starts from 2000Hz. In a high temperature environment, there may be electromagnetic interference generated by thermal radiation. In order to better detect the influence of the electromagnetic interference generated by the high frequency signal by thermal radiation, the high frequency band can be subdivided. For example, the corresponding sub-band thresholds are set to 5000 and 10k. Accordingly, the high frequency band can be subdivided into 3 sub-bands, namely 2000Hz - 5000Hz, 5000Hz - 10kHz, and above 10kHz, so as to analyze the signal characteristics in different high frequency intervals.

[0053] Another example is that in the second type of area, the low frequency band can start from below 0.1Hz. High temperature and high humidity make the electromagnetic characteristics of the earth change complexly. A lower low frequency starting frequency helps to detect the stable electromagnetic signal characteristics in the deep part. The medium frequency band is 0.1 - 1000Hz, and the corresponding sub-band thresholds are set to 1 and 100. Accordingly, the medium frequency band can be subdivided into multiple sub-bands, such as 0.1Hz - 1Hz, 1Hz - 100Hz, and 100Hz - 1000Hz, to cope with the complex and changeable situation of the medium frequency band signal in a complex environment. The high frequency band starts from 1000Hz. Because the high frequency signal is greatly affected by water vapor absorption, scattering, and various complex electromagnetic interferences in a high temperature and high humidity environment, it needs to be more carefully divided. The corresponding sub-band thresholds are set to 3000 and 6000. Accordingly, the high frequency band can be subdivided into 1000Hz - 3000Hz, 3000Hz - 6000Hz, and above 6000Hz.

[0054] Using the same method, the frequency band of the air magnetic field intensity can be further subdivided to obtain the air magnetic field intensity of multiple sub-bands. The air electric field intensity of multiple sub-bands and the air magnetic field intensity of multiple sub-bands are used as the air electromagnetic environment parameters of the target area.

[0055] Correspondingly, in the training stage of the mapping model, the same method can be used to decompose the air electric field intensity in historical data into air electric field intensities of multiple sub-bands, decompose the air magnetic field intensity in historical data into air magnetic field intensities of multiple sub-bands, and train the mapping model so that the model learns the subtle relationship between the air electromagnetic environment parameters and the telluric electromagnetic environment parameters in each sub-band.

[0056] It can be concluded from the above that in this embodiment, the sub-band threshold is determined based on the regional category, and the key frequency band is divided into sub-bands, which can capture more subtle changes of electromagnetic signals in different frequency ranges, so as to extract electromagnetic environment parameters more accurately and provide a more accurate data basis for subsequent analysis.

[0057] Corresponding to the telluric electromagnetic interference detection method in the above embodiment, Figure 2 is a structural block diagram of a telluric electromagnetic interference detection device provided by an embodiment of the present disclosure. For the sake of illustration, only the parts related to the embodiments of the present disclosure are shown. Refer to Figure 2 and the telluric electromagnetic interference detection device 20 includes: a data acquisition module 21 and a data mapping module 22. Among them, the data acquisition module 21 is used to acquire the air electromagnetic environment parameters of the target area; The data mapping module 22 is used to input the air electromagnetic environment parameters of the target area into the mapping model to obtain the telluric electromagnetic environment parameters of the target area; the mapping model is a model obtained by training a neural network model based on historical data, and the historical data includes historical data of air electromagnetic environment parameters and historical data of corresponding telluric electromagnetic environment parameters of the same detection area.

[0058] In an embodiment of the present disclosure, the data mapping module 22 is specifically used for: determining the regional category of the target area based on the temperature and humidity of the target area; selecting a target mapping model from multiple preset mapping models based on the regional category of the target area; inputting the air electromagnetic environment parameters of the target area into the target mapping model to obtain the telluric electromagnetic environment parameters of the target area.

[0059] In an embodiment of the present disclosure, the data mapping module 22 is specifically further used for: classifying the detection area based on the temperature and humidity to obtain multiple regional categories; training a neural network model based on the historical data corresponding to each regional category to obtain multiple preset mapping models.

[0060] In an embodiment of the present disclosure, the neural network model is a multi-layer perceptron model, and the data mapping module 22 is specifically further used for: If the region category is the first type of region, set the number of hidden layers of the multi-layer perceptron model to the first number; the first type of region is the region where the corresponding temperature is less than the first temperature threshold and the humidity is less than the first humidity threshold; If the region category is the second type of region, set the number of hidden layers of the multi-layer perceptron model to the second number; the second type of region is the region where the corresponding temperature is greater than the second temperature threshold and the humidity is greater than the second humidity threshold, the second temperature threshold is greater than the first temperature threshold, and the second humidity threshold is greater than the second humidity threshold; If the region category is the third type of region, set the number of hidden layers of the multi-layer perceptron model to the third number; the third type of region is the region other than the first type of region and the second type of region; The first number, the third number, and the second number increase in sequence.

[0061] In an embodiment of the present disclosure, the data acquisition module 21 is specifically configured to: Obtain the air electric field intensity and air magnetic field intensity of the target region; Perform spectrum analysis on the air electric field intensity of the target region to obtain the air electric field intensity in multiple frequency bands, and perform spectrum analysis on the air magnetic field intensity of the target region to obtain the air magnetic field intensity in multiple frequency bands; Determine the air electromagnetic environment parameters of the target region as the air electric field intensity in multiple frequency bands and the air magnetic field intensity in multiple frequency bands.

[0062] In an embodiment of the present disclosure, the data acquisition module 21 is specifically further configured to: Perform spectrum analysis on the air electric field intensity of the target region to obtain the air electric field intensity spectrum diagram of the target region; Determine the region category of the target region based on the temperature and humidity of the target region; Determine the corresponding first frequency threshold and second frequency threshold based on the region category of the target region; the first frequency threshold is less than the second frequency threshold; Segment the air electric field intensity spectrum diagram of the target region based on the first frequency threshold and the second frequency threshold to obtain the air electric field intensity in multiple frequency bands.

[0063] In an embodiment of the present disclosure, the multiple frequency bands include the first frequency band, the second frequency band, and the third frequency band. The data acquisition module 21 is specifically further configured to: The magnetotelluric interference detection method further includes: Determine the corresponding sub-band threshold based on the region category of the target region; Divide the air electric field intensity spectrum diagram of the second frequency band and / or the air electric field intensity spectrum diagram of the third frequency band into multiple sub-bands based on the corresponding sub-band threshold to obtain the air electric field intensity in multiple sub-bands; Determine the air electric field strengths of multiple sub - frequency bands as the air electromagnetic environment parameters of the target area.

[0064] See Figure 3 , Figure 3 is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. As Figure 3 shown, the electronic device 300 in this embodiment may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The above - mentioned processors 301, input devices 302, output devices 303, and memories 304 communicate with each other through a communication bus 305. The memory 304 is used to store computer programs, and the computer programs include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. Among them, the processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above - mentioned device embodiments, such as Figure 2 the functions of the data acquisition module 21 and the data mapping module 22 shown.

[0065] It should be understood that in the embodiments of the present disclosure, the so - called processor 301 may be a central processing unit (CPU), and this processor may also be other general - purpose processors, digital signal processors (DSPs), application - specific integrated circuits (ASICs), field - programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general - purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0066] The input device 302 may include a touchpad, a fingerprint acquisition sensor (for acquiring the fingerprint information and the direction information of the fingerprint of the user), a microphone, etc., and the output device 303 may include a display (such as an LCD), a speaker, etc.

[0067] The memory 304 may include a read - only memory and a random access memory, and provide instructions and data to the processor 301. A part of the memory 304 may also include a non - volatile random access memory. For example, the memory 304 may also store information about the device type.

[0068] In a specific implementation, the processor 301, the input device 302, and the output device 303 described in the embodiments of the present disclosure may execute the implementation manners described in the first and second embodiments of the magnetotelluric interference detection method provided by the embodiments of the present disclosure, and may also execute the implementation manner of the electronic device described in the embodiments of the present disclosure, which will not be elaborated herein.

[0069] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, all or part of the processes in the method of the above embodiments are implemented. It can also be completed by instructing relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0070] The computer-readable storage medium may be an internal storage unit of the electronic device in any of the foregoing embodiments, such as the hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the computer-readable storage medium may also include both the internal storage unit and the external storage device of the electronic device. The computer-readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store the data that has been output or will be output.

[0071] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of the examples have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this disclosure.

[0072] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described electronic devices and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0073] In several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there can 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. In addition, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces or units, or can also be electrical, mechanical, or other forms of connection.

[0074] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of this disclosure.

[0075] In addition, the functional units in various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0076] The above is only the specific implementation manner of this disclosure, but the protection scope of this disclosure is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope disclosed by this disclosure, and these modifications or substitutions should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be subject to the protection scope of the claims.

Claims

1. A method for detecting electromagnetic interference of the earth, characterized in that: include: Obtain the air electromagnetic environment parameters of the target area; The air electromagnetic environment parameters of the target area are input into a mapping model to obtain the magnetotelluric environment parameters of the target area; the mapping model is a model obtained by training a neural network model based on historical data, and the historical data includes historical data of air electromagnetic environment parameters and historical data of magnetotelluric environment parameters of the corresponding same detection area.

2. The method for detecting electromagnetic interference of the earth as claimed in claim 1, characterized in that: The step of inputting the air electromagnetic environment parameters of the target area into a mapping model to obtain the magnetotelluric environment parameters of the target area includes: determining an area category of the target area based on the temperature and humidity of the target area; Selecting a target mapping model from a plurality of preset mapping models based on the area category of the target area; The air electromagnetic environment parameters of the target area are input into the target mapping model to obtain the ground electromagnetic environment parameters of the target area.

3. The method for detecting electromagnetic interference of the earth as claimed in claim 2, characterized in that: Also includes: Classify the detection area based on temperature and humidity to obtain multiple area categories; The neural network model is trained based on the historical data corresponding to each area category to obtain the multiple preset mapping models.

4. The method for detecting electromagnetic interference of the earth as claimed in claim 3, characterized in that: The neural network model is a multi-layer perceptron model, and the training of the neural network model based on historical data corresponding to each regional category includes: If the region category is a first-category region, the number of hidden layers of the multilayer perceptron model is set to a first number; the first-category region is a region whose corresponding temperature is less than a first temperature threshold and whose humidity is less than a first humidity threshold; If the region category is a second-category region, the number of hidden layers of the multilayer perceptron model is set to a second number; the second-category region is a region whose corresponding temperature is greater than a second temperature threshold and whose humidity is greater than a second humidity threshold, the second temperature threshold is greater than the first temperature threshold, and the second humidity threshold is greater than the second humidity threshold; If the region category is a third-category region, the number of hidden layers of the multilayer perceptron model is set to a third number; the third-category region is a region other than the first-category region and the second-category region; The first number, the third number, and the second number increase sequentially.

5. The method for detecting electromagnetic interference of the earth as claimed in claim 1, characterized in that: The step of obtaining the air electromagnetic environment parameters of the target area includes: Acquire the air electric field strength and air magnetic field strength of the target area; Performing a spectrum analysis on the air electric field strength of the target area to obtain the air electric field strength of multiple frequency bands, and performing a spectrum analysis on the air magnetic field strength of the target area to obtain the air magnetic field strength of multiple frequency bands; The air electric field strengths of the multiple frequency bands and the air magnetic field strengths of the multiple frequency bands are determined as air electromagnetic environment parameters of the target area.

6. The method for detecting electromagnetic interference of the earth as claimed in claim 5, characterized in that: The performing spectrum analysis on the air electric field strength of the target area to obtain the air electric field strength of multiple frequency bands includes: Performing spectrum analysis on the air electric field intensity of the target area to obtain a spectrum diagram of the air electric field intensity of the target area; determining an area category of the target area based on the temperature and humidity of the target area; Determine a corresponding first frequency threshold and a second frequency threshold based on the area category of the target area; the first frequency threshold is less than the second frequency threshold; The air electric field intensity spectrum diagram of the target area is segmented based on the first frequency threshold and the second frequency threshold to obtain air electric field intensity of multiple frequency bands.

7. The method for detecting electromagnetic interference of the earth as claimed in claim 6, characterized in that: The multiple frequency bands include a first frequency band, a second frequency band and a third frequency band, The method for detecting electromagnetic interference of the earth further comprises: Determining a corresponding sub-band threshold based on the area category of the target area; Dividing the air electric field intensity spectrum diagram of the second frequency band and / or the air electric field intensity spectrum diagram of the third frequency band into a plurality of sub-frequency bands based on corresponding sub-frequency band thresholds to obtain air electric field intensities of the plurality of sub-frequency bands; The air electric field strengths of the multiple sub-frequency bands are determined as air electromagnetic environment parameters of the target area.

8. A device for detecting electromagnetic interference of the earth, characterized in that: include: A data acquisition module is used to obtain the air electromagnetic environment parameters of the target area; A data mapping module is used to input the air electromagnetic environment parameters of the target area into a mapping model to obtain the magnetotelluric environment parameters of the target area; the mapping model is a model obtained by training a neural network model based on historical data, and the historical data includes historical data of air electromagnetic environment parameters and historical data of magnetotelluric environment parameters of the same detection area.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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