Rare earth ore deep exploration method and system based on time-frequency electromagnetism and storage medium

Abstract

The invention discloses a rare earth ore deep exploration method and system based on time-frequency electromagnetism, and a storage medium. The exploration method comprises the following steps: identifying a target region in a to-be-explored region; acquiring exploration data of a target area by using a time-frequency electromagnetic method to obtain first time domain data and first frequency domain data; calculating the apparent resistivity of the target region according to the first time domain data, and calculating a total longitudinal conductivity parameter based on the apparent resistivity; an Occam inversion model related to the total longitudinal conductivity parameters is constructed; performing dual-frequency phase calculation and dual-frequency amplitude calculation according to the first frequency domain data to obtain a frequency parameter set of the target region; fitting the Occam inversion model, and performing polarizability inversion based on the frequency parameter set; and delineating the deep geophysical prospecting anomalous body in the target region based on the polarizability inversion result. According to the method, deep rare earth ore can be identified by using the time-frequency electromagnetic technology, the number of exploration drilling wells is reduced, the exploration cost is reduced, the prospecting time is shortened, and the exploration efficiency of the hidden rare earth ore deposit in the thick coverage area is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mineral resource exploration, and in particular to a method, system and storage medium for deep exploration of rare earth mines based on time-frequency electromagnetism. Background Art

[0002] With the development of the national economy, the social demand for rare earth resources is increasing significantly, while outcrop mines and easy-to-find mines in shallow areas of the surface are becoming less and less, especially in the Sichuan Panxi rare earth mineralization belt, which is more than 150km long and 10km wide. So far, only two large rare earth mines in operation have been found (Yakniuping Mine and Continental Trough Mine). The Yakniuping-Continental Trough mining area is a blank area 100km apart. Because the terrain is deeply cut and the Quaternary cover is thick (>50m), there are only a few mining sites and small and medium-sized mines.

[0003] From the prospecting history of the Panxi rare earth mineralization belt in Sichuan, prospecting work around the Panxi mineralization belt has begun since the 1960s, especially the surface outcrops, which have been repeatedly plowed by geologists of all generations, and it is extremely difficult to find new outcrops. A large amount of slope deposits and alluvial deposits cover the surface, and deep exploration has become the focus of prospecting work in this area. What followed was a sharp increase in the difficulty of prospecting, a sudden increase in exploration costs, and a sharp decline in the exploration discovery rate. The mineral exploration process urgently needs the emergence of efficient and low-cost guidance methods for deep hidden body prospecting. Therefore, exploring a method suitable for deep prospecting of rare earth mines is of great practical significance for finding the same type of deep rare earth mine hidden bodies in the future. Summary of the invention

[0004] The embodiments of the present invention provide a method, system and storage medium for deep exploration of rare earth mines based on time-frequency electromagnetics, which can use time-frequency electromagnetic technology to identify deep rare earth mines, reduce the number of exploration drillings, greatly reduce exploration costs, shorten prospecting time, and significantly improve the exploration efficiency of hidden rare earth deposits in thick coverage areas.

[0005] An embodiment of the present invention provides a method for deep exploration of rare earth mines based on time-frequency electromagnetics, comprising:

[0006] identifying a target area in the area to be explored according to regional characteristics of the area to be explored;

[0007] Collecting exploration data of the target area by using a time-frequency electromagnetic method, and preprocessing the exploration data to obtain first time domain data and first frequency domain data;

[0008] Calculating the apparent resistivity of the target area according to the first time domain data, and calculating the total longitudinal conductivity parameter based on the apparent resistivity;

[0009] constructing an Occam inversion model related to the total longitudinal conductivity parameter;

[0010] Perform dual-frequency phase calculation and dual-frequency amplitude calculation according to the first frequency domain data to obtain a frequency parameter set of the target area;

[0011] Fitting the Occam inversion model, and when the fitting result meets the preset error requirement, performing polarizability inversion based on the frequency parameter set;

[0012] Based on the polarizability inversion result, a deep geophysical anomaly is delineated in the target area.

[0013] As an improvement of the above solution, the first time domain data includes: angular frequency, magnetic permeability, electric field strength and magnetic field strength;

[0014] The step of calculating the apparent resistivity of the target area according to the first time domain data, and calculating the total longitudinal conductivity parameter based on the apparent resistivity, comprises:

[0015] The apparent resistivity is calculated by the following formula:

[0016]

[0017] Wherein, ρ is the apparent resistivity, ω is the angular frequency, μ is the magnetic permeability, E is the electric field intensity in the first direction, and H is the magnetic field intensity in the second direction, wherein the first direction is perpendicular to the second direction;

[0018] Based on the apparent resistivity, an apparent resistivity curve is obtained, and the total longitudinal conductivity parameter is calculated by the following formula:

[0019]

[0020] Where S is the total longitudinal conductivity parameter, ρ min is the minimum value of the apparent resistivity curve before the 45° rise, f min is min The corresponding frequency.

[0021] As an improvement of the above scheme, the construction of the Occam inversion model related to the total longitudinal conductivity parameter includes:

[0022] For the Occam inversion model, the following objective function is constructed:

[0023]

[0024] Where λ is the Lagrange multiplier, W is the error weighting matrix, S is the total longitudinal conductivity parameter, m is the Occam inversion model parameter, G(m) is the forward operator, is the roughness matrix, △ is the first-order Laplace operator, t1 is the regularization parameter corresponding to the first-order Laplace operator, △2 is a second-order Laplace operator, and t2 is a regularization parameter corresponding to the second-order Laplace operator.

[0025] As an improvement of the above solution, performing dual-frequency phase calculation and dual-frequency amplitude calculation according to the first frequency domain data to obtain the frequency parameter set of the target area includes:

[0026] The dual frequency phase is calculated by the following formula:

[0027]

[0028] Wherein, DFP is the dual-frequency phase, ω1 is the fundamental frequency collected, ω3 is the frequency of the third harmonic of ω1, and ω1<ω3, is the phase of the fundamental wave, is the phase of the third harmonic;

[0029] The dual frequency amplitude is calculated by the following formula:

[0030]

[0031] Where P_Amp is the dual frequency amplitude, A L is the low frequency amplitude value, A H is the high frequency amplitude value;

[0032] The dual-frequency phase and the dual-frequency amplitude of each frequency point are extracted to obtain the frequency parameter set.

[0033] As an improvement of the above solution, the performing polarizability inversion based on the frequency parameter set includes:

[0034] The Cole-Cole model is used to perform the polarizability inversion through the following formula:

[0035]

[0036] Wherein, ρ' is the polarizability inversion result, ρ is the apparent resistivity, DFP is the dual-frequency phase, P_Amp is the dual-frequency amplitude, i is the imaginary unit, ω is the angular frequency, τ is the time constant, and c is the distribution parameter of the Cole-Cole model.

[0037] As an improvement of the above solution, the step of identifying a target area in the area to be explored according to the area characteristics of the area to be explored includes:

[0038] Based on the regional characteristics of the area to be explored, an initial working area is identified in the area to be explored, wherein the regional characteristics include at least one of element characteristics, surface characteristics, and natural landscape characteristics;

[0039] The initial working area is subjected to radioactive gamma measurement, and the target area is identified according to the measurement result.

[0040] As an improvement of the above solution, the exploration data of the target area is collected by using the time-frequency electromagnetic method, and the exploration data is preprocessed to obtain first time domain data and first frequency domain data, including:

[0041] Performing original signal analysis, data current normalization, and data denoising on the exploration data to obtain the first time domain data;

[0042] The exploration data is subjected to noise analysis, interference elimination, device coefficient normalization, current and frequency response normalization, and zero track and bad point deletion to obtain the first frequency domain data.

[0043] Another embodiment of the present invention provides a rare earth mine deep exploration system based on time-frequency electromagnetic, including:

[0044] A target area identification module, used for identifying a target area in the area to be explored according to the area characteristics of the area to be explored;

[0045] A data preprocessing module, used to collect exploration data of the target area by using a time-frequency electromagnetic method, and preprocess the exploration data to obtain first time domain data and first frequency domain data;

[0046] A time domain analysis module, used to calculate the apparent resistivity of the target area according to the first time domain data, and calculate the total longitudinal conductivity parameter based on the apparent resistivity;

[0047] A model calculation module, used for constructing an Occam inversion model related to the total longitudinal conductivity parameter;

[0048] A frequency calculation module, used for performing dual-frequency phase calculation and dual-frequency amplitude calculation according to the first frequency domain data to obtain a frequency parameter set of the target area;

[0049] A polarizability inversion module, used for fitting the Occam inversion model and performing polarizability inversion based on the frequency parameter set when the fitting result meets the preset error requirement;

[0050] A mineral identification module is used to delineate deep geophysical anomalies in the target area based on the polarizability inversion result.

[0051] Another embodiment of the present invention provides a rare earth mine deep exploration system based on time-frequency electromagnetics, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the rare earth mine deep exploration method based on time-frequency electromagnetics described in the above-mentioned embodiment of the invention is implemented.

[0052] Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the deep exploration method for rare earth mines based on time-frequency electromagnetics as described in the above-mentioned embodiment of the invention.

[0053] Compared with the prior art, in the embodiments of the present invention, the target area is first identified in the area to be explored through regional characteristics, thereby narrowing the scope of prospecting; the target area is detected by using the time-frequency electromagnetic method, and the profile abnormality characteristics are studied and analyzed in both the time domain and the frequency domain. The Occam inversion model is obtained by calculating the apparent resistivity and total longitudinal conductivity parameters, and then the polarizability inversion is performed based on the frequency data. Finally, an effective deep abnormal geological characteristic model is established as a basis for effectively delineating deep abnormal bodies; thereby providing a basis for better arrangement of drilling projects, reducing the number of exploration drillings, greatly reducing exploration costs, and shortening prospecting time, significantly improving the exploration efficiency of concealed rare earth deposits in thick coverage areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic flow chart of a method for deep exploration of rare earth mines based on time-frequency electromagnetics provided by one embodiment of the present invention;

[0055] Figure 2 It is a schematic diagram of an axial dipole device used in field construction provided by an embodiment of the present invention;

[0056] Figure 3 It is a comprehensive geology-geophysical exploration-drilling profile of Longjiagou P01 provided by an embodiment of the present invention;

[0057] Figure 4 It is a schematic structural diagram of a rare earth mine deep exploration system based on time-frequency electromagnetics provided by an embodiment of the present invention;

[0058] Figure 5 It is a schematic structural diagram of a rare earth mine deep exploration system based on time-frequency electromagnetics provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] See also Figure 1, is a flow chart of a method for deep exploration of rare earth mines based on time-frequency electromagnetics provided by an embodiment of the present invention, including steps S101 to S107:

[0061] S101, identifying a target area in the area to be explored according to regional characteristics of the area to be explored;

[0062] Specifically, important surface mineralized areas can be preliminarily identified as target areas based on basic geological data of the area to be explored, large-scale surface rock mapping, large-scale surface detection data, and radioactive gamma measurement prospecting methods.

[0063] S102, collecting exploration data of the target area by using a time-frequency electromagnetic method, and preprocessing the exploration data to obtain first time domain data and first frequency domain data;

[0064] Specifically, this embodiment proposes the use of time-frequency electromagnetic method (TFEM) for deep prospecting of hard rock carbonate rare earth minerals in deep coverage areas. Previous researchers rarely used deep geophysical methods in rare earth mineral exploration, and only a small amount of experimental work was done. The advantages of time-frequency electromagnetic method compared with conventional methods in the prior art include: (1) high-power excitation, which is more than ten times the power of conventional electrical methods, strong anti-interference and high resolution; (2) simultaneous measurement of electric field and magnetic field components, which makes up for the defect of low resolution of high-resistance thin layers caused by observing only a single component, and multi-component inversion calculation reduces multi-solution; (3) simultaneous measurement of time domain and frequency domain information; (4) simultaneous study of resistivity and polarization anomalies in the target area. In view of the above analysis, the deep exploration method for rare earth mines based on time-frequency electromagnetics provided in this embodiment can be used to solve the problems that the existing rare earth mine exploration methods cannot quickly and effectively penetrate thick coverage areas (floating soil thickness > 50m) to delineate hidden anomalies in deep rare earth mines, and have low success rate in prospecting, high cost and long cycle.

[0065] Specifically, the steps of collecting exploration data of the target area using the time-frequency electromagnetic method provided in this embodiment include the following three steps:

[0066] (1) Using a long straight wire source to excite, frequency domain sounding and time domain sounding are combined in one system. Different frequencies and different types of excitation waveforms can be selected according to the depth of the exploration target, which can not only provide resistivity information, but also provide excitation polarization information. Therefore, while studying the electrical structure, it can also detect its high polarizability anomaly. The time-frequency electromagnetic method uses a square wave that does not exceed zero, which can have a variety of different combinations, from high frequency to low frequency continuous excitation, and each frequency is repeatedly excited several times. The number of repetitions depends on the frequency setting. The high frequency has a large number of repetitions, and the low frequency has a relatively small number of repetitions. The data quality and reliability are high.

[0067] (2) Adopt a multi-station arrangement receiving working mode. Each station consists of an electrical acquisition station and a magnetic acquisition station. The signal receiving system receives all signals of each frequency repetitive excitation through GPS and transmission synchronization. Time-frequency electromagnetic technology combines the advantages of frequency domain method and time domain method, so that data processing can have two processing methods, time domain and frequency domain. A variety of methods are used to comprehensively utilize the characteristics of various fields to study a variety of rock properties, effectively improving the exploration accuracy.

[0068] (3) See Figure 2 , is an axial dipole device used in field construction, which is divided into two parts: transmitting and receiving. The transmitting end is composed of multiple parallel aluminum plates to form a horizontal limited length grounding wire source. A high-power transmitter is used to send a series of right-angle pulse currents to the ground at different frequencies. The receiving end measures the electric field component E through the electrodes grounded at the two ends of MN. x .

[0069] S103, calculating the apparent resistivity of the target area according to the first time domain data, and calculating the total longitudinal conductivity parameter based on the apparent resistivity;

[0070] Specifically, in electrical prospecting, the resistivity calculation method of uniform half-space is extended to non-uniform half-space, and the solved ρ is called apparent resistivity. By analyzing the apparent resistivity-frequency section, we can qualitatively understand the electrical distribution on the survey line, the ups and downs of the strata, the distribution of faults, the division of electrical layers and other section characteristics. After the apparent resistivity curves of each survey line are corrected by static displacement such as single-point curve translation and spatial filtering method, it can be seen from the apparent resistivity contour section map that the "hanging noodles" phenomenon of steep and dense contour lines, fluctuating up and down, and changing randomly disappears.

[0071] S104, constructing an Occam inversion model related to total longitudinal conductivity parameters;

[0072] Specifically, in geophysical inversion, it is very difficult to describe the electrical distribution of complex underground media with a simple layered model and very few parameters, which is inconsistent with the actual situation; the electrical properties of underground media should change continuously with depth, or at least be piecewise continuous. Occam inversion is a regularized inversion method that requires the model data to be the smoothest or most rounded while pursuing the maximum fit between the simulated data and the original measurement curve, so that it is less affected by the initial model and can achieve stable convergence. Therefore, this embodiment uses the Occam inversion model for time domain data analysis.

[0073] S105, performing dual-frequency phase calculation and dual-frequency amplitude calculation according to the first frequency domain data to obtain a frequency parameter set of the target area;

[0074] Specifically, when the underground medium has or does not have a polarization effect, the parameters measured by the time-frequency electromagnetic method are different. The contrast characteristics are as follows: if the underground medium has a polarization effect, the real component and amplitude become higher, the imaginary component and phase become lower, and the phase may even become negative at low frequencies. When there is a polarization effect, the calculated apparent resistivity becomes larger, and the above differences become more obvious at lower frequencies. Therefore, this embodiment performs dual-frequency phase calculation and dual-frequency amplitude calculation on the first frequency domain data, thereby obtaining accurate cross-section analysis data based on the frequency parameters.

[0075] S106, fitting the Occam inversion model, and when the fitting result meets the preset error requirement, performing polarizability inversion based on the frequency parameter set;

[0076] Specifically, this embodiment adopts an induced polarization information inversion method: the amplitude and phase of all frequency points in the first frequency domain data are extracted to form section data, and then the induced polarization information is inverted on the entire section data to obtain a polarizability inversion section.

[0077] Specifically, in this embodiment, the Occam inversion model is first fitted before polarizability inversion. Polarizability inversion can be performed only when the fitting error of each measuring point meets the preset error requirement; wherein, the fitting error is calculated as follows:

[0078]

[0079] In the formula, Re i is the fitting error of the ith measurement point, U i obs is the observed value of the i-th measuring point, U i is the Occam inversion model fitting value of the i-th measuring point.

[0080] Specifically, the preset error requirement may be less than 5%, 10%, 15%, etc. The specific error requirement may be set based on actual exploration needs, and this embodiment does not make any specific limitation.

[0081] S107. Based on the polarizability inversion results, deep geophysical anomalies are delineated in the target area.

[0082] Specifically, this embodiment takes the Panzhihua rare earth mineralization belt in Sichuan as an example. The discreteness of the apparent resistivity in the target area of ​​this region is relatively large, among which the largest is the carbonate rock containing nepheline, followed by the light gray alkaline orthoclase, and the discreteness of the remaining rocks (ores) is relatively small; while the overall discreteness of the apparent polarizability is relatively small, the jump is relatively small, and the data distribution is relatively uniform.

[0083] The classification of apparent resistivity difference is relatively complex. In this embodiment, the apparent resistivity value is less than 1000 as low resistance, the resistivity value is 1000-5000 as medium resistance, and the resistivity value is greater than 5000 as high resistance. The low-resistance lithology is mainly gabbro, among which the average resistivity of gabbro is about 97.77Ω. The medium-resistance lithology is mainly rhyolite, schist, granite, and light gray alkaline orthoclase, among which the average resistivity of light gray alkaline orthoclase is 3617.12Ωm, and the resistivity of granite is 20.84Ωm. The average resistivity is 1001.31Ωm, the average resistivity of rhyolite is 1949.73Ωm, and the average resistivity of schist is 1293.5Ωm; the high-resistance rock types are mainly fine-vein ore bodies, rare earth minerals, marble, and nepheline-containing carbonate rocks. The average resistivity of marble is 6751.48Ωm, the average resistivity of fine-vein ore bodies is 6891.81Ωm, the average resistivity of nepheline-containing carbonate rocks is 6942.22Ωm, and the average resistivity of rare earth minerals is 7266.71Ωm.

[0084] Refer to Table 1, which is a statistical table of electrical characteristics of different rocks (ores). According to the difference in physical parameters, the physical characteristics of carbonate rare earth ore bodies or structural fracture zones containing carbonate rare earth ore are high resistivity and low polarizability (average 6751.48Ωm), and the syenite in the surrounding rock is medium resistivity and low polarizability (average 3617.12Ωm); granite is low resistivity and low polarizability (average 1001.31Ωm); in this area, the rare earth ore bodies are mainly high resistivity anomalies, which are significantly different from the surrounding rocks. Because the rare earth ore bodies contain a small amount of sulfides (i.e. pyrite and galena), the polarizability ranges from 0.46 to 1.49. Marble is relatively pure, and there are few sulfides in its strata. The polarizability ranges from 0.15 to 1.48. Because of the increase or decrease in sulfide content, the carbonate rare earth ore and marble are superimposed on each other in polarizability.

[0085] Table 1 Statistics of electrical characteristics of different rocks (ores)

[0086]

[0087]

[0088] In a specific implementation, the types of rocks (ores) at different measuring points can be identified based on the polarizability inversion results and the electrical characteristic parameters of different rocks (ores), so that the areas that meet the polarizability of rare earth minerals can be delineated as deep geophysical anomalies, so that the next step is to carry out drilling projects to verify the mineralization of the deep anomalies.

[0089] In summary, the prospecting method of this embodiment fully combines the deep geophysical anomaly of the geological background and can determine whether there is a rare earth mineralization zone in the deep area, providing a reference for subsequent exploration work.

[0090] See also Figure 3, which is a comprehensive profile of Longjiagou P01 geology-geophysical-drilling. The deep exploration method of rare earth mines based on time-frequency electromagnetic in this embodiment was used to conduct deep prospecting of rare earth mines in the Longjiagou thick coverage area outside the Panxi Yaoniuping rare earth mine. On the basis of existing research results and field geological surveys, the time-frequency electromagnetic deep measurement method was introduced to delineate deep abnormal target bodies. Through deep drilling engineering verification, not only the distribution and change law of deep hidden rare earth mineralization belts was identified, but also a new prospecting method combination for hard rock rare earth mines in the area was constructed, and the rare earth mineralization center of the Longjiagou thick coverage area was successfully delineated.

[0091] It can be seen that the rare earth mineral exploration method for thick cover areas (cover thickness > 50m) provided in this embodiment is based on the physical property differences between rock masses, uses time-frequency electromagnetic geophysical prospecting methods to delineate deep anomalies, and uses the physical property parameters of various rock types summarized in this area to distinguish and identify target anomalies, thereby improving the exploration efficiency of hidden rare earth deposits in thick cover areas.

[0092] In this embodiment, preferably, the first time domain data includes: angular frequency, magnetic permeability, electric field strength and magnetic field strength;

[0093] The apparent resistivity of the target area is calculated according to the first time domain data, and the total longitudinal conductivity parameters are calculated based on the apparent resistivity, including:

[0094] The apparent resistivity is calculated by the following formula:

[0095]

[0096] Wherein, ρ is the apparent resistivity, ω is the angular frequency, μ is the magnetic permeability, E is the electric field intensity in the first direction, and H is the magnetic field intensity in the second direction, wherein the first direction is perpendicular to the second direction;

[0097] Based on the apparent resistivity, the apparent resistivity curve is obtained, and the total longitudinal conductivity parameter is calculated by the following formula:

[0098]

[0099] Where S is the total longitudinal conductance parameter, ρ min is the minimum value of the apparent resistivity curve before it rises 45°, f min is min The corresponding frequency.

[0100] Specifically, when using Figure 2 When the axial dipole device is used for time-frequency electromagnetic acquisition during field construction, the above E is E x , that is, the electric field strength in the x direction, at this time H is H y , that is, the magnetic field strength in the y direction.

[0101] Specifically, in this embodiment, the total longitudinal conductance of the measuring point is defined as the sum of the conductances from the surface to a certain depth, which can be expressed as:

[0102]

[0103] Where σ(L) is the conductivity value and L is the depth value.

[0104] The above formula shows that within the exploration depth range, the lower the resistivity, the greater the total longitudinal conductivity, and the thicker the low-resistance stratum, the greater the total longitudinal conductivity. In geological interpretation, since the resistivity of sedimentary rocks is relatively low, it has some influence on the total longitudinal conductivity, and the greater the thickness, the greater the influence; for the basement, theoretically, it can be considered that the resistivity tends to infinity and the conductivity is 0, which has no effect on the S value, so the total longitudinal conductivity is usually used to divide the thickness of the sedimentary rock cover and delineate the basement burial depth.

[0105] In summary, after derivation, the following formula is obtained in this embodiment to calculate the total longitudinal conductance parameter:

[0106]

[0107] In this embodiment, preferably, an Occam inversion model related to the total longitudinal conductivity parameter is constructed, including:

[0108] For the Occam inversion model, the following objective function is constructed:

[0109]

[0110] Where λ is the Lagrange multiplier, W is the error weighting matrix, S is the total longitudinal conductivity parameter, m is the Occam inversion model parameter, G(m) is the forward operator, is the roughness matrix, △ is the first-order Laplace operator, t1 is the regularization parameter corresponding to the first-order Laplace operator, △ 2 is the second-order Laplace operator, and t2 is the regularization parameter corresponding to the second-order Laplace operator.

[0111] Specifically, the free inversion result can show the macroscopic electrical property variation law, and the lateral resistivity variation law is consistent with the actual stratum fluctuation shape, but due to the volume effect of electrical exploration, it cannot well meet the needs of deep interpretation in the working area. This embodiment selects the constrained inversion method.

[0112] Specifically, when establishing the inversion model, it is necessary to control the key geological layer boundaries. Therefore, this embodiment can also introduce the geological function K(x) to obtain the following new objective function:

[0113]

[0114] When the calculation results of U twice are greater than the preset difference, it can be considered that the geological layer boundary has been reached, and K(x) is assigned a value of 0; otherwise, K(x) is assigned a value of 1, so that the geological layer boundary area appears as a steep curve.

[0115] Specifically, in the area not at the geological boundary, the curve of the objective function needs to be a smooth curve, so this embodiment also introduces a Laplace operator to improve the smoothness of the curve in the area not at the geological boundary.

[0116] In this embodiment, preferably, dual-frequency phase calculation and dual-frequency amplitude calculation are performed according to the first frequency domain data to obtain a frequency parameter set of the target area, including:

[0117] The dual frequency phase is calculated by the following formula:

[0118]

[0119] In the formula, DFP is the dual frequency phase, ω1 is the fundamental frequency collected, ω3 is the frequency of the third harmonic of ω1, and ω1<ω3, is the phase of the fundamental wave, is the phase of the third harmonic;

[0120] The dual frequency amplitude is calculated by the following formula:

[0121]

[0122] Where P_Amp is the dual frequency amplitude, A L is the low frequency amplitude value, A H is the high frequency amplitude value;

[0123] The dual-frequency phase and dual-frequency amplitude of each frequency point are extracted to obtain a frequency parameter set.

[0124] Specifically, for the dual-frequency amplitude: the induced polarization effect has capacitive characteristics, the low-frequency amplitude is large, and the high-frequency amplitude is small. Therefore, the induced polarization anomaly can be extracted by calculating the relative high- and low-frequency normalized amplitude difference in the near field region through the above formula.

[0125] Specifically, for dual-frequency phase: phase is also a parameter for evaluating capacitor characteristics. Strong phase and large capacitance indicate strong polarization effect. Polarization effect can be extracted from phase anomaly. In a specific implementation, in order to eliminate the influence of the induction field, trend filtering can also be performed, just like the dual-frequency amplitude difference processing method.

[0126] In this embodiment, preferably, polarizability inversion is performed based on the frequency parameter set, including:

[0127] The Cole-Cole model is used to invert the polarizability through the following formula:

[0128]

[0129] Where ρ' is the polarizability inversion result, ρ is the apparent resistivity, DFP is the dual-frequency phase, P_Amp is the dual-frequency amplitude, i is the imaginary unit, ω is the angular frequency, τ is the time constant, and c is the distribution parameter of the Cole-Cole model.

[0130] Specifically, the induced polarization information inversion technique first extracts the amplitude and phase of all frequency points to form profile data, and then performs induced polarization information inversion on the entire profile data to obtain the polarizability inversion section.

[0131] In this embodiment, preferably, identifying a target area in the area to be explored according to the regional characteristics of the area to be explored includes:

[0132] Based on regional characteristics of the area to be explored, an initial working area is identified in the area to be explored, wherein the regional characteristics include at least one of element characteristics, surface characteristics, and natural landscape characteristics;

[0133] Perform radioactive gamma measurements on the initial working area and identify the target area based on the measurement results.

[0134] Specifically, based on the area to be explored, the records of element combinations such as La, Ce, Y, Nb, Ba (Br), Th (U), Pb, Mo, F, etc. in the database and the regional geological and mineral data can be identified based on the water sediment measurement database with a scale of 1:200,000 and 1:50,000, so that the range with more records of the above element combinations is preferably selected as the first working range.

[0135] Then collect images of the first working range, compare them with the 1:10,000 scale geological map, and obtain the stone turning data such as the particle size, roundness, and transportation distance of the stones in the first working range through image recognition. Based on the stone turning data, further identify a more preferred second working range in the first working range, for example, identify the alkaline syenite-carbonate rock range as the second working range.

[0136] For the reduced second working range, its natural landscape features are obtained through image recognition, and the slope deposits, alluvial deposits, residual deposits, residual slope deposits and other areas are divided in the above image according to the natural landscape features. According to the preset division principles, the third working range is delineated in the second working range; further, the surface of the third working range is scanned by measuring the total amount of surface gamma, and the target area is identified according to the measurement results.

[0137] In this embodiment, preferably, the exploration data of the target area is collected by using the time-frequency electromagnetic method, and the exploration data is preprocessed to obtain first time domain data and first frequency domain data, including:

[0138] Performing raw signal analysis, data current normalization and data denoising on the exploration data to obtain the first time domain data;

[0139] The exploration data is subjected to noise analysis, interference elimination, device coefficient normalization, current and frequency response normalization, and zero track and bad point deletion to obtain the first frequency domain data.

[0140] Specifically, the data collected in the field is converted using a conversion program to obtain the corresponding current data files and receiving data files, and then the time series signals are played back and analyzed using a signal display program. Finally, after a series of processing such as denoising and normalization, the relevant data in the time domain and frequency domain are obtained.

[0141] After data acquisition, the signal is first analyzed. Through playback display, the data quality and noise level of field records can be understood, and corresponding denoising processing can be performed. Then a normalized database is obtained through spectrum analysis, synchronous superposition and program conversion. In addition, after spectrum analysis, the spectrum characteristics of the original signal and interference can be understood, and different processing methods can be adopted according to the type of interference: for records with zero drift or low-frequency electromagnetic interference, linear interpolation method or positive and negative half-cycle signal reflection is used to correct it; for electromagnetic interference of known frequency, filtering is used, and 50Hz filtering is used to remove noise while converting and decoding the data format; for intermediate frequency electromagnetic interference and irregular pulse interference that cannot improve the signal-to-noise ratio, it is deleted; through the superposition of multiple signals, high-frequency random interference is effectively suppressed.

[0142] Furthermore, it also includes the processing of the transmission signal, which is mainly for current normalization in the later data processing. After obtaining the transmission timing data, first replay the transmission data, check whether the header information is correct, whether the current size meets the requirements, and whether the signal is stable, and whether the signals of each cycle are consistent. Then superimpose the data and perform FFT transformation. After obtaining the receiving timing data, first check the header information, mainly check whether the parameters such as MN distance, point number, field source, etc. are correct, and then replay the timing data to check whether the data of each cycle is normal.

[0143] Specifically, after the acquisition is completed, the processing of time domain data is divided into the following steps: original signal analysis, database establishment, data current normalization, data denoising, and the determination of apparent resistivity and total longitudinal conductance. After the acquisition of the time-frequency electromagnetic method is completed, the parameters such as AB length, excitation current, transceiver distance, MN length, and ground resistance of the receiving electrode of the collected original data are checked. After the verification is completed, the data is thinned. The principle of thinning is: based on the logarithmic half-cycle time axis, 20 sample points are extracted for each order of magnitude for subsequent processing, so as to facilitate subsequent data calculation.

[0144] Field data collection is not only a general reflection of the electrical properties of underground formations, but also related to the response characteristics of the instrument, and there are some interferences from different spectra. The purpose of data preprocessing is to eliminate or suppress the influence of these factors so that the signal can truly reflect the underground electrical characteristics. Signal playback is to understand the quality and noise level of the original data. Signals with too much noise and obviously poor data quality are directly deleted to prevent large interference from affecting the superposition results of the data. By measuring the original signal records of multiple channels in one cycle, it can be found that there are large differences in noise levels between different channels. In view of this feature, the time series signals of each channel are selected to delete the signals with large interference. Spectral analysis is to understand the spectral characteristics of signals and noise, and select different filtering parameters according to the results of spectrum analysis to filter the signal and eliminate the influence of some characteristic noise.

[0145] The square wave current of one cycle of time-frequency electromagnetic emission is composed of a negative DC current and a positive DC current, and the received signal of one cycle is a negative attenuation curve and a positive attenuation curve. If there is no interference, the result of the superposition of positive and negative half-cycle signals should be zero, but due to the existence of zero drift and low-frequency interference, this result is often not zero. Removing the interference value from the original record can correct the zero drift and eliminate the low-frequency interference. The superposition processing of positive and negative half-cycle signals is one of the unique denoising methods of time-frequency electromagnetic method.

[0146] Specifically, the main purpose of frequency domain data processing is to extract polarization information. It can be divided into the following steps:

[0147] (1) Noise analysis and suppression is achieved by analyzing the original signal and using processing techniques such as superposition, filtering, and smoothing to eliminate various interferences; (2) Normalization of device coefficients is aimed at eliminating the influence of device parameters and instrument systems; (3) Normalization of current and frequency response; (4) Browsing frequency domain data and deleting zero channels and bad pixels.

[0148] In addition, repeated measurement points can be analyzed to evaluate the quality of the collected data and the data from repeated measurement points can be averaged.

[0149] In summary, in the embodiments of the present invention, the target area is first identified in the area to be explored through regional characteristics, thereby narrowing the scope of prospecting; the target area is detected by using the time-frequency electromagnetic method, and the profile anomaly characteristics are studied and analyzed in both the time domain and the frequency domain. The Occam inversion model is obtained by calculating the apparent resistivity and total longitudinal conductivity parameters, and then the polarizability inversion is performed based on the frequency meal data. Finally, an effective deep anomaly geological characteristic model is established as a basis for effectively delineating deep anomalies. This provides a basis for better arrangement of drilling projects, reduces the number of exploration drillings, greatly reduces exploration costs, and can also shorten prospecting time, significantly improving the exploration efficiency of concealed rare earth deposits in thick coverage areas.

[0150] See also Figure 4 , is a schematic structural diagram of a rare earth mine deep exploration system based on time-frequency electromagnetics provided by an embodiment of the present invention, comprising:

[0151] The target area identification module 201 is used to identify the target area in the area to be explored according to the regional characteristics of the area to be explored;

[0152] The data preprocessing module 202 is used to collect exploration data of the target area by using the time-frequency electromagnetic method, and preprocess the exploration data to obtain first time domain data and first frequency domain data;

[0153] A time domain analysis module 203 is used to calculate the apparent resistivity of the target area according to the first time domain data, and calculate the total longitudinal conductivity parameter based on the apparent resistivity;

[0154] A model calculation module 204 is used to construct an Occam inversion model related to the total longitudinal conductivity parameter;

[0155] A frequency calculation module 205 is used to perform dual-frequency phase calculation and dual-frequency amplitude calculation according to the first frequency domain data to obtain a frequency parameter set of the target area;

[0156] The polarizability inversion module 206 is used to fit the Occam inversion model and perform polarizability inversion based on the frequency parameter set when the fitting result meets the preset error requirement;

[0157] The mineral identification module 207 is used to delineate deep geophysical anomalies in the target area based on the polarizability inversion results.

[0158] Further, the first time domain data includes: angular frequency, magnetic permeability, electric field strength and magnetic field strength;

[0159] The apparent resistivity of the target area is calculated according to the first time domain data, and the total longitudinal conductivity parameters are calculated based on the apparent resistivity, including:

[0160] The apparent resistivity is calculated by the following formula:

[0161]

[0162] Wherein, ρ is the apparent resistivity, ω is the angular frequency, μ is the magnetic permeability, E is the electric field intensity in the first direction, and H is the magnetic field intensity in the second direction, wherein the first direction is perpendicular to the second direction;

[0163] Based on the apparent resistivity, the apparent resistivity curve is obtained, and the total longitudinal conductivity parameter is calculated by the following formula:

[0164]

[0165] Where S is the total longitudinal conductance parameter, ρ min is the minimum value of the apparent resistivity curve before it rises 45°, f min is min The corresponding frequency.

[0166] Furthermore, an Occam inversion model related to the total longitudinal conductivity parameters is constructed, including:

[0167] For the Occam inversion model, the following objective function is constructed:

[0168]

[0169] Where λ is the Lagrange multiplier, W is the error weighting matrix, S is the total longitudinal conductivity parameter, m is the Occam inversion model parameter, G(m) is the forward operator, is the roughness matrix, △ is the first-order Laplace operator, t1 is the regularization parameter corresponding to the first-order Laplace operator, △ 2 is the second-order Laplace operator, and t2 is the regularization parameter corresponding to the second-order Laplace operator.

[0170] Furthermore, dual-frequency phase calculation and dual-frequency amplitude calculation are performed according to the first frequency domain data to obtain a frequency parameter set of the target area, including:

[0171] The dual frequency phase is calculated by the following formula:

[0172]

[0173] In the formula, DFP is the dual frequency phase, ω1 is the fundamental frequency collected, ω3 is the frequency of the third harmonic of ω1, and ω1<ω3, is the phase of the fundamental wave, is the phase of the third harmonic;

[0174] The dual frequency amplitude is calculated by the following formula:

[0175]

[0176] Where P_Amp is the dual frequency amplitude, A L is the low frequency amplitude value, A H is the high frequency amplitude value;

[0177] The dual-frequency phase and dual-frequency amplitude of each frequency point are extracted to obtain a frequency parameter set.

[0178] Furthermore, polarizability inversion is performed based on the frequency parameter set, including:

[0179] The Cole-Cole model is used to invert the polarizability through the following formula:

[0180]

[0181] Where ρ' is the polarizability inversion result, ρ is the apparent resistivity, DFP is the dual-frequency phase, P_Amp is the dual-frequency amplitude, i is the imaginary unit, ω is the angular frequency, τ is the time constant, and c is the distribution parameter of the Cole-Cole model.

[0182] Further, according to the regional characteristics of the area to be explored, a target area is identified in the area to be explored, including:

[0183] Based on regional characteristics of the area to be explored, an initial working area is identified in the area to be explored, wherein the regional characteristics include at least one of element characteristics, surface characteristics, and natural landscape characteristics;

[0184] Perform radioactive gamma measurements on the initial working area and identify the target area based on the measurement results.

[0185] Furthermore, the exploration data of the target area is collected by using the time-frequency electromagnetic method, and the exploration data is preprocessed to obtain first time domain data and first frequency domain data, including:

[0186] Performing raw signal analysis, data current normalization and data denoising on the exploration data to obtain the first time domain data;

[0187] The exploration data is subjected to noise analysis, interference elimination, device coefficient normalization, current and frequency response normalization, and zero track and bad point deletion to obtain the first frequency domain data.

[0188] In summary, in the embodiments of the present invention, the target area is first identified in the area to be explored through regional characteristics, thereby narrowing the scope of prospecting; the target area is detected by using the time-frequency electromagnetic method, and the profile anomaly characteristics are studied and analyzed in both the time domain and the frequency domain. The Occam inversion model is obtained by calculating the apparent resistivity and total longitudinal conductivity parameters, and then the polarizability inversion is performed based on the frequency meal data. Finally, an effective deep anomaly geological characteristic model is established as a basis for effectively delineating deep anomalies. This provides a basis for better arrangement of drilling projects, reduces the number of exploration drillings, greatly reduces exploration costs, and can also shorten prospecting time, significantly improving the exploration efficiency of concealed rare earth deposits in thick coverage areas.

[0189] See also Figure 5, is a schematic diagram of a rare earth mine deep exploration system based on time-frequency electromagnetics provided in an embodiment of the present invention. The rare earth mine deep exploration system based on time-frequency electromagnetics in this embodiment includes: a processor 1, a memory 2, and a computer program stored in the memory 2 and executable on the processor, such as a rare earth mine deep exploration program based on time-frequency electromagnetics. When the processor 1 executes the computer program, the steps in the above-mentioned embodiments of the rare earth mine deep exploration method based on time-frequency electromagnetics are implemented. Alternatively, when the processor 1 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0190] Exemplarily, the computer program can be divided into one or more modules / units, one or more modules / units are stored in a memory and executed by a processor to complete the present invention. One or more modules / units can be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the rare earth deep exploration system based on time-frequency electromagnetic.

[0191] The rare earth mine deep exploration system based on time-frequency electromagnetic may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that the schematic diagram is only an example of the rare earth mine deep exploration system based on time-frequency electromagnetic, and does not constitute a limitation on the rare earth mine deep exploration system based on time-frequency electromagnetic, and may include more or fewer components than shown in the figure, or combine certain components, or different components, for example, the rare earth mine deep exploration system based on time-frequency electromagnetic may also include input and output devices, network access devices, CAN bus, etc.

[0192] An embodiment of the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the deep exploration method for rare earth mines based on time-frequency electromagnetics as described in Embodiment 1 of the present invention.

[0193] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the deep exploration system for rare earth mines based on time-frequency electromagnetics, and uses various interfaces and lines to connect the various parts of the deep exploration system for rare earth mines based on time-frequency electromagnetics.

[0194] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the deep exploration system of rare earth mines based on time-frequency electromagnetic by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0195] Among them, if the module / unit integrated in the deep exploration system of rare earth mines based on time-frequency electromagnetic is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunications signals.

[0196] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0197] The above are preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for deep exploration of rare earth mines based on time-frequency electromagnetics, characterized in that: include: identifying a target area in the area to be explored according to the regional characteristics of the area to be explored; Collecting exploration data of the target area by using a time-frequency electromagnetic method, and preprocessing the exploration data to obtain first time domain data and first frequency domain data; Calculating the apparent resistivity of the target area according to the first time domain data, and calculating the total longitudinal conductivity parameter based on the apparent resistivity; constructing an Occam inversion model related to the total longitudinal conductivity parameter; Perform dual-frequency phase calculation and dual-frequency amplitude calculation according to the first frequency domain data to obtain a frequency parameter set of the target area; Fitting the Occam inversion model, and when the fitting result meets the preset error requirement, performing polarizability inversion based on the frequency parameter set; Based on the polarizability inversion result, a deep geophysical anomaly is delineated in the target area.

2. The deep exploration method for rare earth mines based on time-frequency electromagnetics according to claim 1, characterized in that: The first time domain data includes: angular frequency, magnetic permeability, electric field strength and magnetic field strength; The step of calculating the apparent resistivity of the target area according to the first time domain data, and calculating the total longitudinal conductivity parameter based on the apparent resistivity, comprises: The apparent resistivity is calculated by the following formula: Wherein, ρ is the apparent resistivity, ω is the angular frequency, μ is the magnetic permeability, E is the electric field intensity in the first direction, and H is the magnetic field intensity in the second direction, wherein the first direction is perpendicular to the second direction; Based on the apparent resistivity, an apparent resistivity curve is obtained, and the total longitudinal conductivity parameter is calculated by the following formula: Where S is the total longitudinal conductivity parameter, ρ min is the minimum value of the apparent resistivity curve before the 45° rise, f min is min The corresponding frequency.

3. The deep exploration method for rare earth mines based on time-frequency electromagnetics according to claim 2, characterized in that: The constructing of the Occam inversion model related to the total longitudinal conductivity parameter comprises: For the Occam inversion model, the following objective function is constructed: Where λ is the Lagrange multiplier, W is the error weighting matrix, S is the total longitudinal conductivity parameter, m is the Occam inversion model parameter, G(m) is the forward operator, is the roughness matrix, △ is the first-order Laplace operator, t1 is the regularization parameter corresponding to the first-order Laplace operator, △ 2 is a second-order Laplace operator, and t2 is a regularization parameter corresponding to the second-order Laplace operator.

4. The deep exploration method for rare earth mines based on time-frequency electromagnetics according to claim 3, characterized in that: The performing dual-frequency phase calculation and dual-frequency amplitude calculation according to the first frequency domain data to obtain the frequency parameter set of the target area includes: The dual frequency phase is calculated by the following formula: Wherein, DFP is the dual-frequency phase, ω1 is the fundamental frequency collected, ω3 is the frequency of the third harmonic of ω1, and ω1<ω3, is the phase of the fundamental wave, is the phase of the third harmonic; The dual frequency amplitude is calculated by the following formula: Where P_Amp is the dual frequency amplitude, A L is the low frequency amplitude value, A H is the high frequency amplitude value; The dual-frequency phase and the dual-frequency amplitude of each frequency point are extracted to obtain the frequency parameter set.

5. The method for deep exploration of rare earth mines based on time-frequency electromagnetics according to claim 4, characterized in that: The performing polarizability inversion based on the frequency parameter set comprises: The Cole-Cole model is used to perform the polarizability inversion through the following formula: Wherein, ρ' is the polarizability inversion result, ρ is the apparent resistivity, DFP is the dual-frequency phase, P_Amp is the dual-frequency amplitude, i is the imaginary unit, ω is the angular frequency, τ is the time constant, and c is the distribution parameter of the Cole-Cole model.

6. The method for deep exploration of rare earth mines based on time-frequency electromagnetics according to claim 1, characterized in that: The step of identifying a target area in the area to be explored according to the area characteristics of the area to be explored comprises: Based on the regional characteristics of the area to be explored, an initial working area is identified in the area to be explored, wherein the regional characteristics include at least one of element characteristics, surface characteristics, and natural landscape characteristics; The initial working area is subjected to radioactive gamma measurement, and the target area is identified according to the measurement result.

7. The deep exploration method for rare earth mines based on time-frequency electromagnetics according to claim 1, characterized in that: The method of collecting the exploration data of the target area by using the time-frequency electromagnetic method and preprocessing the exploration data to obtain first time domain data and first frequency domain data includes: Performing original signal analysis, data current normalization, and data denoising on the exploration data to obtain the first time domain data; The exploration data is subjected to noise analysis, interference elimination, device coefficient normalization, current and frequency response normalization, and zero track and bad point deletion to obtain the first frequency domain data.

8. A rare earth mine deep exploration system based on time-frequency electromagnetic, characterized in that: include: A target area identification module, used for identifying a target area in the area to be explored according to the area characteristics of the area to be explored; A data preprocessing module, used to collect exploration data of the target area by using a time-frequency electromagnetic method, and preprocess the exploration data to obtain first time domain data and first frequency domain data; A time domain analysis module, used to calculate the apparent resistivity of the target area according to the first time domain data, and calculate the total longitudinal conductivity parameter based on the apparent resistivity; A model calculation module, used for constructing an Occam inversion model related to the total longitudinal conductivity parameter; A frequency calculation module, used for performing dual-frequency phase calculation and dual-frequency amplitude calculation according to the first frequency domain data to obtain a frequency parameter set of the target area; A polarizability inversion module, used for fitting the Occam inversion model and performing polarizability inversion based on the frequency parameter set when the fitting result meets the preset error requirement; A mineral identification module is used to delineate deep geophysical anomalies in the target area based on the polarizability inversion result.

9. A rare earth mine deep exploration system based on time-frequency electromagnetic, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a method for deep exploration of rare earth mines based on time-frequency electromagnetics as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the deep exploration method for rare earth mines based on time-frequency electromagnetics as described in any one of claims 1 to 7.

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