Method for automatically identifying PC2 geomagnetic pulsation, electronic equipment and computer storage medium

Abstract

The invention discloses a method for automatically identifying PC2 geomagnetic pulsation, electronic equipment and a computer storage medium, and the method comprises the steps: obtaining magnetotelluric (MT) time series data containing magnetic storm data, carrying out the split-window Fourier transform of electromagnetic field components, and obtaining a frequency spectrum array of each electromagnetic field component in a frequency domain; calculating data quality evaluation parameters based on the spectrum array, wherein the data quality evaluation parameters comprise electromagnetic field linearity, a phase difference between an electric field and a magnetic field, and an amplitude of an electromagnetic field spectrum as an energy parameter; and according to the high-quality data type related to the magnetic storm, determining the distribution characteristic of each parameter of the high-quality data of the measuring point. The geomagnetic pulsation event is automatically detected by identifying MT dead band high-quality data, widely distributed MT observation stations are converted into a geomagnetic pulsation monitoring network, existing infrastructures are fully utilized, and the space weather monitoring cost is reduced.

Description

Technical Field

[0001] This invention relates to the fields of geophysical exploration and space weather monitoring, and in particular to a method, electronic device, and computer storage medium for automatically identifying PC2 geomagnetic pulsations. Background Technology

[0002] Magnetotelluric (MT) sounding is a passive exploration method that uses natural electromagnetic fields to study the electrical conductivity structure of the subsurface. This technology relies on broadband electromagnetic signals; signals of different frequencies penetrate to different depths due to the skin effect, thus enabling the detection of subsurface structures at different depths. However, in the "dead band" frequency range of 0.1–10 Hz, the natural signals during periods of geomagnetic quiescence are typically very weak, comparable to the level of anthropogenic electromagnetic interference, resulting in a low signal-to-noise ratio and poor data quality, severely limiting the imaging capabilities of the subsurface at medium depths corresponding to the dead band frequencies. Traditionally, the poor data quality of the dead band is considered an inherent limitation of MT technology. Researchers mainly improve data quality through post-processing methods such as far-reference techniques, robust impedance estimation, and time-frequency conversion. However, these methods only affect the data processing stage and fail to recognize the intrinsic physical connection between the signal quality of the dead band and geomagnetic activity at the signal source.

[0003] Geomagnetic pulsations are short-period geomagnetic disturbances caused by the interaction between the solar wind and the Earth's magnetosphere in the Sun-Earth space environment. Their amplitudes on the Earth's surface range from a few tenths of a nanotera to hundreds of nanotera, and their periods range from a few tenths of a second to several minutes. According to the classification criteria proposed by the International Association for Geomagnetism and Upper Atmosphere Physics (IAGA) in 1964, geomagnetic pulsations are divided into two main categories: continuous pulsations (Pc) and irregular pulsations (Pi). Continuous pulsations are further subdivided into five subcategories: Pc1 (period 0.2-5 seconds), Pc2 (period 5-10 seconds), Pc3 (period 10-45 seconds), Pc4 (period 45-150 seconds), and Pc5 (period 150-600 seconds). Among these, Pc2 geomagnetic pulsations have a frequency range of 0.1-0.2 Hz, which falls precisely within the MT deadband frequency range. This overlap provides a natural physical basis for cross-disciplinary technological integration. The generation mechanism of Pc2 geomagnetic pulsations involves complex magnetospheric physics processes. In addition, geomagnetic storms, as another important type of large-scale geomagnetic disturbance, typically evolve through three distinct phases: the initial phase, the main phase, and the recovery phase. The initial phase follows the sudden onset of the storm, characterized by a slight increase in the horizontal magnetic field component relative to the magnetostatic background level, lasting generally from one to several hours. The main phase is the most intense stage of the storm, characterized by a significant increase in loop current leading to a rapid and substantial decrease in the horizontal magnetic field component, typically lasting about half a day, during which it poses a serious threat to critical infrastructure. Of particular concern is the complex coupling relationship between geomagnetic pulsations and geomagnetic storms: geomagnetic pulsations can occur independently without a significant geomagnetic storm background, but when a geomagnetic storm erupts, it is often accompanied by significantly enhanced geomagnetic pulsation activity. Accurate identification and real-time monitoring of Pc2 geomagnetic pulsations are of significant scientific importance for space weather early warning.

[0004] Currently, the identification of Pc2 geomagnetic pulsations mainly relies on continuous observations by high-precision magnetometers deployed at dedicated geomagnetic stations. With the decreasing cost of large-scale storage devices and the continuous increase in the number of observation instruments, geomagnetic observation data is growing exponentially. Traditional manual identification methods can no longer meet the real-time processing needs of massive amounts of data, severely restricting the operational monitoring capabilities of Pc2 pulsations. Another commonly used automatic identification method is threshold-based time-frequency analysis. This method identifies pulsation events by searching for spectral peaks exceeding a preset threshold within a specific frequency range. However, this method has significant drawbacks: its performance heavily relies on threshold parameters set manually based on experience, lacks adaptability and objective physical criteria, and is susceptible to human-induced electromagnetic interference, resulting in high false positive and false negative rates; it is also difficult to meet the real-time monitoring requirements with minute-level time resolution and lacks clear physical meaning.

[0005] Notably, geomagnetic pulsations significantly enhance electromagnetic field energy within the dead-band frequency range through physical mechanisms. During these disturbances, the enhanced magnetospheric and ionospheric current systems generate stronger natural-source electromagnetic signals, substantially improving the signal-to-noise ratio of MT measurements and significantly enhancing the quality of dead-band data. This objective physical phenomenon reveals a potential, but long-understood and underutilized, intrinsic connection: changes in MT data quality within the dead band can serve as a sensitive physical indicator of geomagnetic activity. In recent years, several innovative methods have been proposed by the academic community for MT data quality assessment. Chen et al. (2023) proposed a quality assessment parameter system based on electromagnetic field linearity, which identifies noisy data segments by quantitatively comparing the similarity between the observed electric field and the predicted electric field based on the impedance tensor, providing a physical quantitative index for the objective evaluation of data quality. Chen and Zhang (2025) further investigated the intrinsic relationship between electromagnetic field phase difference characteristics and data quality, finding that the ratio of impedance tensor components significantly affects the phase difference distribution pattern between orthogonal and parallel electromagnetic field components. When the dominant impedance component is dominant, the phase difference of the orthogonal components exhibits a clear preferred direction characteristic. However, these existing methods are mainly applied to routine processing steps such as noise identification and quality screening of MT data, failing to break through disciplinary boundaries and organically combine the dynamic changes in dead-band data quality with real-time monitoring of geomagnetic pulsations. They have also failed to fully explore the application potential of this cross-disciplinary correlation in operational space weather monitoring.

[0006] In summary, the existing technologies have the following main shortcomings and technical bottlenecks that urgently need to be overcome: First, MT exploration technology and space weather monitoring have long developed independently, with obvious disciplinary barriers. They have failed to fully utilize the globally distributed MT station network resources to carry out real-time monitoring of Pc2 geomagnetic pulsations, and lack innovative ideas and implementation paths for cross-domain technology integration, resulting in a large amount of existing observation resources not being effectively utilized. Second, existing Pc2 geomagnetic pulsation identification methods mainly rely on observations from dedicated geomagnetic stations or time-frequency analysis techniques based on empirical thresholds. They have prominent problems such as low automation and poor real-time performance, making it difficult to meet the operational real-time monitoring requirements with minute-level time resolution. Moreover, they generally lack quantitative identification algorithms with clear physical meaning and strong interpretability, making it difficult to meet the real-time processing requirements and operational application standards of continuous observation data from large-scale station networks.

[0007] Therefore, there is an urgent need to develop an innovative method based on dynamic analysis of MT data quality that can automatically identify Pc2 geomagnetic pulsations. This method would make full use of existing MT station network resources and provide cost-effective, wide-coverage, and physically meaningful new technologies for operational real-time monitoring and early warning of space weather. It has significant scientific research value and practical application prospects. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, electronic device and computer storage medium for automatically identifying PC2 geomagnetic pulsations.

[0009] The objective of this invention is achieved through the following technical solution: A first aspect of the present invention provides a method for automatically identifying PC2 geomagnetic pulsations, comprising a high-quality data class determination step and a geomagnetic pulsation identification step; the high-quality data class determination step includes the following sub-steps: S11: Obtain magnetotelluric time series data containing geomagnetic storm data from the measuring point, perform windowed Fourier transforms on the electromagnetic field components respectively, and obtain the frequency spectrum array of each electromagnetic field component in the frequency domain. S12: Calculate data quality assessment parameters based on the spectrum array. The data quality assessment parameters include electromagnetic field linearity, phase difference between electric and magnetic fields, and amplitude of the electromagnetic field spectrum as an energy parameter. S13: Based on the data quality assessment parameters, a preset threshold is used to filter high-quality data segments and determine the distribution characteristics of each parameter of the high-quality data at the corresponding measurement point; The geomagnetic pulsation identification step includes the following sub-steps: S21: For measurement points where the distribution characteristics of each parameter of the high-quality data have been determined, a refined analysis of the data quality of the newly acquired magnetotelluric time series data is performed, including: performing Fourier transform on the time series data of multiple electromagnetic field components, and calculating the parameters of electromagnetic field linearity, phase difference, and energy, wherein the amplitudes of electric field and magnetic field are normalized using median values. S22: Based on the high-quality data type determined in step S13, select the corresponding discrimination criteria, evaluate the quality of the data segment in S21, and determine the data segment that meets the discrimination criteria as a geomagnetic pulsation signal.

[0010] Furthermore, in step S12, the calculation method for the electromagnetic field linearity includes: S1211: Divide the observed electromagnetic spectrum data into groups of 20 windows, and use the least squares method to estimate the impedance tensor Z for each group of observed electric field E and observed magnetic field H. S1212: Using the observed magnetic field H and impedance tensor Z, calculate the predicted electric field for each set of data. =ZH; S1213: By comparing observed electric fields Should be consistent with the predicted electric field The linearity of electromagnetic fields is determined by the similarity of their phases, and the linear coherence degree (PLcoh) is used to measure the phase similarity. The calculation formula is as follows: ; in, Indicates the first Observed electric field values With the predicted electric field value The phase difference, where i represents the number of windows; S1214: The amplitude ratio is used to determine the similarity of amplitude PARs. The calculation formula is as follows: = .

[0011] Furthermore, in step S12, the calculation method for the phase difference between the electric field and the magnetic field includes: The phase difference PD was calculated using two observed electromagnetic spectrum data. , The calculation formula is as follows: ; Where j represents the imaginary unit, and These are the first of different channels The electric field spectrum and magnetic field spectrum calculated over a time window; and They represent and The phase is indicated by the overline, which represents the complex conjugate.

[0012] Further, in step S13, a preset threshold is used to filter high-quality data segments, and the distribution characteristics of each parameter of the high-quality data at the measuring point are determined, including: High linearity criterion: linear correlation coefficient between electric and magnetic field components >0.8, and >0.8; Phase difference distribution characteristic criterion: based on the amplitude ratio AR of the impedance tensor = Data type determination: For V-type data, only the orthogonal directions exhibit phase-preferred characteristics, while the parallel directions are randomly distributed within the entire range (±180°); for PV-type data, both the orthogonal and parallel directions show synchronous random variation within a 180° range, with no azimuth selectivity; for P-type data, only the parallel directions exhibit phase-preferred characteristics, while the orthogonal directions are randomly distributed within the entire range (±180°). and Let Z represent the self-impedance in the X direction of the impedance tensor Z, the transverse impedance in the X direction of the impedance tensor Z, and the phase difference, respectively. When the distribution is concentrated within a 90° range, it is defined as having phase-preferred characteristics; Energy enhancement criterion: First, the electric field amplitude and magnetic field amplitude are normalized by median. When the normalized electric field amplitude |E| and magnetic field amplitude |H| are both greater than 2, the data is determined to be energy-enhanced.

[0013] Further, in step S22, the step of selecting the corresponding discrimination criteria based on the high-quality data type determined in step S13, performing quality assessment on the data segment of S21, and determining the data segment that meets the discrimination criteria as a geomagnetic pulsation signal includes: High linearity criterion: linear correlation coefficient between electric and magnetic field components >0.8, and >0.8; Phase difference distribution characteristic criteria: consistent with the data type in step S13; Energy enhancement criterion: First, the electric field amplitude and magnetic field amplitude are normalized by median. When the normalized electric field amplitude |E| and magnetic field amplitude |H| are both greater than 2, the data is determined to be energy-enhanced.

[0014] A second aspect of the present invention provides an electronic device including a memory and a processor, the memory storing computer instructions executable on the processor, the processor performing steps of the method as described in the first aspect when executing the computer instructions.

[0015] A third aspect of the present invention provides a computer storage medium having computer instructions stored thereon, which, when executed, perform the steps of the method as described in the first aspect.

[0016] The beneficial effects of this invention are: (1) Realize the cross-domain integration of MT technology and space weather monitoring: transform the widely distributed MT observation stations into a geomagnetic pulsation monitoring network, make full use of existing infrastructure, and reduce the cost of space weather monitoring.

[0017] (2) Establish a physically meaningful identification index system: Based on multiple physical parameters such as electromagnetic field linearity, phase difference, and energy, construct a comprehensive quality assessment system to achieve reliable identification of Pc2 geomagnetic pulsations.

[0018] (3) Improve the automation and real-time performance of identification: Develop automatic identification algorithms with low computational resource requirements and fast response speed to achieve real-time monitoring with minute-level time resolution. Attached Figure Description

[0019] Figure 1 A flowchart illustrating an exemplary embodiment of the present invention is provided. Figure 2 A schematic diagram illustrating the phase difference distribution under different impedance ratios, provided as an exemplary embodiment of the present invention; Figure 3 A schematic diagram of the impedance calculation results of the Memanbetsu station in Japan, provided as an exemplary embodiment of the present invention; Figure 4 A schematic diagram illustrating the identification results of a geomagnetic storm event in July 2000, provided as an exemplary embodiment of the present invention; Figure 5 A schematic diagram of the geomagnetic storm event identification results in October 2003, provided as an exemplary embodiment of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0021] See Figure 1 , Figure 1 A flowchart illustrating an exemplary embodiment of the present invention is shown, illustrating a method for automatically identifying PC2 geomagnetic pulsations, including a high-quality data class determination step and a geomagnetic pulsation identification step; the high-quality data class determination step includes the following sub-steps: S11: Obtain magnetotelluric time series data containing geomagnetic storm data from the measuring point, perform windowed Fourier transforms on the electromagnetic field components respectively, and obtain the frequency spectrum array of each electromagnetic field component in the frequency domain. S12: Calculate data quality assessment parameters based on the spectrum array. The data quality assessment parameters include electromagnetic field linearity, phase difference between electric and magnetic fields, and amplitude of the electromagnetic field spectrum as an energy parameter. S13: Based on the data quality assessment parameters, a preset threshold is used to filter high-quality data segments and determine the distribution characteristics of each parameter of the high-quality data at the corresponding measurement point; The geomagnetic pulsation identification step includes the following sub-steps: S21: For measurement points where the distribution characteristics of each parameter of the high-quality data have been determined, a refined analysis of the data quality of the newly acquired magnetotelluric time series data is performed, including: performing Fourier transform on the time series data of multiple electromagnetic field components, and calculating the parameters of electromagnetic field linearity, phase difference, and energy, wherein the amplitudes of electric field and magnetic field are normalized using median values. S22: Based on the high-quality data type determined in step S13, select the corresponding discrimination criteria, evaluate the quality of the data segment in S21, and determine the data segment that meets the discrimination criteria as a geomagnetic pulsation signal.

[0022] Specifically, in this exemplary embodiment, the steps include two steps: a high-quality data class determination step and a geomagnetic pulsation identification step. First, in the high-quality data type determination step, steps S11-S13 are established as the benchmark. This is the standard MT impedance estimation preprocessing procedure. The purpose is to first use a general threshold to filter out relatively good data from long data containing magnetic storms (i.e., possibly containing pulsations). Then, in step S14, the measurement point fingerprint is calibrated, and the high-quality data type is identified by combining the impedance amplitude ratio. Furthermore, the phase difference distribution characteristics are determined, and an expected value or concentrated distribution range of the phase difference under clean data is established for this specific measurement point.

[0023] In the geomagnetic pulsation identification step, step S21 is a fine analysis, which involves a sliding window analysis of the continuous time series; step S22 is a comprehensive discrimination, which involves calling the corresponding high-quality template to perform multi-parameter joint discrimination based on the measurement point type determined in S14. Only windows that meet all three criteria are judged as geomagnetic pulsation signals.

[0024] In summary, this exemplary embodiment constructs a multi-parameter comprehensive discrimination system based on linearity, phase difference concentration, and energy for geomagnetic pulsation events. This system analyzes the quality variation characteristics of magnetotelluric sounding data in the dead frequency band (0.1-10 Hz) to achieve automatic identification of Pc2 geomagnetic pulsations. More specifically, leveraging the significant enhancement of electromagnetic field energy in the dead frequency band (0.1-10 Hz, corresponding to a period of approximately 0.1-10 seconds) during Pc2 geomagnetic pulsations, the system comprehensively analyzes the quality changes of MT data in the frequency domain by combining parameters such as electromagnetic field phase difference, linearity, and energy, thereby achieving automatic identification of Pc2 pulsation events. This approach offers the following advantages: (1) Realize the cross-domain integration of MT technology and space weather monitoring: transform the widely distributed MT observation stations into a geomagnetic pulsation monitoring network, make full use of existing infrastructure, and reduce the cost of space weather monitoring.

[0025] (2) Establish a physically meaningful identification index system: Based on multiple physical parameters such as electromagnetic field linearity, phase difference, and energy, construct a comprehensive quality assessment system to achieve reliable identification of Pc2 geomagnetic pulsations.

[0026] (3) Improve the automation and real-time performance of identification: Develop automatic identification algorithms with low computational resource requirements and fast response speed to achieve real-time monitoring with minute-level time resolution.

[0027] The following will describe each preferred embodiment in detail: More preferably, in an exemplary embodiment, the calculation of the electromagnetic field linearity in step S12 includes: S1211: Divide the observed electromagnetic spectrum data into groups of 20 windows, and use the least squares method to estimate the impedance tensor Z for each group of observed electric field E and observed magnetic field H. S1212: Using the observed magnetic field H and impedance tensor Z, calculate the predicted electric field for each set of data. =ZH; S1213: By comparing observed electric fields Should be consistent with the predicted electric field The linearity of electromagnetic fields is determined by the similarity of their phases, and the linear coherence degree (PLcoh) is used to measure the phase similarity. The calculation formula is as follows: ; in, Indicates the first Observed electric field values With the predicted electric field value The phase difference, where i represents the number of windows; S1214: The amplitude ratio is used to determine the similarity of amplitude PARs. The calculation formula is as follows: = .

[0028] Specifically, the magnetotelluric sounding method assumes that the data follows a linear relationship in the frequency domain. = ; Here, E and H correspond to the horizontal electric and magnetic fields at specific frequencies, respectively; the subscripts x and y represent two orthogonal directions, ω represents the angular frequency, and Z is the magnetotelluric (MT) impedance. Z reflects the Earth's induced response to the electromagnetic field and carries information about the distribution of subsurface conductivity. The method for evaluating linearity is similar to that of a simple linear model. Specifically, the main difference between data with high linearity and data with low linearity is that their predicted output values ​​are close to the observed values ​​in most cases.

[0029] Assuming the data has high linearity, the observed electric field Should be consistent with the predicted electric field Similar, among which =ZH, where Z is estimated using the least squares method. The observed electric field can be compared with the measured field. and predicting electric field To identify noisy data with low linearity.

[0030] Complex numbers have two properties: amplitude and phase. Therefore, in this exemplary embodiment: (1) Using linear coherence To measure the similarity of phases, i.e. When linearity is high, the predicted electric field and the observed electric field should be similar, with a phase difference close to 0°, and PLcoh should be close to 1.

[0031] (2) Use amplitude ratio to confirm amplitude. The similarity. The range is from 0 to 1; the higher the PAR, the greater the similarity in amplitude between the two spectra.

[0032] It should be noted that the signal-to-noise ratio of the measured data may change over time, and the linearity will also change accordingly. Assuming regression is performed on all available data, the overall linearity may be low in the presence of significant noise, thus preventing the extraction of locally high linearity data.

[0033] More preferably, in an exemplary embodiment, the calculation of the phase difference between the electric field and the magnetic field in step S12 includes: The phase difference PD was calculated using two observed electromagnetic spectrum data. , The calculation formula is as follows: ; Where j represents the imaginary unit, and These are the first of different channels The electric field spectrum and magnetic field spectrum calculated over a time window; and They represent and The phase is indicated by the overline, which represents the complex conjugate.

[0034] Specifically, the phase difference characteristics between various components under a quiet electromagnetic environment were analyzed through simulation, and the results are as follows: Figure 2 As shown. Taking the relationship between the electromagnetic field components in the x-direction (north-south orientation) as an example, it is found that the phase difference distribution pattern is related to the amplitude ratio AR of the impedance tensor. Closely related. When the phase difference varies randomly within a 90° range, it is defined as having a distinct preferred direction characteristic.

[0035] When the impedance component in the orthogonal direction is dominant (i.e.) Greater than When the phase difference is greater than 2 times, the phase difference between the electric and magnetic fields in the orthogonal direction exhibits a clear preferred direction characteristic, while the phase difference between the electric and magnetic fields in the parallel direction is randomly distributed in the range of -180° to 180°. This data type is named V-type, representing the coherence of the electromagnetic components in the vertical direction.

[0036] When the impedance components in the orthogonal and parallel directions have similar amplitudes (i.e., when the ratio of their impedance amplitudes is close to 1), the phase difference between the electric and magnetic fields in the orthogonal and parallel directions changes synchronously and randomly within a range of 180°. Neither direction exhibits a preferred phase characteristic. This data type is named PV type.

[0037] When the parallel impedance component is dominant (i.e.) Greater than When the phase difference is greater than 2 times, the phase difference between the electric and magnetic fields in the parallel direction exhibits a clear preferred direction characteristic, while the phase difference between the electric and magnetic fields in the orthogonal direction is randomly distributed within the range of -180° to 180°. This data type is named P-type, representing coherent electromagnetic components in the parallel direction.

[0038] More preferably, in an exemplary embodiment, in step S13, a preset threshold is used to filter high-quality data segments to determine the distribution characteristics of each parameter of the high-quality data at the measurement point, including: High linearity criterion: linear correlation coefficient between electric and magnetic field components >0.8, and >0.8; Phase difference distribution characteristic criterion: phase difference When concentrated within a 90° range, it is defined as having phase-optimal characteristics; firstly, based on the amplitude ratio AR of the impedance tensor... The data types are defined as follows: For V-type data, only the orthogonal directions exhibit phase-preferred characteristics, while the parallel directions are randomly distributed within the entire range (±180°); for PV-type data, both the orthogonal and parallel directions show synchronous random variation within a 180° range, with no azimuth selectivity; for P-type data, only the parallel directions exhibit phase-preferred characteristics, while the orthogonal directions are randomly distributed within the entire range (±180°). and These represent the self-impedance in the X direction of the impedance tensor Z and the transverse impedance in the X direction of the impedance tensor Z, respectively. Energy enhancement criterion: First, the electric field amplitude and magnetic field amplitude are normalized by median. When the normalized electric field amplitude |E| and magnetic field amplitude |H| are both greater than 2, the data is determined to be energy-enhanced.

[0039] In summary, in this exemplary embodiment, measured magnetotelluric (MT) data are classified into three high-quality types. By analyzing the phase difference (PD) distribution pattern and linearity, the type of data for each time period can be determined, thereby revealing the temporal variation pattern of data quality. High-linearity data is defined as... >0.8, and >0.8; the phase difference has a preferred orientation, with the phase difference randomly varying within a 90° range.

[0040] Table 1: Criteria for Judging Three Types of High-Quality Data Types More preferably, in an exemplary embodiment, step S22, which involves selecting appropriate discrimination criteria based on the high-quality data types determined in step S13, performing quality assessment on the data segments from S21, and identifying data segments that meet the discrimination criteria as geomagnetic pulsation signals, includes: High linearity criterion: linear correlation coefficient between electric and magnetic field components >0.8, and >0.8; Phase difference distribution characteristic criteria: consistent with the data type in step S13; Energy enhancement criterion: First, the electric field amplitude and magnetic field amplitude are normalized by median. When the normalized electric field amplitude |E| and magnetic field amplitude |H| are both greater than 2, the data is determined to be energy-enhanced.

[0041] The following will be used to conduct experiments to verify the technical effects of the above exemplary embodiments: To verify the effectiveness of the method of this invention, measured data from the Memambetsu (MMB) geomagnetic observatory in Japan were used for testing and analysis. Before data quality analysis, impedance calculation methods with high reliability were obtained by using data from periods of geomagnetic storms and employing pre-screening techniques. Figure 3 As shown, the diagonal impedance component is found to be more than 3 times higher than the off-diagonal component. The high-quality data of the dead band at this site satisfies the fact that the vertical electromagnetic components exhibit a certain phase difference.

[0042] Two typical geomagnetic storm events were selected for in-depth analysis: (1) The moderate geomagnetic storm of July 15-16, 2000: the minimum SYM-H index was approximately -150 nT; (2) The severe geomagnetic storm of October 29-31, 2003 ("Halloween Storm"): the minimum SYM-H index was approximately -400 nT; as shown below. Figure 4 As shown in Figure 5.

[0043] In the process of identifying high-quality data, high linearity data ( >0.8, and Values ​​>0.8 are marked in red, and other data are marked in blue. Both electric and magnetic field amplitudes were normalized using the median. The results show that this method can accurately identify the initial phase of a geomagnetic storm when the SYM-H index begins to decline, providing an early warning time of approximately 1-2 hours. Notably, this method can also identify geomagnetic pulsations generated during non-geomagnetic storm periods, demonstrating good applicability.

[0044] In another exemplary embodiment of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores computer instructions executable on the processor, and the processor executes the steps of the method described in any of the above exemplary embodiments when executing the computer instructions.

[0045] Electronic devices are manifested in the form of general-purpose computing devices. Components of electronic devices may include, but are not limited to: at least one processing unit, at least one storage unit, and a bus connecting different system components (including storage units and processing units).

[0046] The storage unit stores program code that can be executed by the processing unit, causing the processing unit to perform the steps described in the "Exemplary Methods" section above, based on various exemplary embodiments of the present invention. For example, the processing unit can perform actions such as... Figure 1 The method shown in the figure.

[0047] The storage unit may include readable media in the form of volatile storage units, such as random access memory (RAM) and / or cache storage units, and may further include read-only memory (ROM).

[0048] The storage unit may also include a program / utility having a set (at least one) of program modules, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0049] A bus can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus that uses any of the various bus structures.

[0050] The electronic device can also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed via input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0051] Through the above description, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to this exemplary embodiment can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the method according to this exemplary embodiment.

[0052] In another exemplary embodiment of the present invention, a computer storage medium is provided, on which computer instructions are stored, wherein the computer instructions, when executed, perform the steps of the method described in any of the above exemplary embodiments.

[0053] Based on this understanding, the technical solution of this embodiment, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product (program product). The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0054] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0055] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0056] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0057] The program code for performing the operations of this invention can be written in a programming language, including object-oriented programming languages ​​such as MATLAB and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, as a standalone software package, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0058] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for automatically identifying geomagnetic pulsations of PC2, characterized in that: This includes a high-quality data class determination step and a geomagnetic pulsation identification step; the high-quality data class determination step includes the following sub-steps: S11: Obtain magnetotelluric time series data containing geomagnetic storm data from the measuring point, perform windowed Fourier transforms on the electromagnetic field components respectively, and obtain the frequency spectrum array of each electromagnetic field component in the frequency domain. S12: Calculate data quality assessment parameters based on the spectrum array. The data quality assessment parameters include electromagnetic field linearity, phase difference between electric and magnetic fields, and amplitude of the electromagnetic field spectrum as an energy parameter. S13: Based on the data quality assessment parameters, a preset threshold is used to filter high-quality data segments and determine the distribution characteristics of each parameter of the high-quality data at the corresponding measurement point; The geomagnetic pulsation identification step includes the following sub-steps: S21: For measurement points where the distribution characteristics of each parameter of the high-quality data have been determined, a refined analysis of the data quality of the newly acquired magnetotelluric time series data is performed, including: performing Fourier transform on the time series data of multiple electromagnetic field components, and calculating the parameters of electromagnetic field linearity, phase difference, and energy, wherein the amplitudes of electric field and magnetic field are normalized using median values. S22: Based on the high-quality data type determined in step S13, select the corresponding discrimination criteria, evaluate the quality of the data segment in S21, and determine the data segment that meets the discrimination criteria as a geomagnetic pulsation signal.

2. The method for automatically identifying PC2 geomagnetic pulsations according to claim 1, characterized in that: In step S12, the calculation method for electromagnetic field linearity includes: S1211: Divide the observed electromagnetic spectrum data into groups of 20 windows, and use the least squares method to estimate the impedance tensor Z for each group of observed electric field E and observed magnetic field H. S1212: Using the observed magnetic field H and impedance tensor Z, calculate the predicted electric field for each set of data. =ZH; S1213: By comparing observed electric fields Should be consistent with the predicted electric field The linearity of electromagnetic fields is determined by the similarity of their phases, and the linear coherence degree (PLcoh) is used to measure the phase similarity. The calculation formula is as follows: ； in, Indicates the first Observed electric field values With the predicted electric field value The phase difference, where i represents the number of windows; S1214: The amplitude ratio is used to determine the similarity of amplitude PARs. The calculation formula is as follows: = 。 3. The method for automatically identifying PC2 geomagnetic pulsations according to claim 2, characterized in that: In step S12, the phase difference between the electric field and the magnetic field is calculated as follows: The phase difference PD was calculated using two observed electromagnetic field spectrum data. , The calculation formula is as follows: ； Where j represents the imaginary unit, and These are the first of different channels The electric field spectrum and magnetic field spectrum calculated over a time window; and They represent and The phase is indicated by the overline, which represents the complex conjugate.

4. The method for automatically identifying PC2 geomagnetic pulsations according to claim 3, characterized in that: In step S13, a preset threshold is used to filter high-quality data segments and determine the distribution characteristics of each parameter of the high-quality data at the measuring point, including: High linearity criterion: linear correlation coefficient between electric and magnetic field components >0.8, and >0.8; Phase difference distribution characteristic criterion: based on the amplitude ratio AR of the impedance tensor = Data type determination: For V-type data, only the orthogonal directions exhibit phase-preferred characteristics, while the parallel directions are randomly distributed within the entire range (±180°); for PV-type data, both the orthogonal and parallel directions show synchronous random variation within a 180° range, with no azimuth selectivity; for P-type data, only the parallel directions exhibit phase-preferred characteristics, while the orthogonal directions are randomly distributed within the entire range (±180°). and Let Z represent the self-impedance in the X direction of the impedance tensor Z, the transverse impedance in the X direction of the impedance tensor Z, and the phase difference, respectively. When the distribution is concentrated within a 90° range, it is defined as having phase-preferred characteristics; Energy enhancement criterion: First, the electric field amplitude and magnetic field amplitude are normalized by median. When the normalized electric field amplitude |E| and magnetic field amplitude |H| are both greater than 2, the data is determined to be energy-enhanced.

5. The method for automatically identifying PC2 geomagnetic pulsations according to claim 4, characterized in that: In step S22, the step of selecting the corresponding discrimination criteria based on the high-quality data type determined in step S13, and performing quality assessment on the data segment from S21, and determining the data segment that meets the discrimination criteria as a geomagnetic pulsation signal, includes: High linearity criterion: linear correlation coefficient between electric and magnetic field components >0.8, and >0.8; Phase difference distribution characteristic criteria: consistent with the data type in step S13; Energy enhancement criterion: First, the electric field amplitude and magnetic field amplitude are normalized by median. When the normalized electric field amplitude |E| and magnetic field amplitude |H| are both greater than 2, the data is determined to be energy-enhanced.

6. An electronic device comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, characterized in that, When the processor executes the computer instructions, it performs the steps of the method as described in any one of claims 1-5.

7. A computer storage medium storing computer instructions thereon, characterized in that, The computer instructions, when executed, perform the steps of the method as described in any one of claims 1-7.

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