A method, device and computing equipment for inversion of granite pegmatite abundance

Abstract

The present invention provides a method, device, and computing equipment for inverting the abundance of granite pegmatite, relating to the technical field of mineral detection. The present invention obtains multi-angle reflectance spectral data of multiple particle samples, each of which is a different type of end-member mineral. The method then determines the single scattering albedo of each of the multiple end-member minerals at multiple characteristic wavelengths based on the Hapke model. A multivariate linear equation system is then established based on the single scattering albedo corresponding to each of the multiple characteristic wavelengths of the target granite pegmatite determined from the granite pegmatite image and the types of end-member minerals included in the target granite pegmatite. Solving the multivariate linear equation system enables abundance analysis of the granite pegmatite, rapid and accurate determination of the mass fraction of each end-member mineral included in the granite pegmatite, and rapid detection of the granite pegmatite, thereby improving prospecting efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of mineral detection, and in particular to an inversion method, device and computing equipment for granite pegmatite abundance. Background Art

[0002] Lithium is a type of metal mineral resource. Remote sensing is often used to detect lithium-rich minerals. However, remote sensing for granite pegmatites still suffers from issues such as low image target area delineation accuracy and difficulty distinguishing similar spectra. While the type of each end-member mineral in a mineral can be determined, accurate analysis of its abundance—that is, the mass fraction of each end-member mineral in the mineral—is impossible. This results in low mineral detection efficiency and fails to meet user needs.

[0003] Therefore, there is an urgent need for an inversion method for the abundance of granite pegmatite to realize the qualitative analysis and quantitative inversion of granite pegmatite remote sensing, solve the problems of low accuracy in image target area delineation and difficulty in distinguishing similar spectra, and realize rapid detection of the abundance of granite pegmatite, thereby improving the efficiency of mineral exploration. Summary of the Invention

[0004] The embodiments of the present invention provide a method, apparatus, and computing device for inversion of the abundance of granite pegmatite, which can realize abundance analysis of granite pegmatite, quickly and accurately determine the mass fraction of each end-member mineral included in the granite pegmatite, and realize rapid detection of granite pegmatite, thereby improving prospecting efficiency.

[0005] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0006] In a first aspect, a method for inverting the abundance of granite pegmatite is provided, the method comprising: obtaining multi-angle reflectance spectral data of a plurality of particle samples, the plurality of particle samples being end-member minerals of different types, the multi-angle reflectance spectral data comprising the reflectance corresponding to each observation condition parameter in a plurality of observation condition parameters, the observation condition parameters comprising the incident angle of the light source, the observation zenith angle and the phase angle; determining target observation condition parameters according to the multi-angle reflectance spectral data, the target observation condition parameters comprising the incident angle of the target light source, the target observation zenith angle and the target phase angle; determining the single scattering albedo corresponding to each characteristic wavelength in a plurality of characteristic wavelengths of each particle sample under the target observation condition parameters based on the Hapke model; obtaining a granite pegmatite image, the granite pegmatite image, the granite pegmatite image and the granite pegmatite image. The granite pegmatite image is an image of the target granite pegmatite obtained based on target observation condition parameters; the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite and the types of end-member minerals included in the target granite pegmatite are determined based on the granite pegmatite image; a multivariate linear equation system is established based on the single scattering albedo corresponding to each characteristic wavelength of each particle sample, the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite, and the types of end-member minerals included in the target granite pegmatite; the multivariate linear equation system is solved, and the abundance distribution of the target granite pegmatite is determined based on the solution of the multivariate linear equation system, where the abundance distribution is the mass fraction of each end-member mineral included in the target granite pegmatite;

[0007] Among them, the multivariate linear equation system includes multiple multivariate linear equations, and any multivariate linear equation is:

[0008] ω m =∑ j F j ω mj ;

[0009] Among them, ω m is the single scattering albedo of the target granite pegmatite at the characteristic wavelength m, j is the number of end-member minerals included in the target granite pegmatite, ω mj is the single scattering albedo of the particle sample of the jth end-member mineral among multiple end-member minerals at the characteristic wavelength m, F j is the mass fraction of the jth end-member mineral among multiple end-member minerals.

[0010] In a possible implementation of the first aspect, multi-angle reflectance spectral data of multiple particle samples is obtained, including: correcting and calibrating a portable ground object spectrometer; obtaining spectral data of each particle sample through the portable ground object spectrometer; and performing Savitzky-Golay smoothing preprocessing on the spectral data of each particle sample to obtain multi-angle reflectance spectral data of the multiple particle samples.

[0011] In a possible implementation of the first aspect, target observation condition parameters are determined based on multi-angle reflectance spectral data, including: determining the anisotropy factor and dimensionless bireflection factor corresponding to each observation condition parameter of each particle sample under multiple observation condition parameters; determining the target observation condition parameters from multiple observation condition parameters based on the anisotropy factor and dimensionless bireflection factor corresponding to each observation condition parameter of each particle sample under multiple observation condition parameters, wherein the difference between the anisotropy factors of any two particle samples at each wavelength under the target observation condition parameters is greater than or equal to a first threshold, and the difference between the dimensionless bireflection factors of any two particle samples at each wavelength is greater than or equal to a second threshold.

[0012] In a possible implementation of the first aspect, a formula for determining an anisotropy factor ANIF of each observation condition parameter at wavelength x is:

[0013]

[0014] Wherein, R is the reflectivity corresponding to each observation condition parameter at wavelength x, and R0 is the reflectivity corresponding to the preset observation condition parameter at wavelength x. When the target light source incident angle of the observation condition parameter is a°, the target observation zenith angle is b°, and the target phase angle is c°, the preset observation condition parameter has a target light source incident angle of a°, a target observation zenith angle of 0°, and a target phase angle of c°.

[0015] The dimensionless bidirectional reflection factor F at wavelength x for each observation condition parameter NDRF The formula for determining is:

[0016]

[0017] Among them, R max (λ) is the maximum value of the reflectivity corresponding to multiple observation condition parameters of each particle sample at wavelength x, R min (λ) is the minimum value of the reflectivity corresponding to multiple observation condition parameters of each particle sample at wavelength x.

[0018] In a possible implementation of the first aspect, the Hapke model is:

[0019]

[0020] R(i,e,g) is the reflectivity under the target observation condition parameters, ω is the single scattering albedo, B(g) is the backscattering function, and P(g) is the single particle phase angle function; i is the target light source incident angle; e is the target observation zenith angle; g is the target phase angle; μ0 = cosi, μ = cose;

[0021] The formula for determining the function H(x) is:

[0022]

[0023]

[0024]

[0025] Function B(g) is used to characterize the target phase angle g and roughness parameter h;

[0026] The expression of the single particle phase angle function P(g) is:

[0027]

[0028] cos g=cos i cos e+sin i sin e cosφ;

[0029] The parameter c is used to characterize the degree of forward scattering or backward scattering of the particle sample, and the parameter b is used to characterize the shape of the scattering lobe.

[0030] In a possible implementation of the first aspect, determining, based on a granite pegmatite image, a single scattering albedo corresponding to each of multiple characteristic wavelengths of a target granite pegmatite and a type of end-member mineral included in the target granite pegmatite includes:

[0031] Preprocessing the granite pegmatite image to obtain a processed granite pegmatite image, the preprocessing includes radiometric calibration processing and atmospheric correction processing;

[0032] The single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite in the processed granite pegmatite image and the end-member mineral types included in the target granite pegmatite are determined.

[0033] The beneficial effects of the present invention are as follows: the method provided by the present invention determines the single scattering albedo of each characteristic wavelength of multiple end-member minerals at multiple characteristic wavelengths based on the Hapke model, and then establishes a multivariate linear equation system based on the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite determined from the granite pegmatite image and the types of end-member minerals included in the target granite pegmatite. By solving the multivariate linear equation system, it is possible to analyze the abundance of the granite pegmatite, quickly and accurately determine the mass fraction of each end-member mineral included in the granite pegmatite, and realize rapid detection of the granite pegmatite, thereby improving the efficiency of mineral exploration.

[0034] In a second aspect, an inversion device for the abundance of granite pegmatite is provided, the device comprising: a data acquisition unit for acquiring multi-angle reflectance spectral data of a plurality of particle samples, the plurality of particle samples being end-member minerals of different types, the multi-angle reflectance spectral data comprising the reflectance corresponding to each observation condition parameter in a plurality of observation condition parameters, the observation condition parameters comprising the light source incident angle, the observation zenith angle and the phase angle; a first determination unit for determining target observation condition parameters according to the multi-angle reflectance spectral data, the target observation condition parameters comprising the target light source incident angle, the target observation zenith angle and the target phase angle; a second determination unit for determining the single scattering albedo corresponding to each characteristic wavelength in a plurality of characteristic wavelengths of each particle sample under the target observation condition parameters based on the Hapke model; an image acquisition unit for acquiring the granite pegmatite; granite image, the granite pegmatite image is an image of the target granite pegmatite obtained based on the target observation condition parameters; a third determining unit is used to determine the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite and the type of end-member minerals included in the target granite pegmatite based on the granite pegmatite image; a fourth determining unit is used to establish a multivariate linear equation system based on the single scattering albedo corresponding to each characteristic wavelength of each particle sample in the multiple characteristic wavelengths, the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite in the multiple characteristic wavelengths, and the type of end-member minerals included in the target granite pegmatite; a fifth determining unit is used to solve the multivariate linear equation system, and determine the abundance distribution of the target granite pegmatite based on the solution of the multivariate linear equation system, where the abundance distribution is the mass fraction of each end-member mineral included in the target granite pegmatite;

[0035] Among them, the multivariate linear equation system includes multiple multivariate linear equations, and any multivariate linear equation is:

[0036] ω m =∑ j F j ω mj ;

[0037] Among them, ω m is the single scattering albedo of the target granite pegmatite at the characteristic wavelength m, j is the number of end-member minerals included in the target granite pegmatite, ω mj is the single scattering albedo of the particle sample of the jth end-member mineral among multiple end-member minerals at the characteristic wavelength m, F j is the mass fraction of the jth end-member mineral among multiple end-member minerals.

[0038] In a possible implementation of the second aspect, the third determining unit is specifically configured to preprocess the granite pegmatite image to obtain a processed granite pegmatite image, wherein the preprocessing includes radiometric calibration processing and atmospheric correction processing;

[0039] The single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite in the processed granite pegmatite image and the end-member mineral types included in the target granite pegmatite are determined.

[0040] In a third aspect, a computing device is provided, comprising a memory and one or more processors; the memory is coupled to the processor; wherein computer program code is stored in the memory, and the computer program code comprises computer instructions, which, when executed by the processor, enable the computing device to execute the inversion method for granite pegmatite abundance as in any implementation of the first aspect.

[0041] In a fourth aspect, a computer-readable storage medium is provided, comprising computer instructions. When the computer instructions are executed on a computing device, the computing device executes the inversion method for granite pegmatite abundance as in any implementation of the first aspect.

[0042] In a fifth aspect, a computer program product is provided. When the computer program product is run on a computing device, the computing device is enabled to execute the inversion method for granite pegmatite abundance as in any implementation of the first aspect.

[0043] It can be understood that the beneficial effects that can be achieved by the inversion device for the abundance of granite pegmatite described in the second aspect, the computing device described in the third aspect, the computer-readable storage medium described in the fourth aspect, and the computer program product described in the fifth aspect provided above can be referred to the beneficial effects in the first aspect and any possible design method thereof, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of the hardware structure of a computing device according to an embodiment of the present invention;

[0045] Figure 2 A schematic flow chart of a method for inverting the abundance of granite pegmatite according to an embodiment of the present invention;

[0046] Figure 3 A schematic flow chart of another method for inverting the abundance of granite pegmatite according to an embodiment of the present invention;

[0047] Figure 4 A schematic flow chart of another method for inverting the abundance of granite pegmatite according to an embodiment of the present invention;

[0048] Figure 5 A schematic flow chart of another method for inverting the abundance of granite pegmatite according to an embodiment of the present invention;

[0049] Figure 6 The figure is a schematic diagram of the hardware structure of an inversion device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention. In the description of the present invention, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship. For example, A / B can represent A or B. The "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, in the description of the present invention, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0051] In addition, to facilitate a clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, the words "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that the words "first" and "second" do not limit the quantity or execution order, and the words "first" and "second" do not necessarily mean different.

[0052] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to represent examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner to facilitate understanding.

[0053] Lithium is a type of metal mineral resource. Remote sensing is often used to detect lithium-rich minerals. However, remote sensing for granite pegmatites still suffers from issues such as low image target area delineation accuracy and difficulty distinguishing similar spectra. While the type of each end-member mineral in a mineral can be determined, accurate analysis of its abundance—that is, the mass fraction of each end-member mineral in the mineral—is impossible. This results in low mineral detection efficiency and fails to meet user needs.

[0054] Therefore, there is an urgent need for an inversion method for the abundance of granite pegmatite to realize the qualitative analysis and quantitative inversion of granite pegmatite remote sensing, solve the problems of low accuracy in image target area delineation and difficulty in distinguishing similar spectra, and realize rapid detection of the abundance of granite pegmatite, thereby improving the efficiency of mineral exploration.

[0055] In view of this, an embodiment of the present invention provides an inversion method for the abundance of granite pegmatite, the method comprising: obtaining multi-angle reflectance spectral data of a plurality of particle samples, the plurality of particle samples being end-member minerals of different types, the multi-angle reflectance spectral data comprising the reflectance corresponding to each observation condition parameter in a plurality of observation condition parameters, the observation condition parameters comprising the incident angle of the light source, the observation zenith angle and the phase angle; determining target observation condition parameters according to the multi-angle reflectance spectral data, the target observation condition parameters comprising the incident angle of the target light source, the target observation zenith angle and the target phase angle; determining the single scattering albedo corresponding to each characteristic wavelength in a plurality of characteristic wavelengths of each particle sample under the target observation condition parameters based on the Hapke model; obtaining the granite pegmatite shadow The granite pegmatite image is an image of the target granite pegmatite obtained based on target observation condition parameters; the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite and the types of end-member minerals included in the target granite pegmatite are determined based on the granite pegmatite image; a multivariate linear equation system is established based on the single scattering albedo corresponding to each characteristic wavelength of each particle sample, the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite and the types of end-member minerals included in the target granite pegmatite; the multivariate linear equation system is solved, and the abundance distribution of the target granite pegmatite is determined based on the solution of the multivariate linear equation system, where the abundance distribution is the mass fraction of each end-member mineral included in the target granite pegmatite;

[0056] Among them, the multivariate linear equation system includes multiple multivariate linear equations, and any multivariate linear equation is:

[0057] ω m =∑ j F j ω mj ;

[0058] Among them, ω m is the single scattering albedo of the target granite pegmatite at the characteristic wavelength m, j is the number of end-member minerals included in the target granite pegmatite, ω mj is the single scattering albedo of the particle sample of the jth end-member mineral among multiple end-member minerals at the characteristic wavelength m, F j is the mass fraction of the jth end-member mineral among multiple end-member minerals.

[0059] The method provided in an embodiment of the present invention determines the single scattering albedo of multiple end-member minerals at each characteristic wavelength based on the Hapke model. Then, based on the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite determined from the granite pegmatite image and the types of end-member minerals included in the target granite pegmatite, a multivariate linear equation system is established. By solving the multivariate linear equation system, the abundance analysis of the granite pegmatite can be realized, the mass fraction of each end-member mineral included in the granite pegmatite can be quickly and accurately determined, and the granite pegmatite can be quickly detected, thereby improving the efficiency of mineral exploration.

[0060] In some embodiments, the granite pegmatite abundance inversion method provided in embodiments of the present invention can be performed by a granite pegmatite abundance inversion device 100 (hereinafter referred to as inversion device 100). By way of example, inversion device 100 can be any computing device 200 with data processing capabilities, such as a general-purpose computer, personal computer, laptop computer, switch, or tablet computer. The specific implementation of purchase list inversion device 100 is not limited herein.

[0061] Figure 1 FIG2 is a schematic diagram showing the hardware structure of a computing device provided by an embodiment of the present invention. The computing device 200 includes a processor 210 , a memory 220 , and a communication interface 230 .

[0062] The processor 210 may include one or more processing cores. The processor 210 connects various components within the computing device 200 using various interfaces and lines. It executes instructions, programs, code sets, or instruction sets stored in the memory 220, and calls data stored in the memory 220 to perform various functions and process data of the computing device 200. Optionally, the processor 210 may be implemented in the form of at least one of a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing unit (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA).

[0063] The memory 220 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory 220 includes a non-transitory computer-readable storage medium. The memory 220 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 220 may include a program storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a data acquisition function, a data processing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.

[0064] The communication interface 230 is used to communicate with other devices, equipment or communication networks, such as data storage devices, image processing equipment or Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0065] In physical implementation, the aforementioned components (e.g., processor 210, memory 220, and communication interface 230) may be components within the same device (e.g., a laptop). Alternatively, at least two of the components may be provided within the same device, i.e., as different components within a single device, similar to the deployment of devices or components in a distributed system.

[0066] It should be understood that the structure illustrated in this embodiment does not constitute a specific limitation on the computing device 200. In other embodiments of the present invention, the computing device 200 may include more or fewer components than shown, or may combine or separate certain components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0067] The following describes the inversion method for granite pegmatite abundance provided by the embodiment of the present invention in conjunction with the accompanying drawings.

[0068] Figure 2 A flowchart of a method for inverting the abundance of granite pegmatite provided in an embodiment of the present invention. Optionally, the method can be performed by Figure 1 The method is executed by the computing device 200 of the hardware structure shown, that is, by the inversion device 100. The method may include the following steps:

[0069] S1. Obtain multi-angle reflectance spectral data of multiple particle samples.

[0070] Specifically, the multiple particle samples are different types of end-member minerals. Exemplarily, the multiple particle samples include sample A, sample B, sample C and sample D, wherein sample A is end-member mineral A, sample B is end-member mineral B, sample C is end-member mineral C, and sample D is end-member mineral D.

[0071] The multi-angle reflectance spectral data includes the reflectance corresponding to each of the multiple observation condition parameters, including the light source incident angle, the observation zenith angle, and the phase angle. The multi-angle reflectance spectral data for each particle sample includes the reflectance measured for each particle sample under different observation condition parameters. The observation condition parameters include the light source incident angle, the observation zenith angle, and the phase angle. The light source incident angle is the zenith angle of the light source's incident direction; the observation zenith angle is the zenith angle of the observation direction; and the phase angle is the angle between the light source incident direction and the observation direction.

[0072] In one possible implementation, see Figure 3 The above S1 specifically includes the following steps:

[0073] S11. Calibrate and calibrate the portable ground object spectrometer;

[0074] S12, obtaining spectral data of each particle sample by the portable ground object spectrometer;

[0075] S13. Perform Savitzky-Golay smoothing preprocessing on the spectral data of each particle sample to obtain multi-angle reflectance spectral data of the multiple particle samples.

[0076] Savitzky-Golay smoothing is a commonly used spectral data preprocessing method used to smooth spectral curves and remove the effects of high-frequency noise. It is based on least-squares polynomial fitting, which calculates the smoothed curve by fitting local data.

[0077] Specifically, the multi-angle reflectance spectrum data of each particle sample includes a spectrum curve of each particle sample determined according to the arithmetic average of multiple spectrum curves of each particle sample.

[0078] It should be understood that the above method of obtaining multi-angle reflectance spectral data is only an example. The inversion device 100 can also obtain the spectral data of each particle sample in other ways, and pre-process the spectral data of each particle sample in any other way to obtain multi-angle reflectance spectral data of multiple particle samples. The embodiment of the present invention does not impose any special restrictions on this.

[0079] S2. Determine target observation condition parameters based on the multi-angle reflectance spectrum data. The target observation condition parameters include the target light source incident angle, the target observation zenith angle, and the target phase angle.

[0080] Specifically, the multi-angle reflectance spectral data includes the reflectance of each particle sample corresponding to each of multiple observation condition parameters, where the target observation condition parameter is one of the multiple observation condition parameters. Under the target observation condition parameters, the multi-angle reflectance spectral data of each end-member mineral is more diverse, facilitating accurate differentiation between different end-member minerals.

[0081] In one example, the target observation condition parameters include a target light source incident angle of 30°, a target observation zenith angle of 0°, and a target phase angle of 30°.

[0082] In some embodiments, see Figure 4 The above S2 specifically includes the following steps:

[0083] S21. Determine the anisotropy factor and dimensionless bidirectional reflection factor corresponding to each observation condition parameter of each particle sample under multiple observation condition parameters.

[0084] Specifically, the inversion device 100 determines the anisotropy factor and dimensionless bidirectional reflection factor corresponding to each particle sample at each wavelength under each observation condition parameter, and obtains the anisotropy factor and dimensionless bidirectional reflection factor corresponding to each observation condition parameter of each particle sample under multiple observation condition parameters.

[0085] The formula for determining the anisotropy factor ANIF of each observation condition parameter at wavelength x is:

[0086]

[0087] Wherein, R is the reflectivity corresponding to each observation condition parameter at wavelength x, and R0 is the reflectivity corresponding to the preset observation condition parameter at wavelength x, wherein, when the target light source incident angle of the observation condition parameter is a°, the target observation zenith angle is b°, and the target phase angle is c°, the target light source incident angle of the preset observation condition parameter is a°, the target observation zenith angle is 0°, and the target phase angle is c°;

[0088] The dimensionless bidirectional reflection factor F at wavelength x for each observation condition parameter NDRF The formula for determining is:

[0089]

[0090] Among them, R max(λ) is the maximum value of the reflectivity corresponding to multiple observation condition parameters of each particle sample at wavelength x, R min (λ) is the minimum value of the reflectivity corresponding to multiple observation condition parameters of each particle sample at wavelength x.

[0091] S22. Determine a target observation condition parameter from the multiple observation condition parameters according to the anisotropy factor and the dimensionless bidirectional reflection factor corresponding to each observation condition parameter of each particle sample under the multiple observation condition parameters.

[0092] The difference between the anisotropy factors of any two particle samples at each wavelength under the target observation condition parameters is greater than or equal to a first threshold, and the difference between the dimensionless bidirectional reflection factors of any two particle samples at each wavelength is greater than or equal to a second threshold.

[0093] For example, under observation condition parameter A, the anisotropy factor of particle sample A at a wavelength of 600 nm is 5, and the dimensionless bidirectional reflection factor at a wavelength of 600 nm is 2. The anisotropy factor of particle sample B at a wavelength of 600 nm is 6, and the dimensionless bidirectional reflection factor of particle sample B at a wavelength of 600 nm is 3. Under observation condition parameter B, the anisotropy factor of particle sample A at a wavelength of 600 nm is 16, and the dimensionless bidirectional reflection factor at a wavelength of 600 nm is 1. The anisotropy factor of particle sample B at a wavelength of 600 nm is 5, and the dimensionless bidirectional reflection factor of particle sample B at a wavelength of 600 nm is 1. Under the observation condition parameter B, the difference between the anisotropy factors of the two particle samples at a wavelength of 600nm is greater than or equal to the first threshold, and the difference between the dimensionless bidirectional reflection factors of the two particle samples at a wavelength of 600nm is greater than or equal to the second threshold. When the difference between the anisotropy factors of the two particle samples at each wavelength is greater than or equal to the first threshold, and the difference between the dimensionless bidirectional reflection factors of the two particle samples at each wavelength is greater than or equal to the second threshold, the observation condition parameter B is determined as the target observation condition parameter.

[0094] S3. Based on the Hapke model, under the target observation condition parameters, determine the single scattering albedo of each particle sample corresponding to each characteristic wavelength in multiple characteristic wavelengths.

[0095] The multiple characteristic wavelengths are a correlation matrix of multiple particle samples determined based on a principal component analysis method, and the multiple characteristic wavelengths are obtained according to the correlation matrix.

[0096] The formula for the correlation matrix is:

[0097]

[0098] F1, F2, …, Fs The variables x1, x2, ..., x m The first principal component, the second principal component, and so on. ij (i=1,2,…,s;j=1,2,…,m) is the variable x i The factor loadings on each principal component are x1, x2, ..., x m The eigenvectors corresponding to the s largest eigenvalues ​​of the correlation matrix.

[0099] Specifically, principal component analysis is a multidimensional orthogonal linear transformation method based on multivariate statistics. It concentrates useful information in several principal components as much as possible to achieve data compression and dimensionality reduction. Each principal component is a linear combination of variables, and the contribution coefficient of each principal component is used to extract the target object. The principle is: let the original variables be x1, x2, ..., x m , then the new variables of the linear combination are F1, F2, ..., F s (s≤m).

[0100] In one example, the plurality of characteristic wavelengths are 600 nm, 930 nm, 1260 nm, 1600 nm and 2200 nm.

[0101] It should be understood that the above methods and examples for determining the characteristic wavelength are merely illustrative, and the embodiment of the present invention does not impose any particular limitation on the method for selecting the characteristic wavelength.

[0102] The Hapke model is:

[0103]

[0104] R(i,e,g) is the reflectivity under the target observation condition parameters, ω is the single scattering albedo, B(g) is the backscattering function, P(g) is the single particle phase angle function; i is the target light source incident angle; e is the target observation zenith angle; g is the target phase angle; μ0 = cosi, μ = cose;

[0105] The formula for determining the function H(x) is:

[0106]

[0107]

[0108]

[0109] Function B(g) is used to characterize the target phase angle g and roughness parameter h;

[0110] The expression of the single particle phase angle function P(g) is:

[0111]

[0112] cosg=cosicose+sinisinecosφ;

[0113] The parameter c is used to characterize the degree of forward scattering or backward scattering of the particle sample, and the parameter b is used to characterize the shape of the scattering lobe.

[0114] That is to say, in the above-mentioned P(g) function expression, the first and second terms describe the backscattering lobe and the forward scattering lobe, respectively. The parameter c represents the degree of forward scattering or backscattering of the sample, and its value range is between -1 and 1. A negative value indicates that the forward scattering of the sample is greater than the backscattering. On the contrary, a positive value indicates that the sample is more inclined to backscattering behavior. The parameter b describes the shape of the scattering lobe, and its value range is between 0 and 1. Therefore, the scattering behavior of the sample is determined by two parameters. Therefore, the Hapke model used in the method provided in the embodiment of the present invention includes the following three parameters: ω, b, c and h. In other words, the embodiment of the present invention performs processing based on the unknown parameters ω, b, c and h to obtain the single scattering albedo corresponding to each characteristic wavelength of each particle sample in multiple characteristic wavelengths.

[0115] S4. Obtaining a granite pegmatite image. The granite pegmatite image is an image of the target granite pegmatite obtained based on target observation condition parameters.

[0116] S5. Determine, based on the granite pegmatite image, the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite at multiple characteristic wavelengths and the types of end-member minerals included in the target granite pegmatite.

[0117] In some embodiments, see Figure 5 The above S5 specifically includes the following steps:

[0118] S51. Preprocessing the granite pegmatite image to obtain a processed granite pegmatite image. The preprocessing includes radiometric calibration processing and atmospheric correction processing.

[0119] S52: Determine the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite in the processed granite pegmatite image and the end-member mineral types included in the target granite pegmatite.

[0120] S6. establishing a multivariate linear equation system based on the single scattering albedo of each particle sample corresponding to each characteristic wavelength in the plurality of characteristic wavelengths, the single scattering albedo of the target granite pegmatite corresponding to each characteristic wavelength in the plurality of characteristic wavelengths, and the types of end-member minerals included in the target granite pegmatite;

[0121] Among them, the multivariate linear equation system includes multiple multivariate linear equations, and any multivariate linear equation is:

[0122] ω m =∑ j F j ω mj ;

[0123] Among them, ω m is the single scattering albedo of the target granite pegmatite at the characteristic wavelength m, j is the number of end-member minerals included in the target granite pegmatite, ω mj is the single scattering albedo of the particle sample of the jth end-member mineral among multiple end-member minerals at the characteristic wavelength m, F j is the mass fraction of the jth end-member mineral among multiple end-member minerals.

[0124] For example, the target granite pegmatite includes end-member mineral A, end-member mineral B and end-member mineral C, and the single scattering albedo of the target granite pegmatite at 600nm is 0.1, the single scattering albedo of the target granite pegmatite at 930nm is 0.2, and the single scattering albedo of the target granite pegmatite at 1260nm is 0.5; the single scattering albedo of the end-member mineral A at 600nm is 0.4, the single scattering albedo of the end-member mineral A at 930nm is 0.3, and the single scattering albedo of the end-member mineral A at 1 The single scattering albedo at 260nm is 0.2; the single scattering albedo at 600nm of end-member mineral B is 0.3, the single scattering albedo at 930nm of end-member mineral B is 0.5, and the single scattering albedo at 1260nm of end-member mineral B is 0.8; the single scattering albedo at 600nm of end-member mineral C is 0.9, the single scattering albedo at 930nm of end-member mineral C is 0.1, and the single scattering albedo at 1260nm of end-member mineral C is 0.4;

[0125] The multivariate linear equation system of the target granite pegmatite is:

[0126] 0.1=0.4F1+0.3F2+0.9F3;

[0127] 0.2=0.3F1+0.5F2+0.1F3;

[0128] 0.5=0.2F1+0.8F2+0.4F3;

[0129] Wherein, F1 is the mass fraction of end-member mineral A in the target granite pegmatite. F2 is the mass fraction of end-member mineral B in the target granite pegmatite. F3 is the mass fraction of end-member mineral C in the target granite pegmatite.

[0130] S7. Solve the multivariate linear equation system, and determine the abundance distribution of the target granite pegmatite according to the solution of the multivariate linear equation system, where the abundance distribution is the mass fraction of each end-member mineral included in the target granite pegmatite.

[0131] It can also be understood as F in the above equations j There are multiple unknowns. By solving multiple equations included in multiple pairs of equations simultaneously, the value corresponding to each Fj can be obtained, and then the mass fraction of each end-member mineral included in the target granite pegmatite can be obtained.

[0132] It can be seen from the above S1-S7 that the method provided in the embodiment of the present invention determines the single scattering albedo of each characteristic wavelength of multiple end-member minerals at multiple characteristic wavelengths based on the Hapke model, and then establishes a multivariate linear equation system based on the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite determined from the granite pegmatite image and the types of end-member minerals included in the target granite pegmatite. By solving the multivariate linear equation system, it is possible to realize the abundance analysis of the granite pegmatite, quickly and accurately determine the mass fraction of each end-member mineral included in the granite pegmatite, and realize rapid detection of the granite pegmatite, thereby improving the efficiency of mineral exploration.

[0133] The above mainly introduces the solution of the embodiment of the present invention from the perspective of method. It can be understood that in order to realize the above functions, the inversion device 100 includes at least one of the hardware structure and software modules corresponding to the execution of each function. It should be easy for those skilled in the art to realize that, in combination with the units and algorithm steps of each example described in the embodiment disclosed herein, the embodiment of the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the embodiment of the present invention.

[0134] In embodiments of the present invention, the inversion device 100 can be divided into functional units according to the above-described method example. For example, the inversion device 100 can be divided into functional units corresponding to respective functions, or two or more functions can be integrated into a single processing unit. The above-described integrated units can be implemented in the form of hardware or software functional units. It should be noted that the division of units in the embodiments of the present invention is illustrative and merely represents a logical functional division. In actual implementation, other division methods may be used.

[0135] For example, Figure 6The hardware structure diagram of an inversion device 100 provided by an embodiment of the present invention is shown. The inversion device 100 includes: a data acquisition unit 610, for acquiring multi-angle reflectance spectrum data of multiple particle samples, the multiple particle samples are different types of end-member minerals, the multi-angle reflectance spectrum data includes the reflectance corresponding to each observation condition parameter in a plurality of observation condition parameters, the observation condition parameters include the light source incident angle, the observation zenith angle and the phase angle; a first determination unit 620, for determining the target observation condition parameters according to the multi-angle reflectance spectrum data, the target observation condition parameters include the target light source incident angle, the target observation zenith angle and the target phase angle; a second determination unit 630, for determining the single scattering albedo corresponding to each characteristic wavelength in a plurality of characteristic wavelengths of each particle sample under the target observation condition parameters based on the Hapke model; an image acquisition unit, for acquiring granite pegmatite images, the granite pegmatite images The image is an image of the target granite pegmatite obtained based on the target observation condition parameters; a third determining unit 640 is used to determine the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite and the type of end-member minerals included in the target granite pegmatite based on the granite pegmatite image; a fourth determining unit 650 is used to establish a multivariate linear equation system based on the single scattering albedo corresponding to each characteristic wavelength of each particle sample, the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite, and the type of end-member minerals included in the target granite pegmatite; a fifth determining unit 660 is used to solve the multivariate linear equation system and determine the abundance distribution of the target granite pegmatite based on the solution of the multivariate linear equation system, where the abundance distribution is the mass fraction of each end-member mineral included in the target granite pegmatite;

[0136] Among them, the multivariate linear equation system includes multiple multivariate linear equations, and any multivariate linear equation is:

[0137] ω m =∑ j F j ω mj ;

[0138] Among them, ω m is the single scattering albedo of the target granite pegmatite at the characteristic wavelength m, j is the number of end-member minerals included in the target granite pegmatite, ω mj is the single scattering albedo of the particle sample of the jth end-member mineral among multiple end-member minerals at the characteristic wavelength m, F j is the mass fraction of the jth end-member mineral among multiple end-member minerals.

[0139] Optionally, the third determination unit 640 is specifically used to preprocess the granite pegmatite image to obtain a processed granite pegmatite image, where the preprocessing includes radiation calibration processing and atmospheric correction processing; and determine the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite in the processed granite pegmatite image at multiple characteristic wavelengths and the end-member mineral types included in the target granite pegmatite.

[0140] It should be understood that the specific description of the above optional methods can be found in the above method embodiments, which will not be repeated here. In addition, the explanation of any of the above-mentioned inversion devices 100 and the description of the beneficial effects can refer to the above-mentioned corresponding method embodiments, which will not be repeated here.

[0141] An embodiment of the present invention further provides a computer-readable storage medium storing at least one computer instruction, which is loaded and executed by a processor to implement the methods of each of the above embodiments. For explanations of the relevant contents and descriptions of the beneficial effects of any of the above-mentioned computer-readable storage media, reference can be made to the corresponding embodiments described above and will not be repeated here.

[0142] An embodiment of the present invention further provides a chip. The chip integrates a control circuit and one or more ports for implementing the functions of the purchase list inversion device 100. Optionally, the functions supported by the chip can be found above and will not be further described here.

[0143] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned embodiments can be implemented by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a random access memory, etc. The above-mentioned processing unit or processor can be a central processing unit, a general-purpose processor, a specific circuit structure (application specific integrated circuit, ASIC), a microprocessor (digital signal processor, DSP), a field programmable gate array (field programmable gate array, FPGA) or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

[0144] An embodiment of the present invention further provides a computer program product comprising instructions that, when executed on a computer, cause the computer to perform any of the methods described in the above embodiments. The computer program product comprises one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible by a computer or a data storage device such as a server or data center that includes one or more available media. Available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives).

[0145] It should be noted that the above-mentioned devices for storing computer instructions or computer programs provided in the embodiments of the present invention, such as but not limited to the above-mentioned memories, computer-readable storage media and communication chips, etc., all have non-transitory properties. Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present invention can be implemented using hardware, software, firmware or any combination thereof. When implemented using software, these functions can be stored in a computer-readable storage medium or transmitted as one or more instructions or codes on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein the communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0146] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for inverting the abundance of granite pegmatite, characterized in that: The method comprises: Acquiring multi-angle reflectance spectral data of a plurality of particle samples, the plurality of particle samples being different types of end-member minerals, the multi-angle reflectance spectral data including reflectance corresponding to each of a plurality of observation condition parameters, the observation condition parameters including a light source incident angle, an observation zenith angle, and a phase angle; Determining target observation condition parameters based on the multi-angle reflectance spectrum data, wherein the target observation condition parameters include a target light source incident angle, a target observation zenith angle, and a target phase angle; Based on the Hapke model, under the target observation condition parameters, determining the single scattering albedo corresponding to each characteristic wavelength of each particle sample; Acquire a granite pegmatite image, wherein the granite pegmatite image is an image of the target granite pegmatite obtained based on the target observation condition parameters; determining, based on the granite pegmatite image, a single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite at a plurality of characteristic wavelengths and a type of end-member minerals included in the target granite pegmatite; Establishing a multivariate linear equation system according to the single scattering albedo corresponding to each characteristic wavelength of each particle sample, the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite, and the types of end-member minerals included in the target granite pegmatite; Solving the multivariate linear equation system, and determining the abundance distribution of the target granite pegmatite according to the solution of the multivariate linear equation system, wherein the abundance distribution is the mass fraction of each end-member mineral included in the target granite pegmatite; The multivariate linear equation system includes a plurality of multivariate linear equations, any of which is: oh m =∑ j F j oh mj ; Among them, ω m is the single scattering albedo of the target granite pegmatite at the characteristic wavelength m, j is the number of end-member minerals included in the target granite pegmatite, ω mj is the single scattering albedo of the particle sample of the jth end-member mineral among multiple end-member minerals at the characteristic wavelength m, F j is the mass fraction of the jth end-member mineral among multiple end-member minerals.

2. The method according to claim 1, characterized in that The obtaining of multi-angle reflectance spectral data of a plurality of particle samples comprises: Calibrate and calibrate the portable ground spectrometer; Obtaining spectral data of each particle sample by the portable ground object spectrometer; Savitzky-Golay smoothing preprocessing is performed on the spectral data of each particle sample to obtain multi-angle reflectance spectral data of the multiple particle samples.

3. The method according to claim 2, characterized in that Determining target observation condition parameters according to the multi-angle reflectance spectrum data includes: Determining an anisotropy factor and a dimensionless bidirectional reflectance factor corresponding to each observation condition parameter of each particle sample under a plurality of observation condition parameters; The target observation condition parameters are determined from the multiple observation condition parameters according to the anisotropy factor and the dimensionless bidirectional reflection factor corresponding to each observation condition parameter of each particle sample under the multiple observation condition parameters, wherein the difference between the anisotropy factors of any two particle samples at each wavelength under the target observation condition parameters is greater than or equal to a first threshold, and the difference between the dimensionless bidirectional reflection factors of any two particle samples at each wavelength is greater than or equal to a second threshold.

4. The method according to claim 3, characterized in that The formula for determining the anisotropy factor ANIF of each observation condition parameter at wavelength x is: Wherein, R is the reflectivity corresponding to each observation condition parameter at wavelength x, and R0 is the reflectivity corresponding to the preset observation condition parameter at wavelength x, wherein, when the target light source incident angle of the observation condition parameter is a°, the target observation zenith angle is b°, and the target phase angle is c°, the target light source incident angle of the preset observation condition parameter is a°, the target observation zenith angle is 0°, and the target phase angle is c°; The dimensionless bidirectional reflection factor F at wavelength x for each observation condition parameter NDRF The formula for determining is: Among them, R max (λ) is the maximum value of the reflectivity corresponding to multiple observation condition parameters of each particle sample at wavelength x, R min (λ) is the minimum value of the reflectivity corresponding to multiple observation condition parameters of each particle sample at wavelength x.

5. The method according to claim 4, characterized in that The Hapke model is: R(i,e,g) is the reflectivity under the target observation condition parameters, ω is the single scattering albedo, B(g) is the backscattering function, P(g) is the single particle phase angle function; i is the target light source incident angle; e is the target observation zenith angle; g is the target phase angle; μ0 = cosi, μ = cose; The formula for determining the function H(x) is: Function B(g) is used to characterize the target phase angle g and roughness parameter h; The expression of the single particle phase angle function P(g) is: cos g=cosicos e+sinisin e cosφ; The parameter c is used to characterize the degree of forward scattering or backward scattering of the particle sample, and the parameter b is used to characterize the shape of the scattering lobe.

6. The method according to claim 5, characterized in that The determining, based on the granite pegmatite image, the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite at a plurality of characteristic wavelengths and the type of end-member minerals included in the target granite pegmatite comprises: Preprocessing the granite pegmatite image to obtain a processed granite pegmatite image, wherein the preprocessing includes radiometric calibration processing and atmospheric correction processing; The single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite in the processed granite pegmatite image and the end-member mineral types included in the target granite pegmatite are determined.

7. An inversion device for granite pegmatite abundance, characterized in that: The device comprises: a data acquisition unit, configured to acquire multi-angle reflectance spectral data of a plurality of particle samples, the plurality of particle samples being different types of end-member minerals, the multi-angle reflectance spectral data including reflectance corresponding to each of a plurality of observation condition parameters, the observation condition parameters including a light source incident angle, an observation zenith angle, and a phase angle; a first determining unit, configured to determine target observation condition parameters according to the multi-angle reflectance spectrum data, wherein the target observation condition parameters include a target light source incident angle, a target observation zenith angle, and a target phase angle; a second determining unit, configured to determine, based on a Hapke model and under the target observation condition parameters, a single scattering albedo corresponding to each characteristic wavelength of each of the particle samples at a plurality of characteristic wavelengths; An image acquisition unit, configured to acquire a granite pegmatite image, wherein the granite pegmatite image is an image of the target granite pegmatite acquired based on the target observation condition parameters; a third determining unit, configured to determine, based on the granite pegmatite image, a single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite at a plurality of characteristic wavelengths and a type of end-member minerals included in the target granite pegmatite; a fourth determining unit, configured to establish a multivariate linear equation system based on the single scattering albedo corresponding to each characteristic wavelength of each particle sample, the single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite, and the types of end-member minerals included in the target granite pegmatite; a fifth determining unit, configured to solve the multivariate linear equation system, and determine the abundance distribution of the target granite pegmatite according to the solution of the multivariate linear equation system, wherein the abundance distribution is the mass fraction of each end-member mineral included in the target granite pegmatite; The multivariate linear equation system includes a plurality of multivariate linear equations, any of which is: oh m =∑ j F j oh mj ; Among them, ω m is the single scattering albedo of the target granite pegmatite at the characteristic wavelength m, j is the number of end-member minerals included in the target granite pegmatite, ω mj is the single scattering albedo of the particle sample of the jth end-member mineral among multiple end-member minerals at the characteristic wavelength m, F j is the mass fraction of the jth end-member mineral among multiple end-member minerals.

8. The device according to claim 7, characterized in that The third determining unit is specifically configured to: Preprocessing the granite pegmatite image to obtain a processed granite pegmatite image, wherein the preprocessing includes radiometric calibration processing and atmospheric correction processing; The single scattering albedo corresponding to each characteristic wavelength of the target granite pegmatite in the processed granite pegmatite image and the end-member mineral types included in the target granite pegmatite are determined.

9. A computing device, characterized in that include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the inversion method for granite pegmatite abundance according to any one of claims 1 to 6.

10. A computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the inversion method for granite pegmatite abundance according to any one of claims 1 to 6.

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