Inversion method and device for granite pegmatite granularity and computing equipment

Abstract

The invention provides an inversion method and device for granite pegmatite granularity and computing equipment, and relates to the technical field of mineral detection. According to the method, multi-angle reflectivity spectral data of a plurality of particle samples are obtained, the particle samples are ore bodies which comprise one or more kinds of end member minerals and have different particle sizes, and the multi-angle reflectivity spectral data comprise reflectivity corresponding to each observation condition parameter in a plurality of observation condition parameters. Determining target observation condition parameters according to the multi-angle reflectivity spectral data; based on a Hapke model, determining a linear regression equation of the particle size of the granite pegmatite according to the target observation condition parameters; and determining the particle size distribution of the granite pegmatite in the granite pegmatite image according to a linear regression equation. According to the method provided by the invention, qualitative analysis and quantitative inversion of remote sensing of the granite pegmatite can be realized, the problems that the delineation precision of an image target region is low and similar spectrums are difficult to distinguish are solved, rapid detection of the granite pegmatite is realized, and the prospecting efficiency is improved.

Description

Technical Field

[0001] The 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 resources are one of the metal mineral resources. In related technologies, minerals rich in lithium resources are usually detected through remote sensing technology, which has the advantages of high efficiency, low cost and large area detection.

[0003] However, in the process of detecting granite pegmatite by remote sensing technology, there are still problems such as low accuracy of image target area delineation and difficulty in distinguishing similar spectra, which leads to low efficiency of mineral detection and cannot meet the needs of users.

[0004] Therefore, there is an urgent need for an inversion method for the grain size of granite pegmatite to achieve 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 delineation of image target areas and difficulty in distinguishing similar spectra, realize rapid detection of granite pegmatite and improve prospecting efficiency.

[0006] To achieve the above object, 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 grain samples, the grain samples being ore bodies with different grain sizes including one or more end-member minerals, 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 a light source incident angle, an observation zenith angle, and a phase angle; determining target observation condition parameters according to 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 according to 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 according to the linear regression equation.

[0008] In a possible implementation of the first aspect, multi-angle reflectance spectral data of multiple particle samples are obtained, including: calibrating 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, target observation condition parameters are determined based on multi-angle reflectance spectral data, including: determining an anisotropy factor and a dimensionless bidirectional reflection 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 the dimensionless bidirectional reflection factor corresponding to each observation condition parameter of each particle sample under the multiple observation condition parameters, 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.

[0010] In a possible implementation manner of the first aspect, a determination formula for 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 determining the particle size of the granite pegmatite according to the target characteristic parameters includes:

[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 in multiple characteristic wavelengths is determined; according to the single scattering albedo corresponding to each characteristic wavelength of each particle sample in multiple characteristic wavelengths and the particle size, the linear regression equation corresponding to each characteristic wavelength in 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 function of target phase angle g and roughness parameter h;

[0026] The expression of 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 backscattering 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, the particle size distribution of granite pegmatite in a granite pegmatite image is determined according to a linear regression equation, including: 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 is an image acquired based on target observation condition parameters, and the granite pegmatite image includes multiple granite pegmatite areas; determining single scattering albedo data corresponding to each granite pegmatite area in the processed granite pegmatite image, the single scattering albedo data including a single scattering albedo corresponding to each characteristic wavelength in multiple characteristic wavelengths; based on the linear regression equation, obtaining the particle size distribution of the granite pegmatite image according to the single scattering albedo data, the particle size distribution of the granite pegmatite image including the particle size distribution corresponding to each granite pegmatite area.

[0034] In a possible implementation of the first aspect, the particle size distribution of granite pegmatite in the granite pegmatite image is determined according to a linear regression equation, including: acquiring 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 according to 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, thereby realizing qualitative analysis and quantitative inversion of remote sensing of granite pegmatite, solving the problems of low accuracy in delineation of image target areas and difficulty in distinguishing similar spectra, realizing rapid detection of granite pegmatite, and improving 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, used to acquire multi-angle reflectance spectral data of multiple particle samples, the particle samples are ore bodies with different grain sizes including one or more end-member minerals, the multi-angle reflectance spectral data include the reflectance corresponding to each observation condition parameter in multiple observation condition parameters, the observation condition parameters include the light source incident angle, the observation zenith angle and the phase angle; a processing unit, used to determine target observation condition parameters according to the multi-angle reflectance spectral data, 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 is also used to determine the linear regression equation of the grain size of the granite pegmatite according to the target observation condition parameters based on the Hapke model; the processing unit is also used to determine the grain size distribution of the granite pegmatite in the granite pegmatite image according to 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 the memory stores computer program code, the computer program code comprises computer instructions, and when the computer instructions are executed by the processor, the computing device executes the inversion method for the grain size of granite pegmatite as in any implementation of the first aspect.

[0038] In a fourth aspect, a computer-readable storage medium is provided, comprising computer instructions, which, when executed on a computing device, enable the computing device to execute the inversion method for the grain size of granite pegmatite 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 executes the inversion method for the grain size of granite pegmatite 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 shown in an embodiment of the present invention;

[0042] Figure 2 A schematic flow chart of a method for inversion of granite pegmatite grain size shown in 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] Fig. 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 solution in the embodiment of the present invention will be described below in conjunction with the accompanying drawings in the embodiment of the present invention. Among them, 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; "and / or" in the present invention is only a kind of association relationship describing 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, where A and B can be singular or plural. And, in the description of the present invention, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" 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, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, the words "first", "second", etc. are used to distinguish the same items or similar items with substantially the same functions and effects. Those skilled in the art can understand that the words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not necessarily limit the difference.

[0056] Meanwhile, in the embodiments of the present invention, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way for easy understanding.

[0057] Lithium resources are one of the metal mineral resources. In related technologies, minerals rich in lithium resources are usually detected through remote sensing technology, which has the advantages of high efficiency, low cost and large area detection.

[0058] However, in the process of detecting granite pegmatite by remote sensing technology, there are still problems such as low accuracy of image target area delineation and difficulty in distinguishing similar spectra, which leads to low efficiency of mineral detection and cannot meet the needs of users.

[0059] Therefore, there is an urgent need for an inversion method for the grain size of granite pegmatite to achieve 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, an embodiment of the present invention provides an inversion method for the grain size of granite pegmatite, the method comprising: obtaining multi-angle reflectance spectral data of multiple 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 observation condition parameter in multiple observation condition parameters, the observation condition parameters comprising the light source incident angle, 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 target light source incident angle, the target observation zenith angle and the target phase angle; determining a linear regression equation for the grain size of the granite pegmatite according to the target observation condition parameters based on the Hapke model; and determining the grain size distribution of the granite pegmatite in the granite pegmatite image according to the linear regression equation.

[0061] 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 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.

[0062] In some embodiments, the inversion method for granite pegmatite grain size provided by the embodiments of the present invention can be performed by an inversion device 100 for granite pegmatite grain size (hereinafter referred to as the inversion device 100). As an example, the inversion device 100 can be any computing device 200 with data processing capabilities, such as a general-purpose computer, a personal computer, a laptop computer, a switch or a tablet computer, etc. The specific implementation method of the inversion device 100 is not limited here.

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

[0064] The processor 210 may include one or more processing cores. The processor 210 uses various interfaces and lines to connect various parts within the computing device 200, and executes various functions and processes data of the computing device 200 by running or executing instructions, programs, code sets or instruction sets stored in the memory 220, and calling data stored in the memory 220. Optionally, the processor 210 can be implemented in at least one hardware form 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 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.

[0066] 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 networks (WLAN), etc.

[0067] In physical implementation, the above-mentioned components (such as processor 210, memory 220 and communication interface 230) can be components in the same device (such as a laptop). Alternatively, at least two of the components can be set in the same device, that is, as different components in one device, similar to the deployment mode of devices or components in a distributed system.

[0068] It is to 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 in the figure, or combine some components, or split some 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 A flowchart of a granite pegmatite grain size inversion method 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:

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

[0072] Specifically, the particle sample is an ore body with different particle sizes including one or more end-member minerals. Exemplarily, the plurality of 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 a mixture of end-member mineral A and end-member mineral B. And sample A, sample B, sample C and sample D have different particle sizes respectively. It can also be understood that sample A, sample B, sample C and sample D have different particle sizes respectively.

[0073] The multi-angle reflectance spectrum data includes the reflectance corresponding to each observation condition parameter in the multiple observation condition parameters, and the multi-angle reflectance spectrum data of 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; 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 for an embodiment of the present invention, including single scattering albedo, roughness h, phase function coefficients b and c of a particle sample in the VNIR-SWIR band, and 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, calibrating and calibrating the portable ground object spectrometer;

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

[0078] S13, performing 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 preprocessing is a commonly used spectral data preprocessing method used to smooth the spectral curve and remove the influence of high-frequency noise. It is based on least squares polynomial fitting and 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 exemplary description. 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 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.

[0083] Specifically, the multi-angle reflectance spectral data includes the reflectance corresponding to each observation condition parameter of each particle sample in the multiple observation condition parameters, and the target observation condition parameter is one of the multiple observation condition parameters. Under the target observation condition parameter, the multiple end-member minerals and the mixture of end-member minerals included in the multiple particle samples have greater differences in the multi-angle reflectance spectral data, so that the multiple end-member minerals included in the multiple particle samples can be accurately distinguished.

[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 dimensionless bidirectional reflection factor corresponding to each observation condition parameter of each particle sample under multiple observation condition parameters.

[0087] 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.

[0088] The formula for determining 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, 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°;

[0091] The dimensionless bidirectional reflection factor F at wavelength x for each observation condition parameter NDRF The formula for determining is:

[0092]

[0093] 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.

[0094] S22. Determine a target observation condition parameter from multiple observation condition parameters according to an anisotropy factor and a dimensionless bidirectional reflection factor corresponding to each observation condition parameter of each particle sample under multiple observation condition parameters.

[0095] The difference between the anisotropy factors of any two of the 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 of the particle samples at each wavelength is greater than or equal to a second threshold.

[0096] Exemplarily, under observation condition parameter A, the anisotropy factor of particle sample A at a wavelength of 600nm is 5, and the dimensionless bidirectional reflection factor at a wavelength of 600nm is 2. The anisotropy factor of particle sample B at a wavelength of 600nm is 6, and the dimensionless bidirectional reflection factor of particle sample B at a wavelength of 600nm is 3. Under observation condition parameter B, the anisotropy factor of particle sample A at a wavelength of 600nm is 16, and the dimensionless bidirectional reflection factor at a wavelength of 600nm is 1. The anisotropy factor of particle sample B at a wavelength of 600nm is 5, and the dimensionless bidirectional reflection factor of particle sample B at a wavelength of 600nm is 1. Under 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 a 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 a 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.

[0097] S3. Based on the Hapke model, the linear regression equation of the grain size of granite pegmatite is determined according to the target observation condition parameters.

[0098] In some embodiments, see Figure 6 The above S3 specifically includes the following steps:

[0099] S31. 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.

[0100] 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.

[0101] The formula for the correlation matrix is:

[0102]

[0103] F1, F2, …, F s 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 larger eigenvalues ​​of the correlation matrix.

[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 size of 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 determination methods and examples of characteristic wavelengths are merely illustrative, and the embodiments of the present invention do not impose any particular limitation on the method of selecting the characteristic wavelengths.

[0107] S32. Determine a linear regression equation corresponding to each of the multiple characteristic wavelengths according to the single scattering albedo and particle size of each particle sample corresponding to each of 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 function of target phase angle g and roughness parameter h;

[0116] The expression of 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 P(g) function expression, the first term and the second term 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 multiple 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 the 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 figure shows the relationship between the single scattering albedo inverted by the Hapke model and particle samples of different particle sizes at wavelengths of 600nm, 930nm, 1260nm, 1600nm and 2200nm. 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, pre-processing the granite pegmatite image to obtain a processed granite pegmatite image.

[0129] The preprocessing includes radiation calibration processing and atmospheric correction processing. 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 a plurality of 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. This 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.

[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 acquiring 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 scheme of the embodiment of the present invention from the perspective of the 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 each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiment disclosed in this article, 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] The embodiment of the present invention can divide the inversion device 100 into functional units according to the above method example. For example, the inversion device 100 can be divided into functional units corresponding to various functions, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present invention is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0137] For example, Fig. 9 The 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, which is used to acquire multi-angle reflectance spectrum data of multiple particle samples, the particle samples are ore bodies with different particle sizes including one or more end-member minerals, the multi-angle reflectance spectrum data includes the reflectance corresponding to each observation condition parameter in multiple observation condition parameters, and the observation condition parameters include the light source incident angle, the observation zenith angle, and the phase angle; a processing unit 112, which is used to determine the target observation condition parameters according to the multi-angle reflectance spectrum data, and 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 also used to determine the linear regression equation of the particle size of the granite pegmatite according to the target observation condition parameters based on the Hapke model; the processing unit 112 is also used to determine the particle size distribution of the granite pegmatite in the granite pegmatite image according to the linear regression equation.

[0138] It should be understood that the specific description of the above optional methods can refer to the above method embodiments, which will not be repeated here. In addition, the explanation of any inversion device 100 provided above and the description of the beneficial effects can refer to the above corresponding method embodiments, which will not be repeated here.

[0139] The embodiment of the present invention further provides a computer-readable storage medium, in which at least one computer instruction is stored, and the at least one computer instruction is loaded and executed by a processor to implement the methods of the above embodiments. For the explanation of the relevant contents and the description of the beneficial effects in any of the above-mentioned computer-readable storage media, reference can be made to the above-mentioned corresponding embodiments, which will not be repeated here.

[0140] The embodiment of the present invention further provides a chip. The chip integrates a control circuit and one or more ports for realizing the functions of the above-mentioned inversion device 100. Optionally, the functions supported by the chip can be referred to 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 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] The embodiment of the present invention also provides a computer program product including instructions, when the instructions are run on a computer, the computer executes any one of the methods in the above embodiments. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present invention is generated in whole or in part. 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 computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by 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 that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., an SSD), etc.

[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 memory, computer-readable storage medium and communication chip, etc., all have non-transitory. Those skilled in the art should be aware that in one or more of the above-mentioned examples, the functions described in the embodiments of the present invention can be implemented with 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 is convenient for transmitting a computer program from one place to another. The storage medium can be any available medium that a general or special-purpose computer can access.

[0144] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary 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: Acquire multi-angle reflectance spectral data of a plurality of particle samples, wherein the particle samples are ore bodies with different particle sizes including one or more end-member minerals, wherein the multi-angle reflectance spectral data include reflectance corresponding to each observation condition parameter in a plurality of observation condition parameters, wherein the observation condition parameters include a light source incident angle, an observation zenith angle, and a phase angle; 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; 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; The particle size distribution of the granite pegmatite in the granite pegmatite image is determined according to the linear regression equation. The granite pegmatite image is an image acquired based on the target observation condition parameters. The granite pegmatite image includes a plurality of granite pegmatite areas.

2. The method according to claim 1, characterized in that The step of obtaining 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 means of the portable ground object spectrometer; The spectral data of each particle sample is subjected to Savitzky-Golay smoothing preprocessing to obtain multi-angle reflectivity spectral data of the plurality of 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 reflection factor corresponding to each observation condition parameter of each particle sample under a plurality of observation condition parameters; The target observation condition parameter is 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, 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 linear regression equation for determining the particle size of the granite pegmatite based on the Hapke model and the target characteristic 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 of the particle samples in the plurality of characteristic wavelengths; Determine a linear regression equation corresponding to each of the plurality of characteristic wavelengths according to the single scattering albedo and particle size corresponding to each of the plurality of characteristic wavelengths of each of the particle samples; 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 function of target phase angle g and roughness parameter h; The expression of single particle phase angle function P(g) is: Among them, parameter c is used to characterize the degree of forward scattering or backscattering 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.

6. The method according to claim 5, characterized in that 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 radiation calibration processing and atmospheric correction processing; Determine 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, and the particle size distribution of the granite pegmatite image includes the particle size distribution corresponding to each of the granite pegmatite regions.

7. The method according to claim 6, 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 through 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.

8. An inversion device for granite pegmatite grain size, characterized in that: The device comprises: an acquisition unit, for acquiring multi-angle reflectance spectral data of a plurality of 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 include reflectance corresponding to each observation condition parameter in a plurality of observation condition parameters, wherein the observation condition parameters include a light source incident angle, an observation zenith angle, and a phase angle; A processing 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; The processing unit is further used to determine a linear regression equation of the grain size of the granite pegmatite according to the target observation condition parameters based on the Hapke model; The processing unit is further used to determine the particle size distribution of the granite pegmatite in the 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 multiple granite pegmatite areas.

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 granite pegmatite grain size inversion method as described in any one of claims 1-7.

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

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