Geomagnetic disturbance geoelectric field calculation method based on block geoelectric structure

Abstract

The invention discloses a geomagnetic disturbance geoelectric field calculation method based on a block geoelectric structure, relates to the technical field of geomagnetic disturbance geoelectric field calculation, and solves the technical problem of low GIC calculation accuracy in complex geology. According to the method, in a macroscopic scale, a one-dimensional conductivity model is inverted by using long-period magnetotelluric sounding, and a regional structure framework is delimited by combining a geological map, so that the accuracy of a global electrical background is ensured; a three-dimensional electrical structure is constructed through audio magnetotelluric sounding in the mesoscale, a secondary geological unit is recognized, and the problem that a traditional model is insufficient in depicting transverse electrical differences is solved; in the microscopic scale, for a key area, high-density resistivity imaging and a controllable source audio magnetotelluric method are used for capturing a high-conductivity / low-resistance anomalous body, and fine modeling of a local area is achieved; according to the method, the geological complexity is mined through the multi-scale model, so that the calculation precision of the GIC is improved.

Description

Technical Field

[0001] The present invention belongs to the field of geomagnetic disturbance geoelectric field calculation, and specifically relates to a method for calculating geomagnetic disturbance geoelectric field based on block-wise geoelectric structure. Background Art

[0002] Geomagnetically induced current (GIC) is a low-frequency telluric current induced by geomagnetic disturbances. It is injected into the power transmission network through the grid grounding system, which may cause transformer saturation, misoperation of relay protection, and even power grid collapse. Accurately predicting the spatial distribution and intensity of GIC is crucial for the safety protection of power systems.

[0003] Traditional methods rely on one-dimensional conductivity models at a single scale (such as long-period magnetotelluric sounding LMT), and it is difficult to characterize the electrical property differences of mesoscopic secondary geological units (such as fault zones, large mining areas) and microscopic anomalies (such as highly conductive ore bodies). For example, the anisotropic conductivity tensor at the mesoscopic scale and local high-resistance / low-resistance anomalies at the microscopic scale are often ignored, resulting in the inability of geoelectric field calculation to reflect the sudden change characteristics of the electric field at the real geological interface.

[0004] The present invention provides a method for calculating geomagnetic disturbance geoelectric field based on block-wise geoelectric structure to solve the above technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention proposes a method for calculating geomagnetic disturbance geoelectric field based on block-wise geoelectric structure. At the macroscopic scale, this method uses long-period magnetotelluric sounding (LMT) to invert one-dimensional conductivity models, and combines geological maps to delimit the regional tectonic framework to ensure the accuracy of the overall electrical background; at the mesoscopic scale, three-dimensional electrical structures are constructed through audio magnetotelluric sounding (AMT) to identify secondary geological units (such as large mining areas, fault zones), solving the problem of insufficient characterization of lateral electrical property differences in traditional models; at the microscopic scale, for key areas (such as extra-high voltage substations), high-density resistivity imaging (ERT) and controlled-source audio magnetotelluric method (CSAMT) are used to capture highly conductive / low-resistivity anomalies to achieve refined modeling of local areas; the present invention mines geological complexity through multi-scale models, thereby improving the calculation accuracy of GIC.

[0006] To achieve the above object, the first aspect of the present invention provides a method for calculating geomagnetic disturbance geoelectric field based on block-wise geoelectric structure, including: Determine the target area, and divide the target area into several macroscopic blocks according to the geological map and administrative map covering the target area; use LMT detection means to invert the one-dimensional conductivity models of several macroscopic blocks; Obtain the three-dimensional electrical property structure models of several macroscopic blocks by using AMT detection means; combine the three-dimensional electrical property structure models with geological maps to identify secondary geological units, and divide the macroscopic blocks into several mesoscopic blocks according to the identification results; among them, the secondary geological units include large mining areas and specific geological structure zones; Determine the key areas in the mesoscopic blocks, and obtain the abnormal bodies underground in the key areas by using ERT and CSAMT detection means; divide the mesoscopic blocks based on the abnormal bodies to obtain several microscopic blocks; among them, the key areas include ultra-high voltage substations, and the abnormal bodies include high-conductivity / low-resistivity abnormal bodies; Obtain the geoelectric field distribution of the target area based on the electrical property parameters of the macroscopic blocks and mesoscopic blocks; obtain the geoelectric field distribution of the key areas based on the electrical property parameters of the microscopic blocks; calculate the geoelectric field by using the geomagnetic model based on the electrical property parameters of the macroscopic blocks, mesoscopic blocks, microscopic blocks and real-time geomagnetic data; Extract characteristic parameters from the geoelectric fields of the target area and key areas and the geoelectric field calculated by the geomagnetic model, as well as the electrical property parameters at different scales; integrate the characteristic parameters and electrical property parameters into a vector, and input the vector into a machine learning algorithm to predict GIC.

[0007] Preferably, after dividing the target area into several macroscopic blocks, perform verification and adjustment on the several macroscopic blocks, including: Calculate the apparent resistivity based on the one-dimensional conductivity model obtained by inversion, and convert the conductivity data into apparent resistivity data; among them, the formula for calculating the apparent resistivity is: , is the apparent resistivity, is the conductivity; Use the finite difference method to calculate the apparent resistivity gradient at the boundary between adjacent macroscopic blocks; compare the apparent resistivity gradient with the apparent resistivity gradient threshold, and adjust the boundary according to the comparison result.

[0008] Preferably, after dividing the macroscopic blocks into several mesoscopic blocks, perform verification and adjustment on the several mesoscopic blocks, including: Statistically analyze the conductivity data in each mesoscopic block, and calculate the conductivity variation coefficient according to statistical methods; among them, the formula for calculating the conductivity variation coefficient is: , is the standard deviation of the conductivity within the mesoscopic block, is the average conductivity in the mesoscopic block; Compare the conductivity variation coefficient with the set conductivity variation coefficient threshold, and adjust the boundary of the mesoscopic block according to the comparison result.

[0009] Preferably, obtaining the geoelectric field distribution of the target area based on the electrical property parameters of the macroscopic blocks and mesoscopic blocks includes: Obtain the electrical parameters of the macro block and the meso block; among them, the electrical parameters of the macro block include a one-dimensional conductivity model and regional geological structure information, and the electrical parameters of the meso block include an anisotropic conductivity tensor and the average wave impedance of the block; Divide the target area into several grids in the horizontal direction and layer them according to the one-dimensional conductivity model of the macro block in the vertical direction; among them, each grid is associated with an average conductivity; Calculate the skin depth and the surface electric field intensity based on its average conductivity, and integrate the calculation results of each grid to form the ground electric field intensity distribution matrix of the target area.

[0010] Preferably, the calculation formula for the skin depth is: , , is the angular frequency, is the frequency (take the dominant frequency of geomagnetic pulsation 0.01Hz), is the magnetic permeability.

[0011] Preferably, the calculation formula for the surface electric field intensity is: ; the surface electric field intensity represents the induced electric field intensity of the plane wave on the surface, is the amplitude of the geomagnetic field disturbance.

[0012] Preferably, obtaining the ground electric field distribution of the key area based on the electrical parameters of the micro block includes: Determine the micro block, input the electrical parameters of the micro block, and use the ground electric field distribution of the target area as the peripheral background; Based on Maxwell's equations, solve the ground electric field distribution of the local area in the micro block to obtain the ground electric field intensity, gradient distribution, and identify the electric field mutation points in the key area.

[0013] Preferably, calculating the ground electric field using the geomagnetic model includes: Extract the electrical parameters of the macro block, meso block, micro block, and real-time geomagnetic data; Divide the target area with a three-dimensional grid, assign conductivity and magnetic permeability to each grid; calculate the induced ground electric field based on Maxwell's equations, and use the finite element method to solve the electric field intensity and direction of each grid.

[0014] Preferably, integrating the characteristic parameters and the electrical parameters into a vector and inputting the vector into a machine learning algorithm to predict the GIC includes: Extract the characteristic parameters and electrical parameters of each grid, and after preprocessing, splice them into a characteristic vector according to the scale layer by layer; among them, the preprocessing includes normalization processing and denoising processing; Input the feature vector into the trained machine learning algorithm to obtain the GIC value; among them, the machine learning algorithm includes gradient boosting tree or random forest.

[0015] Preferably, the training of the machine learning algorithm includes: Using the multi-scale block electrical property library and real-time geomagnetic data, through the electromagnetic model (calculate the geomagnetic field, extract the characteristic parameters of the geomagnetic field at different scales as the input feature X, and the GIC observation value as the target variable; Perform data partitioning to obtain the training set, test set, and validation set; train, test, and validate the constructed machine learning algorithm through the training set, test set, and validation set to obtain the trained machine learning algorithm.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. Through the integration of the macro-meso-micro three-level block division system and multi-source geophysical means, the present invention significantly improves the fineness of the geomagnetic field calculation; at the macro scale, use long-period magnetotelluric sounding (LMT) to invert the one-dimensional conductivity model, and combine with the geological map to delimit the regional tectonic framework to ensure the accuracy of the global electrical background; at the meso scale, construct a three-dimensional electrical structure through audio magnetotelluric sounding (AMT) to identify secondary geological units (such as large mining areas, fault zones), and solve the problem of insufficient description of lateral electrical differences in traditional models; at the micro scale, for key areas (such as extra-high voltage substations), use high-density resistivity imaging (ERT) and controlled-source audio magnetotelluric method (CSAMT) to capture high-conductivity / low-resistivity anomalies and realize refined modeling of local areas; the present invention mines geological complexity through multi-scale models, thereby improving the calculation accuracy of GIC.

[0017] 2. Through the real-time integration of geomagnetic data and the physical model-machine learning fusion architecture, the present invention significantly improves the response ability of GIC prediction to dynamic magnetic disturbance scenarios. On the one hand, introduce dynamic parameters such as geomagnetic disturbance amplitude, magnetic storm index, and magnetic field change rate, construct an electromagnetic model based on Maxwell's equations, and calculate the induced geomagnetic field in real time, solving the problem of the failure of traditional static models to simulate the sudden change of the electric field during magnetic storms. Moreover, through feature engineering, integrate multi-scale electrical parameters and geomagnetic dynamic characteristics, and establish a non-linear mapping relationship using gradient boosting trees, making up for the defects of time-consuming calculation of pure physical models and insufficient interpretability of pure data models. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0019] Figure 1 Schematic diagram of the method flow for calculating the geomagnetically disturbed geoelectric field in the present invention Figure 1 ; Figure 2 Schematic diagram of the method flow for calculating the geomagnetically disturbed geoelectric field in the present invention Figure 2 ; Figure 3 Schematic diagram for extracting characteristic parameters in the present invention Figure 1 ; Figure 4 Schematic diagram for extracting characteristic parameters in the present invention Figure 2 . Detailed implementation manners

[0020] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0021] Please refer to Figure 1 , an embodiment of the first aspect of the present invention provides a method for calculating the geomagnetically disturbed geoelectric field based on the block large-scale electrical property structure, including: Determine the target area, divide the target area into several macro blocks according to the geological map and administrative map covering the target area; use the LMT detection means to invert the one-dimensional conductivity model of several macro blocks; Use the AMT detection means to obtain the three-dimensional electrical property structure model of several macro blocks; combine the three-dimensional electrical property structure model with the geological map to identify secondary geological units, and divide the macro blocks into several meso blocks according to the identification results; among them, the secondary geological units include large mining areas and specific geological structure zones; Determine the key areas in the meso blocks, use the ERT and CSAMT detection means to obtain the abnormal bodies underground in the key areas; divide the meso blocks based on the abnormal bodies to obtain several micro blocks; among them, the key areas include ultra-high voltage substations, and the abnormal bodies include high-conductivity / low-resistivity abnormal bodies; Obtain the geoelectric field distribution of the target area based on the electrical property parameters of the macro blocks and meso blocks; obtain the geoelectric field distribution of the key areas based on the electrical property parameters of the micro blocks; calculate the geoelectric field using the geomagnetic model based on the electrical property parameters of the macro blocks, meso blocks, micro blocks and real-time geomagnetic data; Extract characteristic parameters, as well as electrical property parameters at different scales, from the geoelectric fields of the target area, key areas and the geoelectric field calculated by the geomagnetic model; integrate the characteristic parameters and electrical property parameters into a vector, and input the vector into a machine learning algorithm to predict GIC.

[0022] To quickly identify large-scale geological frameworks and GIC risk areas, establish a global electrical background, and conduct a macroscopic regional division of the target area: S110: Collect relevant data such as provincial geological maps and provincial administrative maps, and refer to the distribution ranges of large-scale geological structures (such as mountains, sedimentary basins, etc.) and the boundaries of prefecture-level administrative regions to divide the target area into several macroscopic blocks; S130: Use geophysical exploration methods such as long-period magnetotelluric sounding (LMT) to conduct measurements within the above-mentioned several macroscopic blocks to obtain electromagnetic response data at different underground depths. Among them, long-period magnetotelluric sounding can obtain deep electrical structure information, and its measurement period is generally 10 3 -10 4 s, and the inversion depth can reach more than 50 km; S140: Select an inversion algorithm (such as Occam inversion, etc.) to invert the obtained electromagnetic response data, calculate and invert the one-dimensional conductivity model of each macroscopic block; this one-dimensional conductivity model describes the conductivity distribution at different depths and can provide a data basis for subsequent analysis of the electrical characteristics of macroscopic blocks.

[0023] Based on the provincial geological map, provincial administrative map, and large-scale geological structures, dividing the target area into several macroscopic blocks can ensure relatively stable electrical parameters between macroscopic blocks and relatively uniform electrical characteristics within macroscopic blocks.

[0024] In some other preferred embodiments, the apparent resistivity can also be calculated based on the inverted electromagnetic response data, and the boundaries of the already divided macroscopic blocks can be adjusted according to the apparent resistivity gradient. The specific steps can be referred to as follows: S150: Calculate the apparent resistivity based on the inverted one-dimensional conductivity model and convert the conductivity data into apparent resistivity data; among them, the formula for calculating the apparent resistivity is: , is the apparent resistivity, is the conductivity; S160: Use the finite difference method to calculate the apparent resistivity gradient at the boundaries of adjacent macroscopic blocks; compare the apparent resistivity gradient with the apparent resistivity gradient threshold, and adjust the boundary according to the comparison result.

[0025] The setting of the apparent resistivity gradient threshold should comprehensively consider factors such as geological complexity and electromagnetic measurement accuracy. Generally speaking, for areas with relatively simple geological structures and high measurement accuracy, the apparent resistivity gradient threshold can be set to 10%-15%, and for areas with complex geology, it can be set to 20%. For example, in plain areas where the geological structure is relatively single, a lower threshold can be adopted; while in mountainous areas or areas where there are intersections of multiple geological structures, a slightly higher threshold can be adopted.

[0026] When adjusting the boundary according to the comparison result, the main principle is that when the apparent resistivity gradient corresponding to a certain section of the boundary is greater than the set apparent resistivity threshold, it indicates that the electrical property structures of the geological bodies on both sides of the boundary are significantly different, and the gradient mutation line needs to be used as a new boundary reference.

[0027] For example, on the boundary of a preliminarily divided macroscopic block, it is found that the apparent resistivity gradient in a certain area is significantly higher than the threshold, and through the analysis of geological data, it is confirmed that there is a boundary between different geological units in this area (such as the transition from sedimentary rock to igneous rock), then this section of the boundary needs to be adjusted. This adjustment mainly adopts the method of moving the boundary. Moving the boundary can reduce the electrical difference on both sides of the adjusted boundary, make the electrical property structures within each block more similar, conform to the geological actual situation, and provide a more reasonable basis for subsequent electromagnetic calculations.

[0028] After determining the boundary that needs to be adjusted, a tentative movement of the boundary can be carried out. Specifically, move the boundary towards the side with relatively uniform apparent resistivity, and recalculate the apparent resistivity gradient of the moved boundary. If the gradient is still greater than the threshold, continue to move the boundary in this direction and recheck until the apparent resistivity gradient at the new boundary meets the set requirements (that is, less than or equal to the threshold). If the gradient meets the requirements after moving, determine the new boundary position. For example, at the boundary between macroscopic blocks A and B, if the apparent resistivity on the side of block A is low and varies greatly, while the side of block B is relatively uniform, then first move the boundary a short distance into block B (such as the moving distance can be determined according to the measurement accuracy and actual situation, assuming to move 1 - 2 kilometers first).

[0029] When the apparent resistivity gradients of two adjacent macroscopic blocks are both less than the threshold and belong to the same geological unit through geological analysis, it indicates that their electrical property structures are similar. After merging, the number of blocks can be reduced, the subsequent analysis and calculation processes can be simplified, and at the same time, the overall geological electrical characteristics of the region can be more accurately reflected.

[0030] In order to further refine the regional geological differences and identify secondary geological units.

[0031] S210: Collect and sort out geological data within the scope of the prefecture - level city. Specifically, obtain detailed prefecture - level geological sub - maps from channels such as geological bureaus and professional databases; these geological sub - maps contain information such as rock types, geological structures (such as faults, folds, etc.), and stratigraphic distributions in this area; professional databases include the National Geological Data Center, the Digital Geological Map Database of the China Geological Survey, etc.; S220: Use GIS technology to overlay the boundary layer of the county - level administrative region with the prefecture - level geological sub - map to visually see the spatial relationship between geological units and county - level administrative regions; S230: By increasing the line density of audio magnetotelluric sounding (AMT) in the macro block, the electrical structure of the underground three-dimensional space is inverted based on the data obtained from AMT measurements, that is, the conductivity distribution at different underground positions is determined; S240: Based on the three-dimensional electrical structure model and the municipal geological sub-map, the boundary positions of secondary geological units in the study area (macro block) are determined, and the study area is divided into several meso blocks according to the boundaries of the secondary geological units; among them, the secondary geological units include large mining areas, specific geological structure belts, etc.

[0032] In S230, AMT lines can be arranged in the macro block at intervals of 5 - 10 km. The AMT method infers the electrical structure of the underground medium by measuring the electromagnetic response generated by the natural electromagnetic field in the underground medium. By densifying the lines, the electrical differences of different underground geological bodies can be captured more precisely.

[0033] In S240, professional geophysical inversion algorithms and software are used to process the AMT measurement data. By continuously adjusting the model parameters, the electromagnetic response calculated by the model is made to match the actual measurement data, and finally a three-dimensional underground electrical structure model is obtained. For example, through inversion, conductivity distribution images at different depth levels can be obtained, clearly showing the spatial positions and forms of high-conductivity layers, low-conductivity layers, etc. The inversion algorithms include Occam inversion algorithm, damped least squares method or conjugate gradient method, and the inversion software includes GEO-TOMO, EMU or Geosoft Oasismontaj.

[0034] In some other preferred embodiments, in order to improve the rationality of the meso block division, the conductivity data in several meso blocks can also be analyzed, and the meso blocks are adjusted according to the analysis results. The specific steps can refer to: S250: Statistically analyze the conductivity data in each meso block, and calculate the conductivity variation coefficient according to statistical methods; among them, the calculation formula for the conductivity variation coefficient is: , is the standard deviation of conductivity within the meso block, is the average conductivity in the meso block; S260: Compare the conductivity variation coefficient with the set conductivity variation coefficient threshold, and adjust the boundary of the meso block according to the comparison result.

[0035] Exemplarily, the conductivity variation coefficient threshold is set to 30%. If the conductivity variation coefficient of the meso block is greater than 30%, it indicates that the electrical differences within the meso block are large and can be further divided; if there are adjacent meso blocks with similar electrical characteristics, they are merged into one meso block. Here, the similar electrical characteristics include similar average conductivity and standard deviation.

[0036] To distinguish the geological conditions of key areas, several micro-blocks are determined within the meso-block.

[0037] S310: Identify the key areas in the meso-block and obtain the electrical structure information underground in the key areas through high-precision geophysical measurement means; among them, the key areas include ultra-high voltage substations, cross-coast transmission lines, large mining areas, etc., and the measurement means include high-density resistivity imaging (ERT) and controlled-source audio-frequency magnetotellurics (CSAMT); S320: Use professional geophysical inversion algorithms and software to perform three-dimensional inversion calculations on the data obtained from ERT and CSAMT measurements. By continuously adjusting the model parameters, make the electromagnetic response calculated by the model match the actual measurement data, so as to identify the high-conductivity / low-resistivity anomalies underground; S330: According to the results obtained from the three-dimensional inversion, combined with geological professional knowledge and data characteristics, determine the boundaries of the anomalies; based on the delimited anomaly boundaries, divide the meso-block on the geographic information system (GIS) platform, divide the anomalies into one or more sub-blocks separately, and at the same time divide the normal geological areas around the anomalies into corresponding sub-blocks, and these sub-blocks are the micro-blocks.

[0038] Since the geological electrical parameters of the micro-blocks are easily affected by other factors and change, such as the invasion of groundwater will cause changes in the geological electrical parameters, dynamic monitoring nodes can be set within the micro-region, and devices such as geoelectric field sensors and conductivity sensors can be set at the dynamic monitoring nodes to monitor the changes in parameters such as the underground electric field intensity and conductivity in real time.

[0039] Establish a real-time data analysis and early warning system to perform real-time analysis on the data collected by the dynamic monitoring nodes. When it is found that the data change exceeds a certain threshold (such as the conductivity change exceeds 20%), and it is judged by analysis that it is caused by important geological factor changes (such as groundwater invasion, geological tectonic activities, etc.), trigger the micro-block adjustment mechanism. For example, when the conductivity in a certain sub-block changes significantly due to groundwater invasion, re-evaluate the scope and attributes of the sub-block, and it may be necessary to adjust its boundary, or merge or split it with adjacent sub-blocks.

[0040] After the block division at different scales is completed, combine the electrical parameters of each block to predict GIC, please refer to Figures 3 - 4 .

[0041] I. Data acquisition: The electrical parameters of the macro-block: mainly include the one-dimensional conductivity model, regional geological structure information, etc. obtained by means of long-period magnetotelluric sounding, etc., which can be obtained from the macro-parameter database; Electrical parameters of the mesoscopic block: mainly including data such as the anisotropic conductivity tensor obtained by audio magnetotelluric sounding and the average wave impedance of the block, which can be obtained from the mesoscopic database; Electrical parameters of the microscopic block: high-precision underground electrical structure information obtained by using high-density resistivity imaging, controlled-source magnetotelluric method, etc., which can be obtained from the microscopic database.

[0042] Real-time geomagnetic data: collect data such as the electromagnetic field intensity, direction, change rate, etc. recorded in real time by geomagnetic observation stations, as well as geomagnetic activity indices (such as Kp index) and magnetic storm ring current intensity indices (such as Dst index).

[0043] Power grid topology parameters: obtain the topology structure data of the power grid from the power grid operation department, mainly including the length, direction, resistance, inductance of transmission lines, the location of substations, transformer parameters, etc.

[0044] II. Data processing and calculation S410: Based on the electrical parameters of the macroscopic block and the mesoscopic block, use methods such as the plane wave method to perform an initial calculation of the global geoelectric field, and obtain the geoelectric field distribution of the target area; S420: For the key areas of the microscopic block, based on the electrical parameters of the microscopic block, use the finite element method to perform a precise calculation of the local geoelectric field, and obtain the geoelectric field distribution of the key areas; S430: Based on the electrical parameters of the macroscopic block, the mesoscopic block, the microscopic block and the real-time geomagnetic data, use an electromagnetic model (which can be constructed by Maxwell's equations) to calculate the geoelectric field; S440: Extract characteristic parameters from the geoelectric fields of the target area and the key areas and the geoelectric field calculated by the geomagnetic model. The characteristic parameters mainly include electric field strength, direction, gradient, etc., as well as electrical parameters at different scales; these characteristic parameters are used to form the feature vectors input into the machine learning model.

[0045] The specific implementation steps of S410 are as follows: S411: Obtain the following parameters: the electrical parameters of the macroscopic block include a one-dimensional conductivity model and regional geological structure information, etc., and the electrical parameters of the mesoscopic block include an anisotropic conductivity tensor and the average wave impedance of the block; perform preprocessing on the obtained parameters, and the preprocessing mainly includes interpolation processing and outlier removal; Assumptions: Ignore the real-time changes of geomagnetic parameters and only use the average geomagnetic field value during the quiet period (such as B = 50 μT); assume that the conductivity is laterally uniform within the mesoscopic block and only vertically stratified (i.e., a two-dimensional horizontal stratification model).

[0046] S413: Mesh division 1) Horizontal direction: Divide the target area into several grids at a set spacing (e.g., 1 - 5 km), and the average conductivity of each grid corresponds to a mesoscopic sub-block. 2) Vertical direction: Stratify according to the macroscopic one-dimensional conductivity model, such as 0–1 km (soil layer), 1–5 km (sandstone), 5 km+ (bedrock). S414: For each grid, based on its average conductivity Calculate: Skin depth : , , is the angular frequency, is the frequency (take the dominant frequency of geomagnetic pulsation 0.01 Hz), is the magnetic permeability; Surface electric field intensity : ; Surface electric field intensity represents the induced electric field intensity of the plane wave on the surface, is the amplitude of the geomagnetic field disturbance; S415: Integrate the calculation results of each grid to form the geoelectric field intensity distribution matrix of the target area.

[0047] It should be noted that the mesoscopic sub-block is usually the constituent unit of the mesoscopic block, and the two belong to an inclusion relationship in terms of scale level. The mesoscopic block can be further divided into several sub-units with different electrical or lithological properties inside. Example: A certain mesoscopic block is "a depression zone in the North China Plain", and its interior can include sub-blocks such as clay sedimentation areas and sandstone lens areas.

[0048] The specific implementation steps of S420 are as follows: S421: Determine the microscopic block, input the electrical parameters of the microscopic block and use the geoelectric field distribution of the target area as the peripheral background; S422: Based on Maxwell's equations, solve the geoelectric field distribution in the local area of the microscopic block to obtain the geoelectric field intensity, gradient distribution in the key area and identify the electric field mutation points.

[0049] The specific implementation steps of S430 are as follows: S431: Electrical parameter fusion: 1) Macroscopic block: Import the one-dimensional conductivity model, such as σ = 0.05 S / m for the sandstone-shale interbed of 0–10 km, and σ = 0.01 S / m for the bedrock of 10–30 km; 2) Mesoscopic block: Superimpose the anisotropic conductivity tensor (AMT inversion), such as σ_h = 0.8 S / m and σ_v = 0.2 S / m for a certain fault zone; 3) Micro-block: A high-resolution conductivity body embedded with ERT / CSAMT; for example, a karst cave with σ = 10 S / m, three-dimensional coordinates x = 1000–1500 m, y = 2000–2500 m, z = 50–150 m; the grid resolution of the micro-block is set to 10 m. Under this setting, 50 grids can be divided in the x direction ((1500 - 1000) ÷ 10 = 50), 50 grids in the y direction, and 10 grids in the z direction. Each grid has a size of 10 m × 10 m × 10 m, which can finely depict the conductivity distribution characteristics of the karst cave in this area.

[0050] 4) Data verification: Verify the boundaries of micro-anomalies through ground penetrating radar data to ensure the coincidence of the multi-scale parameter space, such as the error being less than 5%.

[0051] S432: Real-time geomagnetic data: Synchronously obtain data from the global geomagnetic network to obtain the real-time values (accuracy 0.1 nT) of the three components of the geomagnetic field ( , , ), as well as the magnetic storm indices (Kp, Dst) and the rate of change (dB / dt), with a time resolution of 1 minute); preprocess the real-time geomagnetic data, such as denoising and extracting the disturbance component; S433: Divide the target area with a three-dimensional grid, assign conductivity and permeability (default is the permeability of vacuum) to each grid; calculate the induced geoelectric field based on Maxwell's equations, and use the finite element method or the fast marching method to solve the electric field strength and direction of each grid.

[0052] The resolution of each scale grid can refer to Table 1 below: Table 1: Resolution of each scale grid It should be noted that due to the uncontrollability of the scale of the anomaly, the horizontal and vertical resolutions of the micro-block can be set according to the estimated size of the anomaly, such as determining the resolution based on 1 / 5–1 / 10 of the estimated size of the anomaly.

[0053] The characteristic parameters extracted by S440 are as shown in Table 2 below: Table 2: Examples of extracted characteristic parameters After normalizing the above-extracted characteristic parameters, they are spliced into a feature vector according to the scale layer. Inputting this feature vector into the trained machine learning algorithm can obtain the predicted GIC current value. Taking the characteristic parameters in the above table as an example, the input vector is .

[0054] The training of the machine learning algorithm can refer to the following steps: S510: Using the multi-scale block electrical property parameter library and real-time geomagnetic data, calculate the geoelectric field through electromagnetic models (such as plane wave model, 1D / 3D magnetotelluric model), and extract different-scale geoelectric field characteristic parameters (such as electric field strength, gradient, direction, etc.) as input feature X, and the GIC observation value as the target variable (Y). S520: After preprocessing the data obtained in S510, perform data partitioning to obtain a training set, a test set, and a validation set; train, test, and validate the gradient boosting tree (XGBoost / LightGBM) or random forest (RandomForest) through the training set, test set, and validation set to obtain a trained GIC prediction model.

[0055] Please refer to Figure 2 , in another preferred embodiment, the characteristic parameters can also be combined with the power grid topology parameters and spliced to generate input variables, such as: Normalize the power grid topology parameters and different-scale electrical property parameters, and splice the processed different-scale electrical property parameters with the power grid topology parameters into an input vector; Input the input vector into the trained machine learning algorithm to obtain the predicted GIC.

[0056] When introducing the power grid topology parameters, it is necessary to establish the spatial mapping relationship between the power grid nodes (substations, transformers) and the geoelectric field grid, which can be realized by GIS spatial connection for coordinate matching. Specifically, for example: convert the longitude and latitude coordinates (WGS84 coordinate system) of the power grid nodes into the projection coordinate system of the geoelectric field grid (such as UTM zone); use GIS buffer analysis (such as 1km radius) to determine the geoelectric field grid corresponding to each power grid node (take the weighted average when there may be multiple corresponding grids).

[0057] Simplify the transmission lines and transformers into DC equivalent circuits, and calculate the equivalent impedance of the nodes to the ground; splice the grid topology parameters such as node equivalent impedance, line projection length, and grounding resistance with the characteristic parameters into a vector, and input the vector into the trained machine learning algorithm.

[0058] It should be noted that if there are categorical variables in the power grid topology parameters, they need to be encoded, and at the same time, the power grid topology parameters and electrical property parameters, etc. need to be spatially registered. Moreover, during the training process of the machine learning algorithm, the input vector also needs to introduce the power grid topology parameters. The core of introducing the power grid structure parameters is to combine physical prior knowledge with the data-driven model to improve the accuracy and interpretability of GIC prediction through topological features, electrical parameters, and spatial associations.

[0059] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for calculating the geomagnetic disturbance geoelectric field based on the block geoelectric structure, characterized in that, Including: Determine the target area, and divide the target area into several macro-blocks according to the geological map and administrative map covering the target area; Use the LMT detection method to invert the one-dimensional conductivity model of several macro-blocks; Use the AMT detection method to obtain the three-dimensional electrical property structure model of several macro-blocks; combine the three-dimensional electrical property structure model with the geological map to identify secondary geological units, and divide the macro-blocks into several meso-blocks according to the identification results; among them, the secondary geological units include large mining areas and specific geological structure belts; Determine the key areas in the meso-blocks, and use the ERT and CSAMT detection methods to obtain the abnormal bodies underground in the key areas; divide the meso-blocks based on the abnormal bodies to obtain several micro-blocks; among them, the key areas include ultra-high voltage substations, and the abnormal bodies include high-conductivity / low-resistivity abnormal bodies; Obtain the geoelectric field distribution of the target area based on the electrical property parameters of the macro-blocks and meso-blocks; obtain the geoelectric field distribution of the key areas based on the electrical property parameters of the micro-blocks; calculate the geoelectric field using the geomagnetic model based on the electrical property parameters of the macro-blocks, meso-blocks, micro-blocks and real-time geomagnetic data; Extract characteristic parameters, as well as electrical property parameters at different scales, from the geoelectric fields of the target area and key areas and the geoelectric field calculated by the geomagnetic model; integrate the characteristic parameters and electrical property parameters into a vector, and input the vector into a machine learning algorithm to predict GIC.

2. The geomagnetic disturbance geoelectric field calculation method based on the block large-scale electrical structure according to claim 1, characterized in that After dividing the target area into several macro-blocks, perform calibration and adjustment on several macro-blocks, including: Calculate the apparent resistivity based on the one-dimensional conductivity model obtained by inversion, and convert the conductivity data into apparent resistivity data; where the formula for calculating the apparent resistivity is: , is the apparent resistivity, is the conductivity; Use the finite difference method to calculate the apparent resistivity gradient at the boundary between adjacent macro-blocks; compare the apparent resistivity gradient with the apparent resistivity gradient threshold, and adjust the boundary according to the comparison result.

3. A geomagnetic disturbance geoelectric field calculation method based on the block large-scale electrical structure according to claim 1, characterized in that, After dividing the macro-blocks into several meso-blocks, perform calibration and adjustment on several meso-blocks, including: Statistically analyze the conductivity data for each mesoscopic block and calculate the coefficient of variation of conductivity according to statistical methods. The calculation formula for the coefficient of variation of conductivity is as follows: , is the standard deviation of conductivity within the mesoscopic block, is the mean value of conductivity in the mesoscopic block. Compare the conductivity variation coefficient with the set conductivity variation coefficient threshold, and adjust the boundary of the meso-block according to the comparison result.

4. The geomagnetic disturbance geoelectric field calculation method based on the block large-scale electrical structure according to claim 1, wherein Obtain the geoelectric field distribution of the target area based on the electrical property parameters of the macro-blocks and meso-blocks, including: Obtain the electrical property parameters of the macro-blocks and meso-blocks; among them, the electrical property parameters of the macro-blocks include the one-dimensional conductivity model and regional geological structure information, and the electrical property parameters of the meso-blocks include the anisotropic conductivity tensor and the average wave impedance of the block; Divide the target area into several grids in the horizontal direction, and layer in the vertical direction according to the one-dimensional conductivity model of the macro-blocks; among them, each grid is associated with an average conductivity; Calculate the skin depth and surface electric field intensity based on its average conductivity, and integrate the calculation results of each grid to form the geoelectric field intensity distribution matrix of the target area.

5. The geomagnetic disturbance geoelectric field calculation method based on the block large-scale electrical structure according to claim 4, wherein The calculation formula for the skin depth is as follows: , , is the angular frequency, is the frequency, with a value of 0.01 Hz, is the magnetic permeability.

6. The geomagnetic disturbance geoelectric field calculation method based on the block large-scale electrical structure according to claim 4, characterized in that, The calculation formula for the surface electric field intensity is as follows: ; the surface electric field intensity represents the induced electric field intensity of the plane wave on the surface, is the amplitude of the geomagnetic field disturbance.

7. A method for calculating the geomagnetic disturbance geoelectric field based on the block geoelectric structure according to claim 1, characterized in that, Obtain the geoelectric field distribution of the key areas based on the electrical property parameters of the micro-blocks, including: Determine the micro-blocks, input the electrical property parameters of the micro-blocks and use the geoelectric field distribution of the target area as the peripheral background; Based on Maxwell's equations, solve the geoelectric field distribution in the local area of the micro-blocks to obtain the geoelectric field intensity, gradient distribution and identify the electric field mutation points in the key areas.

8. A geomagnetic disturbance geoelectric field calculation method based on the block large-scale electrical structure according to claim 1, characterized in that Calculate the geoelectric field using the geomagnetic model, including: Extract the electrical parameters of macro blocks, meso blocks, and micro blocks, as well as real-time geomagnetic data; Divide the target area with a three-dimensional grid, and assign conductivity and permeability to each grid; calculate the induced geoelectric field based on Maxwell's equations, and use the finite element method to solve the electric field strength and direction of each grid.

9. A geomagnetic disturbance geoelectric field calculation method based on a block large-scale electrical structure according to claim 1, characterized in that, Integrate the characteristic parameters and electrical parameters into a vector, and input the vector into a machine learning algorithm to predict GIC, including: Extract the characteristic parameters and electrical parameters of each grid, and after preprocessing, splice them into a feature vector according to the scale layer by layer; among them, the preprocessing includes normalization processing and denoising processing; Input the feature vector into the trained machine learning algorithm to obtain the GIC value; among them, the machine learning algorithm includes gradient boosting tree or random forest.

10. A geomagnetic disturbance geoelectric field calculation method based on the block earth electrical property structure according to claim 1, characterized in that, The training of the machine learning algorithm includes: Use the multi-scale block electrical parameter library and real-time geomagnetic data to calculate the geoelectric field through the electromagnetic model, extract the characteristic parameters of the geoelectric field at different scales as the input feature X, and the GIC observation value as the target variable; Perform data division to obtain a training set, a test set, and a validation set; train, test, and validate the constructed machine learning algorithm through the training set, the test set, and the validation set to obtain the trained machine learning algorithm.

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