A geomagnetic storm prediction method based on multi-station cosmic ray data

Abstract

The present invention discloses a geomagnetic storm prediction method based on multi-station cosmic ray data, which collects multiple cosmic ray signals; performs empirical mode decomposition on the cosmic ray signals to obtain corresponding intrinsic mode functions, and performs fast Fourier transform on the intrinsic mode functions; selects specific frequency components from the frequency domain as precursor signals, and converts the precursor signals into local time to align with the geographical location or the observation time of a specific station; performs weighted processing on the precursor signals of different local times and then sums them to obtain a universal time precursor signal, and performs value determination on the universal time precursor signal, and uses the time when the precursor signal first reaches a threshold as the warning time. The present invention uses multiple cosmic ray stations to process geomagnetic storm precursor signals extracted from cosmic rays, thereby improving the prediction accuracy, reducing the phenomenon of missed reports and false reports, and improving the current situation that medium and small geomagnetic storms cannot be predicted.

Description

Technical Field

[0001] The present invention relates to the field of forecasting technology, and in particular to a geomagnetic storm forecasting method based on cosmic ray data from multiple stations. Background Art

[0002] Geomagnetic storms can have an impact on space-based and ground-based technical systems such as satellites, communications, aviation, and power grids, and are one of the more important forecast parameters in space weather forecasting. Most of the large geomagnetic storms that have a significant impact on space-based and ground-based technical systems are non-reproducible geomagnetic storms caused by a single coronal mass ejection event (CME) or multiple CMEs. Since CMEs are huge, dense clouds of plasma that erupt from the solar atmosphere, and their eruptions are random and diverse, non-reproducible geomagnetic storms caused by CMEs will not only have a strong impact on the Earth system, but are also very difficult to predict.

[0003] At present, most of the geomagnetic storm prediction methods use solar wind data at the L1 point for prediction, and the lead time is mostly between 3 and 6 hours. Some studies will use solar observation data to improve the prediction time, such as CME, solar flares and other information, but as the prediction time increases, the prediction accuracy will still gradually decrease, which is limited by the time scale of the dynamic process of the magnetosphere inside the geomagnetic storm (about 1 hour). Using the same data and prediction model, the root mean square error of the prediction results increases from 0.65 in 3 hours to 0.85 in 6 hours; if the Kp index is predicted by machine learning methods, the root mean square error of the results also increases with the advance of the prediction time. However, it is difficult for the various technical systems affected by geomagnetic storms to respond effectively within a few hours. For example, for the orbit prediction of low-Earth orbit satellites, the geomagnetic storm prediction capability of about one day is more effective. Therefore, although the solar wind data at the L1 point can relatively accurately predict the geomagnetic activity in the next few hours, the longer the prediction lead time, the more beneficial it is for the affected technical systems.

[0004] The geomagnetic storm precursor signals carried in the cosmic ray flux (CRI) data can be used to predict the occurrence of non-reproducible geomagnetic storms 10-25 hours in advance. By predicting the pre-rise or pre-fall of the cosmic ray flux data before the geomagnetic storm, the occurrence of the geomagnetic storm can be predicted more than 6-12 hours in advance, or even 20 hours in advance; the precursor signals of the geomagnetic storm can also be extracted through the changes in the frequency domain of the cosmic ray data, so as to predict the occurrence of the geomagnetic storm, and the lead time is about 20 hours. Using the time-frequency analysis method CEEMDAN-CWT, 65 non-reproducible large geomagnetic storms (Kp≥7) in the 23rd and 24th solar activities were studied. It was found that the use of this method to reconstruct the geomagnetic storm precursor signals in the cosmic rays can predict the occurrence of non-reproducible large geomagnetic storms 22.5h in advance. However, this method only uses the data of a single station, and there are omissions and false alarms. Therefore, a geomagnetic storm prediction method based on multi-station cosmic ray data is needed. Summary of the invention

[0005] The purpose of the present invention is to provide a geomagnetic storm prediction method based on multi-station cosmic ray data.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0007] The present invention comprises the following steps:

[0008] Collect multiple cosmic ray signals;

[0009] Performing empirical mode decomposition on the cosmic ray signal to obtain a corresponding intrinsic mode function, and performing fast Fourier transform on the intrinsic mode function;

[0010] Selecting a specific frequency component from the frequency domain as a precursor signal, and converting the precursor signal into a local time to align with a geographical location or an observation time of a specific station;

[0011] The precursor signals of different local times are weighted and then summed to obtain the precursor signal of world time, and the precursor signal of world time is read and determined.

[0012] Furthermore, the fast Fourier transform is used to convert the signal from the time domain to the frequency domain, add IMF4 and IMF5 to form a new signal X(t), and use the fast Fourier transform method to convert X(t) into a frequency domain signal F(m), the mathematical expression of which is:

[0013]

[0014] Where X(△t) is the sample value of the discrete signal in the time domain, m is the index in the frequency domain, ranging from 0 to N-1; Ω is the angular frequency, equal to 2π / N; e^(-imnΩ) is a complex exponential signal.

[0015] Furthermore, when a specific frequency is selected, a frequency range of 4 to 24 h with the largest change in power spectrum density value is selected from the frequency domain signal as a frequency window, and the frequency window is integrated to obtain the precursor signal of cosmic rays at a single station.

[0016] Furthermore, the precursor signal is converted into a precursor signal with local time as the time series, and the time conversion formula is:

[0017] T local =T UTC +T diff (4)

[0018] Furthermore, the precursor signals at different locations are weighted and then summed according to their cutoff stiffness, and the cutoff stiffness is positively correlated with the weight.

[0019] A cosmic ray signal analysis system, comprising:

[0020] A signal acquisition unit, which collects multiple cosmic ray signals;

[0021] A signal processing unit, performing empirical mode decomposition on the cosmic ray signal to obtain a corresponding intrinsic mode function, and performing fast Fourier transform on the intrinsic mode function;

[0022] a signal selection unit, which selects a specific frequency component from the frequency domain as a precursor signal, and converts the precursor signal into a local time to align the observation time of a geographical location or a specific station;

[0023] The determination unit performs weighted processing on the precursor signals of different local times and then sums them up to obtain a world time precursor signal, and performs value determination on the world time precursor signal.

[0024] In another aspect, an electronic device includes:

[0025] A processor; and a memory arranged to store computer executable instructions which, when executed, cause the processor to perform any of the methods described.

[0026] The beneficial effects of the present invention are:

[0027] The present invention is a geomagnetic storm prediction method based on multi-station cosmic ray data. Compared with the prior art, the present invention has the following technical effects:

[0028] The present invention proposes to utilize multiple cosmic ray stations to process geomagnetic storm precursor signals extracted from cosmic rays while taking into account the longitude and latitude of different stations and the cutoff stiffness of the stations, thereby improving the forecast accuracy, reducing the phenomenon of missed reports and false reports, and improving the current situation where medium and small geomagnetic storms cannot be forecasted. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The present invention is a flowchart of the steps of a geomagnetic storm prediction method based on multi-station cosmic ray data.

[0030] Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0031] The present invention is further described below with specific examples. The illustrative examples and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0032] The geomagnetic storm prediction method based on multi-station cosmic ray data of the present invention comprises the following steps:

[0033] like Figure 1 As shown, the purpose of the present invention is to provide a geomagnetic storm prediction method based on cosmic ray data from multiple stations.

[0034] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0035] The present invention comprises the following steps:

[0036] Cosmic ray station data collection: First, there are multiple cosmic ray stations (cosmic ray station 1, 2, 3...n), which are responsible for collecting cosmic ray signals.

[0037] Empirical Mode Decomposition: The collected signals are first processed using the Empirical Mode Decomposition (EMD) method. EMD is an adaptive time-frequency analysis method that can decompose complex signals into a series of Intrinsic Mode Functions (IMFs) with different frequency characteristics.

[0038] Fast Fourier Transform: Next, the decomposed IMFs are subjected to Fast Fourier Transform (FFT), which is a mathematical method for converting signals from the time domain to the frequency domain and is used to analyze the frequency components of the signal.

[0039] Extracting precursor signals of specific frequencies: In the frequency domain, select specific frequency components as precursor signals. These precursor signals may be associated with certain specific cosmic ray events.

[0040] Precursor signal to local time: Convert the extracted precursor signal to local time, which may be to align with a geographic location or the time of observation at a specific station.

[0041] Weighted summation of precursor signals: Precursor signals at different locations are weighted and then summed. The weighting may be based on signal strength, confidence or other criteria.

[0042] Precursor signal for conversion to universal time: Convert the weighted summed signal to universal time (UTC) to facilitate comparison and analysis between different stations.

[0043] Finally, the converted UT precursor signal is evaluated, which may include the signal strength, frequency characteristics, etc., to determine whether it meets the precursor characteristics of certain specific cosmic ray events.

[0044] Decompose a time series signal Y(t) into N intrinsic mode functions (IMF) and a residual function Res(t). The mathematical formula is as follows:

[0045]

[0046] Where I MFn(t) is the intrinsic mode function. Its construction process is to select the upper envelope function Eup(t) and the lower envelope function Edown(t) of Y(t), take their average value, and then subtract the average value from Y(t) to get the intrinsic mode function. The mathematical expression is:

[0047] IMF n =Y(t)-(E up +E down ) / twenty two)

[0048] Then (E up +E down ) / 2 is regarded as the new Y(t), and the process of formula 2 is repeated until Y(t) becomes a monotonic function, which is the residual function Res(t).

[0049] In this process, N intrinsic mode functions (IMFs) will be formed and will show periods of different lengths (such as a period of about 27 days and a period of about 24 hours). After data analysis, it is found that IMF4 and IMF5 will show obvious fluctuations before a magnetic storm occurs. However, there is still other fluctuation information in the time domain, which needs to be further extracted in the frequency domain.

[0050] IMF4 and IMF5 are added together to form a new signal X(t), and X(t) is converted into a frequency domain signal F(m) using the fast Fourier transform method. The mathematical expression is:

[0051]

[0052] Where X(△t) is the sample value of the discrete signal in the time domain, m is the index in the frequency domain, ranging from 0 to N-1; Ω is the angular frequency, equal to 2π / N; e^(-imnΩ) is a complex exponential signal.

[0053] The frequency range of 4 to 24 h with the largest change in power spectrum density value is selected from the frequency domain signal as the frequency window, and integrated to obtain the geomagnetic storm precursor signal of cosmic rays at a single station.

[0054] IMF4 and IMF5 here refer to the 4th and 5th intrinsic mode functions obtained in the process of empirical mode decomposition (EMD). EMD is an adaptive time-frequency analysis method for signal processing, which can decompose a complex signal into several simple intrinsic mode functions with different frequency characteristics.

[0055] In the entire data set, the number of local maxima and local minima is equal or the difference does not exceed 1. The envelope of the local maximum (upper envelope) and the envelope of the local minimum (lower envelope) do not cross each other. In the EMD process, the signal is decomposed into multiple IMFs, each I MF corresponds to an oscillation component of a different frequency in the signal. Usually, IMF1 contains the highest frequency oscillation, and as the sequence number increases, the frequency of IMFs gradually decreases. Therefore, IMF4 and I MF5 represent the lower frequency oscillation components in the signal.

[0056] When the signal is collected from stations in different geographical locations. Local time refers to the time calculated based on the rotation of the earth and the longitude of the station. It is different from the Universal Coordinated Time (UTC) because the rotation of the earth causes time differences in different geographical locations. According to the longitude of different stations, the precursor signal is converted into a precursor signal with local time as the time series. The time conversion formula is:

[0057] T local =T UTC +T diff (4)

[0058] Then, the precursor signals of different stations are weighted and summed according to the size of their cutoff stiffness. The smaller the cutoff stiffness, the higher the weight. After the local time precursor signal is obtained, it is converted into world time. The time when the precursor signal first reaches the threshold is the warning time. The threshold is 1.2 times the average value of the precursor signal during the geomagnetic calm time (time span 24h) before the CME outbreak.

[0059] Continuous wavelet transform (CWT) reveals some additional information of the signal, especially the time-varying spectral characteristics of non-stationary signals. After applying CWT to CRI data, some features are found in the spectrum, which appear at a fixed position in the spectrum. Therefore, before applying CWT, it is necessary to preprocess the CRI data to filter out information irrelevant to CME.

[0060] The present invention uses the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN) to decompose the adaptive empirical signal, decomposes the CRI signals of multiple stations into different quasi-linear components and quasi-stationary components, and then uses the continuous wavelet transform (CWT) to analyze them, further extracts the information related to the geomagnetic storm, and then predicts the non-reproducible geomagnetic storm based on the information extracted from the data of different stations.

[0061] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0062] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0063] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0064] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a high-resolution weather forecast device based on multi-source detection and machine learning at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the above-mentioned geomagnetic storm forecasting methods based on multi-station cosmic ray data.

[0065] The above application Figure 1 The geomagnetic storm prediction method based on multi-station cosmic ray data disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The various methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0066] The electronic device may also perform Figure 1 A geomagnetic storm prediction method based on multi-station cosmic ray data is implemented Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0067] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple application programs, any one of the aforementioned geomagnetic storm prediction methods based on multi-station cosmic ray data is executed.

[0068] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0069] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0070] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0072] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0073] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0074] Computer readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transit media), such as modulated data signals and carrier waves.

[0075] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0076] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A geomagnetic storm prediction method based on multi-station cosmic ray data, characterized in that: The following steps are involved: Collect multiple cosmic ray signals; Performing empirical mode decomposition on the cosmic ray signal to obtain a corresponding intrinsic mode function, and performing fast Fourier transform on the intrinsic mode function; Selecting a specific frequency component from the frequency domain as a precursor signal, and converting the precursor signal into a local time to align with a geographical location or an observation time of a specific station; The precursor signals at different local times are weighted and summed up to obtain the universal time precursor signal, and the universal time precursor signal is read and judged. The time when the precursor signal first reaches the threshold is used as the warning time. The threshold is 1.2 times the average value of the precursor signal when the geomagnetic field is calm before the CME outbreak.

2. The geomagnetic storm prediction method based on multi-station cosmic ray data according to claim 1, characterized in that: The empirical mode decomposition includes: a. Decompose the signal into several intrinsic mode functions (IMFs); b. Treat the residual function Y(t) as a new signal and repeat the empirical mode decomposition process until Y(t) becomes a monotonic function.

3. The geomagnetic storm prediction method based on multi-station cosmic ray data according to claim 1, characterized in that: The fast Fourier transform is used to convert the signal from the time domain to the frequency domain, add IMF4 and IMF5 to form a new signal X(t), and use the fast Fourier transform method to convert X(t) into a frequency domain signal F(m), the mathematical expression of which is: Where X(△t) is the sample value of the discrete signal in the time domain, m is the index in the frequency domain, ranging from 0 to N-1; Ω is the angular frequency, equal to 2π / N; e^(-imnΩ) is a complex exponential signal.

4. The geomagnetic storm prediction method based on multi-station cosmic ray data according to claim 1, characterized in that: When a specific frequency is selected, a frequency range of 4 to 24 hours with the largest change in power spectrum density value is selected from the frequency domain signal as a frequency window, and the frequency window is integrated to obtain the precursor signal of cosmic rays at a single station.

5. The geomagnetic storm prediction method based on multi-station cosmic ray data according to claim 1, characterized in that: The precursor signal is converted into a precursor signal with local time as the time series. The time conversion formula is: T local =T UTC +T diff (4) 6. The geomagnetic storm prediction method based on multi-station cosmic ray data according to claim 1, characterized in that: After weighted processing of precursor signals at different locations, weighted summation is performed according to the size of their cutoff stiffness, and the cutoff stiffness is positively correlated with the weight.

7. A geomagnetic storm prediction system based on multi-station cosmic ray data, characterized in that: include: A signal acquisition unit, which collects multiple cosmic ray signals; A signal processing unit, performing empirical mode decomposition on the cosmic ray signal to obtain a corresponding intrinsic mode function, and performing fast Fourier transform on the intrinsic mode function; a signal selection unit, which selects a specific frequency component from the frequency domain as a precursor signal, and converts the precursor signal into a local time to align the observation time of a geographical location or a specific station; The determination unit performs weighted processing on the precursor signals of different local times and then sums them up to obtain the universal time precursor signal, performs value determination on the universal time precursor signal, and takes the time when the precursor signal first reaches the threshold as the warning time, and the threshold is 1.2 times the average value of the precursor signal when the geomagnetic field is quiet before the CME outbreak.

8. An electronic device comprising: processor; and a memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1 to 6.

Images