A method, apparatus, and computing device for inversion of granitic pegmatite grain size

Abstract

The application provides a granitic pegmatite granularity inversion method and device and a computing device, and relates to the technical field of mineral exploration.The application obtains multi-angle reflectivity spectrum data of a plurality of particle samples, the particle samples are ore bodies with different particle sizes and include one or more end-member minerals, and the multi-angle reflectivity spectrum data includes reflectivity corresponding to each observation condition parameter in a plurality of observation condition parameters.A target observation condition parameter is determined according to the multi-angle reflectivity spectrum data; a linear regression equation of the particle size of the granitic pegmatite is determined according to the target observation condition parameter based on a Hapke model; and the particle size distribution of the granitic pegmatite in a granitic pegmatite image is determined according to the linear regression equation.The method provided by the application can realize qualitative analysis and quantitative inversion of remote sensing of the granitic pegmatite, solve the problems of low accuracy of target area delineation and difficulty in distinguishing similar spectra, realize rapid detection of the granitic pegmatite, and improve the efficiency of ore prospecting.

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 grain size. Background Art

[0002] Lithium is a type of metal mineral resource. Remote sensing is commonly used to detect lithium-rich minerals, offering advantages such as high efficiency, low cost, and the ability to detect large areas.

[0003] However, in the process of remote sensing detection of granite pegmatites, problems still exist, such as low accuracy in image target delineation and difficulty in distinguishing similar spectra. This results in low mineral detection efficiency and fails to meet user needs.

[0004] Therefore, there is an urgent need for an inversion method for the grain size 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, realize rapid detection of granite pegmatite, and improve prospecting efficiency. Summary of the Invention

[0005] The embodiments of the present invention provide a method, device and computing equipment for inversion of granite pegmatite grain size, which can realize 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, realize rapid detection of granite pegmatite and improve prospecting efficiency.

[0006] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0007] In a first aspect, a method for inverting the grain size of granite pegmatite is provided, the method comprising: obtaining multi-angle reflectance spectral data of a plurality of particle samples, the particle samples being ore bodies with different particle sizes including one or more end-member minerals, the multi-angle reflectance spectral data comprising the reflectance corresponding to each of a plurality of observation condition parameters, the observation condition parameters comprising a light source incident angle, a target observation zenith angle, and a phase angle; determining target observation condition parameters based on the multi-angle reflectance spectral data, the target observation condition parameters comprising a target light source incident angle, a target observation zenith angle, and a target phase angle; determining a linear regression equation for the grain size of the granite pegmatite based on the target observation condition parameters based on a Hapke model; and determining the grain size distribution of the granite pegmatite in the granite pegmatite image based on the linear regression equation.

[0008] 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.

[0009] In a possible implementation of the first aspect, the 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 the multiple observation condition parameters based on the anisotropy factor and dimensionless bireflection 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 bireflection factors of any two particle samples at each wavelength is greater than or equal to a second threshold.

[0010] 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:

[0011]

[0012] 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°;

[0013] The dimensionless bidirectional reflection factor F at wavelength x for each observation condition parameter NDRF The formula for determining is:

[0014]

[0015] 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.

[0016] In a possible implementation of the first aspect, based on the Hapke model, a linear regression equation for the grain size of the granite pegmatite is determined according to target characteristic parameters, including:

[0017] Based on the Hapke model, under the target observation condition parameters, the single scattering albedo corresponding to each characteristic wavelength of each particle sample is determined; based on the single scattering albedo corresponding to each characteristic wavelength of each particle sample and the particle size, the linear regression equation corresponding to each characteristic wavelength of the multiple characteristic wavelengths is determined;

[0018] Wherein, 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, 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]

[0029] Among them, parameter c is used to characterize the degree of forward scattering or backward scattering of the particle sample, and parameter b is used to characterize the shape of the scattering lobe. is the angle between the incident plane and the observation plane, the angle between the incident plane and the horizontal plane is the target light source incident angle, and the angle between the observation plane and the horizontal plane is the target observation zenith angle;

[0030] The linear regression equation is:

[0031] P Vλ =M+N*particle size;

[0032] Among them, P Vλ is the retrieved value of the single scattering albedo ω at wavelength λ, particle size is the particle size of granite pegmatite, and M and N are constants.

[0033] In a possible implementation of the first aspect, determining the particle size distribution of granite pegmatite in a granite pegmatite image according to a linear regression equation includes: preprocessing the granite pegmatite image to obtain a processed granite pegmatite image, the preprocessing including radiation calibration processing and atmospheric correction processing, the granite pegmatite image being an image acquired based on target observation condition parameters, and the granite pegmatite image including multiple granite pegmatite regions; determining single scattering albedo data corresponding to each granite pegmatite region in the processed granite pegmatite image, the single scattering albedo data including a single scattering albedo corresponding to each characteristic wavelength of multiple characteristic wavelengths; and obtaining the particle size distribution of the granite pegmatite image according to the single scattering albedo data based on the linear regression equation, the particle size distribution of the granite pegmatite image including the particle size distribution corresponding to each granite pegmatite region.

[0034] In a possible implementation of the first aspect, the particle size distribution of the granite pegmatite in the granite pegmatite image is determined according to a linear regression equation, including: obtaining ground-based imaging hyperspectral image data of multiple particle samples; obtaining the particle size distribution of the multiple particle samples through a linear regression equation based on the ground-based imaging hyperspectral image data of the multiple particle samples; and determining the particle size distribution of the granite pegmatite in the granite pegmatite image according to the linear regression equation when the particle size of each particle sample is within the particle size distribution of each particle sample.

[0035] The beneficial effects of the present invention are as follows: the method provided by the present invention determines the single scattering albedo corresponding to each particle sample under the target observation condition parameters based on multi-angle reflectance spectral data, and establishes a linear regression equation according to the single scattering albedo and particle size corresponding to each particle sample, which can realize 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, realize rapid detection of granite pegmatite, and improve prospecting efficiency.

[0036] In a second aspect, a device for inverting the grain size of granite pegmatite is provided, the device comprising: an acquisition unit for acquiring multi-angle reflectance spectral data of a plurality of particle samples, the particle samples being ore bodies with different particle sizes including one or more end-member minerals, the multi-angle reflectance spectral data comprising the reflectance corresponding to each of 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 processing unit for determining target observation condition parameters based on 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; the processing unit is further configured to determine a linear regression equation for the grain size of the granite pegmatite based on the target observation condition parameters based on the Hapke model; the processing unit is further configured to determine the grain size distribution of the granite pegmatite in the granite pegmatite image based on the linear regression equation.

[0037] 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 grain size as in any implementation of the first aspect.

[0038] 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 grain size as in any implementation of the first aspect.

[0039] 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 grain size as in any implementation of the first aspect.

[0040] It can be understood that the beneficial effects that can be achieved by the inversion device for the grain size 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

[0041] Figure 1 A schematic diagram of the hardware structure of a computing device according to an embodiment of the present invention;

[0042] Figure 2 A schematic flow chart of a method for inverting the grain size of granite pegmatite according to an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of multi-angle reflectance spectrum data shown in an embodiment of the present invention;

[0044] Figure 4 A schematic flow chart of another method for inversion of granite pegmatite grain size according to an embodiment of the present invention;

[0045] Figure 5 A schematic flow chart of another method for inversion of granite pegmatite grain size according to an embodiment of the present invention;

[0046] Figure 6 A schematic flow chart of another method for inversion of granite pegmatite grain size according to an embodiment of the present invention;

[0047] Figure 7a A graph showing the relationship between the single scattering albedo inverted by the Hapke model at a wavelength of 600 nm and the particle size according to an embodiment of the present invention;

[0048] Figure 7b A graph showing the relationship between the single scattering albedo inverted by the Hapke model at a wavelength of 930 nm and the particle size according to an embodiment of the present invention;

[0049] Figure 7c A graph showing the relationship between the single scattering albedo inverted by the Hapke model at a wavelength of 1260 nm and the particle size according to an embodiment of the present invention;

[0050] Figure 7d A graph showing the relationship between the single scattering albedo inverted by the Hapke model at a wavelength of 1600 nm and the particle size according to an embodiment of the present invention;

[0051] Figure 7e A graph showing the relationship between the single scattering albedo inverted by the Hapke model at a wavelength of 2200 nm and the particle size according to an embodiment of the present invention;

[0052] Figure 8 A schematic flow chart of another method for inversion of granite pegmatite grain size according to an embodiment of the present invention;

[0053] Figure 9 The figure is a schematic diagram of the hardware structure of an inversion device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] 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.

[0055] 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.

[0056] 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.

[0057] Lithium is a type of metal mineral resource. Remote sensing is commonly used to detect lithium-rich minerals, offering advantages such as high efficiency, low cost, and the ability to detect large areas.

[0058] However, in the process of remote sensing detection of granite pegmatites, problems still exist, such as low accuracy in image target delineation and difficulty in distinguishing similar spectra. This results in low mineral detection efficiency and fails to meet user needs.

[0059] Therefore, there is an urgent need for an inversion method for the grain size 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, realize rapid detection of granite pegmatite, and improve prospecting efficiency.

[0060] In view of this, the embodiment of the present application provides a method for inverting the granularity of a granitic pegmatite, which comprises: obtaining multi-angle reflectance spectrum data of a plurality of particle samples, the particle samples being ore bodies with different particle sizes and comprising one or more end-member minerals, the multi-angle reflectance spectrum data comprising reflectance corresponding to each observation condition parameter in a plurality of observation condition parameters, the observation condition parameters including a light source incidence angle, an observation zenith angle and a phase angle; determining target observation condition parameters according to the multi-angle reflectance spectrum data, the target observation condition parameters including a target light source incidence angle, a target observation zenith angle and a target phase angle; determining a linear regression equation of the particle size of the granitic pegmatite based on the Hapke model and according to the target observation condition parameters; and determining the particle size distribution of the granitic pegmatite in a granitic pegmatite image according to the linear regression equation.

[0061] The method provided by the embodiment of the present application can realize qualitative analysis and quantitative inversion of remote sensing of the granitic pegmatite by determining the single scattering albedo corresponding to each particle sample under the target observation condition parameters based on the multi-angle reflectance spectrum data and establishing a linear regression equation according to the single scattering albedo corresponding to each particle sample and the particle size, thereby solving the problems of low accuracy of image target delineation and difficulty in distinguishing similar spectra, realizing rapid detection of the granitic pegmatite and improving the efficiency of ore prospecting.

[0062] In some embodiments, the method for inverting the granularity of the granitic pegmatite provided by the embodiment of the present application can be executed by an inversion device 100 for the granularity of the granitic pegmatite (hereinafter referred to as the inversion device 100). As an example, the inversion device 100 can be any computing device 200 with data processing capability, such as a general-purpose computer, a personal computer, a notebook computer, a switch or a tablet computer, etc., and the specific implementation mode of the inversion device 100 is not limited here.

[0063] Figure 1 A hardware structure schematic diagram of a computing device provided by the embodiment of the present application is shown. The computing device 200 comprises a processor 210, a memory 220 and a communication interface 230.

[0064] The processor 210 can include one or more processing cores. The processor 210 connects various parts within the computing device 200 with various interfaces and lines, performs various functions of the computing device 200 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 220, and calling data stored in the memory 220. Alternatively, the processor 210 can be implemented in at least one of a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA).

[0065] The memory 220 can include a random access memory (RAM) and can also include a read-only memory (ROM). Alternatively, the memory 220 includes a non-transitory computer-readable storage medium. The memory 220 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 220 can include a program storage area. The program storage area can 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 various method embodiments described above, and the like.

[0066] The communication interface 230 is configured to communicate with other devices, equipment or communication networks, such as data storage devices, image processing equipment or Ethernet, a radio access network (RAN), a wireless local area network (WLAN), and the like.

[0067] In physical implementation, the above-mentioned devices (such as the processor 210, the memory 220 and the communication interface 230) can be devices in the same device (such as a notebook computer). Alternatively, at least two of the devices can be arranged in the same device, i.e. as different devices in the same device, similar to the deployment of devices or devices in a distributed system.

[0068] 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.

[0069] The following describes the inversion method for granite pegmatite grain size provided by the embodiment of the present invention in conjunction with the accompanying drawings.

[0070] Figure 2 The flowchart of a method for inverting the grain size of granite pegmatite provided by 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:

[0071] S1. Obtain multi-angle reflectance spectral data of multiple particle samples.

[0072] Specifically, the particle sample is an ore body comprising one or more end-member minerals and having different particle sizes. For example, the plurality of particle samples includes sample A, sample B, sample C, and sample D, where 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 a mixture of end-member mineral A and end-member mineral B. Samples A, B, C, and D each have different particle sizes. Alternatively, it can be understood that sample A, B, C, and D each have different particle sizes.

[0073] The multi-angle reflectance spectrum data includes the reflectance corresponding to each of the multiple observation condition parameters. The multi-angle reflectance spectrum 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 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.

[0074] For example, see Figure 3 , Figure 3 A schematic diagram of multi-angle reflectance spectral data shown in an embodiment of the present invention, including the single scattering albedo, roughness h, phase function coefficients b and c of the particle sample in the VNIR-SWIR band, and the particle diameter sizes of the particle sample, which are <75μm, 75-150μm and 150-380μm, respectively.

[0075] In one possible implementation, see Figure 4The above S1 specifically includes the following steps:

[0076] S11. Calibrate and calibrate the portable ground object spectrometer;

[0077] S12, obtaining spectral data of each particle sample by a portable ground object spectrometer;

[0078] S13. Perform Savitzky-Golay smoothing preprocessing on the spectral data of each particle sample to obtain multi-angle reflectance spectral data of multiple particle samples.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] Specifically, the multi-angle reflectance spectral data includes the reflectance of each particle sample corresponding to each of the 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 the multiple end-member minerals and mixtures of end-member minerals included in the multiple particle samples exhibit greater variability, thereby enabling accurate differentiation of the multiple end-member minerals included in the multiple particle samples.

[0084] 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°.

[0085] In some embodiments, see Figure 5 The above S2 specifically includes the following steps:

[0086] S21, determine the anisotropy factor and the dimensionless bidirectional reflectance factor corresponding to each observation condition parameter of each particle sample under the plurality of observation condition parameters.

[0087] Specifically, the inversion device 100 determines the anisotropy factor and the dimensionless bidirectional reflectance factor corresponding to each wavelength of each particle sample under each observation condition parameter, to obtain the anisotropy factor and the dimensionless bidirectional reflectance factor corresponding to each observation condition parameter of each particle sample under the plurality of observation condition parameters.

[0088] The determination formula of the anisotropy factor ANIF of each observation condition parameter at wavelength x is:

[0089]

[0090] wherein, R is the reflectivity corresponding to each observation condition parameter at wavelength x, R0 is the reflectivity corresponding to the preset observation condition parameter at wavelength x, wherein, in the case that 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°.

[0091] The determination formula of the dimensionless bidirectional reflectance factor F NDRF of each observation condition parameter at wavelength x is:

[0092]

[0093] wherein, R max (λ) is the maximum value of the reflectivity corresponding to the plurality of observation condition parameters of each particle sample at wavelength x, R min (λ) is the minimum value of the reflectivity corresponding to the plurality of observation condition parameters of each particle sample at wavelength x.

[0094] S22, determine the target observation condition parameter from the plurality of observation condition parameters according to the anisotropy factor and the dimensionless bidirectional reflectance factor corresponding to each observation condition parameter of each particle sample under the plurality of observation condition parameters.

[0095] The difference between the anisotropy factors of any two particle samples under the target observation condition parameter at each wavelength is greater than or equal to a first threshold value, and the difference between the dimensionless bidirectional reflectance factors of any two particle samples at each wavelength is greater than or equal to a second threshold value.

[0096] Exemplarily, under the observation condition parameter A, the anisotropy factor of the particle sample A at the wavelength of 600 nm is 5, and the dimensionless bidirectional reflectance factor of the particle sample A at the wavelength of 600 nm is 2. The anisotropy factor of the particle sample B at the wavelength of 600 nm is 6, and the dimensionless bidirectional reflectance factor of the particle sample B at the wavelength of 600 nm is 3. Under the observation condition parameter B, the anisotropy factor of the particle sample A at the wavelength of 600 nm is 16, and the dimensionless bidirectional reflectance factor of the particle sample A at the wavelength of 600 nm is 1. The anisotropy factor of the particle sample B at the wavelength of 600 nm is 5, and the dimensionless bidirectional reflectance factor of the particle sample B at the wavelength of 600 nm is 1. Under the observation condition parameter B, the difference between the anisotropy factors of the two particle samples at the wavelength of 600 nm is greater than or equal to the first threshold value, and the difference between the dimensionless bidirectional reflectance factors of the two particle samples at the wavelength of 600 nm is greater than or equal to the second threshold value, the observation condition parameter B is determined as the target observation condition parameter when the difference between the anisotropy factors of the two particle samples at each wavelength is greater than or equal to the first threshold value, and the difference between the dimensionless bidirectional reflectance factors of the two particle samples at each wavelength is greater than or equal to the second threshold value.

[0097] S3, determining a linear regression equation of the particle size of the granitic pegmatite according to the target observation condition parameter based on the Hapke model.

[0098] In some embodiments, referring to Figure 6 S3, specifically comprising the following steps:

[0099] S31, determining the single scattering albedo corresponding to each characteristic wavelength of each particle sample under the target observation condition parameter based on the Hapke model.

[0100] wherein the plurality of characteristic wavelengths are obtained from a correlation matrix of the plurality of particle samples based on the principal component analysis method.

[0101] The formula of the correlation matrix is:

[0102]

[0103] F1, F2, …, F s are the first principal component, the second principal component, and so on of the variable x m The factor loadings on the principal components are x1, x2, …, x ij The eigenvectors corresponding to the s larger eigenvalues of the correlation matrix of x i The eigenvectors corresponding to the s larger eigenvalues of the correlation matrix of x m The eigenvectors corresponding to the s larger eigenvalues of the correlation matrix of x

[0104] 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).

[0105] In one example, the plurality of characteristic wavelengths are 600 nm, 930 nm, 1260 nm, 1600 nm and 2200 nm.

[0106] 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.

[0107] S32. Determine a linear regression equation corresponding to each characteristic wavelength in the multiple characteristic wavelengths according to the single scattering albedo and particle size of each particle sample corresponding to each characteristic wavelength in the multiple characteristic wavelengths.

[0108] Wherein, the Hapke model is:

[0109]

[0110] 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;

[0111] Wherein, R(i,e,g) is the reflectivity corresponding to each characteristic wavelength x in the multiple characteristic wavelengths under the target observation condition parameters, and ω is the single scattering albedo corresponding to each characteristic wavelength x in the multiple characteristic wavelengths;

[0112] The formula for determining the function H(x) is:

[0113]

[0114]

[0115] Function B(g) is used to characterize the target phase angle g and roughness parameter h;

[0116] The expression of the single particle phase angle function P(g) is:

[0117]

[0118]

[0119] Among them, parameter c is used to characterize the degree of forward scattering or backward scattering of the particle sample, and parameter b is used to characterize the shape of the scattering lobe; is the angle between the incident plane and the observation plane, the angle between the incident plane and the horizontal plane is the target light source incident angle, and the angle between the observation plane and the horizontal plane is the target observation zenith angle;

[0120] 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 a linear regression equation corresponding to each characteristic wavelength in a plurality of characteristic wavelengths.

[0121] The linear regression equation is:

[0122] P Vλ =M+N*particle size;

[0123] Among them, P Vλ is the retrieved value of the single scattering albedo ω at wavelength λ, particle size is the particle size of the granite pegmatite, M and N are constants, N is the slope of the linear regression equation, and M is the intercept of the linear regression equation on the y-axis.

[0124] Specifically, Figure 7a 、 Figure 7b 、 Figure 7c 、 Figure 7d and Figure 7e The relationship between the single scattering albedo inverted by the Hapke model at wavelengths of 600nm, 930nm, 1260nm, 1600nm and 2200nm and particle samples of different sizes is shown. Among them, y1:x1~y11:x11 correspond to the straight lines fitted by particle samples 101-111 respectively; the shaded part represents the 95% confidence interval, R2 and p value as shown in the figure. It can be seen that the parameters inverted by the Hapke model show a negative correlation with the particle size of the particle sample. From Figure 7a 、 Figure 7b 、 Figure 7c 、 Figure 7d and Figure 7eIt can be seen that at 600nm, 930nm, 1260nm, 1600nm and 2200nm, as the particle size increases, the fitting accuracy (R2) decreases overall. This is because the degree of linear relationship fitting decreases when the particle size of the particle sample is larger.

[0125] S4. Determine the particle size distribution of the granite pegmatite in the granite pegmatite image according to the linear regression equation.

[0126] Among them, the granite pegmatite image is an image obtained based on the target observation condition parameters, and the granite pegmatite image includes multiple granite pegmatite areas.

[0127] In some embodiments, see Figure 8 The above S4 specifically includes the following steps:

[0128] S41. Preprocess the granite pegmatite image to obtain a processed granite pegmatite image.

[0129] The pre-processing includes radiometric calibration and atmospheric correction. S42: Determine the single scattering albedo data corresponding to each granite pegmatite region in the processed granite pegmatite image.

[0130] The single scattering albedo data includes the single scattering albedo corresponding to each characteristic wavelength in multiple characteristic wavelengths;

[0131] S43. Based on a linear regression equation, the particle size distribution of the granite pegmatite image is obtained according to the single scattering albedo data. The particle size distribution of the granite pegmatite image includes the particle size distribution corresponding to each granite pegmatite area.

[0132] It can be seen from S1-S4 that the method provided in the embodiment of the present invention determines the single scattering albedo corresponding to each particle sample under the target observation condition parameters based on multi-angle reflectance spectral data, and establishes a linear regression equation according to the single scattering albedo and particle size corresponding to each particle sample. It can 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, realize rapid detection of granite pegmatite, and improve prospecting efficiency.

[0133] Optionally, the above S4 specifically includes the following steps: obtaining ground-based imaging hyperspectral image data of multiple particle samples; obtaining the particle size distribution of the multiple particle samples through a linear regression equation based on the ground-based imaging hyperspectral image data of the multiple particle samples; when the particle size of each particle sample is within the particle size distribution of each particle sample, determining the particle size distribution of the granite pegmatite in the granite pegmatite image according to the linear regression equation.

[0134] The embodiment of the present invention verifies the linear regression equation by obtaining ground-based imaging hyperspectral image data of multiple particle samples, and after the verification is completed, determines the particle size distribution of granite pegmatite in the granite pegmatite image according to the linear regression equation, which can effectively improve the accuracy and precision of the particle size distribution of granite pegmatite and meet the user's usage needs in different usage scenarios.

[0135] 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.

[0136] 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.

[0137] For example, Figure 9 A hardware structure diagram of an inversion device 100 provided by an embodiment of the present invention is shown. The inversion device 100 includes: an acquisition unit 111 for acquiring multi-angle reflectance spectral data of multiple particle samples, wherein the particle samples are ore bodies with different particle sizes including one or more end-member minerals, and the multi-angle reflectance spectral data includes the reflectance corresponding to each of multiple observation condition parameters, including the light source incident angle, the observation zenith angle, and the phase angle; a processing unit 112 for determining target observation condition parameters based on the multi-angle reflectance spectral data, wherein the target observation condition parameters include the target light source incident angle, the target observation zenith angle, and the target phase angle; the processing unit 112 is further configured to determine a linear regression equation for the particle size of the granite pegmatite based on the target observation condition parameters based on the Hapke model; and the processing unit 112 is further configured to determine the particle size distribution of the granite pegmatite in the granite pegmatite image based on the linear regression equation.

[0138] 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.

[0139] 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.

[0140] The present invention also provides a chip that integrates a control circuit and one or more ports for implementing the functions of the inversion device 100. The functions supported by the chip can be found above and will not be described in detail here.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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 inversion of granite pegmatite grain size, characterized in that: The method comprises: Acquiring multi-angle reflectance spectral data for a plurality of particle samples, the particle samples being ore bodies of different particle sizes including one or more 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, a linear regression equation of the grain size of the granite pegmatite is determined according to the target observation condition parameters; determining a particle size distribution of granite pegmatite in a granite pegmatite image according to the linear regression equation, wherein the granite pegmatite image is an image acquired based on the target observation condition parameters, and the granite pegmatite image includes a plurality of granite pegmatite regions; 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; determining a target observation condition parameter from the multiple observation condition parameters according to the anisotropy factor and the dimensionless bireflectance factor corresponding to each observation condition parameter of each particle sample under the multiple observation condition parameters, wherein a difference between the anisotropy factors of any two particle samples at each wavelength under the target observation condition parameter is greater than or equal to a first threshold, and a difference between the dimensionless bireflectance factors of any two particle samples at each wavelength is greater than or equal to a second threshold; The linear regression equation for determining the particle size of the granite pegmatite based on the Hapke model and the target observation condition parameters includes: 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; Determine a linear regression equation corresponding to each characteristic wavelength in the plurality of characteristic wavelengths according to the single scattering albedo and particle size corresponding to each characteristic wavelength in the plurality of characteristic wavelengths of each particle sample; Wherein, 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: Among them, parameter c is used to characterize the degree of forward scattering or backward scattering of the particle sample, and parameter b is used to characterize the shape of the scattering lobe. is the angle between the incident plane and the observation plane, the angle between the incident plane and the horizontal plane is the target light source incident angle, and the angle between the observation plane and the horizontal plane is the target observation zenith angle; The linear regression equation is: P Vλ =M+N*particle size; Among them, P Vλ is the retrieved value of the single scattering albedo ω at wavelength λ, particle size is the particle size of granite pegmatite, and M and N are constants.

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 1, 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.

4. The method according to claim 1, wherein Determining the particle size distribution of the granite pegmatite in the granite pegmatite image according to the linear regression equation includes: Preprocessing the granite pegmatite image to obtain a processed granite pegmatite image, wherein the preprocessing includes radiometric calibration processing and atmospheric correction processing; Determining single scattering albedo data corresponding to each of the granite pegmatite regions in the processed granite pegmatite image, wherein the single scattering albedo data includes a single scattering albedo corresponding to each of the multiple characteristic wavelengths; Based on the linear regression equation, the particle size distribution of the granite pegmatite image is obtained according to the single scattering albedo data. The particle size distribution of the granite pegmatite image includes the particle size distribution corresponding to each granite pegmatite region.

5. The method according to claim 4, characterized in that Determining the particle size distribution of the granite pegmatite in the granite pegmatite image according to the linear regression equation includes: Acquiring ground-based imaging hyperspectral image data of the plurality of particle samples; Obtaining the particle size distribution of the plurality of particle samples by using the linear regression equation according to the ground-based imaging hyperspectral image data of the plurality of particle samples; In a case where the particle size of each of the particle samples is within the particle size distribution of each of the particle samples, the particle size distribution of the granite pegmatite in the granite pegmatite image is determined according to the linear regression equation.

6. An inversion device for granite pegmatite grain size, characterized in that: The device comprises: an acquisition unit, configured to acquire multi-angle reflectance spectral data of a plurality of particle samples, the particle samples being ore bodies having different particle sizes and including one or more 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 processing unit, configured to determine 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; The processing unit is further configured to determine a linear regression equation of the grain size of the granite pegmatite based on the Hapke model and the target observation condition parameters; The processing unit is further configured to determine a particle size distribution of granite pegmatite in a granite pegmatite image according to the linear regression equation, wherein the granite pegmatite image is an image acquired based on the target observation condition parameters, and the granite pegmatite image includes a plurality of granite pegmatite regions; The processing unit is specifically configured to: 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; determining a target observation condition parameter from the multiple observation condition parameters according to the anisotropy factor and the dimensionless bireflectance factor corresponding to each observation condition parameter of each particle sample under the multiple observation condition parameters, wherein a difference between the anisotropy factors of any two particle samples at each wavelength under the target observation condition parameter is greater than or equal to a first threshold, and a difference between the dimensionless bireflectance factors of any two particle samples at each wavelength is greater than or equal to a second threshold; The processing unit is further specifically configured to: 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; Determine a linear regression equation corresponding to each characteristic wavelength in the plurality of characteristic wavelengths according to the single scattering albedo and particle size corresponding to each characteristic wavelength in the plurality of characteristic wavelengths of each particle sample; Wherein, 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: Among them, parameter c is used to characterize the degree of forward scattering or backward scattering of the particle sample, and parameter b is used to characterize the shape of the scattering lobe. is the angle between the incident plane and the observation plane, the angle between the incident plane and the horizontal plane is the target light source incident angle, and the angle between the observation plane and the horizontal plane is the target observation zenith angle; The linear regression equation is: P Vλ =M+N*particle size; Among them, P Vλ is the retrieved value of the single scattering albedo ω at wavelength λ, particle size is the particle size of granite pegmatite, and M and N are constants.

7. 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 granite pegmatite grain size inversion method according to any one of claims 1 to 5.

8. 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 grain size according to any one of claims 1 to 5.

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