CN113588919A - Prediction method suitable for ore prospecting of granite type rare metal deposit - Google Patents
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- 239000010438 granite Substances 0.000 title claims abstract description 39
- 229910052751 metal Inorganic materials 0.000 title claims abstract description 35
- 238000000034 method Methods 0.000 title claims abstract description 17
- 239000002184 metal Substances 0.000 title claims abstract description 16
- 238000004458 analytical method Methods 0.000 claims abstract description 87
- 229910052500 inorganic mineral Inorganic materials 0.000 claims abstract description 39
- 239000011707 mineral Substances 0.000 claims abstract description 39
- 238000012360 testing method Methods 0.000 claims abstract description 17
- 239000011435 rock Substances 0.000 claims abstract description 11
- 238000011065 in-situ storage Methods 0.000 claims abstract description 4
- 238000005065 mining Methods 0.000 claims description 13
- 239000000203 mixture Substances 0.000 claims description 11
- 238000012937 correction Methods 0.000 claims description 10
- 230000004075 alteration Effects 0.000 claims description 6
- 238000001095 inductively coupled plasma mass spectrometry Methods 0.000 claims description 6
- 238000000918 plasma mass spectrometry Methods 0.000 claims description 6
- 238000010183 spectrum analysis Methods 0.000 claims description 6
- 239000000126 substance Substances 0.000 claims description 6
- 238000004876 x-ray fluorescence Methods 0.000 claims description 6
- 238000000095 laser ablation inductively coupled plasma mass spectrometry Methods 0.000 claims description 4
- 208000035126 Facies Diseases 0.000 claims description 3
- 238000012863 analytical testing Methods 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 claims description 3
- 238000010998 test method Methods 0.000 claims description 2
- 238000013277 forecasting method Methods 0.000 claims 1
- 238000013507 mapping Methods 0.000 abstract description 2
- 230000002349 favourable effect Effects 0.000 abstract 1
- 229910052701 rubidium Inorganic materials 0.000 description 16
- IGLNJRXAVVLDKE-UHFFFAOYSA-N rubidium atom Chemical compound [Rb] IGLNJRXAVVLDKE-UHFFFAOYSA-N 0.000 description 14
- 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 7
- 229910052627 muscovite Inorganic materials 0.000 description 7
- 239000002245 particle Substances 0.000 description 6
- 239000010453 quartz Substances 0.000 description 6
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 6
- 238000011161 development Methods 0.000 description 5
- 239000010955 niobium Substances 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 229910052758 niobium Inorganic materials 0.000 description 3
- 238000011084 recovery Methods 0.000 description 3
- 229910052715 tantalum Inorganic materials 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 239000010433 feldspar Substances 0.000 description 2
- NLYAJNPCOHFWQQ-UHFFFAOYSA-N kaolin Chemical compound O.O.O=[Al]O[Si](=O)O[Si](=O)O[Al]=O NLYAJNPCOHFWQQ-UHFFFAOYSA-N 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- ZKATWMILCYLAPD-UHFFFAOYSA-N niobium pentoxide Chemical compound O=[Nb](=O)O[Nb](=O)=O ZKATWMILCYLAPD-UHFFFAOYSA-N 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 239000005995 Aluminium silicate Substances 0.000 description 1
- 229910021532 Calcite Inorganic materials 0.000 description 1
- 238000003723 Smelting Methods 0.000 description 1
- 235000012211 aluminium silicate Nutrition 0.000 description 1
- 229910052586 apatite Inorganic materials 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000012267 brine Substances 0.000 description 1
- 239000003054 catalyst Substances 0.000 description 1
- 229910001919 chlorite Inorganic materials 0.000 description 1
- 229910052619 chlorite group Inorganic materials 0.000 description 1
- QBWCMBCROVPCKQ-UHFFFAOYSA-N chlorous acid Chemical compound OCl=O QBWCMBCROVPCKQ-UHFFFAOYSA-N 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 239000010459 dolomite Substances 0.000 description 1
- 229910000514 dolomite Inorganic materials 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000010249 in-situ analysis Methods 0.000 description 1
- 229910052622 kaolinite Inorganic materials 0.000 description 1
- 239000010445 mica Substances 0.000 description 1
- 229910052618 mica group Inorganic materials 0.000 description 1
- 239000002366 mineral element Substances 0.000 description 1
- GUCVJGMIXFAOAE-UHFFFAOYSA-N niobium atom Chemical compound [Nb] GUCVJGMIXFAOAE-UHFFFAOYSA-N 0.000 description 1
- VSIIXMUUUJUKCM-UHFFFAOYSA-D pentacalcium;fluoride;triphosphate Chemical compound [F-].[Ca+2].[Ca+2].[Ca+2].[Ca+2].[Ca+2].[O-]P([O-])([O-])=O.[O-]P([O-])([O-])=O.[O-]P([O-])([O-])=O VSIIXMUUUJUKCM-UHFFFAOYSA-D 0.000 description 1
- 239000011028 pyrite Substances 0.000 description 1
- 229910052683 pyrite Inorganic materials 0.000 description 1
- NIFIFKQPDTWWGU-UHFFFAOYSA-N pyrite Chemical compound [Fe+2].[S-][S-] NIFIFKQPDTWWGU-UHFFFAOYSA-N 0.000 description 1
- 229910052761 rare earth metal Inorganic materials 0.000 description 1
- 150000002910 rare earth metals Chemical class 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 150000003298 rubidium compounds Chemical class 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 229910052604 silicate mineral Inorganic materials 0.000 description 1
- HPALAKNZSZLMCH-UHFFFAOYSA-M sodium;chloride;hydrate Chemical compound O.[Na+].[Cl-] HPALAKNZSZLMCH-UHFFFAOYSA-M 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- GUVRBAGPIYLISA-UHFFFAOYSA-N tantalum atom Chemical compound [Ta] GUVRBAGPIYLISA-UHFFFAOYSA-N 0.000 description 1
- PBCFLUZVCVVTBY-UHFFFAOYSA-N tantalum pentoxide Inorganic materials O=[Ta](=O)O[Ta](=O)=O PBCFLUZVCVVTBY-UHFFFAOYSA-N 0.000 description 1
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/24—Earth materials
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V9/00—Prospecting or detecting by methods not provided for in groups G01V1/00 - G01V8/00
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Abstract
The invention discloses a prediction method suitable for granite porphyry rare metal ore deposit prospecting, which carries out the prospecting prediction of the granite porphyry rare metal ore deposit by carrying out whole rock test, main mineral proportion and in-situ micro-area analysis on a granite porphyry sample on the basis of carrying out alteration-lithofacies mapping on altered granite porphyry. The method is favorable for finding out the distribution condition of rare metal elements possibly existing in granite porphyry type ore deposit.
Description
The invention relates to a prediction method suitable for granite porphyry rare metal deposit prospecting exploration, and belongs to the field of rare metal prospecting exploration.
Background
The rare (rare, rare earth and rare dispersion) resources are important strategic materials in the 21 st century, are important scarce resources for guiding future economic and social development and guaranteeing international competitiveness, and have a strategic position of playing a key role in development of high, fine and top-end science and technology in the future and future energy. The sustainable supply of strategic key mineral resources is an important guarantee for the economic growth of China, the national resource safety and the improvement of the international speaking right, and is also a priority theme for the resource exploration and storage increase in the national science and technology development planning. Therefore, how to effectively acquire strategic key mineral resources is always the key point in mineral exploration work.
Rubidium is an important strategic key metal in China, is known as a metal with long eyes due to excellent photoelectric performance, and is a photoelectric material with the most potential in the 21 st century. Rubidium and rubidium compounds are widely applied to the traditional fields of biomedicine, electronic devices, catalysts, special glass and the like, and have wide prospects in the emerging application fields of rubidium atomic frequency standard, magnetohydrodynamic power generation, thermionic power generation, new energy, aerospace, biology, new medical technology and the like along with the rapid development of high and new technology industries. With the wide application of rubidium, the demand of rubidium resources is increasing.
At present, the rubidium resources in China are relatively rich, but the resource distribution is unbalanced, and the rubidium resources in Jiangxi, Xinjiang, Guangdong and Hunan 4 provinces are more. The rubidium ore formation types can be divided into six types, namely granite type, granite pegmatite type, yunnan type, magma hydrothermal type, salt lake type and underground brine type, and the granite type and granite pegmatite type are mostly reported, and the granite type rubidium ore is fresh. Meanwhile, the independent rubidium ore deposit is few and is often (accompanied) with other rare metal ore species such as Li, Cs, Nb, Ta and the like, most of the rubidium ore deposits are low in level, the embedded particle size is fine and dispersed, and the occurrence in feldspar minerals is increased, so that certain difficulty is brought to industrial separation, development and utilization.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a prediction method suitable for the exploration of the granite porphyry rare metal deposit, which is used for analyzing and testing by collecting a representative sample on the basis of carrying out alteration lithofacies mapping and zonation in the field and analyzing and summarizing rare metal elements possibly existing in the granite porphyry deposit and the distribution rule thereof.
(II) technical scheme
In order to achieve the technical effects, the invention is realized by the following technical scheme:
a prospecting prediction system suitable for prospecting of granite type rare metal deposits, comprising: the device comprises a sample testing module, a sample analyzing module and an analysis result correcting module;
the sample testing module includes: the system comprises an under-mirror identification device, an X-ray fluorescence spectrum analysis device, an inductively coupled plasma mass spectrometry device, a Thermo Element II plasma mass spectrometry device, an MLA mineral automatic analysis device and a corresponding sample preparation device;
the sample analysis module includes: ore analysis and prospecting prediction analysis in a mining area;
the analysis result correction module comprises: when the output ore analysis and/or ore prospecting prediction analysis conclusion is accurate, the analysis of the current analysis model is accurate plus one; and when the output ore analysis and/or ore-finding prediction analysis conclusion of the mining area is wrong, opening the analysis conclusion correction authority to a user with the correction authority, correcting the analysis conclusion and the analysis model by the authority user, counting the analysis error plus one of the current analysis model, and forming an analysis model 2-N according to the corrected analysis model.
Preferably, the ore analysis of the mining area is formed by obtaining analysis results of an under-mirror identification device, an X-ray fluorescence spectrum analysis device, an inductively coupled plasma mass spectrometry device, a Thermo Element ii plasma mass spectrometry device and an MLA mineral automatic analysis device: the method comprises the following steps of analyzing the composition of ore substances, analyzing the embedding characteristics of main minerals, analyzing the occurrence state of rare metal elements in the ore and analyzing the selectivity; the prospecting prediction analysis comprises the following steps: and geological information of the target deposit is obtained, and the prospecting prediction analysis is formed according to the geological information and the ore analysis of the mining area.
Another object of the present invention is to provide an ore-prospecting prediction method suitable for an ore-prospecting prediction system for granite type rare metal deposits, comprising:
(1) metamorphic lithofacies zonal stages
1) According to the degree of the granite alteration, delineating different altered rock facies zones;
2) collecting different altered granite samples at equal intervals, and determining the type of rock alteration under a microscope;
(2) analytical testing phase
1) The test method comprises the following steps: the method comprises the following steps of (1) performing whole-rock main micro-testing, main mineral LA-ICP-MS in-situ micro-area analysis and MLA mineral automatic analysis;
2) and (3) analyzing a test result: obtaining mineral composition and chemical composition in granite porphyry through testing, and judging whether the content of rare metal elements in different altered granite porphyry reaches industrial exploitation grade or not according to the element composition of main rock-making minerals;
(3) summarizing occurrence states and rules of rare metal elements
1) Determining main occurrence minerals of rare metal elements according to the test result;
2) and calculating the distribution condition of the rare metal elements in the main mineral under the grinding fineness of 100 mu m, -100+40 mu m, -40+20 mu m and-20 mu m, and determining the enrichment rule of the rare metal elements under different grain sizes.
(III) advantageous effects
The invention has the beneficial effects that:
1. the method analyzes granite porphyry ore deposits to obtain the distribution condition of rare metal elements possibly existing in the granite porphyry ore deposits and an adopted recycling method. The exploration efficiency of rare metal elements in the ore deposit is effectively improved.
2. The analysis model has the correction and self-adjustment capabilities, and the analysis accuracy of the analysis model can be continuously improved in the working process.
Drawings
FIG. 1 is a chart of the catalog of the Wuya mountain spot rock mass in the middle section of the Changcheng mountain rubidium polymetallic ore bed 550
FIG. 2 is a graph showing mineral at different size fractions
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
A prospecting prediction system suitable for prospecting of granite type rare metal deposits, comprising: the device comprises a sample testing module, a sample analyzing module and an analysis result correcting module;
the sample testing module includes: the system comprises an under-mirror identification device, an X-ray fluorescence spectrum analysis device, an inductively coupled plasma mass spectrometry device, a Thermo Element II plasma mass spectrometry device, an MLA mineral automatic analysis device and a corresponding sample preparation device;
the sample analysis module includes: ore analysis and prospecting prediction analysis in a mining area;
the analysis result correction module comprises: when the output ore analysis and/or ore prospecting prediction analysis conclusion is accurate, the analysis of the current analysis model is accurate plus one; and when the output ore analysis and/or ore-finding prediction analysis conclusion of the mining area is wrong, opening the analysis conclusion correction authority to a user with the correction authority, correcting the analysis conclusion and the analysis model by the authority user, counting the analysis error plus one of the current analysis model, and forming an analysis model 2-N according to the corrected analysis model.
Preferably, the ore analysis of the mining area is formed by obtaining analysis results of an under-mirror identification device, an X-ray fluorescence spectrum analysis device, an inductively coupled plasma mass spectrometry device, a Thermo Element ii plasma mass spectrometry device and an MLA mineral automatic analysis device: the method comprises the following steps of analyzing the composition of ore substances, analyzing the embedding characteristics of main minerals, analyzing the occurrence state of rare metal elements in the ore and analyzing the selectivity; the prospecting prediction analysis comprises the following steps: and geological information of the target deposit is obtained, and the prospecting prediction analysis is formed according to the geological information and the ore analysis of the mining area.
Example 2
Prediction method suitable for ore prospecting of granite type rare metal deposit
(1) Metamorphic lithofacies zonal stages
The method comprises the steps of carrying out altered rock facies filling and zonation research on granite distributed in different directions in a great wall mining area, integrally dividing the granite into a strong altered granite zone and a weak altered granite zone (figure 1) according to rock mass alteration degree, collecting granite samples in different altered zones, observing under a mirror, wherein feldspar speckles are widely and seriously altered, the surface of the granite speckles is turbid and brownish gray, most of the granite speckles only remain, and sericitization and carbonation are actually carried out, and part of the granite speckles is kaolin.
(2) Analytical testing phase
Rb in ore by carrying out main trace test of whole rock on collected samples2O grade of 0.16%, Nb2O5+Ta2O5Grade (L) of a material0.0144% of Li2The O grade is 0.14 percent (Table 1), Rb is the main recovery element by referring to industrial indexes, and Nb and Ta can be considered as associated comprehensive recovery.
TABLE 1 results of ore chemical analysis (%)
MLA mineral autoanalysis showed that the ore consisted primarily of quartz, sericite, followed by calcite and muscovite, small amounts of kaolinite, chlorite, dolomite, pyrite, apatite, etc., and lower levels of other minerals (see table 2).
Table 2 raw ore mineral composition and content (%)
The grain size composition under the grinding fineness of 100 mu m, -100+40 mu m, -40+20 mu m and-20 mu m has larger change (figure 2), and as the grain size is reduced, the content of layered silicate minerals such as sericite and the like is increased, and the content of quartz is reduced; the particle size of quartz is mainly distributed above the plus 40 mu m grade, the particle size of sericite mineral is mainly distributed within the minus 20 mu m grade and the content is over 75 percent, and the particle size of muscovite mineral is mainly distributed within the minus 40 plus 20 mu m grade and the content is 6.83 percent. The columbite content is low, the disseminated particle size is fine, and the content is mostly concentrated in the particle size range of-20.
The main mineral LA-ICP-MS in-situ micro-area analysis shows that the content of rare metal elements such as Rb in quartz is extremely low, and only the content of trace Li is detected; the content of Rb and Li in muscovite is much higher than that of Rb and Li in sericite, and the content of Nb + Ta in muscovite is slightly higher than that of Nb + Ta in sericite, so that the muscovite is characterized by relatively enriching niobium and sericite relatively enriching tantalum (see table 3).
TABLE 3 Single mineral element analysis results (%)
Note: single mineral LA-ICP-MS in situ analysis data (averaged): 42 pieces of muscovite, 15 pieces of quartz and 18 pieces of sericite.
(3) Summarizing occurrence states and rules of rare metal elements
The distribution of Rb in the ore can be calculated according to the content of each mineral and the content of rubidium in each mineral in the sample, and the result is shown in Table 4, wherein Rb is mainly presented in the form of isomorphism in sericite and muscovite, and the distribution rate is 68.68% and 31.32%. Therefore, excellent rubidium recovery index can be obtained by carrying out dressing and smelting work on mica minerals.
TABLE 4 distribution of Rb in mineral (%)
Note: "-" not detected or below detection limit; error analysis (sericite is mostly associated with fine quartz, and the content of the sericite mineral is slightly higher).
Claims (3)
1. A prospecting prediction system suitable for prospecting of granite type rare metal deposits, comprising: the device comprises a sample testing module, a sample analyzing module and an analysis result correcting module;
the sample testing module includes: the system comprises an under-mirror identification device, an X-ray fluorescence spectrum analysis device, an inductively coupled plasma mass spectrometry device, a Thermo Element II plasma mass spectrometry device, an MLA mineral automatic analysis device and a corresponding sample preparation device;
the sample analysis module includes: ore analysis and prospecting prediction analysis in a mining area;
the analysis result correction module comprises: when the output ore analysis and/or ore prospecting prediction analysis conclusion is accurate, the analysis of the current analysis model is accurate plus one; and when the output ore analysis and/or ore-finding prediction analysis conclusion of the mining area is wrong, opening the analysis conclusion correction authority to a user with the correction authority, correcting the analysis conclusion and the analysis model by the authority user, counting the analysis error plus one of the current analysis model, and forming an analysis model 2-N according to the corrected analysis model.
2. The system of claim 1, wherein the ore analysis in the mining area is performed by obtaining the analysis results of an under-mirror identification device, an X-ray fluorescence spectrum analysis device, an inductively coupled plasma mass spectrometry device, a Thermo Element ii plasma mass spectrometry device, and an MLA mineral automatic analysis device, and the system is characterized in that: the method comprises the following steps of analyzing the composition of ore substances, analyzing the embedding characteristics of main minerals, analyzing the occurrence state of rare metal elements in the ore and analyzing the selectivity; the prospecting prediction analysis comprises the following steps: and geological information of the target deposit is obtained, and the prospecting prediction analysis is formed according to the geological information and the ore analysis of the mining area.
3. The forecasting method of the prospecting forecasting system for the granite type rare metal deposit according to any one of claims 1 to 2, characterized by comprising the following steps:
(1) metamorphic lithofacies zonal stages
1) According to the degree of the granite alteration, delineating different altered rock facies zones;
2) collecting different altered granite samples at equal intervals, and determining the type of rock alteration under a microscope;
(2) analytical testing phase
1) The test method comprises the following steps: the method comprises the following steps of (1) performing whole-rock main micro-testing, main mineral LA-ICP-MS in-situ micro-area analysis and MLA mineral automatic analysis;
2) and (3) analyzing a test result: obtaining mineral composition and chemical composition in granite porphyry through testing, and judging whether the content of rare metal elements in different altered granite porphyry reaches industrial exploitation grade or not according to the element composition of main rock-making minerals;
(3) summarizing occurrence states and rules of rare metal elements
1) Determining main occurrence minerals of rare metal elements according to the test result;
2) and calculating the distribution condition of the rare metal elements in the main mineral under the grinding fineness of 100 mu m, -100+40 mu m, -40+20 mu m and-20 mu m, and determining the enrichment rule of the rare metal elements under different grain sizes.
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| 朱恩异等: "湘南长城岭超大型铷铌钽矿金属元素赋存状态及规律", 《中南大学学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114646682A (en) * | 2022-03-18 | 2022-06-21 | 西藏巨龙铜业有限公司 | Ore finding method based on trace elements of green cord stone |
| CN114646682B (en) * | 2022-03-18 | 2023-09-08 | 西藏巨龙铜业有限公司 | Mineral prospecting method based on trace elements of green-curtain stone |
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