A method for fast imaging of apparent conductivity based on magnetic phase gradient and application thereof

Abstract

The present application relates to a kind of based on the apparent conductivity fast imaging method of magnetic phase gradient and its application, the method combines magnetic phase and electromagnetic field gradient identification ability to underground electrical structure, with the phase gradient of magnetic field Hz component as imaging parameter and carries out imaging, avoids the error interference of magnetic field multiple component acquisition.The present application can be applied to the fast imaging of underground electrical structure;Particularly, the metal mine area of conductivity far greater than dielectric loss.Compared with the traditional apparent resistivity imaging method, the present application carries out imaging by the phase gradient of magnetic field Hz component, shows strong adaptability under different burial depth, electrical characteristics and noise environment, without background field correction in imaging process, avoids the adverse effects of background resistivity calculation error on imaging result, while simplifying the calculation process, reduces the complexity of imaging process, effectively improves the working efficiency and practicality of the method.

Description

A Fast Imaging Method Based on Apparent Conductivity Using Magnetic Phase Gradient and Its Application Technical Field

[0001] This invention belongs to the field of metal exploration technology, and relates to a ground-to-space frequency domain electromagnetic detection method, particularly to a rapid imaging method based on apparent conductivity of magnetic phase gradient and its application. Background Technology

[0002] Frequency-domain electromagnetic methods (FDMA) are geophysical exploration methods used to study the electrical distribution and properties of underground structures. Based on the principle of electromagnetic induction, it obtains information about underground structures by measuring their response to electromagnetic fields of different frequencies. FDMA has wide applications in underground structure detection, mineral resource exploration, hydrogeological surveys, polar research, underground pipeline detection, and other environmental and engineering exploration fields. Currently, there are three main types of FDMA used in geophysical exploration: Controlled-Source Audio-Frequency Magnetotelluric (CSAMT), Airborne Frequency-Domain Electromagnetic (AFEM), and Ground-to-Air Frequency-Domain Electromagnetic (GAFDEM). CSAMT has a high signal-to-noise ratio and strong observation signal, thus offering advantages such as large detection depth and wide detection range; however, it requires the deployment of a transmitting loop and multiple receiving devices on the ground, making it difficult to operate in complex terrain areas, resulting in low efficiency and high cost. AFEM (Aerial Magnetic Field Mechanism) utilizes aircraft-mounted electromagnetic exploration equipment, eliminating the need for personnel to enter the detection area. It can cover large areas in a short time, offering advantages such as wide detection range, high efficiency, and the ability to conduct exploration in complex terrain and extreme environments. However, due to limitations in the high-speed movement of the aircraft platform and measurement altitude, this method has low resolution and poor ability to identify small underground targets. Furthermore, it is susceptible to noise interference from the surface and near-surface, increasing the complexity of data interpretation. Limited by transmitter power and the distance between the transmitting and receiving coils, this method has shallow detection depth and is costly and insecure. GAFDEM (Gas-Oriented Magnetic Field Mechanism) combines the advantages of CSAMT (Central Surface Electromagnetic Detection) and AFEM. It possesses the capability for rapid detection of large-scale, deep underground electrical structures in complex terrain areas, and offers advantages such as high safety and low cost.

[0003] Currently, the instruments used in frequency domain electromagnetic methods (GDMA) have achieved high performance. However, the corresponding data interpretation methods are relatively lagging, severely restricting the practical application and research development of GAFDEM. There are two main methods for interpreting GAFDEM data: inversion methods and rapid imaging methods. Inversion methods iteratively calculate the initial model using an appropriate inversion algorithm, then compare the differences between the forward model data and the measured data, continuously optimizing the forward model parameters to ultimately interpret the subsurface structure. This method has the advantages of high resolution and strong adaptability. However, inversion methods are affected by many factors, such as the selection of the initial model and the geometric configuration of the observation system; moreover, the inversion process consumes a large amount of computational resources, resulting in long computation times and low efficiency. It cannot meet the needs of instrument testing, data quality evaluation, and general geological structure surveys during actual exploration, and is more suitable for data processing after the exploration work is completed. Therefore, rapid imaging methods have become an important means of rapid interpretation of GAFDEM data. It can meet our needs for instrument calibration, data quality monitoring, and real-time interpretation during exploration, and better reflects the actual changes in subsurface electrical structures, providing a reference for the selection of the initial model for subsequent inversion.

[0004] Currently, the main rapid imaging methods are tilt divergence imaging and apparent resistivity imaging. Tilter divergence imaging has significant advantages in terms of resolution and imaging time; however, it cannot accurately identify the upper and lower boundaries of anomalies when used to image underground electrical structures. By comparing the similarities and differences in the response characteristics of tilt divergence in natural field sources and orthogonal electric field sources, it was found that the deployment of orthogonal field sources improves the detection effect of low resistivity anomalies. However, tilt divergence imaging requires orthogonal excitation sources, and the deployment of ground excitation sources in complex terrain areas is limited in terms of space and location selection, making excitation source deployment more time and resources-intensive, thus reducing the efficiency and usability of tilt divergence imaging.

[0005] Apparent resistivity (ARI) imaging methods require only the z-component data of the magnetic field under a single source excitation to achieve rapid imaging of subsurface structures. However, this method has poor ability to identify anomaly boundaries, only roughly identifying the center location of the anomaly. Due to the shadowing effect, the anomaly response extends to a deeper region, making the size of the anomaly in the imaging profile much larger than its actual size, making it impossible to accurately identify the anomaly outline. Currently, researchers have proposed a GAFDEM rapid imaging method based on spatial magnetic field gradient anomalies. This method can effectively identify the location and outline of anomalies using only GAFDEM data, significantly improving the imaging accuracy of subsurface anomalies. However, this method requires using ARIMAD to calculate the apparent resistivity as the background resistivity, and then calculating the background field data. The background resistivity estimated by this method has a certain gap with the true value, thus leading to the appearance of false anomalies. Summary of the Invention

[0006] To address the problems existing in current rapid imaging methods for GAFDEM data, this invention proposes a rapid imaging method based on apparent conductivity using magnetic phase gradient. This method combines the ability to identify underground electrical structures using magnetic phase and electromagnetic field gradients, using the phase gradient of the Hz component of the magnetic field as the imaging parameter, thus avoiding errors caused by multi-component acquisition of the magnetic field.

[0007] This invention provides a fast CMPG imaging method based on apparent conductivity using magnetic phase gradient, the calculation formula of which is shown below:

[0008]

[0009] Wherein, CMPG(X,Y,Z) represents the apparent conductivity of the magnetic phase gradient. The spatial coordinates corresponding to the magnetic phase gradient conductivity CMPG at different frequencies are (X(i),Y(i),Z(i)), H z (i,j) represents the z-component of the magnetic field at each measuring point on the measuring line at different frequencies, i = 1, 2, ..., p, where p is the total number of measuring points on a measuring line, j = 1, 2, ..., q, where q is the total number of transmission frequencies, μ0 is the free permeability, and ω(j) is the angular velocity corresponding to each transmission frequency, j = 1, 2, ..., q. For the phase of the magnetic field component Hz, Re(H) z (i,j)) represents the real part of the magnetic field component Hz, Im(H z (i,j)) represents the imaginary part of the magnetic field component Hz, r(i) represents the transmitter-receiver distance at the measuring point, and ρ0 represents the background resistivity.

[0010] The present invention provides a rapid imaging method based on apparent conductivity of magnetic phase gradient, which can be applied to rapid imaging of underground electrical structures; especially in metal mining areas where conductivity is much greater than dielectric loss, the propagation of electromagnetic waves is mainly determined by conductivity, and the influence of dielectric constant is negligible.

[0011] This method can accurately depict the background conductivity distribution in the absence of anomalies; compared with the uniform half-space model, when anomalies are present underground, high-value CMPG traps appear at the locations of the anomalies.

[0012] Since the above methods always result in high-value traps at the location of the anomalous body in the imaging results, regardless of whether it is a low-resistivity or high-resistivity anomalous body, it is impossible to directly compare the relationship between the resistivity of the anomalous body and the resistivity of the background. Therefore, this invention further provides an improved imaging method based on apparent conductivity using magnetic phase gradient (ICMPG), defined as follows:

[0013]

[0014] ICMPG(X,Y,Z) represents the apparent conductivity of the improved magnetic phase gradient. The spatial coordinates of the magnetic phase gradient conductivity CMPG at different frequencies are (X(i),Y(i),Z(i)), μ0 is the free permeability, ω(j) is the angular velocity corresponding to each transmission frequency, j=1,2……q, and q is the total number of transmission frequencies. Let r(i) be the phase of the magnetic field component Hz, r(i) be the transmitter-receiver distance between the measuring points, and i = 1, 2, ..., p, where p is the total number of measuring points on a measuring line. For the phase of the magnetic field component Hz, Re(H) z (i,j)) represents the real part of the magnetic field component Hz, Im(H z (i,j)) represents the imaginary part of the magnetic field component Hz, r(i) represents the transmitter-receiver distance at the measuring point, and ρ0 represents the background resistivity.

[0015] The present invention provides an improved imaging method based on apparent conductivity of magnetic phase gradient, which can be applied to rapid imaging of underground electrical structures; especially in metal mining areas where conductivity is much greater than dielectric loss, the propagation of electromagnetic waves is mainly determined by conductivity, and the influence of dielectric constant is negligible.

[0016] When the resistivity of an anomalous body is lower than that of the background resistivity, the location of the anomalous body appears as a high-value trap, and vice versa. An improved imaging method based on apparent conductivity using magnetic phase gradients leverages the difference in sign of the magnetic phase gradient: when the resistivity of the anomalous body is lower than that of the background, the magnetic phase gradient is positive, and vice versa. Therefore, this improved imaging method based on apparent conductivity using magnetic phase gradients not only indicates the location and boundary contour of the anomalous body but also further reflects the relationship between the resistivity of the anomalous body and the background resistivity, providing a more intuitive understanding of the electrical characteristics of the anomalous body.

[0017] This invention also provides a method for rapid imaging of underground electrical structures, comprising the following steps:

[0018] A long conductor source is laid on the ground as an excitation source. An alternating current of a fixed frequency is passed into the long conductor source to generate electromagnetic waves. When the electromagnetic waves are incident on the ground, eddy currents are induced in the ground. These eddy currents generate magnetic fields, which in turn affect the magnetic field data collected by the magnetic field data acquisition device.

[0019] The magnetic field generated by eddy currents is related to the electrical conductivity of underground structures. Therefore, by detecting the phase data of the magnetic field generated by eddy currents, the underground electrical structure can be detected. A drone carrying a magnetic field data acquisition device is used to fly along a preset survey line, maintaining a certain safe altitude above the ground. The magnetic field data acquisition device is used to collect the vertical component (Hz) of the magnetic field above the survey line.

[0020] The vertical component of the acquired magnetic field is denoised using wavelet transform and other methods to remove noise from power frequency interference and human interference, resulting in high-quality magnetic field data with a high signal-to-noise ratio. Fourier transform is performed on the magnetic field data to obtain the real and imaginary parts of the frequency point of the detection target, and the magnetic field phase is calculated.

[0021] According to the above-mentioned fast imaging method for apparent conductivity based on magnetic phase gradient, or an improved imaging method for apparent conductivity based on magnetic phase gradient, the apparent conductivity values ​​of underground structures are calculated based on the magnetic field phase gradient; according to the skin depth formula, the skin depth corresponding to each target frequency point is calculated, and combined with spatial information such as latitude and longitude, the underground apparent conductivity distribution matrix is ​​obtained.

[0022] Interpolation methods were used to plot the apparent conductivity distribution matrix to obtain a map of the underground electrical structure.

[0023] The beneficial effects of this invention are:

[0024] This invention provides a fast imaging method for apparent conductivity based on magnetic phase gradient, and an improved imaging method for apparent conductivity based on magnetic phase gradient. Compared with the traditional apparent resistivity imaging method, this invention performs imaging through the phase gradient of the Hz component of the magnetic field. No background field correction is required during the imaging process, avoiding the adverse effects of background resistivity calculation errors on the imaging results. At the same time, it simplifies the calculation process, reduces the complexity of the imaging process, and effectively improves the working efficiency and practicality of the method.

[0025] Through imaging simulation verification of different electrical anomalies, the fast imaging method based on apparent conductivity using magnetic phase gradient and the improved imaging method based on apparent conductivity using magnetic phase gradient provided by this invention demonstrate strong adaptability under different burial depths, electrical characteristics, and noise environments. In particular, the improved imaging method based on apparent conductivity using magnetic phase gradient, ICMPG, can effectively identify the boundaries and locations of anomalies in both single anomaly models and complex models with multiple anomalies, and has high imaging accuracy. In noisy environments, although Gaussian white noise interference affects the clarity of the boundaries, the location and electrical characteristics of the anomalies can still be accurately identified, demonstrating the excellent noise resistance of this method.

[0026] In practical geological exploration applications, the method of this invention was successfully compared with actual geological background data of the Gangcobalt (copper-nickel) deposit in Linjiang City, Baishan City, Jilin Province, verifying its effectiveness in actual exploration. The imaging results accurately reflect the location and distribution of the deposit, showing significant advantages, especially in the identification of deep deposits. The ICMPG method provides a new approach for rapid imaging of subsurface electrical structures, particularly suitable for complex terrain and deep exploration environments, providing reliable technical support for geophysical exploration. Future research can further optimize imaging accuracy and expand its application potential in mineral resource exploration, environmental surveys, and other fields. Attached Figure Description

[0027] Figure 1 shows the imaging model of magnetic phase gradient conductivity (CMPG) in Example 1;

[0028] Wherein: (a) 3D schematic diagram; (b) yz cross-sectional view; (c) xy cross-sectional view;

[0029] Figure 2 shows the CMPG imaging results of magnetic phase gradient conductivity in Example 1;

[0030] Among them: (a) uniform half-space model; (b) low-resistivity anomaly model; (c) high-resistivity anomaly model;

[0031] Figure 3 shows the results of ICMPG imaging of the improved magnetic phase gradient conductivity in Example 2;

[0032] Among them: (a) ICMPG imaging results of the low-resistivity anomaly model; (b) ICMPG imaging results of the high-resistivity anomaly model;

[0033] Figure 4 shows the imaging model of ICMPG in Example 2;

[0034] Wherein: (a) 3D schematic diagram; (b) yz cross-sectional view; (c) xy cross-sectional view;

[0035] Figure 5 shows a three-dimensional slice of the ICMPG imaging results of the low-resistivity anomaly model in Example 3.

[0036] Where: (a) x = 0m; (b) x = 100m; (c) x = 200m; (d) x = 300m;

[0037] Figure 6 shows a three-dimensional slice of the ICMPG imaging results of the high-resistivity anomaly model in Example 3;

[0038] Where: (a) x = 0m; (b) x = 100m; (c) x = 200m; (d) x = 300m;

[0039] Figure 7 shows the imaging results of different resistivity anomaly models in Example 4;

[0040] Among them: (a) low resistance anomaly ρ1 = 50Ωm model; (b) high resistance anomaly ρ1 = 200Ωm model;

[0041] Figure 8 shows imaging models of different burial depths of anomalies in Example 5;

[0042] Among them: (a) a three-dimensional schematic diagram; (b) yz profiles of different burial depths of anomalies; (c) xy profiles of different burial depths of anomalies;

[0043] Figure 9 shows the imaging results of different burial depth models of anomalies in Example 5;

[0044] Among them: (a) imaging results of low-resistivity anomalies buried at a depth of 200m; (b) imaging results of high-resistivity anomalies buried at a depth of 200m; (c) imaging results of low-resistivity anomalies buried at a depth of 600m; (d) imaging results of high-resistivity anomalies buried at a depth of 600m.

[0045] Figure 10 shows the multi-anomaly model in Example 6;

[0046] Wherein: (a) 3D schematic diagram; (b) yz cross-sectional view; (c) xy cross-sectional view;

[0047] Figure 11 shows the imaging results of the multi-anomaly model in Example 6;

[0048] Among them: (a) dual low-resistivity anomalies ρ1=10Ωm, ρ2=10Ωm; (b) dual high-resistivity anomalies ρ1=1000Ωm, ρ2=1000Ωm; (c) near-source low-resistivity anomalies and far-source high-resistivity anomalies ρ1=10Ωm, ρ2=1000Ωm; (d) near-source high-resistivity anomalies and far-source low-resistivity anomalies ρ1=1000Ωm, ρ2=10Ωm;

[0049] Figure 12 shows the data imaging results under simulated noise environment in Example 7; (a) low-resistivity anomaly model; (b) high-resistivity anomaly model;

[0050] Figure 13 is a schematic diagram of the detection experiment in Example 8;

[0051] Figure 14 shows the geological ICMPG imaging results of the Shansonggang cobalt (copper-nickel) deposit in Example 8. Detailed Implementation

[0052] Example 1

[0053] During electromagnetic wave propagation, the Earth's interior attenuates the energy of the waves. The degree of attenuation is determined by the electrical parameters of the underground structures, such as dielectric constant and conductivity. Therefore, the data can be processed based on the observed attenuation information to understand the distribution of electrical parameters of the underground structures.

[0054] In the frequency domain, Maxwell's equations can be written as:

[0055]

[0056] Where ω is the angular frequency of the electromagnetic wave, μ is the magnetic permeability of the medium, σ is the electrical conductivity of the medium, ε is the dielectric constant of the medium, E is the electric field strength, H is the magnetic field strength, and i is the imaginary unit.

[0057] Through the Take the curl and use the vector identity. In a homogeneous medium, the following can be obtained:

[0058]

[0059] Will Substituting the values, we obtain the wave equation for the electric field intensity E:

[0060]

[0061] After simplification, we get:

[0062]

[0063] Similarly, the wave equation for the magnetic field is obtained as follows:

[0064]

[0065] In the far region, assuming the electromagnetic wave propagates along the z-direction as a plane wave, the solution for the magnetic field Hz is:

[0066] Hz = H0e ikz (6)

[0067] Where k is the complex wave number, H0 is the initial amplitude of the magnetic field; e ikz Let z be a complex number, where z is a spatial coordinate representing the distance along the propagation direction.

[0068] Substituting the plane wave solution into the wave equation From this, we can obtain:

[0069]

[0070] therefore:

[0071] k 2 =μ(σ+iωε)ω 2 (8)

[0072] Then the expression for k is:

[0073]

[0074] Expressing σ+iωε in polar coordinates, we have:

[0075]

[0076] Where e is the base of the natural logarithm;

[0077]

[0078] The complex wave number k is then expressed as:

[0079]

[0080] Using the formula for the square root of a complex number:

[0081]

[0082] Where r is the distance between the measuring point and the transmitter;

[0083] The wavenumber is divided into a real part (attenuation constant α) and an imaginary part (phase constant β):

[0084]

[0085] The attenuation constant (which describes the attenuation of the electromagnetic wave amplitude with distance) is:

[0086]

[0087] The phase constant (which describes how the phase of an electromagnetic wave changes with distance) is:

[0088]

[0089] The phase constant can also be written as:

[0090]

[0091] Where μ0 is the free permeability, ε0 ​​is the free permittivity, and ε r Let σ be the relative permittivity of the dielectric. When the conductivity is much greater than the dielectric loss, i.e., σ >> ωε0ε r The wavenumber is mainly determined by conductivity, therefore:

[0092]

[0093] In metal mining areas, the electrical conductivity of metal deposits is very high, while the dielectric term in the geophysical exploration frequency range is very small; in this case, σ>>ωε0ε r This is basically true. At this point, the propagation of electromagnetic waves is mainly determined by conductivity, and the influence of dielectric constant can be ignored.

[0094] Based on the above formula, the phase relationship between the conductivity of the electromagnetic wave propagation medium and the magnetic field component Hz can be derived:

[0095]

[0096] in, The phase of the magnetic field component Hz;

[0097] The formula for calculating conductivity imaging based on magnetic phase gradient is defined as follows:

[0098]

[0099] Among them, H z (i,j) represents the z-component of the magnetic field at each measuring point on the measuring line at different frequencies (i = 1, 2...p, p is the total number of measuring points on a measuring line, j = 1, 2...q, q is the total number of transmission frequencies), ω(j) represents the angular velocity corresponding to each transmission frequency (j = 1, 2...q, q is the total number of transmission frequencies), and μ0 is the vacuum permeability. When performing underground structure imaging, the spatial coordinates of the CMPG corresponding to the measuring points at different frequencies are (X(i), Y(i), Z(i)), and ρ0 is the background resistivity. For the phase of the magnetic field component Hz, Re(H) z (i,j)) represents the real part of the magnetic field component Hz, Im(H z (i,j)) represents the imaginary part of the magnetic field component Hz, and r(i) represents the distance between the measuring point and the transmitter.

[0100] The finite element method (FEM) was used to perform forward modeling on the model shown in Figure 1 to obtain GAFDEM data. The blue line segment represents a long conductor electromagnetic emission source located at x = 0m, y = -250m to 250m, with a length of 500m and a center at the origin. The emission current amplitude is 40A, and the emission frequency is an equally spaced frequency of 32Hz-1024Hz, with an interval of 16Hz. The green cuboid represents an anomaly, located as shown in the figure, with dimensions of 400m * 400m * 200m, a center at x = 0m, y = 5000m, and a burial depth of 400m. The red dashed line represents the survey line location, located at x = 0m, y = 4000m to 6000m, with a length of 2000m. The earth resistivity, i.e., the background resistivity, is 100Ωm. To ensure that the electromagnetic wave can be considered a perpendicularly incident plane wave, the transmitter-receiver distance is 4800m. The resistivity of the anomaly is divided into two cases: low resistivity and high resistivity. The low-resistivity anomaly has ρ1 = 10 Ωm, and the high-resistivity anomaly has ρ1 = 1000 Ωm. Since magnetic field data is collected in the air, a straight line with x = 0 m, y = 4000 m to 6000 m, and z = 100 m is used as the measurement line. Phase information of the Hz component of the magnetic field at different transmission frequencies is extracted using a 10 m step size. CMPG is then used to image the uniform half-space model and the model data shown in Figure 1. The imaging result of the yz profile at x = 0 is shown in Figure 2. The white dashed box indicates the location of the anomaly.

[0101] As shown in Figure 2, the CMPG imaging results of the uniform half-space model show a consistent conductivity distribution with no anomalous regions. This is because the model is free from the influence of underground anomalies, verifying that the method can accurately depict the background conductivity distribution even in the absence of anomalies.

[0102] Compared to the uniform half-space model, when anomalies exist underground, high CMPG values ​​appear as traps at the anomaly's location. While there is a slight deviation between the trap location and the actual anomaly location, it still effectively indicates the anomaly region and its boundaries. Low-resistivity anomalies correspond to even higher CMPG values ​​than high-resistivity anomalies.

[0103] Example 2

[0104] Since both low-resistivity and high-resistivity anomalies appear as high-value closures in the imaging results, it is impossible to directly compare the relationship between the anomaly resistivity and the background resistivity. The study found that the magnetic phase gradient in the imaging method of Example 1 has a positive or negative value depending on whether the anomaly resistivity is higher or lower than the background resistivity. When the anomaly resistivity is lower than the background resistivity, the magnetic phase gradient is positive; when the anomaly resistivity is higher than the background resistivity, the magnetic phase gradient is negative.

[0105] Therefore, Example 2 proposes an improved imaging method based on CMPG, ICMPG, with the following formula defined:

[0106]

[0107] The model shown in Figure 1 was imaged using ICMPG, and the imaging result of the yz profile at x = 0 m is shown in Figure 3. It can be seen from the figure that when the resistivity of the anomalous body is lower than the background resistivity, the location of the anomalous body is characterized by a high-value trap, and vice versa. The improved magnetic phase gradient apparent conductivity imaging method is based on the difference between positive and negative magnetic phase gradients; that is, when the resistivity of the anomalous body is lower than the background resistivity, the magnetic phase gradient is positive, and vice versa. Therefore, ICMPG can not only indicate the location and boundary contour of the anomalous body, but also further reflect the relationship between the resistivity of the anomalous body and the background resistivity, providing a more intuitive understanding of the electrical characteristics of the anomalous body.

[0108] Example 3

[0109] To verify the three-dimensional imaging capability of this imaging method, the influence of the relative position of the survey line and the anomaly on the imaging results was analyzed. Magnetic field component data of multiple survey lines in the model shown in Figure 4 were extracted. The red dashed lines represent the survey line positions. With y = 4000m to 6000m and z = 100m remaining constant, survey line data at x = 0m, 100m, 200m, and 300m were extracted respectively. The imaging method of Example 2 was then applied to image each survey line data, and the results are shown in Figures 5 and 6. Figure 5 shows the imaging results of the low-resistivity anomaly model, and Figure 6 shows the imaging results of the high-resistivity anomaly model.

[0110] As can be seen from the imaging results in Figures 5 and 6, the imaging method can accurately identify the location and electrical characteristics of both low-resistivity and high-resistivity anomalies. Especially when anomalies are present below the survey line, the imaging results clearly show high- or low-value traps, reflecting the conductivity information of the anomaly. However, when the survey line is at the edge of the anomaly or far from it, the imaging results show blurred traps or smooth backgrounds, verifying the influence of the relative position of the survey line and the anomaly on the imaging results. Furthermore, the good continuity between the slices demonstrates the excellent three-dimensional recognition capability of this imaging method. The imaging results show high stability and consistency under different survey line locations, providing strong support for the three-dimensional imaging of underground electrical structures. These imaging results demonstrate the advantages of this method in three-dimensional imaging, effectively reflecting the location, outline, and electrical characteristics of underground anomalies, and adapting well to different survey line configurations and measurement conditions.

[0111] Example 4

[0112] To verify the ability of ICMPG to identify anomalous objects when their resistivity is close to that of the background, the resistivity of the anomalous objects in the model in Figure 1 was set to values ​​closer to the background resistivity. Specifically, low-resistivity anomalous objects were set to ρ1 = 50 Ωm, and high-resistivity anomalous objects were set to ρ1 = 200 Ωm. The improved CMPG-based imaging method ICMPG from Example 2 was applied to image the model, and the imaging result of the yz profile at x = 0 m is shown in Figure 7.

[0113] As shown in Figure 7, the imaging results demonstrate that the imaging method can still effectively identify the location and main features of anomalies when their resistivity is close to that of the background. Although the boundaries of the anomalies exhibit some blurring and distortion due to low contrast, especially in the edge regions, the overall imaging results still accurately locate the center and approximate outline of the anomalies. This verifies the effectiveness of the imaging method under conditions where the resistivity of the anomaly is close to that of the background, particularly maintaining good imaging capability and stability under low contrast conditions. Therefore, the imaging method proposed in this invention is not only suitable for anomaly detection under high contrast conditions but also maintains effectiveness under complex conditions where the resistivity of the anomaly is close to that of the background, ensuring its potential application in practical exploration.

[0114] Example 5

[0115] To verify the effectiveness of the proposed rapid imaging method for electrical anomalies at different burial depths, imaging models of different anomaly depths were established, as shown in Figure 8. The green cuboids represent anomalies at burial depths of 200m and 600m, with a ground resistivity (background resistivity) of 100Ωm. The resistivity of the anomalies is categorized into low-resistivity and high-resistivity cases, where low-resistivity anomaly ρ1 = 10Ωm and high-resistivity anomaly ρ1 = 1000Ωm. The ICMPG method from Example 2 was used to image the model in Figure 8, and the imaging results of the yz profile at x = 0m are shown in Figure 9.

[0116] As can be seen from the imaging results in Figure 9, when the burial depth of the anomaly is 200m, the trap position of the low-resistivity anomaly is slightly lower and cannot be completely closed, which is due to the limitation of the detection depth. Since the emission frequency range of the excitation source is 32Hz-1024Hzs, according to formula (25), the corresponding detection depth range is 222m-1257m. Therefore, underground electrical structures shallower than 222m cannot be detected, resulting in the upper edge of the trap not being closed in the imaging results. The imaging of the high-resistivity anomaly is clearer, and the position of the anomaly is accurate. As the burial depth increases to 600m, the outline of the anomaly in the imaging results gradually becomes blurred, and the imaging accuracy decreases. However, even under the condition of deeper burial, the imaging method can still effectively identify the position of the anomaly and demonstrate good depth detection capability. The rapid imaging method proposed in this invention still shows strong adaptability to electrical anomalies at different burial depths. It can overcome the influence of burial depth changes to a certain extent, accurately locate the spatial position of the anomaly, and show good depth detection capability. Especially under low contrast and deeper burial conditions, it can still effectively identify the existence of the anomaly.

[0117] Example 6

[0118] In actual exploration, multiple electrical anomalies may exist simultaneously underground. Therefore, it is necessary to verify the ICMPG's ability to identify multiple electrical anomalies when they coexist. First, a multi-anomaly model as shown in Figure 10 is established. The resistivity of the anomalies is divided into the following four cases: 1. Double low-resistivity anomaly ρ1=10Ωm, ρ2=10Ωm; 2. Double high-resistivity anomaly ρ1=1000Ωm, ρ2=1000Ωm; 3. Near-source low-resistivity anomaly and far-source high-resistivity anomaly ρ1=10Ωm, ρ2=1000Ωm; 4. Near-source high-resistivity anomaly and far-source low-resistivity anomaly ρ1=1000Ωm, ρ2=10Ωm. The ICMPG in Example 2 is used to image the multi-anomaly model in Figure 10. The imaging result of the yz profile at x=0m is shown in Figure 11.

[0119] As shown in Figure 11, the ICMPG method effectively identifies the location and contour of each anomaly when multiple electrical anomalies coexist. For low-resistivity anomalies, ICMPG's identification ability is better than that for high-resistivity anomalies, exhibiting a clearer high-value trap. However, when multiple anomalies coexist, especially those with significant resistivity differences, the imaging effect is affected by coupling and mutual influence, leading to singularities or reduced imaging accuracy in certain areas. Particularly in different electrical combinations of near-source and far-source anomalies, ICMPG can identify the location of anomalies well, but small-scale singularities or distortions may occur during imaging. Compared to a single anomaly model, ICMPG's imaging effect in multi-anomaly models is slightly worse, but it can still effectively identify the location, contour, and electrical characteristics of anomalies, verifying the good identification ability of the ICMPG method in multi-anomaly situations. This performance demonstrates that the ICMPG method can still provide stable and practical imaging results in actual detection, especially in complex environments where multiple anomalies coexist.

[0120] Example 7

[0121] In actual exploration, human noise and electromagnetic interference in the experimental environment can affect data acquisition, introducing environmental noise into the data. Furthermore, since the magnetic field data acquisition device is mounted on a flight platform, motion noise is also introduced into the acquired magnetic field data. To verify the noise immunity of the ICMPG, this embodiment simulates noisy data from actual exploration, adding 10% Gaussian white noise to the magnetic field data and imaging the model in Figure 1. The imaging result of the yz profile at x = 0 m is shown in Figure 12.

[0122] As shown in Figure 12, despite the addition of 10% Gaussian white noise, the ICMPG method can still effectively identify the location and electrical characteristics of anomalies in noisy environments. Although noise causes slight distortion of the trap boundaries in the imaging results, the high- or low-value traps of the anomalies are still clearly visible and accurately located. This verifies the strong noise resistance of the ICMPG method in noisy environments, enabling its application in complex exploration environments. This characteristic provides theoretical support and practical assurance for the application of the ICMPG method in actual geophysical exploration, especially under actual measurement conditions with electromagnetic interference and human noise, ensuring the stability and reliability of the imaging results. Therefore, this method can not only provide accurate imaging results under ideal conditions, but also maintain good performance in environments with severe noise interference, adapting to more complex application scenarios.

[0123] Example 8

[0124] To further verify the practical effectiveness of the ICMPG imaging method, this embodiment uses the Gangcobalt (copper-nickel) deposit in Linjiang City, Baishan City, Jilin Province, China as an example. BIGEMAP software was used to acquire actual topographic data of the deposit area, and an equivalent model was established in COMSOL. The surrounding rocks of the deposit in this area are mainly composed of phyllite schist, phyllite metamorphic siltstone, and thin-layered marble, exhibiting high resistivity. The cobalt (copper-nickel) deposit, on the other hand, is mainly composed of metallic elements and exhibits low resistivity. Based on this geological information, we utilized the low resistivity characteristics of the cobalt-copper-nickel ore body for ICMPG imaging. Based on the agreement between the imaging results and the actual geological data, we evaluated the feasibility and effectiveness of the ICMPG method in actual field exploration.

[0125] Based on the actual detection conditions, the excitation source and survey lines were set up, and the actual terrain simulation is shown in Figure 13. In the figure, the red line segment represents the location of the excitation source, which is approximately 1000m long; the yellow dashed line represents the location of the survey line, which is approximately 2300m long and perpendicular to the direction of the excitation source. The distance of the survey line ranges from approximately 3.3km to 5.7km from the midpoint of the excitation source, with a total of 249 survey points and a spacing of 10m between the points. To simulate actual detection conditions, the excitation source emission current was 20A, and the frequencies were set to 32Hz, 64Hz, 96Hz, 128Hz, 160Hz, 256Hz, 512Hz, 1024Hz, and 2048Hz.

[0126] Figure 14 shows a vertical profile of the survey line, with the horizontal axis representing the transmit / receive distance at each point along the line and the vertical axis representing altitude. In actual exploration, based on geological data and other physical methods, the resistivity of the land in this area is approximately 1000 Ωm. Two cobalt (copper-nickel) deposits exist below the survey line. Deposit A ​​is located between 4400 m and 5200 m transmit / receive distances, with a vertical dimension of 200 m and a burial depth between 400 m and 600 m above sea level; Deposit B is located between 5350 m and 5550 m transmit / receive distances, with a vertical dimension of 200 m and a burial depth of approximately 200 m above sea level. The resistivity of the cobalt (copper-nickel) ore body is set to 10⁻³⁶ Ωm. In this embodiment, atmospheric magnetic field data was acquired at an altitude of approximately 1000 m and converted to apparent depth based on frequency. ICMPG imaging was then used to image the topography and electrical structure, as shown in Figure 14.

[0127] Figure 14 shows the ICMPG imaging results of the Shansonggang cobalt (copper-nickel) deposit area, where the black dashed box indicates the location of the low-resistivity deposit. The horizontal axis represents the transmit / receive distance, the vertical axis represents the altitude, and the color bars represent the variation in apparent conductivity, with red areas representing higher conductivity (low resistivity) and blue to purple areas representing lower conductivity (high resistivity). The figure shows that within a transmit / receive distance of approximately 4400m–5200m and an altitude of approximately 400m–600m, the imaging results show a significant low-resistivity area, whose location closely matches the actual location of deposit A, indicating that the ICMPG method accurately reflects the distribution characteristics of deposit A. In the area between a transmit / receive distance of approximately 5350m–5550m and an altitude of approximately 200m, a smaller low-resistivity area appears in the imaging results, which also corresponds to the actual location of deposit B. This further verifies the reliability of the detection method. Most of the area below the survey line exhibits a uniform high-conductivity background, consistent with the stable high-resistivity surrounding rock characteristics of the region.

[0128] Overall, the imaging results in Figure 14 are highly consistent with the actual geological background. The location and extent of the low resistivity anomaly area accurately reflect the distribution characteristics of the ore deposit, verifying the effectiveness and feasibility of the ICMPG method in the electrical detection of ore deposits. This method shows promising application prospects in complex real-world environments and provides reliable data support for subsequent research.

Claims

1. A fast imaging method based on apparent conductivity using magnetic phase gradient, characterized in that: The calculation formula is as follows: Where CMPG(X,Y,Z) is the apparent conductivity of the magnetic phase gradient, and the spatial coordinates corresponding to the magnetic phase gradient conductivity of the measurement point at different frequencies are (x(i),y(i),z(i)), H z (i,j) represents the z-component of the magnetic field at each measuring point on the measuring line at different frequencies, i = 1, 2, ..., p, where p is the total number of measuring points on a measuring line, j = 1, 2, ..., q, where q is the total number of transmission frequencies, μ0 is the free permeability, and ω(j) is the angular velocity corresponding to each transmission frequency, j = 1, 2, ..., q. For the phase of the magnetic field component Hz, Re(H) z (i,j)) represents the real part of the magnetic field component Hz, Im(H z (i,j)) represents the imaginary part of the magnetic field component Hz, r(i) represents the transmitter-receiver distance at the measuring point, and ρ0 represents the background resistivity.

2. The fast imaging method based on apparent conductivity according to claim 1, characterized in that: It is applied to rapid imaging of underground electrical structures.

3. A rapid imaging method based on apparent conductivity using magnetic phase gradient according to claim 1 or 2, characterized in that: It can be applied to rapid imaging of underground electrical structures in metal mining areas where conductivity is much greater than dielectric loss, and the influence of dielectric constant is negligible.

4. An improved imaging method based on apparent conductivity using magnetic phase gradient, characterized in that: The calculation formula is as follows: Where ICMPG(X,Y,Z) represents the apparent conductivity of the improved magnetic phase gradient, and the spatial coordinates corresponding to the magnetic phase gradient conductivity of the measurement point at different frequencies are (x(i),y(i),z(i)), μ0 is the free permeability, ω(j) is the angular velocity corresponding to each transmission frequency, j=1,2……q, and q is the total number of transmission frequencies. Let r(i) be the phase of the magnetic field component Hz, r(i) be the transmitter-receiver distance between the measuring points, and i = 1, 2, ..., p, where p is the total number of measuring points on a measuring line. For the phase of the magnetic field component Hz, Re(H) z (i,j)) represents the real part of the magnetic field component Hz, Im(H z (i,j)) represents the imaginary part of the magnetic field component Hz, r(i) represents the transmitter-receiver distance at the measuring point, and ρ0 represents the background resistivity.

5. An improved imaging method based on apparent conductivity using magnetic phase gradient according to claim 4, characterized in that: It is applied to rapid imaging of underground electrical structures.

6. An improved imaging method based on apparent conductivity using magnetic phase gradient according to claim 4 or 5, characterized in that: It can be applied to rapid imaging of underground electrical structures in metal mining areas where conductivity is much greater than dielectric loss, and the influence of dielectric constant is negligible.

7. A rapid imaging method for underground electrical structures, characterized in that: Includes the following steps: A long conductor source is deployed on the ground as an excitation source. A fixed-frequency alternating current is passed through the conductor source to generate electromagnetic waves. These waves strike the earth, inducing eddy currents within the earth, which in turn generate a magnetic field. A drone carrying a magnetic field data acquisition device flies along a pre-set survey line, maintaining a safe altitude above the ground. The magnetic field data acquisition device collects the vertical component (Hz) of the magnetic field above the survey line. By detecting the phase data of the magnetic field generated by the eddy currents, the underground electrical structure can be detected. Noise reduction methods are used to remove power frequency interference and human interference from the collected vertical component of the magnetic field, resulting in high-quality magnetic field data. Perform a Fourier transform on the magnetic field data to obtain the real and imaginary parts of the frequency of the target detection point, and calculate the magnetic field phase. According to any one of claims 1-3, a rapid imaging method for apparent conductivity based on magnetic phase gradient, or according to any one of claims 4-6, an improved imaging method for apparent conductivity based on magnetic phase gradient, the apparent conductivity values ​​of the underground structure are calculated based on the magnetic field phase gradient; according to the skin depth formula, the skin depth corresponding to each target frequency point is calculated, and combined with spatial information, the underground apparent conductivity distribution matrix is ​​obtained; the apparent conductivity distribution matrix is ​​plotted using an interpolation method to obtain an underground electrical structure map.

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