Cooperative detection method for ore concentration area transparency

Abstract

The invention relates to the technical field of general image data processing or generation, in particular to a collaborative detection method for ore concentration area transparency, which is executed by a processor and comprises the following steps: acquiring a gravity data set, a magnetic method data set and an electrical method data set; executing 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; forming an optimization model; executing hybrid joint inversion on the optimization 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 geology-geophysical model and performing forward modeling calculation on the model; and an optimal three-dimensional geology-geophysics model is obtained. According to the method and the device, the problems that in the process of realizing the transparency of the ore concentration area, the existing joint inversion cannot meet the requirement of realizing the transparency of the ore concentration area on underground high-precision physical property distribution and the modeling precision is low are solved.

Description

Technical Field

[0001] The present application relates to the general field of image data processing or generation technology, and in particular to a collaborative detection method for transparency of mining areas. Background Art

[0002] In actual work, in the process of realizing transparency of mineral clusters, for the joint inversion of multiple geophysical data, existing methods often use constraints such as cross gradients to directly establish the structural consistency relationship between the physical properties of each geophysical data during the inversion process. This does not take into account the problems of inconsistent resolution and accuracy of inversion of different exploration data and the coupling relationship between different fields. This can easily lead to distortion of the results of the joint inversion of multiple geophysical data and the occurrence of false physical property anomalies, and cannot meet the demand for high-precision underground physical property distribution to realize transparency of mineral clusters.

[0003] In addition, in terms of modeling, the existing three-dimensional modeling process mainly uses the interpretation results of various underground physical properties, namely lithology and boundary information for modeling, without considering the full utilization of the advantageous characteristics between different physical properties obtained by joint inversion, and without verifying whether the final modeling results match the geophysical exploration data. As a result, the geological-geophysical model established for the transparency of the mineral cluster area is of low accuracy and does not conform to the actual detection data characteristics, resulting in large differences in the distribution of geological bodies in the actual mineral cluster area. Summary of the invention

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

[0005] The technical solution of this application: The present application provides a collaborative detection method for transparent mining areas, which is executed by a processor and is characterized by comprising: Obtain gravity data sets, magnetic data sets, and electrical data sets; Perform structural consistency joint inversion on gravity data set, magnetic data set and electrical data set under the constraint of physical constraint information to form an initial model; Obtaining seismic data sets and forming an optimized model based on the initial model; Perform hybrid joint inversion on the optimization model under the constraints of physical constraint information to obtain wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data; Construct a three-dimensional geological-geophysical model based on wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data; Perform forward modeling on three-dimensional geological-geophysical models; The three-dimensional geological-geophysical model is adjusted based on the gravity data set and the magnetic data set to obtain the optimal three-dimensional geological-geophysical model.

[0006] In some embodiments, the physical constraint information includes at least drilling data.

[0007] In some embodiments, the gravity data set, the magnetic data set, and the electrical data set are subjected to structural consistency joint inversion under the constraints of physical constraint information to form an initial model, specifically: Divide the target underground space to obtain n layers of closely arranged hexahedral units; Based on the coordinates of the hexahedral unit and the surface observation point, the mapping relationship is calculated to obtain the objective function of the observation data and underground physical properties; Add physical constraint information to the objective function and enforce the constraints; Based on the gravity data set, magnetic data set and electrical data set, the objective function is inverted to obtain the initial model.

[0008] In some embodiments, the obtaining of the seismic data set specifically includes: Convert density to wave speed; Based on the wave velocity, the seismic data set is obtained; the conversion formula is:

[0009] In the formula, v is the wave velocity of the earthquake; a and b are constants; v is the velocity; is the density.

[0010] In some embodiments, the optimization model is subjected to hybrid joint inversion under the constraints of physical constraint information to obtain wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data, specifically: Based on the initial function, the gravity data set is inverted to obtain the gravity inversion density parameters; Perform joint inversion on the seismic data set under the constraints of physical constraint information to obtain seismic inversion wave velocity parameters; Based on the gravity inversion density parameters and the seismic inversion wave velocity parameters, a hybrid joint inversion formula is obtained; 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:

[0011] Where: is the three-dimensional magnetic susceptibility of the subsurface; is the three-dimensional resistivity of the subsurface; 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 represents gravity, magnetic and electrical measurement data respectively; d 4 is earthquake data; A 1 is the mapping relationship matrix between underground density and gravity measurement data, A 2 is the mapping relationship matrix between underground magnetic susceptibility and magnetic measurement data, is the mapping function between resistivity and electrical measurement data, , and Gravity, magnetic and electrical data based on forward modeling of 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 is the regularization factor, which is a constant; W is the depth weight function matrix; L is the diagonal matrix of the number of diagonal elements of the subdivision unit, , , is the physical property value interpreted from the drilling data; 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, which is a constant.

[0012] In some embodiments, the forward modeling of the three-dimensional geological-geophysical model is specifically performed as follows: The space of the three-dimensional geological-geophysical model is divided to obtain n layers of closely arranged hexahedral units; The density of the geological-geophysical model corresponding to a single hexahedral unit is taken as the density of the hexahedral unit; The magnetic susceptibility of the geological-geophysical model corresponding to a single hexahedral unit is taken as the magnetic susceptibility of the hexahedral unit; Based on the forward modeling formula, the gravity data and magnetic anomaly data of a single hexahedral unit at the observation point are calculated; Calculate the sum of gravity data and magnetic anomaly data of all hexahedral units to obtain total gravity data and total magnetic anomaly data; Based on the total gravity data and total magnetic anomaly data, forward modeling result data are obtained.

[0013] In some embodiments, the three-dimensional geological-geophysical model is adjusted based on the gravity data set and the magnetic data set to obtain the optimal three-dimensional geological-geophysical model, specifically: Based on the forward modeling data, gravity data set, and magnetic data set, the 3D geological-geophysical model is adjusted to obtain the optimal forward modeling data; The three-dimensional geological-geophysical model is verified and fitted with the optimal forward modeling result data to obtain the optimal three-dimensional geological-geophysical model.

[0014] The technical solution of the present application has at least the following advantages and beneficial effects: The present application provides a collaborative detection method for transparency of mineral concentration areas, based on the coupling relationship and characteristics of each geophysical field, the joint inversion of gravity, magnetic method and electrical method data is performed, and then the seismic data is added to the joint inversion in the form of physical property correlation to perform the joint inversion again, and wave velocity distribution data, resistivity distribution data, density distribution data and magnetic susceptibility distribution data are obtained, wherein, in the process of the two joint inversions, the constraints of physical constraint information are used to improve the inversion accuracy, and better realize the demand for high-precision underground physical property distribution for transparency of mineral concentration areas. Then, a three-dimensional geological-geophysical model is built based on the above-mentioned wave velocity distribution data, resistivity distribution data, density distribution data and magnetic susceptibility distribution data, and the three-dimensional geological-geophysical model is forward calculated, that is, the gravity and magnetic response of the geological-geophysical model is forward calculated, and then, the three-dimensional geological-geophysical model is continuously adjusted to obtain the optimal three-dimensional geological-geophysical model with higher accuracy fitted by interactive potential field data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of the process flow of the collaborative detection method provided in this application; Figure 2 This is a schematic diagram of the comprehensive geophysical detection system in Example 1 of the present application; Figure 3 This is a schematic diagram of the two joint inversions in Example 1 of the present application; Figure 4 A schematic diagram of the optimal three-dimensional geological-geophysical model constructed in Example 1 of the present application; Figure 5 This is a schematic diagram of the results of constructing an optimal three-dimensional geological-geophysical model using a certain mining area as an example in Example 2 of the present application; Figure 6 This is a schematic diagram of the transparency results of the prospecting target area taking a certain mountain as an example in Example 2 of the present application. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0017] Application Overview The transparency of mineral clusters is mainly achieved through geophysical exploration, which mainly consists of three parts: data collection, inversion, and modeling. First, various geophysical explorations 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 mineral cluster area. Then, joint inversion methods such as cross gradient are used to jointly invert various geophysical data to obtain the distribution of underground density, magnetism, electrical properties, wave velocity, and other physical properties. During the joint inversion, borehole physical property constraints are added to improve the degree of conformity between the physical property distribution results and the actual situation. Finally, based on the geological knowledge of the area, the various physical property combinations underground in the mineral cluster area are classified into lithology and the boundaries of geological bodies are divided. The lithology, physical properties, and boundary information are used to form a three-dimensional geological-geophysical model to achieve transparency in the mineral cluster area and provide a direct basis for prospecting prediction.

[0018] However, the joint inversion of multiple geophysical data can easily lead to distortion of the results of the joint inversion of multiple geophysical data and the occurrence of false physical property anomalies, which cannot meet the demand for high-precision underground physical property distribution for achieving transparency of mineral clusters. At the same time, in terms of modeling, the existing technology does not consider the full utilization of the advantageous characteristics between different physical properties obtained by joint inversion, resulting in the geological-geophysical model established for the transparency of mineral clusters having low accuracy and not conforming to the actual detection data characteristics, resulting in large differences in the distribution of geological bodies in actual mineral clusters.

[0019] Based on the foregoing, the present application provides a collaborative detection method for transparency of mineral concentration areas, based on the coupling relationship and characteristics of each geophysical field, the gravity, magnetic method, and electrical method data structure consistency joint inversion is performed, and then the seismic data is added to the joint inversion in the form of physical property correlation to perform the hybrid joint inversion again, and the wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data are obtained. In the process of the two joint inversions, the physical constraint information is constrained to improve the inversion accuracy, and the demand for underground high-precision physical property distribution for transparency of mineral concentration areas is better realized. Then, a three-dimensional geological-geophysical model is built based on the above-mentioned wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data, and the three-dimensional geological-geophysical model is forward calculated, that is, the gravity and magnetic response of the geological-geophysical model is forward calculated, and then, the three-dimensional geological-geophysical model is continuously adjusted to obtain the optimal three-dimensional geological-geophysical model with higher accuracy fitted by interactive potential field data.

[0020] Example 1 Please refer to Figure 1-Figure 6 The present application provides a collaborative detection method for transparent mining areas, which is executed by a processor and includes: Obtain gravity data sets, magnetic data sets, and electrical data sets; Perform structural consistency joint inversion on gravity data set, magnetic data set and electrical data set under the constraint of physical constraint information to form an initial model; Obtaining seismic data sets and forming an optimized model based on the initial model; Perform hybrid joint inversion on the optimization model under the constraints of physical constraint information to obtain wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data; Construct a three-dimensional geological-geophysical model based on wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data; Perform forward modeling on three-dimensional geological-geophysical models; The three-dimensional geological-geophysical model is adjusted based on the gravity data set and the magnetic data set to obtain the optimal three-dimensional geological-geophysical model.

[0021] It is worth noting that the collaborative detection method for the transparency of mineral clusters provided in this embodiment is clearer in the selection of detection methods and significantly reduces the detection cost when it is actually applied to the process of realizing the transparency of mineral clusters in mineral exploration; the joint inversion algorithm is more stable and has a higher resolution, which significantly improves the practical ability of the joint inversion algorithm; the modeling results are more consistent with the actual stratigraphic and geological conditions, which significantly improves the accuracy of the geological-geophysical model, and generally improves the practicality of the technical system for the transparency of mineral clusters. It solves the problem that the existing joint inversion cannot meet the demand for high-precision underground physical property distribution and low modeling accuracy in realizing the transparency of mineral clusters in the process of realizing the transparency of mineral clusters, and at the same time solves the problems of insufficient accuracy and poor practicality in realizing the transparency of mineral clusters in the prior art.

[0022] This application provides a collaborative detection method for transparent mining areas that combines "multi-geophysical data joint inversion technology driven by multi-field coupling relationships" and "geological-geophysical modeling technology for interactive potential field data fitting". The following is a detailed description of the method of this application: It should be noted that in the prior art, in the process of achieving transparency in mineral clusters, there is no good exploration method selection plan for geophysical data collection. According to the characteristics of geophysical exploration methods, targets at different depths have the best geophysical detection methods. Without a good exploration method selection plan, it is impossible to efficiently obtain the exploration data necessary for the transparency of mineral clusters, resulting in serious waste of human and material resources.

[0023] Based on the above, in some embodiments, in the data collection part, such as Figure 2 As shown, Figure 2 The schematic diagram of the comprehensive geophysical detection system guided by the target body's buried depth information is shown in the figure. Based on the characteristics of geophysical data such as gravity field, magnetic field, electromagnetic field, and seismic wave signals, a comprehensive geophysical detection system for different target depths shallower than 3 kilometers is established. The system determines the optimal detection combination required for mineral concentration areas at different depths. For the transparency of mineral concentration areas with a buried depth of less than 1 kilometer, the optimal detection combination is gravity measurement, magnetic gradient measurement, transient electromagnetic method measurement, controlled source audio magnetotelluric measurement, seismic measurement, and core measurement. For the transparency of mineral concentration areas with a buried depth of more than 2 kilometers, the optimal detection combination is gravity measurement, total magnetic field measurement, controlled source audio magnetotelluric / wide-area electromagnetic method measurement, seismic measurement, well magnetic three-component, ground-well electromagnetic method, and ground-well seismic measurement. The optimal detection combination for targets with a buried depth between 1 km and 2 km is gravity measurement, magnetic gradient / total magnetic field measurement, controlled source audio magnetotelluric / wide-area electromagnetic method measurement, seismic measurement, core measurement, well magnetic three-component, ground-well electromagnetic method, and ground-well seismic measurement. In the collaborative detection method provided in this embodiment, the detection method adopted by the data collection part is more clearly selected and can significantly reduce the detection cost.

[0024] In some embodiments, the physical constraint information includes at least drilling data. Specifically, the physical property amplitude and depth information interpreted by the drilling data are used to constrain the two joint inversions, thereby improving the accuracy of the joint inversion.

[0025] In some embodiments, a structural consistency joint inversion is performed on the gravity data set, the magnetic data set, and the electrical data set under the constraints of the physical constraint information to form an initial model, specifically: Divide the target underground space to obtain n layers of closely arranged hexahedral units; Based on the coordinates of the hexahedral unit and the surface observation point, the mapping relationship is calculated to obtain the objective function of the observation data and underground physical properties; Add physical constraint information to the objective function and enforce the constraints; Based on the gravity data set, magnetic data set and electrical data set, the objective function is inverted to obtain the initial model.

[0026] Please note that, please refer to Figure 2 and Figure 3 , Figure 3 The schematic diagram of the two joint inversions is shown in Figure 3 Based on the combination Figure 2 After obtaining gravity data, seismic data, magnetic data, electrical data and drilling data based on the best detection combination, according to the coupling relationship between geophysical field data such as gravity field, magnetic field, electromagnetic field, seismic wave signal, etc., firstly, the gravity data, magnetic data and electrical data are subjected to structural consistency joint inversion under the constraint of physical property constraint information composed of physical property and depth information to obtain density data, magnetic susceptibility data and resistivity data, and then the initial model is obtained based on density data, magnetic susceptibility data and resistivity data. Among them, when performing structural consistency joint inversion, it is necessary to divide the underground space of the target into n Layers of closely arranged hexahedral units are formed, and then the mapping relationship between each hexahedral unit and the coordinates of the surface observation points is calculated to establish the objective function of the observation data and the underground physical properties. The inversion of the underground physical properties is achieved by solving the objective function. In detail, some constraints need to be added to the objective function to make the inversion result closer to the actual situation and multi-field coupling characteristics. The objective function established based on the above is as follows:

[0027] In the formula, is the three-dimensional density of the subsurface; is the three-dimensional magnetic susceptibility of the subsurface; is the three-dimensional resistivity of the subsurface; d 1 is gravity data; d 2 is magnetic data; d 3 is the electrical measurement data;A 1 is the mapping relationship matrix between underground density and gravity measurement data; A 2 is the mapping relationship matrix between underground magnetic susceptibility and magnetic measurement data; It is the mapping function between resistivity and electrical measurement data; Based on the existing gravity data of density, magnetic susceptibility and resistivity forward calculation, Based on the magnetic data of density, magnetic susceptibility and resistivity forward modeling, It is electrical data calculated based on the existing density, magnetic susceptibility and resistivity forward modeling; 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, which are constants used to balance the weights of the two-norm terms in the objective function. They are empirical parameters and preferably integer powers of 10; W is the depth weight function matrix, which is used to reduce the tendency that the weight of the inversion physical property results in the shallow layer is much greater than that in the deep layer; L is the diagonal matrix of the number of subdivision units, and if the physical property results of the drilling interpretation pass through a subdivision unit, the diagonal element corresponding to the subdivision unit is 1, otherwise it is 0; , , is the physical property value interpreted from the drilling data; t is a structural consistency constraint function, which is used to establish the relationship between density, magnetic susceptibility, and resistivity, thereby achieving joint inversion. W and t The specific formula is as follows:

[0028]

[0029]

[0030] in, diag () is to form a diagonal matrix by taking the one-dimensional matrix elements in the brackets as the diagonal elements of the diagonal matrix, where z 1 to z n for n The depth value corresponding to each layer in the layer division unit; It is an empirical parameter, preferably 1-1.5; i and jAccording to the different variables in the actual objective function, it can be , , .

[0031] It should be noted that the objective function solution process is an iterative process, which is achieved by first assigning , , , v When the target function is gradually fitted when it is 0 or the initial value, the underground , , , v Four physical properties.

[0032] Furthermore, the earthquake data set is obtained, specifically: Convert density to wave speed; Based on the wave velocity, the seismic data set is obtained; the conversion formula is:

[0033] In the formula, v is the wave velocity of the earthquake; a and b are constants; v is the velocity; is the density.

[0034] In detail, after the structural consistency joint inversion is performed, an initial model is obtained, the initial model includes an initial wave velocity, and the density is converted into the initial wave velocity, and the initial wave velocity is used as the initial wave velocity of the initial model in the hybrid joint inversion. Under the constraints of the physical property constraint information composed of the physical property and the depth information, the hybrid joint inversion is performed. It should be noted that a and b are obtained by solving the equations after measuring the density and wave velocity of rock samples in the ore concentration area.

[0035] In some embodiments, hybrid joint inversion is performed on the optimization model under the constraints of physical constraint information to obtain wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data, specifically: Based on the initial function, the gravity data set is inverted to obtain the gravity inversion density parameters; Perform joint inversion on the seismic data set under the constraints of physical constraint information to obtain seismic inversion wave velocity parameters; Based on the gravity inversion density parameters and the seismic inversion wave velocity parameters, a hybrid joint inversion formula is obtained; 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:

[0036] Where: is the three-dimensional magnetic susceptibility of the subsurface; is the three-dimensional resistivity of the subsurface; 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 represents gravity, magnetic and electrical measurement data respectively; d 4 is earthquake data; A 1 is the mapping relationship matrix between underground density and gravity measurement data, A 2 is the mapping relationship matrix between underground magnetic susceptibility and magnetic measurement data, is the mapping function between resistivity and electrical measurement data, , and Gravity, magnetic and electrical data based on forward modeling of 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 is the regularization factor, which is a constant; W is the depth weight function matrix; L is the diagonal matrix of the number of diagonal elements of the subdivision unit, , , is the physical property value interpreted from the drilling data; 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, which is a constant.

[0037] It is worth mentioning that please refer to Figure 2 and Figure 3 After performing the structural consistency joint inversion, the velocity data is obtained through the empirical conversion formula between density and velocity, and then the velocity data is input into the initial model to form an optimization model. Under the constraints of the physical property constraint information composed of physical properties and depth information, the density data, magnetic susceptibility data, resistivity data, and velocity data in the optimization model are subjected to a mixed joint inversion of structural consistency and physical property correlation to obtain the final velocity distribution data, density distribution data, magnetic susceptibility distribution data, and resistivity distribution data.

[0038] In some embodiments, forward modeling is performed on the three-dimensional geological-geophysical model, specifically: The space of the three-dimensional geological-geophysical model is divided to obtain n layers of closely arranged hexahedral units; The density of the geological-geophysical model corresponding to a single hexahedral unit is taken as the density of the hexahedral unit; The magnetic susceptibility of the geological-geophysical model corresponding to a single hexahedral unit is taken as the magnetic susceptibility of the hexahedral unit; Based on the forward modeling formula, the gravity data and magnetic anomaly data of a single hexahedral unit at the observation point are calculated; Calculate the sum of gravity data and magnetic anomaly data of all hexahedral units to obtain total gravity data and total magnetic anomaly data; Based on the total gravity data and total magnetic anomaly data, forward modeling result data are obtained.

[0039] It should be noted that the three-dimensional geological-geophysical model can be initially formed through the four physical properties of wave velocity distribution, density distribution, magnetic susceptibility distribution, and resistivity distribution, but the degree of fit between the model and the measured data is unknown, so it is necessary to perform forward calculation on the model, and adjust the model geological body and stratum boundary to make the model forward modeling results consistent with the measured data. Among them, gravity data and magnetic data are used to verify the degree of fit between the three-dimensional geological-geophysical model and the measured data because of the simplicity of forward calculation, and serve as the basis for model adjustment. In the forward calculation process of the gravity and magnetic response of the three-dimensional geological-geophysical model, it is first necessary to divide the three-dimensional geological-geophysical model into closely arranged hexahedrons in space, and the density and magnetic susceptibility of the geological-geophysical model corresponding to each subdivided unit are used as the density and magnetic susceptibility of the subdivided unit. Then, the gravity and magnetic anomalies of each subdivided unit at the observation point are calculated by the forward formula, and finally the gravity and magnetic anomalies calculated by all subdivided units are added to obtain the forward calculation results of the gravity and magnetic response of the three-dimensional geological-geophysical model. Among them, the calculation formula of gravity and magnetic anomaly of a single subdivision unit at the observation point is:

[0040] In the formula, ; G is the gravitational constant, which is 6.67×10 -11 Nm 2 / kg 2 ; is the coordinate of the observation point; It is the minimum coordinate point of the hexahedral unit in the rectangular coordinate system; It is the maximum coordinate point of the hexahedral unit in the rectangular coordinate system. The magnetic anomaly is composed of the direction of the geomagnetic field and the three components of the magnetic anomaly. The specific formula is:

[0041] In the formula, I 0 and D 0 are the inclination and declination of the geomagnetic field respectively. B x , B y and B z The formulas are:

[0042]

[0043]

[0044] in, is the magnetic permeability in vacuum, H / m; H is the strength of the Earth's magnetic field; i is the magnetic inclination of the geological body, It is the magnetization declination of the geological body. When there is no remanent magnetization, it is consistent with the inclination and declination of the geomagnetic field.

[0045] In some embodiments, the three-dimensional geological-geophysical model is adjusted based on the gravity data set and the magnetic data set to obtain the optimal three-dimensional geological-geophysical model, specifically: Based on the forward modeling data, gravity data set, and magnetic data set, the 3D geological-geophysical model is adjusted to obtain the optimal forward modeling data; The three-dimensional geological-geophysical model is verified and fitted with the optimal forward modeling result data to obtain the optimal three-dimensional geological-geophysical model.

[0046] It is worth mentioning that please refer to Figure 4 , Figure 4In order to construct the schematic diagram of 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 stratum interfaces, faults, top and bottom boundaries of geological bodies, and horizontal boundaries of geological bodies are constructed with reference to the distribution of several physical properties. The mean values ​​of physical properties within the corresponding range of each stratum and geological body are delineated based on the structural model to assign physical properties to the structural model. At the same time, based on the correspondence between lithology and physical properties in the ore-concentrated area, lithology classification is performed on strata and geological bodies with multiple physical property combinations to obtain a preliminary three-dimensional geological-geophysical model. The three-dimensional geological-geophysical model with physical property information is forward modeled by gravity and magnetic methods, and compared with the actual measured gravity and magnetic data. The stratum or geological body boundaries of the geological-geophysical model are adjusted through human-computer interaction to make the model forward modeling results fit the actual measured gravity and magnetic data, and obtain the optimal geological-geophysical model that meets the transparency requirements of the ore-concentrated area.

[0047] Example 2 Please refer to Figure 1-Figure 6 On the basis of Example 1, taking a certain mineral cluster area in a certain place as an example, multi-geophysical field detection, joint inversion and modeling are carried out, and finally a transparent model of the mineral cluster area 3 kilometers underground in the study area is obtained to realize mineral prospecting prediction, as follows: In order to discover more mineral deposits in a certain place, the method provided in this embodiment is used to identify the lithology and underground structure based on the analysis of the relationship between density, magnetic susceptibility and lithology, such as Figure 5 As shown, the three-dimensional spatial characteristics of five major types of rock types are depicted, and transparency is initially achieved.

[0048] The results show that the possible lithologies of the highest density geological bodies in this area are: skarn, anhydrite, basic rock or other high density geological bodies; intermediate-basic igneous rocks and intermediate-acidic igneous rocks are mainly distributed in the intersection of a certain place and the adjacent place, but the burial depth is different; Triassic to Ordovician sedimentary rocks are mainly distributed on both sides of the Ningwu-Fanchang magmatic 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 believed that the southern section of a certain volcanic basin is a favorable area for finding "Daye-style" iron-copper mines, and several layers of copper-zinc ore bodies with industrial value were found in the predicted target area ZK02 of a certain mountain in a certain place, such as Figure 6 As shown, the geophysical and geochemical anomalies are somewhat correlated with deep polymetallic ore bodies, and have the potential for copper-lead-zinc polymetallic mineralization.

[0049] The above description is only a preferred implementation mode of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A collaborative detection method for transparent mining areas, the method being executed by a processor, characterized in that: include: Obtain gravity data sets, magnetic data sets, and electrical data sets; Perform structural consistency joint inversion on gravity data set, magnetic data set and electrical data set under the constraint of physical constraint information to form an initial model; Obtaining seismic data sets and forming an optimized model based on the initial model; Perform hybrid joint inversion on the optimization model under the constraints of physical constraint information to obtain wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data; Construct a three-dimensional geological-geophysical model based on wave velocity distribution data, resistivity distribution data, density distribution data, and magnetic susceptibility distribution data; Perform forward modeling on three-dimensional geological-geophysical models; The three-dimensional geological-geophysical model is adjusted based on the gravity data set and the magnetic data set to obtain the 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 drilling data.

3. The collaborative detection method according to claim 1, characterized in that: The gravity data set, magnetic data set, and electrical data set are subjected to structural consistency joint inversion under the constraints of physical constraint information to form an initial model, specifically: Divide the target underground space to obtain n layers of closely arranged hexahedral units; Based on the coordinates of the hexahedral unit and the surface observation point, the mapping relationship is calculated to obtain the objective function of the observation data and underground physical properties; Add physical constraint information to the objective function and enforce the constraints; Based on the gravity data set, magnetic data set and electrical data set, the objective function is inverted to obtain the initial model.

4. The collaborative detection method according to claim 1, characterized in that: The obtaining of seismic data set is specifically as follows: Convert density to wave speed; Based on the wave velocity, the seismic data set is obtained; the conversion formula is: In the formula, v is the wave velocity of the earthquake; a and b are constants; v is the velocity; is the density.

5. The collaborative detection method according to claim 4, characterized in that: The optimization model is subjected to hybrid joint inversion 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: Based on the initial function, the gravity data set is inverted to obtain the gravity inversion density parameters; Perform joint inversion on the seismic data set under the constraints of physical constraint information to obtain seismic inversion wave velocity parameters; Based on the gravity inversion density parameters and the seismic inversion wave velocity parameters, a hybrid joint inversion formula is obtained; 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: Where: is the three-dimensional magnetic susceptibility of the subsurface; is the three-dimensional resistivity of the subsurface; 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 represents gravity, magnetic and electrical measurement data respectively; d 4 is earthquake data; A 1 is the mapping relationship matrix between underground density and gravity measurement data, A 2 is the mapping relationship matrix between underground magnetic susceptibility and magnetic measurement data, is the mapping function between resistivity and electrical measurement data, , and Gravity, magnetic and electrical data based on forward modeling of 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 is the regularization factor, which is a constant; W is the depth weight function matrix; L is the diagonal matrix of the number of diagonal elements of the subdivision unit, , , is the physical property value interpreted from the drilling data; 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, which is a constant.

6. The collaborative detection method according to claim 4, characterized in that: The forward modeling of the three-dimensional geological-geophysical model is specifically performed as follows: The space of the three-dimensional geological-geophysical model is divided to obtain n layers of closely arranged hexahedral units; The density of the geological-geophysical model corresponding to a single hexahedral unit is taken as the density of the hexahedral unit; The magnetic susceptibility of the geological-geophysical model corresponding to a single hexahedral unit is taken as the magnetic susceptibility of the hexahedral unit; Based on the forward modeling formula, the gravity data and magnetic anomaly data of a single hexahedral unit at the observation point are calculated; Calculate the sum of gravity data and magnetic anomaly data of all hexahedral units to obtain total gravity data and total magnetic anomaly data; Based on the total gravity data and total magnetic anomaly data, forward modeling result data are obtained.

7. The collaborative detection method according to claim 6, characterized in that: The three-dimensional geological-geophysical model is adjusted based on the gravity data set and the magnetic data set to obtain the optimal three-dimensional geological-geophysical model, specifically: Based on the forward modeling data, gravity data set, and magnetic data set, the 3D geological-geophysical model is adjusted to obtain the optimal forward modeling data; The three-dimensional geological-geophysical model is verified and fitted with the optimal forward modeling result data to obtain the optimal three-dimensional geological-geophysical model.

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