CN111402194B - Method suitable for identifying exposed and hidden fracture structure of granite uranium mining area - Google Patents
Method suitable for identifying exposed and hidden fracture structure of granite uranium mining area Download PDFInfo
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Abstract
The invention belongs to the technical field of extraction of geologic information, and particularly relates to a method suitable for identifying exposed and hidden fracture structures of a granite uranium mining area. The invention comprises the following steps: 1. optical and radar remote sensing data acquisition; 2. preprocessing optical remote sensing data; 3. preprocessing radar remote sensing data; 4. optical remote sensing data processing and information extraction; 5. extracting radar remote sensing data information and fusing the radar remote sensing data information and optical remote sensing data; 6. constructing a remote sensing identification mark of the exposed fracture structure; 7. constructing a hidden fracture construction remote sensing identification mark; 7. and (5) identifying exposed and hidden fracture structures. The method can quickly identify the exposed and hidden fracture structure of the uranium mining area of the granite, and has important significance for analyzing the uranium mining environment of the area and guiding the uranium mining work deployment.
Description
Technical Field
The invention belongs to the technical field of extraction of geologic information, and particularly relates to a method suitable for identifying exposed and hidden fracture structures of a granite uranium mining area.
Background
Granite uranium ore refers to a hydrothermal type deposit having a close spatial and causative relationship with granite, which is produced inside a rock mass or within a certain range of its periphery. The granite uranium ore deposit is quite widely distributed in China, and according to statistics of related documents, china is the country with the most developed and most diversified types and most widely distributed granite uranium ore deposit in the world, and the granite uranium ore is said to be the primary dominant type of the uranium ore in China.
The fracture structure is one of the main factors for controlling the formation and distribution of granite uranium ores, and not only provides migration channels and aggregation sites for ore-bearing solutions, but also creates necessary physicochemical conditions for redistribution and enrichment of minerals. However, the coverage rate of vegetation and a fourth system in the uranium mining area of granite is high, part of fracture structures are exposed, and part of fracture structures are hidden. Therefore, the accurate identification of the exposed and hidden fracture structure has important guiding significance for analysis of the ore forming environment and prospect prediction of the granite uranium ore.
Nowadays, the technical methods for identifying the exposed and hidden fracture structures mainly comprise conventional geology methods (such as petrology, mineralogy methods, field actual measurement methods and the like), exploration geophysics (such as gravity, electromagnetism and the like), exploration geochemistry (such as geogas-X fluorescence-radon gas measurement, gas emission measurement, ore-forming element geochemistry methods and the like), biological methods and the like, and the technical methods play positive and effective roles in identifying the exposed and hidden fracture structures, but the methods have high labor and economic cost, long period and limited detection range and are unfavorable for large-scale popularization and use. The traditional fracture structure remote sensing recognition technology is taken as an advanced technical method for realizing modernization of geological work in China, and plays a positive role in exposing and hidden fracture structures, but has the disadvantages of low spatial resolution, uncertain interpretation, high difficulty in recognizing hidden fracture structures and the like of remote sensing images, and is questioned by a plurality of students in practical application.
With the continuous improvement of the spatial resolution of the remote sensing data, the increasingly perfect technical means and the continuous deep application of the radar remote sensing, the precise identification of the exposed and hidden fracture structures by using the multi-source remote sensing technology is possible. Therefore, aiming at the current situation that the granite uranium mining area has both exposed fracture structures and hidden fracture structures, it is necessary to develop a method suitable for identifying the exposed and hidden fracture structures of the granite uranium mining area.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method suitable for identifying exposed and hidden fracture structures of a granite uranium mining area, which can quickly and accurately identify the fracture structures and reduce the working cost.
In order to solve the technical problems, the method suitable for identifying the exposed and hidden fracture structure of the granite uranium mining area comprises the following steps in sequence:
step one, optical and radar remote sensing data acquisition: selecting ETM+ optical remote sensing data and Radar Sat2 radar remote sensing data which cover a certain granite uranium mining area in China and have high data acquisition quality;
step two, preprocessing optical remote sensing data: preprocessing the ETM+ optical remote sensing data obtained in the first step, wherein the preprocessing comprises radiation correction, geometric correction and noise removal, and the preprocessed ETM+ optical remote sensing data is obtained;
step three, preprocessing radar remote sensing data: preprocessing the Radar Sat-2 radar remote sensing data obtained in the first step, including focusing, multi-vision, radiation correction, geometric correction and filtering, to obtain preprocessed Radar Sat-2 radar remote sensing data;
step four, optical remote sensing data processing and information extraction: performing three-band color synthesis and color and panchromatic data fusion on the ETM+ optical remote sensing data obtained in the second step to obtain a color fusion image, and performing texture information extraction on a single band of the ETM+ optical remote sensing data to obtain a texture information image;
step five, extracting radar remote sensing data information and fusing the radar remote sensing data information and optical remote sensing data: extracting texture information of the Radar Sat-2 radar remote sensing data obtained in the step three, and carrying out data fusion with the three-band color synthesized image of the ETM+ optical remote sensing data obtained in the step four to obtain an optical and radar data fusion image containing not only electromagnetic reflection spectrum characteristics of different rocks but also texture information such as topography and landform;
step six, building a remote sensing identification mark of the exposed fracture structure: and (3) judging the exposed fracture structure when the three-band color image of the ETM+optical remote sensing data obtained in the step four simultaneously meets the following marks. Sign 1: the specific linear shadow line characteristics are presented, rock mass and stratum in the image are cut or staggered, and stratum shadow lines are discontinuous and disordered; sign 2: the shadow and the bright color tone are tightly connected on the optical and radar data color fusion image obtained in the step five, the landform is in the form of broken mountain bealock, rocks are broken and scattered to form ridge posts, a single-face mountain is formed locally, a silicide strip is developed at the top of the broken mountain, and the broken mountain has strong corrosion resistance and forms specific topography; sign 3: the mountain body fracture surface has thinner soil covering layer, vegetation is rare, and water system does not develop.
Step seven, constructing a hidden fracture structure remote sensing identification mark: the following marks are presented in the ETM+ optical remote sensing data single-band texture information extracted image obtained in the step four: sign 1: the obvious 'inverted hook' river course water system is characterized in that a plurality of tributaries intersect with the main stream at an obtuse angle, and the existence of the abnormal river course is a typical sign of fracture control. Sign 2: the two sides of the main river channel have obvious confluence concentration phenomenon; and (3) presenting the following marks in the texture information extraction image of the Radar Sat-2 radar remote sensing data obtained in the step five: sign 1: obvious dark tone abnormality of the serpentine curve; sign 2: the soil cover layer is generally less than a certain thickness and the vegetation develops. And when the 4 marks are satisfied, the hidden fracture structure can be judged.
Step eight, identifying exposed and hidden fracture structures: under a geographic information system software platform, identifying all exposed fracture structures in the remote sensing image range based on the exposed fracture structure identification mark constructed in the step six, and marking by a solid line with a specific color; identifying all hidden fracture structures in the remote sensing image range based on the hidden fracture structure identification mark constructed in the step seven, and marking by using a dotted line with a specific color; all solid and dashed lines marked by specific colors are identified exposed and hidden fracture configurations.
The acquisition time for acquiring the optical and radar remote sensing data is at noon, and the sky is cloud-free and has high signal to noise ratio; the optical remote sensing data refer to Landsat7 ETM+ data with visible-shortwave-thermal infrared 8 wave bands, which are emitted by the American aerospace agency and have the maximum spatial resolution of 15 meters; the radar remote sensing data is high-resolution Radar Sat-2 synthetic aperture imaging radar data of a C wave band carried by Canadian space agency, the frequency of the radar remote sensing data is 5.4GHZ sensor, the data adopts a full-polarization fine mode, and the nominal resolution is 8 meters.
In the second step, radiation correction is completed by adopting a radiation regression analysis method, geometric correction is completed by adopting a polynomial correction method, and noise removal is completed by adopting a median filtering method.
In the third step, focusing means processing the acquired original data of the radar data and directly outputting single-vision complex product data; multiview refers to processing performed in the transverse frequency domain in order to suppress speckle noise in radar data, improve the signal-to-noise ratio of an image; the radiation correction is accomplished using look-up table data provided by the radar data; the geometric correction is completed by adopting a rational polynomial model provided by radar data and actual coordinates of control points; the filtering is accomplished using a normalized Freeman decomposition method.
In the fourth step, three-band color synthesis means that color conversion is performed on the seventh, fifth and second 3 bands of ETM+ remote sensing data, and a color image is formed through contrast stretching; the data fusion is to fuse the synthesized three-band color image and an eighth band of ETM+ remote sensing data based on a principal component analysis method to obtain an ETM+ color fusion image with a spatial resolution of 15 meters; the single-band texture information extraction refers to extracting texture information of a second band of ETM+ remote sensing data by a filtering method based on probability statistics.
In the fifth step, the texture information extraction refers to extracting texture information of Radar Sat-2 radar data based on a filtering method of second-order probability statistics; the data fusion is to perform HSB conversion on the ETM+ color synthesized image obtained in the step four, replace the B component after the HSB conversion with the obtained Radar Sat-2 radar texture information data, and convert the replaced HSB image into an RGB color image.
In the sixth step, the etm+three-band color image refers to etm+seventh, fifth and second 3-band color composite images; the specific linear schlieren features are schlieren features which are wide and narrow, invisible and intermittent in extension; the optical and radar data color fusion image refers to an image obtained by fusing ETM+a color synthesized image of a seventh wave band, a fifth wave band and a second wave band with the Radar Sat-2 radar texture information data; the specific topography is referred to as potato ridge-shaped topography.
In the seventh step, the etm+single-band texture information extraction image refers to an etm+second-band texture information extraction image; the intersection of the plurality of branches with the main flow at an obtuse angle means that the acute angle at which the main flow intersects with the branches is directed opposite to the flow direction; a particular thickness refers to a thickness of less than 30cm.
In the eighth step, the geographic information system software refers to general geographic drawing software such as ARCGIS, mapGIS; the remote sensing image range refers to the color fusion image range of the ETM+seventh, fifth and second 3 wave bands with the spatial resolution of 15 meters obtained in the step four; the specific color is red, the solid line represents the exposed fracture configuration, and the broken line represents the hidden fracture configuration.
The beneficial technical effects of the invention are as follows:
(1) The method for identifying the exposed and hidden fracture structures of the granite uranium mining area provided by the invention can be used for rapidly identifying the exposed and hidden fracture structures of the granite uranium mining area, so that the geological investigation and physical and chemical detection method detection cost of the fracture structures is greatly reduced;
(2) The method suitable for identifying the exposed and hidden fracture structure of the granite uranium mining area has important significance for analyzing the uranium mining environment of the granite uranium mining area, and provides an important basis for the uranium mining exploration work deployment of the area.
Detailed Description
The present invention will be described in further detail with reference to examples.
The invention discloses a method suitable for identifying exposed and hidden fracture structures of a granite uranium mining area, which comprises the following steps:
step one, optical and radar remote sensing data acquisition. Selecting a granite uranium ore deposit (point) which is covered in China, has higher vegetation and fourth-line coverage rate and is formed by breaking structures, wherein optical remote sensing data are Landsat7 ETM+ optical remote sensing data which are emitted by the American aviation space agency (NASA) and have the maximum spatial resolution of 15 meters and visible-shortwave-thermal infrared 8 wave bands; the radar remote sensing data are radar remote sensing data of a radar Sat-2 synthetic aperture imaging radar with a nominal resolution of 8 meters, wherein the radar remote sensing data are shot by a sensor with a carried C wave band (with a frequency of 5.4 GHZ) transmitted by Canadian space agency, and a full polarization fine mode is adopted; the data acquisition time is noon time division, the sky is cloud-free and the signal to noise ratio is high;
and step two, preprocessing optical remote sensing data. Performing radiation correction on the ETM+ optical remote sensing data obtained in the first step by adopting a radiation regression analysis method, performing geometric correction by adopting a polynomial correction method, performing pretreatment such as noise removal by adopting a median filtering method, and obtaining the pretreated ETM+ optical remote sensing data;
and thirdly, preprocessing radar remote sensing data. Processing the Radar Sat-2 radar remote sensing data obtained in the first step, directly outputting single-view complex product data, and further processing on a transverse frequency domain in order to inhibit speckle noise in the radar data, improve the signal-to-noise ratio of an image; performing radiation correction by using lookup table data provided by radar data, performing geometric correction by using a rational polynomial model provided by the radar data and actual coordinates of control points, performing filtering processing by using a normalized Freeman decomposition method, and obtaining preprocessed Radar Sat-2 radar remote sensing data;
and fourthly, optical remote sensing data processing and information extraction. Performing color conversion on the seventh, fifth and second 3 wave bands of the ETM+ optical remote sensing data obtained in the second step, forming a color image through contrast stretching, fusing the synthesized three-wave band color image and the eighth wave band of the ETM+ optical remote sensing data based on a principal component analysis method to obtain an ETM+752 color fusion image with the spatial resolution of 15 meters, and extracting texture information of the second wave band of the ETM+ remote sensing data based on a filtering method of probability statistics to obtain a texture information image;
and fifthly, extracting radar remote sensing data information and fusing the radar remote sensing data information and the optical remote sensing data. The filtering method based on second-order probability statistics is used for extracting texture information of Radar Sat-2 radar remote sensing data obtained in the step three, obtaining radar data texture information images, performing HSB (high-speed binary) conversion on the ETM+752 color fusion images obtained in the step four, replacing B components after the HSB conversion with the radar texture information images, and converting the replaced HSB images into RGB color images to obtain optical and radar data fusion images containing not only different rock electromagnetic reflection spectrum characteristics but also texture information such as topography and the like;
and step six, building a remote sensing identification mark of the exposed fracture structure. The ETM+752 color fusion image obtained in the step four shows the characteristics of wide and narrow range, time stealth and intermittent extending shadow patterns, the rock mass and stratum images are cut or staggered, and stratum shadow patterns are discontinuous and disordered; the shadow and the bright tone are displayed on the image obtained after the fusion of the ETM+752 color image and the RadarSat-2 texture information image obtained in the step five and are tightly connected, the landform is the broken mountain bealock form, rocks are broken and scattered to form ridge posts, a single mountain is formed locally, a silicide strip is developed at the top of the broken mountain, and the corrosion resistance is strong, so that a potato ridge-shaped topography is formed; the mountain body fracture surface is provided with a thinner soil covering layer, vegetation is rare, and a water system does not develop; when the mark is satisfied, the exposed fracture structure can be identified;
and seventhly, constructing a hidden fracture structure remote sensing identification mark. On the ETM+second wave band texture information extraction image obtained in the step four, an obvious 'inverted hook' river channel system is presented, and the system is characterized in that a plurality of tributaries intersect with a main stream at an obtuse angle, namely, the acute angle direction of intersection of the main stream and the tributaries is opposite to the flow direction, and the existence of the abnormal river channel is a typical sign of fracture control. In addition, the two sides of the main river channel have obvious confluence and concentration phenomenon, which indicates that sharp folds of the stratum are caused at the passing position of the fracture zone; in the Radar Sat-2 texture information extraction image obtained in the step five, obvious dark tone abnormality of a serpentine curve is shown; the soil cover layer in this type of region is generally less than 30cm thick and the vegetation develops. When the mark is satisfied, the invisible fracture structure can be identified;
and step eight, identifying exposed and hidden fracture structures. Under a general geographic information system software platform such as ARCGIS, mapGIS, identifying all exposed fracture structures in the ETM+752 color fusion image range with the spatial resolution of 15 meters based on the exposed fracture structure identification mark constructed in the step six, and marking by a red solid line; identifying all hidden fracture structures in the remote sensing image range based on the hidden fracture structure identification mark constructed in the step seven, and marking by using red dotted lines; all the marked red solid lines and red dashed lines are the identified exposed and hidden fracture structures.
While the embodiments of the present invention have been described in detail, the foregoing embodiments are merely preferred embodiments of the present invention, the present invention is not limited to the above embodiments, and various changes may be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (5)
1. The method suitable for identifying the exposed and hidden fracture structure of the granite uranium mining area is characterized by comprising the following steps of:
step one, optical and radar remote sensing data acquisition: selecting ETM+ optical remote sensing data and Radar Sat2 radar remote sensing data which cover a certain granite uranium mining area in China and have high data acquisition quality;
step two, preprocessing optical remote sensing data: preprocessing the ETM+ optical remote sensing data obtained in the first step, wherein the preprocessing comprises radiation correction, geometric correction and noise removal, and the preprocessed ETM+ optical remote sensing data is obtained;
step three, preprocessing radar remote sensing data: preprocessing the Radar Sat-2 radar remote sensing data obtained in the first step, including focusing, multi-vision, radiation correction, geometric correction and filtering, to obtain preprocessed Radar Sat-2 radar remote sensing data;
step four, optical remote sensing data processing and information extraction: performing three-band color synthesis and color and panchromatic data fusion on the ETM+ optical remote sensing data obtained in the second step to obtain a color fusion image, and performing texture information extraction on a single band of the ETM+ optical remote sensing data to obtain a texture information image;
in the fourth step, three-band color synthesis means that color conversion is performed on the seventh, fifth and second 3 bands of ETM+ remote sensing data, and a color image is formed through contrast stretching; the data fusion is to fuse the synthesized three-band color image and an eighth band of ETM+ remote sensing data based on a principal component analysis method to obtain an ETM+ color fusion image with a spatial resolution of 15 meters; the single-band texture information extraction refers to extracting texture information of a second band of ETM+ remote sensing data by a filtering method based on probability statistics;
step five, extracting radar remote sensing data information and fusing the radar remote sensing data information and optical remote sensing data: extracting texture information of the Radar Sat-2 radar remote sensing data obtained in the step three, and carrying out data fusion with the three-band color synthesized image of the ETM+ optical remote sensing data obtained in the step four to obtain an optical and radar data fusion image containing electromagnetic reflection spectrum characteristics of different rocks and topographic texture information;
step six, building a remote sensing identification mark of the exposed fracture structure: judging that the exposed fracture structure exists when the following marks are simultaneously met on the ETM+optical remote sensing data three-band color image obtained in the step four; sign 1: the specific linear shadow line characteristics are presented, rock mass and stratum in the image are cut or staggered, and stratum shadow lines are discontinuous and disordered; sign 2: the shadow and the bright color tone are tightly connected on the optical and radar data color fusion image obtained in the step five, the landform is in the form of broken mountain bealock, rocks are broken and scattered to form ridge posts, a single-face mountain is formed locally, a silicide strip is developed at the top of the broken mountain, and the broken mountain has strong corrosion resistance and forms specific topography; sign 3: the mountain body fracture surface is provided with a thinner soil covering layer, vegetation is rare, and a water system does not develop;
in the sixth step, the etm+three-band color image refers to etm+seventh, fifth and second 3-band color composite images; the specific linear schlieren features are schlieren features which are wide and narrow, invisible and intermittent in extension; the optical and radar data color fusion image refers to an image obtained by fusing ETM+a color synthesized image of a seventh wave band, a fifth wave band and a second wave band with the Radar Sat-2 radar texture information data; the specific topography is potato ridge-shaped topography;
step seven, constructing a hidden fracture structure remote sensing identification mark: the following marks are presented in the ETM+ optical remote sensing data single-band texture information extracted image obtained in the step four: sign 1: the obvious 'inverted hook' river course water system is characterized in that a plurality of tributaries intersect with the main stream at an obtuse angle, and the existence of the river course water system is a typical mark for fracture control; sign 2: the two sides of the main river channel have obvious confluence concentration phenomenon; and (3) presenting the following marks in the texture information extraction image of the Radar Sat-2 radar remote sensing data obtained in the step five: sign 1: obvious dark tone abnormality of the serpentine curve; sign 2: the soil covering layer is smaller than a certain specific thickness, and vegetation develops; when the 4 marks are met, judging a hidden fracture structure;
in the seventh step, the etm+single-band texture information extraction image refers to an etm+second-band texture information extraction image; the intersection of the plurality of branches with the main flow at an obtuse angle means that the acute angle at which the main flow intersects with the branches is directed opposite to the flow direction; a certain specific thickness refers to 30cm;
step eight, identifying exposed and hidden fracture structures: under a geographic information system software platform, identifying all exposed fracture structures in the remote sensing image range based on the exposed fracture structure identification mark constructed in the step six, and marking by a solid line with a specific color; identifying all hidden fracture structures in the remote sensing image range based on the hidden fracture structure identification mark constructed in the step seven, and marking by using a dotted line with a specific color; all solid lines and broken lines marked by specific colors are the identified exposed and hidden fracture structures;
in the eighth step, the geographic information system software refers to ARCGIS, mapGIS general geographic drawing software; the remote sensing image range refers to the color fusion image range of the ETM+seventh, fifth and second 3 wave bands with the spatial resolution of 15 meters obtained in the step four; the specific color is red, the solid line represents the exposed fracture configuration, and the broken line represents the hidden fracture configuration.
2. The method for identifying exposed and hidden fracture structures of granite uranium mining areas according to claim 1, wherein the method comprises the following steps: the acquisition time for acquiring the optical and radar remote sensing data is at noon, and the sky is cloud-free and has high signal to noise ratio; the optical remote sensing data refer to Landsat7 ETM+ data with visible-shortwave-thermal infrared 8 wave bands, which are emitted by the American aerospace agency and have the maximum spatial resolution of 15 meters; the radar remote sensing data is high-resolution Radar Sat-2 synthetic aperture imaging radar data of a C wave band carried by Canadian space agency, the frequency of the radar remote sensing data is 5.4GHZ sensor, the data adopts a full-polarization fine mode, and the nominal resolution is 8 meters.
3. The method for identifying exposed and hidden fracture structures of granite uranium mining areas according to claim 1, wherein the method comprises the following steps: in the second step, radiation correction is completed by adopting a radiation regression analysis method, geometric correction is completed by adopting a polynomial correction method, and noise removal is completed by adopting a median filtering method.
4. The method for identifying exposed and hidden fracture structures of granite uranium mining areas according to claim 1, wherein the method comprises the following steps: in the third step, focusing means processing the acquired original data of the radar data and directly outputting single-vision complex product data; multiview refers to processing performed in the transverse frequency domain in order to suppress speckle noise in radar data, improve the signal-to-noise ratio of an image; the radiation correction is accomplished using look-up table data provided by the radar data; the geometric correction is completed by adopting a rational polynomial model provided by radar data and actual coordinates of control points; the filtering is accomplished using a normalized Freeman decomposition method.
5. The method for identifying exposed and hidden fracture structures of granite uranium mining areas according to claim 1, wherein the method comprises the following steps: in the fifth step, the texture information extraction refers to extracting texture information of Radar Sat-2 radar data based on a filtering method of second-order probability statistics; the data fusion is to perform HSB conversion on the ETM+ color synthesized image obtained in the step four, replace the B component after the HSB conversion with the obtained Radar Sat-2 radar texture information data, and convert the replaced HSB image into an RGB color image.
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| KR102243782B1 (en) | 2016-10-28 | 2021-04-23 | 피피지 인더스트리즈 오하이오 인코포레이티드 | Coating to increase near-infrared detection distance |
| CN109685028B (en) * | 2018-12-28 | 2022-12-09 | 内蒙古工业大学 | River ice thickness inversion method based on complete polarization SAR data |
| CN113189656A (en) * | 2020-11-20 | 2021-07-30 | 核工业二〇八大队 | Method for delineating ore-forming target area of sandstone-type uranium ore |
| CN112465659B (en) * | 2020-11-24 | 2022-07-26 | 核工业北京地质研究院 | Method for constructing granite vault uranium mineralization mode |
| CN112464834B (en) * | 2020-12-02 | 2024-02-13 | 核工业北京地质研究院 | Multisource remote sensing identification method for different-period secondary rock mass in white sentry uranium mining area |
| CN112731549A (en) * | 2020-12-07 | 2021-04-30 | 核工业北京地质研究院 | Method for predicting sandstone-type uranium mineralization scenic spot based on multi-source geological information |
| CN112748479A (en) * | 2020-12-25 | 2021-05-04 | 核工业北京地质研究院 | Geophysical prospecting combination method for evaluating potential of known hydrothermal uranium deposit peripheral resources |
| CN112837268A (en) * | 2021-01-11 | 2021-05-25 | 黄河勘测规划设计研究院有限公司 | Collapse damage house disaster body information extraction-oriented multi-source remote sensing data fusion method |
| CN113406041B (en) * | 2021-05-31 | 2023-03-17 | 核工业北京地质研究院 | Method for obtaining key altered mineral combination of sodium-substituted rock type uranium ore |
| CN114200953B (en) * | 2021-10-28 | 2024-05-28 | 山东省物化探勘查院 | Pollutant diffusion channel identification method and system based on unmanned aerial vehicle aeromagnetic measurement |
| CN114415237B (en) * | 2022-01-24 | 2022-08-23 | 东北石油大学 | Sandstone-type uranium ore control fracture identification method and system based on three-dimensional seismic data |
| CN117746242A (en) * | 2023-12-20 | 2024-03-22 | 中国科学院空天信息创新研究院 | Remote sensing prospecting method, device, equipment and medium based on multi-metal deposit remote sensing prospecting model |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103512661A (en) * | 2012-12-07 | 2014-01-15 | 核工业北京地质研究院 | Method for quickly identifying buried fault structure of salt lake water areas based on remote-sensing images |
| CN104076393A (en) * | 2014-07-10 | 2014-10-01 | 核工业北京地质研究院 | Recognizing method for concealed mineralization tectonic zone of granite type uranium deposit |
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| CN103512661A (en) * | 2012-12-07 | 2014-01-15 | 核工业北京地质研究院 | Method for quickly identifying buried fault structure of salt lake water areas based on remote-sensing images |
| CN104076393A (en) * | 2014-07-10 | 2014-10-01 | 核工业北京地质研究院 | Recognizing method for concealed mineralization tectonic zone of granite type uranium deposit |
Non-Patent Citations (1)
| Title |
|---|
| 王俊虎 ; 张杰林 ; 张静波 ; .基于热红外数据的花岗岩铀矿田地表SiO_2含量反演及其铀成矿要素识别.地球信息科学学报.2010,第12卷(第1期),全文. * |
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