AU2020102682A4 - A prediction method of skarn deposit based on hyperspectral remote sensing images - Google Patents
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- 229910052619 chlorite group Inorganic materials 0.000 description 3
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V8/00—Prospecting or detecting by optical means
- G01V8/02—Prospecting
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
- G01J3/2823—Imaging spectrometer
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Abstract
The invention discloses a prediction method of skarn deposit based on hyperspectral remote
sensing images, which comprises the following steps: obtaining information about carbonatite
spatial distribution and magmatic intrusions according to hyperspectral remote sensing images
and then based on the information, to get skarn distribution information; obtaining mineral
group information from the skam distribution information; according to the mineral group
information, obtaining the specific mineral categories and mapping the mineral combination
characteristics; getting the wavelength change information of mineral categories, and based on
this, to obtain the element change information; finally predicting the skarn deposit according
to information of skarn distribution, mineral group, specific mineral categories and element
change. Adopting the hierarchical information extraction method of "rock category-group
mineral-element", the invention can accurately predict the combination characteristics and
spatial distribution of minerals in skarn deposits.
1 / 1
Obtaining information about carbonatite spatial distribution
and magmatic intrusions according to hyperspectral remote
sensing images and then based on the information, to get
skarn distribution information
Obtaining mineral group information from the skarn
distribution information
According to the mineral group information, to obtain the
specific mineral categories and map the mineral combination
characteristics
Getting the wavelength change information of mineral
categories, and based on this, to obtain the element change
information
Predicting the skarn deposit according to information of skarn
distribution, mineral group, specific mineral categories and
element change
FIG, 1
A schematic flow chart of the prediction method of skarn deposit based on
hyperspectral remote sensing of the present invention
Description
1 /1
Obtaining information about carbonatite spatial distribution and magmatic intrusions according to hyperspectral remote sensing images and then based on the information, to get skarn distribution information
Obtaining mineral group information from the skarn distribution information
According to the mineral group information, to obtain the specific mineral categories and map the mineral combination characteristics
Getting the wavelength change information of mineral categories, and based on this, to obtain the element change information
Predicting the skarn deposit according to information of skarn distribution, mineral group, specific mineral categories and element change
FIG, 1
A schematic flow chart of the prediction method of skarn deposit based on hyperspectral remote sensing of the present invention
A prediction method of skarn deposit based on hyperspectral remote sensing images
[01] The invention belongs to the technical field of remote sensing geological survey, and particularly relates to a prediction method of skarn deposit based on hyperspectral remote sensing images.
[02] Various of silicate minerals with Ca, Mg, Fe, Al, Mn and the like are often found in skam. According to previous studies, skam minerals mainly include anhydrous silicate minerals in evolutionary metasomatism stage, such as garnet, pyroxene, wollastonite, and hydrous silicate minerals in degradation and alteration stage, such as hornblende and epidote. At present, the research on skam minerals focuses on mineralogy and mineral chemistry, mainly adopting traditional geologic techniques such as microscopic identification and electron probing analysis. Compared with traditional geologic techniques, spectral analysis has the advantages of short time, low cost and high accuracy.
[03] Integrating with aerospace, aviation and ground measuring techniques, hyperspectral remote sensing can obtain more abundant spectral information in visible light, near infrared and thermal infrared spectral range. Super spectral resolution can identify physical properties of rocks and minerals. Under the influence of electron energy level transition, silicate minerals with Fe and Mn cations will produce characteristic absorption peaks in visible-near infrared spectrum, thereby achieving the purpose of mineral categories recognition. However, hydrous silicate minerals, sulphates, carbonates and the like will undergo energy level transition of mineral cations in short-wave infrared range, resulting in absorption characteristics at specific wavelength positions. The frequency (or energy) of electromagnetic radiation in the thermal infrared spectrum (TIR, 6,000-14,500nm) is similar to the vibration frequency of common chemical bonds such as Si-0, Al-O, S-O and C-O in the crystal structure of infrared minerals, which leads to the selective absorption of this electromagnetic wave and the formation of ground state vibration band in the spectrum. Therefore, anhydrous silicate minerals such as garnet, pyroxene and wollastonite can be identified through the vibration of SinOk radical group in TIR.
[04] So hyperspectral remote sensing technology can not only identify typical skarn minerals in different stages, but also identify typical altered mineral assemblage in different metallogenetic environments quickly and accurately. However, the spectral resolutions of aerospace, aviation and ground remote sensing are different, ranging from l0nm to 2-3nm, which also leads to the problem of whether the migration and substitution characteristics of mineral crystal chemical elements can be recorded in detail.
[05] The distribution and changes of altered mineral assemblage seriously affect the discrimination of metallogenetic environment and hydrothermal fluid action, and further determine the grasp of the generation-evolution-degeneration mechanism of skarn mineral. Therefore, it is necessary to play the role of hyperspectral remote sensing technology in the study of pointer mineralogy.
[06] The invention provides a prediction method of skarn deposit based on hyperspectral remote sensing images, which constructs a prospecting and exploration model for extracting skarn and mineralogical information by hyperspectral remote sensing technology with different scales, and establishes a hierarchical information extraction method of "rock category-group-mineral-element", thus solving the problem of identifying skarn by hyperspectral remote sensing technology.
[07] To achieve the above purpose, the present invention provides the following scheme.
[08] The present invention provides a prediction method of skarn deposit based on hyperspectral remote sensing images, which is characterized by comprising the following steps.
[09] Obtaining information about carbonatite spatial distribution and magmatic intrusions according to hyperspectral remote sensing images and then based on the information, to get skarn distribution information.
[010] Obtaining mineral group information from the skarn distribution information.
[011] According to the mineral group information, obtaining the specific mineral categories and mapping the mineral combination characteristics. Getting the wavelength change information of mineral categories, and based on this, to obtain the element change information.
[012] Finally predicting the skarn deposit according to information of skam distribution, mineral group, specific mineral categories and element change.
[013] Preferably, the method for obtaining carbonatite spatial distribution information is to extract reflection spectrum absorption characteristics by using a specific band of hyperspectral images and then carry out image conversion on that remote sensing images. According to the absorption characteristics of the reflection spectrum and the image conversion results, the invention obtains that carbonatite spatial distribution information.
[014] Preferably, the method for obtaining magmatic intrusions information is to extract wavelength change information of Al-OH groups adopting a specific band of hyperspectral images and then obtain annular image features according to texture features of remote sensing images. Based on the wavelength change information and the annular image features, the invention obtains the magmatic intrusions information.
[015] Preferably, the method for obtaining skam distribution information is to calculate Euclidean distance between the magmatic intrusions and the carbonatite distribution and divide contact parts between them at different distances according to the existence range of the magmatic intrusions. And then, calculate fault information between them and acquire skarn colour information compounded by visible light band colour. According to the contact range, the fracture information and the skam colour information, the invention obtains the skarn distribution information.
[016] Preferably, the acquisition process of the mineral categories is to resample the data of a typical mineral standard database and obtain a new mineral database according to the resampling results and the spatial resolution of remote sensing images. Then extract the skam mineral group information as region of interest (ROls) and construct a spectral database according to the ROIs. Carry out characteristic absorption position corresponding processing and waveform processing on the mineral database and the spectral database to obtain specific mineral categories.
[017] The invention discloses the following technical effects: based on hyperspectral remote sensing, the prediction method of skam deposit adopts a hierarchical information extraction method of "rock category-group-mineral-element". It can determine skarn mineral assemblage information through visible light-near infrared bands, predicts the existence possibility of skarn deposits, completes the mapping of altered minerals on the surface of skarn deposits and judges the possible denudation degree of skam deposits and the position of ore bodies. Furthermore, it can analyse the mineral characteristics and remote sensing interpretation method of skarn deposits from macro characteristics to micro characteristics and can accurately predict the assemblage characteristics and spatial distribution of minerals in skam deposits.
[018] In order to explain the embodiments of the present invention or the technical scheme in the prior art more clearly, the figures needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and for ordinary technicians in the field, other figures can be obtained according to these figures without paying creative labour.
[019] FIG. 1 is a schematic flow chart of the prediction method of skarn deposit based on hyperspectral remote sensing of the present invention.
[020] The technical scheme in the embodiments of the present invention will be described clearly and completely with reference to the figures in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without creative labour should belong to the protection scope of the present invention.
[021] In order to make the above objects, features and advantages of the present invention more obvious and easier to understand, the present invention will be further explained in detail with reference to the figures and specific embodiments.
[022] As shown in Fig. 1, the present invention provides a prediction method of skarn deposit based on hyperspectral remote sensing image, which comprises the following steps.
[023] Si. Obtaining information about carbonatite spatial distribution and magmatic intrusions according to hyperspectral remote sensing images and then based on the information, to get skarn distribution information.
[024] Because skarn is formed by hydrothermal fluid and carbonatite surrounding rock through contact metasomatic metamorphism, it is very important to identify the contact relationship between carbonatite and magma intrusion.
[025] Identification of carbonatite. Carbonatites are usually light grey to grey white, mainly composed of calcite, dolomite and ankerite, so they are rich in Ca2
, Mg2+and Fe2+ ions. According to different genesis, the common carbonatites closely related to skarn formation in the field mainly include marble, limestone and dolomite.
[026] This embodiment utilize the remote sensing band near 2,320-2,340nm to directly extract the absorption characteristics of reflection spectrum, which shows the change of Mg 2 +--Ca2+ from short wave to long wave, and then the rock category is determined to be rich in magnesium or calcium, so as to determine the later altered mineral category.
[027] Because the rock colour is light and the brightness value in the remote sensing brightness image is large, converting the remote sensing colour image from RGB to HLS, so as to obtain the brightness (component of L) and extract the larger part of the value range, standard deviation +3x mean value specifically. Wherein, the standard deviation and mean value are obtained by calculating the brightness value in the image generally through the statistical analysis function in ENVI software.
[028] Carrying out spatial superposition analysis of raster data on the extracted Mg2+--Ca2+ change results and RGB-HLS conversion results to calculate the intersection (n), and then extract spatial distribution information of carbonatite closely related to skarn.
[029] Identification of magmatic intrusions. Skarn deposits belong to the category of porphyry system, and their genesis is closely related to porphyry intrusion, so it is very important to identify porphyry near carbonatite.
[030] This embodiment utilizes the spectral characteristics of 2,200nm to extract the wavelength change information of Al-OH group. Generally, the porphyry invades from the centre to the periphery, and there will be a change trend of long wave to short wave. This embodiment extracts texture features with obvious rings, domes and the like according to the texture features of remote sensing images. The images often appear ring-shaped features due to intrusion of rock mass.
[031] Identification of contact relationship. Skarn is formed by contact metasomatic metamorphism between intrusions and carbonatites.
[032] Calculating Euclidean distance between the magmatic intrusions and the carbonatite distribution and divide contact parts between them at different distances according to the existence range of the magmatic intrusions. Skamization will occur through the contact between magmatic intrusions and carbonatites, so the distribution range of skam can be obtained by dividing the contact parts. To get the fracture information between intrusions and carbonatites through interpretation, and it is fault, the channel provided for hydrothermal intrusion. Judging the difference of image color information, skarn often appears green or dark green due to the existence of garnet in visible light band color synthesis, and at the same time there is a banded abnormal colour distribution area formed. The skarn distribution information can be finally obtained according to the contact parts, fracture information and the image colour difference.
[033] S2. To obtain mineral group information from the skam distribution information.
[034] The determination of Si-O group in skarn minerals mainly depends on thermal infrared band, however, the band of 350-2,500nm visible light-short wave infrared remote sensing detector widely used at present cannot directly identify skarn minerals. Therefore, the identification of skarn minerals can be achieved by using short wave infrared spectrum combined with remote sensing image features. Because of the high temperature when skarn is formed, there are characteristics of zonal combination formed in the process of fluid advance from, appearing inside anhydrous to outside hydrous minerals. In addition, a large number of hydrous minerals will appear in the stage of fluid retreat due to late fluid superposition. These minerals have obvious absorption characteristics between 350-2500nm. Extracting the information of Al-OH, Mg-OH, Fe-OH, C0 32 , SO 2 and other groups, and determine their spatial distribution position relationship so as to judge the coexistence relationship between skarn minerals and them.
[035] S3. To obtain the specific mineral categories and map the mineral combination characteristics, according to the mineral group information in step2
[036] Under the influence of mineral groups, a variety of minerals may have similar spectral characteristics (for example, Al-OH groups may be sericite, kaolinite and other minerals), which may cause inaccuracy in determining the categories of altered minerals in remote sensing interpretation.
[037] Therefore, this embodiment selects typical minerals to construct a standard database, and the interpretation information of skarn mineral groups and the standard database of typical minerals are subjected to "gene recognition" according to following steps:
[038] Unifying the typical mineral standard database with image resolution by data resampling method and constructing the standard database. Extracting the image group information as the ROIs to construct the spectral database of "pixels". Comparing and analysing the standard database and spectral database, wherein the analysis mainly includes the corresponding analysis of typical mineral characteristic absorption position and waveform analysis. The analysis of characteristic absorption position is mainly viewed through the wavelength position corresponding to "absorption valley". The waveform analysis is to determine by the transformation of fitting function, that is, fit the waveform curve by polynomial function. If the fitting function is linear, the waveform is consistent, meaning they are the same mineral.
[039] Mapping the characteristics of mineral assemblages according to specific mineral categories. In hydrothermal deposits, magma gas and liquid will interact with the surrounding rocks in the process of upward movement, which will further cause surrounding rocks alteration. According to the change of metallogenetic environment, the distribution of altered minerals in surrounding rocks has certain patterns. Mineral mapping is to make the spatial distribution of mineral assemblages and find the patterns indicating the change of metallogenetic environment.
[040] S4. Based on the mineral category in step S3, determining the element change information caused by electronic transition or molecular vibration in mineral molecules.
[041] The change of elements in general molecules can be analyzed by the shift of characteristic wavelengths. Extracting the wavelength change information of sericite, chlorite and other minerals in skam mineral information to check the change information of Al, Fe and Mg, and further determine the vectorization characteristics of fluid migration. Wavelength is the wavelength change of electromagnetic wave, which is the short-wave infrared spectrum of electromagnetic wave, while skam minerals such as garnet are mainly in the thermal infrared spectrum. That's because different substances have different electromagnetic wave energy responses, so are the wavelength variation characteristics. From inside to outside, the vectorization distribution direction of fluid is respectively: pyroxene-garnet (long wave sericite + biotite + gypsum) --* pyroxene-wollastonite (medium wave superimposed short wave sericite + epidote + serpentine + Mg chlorite) --* diopside-talcum (short wave sericite + Fe chlorite + calcite + kaolinite).
[042] S5. Predicting the skarn deposit according to information of skam distribution, mineral group, specific mineral categories and element change.
[043] Although the invention has been described with reference to specific examples, it will be appreciated by those skilled in the art that the invention may be embodied in many other forms, in keeping with the broad principles and the spirit of the invention described herein.
[044] The present invention and the described embodiments specifically include the best method known to the applicant of performing the invention. The present invention and the described preferred embodiments specifically include at least one feature that is industrially applicable
Claims (5)
1. A prediction method of skam deposit based on hyperspectral remote sensing images, characterized by comprising the following steps.
Obtaining information about carbonatite spatial distribution and magmatic intrusions according to hyperspectral remote sensing images and then based on the information, to get skam distribution information.
Obtaining mineral group information from the skarn distribution information.
According to the mineral group information, obtain the specific mineral categories and map the mineral combination characteristics.
Getting the wavelength change information of mineral categories, and based on this, obtaining the element change information.
Finally predicting the skarn deposit according to information of skam distribution, mineral group, specific mineral categories and element change.
2. The prediction method of skarn deposit based on hyperspectral remote sensing images according to claim 1, characterized in that the method for obtaining carbonatite spatial distribution information is to extract reflection spectrum absorption characteristics by using a specific band of hyperspectral images and then carry out image conversion on that remote sensing images. According to the absorption characteristics of the reflection spectrum and the image conversion results, the invention obtains that carbonatite spatial distribution information.
3. The prediction method of skarn deposit based on hyperspectral remote sensing images according to claim 1, characterized in that the method for obtaining magmatic intrusions information is to extract wavelength change information of Al-OH groups adopting a specific band of hyperspectral images and then obtain annular image features according to texture features of remote sensing images. Based on the wavelength change information and the annular image features, the invention obtains the magmatic intrusions information.
4. The prediction method of skarn deposit based on hyperspectral remote sensing images according to claim 1, characterized in that the method for obtaining skarn distribution information is to calculate Euclidean distance between the magmatic intrusions and the carbonatite distribution, and divide contact parts between them at different distances according to the existence range of the magmatic intrusions. And then, calculate fault information between them and acquire skarn colour information compounded by visible light band colour. According to the contact range, the fracture information and the skarn colour information, the invention obtains the skam distribution information.
5. The prediction method of skarn deposit based on hyperspectral remote sensing images according to claim 1, characterized in that the acquisition process of the mineral categories is to resample the data of a typical mineral standard database, and obtain a new mineral database according to the resampling results and the spatial resolution of remote sensing images. Then extract the skarn mineral group information as ROIs and construct a spectral database according to the ROIs. Carry out characteristic absorption position corresponding processing and waveform processing on the mineral database and the spectral database to obtain specific mineral categories.
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| CN114999592A (en) * | 2022-05-13 | 2022-09-02 | 中国地质调查局西安矿产资源调查中心 | Method for identifying mineral sources by thermal infrared spectroscopy technology |
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