CN110705082A - Mineral mixed spectrum simulation method - Google Patents
Mineral mixed spectrum simulation method Download PDFInfo
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- CN110705082A CN110705082A CN201910911169.5A CN201910911169A CN110705082A CN 110705082 A CN110705082 A CN 110705082A CN 201910911169 A CN201910911169 A CN 201910911169A CN 110705082 A CN110705082 A CN 110705082A
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- 238000001228 spectrum Methods 0.000 title claims abstract description 80
- 229910052500 inorganic mineral Inorganic materials 0.000 title claims abstract description 58
- 239000011707 mineral Substances 0.000 title claims abstract description 58
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000004088 simulation Methods 0.000 title claims abstract description 19
- 230000003595 spectral effect Effects 0.000 claims abstract description 21
- 239000011159 matrix material Substances 0.000 claims abstract description 20
- 238000002310 reflectometry Methods 0.000 claims abstract description 19
- 239000011435 rock Substances 0.000 claims abstract description 16
- 239000013598 vector Substances 0.000 claims abstract description 12
- 238000000605 extraction Methods 0.000 abstract description 4
- 239000000203 mixture Substances 0.000 description 5
- 229910021532 Calcite Inorganic materials 0.000 description 3
- YGANSGVIUGARFR-UHFFFAOYSA-N dipotassium dioxosilane oxo(oxoalumanyloxy)alumane oxygen(2-) Chemical compound [O--].[K+].[K+].O=[Si]=O.O=[Al]O[Al]=O YGANSGVIUGARFR-UHFFFAOYSA-N 0.000 description 3
- 229910000514 dolomite Inorganic materials 0.000 description 3
- 239000010459 dolomite Substances 0.000 description 3
- 239000010433 feldspar Substances 0.000 description 3
- 229910052627 muscovite Inorganic materials 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Abstract
The invention belongs to the technical field of geological exploration, and particularly relates to a mineral mixed spectrum simulation method. The invention comprises the following steps: the method comprises the following steps: acquiring the spectral reflectivity of single mineral as an end member spectrum, acquiring the spectral reflectivity value of each end member, and recording the incidence angle and the emergence angle of each end member spectrum; step two: converting the spectral reflectance value, the incidence angle and the emergence angle in the step one into a column vector, and calculating the single scattering reflectance of the end member spectrum; step three: determining a mineral type M and a simulation spectrum number N according to the rock to be simulated, and generating an MxN mineral abundance matrix e; step four: calculating a single scattering albedo mixed spectrum; step five: and calculating a final mixed spectrum simulation reflectivity result. The method can quickly and accurately simulate the mineral content and the spectrum of the surface rock, and further provides high-precision sample data for intelligent extraction of rock spectrum information.
Description
Technical Field
The invention belongs to the technical field of geological exploration, and particularly relates to a mineral mixed spectrum simulation method based on vector coding and a Hapke model.
Background
The rock is a mixture of minerals, the hyperspectral technology is adopted to evaluate the types and the content of the minerals in the rock, and the mixed spectrum of the minerals is generally required to be processed to carry out spectrum unmixing research. An artificial intelligence method represented by deep learning provides an efficient and high-precision solution mixing model, but a large amount of spectral sample data of known mineral compositions are needed for training the network, and test data are difficult to obtain. Therefore, in order to improve the quantification level of the hyperspectral geological exploration technology, a technical method which can quickly and accurately simulate the rock mineral content and the spectrum on the earth surface according to the rock mineral composition and the spectrum mechanism needs to be developed, and high-precision sample data is further provided for the intelligent extraction of rock spectrum information.
Disclosure of Invention
The invention aims to solve the technical problem of providing a mineral mixed spectrum simulation method based on vector coding and a Hapke model, which can quickly and accurately simulate the mineral content and spectrum of surface rock according to the rock mineral composition and the spectrum mechanism, and further provide high-precision sample data for the intelligent extraction of rock spectrum information.
The technical scheme of the invention is as follows:
a mineral mixed spectrum simulation method comprises the following steps:
the method comprises the following steps: acquiring the spectral reflectivity of single mineral as an end member spectrum, acquiring the spectral reflectivity value of each end member, and recording the incidence angle and the emergence angle of each end member spectrum;
step two: converting the spectral reflectance value, the incidence angle and the emergence angle in the step one into a column vector, and calculating the single scattering reflectance of the end member spectrum;
step three: determining mineral types m and simulated spectrum numbers n according to rocks to be simulated, and according to the constraint conditions of mineral abundance: the sum of the abundance of the end members is 1, the abundance is non-negative, and an M multiplied by N mineral abundance matrix e is generated by using a Monte Carlo random number method;
step four: converting the single scattering albedo omega of the mineral into a matrix omega of Bx 1 by adopting a vector coding method, wherein B represents the number of wave bands; converting corresponding mineral abundance data into a matrix e of 1 XN, wherein N represents the number of simulated spectra, and calculating a single scattering albedo mixed spectrum;
step five: and calculating a final mixed spectrum simulation reflectivity result.
In the first step, the reflectance value may be measured in a laboratory or a spectrum of a spectrum library may be selected.
In the second step, the single scattering albedo of the end member spectrum is calculated through a formula (1),
wherein: omega is the desired single scattering albedo, mu0The incidence angle cosine value, the mu emergence angle cosine value and x are the end member spectral reflectivity.
In the third step, the formula that the sum of the abundance of the end members is 1 is expressed as follows:
emnot less than 0, M not more than 1 … M (ANC constraint) (2)
The formula where the abundance is non-negative is given by:
in the fourth step, the mixed spectrum of the single scattering albedo is calculated by substituting the formula (4),
x represents the single-shot albedo mixed spectrum, ωm＝(ω1ω2…ωM)TRepresenting the end-member spectral matrix, em＝(e1e2…eM) And representing an end member abundance matrix, wherein M is the number of end members, and epsilon is a model error term.
In the fifth step, the mixed spectrum result x calculated in the fourth step is substituted into the formula (5) to calculate the final mixed spectrum simulation reflectivity result,
wherein: gamma is the desired mixed spectral reflectance, mu0The incidence angle cosine value, the mu emergence angle cosine value, and x are the single-pass scattered reflectance mixed spectrum.
And the mixed spectrum simulated in the fifth step is nonlinear.
The invention has the beneficial effects that:
(1) according to the mineral spectrum simulation method based on the vector coding and the Hapke model, the technical method for simulating the mineral content and the spectrum of the surface rock can be rapidly and accurately carried out according to the rock mineral composition and the spectrum mechanism, and high-precision sample data is further provided for the intelligent extraction of rock spectrum information;
(2) in the steps two to five, a mineral spectrum mixed simulation structure based on a Hapke model is provided, and mixed simulation of different minerals can be performed through the structure.
(3) Generating a mineral abundance matrix by utilizing a Monte Carlo random number method, wherein the generated abundance matrix meets two constraint conditions of mineral abundance, namely the sum of the end member abundances is 1, the abundance is non-negative, and sufficient data of mineral abundance data can be generated to serve as mineral abundance label data of a mixed spectrum;
(4) and step four, the idea of vector coding is adopted, the traditional calculation of the spectrum one by one is converted into the matrix multiplication operation of the single scattering albedo and the abundance, the operation efficiency can be greatly improved, and the application requirement of the subsequent mass data is met.
Detailed Description
The following describes a mineral hybrid spectrum simulation method based on vector coding and Hapke model in detail with reference to the examples.
Four minerals of muscovite, calcite, dolomite and feldspar are selected for mineral mixing spectrum simulation, and the steps in the embodiment can be referred to for other minerals.
(1) Selecting four monominerals of muscovite, calcite, dolomite and feldspar as end members to be mixed, and performing spectral measurement on the four monominerals in a laboratory to obtain muscovite reflectivity data SmusCalcite reflectance data ScalDolomite reflectivity data SdolFeldspar reflectivity data Sort. The incidence angle of the four mineral spectra was measured at 0 ° and the emergence angle at 40 °.
(2) Respectively converting the spectral reflectivity data of the end members of the four single minerals into column vectors x, and calculatingCosine values of incident and exit angles, μ0The cosine values of the incident angle and the cosine values of the mu emergent angle are substituted into the formula (1) to calculate the single scattering albedo omega of the four end member spectrumscal，ωmus，ωdol，ωort。
Wherein: omega is the desired single scattering albedo, mu0The incidence angle cosine value, the mu emergence angle cosine value and x are the end member spectral reflectivity.
(3) Determining a mineral type m to be 4 according to 4 end-member minerals, calculating an analog spectrum number n to be 10000, and according to the constraint condition of mineral abundance: the sum of the end member abundances is 1 (formula 2) and the abundance is non-negative (formula 3), and a 4 x 10000 mineral abundance matrix e is generated by using a Monte Carlo random number method.
emNot less than 0, M not more than 1 … M (ANC constraint) (2)
(4) Adopting a vector coding method to obtain the single scattering albedo omega of the four end member spectrumscal，ωmus，ωdol，ωortConverting into a matrix omega of Bx 1, wherein B represents the number of wave bands; and (3) converting corresponding mineral abundance data into a matrix e of 1 XN, wherein N represents the number of the simulated spectrum, and substituting the matrix into a formula (4) to calculate the single scattering albedo mixed spectrum.
x represents the single-shot albedo mixed spectrum, ωm＝(ω1ω2…ωM)TRepresenting the end-member spectral matrix, em＝(e1e2…eM) And representing an end member abundance matrix, wherein M is the number of end members, and epsilon is a model error term.
(5) Substituting the calculated mixed spectrum results of the four minerals into a formula 5 to calculate a final mixed spectrum simulation reflectivity result.
Wherein: gamma is the desired mixed spectral reflectance, mu0The incidence angle cosine value, the mu emergence angle cosine value, and x are the single-pass scattered reflectance mixed spectrum.
Claims (7)
1. A mineral mixed spectrum simulation method is characterized in that: the method comprises the following steps:
the method comprises the following steps: acquiring the spectral reflectivity of single mineral as an end member spectrum, acquiring the spectral reflectivity value of each end member, and recording the incidence angle and the emergence angle of each end member spectrum;
step two: converting the spectral reflectance value, the incidence angle and the emergence angle in the step one into a column vector, and calculating the single scattering reflectance of the end member spectrum;
step three: determining mineral types m and simulated spectrum numbers n according to rocks to be simulated, and according to the constraint conditions of mineral abundance: the sum of the abundance of the end members is 1, the abundance is non-negative, and an M multiplied by N mineral abundance matrix e is generated by using a Monte Carlo random number method;
step four: converting the single scattering albedo omega of the mineral into a matrix omega of Bx 1 by adopting a vector coding method, wherein B represents the number of wave bands; converting corresponding mineral abundance data into a matrix e of 1 XN, wherein N represents the number of simulated spectra, and calculating a single scattering albedo mixed spectrum;
step five: and calculating a final mixed spectrum simulation reflectivity result.
2. The method for simulating a mineral mixed spectrum according to claim 1, wherein: in the first step, the reflectance value may be measured in a laboratory or a spectrum of a spectrum library may be selected.
3. The method for simulating a mineral mixed spectrum according to claim 2, wherein: in the second step, the single scattering albedo of the end member spectrum is calculated through a formula (1),
wherein: omega is the desired single scattering albedo, mu0The incidence angle cosine value, the mu emergence angle cosine value and x are the end member spectral reflectivity.
4. The method for simulating mixed spectrum of minerals according to claim 3, wherein: in the third step, the formula that the sum of the abundance of the end members is 1 is expressed as follows:
emnot less than 0, M not more than 1 … M (ANC constraint) (2)
The formula where the abundance is non-negative is given by:
5. the method for simulating mixed spectrum of minerals according to claim 4, wherein: in the fourth step, the mixed spectrum of the single scattering albedo is calculated by substituting the formula (4),
x represents the single-shot albedo mixed spectrum, ωm＝(ω1ω2…ωM)TRepresenting the end-member spectral matrix, em＝(e1e2…eM) And representing an end member abundance matrix, wherein M is the number of end members, and epsilon is a model error term.
6. The method for simulating mixed spectrum of minerals according to claim 5, wherein: in the fifth step, the mixed spectrum result x calculated in the fourth step is substituted into the formula (5) to calculate the final mixed spectrum simulation reflectivity result,
wherein: gamma is the desired mixed spectral reflectance, mu0The incidence angle cosine value, the mu emergence angle cosine value, and x are the single-pass scattered reflectance mixed spectrum.
7. The method for simulating mixed mineral spectrum according to claim 6, wherein: and the mixed spectrum simulated in the fifth step is nonlinear.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115656053A (en) * | 2022-10-19 | 2023-01-31 | 山东大学 | Rock mineral content testing method and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102645279A (en) * | 2012-04-18 | 2012-08-22 | 中国科学院遥感应用研究所 | Interference imaging spectrometer hyperspectral data simulation method for lunar-surface minerals |
| US8659656B1 (en) * | 2010-10-12 | 2014-02-25 | The Boeing Company | Hyperspectral imaging unmixing |
| CN108664899A (en) * | 2018-04-19 | 2018-10-16 | 中兵勘察设计研究院有限公司 | The mixed pixel of hyper-spectral image decomposition method returned based on model-driven and RVM |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8659656B1 (en) * | 2010-10-12 | 2014-02-25 | The Boeing Company | Hyperspectral imaging unmixing |
| CN102645279A (en) * | 2012-04-18 | 2012-08-22 | 中国科学院遥感应用研究所 | Interference imaging spectrometer hyperspectral data simulation method for lunar-surface minerals |
| CN108664899A (en) * | 2018-04-19 | 2018-10-16 | 中兵勘察设计研究院有限公司 | The mixed pixel of hyper-spectral image decomposition method returned based on model-driven and RVM |
Non-Patent Citations (2)
| Title |
|---|
| 智通祥等: "基于丰度约束核非负矩阵分解的高光谱图像非线性解混", 《复旦学报(自然科学版)》 * |
| 林红磊等: "基于单次散射反照率的矿物高光谱稀疏解混", 《遥感学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115656053A (en) * | 2022-10-19 | 2023-01-31 | 山东大学 | Rock mineral content testing method and system |
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Application publication date: 20200117 |