A collaborative detection method for the transparency of ore concentration areas

Abstract

This application relates to the technical field of general image data processing or generation, and specifically relates to a collaborative detection method for the transparency of ore concentration areas. This method is executed by a processor and includes: obtaining a gravity data set, a magnetic method data set, and an electrical method data set; performing a structure-consistent joint inversion on the gravity data set, the magnetic method data set, and the electrical method data set under the constraint of physical constraint information to form an initial model; forming an optimized model; performing a hybrid joint inversion on the optimized model under the constraint of physical constraint information to obtain wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data; constructing a three-dimensional geological-geophysical model and performing forward calculation on it; obtaining an optimal three-dimensional geological-geophysical model. This application solves the problems that in the process of realizing the transparency of ore concentration areas, the existing joint inversion cannot meet the requirements for high-precision physical property distribution underground for realizing the transparency of ore concentration areas and the low modeling accuracy.

Description

Technical Field

[0001] The present application relates to the technical field of general image data processing or generation, and more particularly, to a collaborative detection method for the transparency of ore concentration areas. Background Art

[0002] During actual work, in the process of realizing the transparency of ore concentration areas, for the joint inversion of multi-geophysical data, existing methods often directly establish the structural consistency relationship of the physical properties corresponding to each geophysical data in the inversion process by using constraint terms such as cross-gradient, without considering the problems of inconsistent resolution and accuracy of different exploration data inversions and the coupling relationship between different fields, which easily leads to distortion of the joint inversion results of multi-geophysical data, the appearance of false physical property anomalies, and the inability to meet the requirements for high-precision underground physical property distribution in realizing the transparency of ore concentration areas.

[0003] In addition, in terms of modeling, the existing three-dimensional modeling process mainly uses the interpretation results of various underground physical properties, that is, lithology and boundary information for modeling, without considering the full utilization of the dominant characteristics between different physical properties obtained by joint inversion, and without verifying whether the final modeling result matches the geophysical exploration data, resulting in a low-precision geological-geophysical model established for the transparency of ore concentration areas that does not conform to the characteristics of actual detection data, and thus there are significant differences in the distribution of geological bodies in actual ore concentration areas. Summary of the Invention

[0004] The purpose of the present application is to provide a collaborative detection method for the transparency of ore concentration areas, which solves the problems that in the process of realizing the transparency of ore concentration areas, the existing joint inversion cannot meet the requirements for high-precision underground physical property distribution in realizing the transparency of ore concentration areas and the low modeling accuracy.

[0005] Technical Solution of the Present Application

[0006] The present application provides a collaborative detection method for the transparency of ore concentration areas, which is executed by a processor and is characterized by including:

[0007] Obtain a gravity data set, a magnetic method data set, and an electrical method data set;

[0008] Perform a structural consistency joint inversion on the gravity data set, the magnetic method data set, and the electrical method data set under the constraint of physical constraint information to form an initial model;

[0009] Obtain a seismic data set and form an optimized model based on the initial model;

[0010] Perform a hybrid joint inversion on the optimized model under the constraint of physical constraint information to obtain wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data;

[0011] Construct a three-dimensional geological-geophysical model based on wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data;

[0012] Perform forward modeling on the three-dimensional geological-geophysical model;

[0013] Adjust the three-dimensional geological-geophysical model based on the gravity data set and the magnetic method data set to obtain an optimal three-dimensional geological-geophysical model.

[0014] In some embodiments, the physical constraint information at least includes borehole data.

[0015] In some embodiments, under the constraint of the physical constraint information, perform a structure-consistent joint inversion on the gravity data set, the magnetic method data set, and the electrical method data set to form an initial model, specifically:

[0016] Divide the target underground space to obtain n layers of closely arranged hexahedral elements;

[0017] Based on the coordinates of the hexahedral elements and the surface observation points, calculate their mapping relationship to obtain the objective function of the observed data and the underground physical properties;

[0018] Add the physical constraint information to the objective function and perform the constraint;

[0019] Based on the gravity data set, the magnetic method data set, and the electrical method data set, perform an inversion on the objective function to obtain an initial model.

[0020] In some embodiments, the obtaining of the seismic data set is specifically:

[0021] Convert density to wave velocity;

[0022] Based on the wave velocity, obtain the seismic data set; where the conversion formula is:

[0023]

[0024] In the formula, v is the wave velocity of the earthquake; both a and b are constants; v is the velocity; is the density.

[0025] In some embodiments, the performing of the hybrid joint inversion on the optimized model under the constraint of the physical constraint information to obtain the wave velocity distribution data, the resistivity distribution data, the density distribution data, and the magnetic susceptibility distribution data is specifically:

[0026] Based on the initial function, perform an inversion on the gravity data set to obtain the gravity inversion density parameter;

[0027] Perform a joint inversion on the seismic data set under the constraint of the physical constraint information to obtain the seismic inversion wave velocity parameter;

[0028] Based on the gravity inversion density parameter and the seismic inversion wave velocity parameter, a hybrid joint inversion formula is obtained;

[0029] Based on the hybrid joint inversion formula, wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data are obtained; the hybrid joint inversion formula is:

[0030]

[0031] In the formula: is the three-dimensional magnetic susceptibility underground; is the three-dimensional resistivity underground; v is the seismic wave velocity; v 0 is the wave velocity value interpreted from borehole data; d 1, d 2 and d 3 represent gravity, magnetic, and electrical measurement data respectively; d 4 is the seismic data; A 1 is the mapping relationship matrix between the underground density and the gravity measurement data, A 2 is the mapping relationship matrix between the underground magnetic susceptibility and the magnetic measurement data, is the mapping relationship function between the resistivity and the electrical measurement data, , and are the gravity, magnetic, and electrical data calculated based on the forward modeling of the existing density, magnetic susceptibility, and resistivity; u 1, u 2, u 3, u 4, u 5, u 6, a 1, a 2, a 3, a 4, a 5, a 6 are regularization factors, all of which are constants; W is the depth weight function matrix; L is a diagonal matrix with the number of dissection elements as diagonal elements, , , are the physical property values interpreted from borehole data; t is the structure consistency constraint function; is the mapping relationship function between the seismic wave velocity and the seismic data; c 1, c 2, u 7, u 8 are regularization factors, all of which are constants.

[0032] In some embodiments, the forward calculation performed on the three-dimensional geological-geophysical model is specifically as follows:

[0033] Divide the space of the three-dimensional geological-geophysical model to obtain n layers of closely arranged hexahedral elements;

[0034] The density of the geological-geophysical model corresponding to a single hexahedral element is used as the density of the hexahedral element;

[0035] The magnetic susceptibility of the geological-geophysical model corresponding to a single hexahedral element is used as the magnetic susceptibility of the hexahedral element;

[0036] Based on the forward formula, calculate the gravity data and magnetic anomaly data of a single hexahedral element at the observation point;

[0037] Calculate the sum of the gravity data and magnetic anomaly data of all hexahedral elements to obtain the total gravity data and total magnetic anomaly data;

[0038] Based on the total gravity data and total magnetic anomaly data, obtain the forward result data.

[0039] In some embodiments, the adjustment of the three-dimensional geological-geophysical model based on the gravity data set and magnetic method data set to obtain the optimal three-dimensional geological-geophysical model is specifically as follows:

[0040] Based on the forward result data, gravity data set, and magnetic method data set, perform adjustment on the three-dimensional geological-geophysical model to obtain the optimal forward result data;

[0041] Perform verification fitting on the three-dimensional geological-geophysical model and the optimal forward result data to obtain the optimal three-dimensional geological-geophysical model.

[0042] The technical solution of this application has at least the following advantages and beneficial effects: This application provides a collaborative detection method for the transparency of ore concentration areas. Based on the coupling relationship and characteristics of various geophysical fields, joint inversion of gravity, magnetic, and electrical data is carried out. Then, seismic data is added to the joint inversion in the form of physical property correlation and the joint inversion is performed again to obtain wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data. Among them, during the two joint inversion processes, the inversion accuracy is improved through the constraint of physical constraint information, and the demand for high-precision physical property distribution underground for the transparency of ore concentration areas is better realized. Then, a three-dimensional geological-geophysical model is built based on the above wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data. By performing forward calculation on the three-dimensional geological-geophysical model, that is, forward calculating the gravity and magnetic responses of the geological-geophysical model. Then, the three-dimensional geological-geophysical model is continuously adjusted to obtain an optimal three-dimensional geological-geophysical model with higher accuracy through interactive potential field data fitting. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic flow chart of the collaborative detection method provided by this application;

[0044] Figure 2 It is a schematic diagram of the integrated geophysical exploration system in Embodiment 1 of this application;

[0045] Figure 3 It is a schematic diagram of the principle of the two joint inversions in Embodiment 1 of this application;

[0046] Figure 4 It is a schematic diagram of the principle of constructing the optimal three-dimensional geological-geophysical model in Embodiment 1 of this application;

[0047] Figure 5 It is a schematic diagram of the result of constructing the optimal three-dimensional geological-geophysical model with a certain mining area as an example in Embodiment 2 of this application;

[0048] Figure 6 It is a schematic diagram of the transparency result of the ore prospecting target area with a certain mountain as an example in Embodiment 2 of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0050] APPLICATION OVERVIEW

[0051] The transparency of the ore concentration area is mainly achieved through geophysical exploration, which mainly includes three parts: data acquisition, inversion, and modeling. First, various geophysical detections such as gravity exploration, magnetic exploration, electrical exploration, seismic exploration, and borehole core measurement are used to obtain the gravity field, magnetic field, electric field, seismic waves, and borehole physical property information of the ore concentration area. Then, joint inversion methods such as cross-gradient are used to jointly invert various geophysical data to obtain physical property distributions such as underground density, magnetism, electric property, and wave velocity. During the joint inversion, borehole physical property constraints are added to improve the compliance of the physical property distribution results with the actual situation. Finally, based on the geological understanding of the area, lithological classification and geological body boundary division are carried out for each physical property combination underground in the ore concentration area, and a three-dimensional geological-geophysical model is formed using lithology, physical properties, and boundary information to achieve the transparency of the ore concentration area and provide a direct basis for prospecting prediction.

[0052] However, for the joint inversion of multi-geophysical data, it is easy to cause the distortion of the joint inversion results of multi-geophysical data, resulting in false physical property anomalies, which cannot meet the requirements of the transparency of the ore concentration area for high-precision underground physical property distribution. At the same time, in the existing technology in terms of modeling, the full utilization of the advantageous characteristics between different physical properties obtained by joint inversion is not considered, resulting in a low-precision geological-geophysical model established for the transparency of the ore concentration area and not conforming to the characteristics of actual detection data, so there are significant differences in the actual distribution of geological bodies in the ore concentration area.

[0053] Based on the above, the present application provides a collaborative detection method for the transparency of the ore concentration area. Based on the coupling relationship and characteristics of each geophysical field, joint inversion of the consistency of the gravity, magnetic, and electrical data structures is carried out, and then the seismic data is added to the joint inversion in the form of physical property correlation and the hybrid joint inversion is performed again to obtain wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data. Among them, during the two joint inversion processes, the inversion accuracy is improved through the constraint of physical constraint information, and the requirements of the transparency of the ore concentration area for high-precision underground physical property distribution are better realized. Then, a three-dimensional geological-geophysical model is built based on the above wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data. By performing forward calculation on the three-dimensional geological-geophysical model, that is, forward calculating the gravity and magnetic responses of the geological-geophysical model, and then continuously adjusting the three-dimensional geological-geophysical model, an optimal three-dimensional geological-geophysical model with higher accuracy and fitted by interactive potential field data is obtained.

[0054] Embodiment 1

[0055] Please refer to Figures 1-6 simultaneously, the present application provides a collaborative detection method for the transparency of the ore concentration area, which is executed by a processor and includes:

[0056] Obtain a gravity data set, a magnetic method data set, and an electrical method data set;

[0057] Perform a structural consistency joint inversion on the gravity data set, magnetic data set, and electrical data set under the constraint of physical constraint information to form an initial model;

[0058] Obtain a seismic data set and form an optimized model based on the initial model;

[0059] Perform a hybrid joint inversion on the optimized model under the constraint of physical constraint information to obtain wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data;

[0060] Construct a three-dimensional geological-geophysical model based on the wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data;

[0061] Perform forward calculation on the three-dimensional geological-geophysical model;

[0062] Adjust the three-dimensional geological-geophysical model based on the gravity data set and magnetic data set to obtain an optimal three-dimensional geological-geophysical model.

[0063] It should be noted that a collaborative detection method for ore concentration area transparency provided in this embodiment, in the actual application of mineral exploration to achieve the transparency of the ore concentration area, the selection of detection methods is clearer, significantly reducing the detection cost; the joint inversion algorithm is more stable and has higher resolution, significantly improving the practical ability of the joint inversion algorithm; the modeling results are more in line with the actual strata and geological bodies, significantly improving the accuracy of the geological-geophysical model, and overall enhancing the practicality of the ore concentration area transparency technology system, solving the problems that in the process of realizing the transparency of the ore concentration area, the existing joint inversion cannot meet the requirements of the high-precision physical property distribution underground for realizing the transparency of the ore concentration area and the low modeling accuracy, and at the same time solving the problems of insufficient accuracy and poor practicality in realizing the transparency of the ore concentration area in the existing technology.

[0064] This application provides a collaborative detection method for ore concentration area transparency that combines the "joint inversion technology of multi-geophysical data driven by multi-field coupling relationship" and the "geological-geophysical modeling technology of interactive potential field data fitting". Hereinafter, the method of this application will be described in detail:

[0065] It should be noted that in the prior art, in the process of realizing the transparency of the ore concentration area, there is no good exploration method selection scheme for geophysical data acquisition. According to the characteristics of geophysical exploration means, there is an optimal geophysical detection means for target bodies at different depths. Without a good exploration method selection scheme, it is impossible to efficiently obtain the exploration data necessary for the transparency of the ore concentration area, resulting in serious waste of human and material resources.

[0066] Based on the above, in some embodiments, in the data acquisition section, such as Figure 2 shown, Figure 2 is a schematic diagram of a comprehensive geophysical exploration system guided by the burial depth information of the target body. According to the characteristics of geophysical data such as gravitational field, magnetic field, electromagnetic field, and seismic wave signals, a comprehensive geophysical exploration system for different target depths shallower than 3 kilometers is established. This system determines the optimal exploration combination scheme required for ore concentration areas at different depths. For the transparency of ore concentration areas shallower than 1 kilometer in burial depth, the optimal exploration combination is gravity measurement, magnetic gradient measurement, transient electromagnetic method measurement, controlled-source audio-frequency magnetotelluric measurement, seismic measurement, and core measurement. For the transparency of ore concentration areas deeper than 2 kilometers in burial depth, the optimal exploration combination is gravity measurement, total magnetic field measurement, controlled-source audio-frequency magnetotelluric / wide-area electromagnetic method measurement, seismic measurement, downhole magnetic triaxial, borehole-to-surface electromagnetic method, and borehole-to-surface seismic measurement. The optimal exploration combination for targets between 1 kilometer and 2 kilometers in burial depth is gravity measurement, magnetic gradient / total magnetic field measurement, controlled-source audio-frequency magnetotelluric / wide-area electromagnetic method measurement, seismic measurement, core measurement, downhole magnetic triaxial, borehole-to-surface electromagnetic method, and borehole-to-surface seismic measurement. In the collaborative exploration method provided in this embodiment, the exploration methods adopted in the data acquisition section are more clearly selected, which can significantly reduce the exploration cost.

[0067] In some embodiments, the physical constraint information at least includes borehole data. Specifically, the physical property amplitude and depth information interpreted from the borehole data are used to constrain the two joint inversions, thereby improving the accuracy of the joint inversion.

[0068] In some embodiments, under the constraint of the physical constraint information, a structural consistency joint inversion is performed on the gravity data set, magnetic method data set, and electrical method data set to form an initial model. Specifically:

[0069] The target underground space is partitioned to obtain n layers of closely arranged hexahedral elements;

[0070] Based on the coordinates of the hexahedral elements and surface observation points, their mapping relationship is calculated to obtain the objective function of the observed data and underground physical properties;

[0071] The physical constraint information is added to the objective function and constrained;

[0072] Based on the gravity data set, magnetic method data set, and electrical method data set, an inversion is performed on the objective function to obtain the initial model.

[0073] It should be noted that please refer to Figure 2 and Figure 3 , Figure 3 are the schematic diagrams of the two joint inversions. On the basis of Figure 3 combined with Figure 2, after obtaining gravity data, seismic data, magnetic data, electrical data, and borehole data based on the optimal detection combination, according to the coupling relationship between geophysical field data such as the gravity field, magnetic field, electromagnetic field, and seismic wave signals, first, under the constraint of the physical property constraint information composed of physical properties and depth information, the gravity data, magnetic data, and electrical data are jointly inversed with structural consistency to obtain density data, magnetic susceptibility data, and resistivity data, and an initial model is obtained based on the density data, magnetic susceptibility data, and resistivity data. Among them, when performing the joint inversion of structural consistency, the underground space of the target needs to be divided into n layers of closely arranged hexahedral elements, and then the mapping relationship between each hexahedral element and the surface observation point coordinates is calculated to establish an objective function for the observed data and the underground physical properties. By solving the objective function, the inversion of the underground physical properties is realized. Specifically, some constraint conditions need to be added to the objective function to make the inversion result closer to the actual situation and the multi-field coupling characteristics. The objective function established above is as follows:

[0074]

[0075] In the formula, is the three-dimensional density underground; is the three-dimensional magnetic susceptibility underground; is the three-dimensional resistivity underground; d 1 is the gravity data; d 2 is the magnetic data; d 3 is the electrical measurement data; A 1 is the mapping relationship matrix between the underground density and the gravity measurement data; A 2 is the mapping relationship matrix between the underground magnetic susceptibility and the magnetic measurement data; is the mapping relationship function between the resistivity and the electrical measurement data; Based on the gravity data calculated by the forward modeling of the existing density, magnetic susceptibility, and resistivity, is the magnetic data calculated by the forward modeling of the existing density, magnetic susceptibility, and resistivity, is the electrical data calculated by the forward modeling of the existing density, magnetic susceptibility, and resistivity; u 1, u 2, u 3, u 4, u 5, u 6, a 1, a 2, a 3, a 4, a 5, a 6 are all regularization factors, all of which are constants, used to balance the weights of each two-norm term in the objective function, and belong to empirical parameters, preferably integer powers of 10; Wis the depth weight function matrix, which is used to weaken the trend that the weight of the inversion physical property results in the shallow layer is much greater than that in the deep layer; L is a diagonal matrix with the number of dissection units as diagonal elements. If the physical property results interpreted by drilling pass through a certain dissection unit, the corresponding diagonal element of this dissection unit is 1, otherwise it is 0; and and are the physical property values interpreted from borehole data; t is the structural consistency constraint function, which is used to establish the relationship between density, magnetic susceptibility and resistivity, so as to realize joint inversion. Among them, W and t The specific formulas are as follows:

[0076]

[0077]

[0078]

[0079] Among them, diag () forms a diagonal matrix by taking the one-dimensional matrix elements in the brackets as the diagonal elements of the diagonal matrix, where z from 1 to z n is n the depth values corresponding to each layer in the layer dissection unit; is an empirical parameter, preferably taking 1 - 1.5; i and j can be and and according to the different variables in the actual objective function.

[0080] It should be noted that the solution process of the objective function is an iterative process. By first assigning and and and v to be 0 or the initial value, the objective function is gradually fitted, and finally the four physical properties of and and and v under the ground in the ore concentration area are obtained.

[0081] Furthermore, a seismic data set is obtained, specifically:

[0082] Convert density to wave velocity;

[0083] Based on the wave velocity, a seismic data set is obtained; among them, the conversion formula is:

[0084]

[0085] Wherein, v is the wave velocity of the earthquake; both a and b are constants; v is the velocity; is the density.

[0086] Specifically, after performing the structural consistency joint inversion, an initial model is obtained. The initial model includes the initial wave velocity. By converting the density into the initial wave velocity and using this initial wave velocity as the initial wave velocity of the initial model in the hybrid joint inversion, the hybrid joint inversion is performed under the constraint of the physical property constraint information composed of the physical property and depth information. It should be noted that a and b are obtained by solving a system of equations after measuring the density and wave velocity of the rock specimens in the ore concentration area.

[0087] In some embodiments, the hybrid joint inversion is performed on the optimized model under the constraint of the physical constraint information to obtain the wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data. Specifically:

[0088] Based on the initial function, an inversion is performed on the gravity data set to obtain the gravity inversion density parameter;

[0089] The seismic data set is subjected to joint inversion under the constraint of the physical constraint information to obtain the seismic inversion wave velocity parameter;

[0090] Based on the gravity inversion density parameter and the seismic inversion wave velocity parameter, a hybrid joint inversion formula is obtained;

[0091] Based on the hybrid joint inversion formula, the wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data are obtained; the hybrid joint inversion formula is:

[0092]

[0093] Wherein: is the three-dimensional magnetic susceptibility underground; is the three-dimensional resistivity underground; v is the seismic wave velocity; v 0 is the wave velocity value interpreted from the borehole data; d 1, d 2 and d 3 respectively represent the gravity, magnetic and electrical measurement data; d 4 is the seismic data; A 1 is the mapping relationship matrix between the underground density and the gravity measurement data, A 2 is the mapping relationship matrix between the underground magnetic susceptibility and the magnetic measurement data, is the mapping relationship function between the resistivity and the electrical measurement data, , and are the gravity, magnetic and electrical data calculated based on the existing density, magnetic susceptibility and resistivity forward calculations; u 1,u 2、 u 3、 u 4、 u 5、 u 6、 a 1、 a 2、 a 3、 a 4、 a 5、 a 6 is the regularization factor, all being constants; W is the depth weight function matrix; L is a diagonal matrix of the diagonal elements of the number of subdivision units, 、 、 are the physical property values for borehole data interpretation; t is the structural consistency constraint function; is the mapping relationship function between seismic wave velocity and seismic data; c 1、 c 2、 u 7、 u 8 is the regularization factor, all being constants.

[0094] It should be noted that, please refer to Figure 2 and Figure 3 After performing the joint inversion of structural consistency, the wave velocity data is obtained through the empirical conversion formula between density and wave velocity, and then the wave velocity data is input into the initial model to form an optimized model. Under the constraint of the physical property constraint information composed of physical properties and depth information, the joint inversion of the mixture of structural consistency and physical property correlation is performed on the density data, magnetic susceptibility data, resistivity data, and wave velocity data in the optimized model to obtain the final wave velocity distribution data, density distribution data, magnetic susceptibility distribution data, and resistivity distribution data.

[0095] In some embodiments, forward calculation is performed on the three-dimensional geological-geophysical model, specifically:

[0096] The space of the three-dimensional geological-geophysical model is partitioned to obtain n layers of closely arranged hexahedral elements;

[0097] The density of the geological-geophysical model corresponding to a single hexahedral element is used as the density of this hexahedral element;

[0098] The magnetic susceptibility of the geological-geophysical model corresponding to a single hexahedral element is used as the magnetic susceptibility of this hexahedral element;

[0099] Based on the forward formula, the gravity data and magnetic anomaly data of a single hexahedral element at the observation point are calculated;

[0100] The sum of the gravity data and magnetic anomaly data of all hexahedral elements is calculated to obtain the total gravity data and total magnetic anomaly data;

[0101] Based on the total gravity data and the total magnetic anomaly data, forward modeling result data is obtained.

[0102] It should be noted that a three-dimensional geological-geophysical model can be preliminarily formed through four physical properties, namely wave velocity distribution, density distribution, magnetic susceptibility distribution, and resistivity distribution. However, the fitting degree between the model and the measured data is unknown. Therefore, forward modeling calculations need to be performed on the model, and by adjusting the geological bodies and formation boundaries of the model, the forward modeling results of the model can be made to conform to the measured data. Among them, due to the simplicity of forward modeling calculations, gravity data and magnetic method data are used to verify the fitting degree between the three-dimensional geological-geophysical model and the measured data and serve as the basis for model adjustment. During the forward modeling calculation of the gravity and magnetic responses of the three-dimensional geological-geophysical model, first, the hexahedral meshing of the three-dimensional geological-geophysical model in space needs to be carried out closely. The density and magnetic susceptibility of the geological-geophysical model corresponding to each meshing unit are used as the density and magnetic susceptibility of this meshing unit. Then, through the forward modeling formula, the gravity and magnetic anomalies of each meshing unit at the observation point are calculated. Finally, the gravity and magnetic anomalies calculated for all meshing units are summed up to obtain the forward modeling calculation results of the gravity and magnetic responses of the three-dimensional geological-geophysical model. Among them, the calculation formulas for the gravity and magnetic anomalies of a single meshing unit at the observation point are as follows:

[0103]

[0104] In the formula, ; G is the universal gravitational constant, which is 6.67×10 -11 Nm 2 / kg 2 ; is the coordinate of the observation point; is the minimum coordinate point of the hexahedral element in the rectangular coordinate system; is the maximum coordinate point of the hexahedral element in the rectangular coordinate system. The magnetic anomaly consists of the direction of the geomagnetic field and the three components of the magnetic anomaly. The specific formula is:

[0105]

[0106] In the formula, I 0 and D 0 are the inclination and declination of the geomagnetic field respectively. The three components of the magnetic anomaly B x , B y and B z The formulas are respectively:

[0107]

[0108]

[0109]

[0110] Among them, is the magnetic permeability in vacuum, which is H / m; H is the geomagnetic field intensity; i is the magnetic dip angle of the geological body, is the magnetic declination of the geological body. When there is no remanent magnetism, it is consistent with the dip angle and declination of the geomagnetic field.

[0111] In some embodiments, based on the gravity data set and the magnetic method data set, the three-dimensional geological-geophysical model is adjusted to obtain the optimal three-dimensional geological-geophysical model. Specifically:

[0112] Based on the forward modeling result data, the gravity data set, and the magnetic method data set, the three-dimensional geological-geophysical model is adjusted to obtain the optimal forward modeling result data;

[0113] The three-dimensional geological-geophysical model and the optimal forward modeling result data are verified and fitted to obtain the optimal three-dimensional geological-geophysical model.

[0114] It should be noted that please refer to Figure 4 , Figure 4 is the schematic diagram for constructing the optimal three-dimensional geological-geophysical model. By making full use of the vertical resolution advantages of wave velocity and resistivity in the geophysical joint inversion results, and the horizontal resolution advantages of density and magnetic susceptibility in the joint inversion results, the structural models of formation interfaces, faults, the top and bottom boundaries of geological bodies, and the horizontal boundaries of geological bodies are constructed by referring to several physical property distributions respectively. Based on the structural models, the physical property means within the corresponding ranges of each formation and geological body are assigned to the structural models. At the same time, based on the correspondence between the lithology and physical properties in the ore concentration area, the strata and geological bodies with multiple physical property combinations are classified by lithology to obtain a preliminary three-dimensional geological-geophysical model. The three-dimensional geological-geophysical model with physical property information is subjected to gravity and magnetic forward modeling and compared with the actually measured gravity and magnetic data. Through human-computer interaction, the boundaries of the strata or geological bodies of the geological-geophysical model are adjusted so that the forward modeling results of the model fit the actually measured gravity and magnetic data, and the optimal geological-geophysical model that meets the requirements of the transparency of the ore concentration area is obtained.

[0115] Example 2

[0116] Please refer to Figures 1-6 together. On the basis of Example 1, taking a certain ore concentration area in a certain place as an example, multi-geophysical field exploration, joint inversion, and modeling are carried out, and finally a transparent model of the ore concentration area 3 kilometers underground in the study area is obtained to realize ore prospecting prediction. Specifically as follows:

[0117] In a certain ore concentration area in a certain place, in order to discover more ore deposits, according to the method provided in this embodiment, based on the analysis of density, magnetic susceptibility and lithological relationship, the lithology and underground structure were identified. As Figure 5 shown, the three-dimensional spatial characteristics of 5 main lithologies were characterized, and transparency was initially achieved.

[0118] The results show that the possible lithologies of the geological bodies with the highest density in this area are: skarn, anhydrite rock, basic rock mass or other geological bodies with higher density; intermediate-basic igneous rocks and intermediate-acid igneous rocks are mainly distributed at the position of the intersection line between a certain place and its adjacent area, but the burial depths are different; sedimentary rocks from the Triassic to the Ordovician are mainly distributed on both sides of the Ningwu-Fanchang magmatic rock belt; Quaternary cover and red basin sedimentary lithologies are mainly distributed in a certain basin and another basin area. According to the transparency results, a number of deep prospecting target areas were predicted in the study area, and it is considered that the southern section of the volcanic rock basin in a certain place is a favorable area for searching for "Daye-type" copper-iron ore deposits. Industrial-value copper-zinc ore bodies were found in several layers at ZK02 in the predicted target area of a certain mountain in a certain place. As Figure 6 shown, there is a certain correlation between geophysical and geochemical anomalies and deep polymetallic ore bodies, and there is the potential for forming copper-lead-zinc polymetallic ore deposits.

[0119] The above is only the preferred embodiment of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A collaborative detection method for the transparency of ore concentration areas, which is executed by a processor, characterized in that, Including: Obtaining a gravity data set, a magnetic method data set, and an electrical method data set; Performing a structure-consistent joint inversion on the gravity data set, the magnetic method data set, and the electrical method data set under the constraint of physical constraint information to form an initial model; Obtaining a seismic data set and forming an optimized model based on the initial model, specifically: Converting density to wave velocity; based on the wave velocity, obtaining a seismic data set; where the conversion formula is: ; Wherein, v is the wave velocity of the earthquake; both a and b are constants; v is the velocity; is the density; Performing a hybrid joint inversion on the optimized model under the constraint of physical constraint information to obtain wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data, specifically: Performing an inversion on the gravity data set based on an initial function to obtain gravity inversion density parameters; Performing a joint inversion on the seismic data set under the constraint of physical constraint information to obtain seismic inversion wave velocity parameters; Obtaining a hybrid joint inversion formula based on the gravity inversion density parameters and the seismic inversion wave velocity parameters; Obtaining wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data based on the hybrid joint inversion formula; the hybrid joint inversion formula is: ; In the formula: is the underground three-dimensional magnetic susceptibility; is the underground three-dimensional resistivity; v is the seismic wave velocity; v 0 is the wave velocity value interpreted from borehole data; d 1, d 2 and d 3 represent gravity, magnetic and electrical measurement data respectively; d 4 is the seismic data; A 1 is the mapping relationship matrix between the underground density and gravity measurement data, A 2 is the mapping relationship matrix between the underground magnetic susceptibility and magnetic measurement data, is the mapping relationship function between the resistivity and electrical measurement data, , and are the gravity, magnetic and electrical data calculated by forward modeling based on the existing density, magnetic susceptibility and resistivity; u 1, u 2, u 3, u 4, u 5, u 6, a 1, a 2, a 3, a 4, a 5, a 6 are regularization factors, all of which are constants; W is the depth weight function matrix; L is the diagonal matrix of the diagonal elements of the number of subdivision units, , , are the physical property values interpreted from borehole data; t is the structural consistency constraint function; is the mapping relationship function between the seismic wave velocity and seismic data; c 1, c 2, u 7, u 8 are regularization factors, all of which are constants; Constructing a three-dimensional geological-geophysical model based on the wave velocity distribution data, the resistivity distribution data, the density distribution data, and the magnetic susceptibility distribution data; Performing forward calculation on the three-dimensional geological-geophysical model, specifically: Dividing the space of the three-dimensional geological-geophysical model to obtain n layers of closely arranged hexahedral elements; Taking the density of the geological-geophysical model corresponding to a single hexahedral element as the density of the hexahedral element; Taking the magnetic susceptibility of the geological-geophysical model corresponding to a single hexahedral element as the magnetic susceptibility of the hexahedral element; Calculating the gravity data and magnetic anomaly data of a single hexahedral element at an observation point based on a forward formula; Calculating the sum of the gravity data and magnetic anomaly data of all hexahedral elements to obtain total gravity data and total magnetic anomaly data; Obtaining forward result data based on the total gravity data and the total magnetic anomaly data; Adjusting the three-dimensional geological-geophysical model based on the gravity data set and the magnetic method data set to obtain an optimal three-dimensional geological-geophysical model, specifically: Adjusting the three-dimensional geological-geophysical model based on the forward result data, the gravity data set, and the magnetic method data set to obtain optimal forward result data; Performing verification fitting on the three-dimensional geological-geophysical model and the optimal forward result data to obtain an optimal three-dimensional geological-geophysical model.

2. The collaborative detection method according to claim 1, characterized in that The physical constraint information at least includes borehole data.

3. The collaborative detection method according to claim 1, characterized in that The performing a structure-consistent joint inversion on the gravity data set, the magnetic method data set, and the electrical method data set under the constraint of physical constraint information to form an initial model is specifically: Dividing the target underground space to obtain n layers of closely arranged hexahedral elements; Calculating its mapping relationship based on the hexahedral elements and the surface observation point coordinates to obtain an objective function of the observed data and the underground physical properties; Adding the physical constraint information to the objective function and performing a constraint; Performing an inversion on the objective function based on the gravity data set, the magnetic method data set, and the electrical method data set to obtain an initial model.

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