A method for evaluating the MT anomaly response of a rock vein

Abstract

The application provides a method for evaluating the MT anomaly response of a rock vein, comprising the following steps: S1. measuring the resistivity of a rock sample and the resistivity of surrounding rock in a target rock vein by using a sample measuring device, and calculating the resistivity of the target rock vein according to the resistivity of the rock sample; S2. constructing a rock vein model and a surrounding rock model; changing the exploration parameters of the rock vein model, and calculating the relationship between the exploration parameters and the anomaly body identification index; S3. calculating the resistivity difference multiple of the rock vein and the surrounding rock according to the respective resistivity of the target rock vein and the surrounding rock; S4. calculating the anomaly body identification index of the target rock vein according to the width of the target rock vein, the resistivity difference multiple, the detection pole distance and the detection frequency of the exploration device, and the relationship between the exploration parameters and the anomaly body identification index; when the anomaly body identification index is greater than an anomaly reference value, the target rock vein is an anomaly body. The method for calculating the anomaly body identification index of the application combines multiple exploration parameters, and is beneficial to providing guidance for MT field work.

Description

Technical Field

[0001] This invention relates to a method for detecting rock veins, specifically a method for evaluating the MT anomaly response of rock veins. Background Technology

[0002] Both surrounding rock and dikes are types of rock. Dikes are rock types with specific morphology and origin, while surrounding rock is a reference rock relative to dikes (or other intrusive / filling geological bodies). Rocks are natural solid mineral aggregates composed of one or more minerals (in rare cases, glassy, ​​such as obsidian) arranged according to certain rules on the Earth's surface or interior. They are the basic material units constituting the Earth's crust and mantle. Dikes refer to geological bodies formed after rock formation, by mineral-containing hydrothermal fluids, magma, etc., intruding and filling along the weak points such as fissures and fractures of the surrounding rock (i.e., the original surrounding rock). They occur in banded or vein-like patterns. Surrounding rock, relative to dikes (or target geological bodies such as rock masses or ore bodies), generally refers to the pre-formed rocks that provide the space for the target geological body.

[0003] In geology, "rock anomalies" refer to rock masses whose material composition, structure, physicochemical properties, etc., deviate significantly from the baseline state of the surrounding normal rocks (i.e., host rocks) within a specific geological environment. These differences are often related to mineralization, tectonic activity, and special geological processes, and are key targets for mineral exploration or the study of geological evolution. Quartz veins and pegmatite veins, as important ore-bearing structures, are usually distributed in bands and are key locations for important metallic minerals such as gold, tungsten, tin, and especially uranium. Effective detection of these veins has significant geological importance and mineral exploration value. In geophysical exploration, there are many methods for finding anomalies, but the accuracy of detected anomalies is generally low when field data acquisition parameters are based on experience or rough estimations. Summary of the Invention

[0004] The purpose of this invention is to provide a method for evaluating the MT anomaly response of rock veins, thereby solving the problem of low accuracy in existing methods for detecting anomalies in rock veins.

[0005] The objective of this invention is achieved in this way:

[0006] A method for evaluating the MT anomaly response of a rock vein includes the following steps:

[0007] S1. Measure the resistivity of the rock sample in the target vein and the resistivity of the surrounding rock using a sample measuring device, and calculate the resistivity of the target vein based on the resistivity of the rock sample.

[0008] S2. Construct a dike model and an associated surrounding rock model; change the exploration parameters of the dike model and calculate the relationship between the exploration parameters of the dike and the anomaly identification index;

[0009] S3. Calculate the resistivity difference factor between the target vein and the surrounding rock based on their respective resistivities;

[0010] S4. Calculate the anomaly identification index of the target dike based on the width of the target dike, the resistivity difference multiple, the detection electrode distance and detection frequency of the exploration device, and the relationship between the exploration parameters and the anomaly identification index; when the anomaly identification index is greater than the anomaly reference value, the target dike is an anomaly.

[0011] Furthermore, the specific method for calculating the resistivity of the target dike is as follows:

[0012] Measure the porosity of the target rock vein;

[0013] The matrix conductivity of the target dike is calculated based on the porosity of the target dike and the resistivity of the rock sample in the target dike.

[0014] Calculate the fracture conductivity of the target dike based on the porosity of the target dike and the resistivity of water.

[0015] The resistivity of the target vein is calculated based on the matrix conductivity and fracture conductivity.

[0016] Furthermore, the matrix electrical conductivity of the target dike for:

[0017]

[0018] in, The porosity of the target dike. The resistivity of the rock sample in the target vein.

[0019] Furthermore, the fracture electrical conductivity of the target dike for:

[0020]

[0021] in, The porosity of the target dike. The resistivity of water in the target vein.

[0022] Furthermore, the resistivity of the target dike for:

[0023]

[0024] in, The fracture electrical conductivity of the target dike. The matrix electrical conductivity of the target vein.

[0025] Furthermore, the exploration parameters of the dike include the width of the dike, the resistivity difference multiple between the dike and its surrounding rock, and the detection electrode distance and detection frequency of the exploration device used to explore the dike.

[0026] The specific method for calculating the relationship between the exploration parameters of the dike and the anomaly identification index in step S2 is as follows:

[0027] S2-1. Establish a fitting function for a single exploration parameter and anomaly identification index, and change the exploration parameter to fit the fitting function to obtain the index of the exploration parameter.

[0028] S2-2. Increase the coefficient and determine the value of the coefficient based on the index of the exploration parameters and the anomaly reference value of the set anomaly identification index;

[0029] S2-3. Based on the values ​​of the coefficients and the indices of the exploration parameters, the relationship between the exploration parameters of the dike and the anomaly identification index is obtained.

[0030] Further, step S2-1 includes the following sub-steps:

[0031] S2-1-1. Establish a fitting function for the width of the dike with respect to the anomaly identification index; fix the detection electrode distance, detection frequency and resistivity difference multiple, change the width of the dike model, and use magnetotelluric two-dimensional forward modeling to obtain the anomaly identification index corresponding to different widths of the dike model; fit the obtained data to the fitting function between the anomaly identification index and the width of the dike to obtain the width index.

[0032] S2-1-2. Establish the fitting function of the detection electrode distance with respect to the anomaly identification index; fix the resistivity difference multiple, detection frequency and the width of the dike model, change the detection electrode distance, and use magnetotelluric two-dimensional forward modeling to obtain the anomaly identification index corresponding to different detection electrode distances; fit the obtained data to the fitting function between the anomaly identification index and the detection electrode distance to obtain the index of the detection electrode distance.

[0033] S2-1-3. Establish a fitting function for the resistivity difference multiple with respect to the anomaly identification index; fix the detection electrode spacing, detection frequency and the width of the dike model, change the resistivity difference multiple, and use magnetotelluric two-dimensional forward modeling to obtain the anomaly identification index corresponding to different resistivity difference multiples; fit the obtained data to the fitting function between the anomaly identification index and the resistivity difference multiple to obtain the index of the resistivity difference multiple;

[0034] S2-1-4. Establish a fitting function for the detection frequency with respect to the anomaly identification index; fix the detection electrode spacing, resistivity difference multiple, and width of the dike model, change the detection frequency, and use magnetotelluric two-dimensional forward modeling to obtain the anomaly identification index corresponding to different detection frequencies of the dike model; fit the obtained data to the fitting function between the anomaly identification index and the detection frequency to obtain the index of the detection frequency.

[0035] When calculating the resistivity of a target dike, this invention takes into account the impact of water presence in the dike's fissures on the resistivity calculation. By combining the resistivity of both water and rock samples, this invention achieves a more accurate calculation of the dike's resistivity. The Anomaly Recognition Index (ARI) proposed in this invention effectively integrates pulse width W, detector distance D, and resistivity difference factor C. ρ Based on the detection frequency f and other key detection parameters, a semi-quantitative evaluation method for detection sensitivity was established. Case studies demonstrate that, when the coefficient Q is determined, setting the anomaly reference value m=1 results in anomalies that are identifiable when the ARI ≥ 1, and significantly improved identification performance when it ≥ 2. This index provides a clear basis for the optimized design of fieldwork plans and improves the accuracy of anomaly detection methods for dikes.

[0036] In this invention, porosity is a key geological factor controlling the resistivity response of quartz veins / pegmatites. Simulation calculations and case studies both show that even a small amount of fracture (φ≈5%) can cause the resistivity to drop significantly to below 10% of that of intact rock; when the porosity exceeds 10%, the resistivity decreases sharply and approaches the resistivity value of the fluid filling the fracture. This "sudden drop in resistivity" phenomenon is of great significance for identifying tectonic fracture zones and hydrothermal activity areas.

[0037] The resistivity model and ARI identification method established in this invention show promising application prospects in uranium exploration. Low resistivity anomalies caused by high fracture density can be used to delineate hydrothermal migration channels and favorable uranium mineralization areas, while the high resistivity characteristics of pegmatite veins help to directly locate uranium source bodies or rare metal enrichment zones. The combination of electromagnetic detection and ARI evaluation provides an effective geophysical technique for achieving breakthroughs in deep uranium exploration. Through theoretical innovation and practical verification, this invention deepens the understanding of the resistivity response laws of fractured vein-like geological bodies. The resulting resistivity calculation model and anomaly identification method can provide scientific methodological support and practical guidance for vein detection under complex geological conditions, especially for target location in uranium exploration. Future research can further focus on the construction of three-dimensional resistivity models, anisotropic response analysis, and multi-method fusion detection, continuously improving the ability to detect and evaluate the mineralization environments of various deep hydrothermal mineral deposits. Attached Figure Description

[0038] Figure 1 This is a flowchart of the present invention.

[0039] Figure 2 This is the MT result cross-sectional view of the fault zone in Example 1.

[0040] Figure 3 This is a cross-sectional view of the CSAMT measurement results corresponding to the pegmatite vein in Example 2. Detailed Implementation

[0041] The invention will now be described in further detail with reference to the accompanying drawings.

[0042] like Figure 1 As shown, S1. The resistivity of the rock sample in the target vein and the resistivity of the surrounding rock are measured using a sample measuring device, and the resistivity of the target vein is calculated based on the resistivity of the rock sample in the target vein.

[0043] First, based on the fissure degree of the target dike... Calculate the fracture electrical conductivity of the target dike. and matrix conductivity .

[0044] Target dike fracture electrical conductivity for:

[0045]

[0046] in, The porosity of the target dike. The resistivity of water in the target vein.

[0047] Matrix electrical conductivity of the target dike for:

[0048]

[0049] in, The resistivity of the rock sample in the target vein.

[0050] Resistivity of the target dike with fractures for:

[0051]

[0052] The resistivity formula for fractured rocks obtained in this invention simplifies the resistivity calculation of fractured rocks. It assumes that the current flows independently in the fracture fluid and matrix and ignores the interfacial polarization effect, making it applicable to resistivity estimation in the full fracture range of 0% to 100%.

[0053] In this invention, the resistivity of water can be measured using a conductivity meter, and the reciprocal of the water's conductivity can be calculated. Corresponding rock samples can be collected from the target vein and the surrounding rock, and measurements can be taken using a sample measuring device. The resistivity of the rock sample in the target vein is measured, and the resistivity of the rock sample in the surrounding rock is measured; the resistivity ρ of the surrounding rock is then calculated. bgd For example, the four-electrode method and the two-electrode method. Methods for measuring porosity include the drainage method and visual inspection.

[0054] S2. Construct a vein model and a surrounding rock model; change the exploration parameters of the vein model and calculate the relationship between the exploration parameters of the vein and the anomaly identification index.

[0055] The exploration parameters for the dike include the width of the dike, the resistivity difference multiple between the dike and its surrounding rock (referred to as resistivity difference multiple), and the detection electrode distance and detection frequency of the exploration device used for magnetotellurics (MT) exploration of the dike.

[0056] The variation pattern of the Anomaly Identification Index (ARI) with exploration parameters is as follows:

[0057]

[0058] Where W is the width of the dike, D is the detection range of the exploration device, and C is... ρ denoted as the resistivity difference factor, f as the detection frequency, and ∝ as proportional to.

[0059] The model is constructed using the two-dimensional forward modeling method of magnetotelluric simulation: the surrounding rock is assumed to be a uniform half-space, and a two-dimensional body (i.e., a cuboid) is embedded in it as a dike model, while the fracture degree parameter of the dike model is set.

[0060] S2-1. Establish a fitting function for a single exploration parameter and anomaly identification index, and change the exploration parameter to fit the fitting function to obtain the index of the exploration parameter.

[0061] S2-1-1. Establish a fitting function for the width of the dike with respect to the anomaly identification index; fix the detection frequency f as d, the detection electrode distance D as b, and the resistivity difference factor C. ρ The value of c is used to change only the width W of the dike model, at least twice. The anomaly identification index corresponding to different widths of the dike model is obtained by using magnetotelluric two-dimensional forward modeling. Based on the obtained data, the function to be fitted between the anomaly identification index and the width of the dike is fitted to obtain the width index.

[0062] The specific steps for obtaining the anomaly identification index corresponding to different widths of the dike model using two-dimensional magnetotelluric forward modeling are as follows:

[0063] The function to be fitted for the width of the dike with respect to the Anomaly Identification Index (ARI) is:

[0064]

[0065] Where K1 is a constant.

[0066] After fitting, the value of the exponent α of the dike width can be obtained.

[0067] During the simulation, the apparent resistivity of the dike model and the apparent resistivity of the surrounding rock can be obtained by changing the width of the dike model each time using magnetotelluric two-dimensional forward modeling. The ratio of the apparent resistivity of the dike model to the apparent resistivity of the surrounding rock is the value of the Anomaly Identification Index (ARI). The calculation method for the ARI value is the same when changing other exploration parameters in subsequent simulations.

[0068] S2-1-2. Establish a fitting function for the detection electrode distance with respect to the anomaly identification index; fix the resistivity difference factor C. ρ The value of c is the value of the detection frequency f, the value of d is the value of the width W of the dike model, and the value of a is the value of a. The detection pole distance D is changed at least twice. The anomaly identification index corresponding to different detection pole distances is obtained by using magnetotelluric two-dimensional forward modeling. The obtained data is fitted to the function to be fitted between the anomaly identification index and the detection pole distance to obtain the index of the detection pole distance.

[0069] The function to be fitted for the detection range with respect to the anomaly identification index (ARI) is:

[0070]

[0071] Where K2 is a constant.

[0072] The value of the exponent β of the detection polar distance is obtained after fitting.

[0073] S2-1-3. Establish a fitting function for the resistivity difference multiple with respect to the anomaly identification index; then fix the value of the width W of the dike model as a, the value of the detection frequency f of the exploration device as d, and the value of the detection electrode distance D as b, and only change the resistivity difference multiple C. ρ The data is modified at least twice, and the anomaly identification index corresponding to different resistivity difference multiples is obtained by using two-dimensional forward modeling of magnetotellurics. The obtained data is then fitted to the function to be fitted between the anomaly identification index and the resistivity difference multiple to obtain the index of resistivity difference multiple.

[0074] The function to be fitted for the resistivity difference factor with respect to the anomaly identification index is:

[0075]

[0076] Where K3 is a constant.

[0077] The resistivity difference factor C can be obtained. ρ The value of the exponent γ.

[0078] By changing the resistivity difference factor C between the vein model and its surrounding rock model ρ At that time, the resistivity difference factor C can be changed by altering the fracture degree of the dike model and the surrounding rock model. ρ .

[0079] S2-1-4. Establish a fitting function for the detection frequency with respect to the anomaly identification index; finally, fix the value of the width W of the dike model as a, the value of the detection electrode distance D of the exploration device as b, and the resistivity difference factor C. ρ The value of c is used to change the detection frequency f of the exploration device at least twice. The anomaly identification index corresponding to different detection frequencies is obtained by using two-dimensional forward modeling of magnetotellurics. The obtained data is then fitted to the function between the anomaly identification index and the detection frequency to obtain the index of the detection frequency.

[0080] The function to be fitted for the detection frequency with respect to the anomaly identification index is:

[0081]

[0082] Where K4 is a constant.

[0083] The value of the exponent δ of the detection frequency can be obtained.

[0084] During the simulation, the ARI value is the ratio of the resistivity of the surrounding rock model to the resistivity of the dike model.

[0085] S2-2. Increase the coefficient. Determine the value of the coefficient based on the index of the exploration parameters and the anomaly reference value of the set anomaly identification index.

[0086] By substituting the values ​​of the exploration parameters when they are fixed during simulation and the anomaly reference value of the Anomaly Identification Index (ARI) into the variation pattern of the anomaly reference value and the exploration parameters, and adding a coefficient Q, the value of coefficient Q can be obtained:

[0087]

[0088] By setting the anomaly reference value of the Anomaly Identification Index (ARI) to a fixed value m (i.e., setting a value), the value of Q can be obtained.

[0089] S2-3. Based on the values ​​of the coefficients and the indices of the exploration parameters, the relationship between the exploration parameters of the dike and the anomaly identification index is obtained.

[0090] Finally, based on the coefficient Q, the index obtained during simulation, and the values ​​when the exploration parameters are fixed, the relationship between the exploration parameters of the dike and the anomaly identification index (ARI) can be obtained as follows:

[0091]

[0092] As shown in Table 1, the width a of the dike model is fixed at 10m, the detection electrode distance b of the exploration device is fixed at 20m, the resistivity difference multiple c is fixed at 5.88, and the detection frequency d of the exploration device is 10Hz.

[0093] Table 1: ARI parameter fitting settings

[0094]

[0095] According to Table 1, the relationship between the exploration parameters of the dikes and the Anomaly Identification Index (ARI) is as follows:

[0096]

[0097] Therefore, the resistivity difference factor C ρ The frequency f has the greatest impact on the identification of measurement anomalies, followed by the pulse width W and the detection electrode spacing D of the exploration device, while the frequency f has a relatively small impact. Therefore, the wider the pulse and the more developed the fractures (the lower the resistivity), the more obvious the anomaly response. Based on this, the electrode spacing selection strategy can be summarized as follows: For wide pulses, the gain effect in the formula can be directly utilized to appropriately widen the electrode spacing and save costs. For narrow pulses, the electrode spacing should be appropriately reduced to improve resolution, but the electrode spacing should not be too small to avoid weak signal and reduced signal-to-noise ratio. A moderate electrode spacing combined with a small moving step size can solve this problem.

[0098] S3. Calculate the resistivity difference factor between the target vein and the surrounding rock based on their respective resistivities.

[0099] The resistivity difference C between the target dike and its surrounding rock ρ for:

[0100]

[0101] in, The resistivity of the target dike. denoted as , where is the resistivity of the surrounding rock.

[0102] S4. Calculate the anomaly identification index of the target dike based on the width of the target dike, the resistivity difference multiple, the detection electrode distance and detection frequency of the exploration device, and the relationship between the exploration parameters and the anomaly identification index; when the anomaly identification index is greater than the anomaly reference value, the target dike is an anomaly.

[0103] The width of the target dike can be measured directly.

[0104] After obtaining the relationship between the exploration parameters of the dike and the Anomaly Identification Index (ARI), the width of the target dike, the resistivity difference multiple, the detection electrode distance and detection frequency of the exploration device are substituted into the relationship between the exploration parameters of the dike and the Anomaly Identification Index (ARI) to obtain the value of the Anomaly Identification Index (ARI) of the target dike.

[0105] In step S2, the value of ARI has been set to m. That is, when the value of ARI exceeds the abnormal reference value m, the target dike is an abnormal body, and when the value of ARI does not exceed the abnormal reference value m, the target dike is a non-abnormal body.

[0106] S6. Method Evaluation.

[0107] As shown in Table 2, this invention simulates the DC resistivity of quartz veins under different fracture densities. Assuming the resistivity of intact quartz is 10000 Ω·m and the resistivity of water-filled fractures is 10 Ω·m, the DC resistivity ρ of the quartz veins under different fracture densities is calculated. dc , where σ dc is the electrical conductivity of the quartz vein.

[0108] Table 2: DC resistivity of quartz under different fracture conditions

[0109]

[0110] As shown in Table 2, the resistivity exhibits a nonlinear and sharp decrease as the porosity increases: when When the resistivity is 1%, it drops to 50.25% (5025.13 Ω·m), indicating that even a small number of cracks can significantly reduce the resistivity. When the resistivity reaches 10%, it plummets to 3.07% (307.48 Ω·m), at which point the fracture network forms interconnected channels, exhibiting over-permeability behavior. This result is largely consistent with the numerical simulation results of resistivity in randomly distributed fractured rocks under saturation conditions by Wang Zukun et al.

[0111] like Figure 1 As shown in Example 1, a fault zone exhibits frequent tectonic activity and intense magmatic-hydrothermal processes, forming a silicified fault zone dominated by quartz veins. This area, as an important prospective uranium polymetallic mineralization zone, serves not only as a conduit for hydrothermal migration but also as a key location for the precipitation and enrichment of uranium and other metallic elements. Therefore, conducting detailed tectonic exploration is of great significance for revealing the uranium mineralization mechanism. Among these, Figure 1 QF2, QF3, F3, and QF5 are the numbers representing the fracture locations.

[0112] The quartz veins filling the fault zone in the exploration area have been affected by multiple phases of tectonic activity, resulting in highly developed fractures. Based on field outcrop observations and core logging statistics, the estimated fracture density is φ≈15%~25%, exhibiting an overall fractured structure with localized limonite and uranium mineralization. The rock has undergone intense weathering and fracturing, resulting in a relatively loose structure; the footwall is composed of late-stage granite (…). The integrity is relatively good, but the resistivity is generally high.

[0113] The magnetotelluric (MT) method was used for detection, with the survey line laid perpendicular to the fault strike, a point spacing of 200m, and a detection electrode spacing of 60m. A full vector measurement mode (TM+TE) was employed, with an effective frequency band of 10. -2 ~10 3 Hz, where the point spacing is the distance between measurement points. The cross-sectional resistivity distribution was obtained by performing Robust processing, impedance tensor decomposition, and nonlinear conjugate gradient inversion on the original data.

[0114] Resistivity tests on rock samples showed that the resistivity of intact small quartz fragments ranged from 20,000 to 80,000 Ω·m, while that of granite ranged from 2,000 to 20,000 Ω·m. Exposed quartz veins on the surface had widths of 20 to 50 m, with visually estimated fissures exceeding 10%. Using the method of this invention, the resistivity of fissure-containing quartz veins ranged from 608 to 2432 Ω·m. Using the method of this invention at a frequency of 1000 Hz, with the anomaly reference value m set to 1 when calculating the coefficient Q, the estimated ARI index was greater than 2.1, indicating that the anomaly should possess good identifiability.

[0115] The resistivity inversion profile shows that the fault zone exhibits a significant low resistivity anomaly (ρ≈100~300Ω·m), far lower than the theoretical resistivity of intact quartz veins (10000Ω·m), consistent with the resistivity drop model under high fracture density (φ>10%) conditions. The estimated ARI index is greater than 2.1, further confirming the good identifiability of this anomaly. Furthermore, the resistivity of the early granite on the left side of the profile (ρ≈200~2000Ω·m) is significantly lower than that of the intact granite on the right side (ρ≈5000~15000Ω·m), reflecting a more fragmented geological background, consistent with Huang Zhongfeng's (2012) research conclusions on the resistivity characteristics of granites in South China.

[0116] like Figure 2 As shown in Example 2, in another region, there are several nearly vertical pegmatite dikes intruding into Proterozoic schist strata. Pegmatite-type uranium mineralization is one of the main prospecting targets in this area. The dikes themselves and their contact zones with the surrounding rocks are often accompanied by enrichment of elements such as uranium, thorium, and rare earth elements. Therefore, the spatial location and structural characteristics of the pegmatite dikes have direct prospecting significance. Among them, Pt2P in the figure... 3It is a code for rock strata.

[0117] The target pegmatite dike is approximately 5–8 m wide, nearly vertical in orientation, and its outcrops in the field show a dense structure with poorly developed fractures; the estimated fracture density is φ<5%, and it exhibits typical high resistivity characteristics overall. The surrounding rock is schist with moderate resistivity (ρ≈800–1200 Ω·m), showing a significant electrical difference from the pegmatite.

[0118] Detailed surveys were conducted using controlled-source audio-frequency magnetotellurics (CSAMT). The survey lines were arranged perpendicular to the pulse direction, with a point spacing of 20m and a transmitter distance of 10.5km. The frequency band was 0.25~8192Hz, and the transmission current was 7~15A. The data were processed by bandpass filtering, far-field correction, and two-dimensional inversion to obtain resistivity profiles.

[0119] The resistivity test results of the rock samples showed that the resistivity of schist was 600~3000 Ω·m, and that of pegmatite was 5000~8000 Ω·m. Using a detection electrode distance of 20m and a frequency of 5000Hz, the ARI index was estimated to be 1.24 based on the relationship between the anomaly identification index and the changes in exploration parameters.

[0120] The inversion results show that the pegmatite dikes exhibit a relatively sharp high-resistivity anomaly (ρ>2000Ω·m) in the profile, with an anomaly width of approximately 10–15 m, slightly larger than the actual dike width, reflecting the volumetric effect under the influence of limited point spacing. When calculating the coefficient Q, the anomaly reference value m was set to 1, and the ARI index was 1.24, above the recognizable threshold, indicating that the anomaly is distinguishable under the current observation configuration. This result also suggests that further reducing the point spacing to 10 m or increasing the frequency band resolution could potentially further improve the ability to characterize narrow dike boundaries.

[0121] As shown in Table 3, this invention summarizes the resistivity response and detection effect of quartz veins / pegmatites under different fracture conditions, and compares the two examples with the anomaly identification index (ARI) in this invention.

[0122] Table 3: Comparison of Examples and Correspondence with the Anomaly Recognition Index (ARI) in this Invention

[0123]

[0124] Table 3 shows that Example 1 verifies that high fracture density leads to a sharp decrease in the resistivity of quartz veins, thus forming a significant low-resistivity anomaly. Example 2 indicates that low-fracture pegmatite veins still maintain high-resistivity characteristics, but their identification effectiveness is greatly affected by geometric factors. Both examples illustrate that fracture density is a key geological factor controlling the resistivity response of veins, and the ARI index can provide a valid basis for selecting field work parameters, making it valuable for widespread application in complex geological scenarios similar to vein exploration.

[0125] The examples analyzed in this invention reveal the differentiated response characteristics of fractured quartz veins / pegmatites in electromagnetic detection. The high-fracture quartz vein in Example 1 exhibits a typical low-resistivity anomaly, directly reflecting the development characteristics of the tectonic fracture zone, while the low-fracture pegmatite vein in Example 2 maintains a significantly high-resistivity characteristic. These two typical electrical response patterns provide geophysical evidence for identifying and distinguishing different types of vein-like geological bodies.

Claims

1. A method for evaluating the MT anomaly response of a rock vein, characterized in that, Includes the following steps: S1. Measure the resistivity of the rock sample in the target vein and the resistivity of the surrounding rock using a sample measuring device, and calculate the resistivity of the target vein based on the resistivity of the rock sample. S2. Construct a dike model and an associated surrounding rock model; change the exploration parameters of the dike model and calculate the relationship between the exploration parameters of the dike and the anomaly identification index; S3. Calculate the resistivity difference factor between the target vein and the surrounding rock based on their respective resistivities; S4. Calculate the anomaly identification index of the target dike based on the width of the target dike, the resistivity difference multiple, the detection electrode distance and detection frequency of the exploration device, and the relationship between the exploration parameters and the anomaly identification index; when the anomaly identification index is greater than the anomaly reference value, the target dike is an anomaly. The specific method for calculating the resistivity of the target dike is as follows: Measure the porosity of the target rock vein; The matrix conductivity of the target dike is calculated based on the porosity of the target dike and the resistivity of the rock sample in the target dike. Calculate the fracture conductivity of the target dike based on the porosity of the target dike and the resistivity of water. Calculate the resistivity of the target vein based on its matrix conductivity and fracture conductivity. The exploration parameters of the dike include the width of the dike, the resistivity difference multiple between the dike and its surrounding rock, and the detection electrode distance and detection frequency of the exploration device used to explore the dike. The specific method for calculating the relationship between the exploration parameters of the dike and the anomaly identification index in step S2 is as follows: S2-1. Establish a fitting function for a single exploration parameter and anomaly identification index, and change the exploration parameter to fit the fitting function to obtain the index of the exploration parameter. S2-2. Increase the coefficient and determine the value of the coefficient based on the index of the exploration parameters and the anomaly reference value of the set anomaly identification index; S2-3. Based on the values ​​of the coefficients and the indices of the exploration parameters, the relationship between the exploration parameters of the dike and the anomaly identification index is obtained.

2. The method for evaluating the MT anomaly response of a rock vein according to claim 1, characterized in that, Matrix electrical conductivity of the target dike for: , in, The porosity of the target dike. The resistivity of the rock sample in the target vein.

3. The method for evaluating the MT anomaly response of a rock vein according to claim 1, characterized in that, Target dike fracture electrical conductivity for: , in, The porosity of the target dike. The resistivity of water in the target vein.

4. The method for evaluating the MT anomaly response of a rock vein according to claim 1, characterized in that, resistivity of the target dike for: , in, The fracture electrical conductivity of the target dike. The matrix electrical conductivity of the target vein.

5. The method for evaluating the MT anomaly response of a rock vein according to claim 1, characterized in that, Step S2-1 includes the following sub-steps: S2-1-1. Establish a fitting function for the width of the dike with respect to the anomaly identification index; fix the detection electrode distance, detection frequency and resistivity difference multiple, change the width of the dike model, and use magnetotelluric two-dimensional forward modeling to obtain the anomaly identification index corresponding to different widths of the dike model; fit the obtained data to the fitting function between the anomaly identification index and the width of the dike to obtain the width index. S2-1-2. Establish the fitting function of the detection electrode distance with respect to the anomaly identification index; fix the resistivity difference multiple, detection frequency and the width of the dike model, change the detection electrode distance, and use magnetotelluric two-dimensional forward modeling to obtain the anomaly identification index corresponding to different detection electrode distances; fit the obtained data to the fitting function between the anomaly identification index and the detection electrode distance to obtain the index of the detection electrode distance. S2-1-3. Establish a fitting function for the resistivity difference multiple with respect to the anomaly identification index; fix the detection electrode spacing, detection frequency and the width of the dike model, change the resistivity difference multiple, and use magnetotelluric two-dimensional forward modeling to obtain the anomaly identification index corresponding to different resistivity difference multiples; fit the obtained data to the fitting function between the anomaly identification index and the resistivity difference multiple to obtain the index of the resistivity difference multiple; S2-1-4. Establish a fitting function for the detection frequency with respect to the anomaly identification index; fix the detection electrode spacing, resistivity difference multiple, and width of the dike model, change the detection frequency, and use magnetotelluric two-dimensional forward modeling to obtain the anomaly identification index corresponding to different detection frequencies of the dike model; fit the obtained data to the fitting function between the anomaly identification index and the detection frequency to obtain the index of the detection frequency.

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