A method and system for detecting a hydrothermal uranium metallogenic environment

Abstract

The present invention relates to a method and system for detecting a hydrothermal uranium metallogenic environment. The method includes determining a density-magnetic susceptibility crossplot based on the rock density and magnetic susceptibility of each geological unit; determining the geometric parameters of a cuboid grid according to the hydrothermal uranium ore exploration area; determining an initial model based on the cuboid grid; using the conjugate gradient method to respectively determine a density iterative model and a magnetic susceptibility iterative model according to the initial model of gravity three-dimensional inversion, gravity data, the initial model of magnetic force three-dimensional inversion, magnetic force data, and the cuboid grid; using the conjugate gradient method to respectively determine a density update model and a magnetic susceptibility update model according to the density iterative model, gravity data, cuboid grid, magnetic susceptibility iterative model, magnetic force data, and mutual introduction of structural information; and inferring the three-dimensional structure of the hydrothermal uranium metallogenic environment based on the iterative results of the density update model and the magnetic susceptibility update model and the density-magnetic susceptibility crossplot. The present invention can reduce the difficulty of interpreting the uranium metallogenic environment.

Description

Technical Field

[0001] The invention relates to the field of uranium geophysical exploration, and in particular to a method and system for detecting a hydrothermal uranium mineralization environment. Background Art

[0002] The key to the prospecting of hydrothermal uranium deposits is to identify the characteristics of the underground uranium mineralization environment, which include the undulation of volcanic strata and basement strata, the distribution of fault structures and the spatial distribution of rock mass. The geological units in the hydrothermal uranium exploration area usually have obvious differences in physical properties. The three-dimensional spatial structure of underground density and magnetic susceptibility can be obtained by using gravity and magnetic three-dimensional inversion, and the characteristics of uranium mineralization environment can be inferred based on this, so as to achieve the purpose of hydrothermal uranium deposit prospecting.

[0003] However, conventional gravity and magnetic 3D inversion has the problem that the resolution capability decreases rapidly with increasing depth, and the inversion results will have redundant structures at depth, making interpretation more difficult. In addition, gravity and magnetic 3D inversion has different sensitivities to density and magnetic susceptibility, and its inversion results often show incompatible or even contradictory results.

[0004] Therefore, there is an urgent need to further extract useful information from gravity and magnetic data and form an effective combination of gravity and magnetic methods to obtain interpretation results with higher accuracy and better compatibility. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for detecting hydrothermal uranium mineralization environment, which can improve the accuracy and compatibility of determining the three-dimensional spatial structure of underground density and magnetic susceptibility, reduce the difficulty of interpreting the uranium mineralization environment, and provide high-quality data support for hydrothermal uranium deposit exploration.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for detecting a hydrothermal uranium mineralization environment, comprising:

[0008] Obtain rock density, magnetic susceptibility, gravity data and magnetic data of each geological unit in the hydrothermal uranium exploration area, and determine the density-magnetic susceptibility cross-plot based on the rock density and magnetic susceptibility of each geological unit;

[0009] Determine the geometric parameters of the rectangular grid used for three-dimensional inversion according to the hydrothermal uranium exploration area; the geometric parameters include: the north-south scale, the east-west scale and the vertical scale of the rectangular grid and the grid division parameters in each direction;

[0010] Determine the initial model of gravity three-dimensional inversion and the initial model of magnetic three-dimensional inversion based on the rectangular grid;

[0011] According to the initial model of 3D gravity inversion, gravity data, and cuboid grids, the conjugate gradient method is used to determine the density iteration model; according to the initial model of 3D magnetic inversion, magnetic data, and cuboid grids, the conjugate gradient method is used to determine the magnetic susceptibility iteration model;

[0012] According to the density iteration model, gravity data, cuboid grids, and the structural information of the magnetic susceptibility iteration model, the conjugate gradient method is used for 3D inversion calculation to determine the density update model; according to the magnetic susceptibility iteration model, magnetic data, cuboid grids, and the structural information of the density iteration model, the conjugate gradient method is used for 3D inversion calculation to determine the magnetic susceptibility update model;

[0013] Based on the iteration results of the density update model and the magnetic susceptibility update model, as well as the density-magnetic susceptibility cross-plot, the 3D structure of the hydrothermal uranium metallogenic environment is inferred.

[0014] Optionally, the obtaining of the rock density, magnetic susceptibility, gravity data, and magnetic data of each geological unit in the hydrothermal uranium ore exploration area, and the determination of the density-magnetic susceptibility cross-plot based on the rock density and magnetic susceptibility of each geological unit specifically includes:

[0015] Obtain the gravity survey data and magnetic survey data of each geological unit in the hydrothermal uranium ore exploration area;

[0016] Determine the Bouguer gravity anomaly data based on the gravity survey data, and use the discrete plane data gridding method to convert the Bouguer gravity anomaly and the corresponding elevation data into a plane grid, and then use the trend surface analysis method to determine the gravity residual anomaly plane grid; the gravity residual anomaly plane grid is the gravity data;

[0017] Determine the total magnetic field anomaly data based on the magnetic survey data, and use the discrete plane data gridding method to convert the total magnetic field anomaly and the corresponding elevation data into a total magnetic field anomaly plane grid, and perform reduction to the pole processing on the total magnetic field anomaly plane grid, and then use the trend surface analysis method to determine the plane grid of the magnetic residual anomaly; the plane grid of the magnetic residual anomaly is the magnetic data.

[0018] Optionally, the using of the conjugate gradient method to determine the density iteration model according to the initial model of 3D gravity inversion, gravity data, and cuboid grids; and the using of the conjugate gradient method to determine the magnetic susceptibility iteration model according to the initial model of 3D magnetic inversion, magnetic data, and cuboid grids specifically includes:

[0019] Using the formula p (n+1)(g) = p (n)(g) + M (g) -1 v (g) to determine the density iteration model;

[0020] Using the formula p(n+1)(m) = p (n)(m) + M (m) -1 v (m) Determine the magnetic susceptibility iteration model;

[0021] where p (n+1)(g) is the density iteration model for the (n + 1)-th iteration, p (n)(g) is the density iteration model for the n-th iteration, M (g) and v (g) are the iteration matrix and vector of the density iteration model respectively, p (n+1)(m) is the magnetic susceptibility iteration model for the (n + 1)-th iteration, p (n)(m) is the magnetic susceptibility iteration model for the n-th iteration, n is the number of iterations, M (m) and v (m) are the iteration matrix and vector of the magnetic susceptibility iteration model respectively.

[0022] Optionally, according to the density iteration model, gravity data, cuboid grid and the structural information of the magnetic susceptibility iteration model, use the conjugate gradient method to perform 3D inversion calculation to determine the density update model; according to the magnetic susceptibility iteration model, magnetic data, cuboid grid and the structural information of the density iteration model, use the conjugate gradient method to perform 3D inversion calculation to determine the magnetic susceptibility update model, specifically including:

[0023] Use the formula p (n+1)(g)(r) = p (n+1)(g) + M (g)(r) -1 v (g)(r) to determine the density update model;

[0024] Use the formula p (n+1)(m)(r) = p (n+1)(m) + M (m)(r) -1 v (m)(r) to determine the magnetic susceptibility update model;

[0025] where p (n+1)(g)(r) is the density update model for the (n + 1)-th iteration, M (g)(r) and v (g)(r) are the matrix and vector of the update amount of the density update model respectively, p (n+1)(m)(r) is the magnetic susceptibility update model for the (n + 1)-th iteration, M (m)(r) and v (m)(r) are the matrix and vector of the update amount of the magnetic susceptibility update model respectively.

[0026] Optionally, infer the 3D structure of the hydrothermal uranium metallogenic environment according to the iteration results of the density update model and the magnetic susceptibility update model and the density-magnetic susceptibility cross plot, specifically including:

[0027] Determine the first forward residual of the density update model under the action of covariance and the second forward residual of the magnetic susceptibility update model under the action of covariance;

[0028] Judge whether both the first forward residual and the second forward residual are less than the corresponding residual thresholds;

[0029] If both are less, determine the iteration result; if not both are less, replace the density update model and the magnetic susceptibility update model with the initial models of the three-dimensional gravity inversion and the three-dimensional magnetic inversion respectively, and continue the iteration until both the first forward residual and the second forward residual are less than the corresponding residual thresholds, and then determine the iteration result.

[0030] A hydrothermal uranium metallogenic environment detection system, comprising:

[0031] A density-magnetic susceptibility cross-plot determination module, configured to obtain the rock density, magnetic susceptibility, gravity data, and magnetic data of each geological unit in the hydrothermal uranium ore exploration area, and determine a density-magnetic susceptibility cross-plot according to the rock density and magnetic susceptibility of each geological unit;

[0032] A cuboid grid determination module, configured to determine the geometric parameters of the cuboid grid used for three-dimensional inversion according to the hydrothermal uranium ore exploration area; the geometric parameters include: the north-south scale, the east-west scale, and the vertical scale of the cuboid grid, as well as the grid subdivision parameters in each direction;

[0033] An initial model determination module, configured to determine the initial model of the three-dimensional gravity inversion and the initial model of the three-dimensional magnetic inversion according to the cuboid grid;

[0034] An iterative model determination module, configured to determine a density iterative model according to the initial model of the three-dimensional gravity inversion, the gravity data, and the cuboid grid by using the conjugate gradient method; determine a magnetic susceptibility iterative model according to the initial model of the three-dimensional magnetic inversion, the magnetic data, and the cuboid grid by using the conjugate gradient method;

[0035] An update model determination module, configured to perform three-dimensional inversion calculation by using the conjugate gradient method according to the density iterative model, the gravity data, the cuboid grid, and the structural information of the magnetic susceptibility iterative model to determine a density update model; perform three-dimensional inversion calculation by using the conjugate gradient method according to the magnetic susceptibility iterative model, the magnetic data, the cuboid grid, and the structural information of the density iterative model to determine a magnetic susceptibility update model;

[0036] A hydrothermal uranium metallogenic environment three-dimensional structure inference module, configured to infer the three-dimensional structure of the hydrothermal uranium metallogenic environment according to the iteration results of the density update model and the magnetic susceptibility update model and the density-magnetic susceptibility cross-plot.

[0037] Optionally, the density-magnetic susceptibility cross-plot determination module specifically includes:

[0038] A gravity survey data and magnetic survey data determination unit, configured to obtain gravity survey data and magnetic survey data of each geological unit in a hydrothermal uranium ore exploration area;

[0039] A gravity data determination unit, configured to determine Bouguer gravity anomaly data based on the gravity survey data, convert the Bouguer gravity anomaly and the corresponding elevation data into a plane grid by using a discrete plane data gridding method, and then determine a gravity residual anomaly plane grid by using a trend surface analysis method; the gravity residual anomaly plane grid is the gravity data;

[0040] A magnetic data determination unit, configured to determine total magnetic field anomaly data based on the magnetic survey data, convert the total magnetic field anomaly and the corresponding elevation data into a total magnetic field anomaly plane grid by using a discrete plane data gridding method, perform reduction to the pole processing on the total magnetic field anomaly plane grid, and then determine a plane grid of magnetic residual anomaly by using a trend surface analysis method; the plane grid of magnetic residual anomaly is the magnetic data.

[0041] Optionally, the iterative model determination module specifically includes:

[0042] A density iterative model determination unit, configured to determine a density iterative model by using the formula p (n+1)(g) = p (n)(g) + M (g) -1 v (g) ;

[0043] A magnetic susceptibility iterative model determination unit, configured to determine a magnetic susceptibility iterative model by using the formula p (n+1)(m) = p (n)(m) + M (m) -1 v (m) ;

[0044] where p (n+1)(g) is the density iterative model of the (n + 1)-th iteration, p (n)(g) is the density iterative model of the n-th iteration, M (g) and v (g) are respectively the iteration matrix and vector of the density iterative model, p (n+1)(m) is the magnetic susceptibility iterative model of the (n + 1)-th iteration, p (n)(m) is the magnetic susceptibility iterative model of the n-th iteration, n is the number of iterations, M (m) and v (m) are respectively the iteration matrix and vector of the magnetic susceptibility iterative model.

[0045] Optionally, the update model determination module specifically includes:

[0046] A density update model determination unit, configured to use the formula p (n+1)(g)(r)= p (n+1)(g) + M (g)(r) -1 v (g)(r) Determine the density update model;

[0047] The magnetic susceptibility update model determination unit is used to use the formula p (n+1)(m)(r) = p (n+1)(m) + M (m)(r) -1 v (m)(r) Determine the magnetic susceptibility update model;

[0048] Where p (n+1)(g)(r) is the density update model for the (n + 1)-th iteration, M (g)(r) and v (g)(r) are respectively the matrix and vector of the update amount of the density update model, and p (n+1)(m)(r) is the magnetic susceptibility update model for the (n + 1)-th iteration, M (m)(r) and v (m)(r) are respectively the matrix and vector of the update amount of the magnetic susceptibility update model.

[0049] Optionally, the three-dimensional structure inference module for the hydrothermal uranium metallogenic environment specifically includes:

[0050] The forward residual determination unit is used to determine the first forward residual of the density update model under the action of covariance and the second forward residual of the magnetic susceptibility update model under the action of covariance;

[0051] The judgment unit is used to judge whether the first forward residual and the second forward residual are both less than the corresponding residual thresholds;

[0052] The iteration result determination unit is used to, if both are less, determine the iteration result; if not both are less, replace the density update model and the magnetic susceptibility update model with the initial models of the gravity three-dimensional inversion and the magnetic three-dimensional inversion respectively, and continue the iteration until the first forward residual and the second forward residual are both less than the corresponding residual thresholds, and determine the iteration result.

[0053] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0054] A method and system for detecting the hydrothermal uranium metallogenic environment provided by the present invention uses a joint gravity and magnetic three-dimensional inversion method to detect the hydrothermal uranium metallogenic environment. Large-scale gravity and magnetic data are used to form a planar grid as input data. The conjugate gradient method is used to obtain iterative models of density and magnetic susceptibility. Then, structural information is introduced and the conjugate gradient method is used again to solve, obtaining updated models of density and magnetic susceptibility. Through continuous iteration, the joint inversion result is finally obtained. This result can obtain a three-dimensional spatial structure of underground density and magnetic susceptibility with higher accuracy and better compatibility, thereby reducing the difficulty of interpreting the uranium metallogenic environment and providing high-quality data support for the prospecting of hydrothermal uranium deposits. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0056] Figure 1 Schematic flow chart of a method for detecting the hydrothermal uranium metallogenic environment provided by the present invention;

[0057] Figure 2 Flow chart for determining the iteration result;

[0058] Figure 3 Schematic structural diagram of a system for detecting the hydrothermal uranium metallogenic environment provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0060] The purpose of the present invention is to provide a method and system for detecting the hydrothermal uranium metallogenic environment, which can improve the accuracy and compatibility of determining the three-dimensional spatial structure of underground density and magnetic susceptibility, reduce the difficulty of interpreting the uranium metallogenic environment, and provide high-quality data support for the prospecting of hydrothermal uranium deposits.

[0061] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0062] Figure 1 Schematic flow chart of a method for detecting the hydrothermal uranium metallogenic environment provided by the present invention, asFigure 1 As shown in Figure 1 , a method for detecting hydrothermal uranium metallogenic environment provided by the present invention includes:

[0063] S101. Obtain the rock density, magnetic susceptibility, gravity data, and magnetic data of each geological unit in the hydrothermal uranium ore exploration area, and determine the density-magnetic susceptibility cross-plot according to the rock density and magnetic susceptibility of each geological unit;

[0064] Measure the density and magnetic susceptibility physical property parameters of the rock sampling specimens (including surface sampling specimens and core sampling specimens) of each geological unit in the hydrothermal rock-type uranium ore exploration area, and combine the existing physical property data to statistically analyze the density and magnetic susceptibility data of the rock physical properties in the exploration area and draw a cross-plot.

[0065] S101 specifically includes:

[0066] Obtain the gravity survey data and magnetic survey data of each geological unit in the hydrothermal uranium ore exploration area;

[0067] Determine the Bouguer gravity anomaly data according to the gravity survey data, and use the discrete plane data gridding method to convert the Bouguer gravity anomaly and the corresponding elevation data into a plane grid, and then use the trend surface analysis method to determine the gravity residual anomaly plane grid; the gravity residual anomaly plane grid is gravity data; the software for the trend surface analysis method includes but is not limited to: Oasis Montaj or GeoIPAS;

[0068] Determine the total magnetic field anomaly data according to the magnetic survey data, and use the discrete plane data gridding method to convert the total magnetic field anomaly and the corresponding elevation data into a total magnetic field anomaly plane grid, and use software such as Oasis Montaj and GeoIPAS to perform pole transformation on the total magnetic field anomaly plane grid, and then use the trend surface analysis method to determine the plane grid of the magnetic residual anomaly; the plane grid of the magnetic residual anomaly is magnetic data.

[0069] S102. Determine the geometric parameters of the cuboid grid used for three-dimensional inversion according to the hydrothermal uranium ore exploration area; the geometric parameters include: the north-south scale, east-west scale, and vertical scale of the cuboid grid, as well as the grid subdivision parameters in each direction;

[0070] The plane scale of the cuboid grid should cover the entire hydrothermal uranium ore exploration area, and the vertical depth can be taken as about 1 / 2 of the horizontal long side. In addition, the grid needs to be appropriately widened outward and downward, and the expansion width can be taken as 1 / 10 to 1 / 5 of the original grid scale.

[0071] Mark the north direction of the cuboid grid as the x direction, the east direction as the y direction, and the vertical downward direction as the z direction. The corresponding number of grids are nx, ny, and nz respectively, then the total number of grid cells is nm = nx × ny × nz.

[0072] S103. Determine the initial model for 3D gravity inversion and the initial model for 3D magnetic inversion according to the cuboid grid;

[0073] Assign a density attribute to each cell of the cuboid grid and arrange them in the order from top to bottom, from west to east, and from south to north to obtain a vector p (n)(g) , whose dimension is nm. Let all its elements be zero to serve as the initial model for 3D gravity inversion;

[0074] Assign a magnetic susceptibility attribute to each cell of the cuboid grid and arrange them in the order from top to bottom, from west to east, and from south to north to obtain a vector p (n)(m) , whose dimension is nm. Let all its elements be zero to serve as the initial model for 3D magnetic inversion;

[0075] S104. According to the initial model for 3D gravity inversion, gravity data, and the cuboid grid, use the conjugate gradient method to determine the density iteration model; according to the initial model for 3D magnetic inversion, magnetic data, and the cuboid grid, use the conjugate gradient method to determine the magnetic susceptibility iteration model;

[0076] S104 specifically includes:

[0077] Arrange the gravity residual anomaly plane grid data in the order from west to east and from south to north to form a vector d 0(g) , and the covariance matrix C d(g) is the product of the unit diagonal matrix and the total accuracy of the Bouguer gravity anomaly. Calculate the regularization parameter for gravity inversion using the L-curve method and multiply it by the unit diagonal matrix as the covariance matrix C of the density model p(g) ; Arrange the magnetic residual anomaly plane grid data in the order from west to east and from south to north to form a vector d 0(m) , and the covariance matrix C d(m) is the product of the unit diagonal matrix and the total accuracy of the total magnetic field anomaly. Calculate the regularization parameter for magnetic inversion using the L-curve method and multiply it by the unit diagonal matrix as the covariance matrix C of the magnetic susceptibility model p(m) ;

[0078] Use the formula p (n+1)(g) =p (n)(g) +M (g) -1 v (g) to determine the density iteration model;

[0079] Use the formula p (n+1)(m) =p (n)(m) +M (m) -1 v (m) to determine the magnetic susceptibility iteration model;

[0080] where p (n+1)(g) is the density iteration model for the (n + 1)-th iteration, p (n)(g) is the density iteration model for the n-th iteration, M (g) and v (g) are the iteration matrix and vector of the density iteration model respectively, p (n+1)(m) is the magnetic susceptibility iteration model for the (n + 1)-th iteration, p (n)(m) is the magnetic susceptibility iteration model for the n-th iteration, n is the number of iterations, M (m) and v (m) are the iteration matrix and vector of the magnetic susceptibility iteration model respectively.

[0081] where M (g) = G (g) T C d(g) -1 G (g) + W z(g) T C p(g) -1 W z(g) ;

[0082] v (g) = G (g) T C d(g) -1 (G (g) p (n)(g) - d 0(g) ) + W z(g) T C p(g) -1 W z(g) p (n)(g) ;

[0083] In the formula, G (g) is the gravity forward modeling matrix, the elements of which are calculated from the regular cuboid three-dimensional grid parameters (and the inversion input data C), and the reference gravity forward formula can be found in formula 9-3 of the book "Potential theory in gravity and magnetic applications" written by Richard Blakely, published by Cambridge University Press, ISBN: 0-521-41508-X), W z(g) is the depth weighting matrix. The linear equations are solved by a finite number of conjugate gradient iterations, and the number of iterations can be set to 10 - 100.

[0084] M (m) = G (m) T C d(m) -1 G (m) + Wz(m) T C p(m) -1 W z(m) ;

[0085] v (m) = G (m) T C d(m) -1 (G (m) p (n)(m) -d 0(m) ) + W z(m) T C p(m) -1 W z(m) p (n)(m) ;

[0086] In the formula, G (m) is the forward magnetic matrix, and its elements are calculated from the three-dimensional grid parameters of the regular cuboid (and the inversion input data C). The forward magnetic formula for reference can be found in formula 9 - 19 of the book "Potential theory in gravity and magnetic applications" written by Richard Blakely and published by Cambridge University Press, ISBN: 0 - 521 - 41508 - X. W z(g) is the depth weight matrix. The linear equations are solved by a finite number of conjugate gradient iterations, and the number of iterations is the same as that in gravity solution.

[0087] S105. According to the density iteration model, gravity data, cuboid grid, and the structural information of the susceptibility iteration model, perform three-dimensional inversion calculation using the conjugate gradient method to determine the density update model; according to the susceptibility iteration model, magnetic data, cuboid grid, and the structural information of the density iteration model, perform three-dimensional inversion calculation using the conjugate gradient method to determine the susceptibility update model;

[0088] S105 specifically includes:

[0089] Use the formula p (n+1)(g)(r) = p (n+1)(g) + M (g)(r) -1 v (g)(r) to determine the density update model;

[0090] Use the formula p (n+1)(m)(r) = p (n+1)(m) + M (m)(r) -1 v (m)(r) to determine the susceptibility update model;

[0091] where p (n+1)(g)(r)is the density update model for the (n + 1)-th iteration, M (g)(r) and v (g)(r) are respectively the matrix and vector of the update amount of the density update model, p (n+1)(m)(r) is the magnetic susceptibility update model for the (n + 1)-th iteration, M (m)(r) and v (m)(r) are respectively the matrix and vector of the update amount of the magnetic susceptibility update model.

[0092] Among them, M (g)(r) = G (g) T C d(g) -1 G (g) + W z(g) T C p(g) -1 W z(g) + β (g) B (g) T B (g) ;

[0093] v (g)(r) = G (g) T C d(g) -1 (G (g) p (n+1)(g) - d 0(g) ) + W z(g) T C p(g) -1 W z(g) p (n+1)(g) ;

[0094] In the formula, β (g) is the weight coefficient, taking values between 10 5 and 10 10 , B (g) is the Jacobian matrix introducing the structural information of the magnetic susceptibility iteration model p (k+1)(m) , and is composed of [B (g)(x) B (g)(y) B (g)(z) T . The formula for each element of its matrix is:

[0095] B (g)(x)([(i-1)×nx+j-1]×ny+k,[(i-1)×nx+j]×ny+k) = (p (n+1)(m)(i,j,k+1) - p (n+1)(m)(i,j,k) ) / dy / dz;

[0096] B (g)(x)([(i-1)×nx+j-1]×ny+k,[(i-1)×nx+j-1]×ny+k) = (p (n+1)(m)(i,j+1,k) - p (n+1)(m)(i,j,k+1) ) / dy / dz;

[0097] B​(g)(x)([(i-1)×nx+j-1]×ny+k,[(i-1)×nx+j-1]×ny+k+1) =(p (n+1)(m)(i,j,k) -p (n+1)(m)(i,j+1,k) ) / dy / dz;

[0098] B (g)(y)([(i-1)×nx+j-1]×ny+k,[i×nx+j-1]×ny+k) =(p (n+1)(m)(i,j,k+1) -p (n+1)(m)(i,j,k) ) / dx / dz;

[0099] B (g)(y)([(i-1)×nx+j-1]×ny+k,[(i-1)×nx+j-1]×ny+k) =(p (n+1)(m)(i+1,j,k) -p (n+1)(m)(i,j,k+1) ) / dx / dz;

[0100] B (g)(y)([(i-1)×nx+j-1]×ny+k,[(i-1)×nx+j-1]×ny+k+1) =(p (n+1)(m)(i,j,k) -p (n+1)(m)(i+1,j,k) ) / dx / dz;

[0101] B (g)(z)([(i-1)×nx+j-1]×ny+k,[i×nx+j-1]×ny+k) =(p (n+1)(m)(i,j+1,k) -p (n+1)(m)(i,j,k) ) / dx / dy;

[0102] B (g)(z)([(i-1)×nx+j-1]×ny+k,[(i-1)×nx+j-1]×ny+k) =(p (n+1)(m)(i+1,j,k) -p (n+1)(m)(i,j+1,k) ) / dx / dy;

[0103] B (g)(z)([(i-1)×nx+j-1]×ny+k,[(i-1)×nx+j]×ny+k) =(p (n+1)(m)(i,j,k) -p (n+1)(m)(i+1,j,k) ) / dx / dy;

[0104] In the formula, the subscripts i, j, and k respectively represent the element numbers in the x, y, and z directions, and dx and dy respectively represent the length, width, and height dimensions of the element. B (g) For the elements not mentioned in the solution formula, they are all 0. The linear equations are solved by using a finite number of conjugate gradient iterations, and the number of iterations can be set to 10 - 100.

[0105] M (m)(r) =G (m) T C d(m) -1 G (m) +W z(m) T C p(m) -1 W z(m) +β (m) B (m) T B (m) ;

[0106] v (m)(r) =G (m) T C d(m) -1 (G (m)p (n+1)(m) -d 0(m) )+W z(m) T C p(m) -1 W z(m) p (n+1)(m) ;

[0107] In the formula, β (m) is the weight coefficient, taking values between 10 5 and 10 10 , B (m) is the Jacobian matrix of the introduced density iteration model p k+1(g) structural information, which is composed of [B (m)(x) B (m)(y) B (m)(z) T . The formula for each element of the matrix is as follows:

[0108] B (m)(x)([(i-1)×nx+j-1]×ny+k,[(i-1)×nx+j-1]×ny+k+1) =(p (n+1)(m)(i,j+1,k) -p (n+1)(m)(i,j,k) ) / dy / dz;

[0109] B (m)(x)([(i-1)×nx+j-1]×ny+k,[(i-1)×nx+j-1]×ny+k) =(p (n+1)(m)(i,j,k+1) -p (n+1)(m)(i,j+1,k) ) / dy / dz;

[0110] B (m)(x)([(i-1)×nx+j-1]×ny+k,[(i-1)×nx+j]×ny+k) =(p (n+1)(m)(i,j,k) -p (n+1)(m)(i,j,k+1) ) / dy / dz;

[0111] B (m)(y)([(i-1)×nx+j-1]×ny+k,[(i-1)×nx+j-1]×ny+k+1) =(p (n+1)(m)(i+1,j,k) -p (n+1)(m)(i,j,k) ) / dx / dz;

[0112] B (m)(y)([(i-1)×nx+j-1]×ny+k,[(i-1)×nx+j-1]×ny+k) =(p (n+1)(m)(i,j,k+1) -p (n+1)(m)(i+1,j,k) ) / dx / dz;

[0113] B (m)(y)([(i-1)×nx+j-1]×ny+k,[i×nx+j-1]×ny+k =(p (n+1)(m)(i,j,k) -p (n+1)(m)(i,j,k+1) ) / dx / dy;

[0114] B (m)(z)([(i-1)×nx+j-1]×ny+k,[(i-1)×nx+j]×ny+k) =(p (n+1)(m)(i+1,j,k) -p (n+1)(m)(i,j,k) ) / dx / dy;

[0115] B (m)(z)([(i-1)×nx+j-1]×ny+k,[(i-1)×nx+j-1]×ny+k) =(p (n+1)(m)(i,j+1,k) -p (n+1)(m)(i+1,j,k) ) / dx / dy;

[0116] B (g)(z)([(i-1)×nx+j-1]×ny+k,[i×nx+j-1]×ny+k) =(p​(n+1)(m)(i,j,k) -p (n+1)(m)(i,j+1,k) ) / dx / dy;

[0117] In the formula, the subscripts i, j, and k respectively represent the element numbers in the x, y, and z directions, and dx and dy respectively represent the length, width, and height dimensions of the element. B (g) All elements not mentioned in the solution formula are 0. The linear equations are solved by a finite number of conjugate gradient iterations, and the number of iterations is the same as that in gravity solution.

[0118] S106. Infer the three-dimensional structure of the hydrothermal uranium metallogenic environment based on the iteration results of the density update model and the magnetic susceptibility update model and the density-magnetic susceptibility cross plot.

[0119] As Figure 2 shown, S106 specifically includes:

[0120] Determine the first forward modeling residual of the density update model under the action of covariance and the second forward modeling residual of the magnetic susceptibility update model under the action of covariance;

[0121] The first forward modeling residual is: ε (g) =(d 0(g) -G (g) p (n+1)(g)(r) ) T C d -1 (d 0(g) -G (g) p (n+1)(g)(r) );

[0122] The second forward modeling residual is: ε (m) =(d 0(m) -G (m) p (n+1)(m)(r) ) T C d -1 (d 0(m) -G (m) p (n+1)(m)(r) );

[0123] Judge whether both the first forward modeling residual and the second forward modeling residual are less than the corresponding residual thresholds;

[0124] If both are less, determine the iteration result; if not both are less, replace the density update model and the magnetic susceptibility update model with the initial models of three-dimensional gravity inversion and three-dimensional magnetic inversion respectively, and continue the iteration until both the first forward modeling residual and the second forward modeling residual are less than the corresponding residual thresholds, and then determine the iteration result.

[0125] First, based on the spatial variations of density and magnetic susceptibility in the 3D inversion results, infer the spatial distributions of various geological units, and simultaneously statistically analyze the numerical characteristics of density and magnetic susceptibility of the grid cells contained in each geological unit.

[0126] Compare whether the numerical characteristics of density and magnetic susceptibility of each geological unit match the characteristics of the obtained density - magnetic susceptibility cross - plot. If the numerical characteristics of density and magnetic susceptibility of a certain geological unit match the characteristics of the density - magnetic susceptibility cross - plot, then determine the inferred geological unit as the final interpretation result.

[0127] If the numerical characteristics of density and magnetic susceptibility of a certain geological unit do not match the characteristics of the density - magnetic susceptibility cross - plot, then adjust the inference of the spatial distribution of this geological unit and re - conduct the inference and determination. If an ideal result still cannot be obtained after multiple repetitions, then cancel the inference of this geological unit and do not use it as the final interpretation result.

[0128] Figure 3 The following is a schematic structural diagram of a hydrothermal uranium metallogenic environment detection system provided by the present invention. As Figure 3 shown, a hydrothermal uranium metallogenic environment detection system provided by the present invention includes:

[0129] A density - magnetic susceptibility cross - plot determination module 301, configured to obtain the rock density, magnetic susceptibility, gravity data, and magnetic force data of each geological unit in the hydrothermal uranium ore exploration area, and determine a density - magnetic susceptibility cross - plot based on the rock density and magnetic susceptibility of each geological unit;

[0130] A cuboid grid determination module 302, configured to determine the geometric parameters of the cuboid grid used for 3D inversion according to the hydrothermal uranium ore exploration area; the geometric parameters include: the north - south scale, east - west scale, and vertical scale of the cuboid grid, as well as the grid subdivision parameters in each direction;

[0131] An initial model determination module 303, configured to determine an initial model for 3D gravity inversion and an initial model for 3D magnetic inversion according to the cuboid grid;

[0132] An iterative model determination module 304, configured to determine a density iterative model by using the conjugate gradient method according to the initial model for 3D gravity inversion, gravity data, and the cuboid grid; determine a magnetic susceptibility iterative model by using the conjugate gradient method according to the initial model for 3D magnetic inversion, magnetic force data, and the cuboid grid;

[0133] The updated model determination module 305 is configured to perform three-dimensional inversion calculations using the conjugate gradient method based on the density iteration model, gravity data, cuboid grid, and structural information of the magnetic susceptibility iteration model to determine the density updated model; and perform three-dimensional inversion calculations using the conjugate gradient method based on the magnetic susceptibility iteration model, magnetic data, cuboid grid, and structural information of the density iteration model to determine the magnetic susceptibility updated model.

[0134] The three-dimensional structure inference module 306 for hydrothermal uranium metallogenic environment is configured to infer the three-dimensional structure of the hydrothermal uranium metallogenic environment based on the iteration results of the density updated model and the magnetic susceptibility updated model and the density-magnetic susceptibility cross plot.

[0135] The density-magnetic susceptibility cross plot determination module 301 specifically includes:

[0136] The gravity survey data and magnetic survey data determination unit is configured to obtain the gravity survey data and magnetic survey data of each geological unit in the hydrothermal uranium ore exploration area.

[0137] The gravity data determination unit is configured to determine the Bouguer gravity anomaly data based on the gravity survey data, convert the Bouguer gravity anomaly and the corresponding elevation data into a plane grid using the discrete plane data gridding method, and then determine the gravity residual anomaly plane grid using the trend surface analysis method; the gravity residual anomaly plane grid is the gravity data.

[0138] The magnetic data determination unit is configured to determine the total magnetic field anomaly data based on the magnetic survey data, convert the total magnetic field anomaly and the corresponding elevation data into a total magnetic field anomaly plane grid using the discrete plane data gridding method, perform reduction to the pole processing on the total magnetic field anomaly plane grid, and then determine the plane grid of the magnetic residual anomaly using the trend surface analysis method; the plane grid of the magnetic residual anomaly is the magnetic data.

[0139] The iteration model determination module 304 specifically includes:

[0140] The density iteration model determination unit is configured to determine the density iteration model using the formula p (n+1)(g) = p (n)(g) + M (g) -1 v (g)

[0141] The magnetic susceptibility iteration model determination unit is configured to determine the magnetic susceptibility iteration model using the formula p (n+1)(m) = p (n)(m) + M (m) -1 v (m)

[0142] where p (n+1)(g) is the density iteration model for the (n + 1)-th iteration, p​​(n)(g) is the density iteration model for the nth iteration, M (g) and v (g) are the iteration matrix and vector of the density iteration model respectively, p (n+1)(m) is the magnetic susceptibility iteration model for the (n + 1)th iteration, p (n)(m) is the magnetic susceptibility iteration model for the nth iteration, n is the number of iterations, M (m) and v (m) are the iteration matrix and vector of the magnetic susceptibility iteration model respectively.

[0143] The update model determination module 305 specifically includes:

[0144] The density update model determination unit is used to determine the density update model by using the formula p (n+1)(g)(r) = p (n+1)(g) + M (g)(r) -1 v (g)(r) ;

[0145] The magnetic susceptibility update model determination unit is used to determine the magnetic susceptibility update model by using the formula p (n+1)(m)(r) = p (n+1)(m) + M (m)(r) -1 v (m)(r) ;

[0146] Among them, p (n+1)(g)(r) is the density update model for the (n + 1)th iteration, M (g)(r) and v (g)(r) are the matrix and vector of the update amount of the density update model respectively, p (n+1)(m)(r) is the magnetic susceptibility update model for the (n + 1)th iteration, M (m)(r) and v (m)(r) are the matrix and vector of the update amount of the magnetic susceptibility update model respectively.

[0147] The three-dimensional structure inference module 306 for the hydrothermal uranium metallogenic environment specifically includes:

[0148] The forward modeling residual determination unit is used to determine the first forward modeling residual of the density update model under the covariance and the second forward modeling residual of the magnetic susceptibility update model under the covariance;

[0149] The judgment unit is used to judge whether the first forward modeling residual and the second forward modeling residual are both less than the corresponding residual thresholds;

[0150] An iteration result determination unit is configured to determine the iteration result if both are less than; if not both are less than, replace the density update model and the magnetic susceptibility update model with the initial models of the three-dimensional gravity inversion and the three-dimensional magnetic inversion respectively, and continue the iteration until both the first forward modeling residual and the second forward modeling residual are less than the corresponding residual thresholds, and then determine the iteration result.

[0151] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description in the method section.

[0152] Specific examples are used in this article to elaborate on the principles and implementation manners of the present invention. The descriptions of the above embodiments are only used to help understand the method of the present invention and its core idea. At the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for detecting a hydrothermal uranium metallogenic environment, characterized in that, Including: Obtaining the rock density, magnetic susceptibility, gravity data, and magnetic data of each geological unit in the hydrothermal uranium ore exploration area, and determining the density-magnetic susceptibility cross-plot based on the rock density and magnetic susceptibility of each geological unit; Determining the geometric parameters of the cuboid grid used for three-dimensional inversion according to the hydrothermal uranium ore exploration area; the geometric parameters include: the north-south scale, east-west scale, and vertical scale of the cuboid grid, as well as the grid division parameters in each direction; Determining the initial model for three-dimensional gravity inversion and the initial model for three-dimensional magnetic inversion according to the cuboid grid; According to the initial model for three-dimensional gravity inversion, gravity data, and the cuboid grid, using the conjugate gradient method to determine the density iteration model; according to the initial model for three-dimensional magnetic inversion, magnetic data, and the cuboid grid, using the conjugate gradient method to determine the magnetic susceptibility iteration model; According to the density iteration model, gravity data, cuboid grid, and the structural information of the magnetic susceptibility iteration model, using the conjugate gradient method for three-dimensional inversion calculation to determine the density update model; according to the magnetic susceptibility iteration model, magnetic data, cuboid grid, and the structural information of the density iteration model, using the conjugate gradient method for three-dimensional inversion calculation to determine the magnetic susceptibility update model; Inferring the three-dimensional structure of the hydrothermal uranium metallogenic environment based on the iteration results of the density update model and the magnetic susceptibility update model and the density-magnetic susceptibility cross-plot.

2. The exploration method for hydrothermal uranium metallogenic environment according to claim 1, wherein The obtaining of the rock density, magnetic susceptibility, gravity data, and magnetic data of each geological unit in the hydrothermal uranium ore exploration area, and determining the density-magnetic susceptibility cross-plot based on the rock density and magnetic susceptibility of each geological unit specifically includes: Obtaining the gravity survey data and magnetic survey data of each geological unit in the hydrothermal uranium ore exploration area; Determining the Bouguer gravity anomaly data based on the gravity survey data, and using the discrete plane data gridding method to convert the Bouguer gravity anomaly and the corresponding elevation data into a plane grid, and then using the trend surface analysis method to determine the gravity residual anomaly plane grid; the gravity residual anomaly plane grid is the gravity data; Determining the total magnetic field anomaly data based on the magnetic survey data, and using the discrete plane data gridding method to convert the total magnetic field anomaly and the corresponding elevation data into a total magnetic field anomaly plane grid, and performing pole transformation on the total magnetic field anomaly plane grid, and then using the trend surface analysis method to determine the plane grid of the magnetic residual anomaly; the plane grid of the magnetic residual anomaly is the magnetic data.

3. A method for detecting a hydrothermal uranium metallogenic environment according to claim 1, characterized in that, The determining of the density iteration model according to the initial model for three-dimensional gravity inversion, gravity data, and the cuboid grid, using the conjugate gradient method; The determining of the magnetic susceptibility iteration model according to the initial model for three-dimensional magnetic inversion, magnetic data, and the cuboid grid, using the conjugate gradient method specifically includes: Using the formula p (n+1)(g) = p (n)(g) + M (g) -1 v (g) to determine the density iteration model; Using the formula p (n+1)(m) = p (n)(m) + M (m) -1 v (m) to determine the magnetic susceptibility iterative model; Among them, p (n+1)(g) is the density iteration model for the (n + 1)-th iteration, p (n)(g) is the density iteration model for the n-th iteration, M (g) and v (g) are the iteration matrix and vector of the density iteration model respectively, p (n+1)(m) is the magnetic susceptibility iteration model for the (n + 1)-th iteration, p (n)(m) is the magnetic susceptibility iteration model for the n-th iteration, n is the number of iterations, M (m) and v (m) are the iteration matrix and vector of the magnetic susceptibility iteration model respectively.

4. The exploration method for a hydrothermal uranium metallogenic environment according to claim 3, characterized in that, The determining of the density update model according to the density iteration model, gravity data, cuboid grid, and the structural information of the magnetic susceptibility iteration model, using the conjugate gradient method for three-dimensional inversion calculation; the determining of the magnetic susceptibility update model according to the magnetic susceptibility iteration model, magnetic data, cuboid grid, and the structural information of the density iteration model, using the conjugate gradient method for three-dimensional inversion calculation specifically includes: Using the formula p (n+1)(g)(r) = p (n+1)(g) + M (g)(r) -1 v (g)(r) to determine the density update model; Using the formula p (n+1)(m)(r) = p (n+1)(m) + M (m)(r) -1 v (m)(r) to determine the magnetic susceptibility update model; Among them, p (n+1)(g)(r) is the density update model for the (n + 1)-th iteration, M (g)(r) and v (g)(r) are the matrix and vector of the update amount of the density update model respectively, p (n+1)(m)(r) is the magnetic susceptibility update model for the (n + 1)-th iteration, M (m)(r) and v (m)(r) are the matrix and vector of the update amount of the magnetic susceptibility update model respectively.

5. A method for detecting a hydrothermal uranium metallogenic environment according to claim 1, characterized in that, Infer the three-dimensional structure of the hydrothermal uranium metallogenic environment based on the iterative results of the density update model and the magnetic susceptibility update model and the density-magnetic susceptibility cross-plot, specifically including: Determine the first forward modeling residual of the density update model under the action of covariance and the second forward modeling residual of the magnetic susceptibility update model under the action of covariance; Judge whether both the first forward modeling residual and the second forward modeling residual are less than the corresponding residual thresholds; If both are less than, determine the iterative result; if not both are less than, replace the density update model and the magnetic susceptibility update model with the initial model of the gravity three-dimensional inversion and the initial model of the magnetic three-dimensional inversion respectively, and continue the iteration until both the first forward modeling residual and the second forward modeling residual are less than the corresponding residual thresholds, and determine the iterative result.

6. A hydrothermal uranium metallogenic environment detection system, characterized in that, Including: A density-magnetic susceptibility cross-plot determination module, which is used to obtain the rock density, magnetic susceptibility, gravity data and magnetic data of each geological unit in the hydrothermal uranium ore exploration area, and determine the density-magnetic susceptibility cross-plot according to the rock density and magnetic susceptibility of each geological unit; A cuboid grid determination module, which is used to determine the geometric parameters of the cuboid grid used for three-dimensional inversion according to the hydrothermal uranium ore exploration area; the geometric parameters include: the north-south scale, the east-west scale and the vertical scale of the cuboid grid, and the grid division parameters in each direction; An initial model determination module, which is used to determine the initial model of the gravity three-dimensional inversion and the initial model of the magnetic three-dimensional inversion according to the cuboid grid; An iterative model determination module, which is used to determine the density iterative model by using the conjugate gradient method according to the initial model of the gravity three-dimensional inversion, the gravity data and the cuboid grid; determine the magnetic susceptibility iterative model by using the conjugate gradient method according to the initial model of the magnetic three-dimensional inversion, the magnetic data and the cuboid grid; An updated model determination module, which is used to perform three-dimensional inversion calculation by using the conjugate gradient method according to the density iterative model, the gravity data, the cuboid grid and the structural information of the magnetic susceptibility iterative model to determine the density updated model; perform three-dimensional inversion calculation by using the conjugate gradient method according to the magnetic susceptibility iterative model, the magnetic data, the cuboid grid and the structural information of the density iterative model to determine the magnetic susceptibility updated model; A three-dimensional structure inference module for the hydrothermal uranium metallogenic environment, which is used to infer the three-dimensional structure of the hydrothermal uranium metallogenic environment based on the iterative results of the density updated model and the magnetic susceptibility updated model and the density-magnetic susceptibility cross-plot.

7. The hydrothermal uranium metallogenic environment detection system according to claim 6, characterized in that The density-magnetic susceptibility cross-plot determination module specifically includes: A gravity survey data and magnetic survey data determination unit, which is used to obtain the gravity survey data and magnetic survey data of each geological unit in the hydrothermal uranium ore exploration area; A gravity data determination unit, which is used to determine the Bouguer gravity anomaly data according to the gravity survey data, and convert the Bouguer gravity anomaly and the corresponding elevation data into a plane grid by using the discrete plane data gridding method, and then determine the gravity residual anomaly plane grid by using the trend surface analysis method; the gravity residual anomaly plane grid is the gravity data; A magnetic data determination unit, which is used to determine the total magnetic field anomaly data according to the magnetic scanning data, and use the discrete plane data gridding method to convert the total magnetic field anomaly and the corresponding elevation data into a total magnetic field anomaly plane grid, and perform pole reduction processing on the total magnetic field anomaly plane grid, and then use the trend surface analysis method to determine the plane grid of the magnetic residual anomaly; the plane grid of the magnetic residual anomaly is the magnetic data.

8. The hydrothermal uranium metallogenic environment detection system according to claim 6, characterized in that, The iterative model determination module specifically includes: Density iteration model determination unit, for using the formula p (n+1)(g) = p (n)(g) + M (g) -1 v (g) to determine the density iteration model; Magnetic susceptibility iteration model determination unit, for using the formula p (n+1)(m) = p (n)(m) + M (m) -1 v (m) to determine the magnetic susceptibility iteration model; Among them, p (n+1)(g) is the density iteration model of the (n + 1)-th iteration, p (n)(g) is the density iteration model of the n-th iteration, M (g) and v (g) are the iteration matrix and vector of the density iteration model respectively, p (n+1)(m) is the magnetic susceptibility iteration model of the (n + 1)-th iteration, p (n)(m) is the magnetic susceptibility iteration model of the n-th iteration, n is the number of iterations, M (m) and v (m) are the iteration matrix and vector of the magnetic susceptibility iteration model respectively.

9. The hydrothermal uranium metallogenic environment detection system according to claim 8, wherein, The updated model determination module specifically includes: A density update model determination unit, which is used to use the formula p (n+1)(g)(r) = p (n+1)(g) + M (g)(r) -1 v (g)(r) to determine a density update model; A magnetic susceptibility update model determination unit for determining a magnetic susceptibility update model by using the formula p (n+1)(m)(r) = p (n+1)(m) + M (m)(r) -1 v (m)(r) ; where p (n+1)(g)(r) is the density update model for the (n + 1)-th iteration, M (g)(r) and v (g)(r) are the matrix and vector of the update amounts of the density update model respectively, and p (n+1)(m)(r) is the magnetic susceptibility update model for the (n + 1)-th iteration, M (m)(r) and v (m)(r) are the matrix and vector of the update amounts of the magnetic susceptibility update model respectively.

10. A hydrothermal uranium metallogenic environment detection system according to claim 6, characterized in that, The three-dimensional structure inference module for hydrothermal uranium metallogenic environment specifically includes: A forward modeling residual determination unit, which is used to determine the first forward modeling residual of the density updated model under the action of covariance and the second forward modeling residual of the magnetic susceptibility updated model under the action of covariance; A judgment unit, which is used to judge whether both the first forward modeling residual and the second forward modeling residual are less than the corresponding residual thresholds; An iterative result determination unit, which is used to determine the iterative result if both are less than; if not both are less than, then replace the initial models of gravity three-dimensional inversion and magnetic three-dimensional inversion with the density updated model and the magnetic susceptibility updated model respectively, and continue the iteration until both the first forward modeling residual and the second forward modeling residual are less than the corresponding residual thresholds, and determine the iterative result.

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