AU2020101520A4 - Method for identifying exposed and buried fault structures in granite-type uranium province - Google Patents
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Abstract
The present invention belongs to the technical field of geological information extraction, and in
particular relates to a method for identifying exposed and buried fault structures in a granite-type
uranium province.
Description
METHOD FOR IDENTIFYING EXPOSED AND BURIED FAULT STRUCTURES IN GRANITE-TYPE URANIUM PROVINCE TECHNICAL FIELD The present invention belongs to the technical field of geological information extraction, and in particular relates to a method for identifying exposed and buried fault structures in a granite-type uranium province.
BACKGROUND Granite-type uranium deposits refer to hydrothermal deposits that have close spatial and genetic relationships with granite, which occur within a certain range in the pluton or on the periphery thereof. China's uranium deposits are dominated by granite-type uranium deposits. Relevant literature shows that China has the most developed, most diverse and most widely distributed granite-type uranium deposits in the world.
The fault structure is one of the main factors for the formation and distribution of granite-type uranium deposits. It provides migration channels and accumulation sites for ore-bearing solutions, and also creates the necessary physiochemical conditions for the redistribution and enrichment of ore-forming materials. The granite-type uranium province features high vegetation cover and thick Quaternary sediments, where some fault structures are exposed and some are buried. The accurate identification of the exposed and buried fault structures is of great significance for analyzing the metallogenic environment of granite-type uranium and predicting the exploration potential thereof.
Currently, the identification methods mainly include conventional geological methods (such as petrology, mineralogy and field measurement), exploration geophysics (such as gravimeter and magnetometer), exploration geochemistry (such as geogas prospectingX-ray fluorescence (XRF)_radon survey, emanation survey and ore-forming element geochemistry) and biological methods. These methods have played a positive and effective role in identifying exposed and buried fault structures, but their use is limited due to the high cost, a long cycle and a small detection range. The traditional remote sensing (RS) technique is an advanced method to realize the geological modernization in China. However, although it has played an active role in the identification of exposed and buried fault structures, its practical application is also limited due to the low spatial resolution of RS images, high interpretation uncertainty and difficulty in identifying buried fault structures.
With the increasing spatial resolution of the RS data, the increasingly perfect technical means and the deepening of radar remote sensing (RRS) applications, it has become possible to use the multi-source remote sensing (MSRS) technique to accurately identify exposed and buried fault structures. Since there are both exposed and buried fault structures developed in the granite-type uranium province, it is necessary to provide a method for identifying the exposed and buried fault structures in the granite-uranium province.
SUMMARY In order to solve the above technical problem, the present invention provides a method for identifying exposed and buried fault structures in a granite-type uranium province. The present invention quickly and accurately identifies the fault structures and reduces the working costs, or provides a customer with a useful choice.
To resolve the above problems, the present invention provides a method for identifying exposed and buried fault structures in a granite-type uranium province, including the following steps:
step 1: obtaining optical remote sensing (ORS) data and radar remote sensing (RRS) data: selecting high-quality enhanced thematic mapper plus (ETM*) ORS data and RadarSat-2 RRS data covering a granite-type uranium province in China;
step 2: preprocessing the ORS data: preprocessing the ETM* ORS data obtained in step 1 to obtain preprocessed ETM* ORS data, the preprocessing including radiation correction, geometric correction and noise reduction;
step 3: preprocessing the RRS data: preprocessing the RadarSat-2 RRS data obtained in step 1 to obtain preprocessed RadarSat-2 RRS data, the preprocessing including focusing, multi-look, radiation correction, geometric correction and filtering;
step 4: processing the ORS data and extracting information: performing three-band color synthesis on the ETM* ORS data obtained in step 2, fusing a color image with panchromatic data to obtain a color fused image, and extracting texture information from a single band of the ETM* ORS data to obtain a texture information image;
step 5: extracting information of the RRS data and fusing with the ORS data: extracting texture information of the RadarSat-2 RRS data obtained in step 3, and fusing the data with the three-band color synthesized image of the ETM* ORS data obtained in step 4 to obtain an ORS/RRS fused image including electromagnetic spectral characteristics of different rock and texture information such as topography;
step 6: constructing RS identification marks for an exposed fault structure, and identifying a fault structure covered by the three-band color image of the ETM* ORS data obtained in step 4 as exposed when all the following marks are met: Mark 1: the color image presents particular linear shadow textures, the rock mass and strata on the image are cut or staggered, and the textures of the strata are discontinuous and disorderly; Mark 2: the color fused image of the ORS/RRS data obtained in step 5 shows that: shadows and bright tones are closely connected; the landform is in the form of a broken mountain pass; the rock is broken and scattered into ridges, and a cuesta is formed locally; the top of a mountain through which a fault passes develops a silicified zone, which has strong resistance to erosion and forms a particular terrain; and Mark 3: a mountain fracture surface features a thin soil cover, scarce vegetation and undeveloped water system; step 7: constructing RS identification marks for a buried fault structure, where the single-band texture information image of the ETM* ORS data obtained in step 4 shows the following marks: Mark 1: an obvious "barb-shaped" river system, where multiple tributaries intersect a main stream at obtuse angles; the existence of the abnormal river system is a typical sign of fault control; and Mark 2: clear confluence on both sides of the main channel; the texture information image of the RadarSat-2 RRS data obtained in step 5 shows the following marks: Mark 1: a clear sinusoidal dark tone anomaly; and Mark 2: a soil cover generally less than a particular thickness, and developed vegetation; a fault structure is identified as buried when all these four marks are met; and step 8: identifying exposed and buried fault structures: using a geographic information system (GIS) software platform to identify the fault structures as follows: identifying all exposed fault structures covered by the RS image based on the exposed fault structure identification marks constructed in step 6, and marking with solid lines of a particular color; identifying all buried fault structures covered by the RS image based on the buried fault structure identification marks constructed in step 7, and marking with dotted lines of a particular color, where all the marked solid lines and dotted lines of the particular color represent the identified exposed and buried fault structures.
The ORS data and the RRS data are acquired at noon, when the sky is cloud-free and the signal to-noise ratio (SNR) is high; the ORS data are captured by the Landsat7 ETM' of the National Aeronautics and Space Administration (NASA), which have a maximum spatial resolution of 15 m and eight bands including visible (V), shortwave (SW) and thermal infrared (TIR); the RRS data are captured by a synthetic aperture radar (SAR, operating at C-band at frequency 5.4 GHZ) aboard the RadarSat-2 launched by the Canadian Space Agency (CSA), using a fully polarimetric (fine) mode and an 8 m nominal resolution.
In step 2, the radiation correction is performed by radiation regression analysis, the geometric correction is performed by polynomial correction, and the noise reduction is performed by median filtering.
In step 3, the focusing refers to a process performed on the obtained raw RSS data to directly output single-look complex (SLC) product data; the multi-look refers to a process performed in a transverse frequency domain to suppress speckle noise in the RRS data and improve the SNR of the image; the radiation correction is performed with lookup table data provided by the RRS data; the geometric correction is performed with a rational polynomial model and actual control point coordinates provided by the RRS data; the filtering is performed by a normalized Freeman decomposition method.
In step 4, the three-band color synthesis refers to color transformation performed on seventh, fifth and second bands of the ETM* ORS data to form a color image by contrast stretching; the data fusion refers to fusion of the synthesized three-band color image with an eighth band of the ETM* ORS data by principal component analysis (PCA) to obtain an ETM* color fused image with a spatial resolution of 15 m; the single-band texture information extraction refers to extraction of texture information of the second band of the ETM* ORS data by using a probability-based filtering method.
In step 5, the texture information extraction refers to extraction of texture information of the RadarSat-2 RRS data by using a filtering method based on second-order probability; the data fusion includes: subjecting the ETM* color fused image obtained in step 4 to hue, saturation and brightness (HSB) conversion, replacing a B component after the HSB conversion with the obtained texture information data of the RadarSat-2 RRS data, and converting the HSB image after replacement into a red, green and blue (RGB) color image.
In step 6, the ETM* three-band color image refers to a color synthesized image of the seventh, fifth and second bands of the ETM* ORS data; the particular linear shadow textures refer to shadow textures that have uneven width, break, and intermittently extend; the color fused image of the ORS/RRS data refers to an image formed by fusing the color synthesized image of the seventh, fifth and second bands of the ETM* ORS data with the texture information data of the RadarSat-2 RRS data; the particular terrain refers to a terrain shaped like a potato ridge.
In step 7, the single-band texture information image of the ETM* ORS data refers to a texture information image of the second band of the ETM* ORS data; the intersection of the multiple tributaries with the main stream at obtuse angles indicates that acute angles at which the main stream intersects the tributaries point in opposite directions; the particular thickness refers to a thickness of less than 30 cm.
In step 8, the GIS software refers to general geographic mapping software such as ARCGIS or MapGIS; the RS image refers to the color synthesized image of the seventh, fifth and second bands of the ETM* ORS data obtained in step 4, which has a spatial resolution of 15 m; the particular color refers to red; the solid lines represent exposed fault structures, and the dotted lines represent buried fault structures.
The present invention has the following beneficial technical effects: (1) The method provided by the present invention quickly identifies the exposed and buried fault structures in the granite-type uranium province, greatly reducing the costs of geological survey and geophysical/geochemical exploration of fault structures.
(2) The method provided by the present invention is of great significance for analyzing the metallogenic environment of the granite-type uranium province, and provides an important basis for the uranium ore prospecting work in the granite-type uranium province. DETAILED DESCRIPTION The present invention is described in further detail below with reference to the examples.
The present invention provides a method for identifying exposed and buried fault structures in a granite-type uranium province, including the following steps:
Step 1: obtain optical remote sensing (ORS) data and radar remote sensing (RRS) data. The obtained data covered a granite-type uranium province with high vegetation cover and thick Quaternary sediments in China. Granite-type uranium deposits had been found in this granite-type uranium province where fault structures were developed. The ORS data were captured by the Landsat7 enhanced thematic mapper plus (ETM*) of the National Aeronautics and Space Administration (NASA), which had a maximum spatial resolution of 15 m and eight bands including visible (V), shortwave (SW) and thermal infrared (TIR). The RRS data were captured by a synthetic aperture radar (SAR, operating at C-band at frequency 5.4 GHZ) aboard the RadarSat-2 launched by the Canadian Space Agency (CSA), using a fully polarimetric (fine) mode and an 8 m nominal resolution. The data were all acquired at noon, when the sky was cloud-free and the signal-to-noise ratio (SNR) was high.
Step 2: preprocess the ORS data. The ETM* ORS data obtained in Step 1 were preprocessed to obtain preprocessed ETM* ORS data. The preprocessing included radiation correction by radiation regression analysis, geometric correction by polynomial correction and noise reduction by median filtering.
Step 3: preprocess the RRS data. The RadarSat-2 RRS data obtained in Step 1 were preprocessed to directly output single-look complex (SLC) product data. In order to suppress the speckle noise in the RRS data and improve the SNR of the image, further processing was performed in a transverse frequency domain. Lookup table data provided by the RRS data were used for radiation correction, a rational polynomial model and actual control point coordinates provided by the RRS data were used for geometric correction, and a normalized Freeman decomposition method was used for a filtering process. In this way, preprocessed RadarSat-2 RRS data were obtained.
Step 4: process the ORS data and extract information. Color transformation was performed on seventh, fifth, and second bands of the ETM* ORS data obtained in Step 2 to form a color image by contrast stretching. The synthesized three-band color image was fused with an eighth band of the ETM* ORS data by principal component analysis (PCA) to obtain an ETM* 752 color fused image with a spatial resolution of 15 m. Texture information of the second band of the ETM* ORS data was extracted by using a probability-based filtering method, so as to obtain a texture information image.
Step 5: extract information of the RRS data and fuse with the ORS data. Texture information of the RadarSat-2 RRS data obtained in Step 3 was extracted by using a filtering method based on second-order probability to obtain a texture information image of the RRS data. The ETM* 752 color fused image obtained in Step 4 was subjected to hue, saturation and brightness (HSB) conversion. After the HSB conversion, a B component was replaced with the texture information image of the RRS data, and then the HSB image was converted into a red, green and blue (RGB) color image. In this way, an ORS/RRS fused image including electromagnetic spectral characteristics of different rock and texture information such as topography was obtained.
Step 6: construct RS identification marks for an exposed fault structure. The ETM* 752 color fused image obtained in Step 4 presented shadow textures that had uneven width, broke, and intermittently extended. The rock mass and strata were cut or staggered, and the textures of the strata were discontinuous and disorderly. The image obtained in Step 5 by fusing the ETM* 752 color image with the RadarSat-2 texture information image showed that shadows and bright tones were closely connected. The landform was in the form of a broken mountain pass. The rock was broken and scattered into ridges, and a cuesta was formed locally. The top of a mountain through which a fault passed developed a silicified zone, which had strong resistance to erosion and formed a terrain shaped like a potato ridge. A mountain fracture surface featured a thin soil cover, scarce vegetation and undeveloped water system. A fault structure was identified as exposed when all these marks were met.
Step 7: construct RS identification marks for a buried fault structure. The texture information image of the second band of the ETM* data obtained in Step 4 presented an obvious "barb-shaped" river system. Multiple tributaries intersected a main stream at obtuse angles, that is, the acute angles at which the main stream intersected the tributaries pointed in opposite directions. The existence of the abnormal river system was a typical sign of fault control. In addition, there was a clear confluence on both sides of the main channel, indicating that sharp folds of the stratum were caused at the passage of the fault zone. The RadarSat-2 texture information image obtained in Step 5 showed a clear sinusoidal dark tone anomaly, where the thickness of the soil covering this type of area was generally less than 30 cm, and the vegetation was developed. A fault structure was identified as buried when all these marks were met.
Step 8: identify exposed and buried fault structures. A general geographic information system (GIS) software platform, for example, ArcGIS or MapGIS, was used to identify the fault structures. All exposed fault structures covered by the ETMm 752 color fused image with a spatial resolution of m were identified based on the exposed fault structure identification marks constructed in Step 6, and marked with red solid lines. All buried fault structures covered by the RS image were identified based on the buried fault structure identification marks constructed in Step 7, and marked with red dotted lines. All the marked red solid lines and red dotted lines represented the identified exposed and buried fault structures.
The specific examples of the present invention are described in detail above. However, they are merely preferred examples of the present invention, and the present invention is not limited thereto. Those of ordinary skill in the art may further make various changes without departing from the spirit of the present invention.
Claims (5)
1. A method for identifying exposed and buried fault structures in a granite-type uranium province, including the following steps:
step 1: obtaining optical remote sensing (ORS) data and radar remote sensing (RRS) data: selecting high-quality enhanced thematic mapper plus (ETM*) ORS data and RadarSat-2 RRS data covering a granite-type uranium province in China; step 2: preprocessing the ORS data: preprocessing the ETM* ORS data obtained in step 1 to obtain preprocessed ETM* ORS data, the preprocessing including radiation correction, geometric correction and noise reduction;
step 3: preprocessing the RRS data: preprocessing the RadarSat-2 RRS data obtained in step 1 to obtain preprocessed RadarSat-2 RRS data, the preprocessing including focusing, multi-look, radiation correction, geometric correction and filtering;
step 4: processing the ORS data and extracting information: performing three-band color synthesis on the ETM* ORS data obtained in step 2, fusing a color image with panchromatic data to obtain a color fused image, and extracting texture information from a single band of the ETM* ORS data to obtain a texture information image; step 5: extracting information of the RRS data and fusing with the ORS data: extracting texture information of the RadarSat-2 RRS data obtained in step 3, and fusing the data with the three-band color synthesized image of the ETM* ORS data obtained in step 4 to obtain an ORS/RRS fused image including electromagnetic spectral characteristics of different rock and texture information such as topography;
step 6: constructing RS identification marks for an exposed fault structure, and identifying a fault structure covered by the three-band color image of the ETM* ORS data obtained in step 4 as exposed when all the following marks are met: Mark 1: the color image presents particular linear shadow textures, the rock mass and strata on the image are cut or staggered, and the textures of the strata are discontinuous and disorderly; Mark 2: the color fused image of the ORS/RRS data obtained in step 5 shows that: shadows and bright tones are closely connected; the landform is in the form of a broken mountain pass; the rock is broken and scattered into ridges, and a cuesta is formed locally; the top of a mountain through which a fault passes develops a silicified zone, which has strong resistance to erosion and forms a particular terrain; and Mark 3: a mountain fracture surface features a thin soil cover, scarce vegetation and undeveloped water system; step 7: constructing RS identification marks for a buried fault structure, wherein the single-band texture information image of the ETM* ORS data obtained in step 4 shows the following marks:
Mark 1: an obvious "barb-shaped" river system, where multiple tributaries intersect a main stream at obtuse angles; the existence of the abnormal river system is a typical sign of fault control; and Mark 2: clear confluence on both sides of the main channel; the texture information image of the RadarSat-2 RRS data obtained in step 5 shows the following marks: Mark 1: a clear sinusoidal dark tone anomaly; and Mark 2: a soil cover generally less than a particular thickness, and developed vegetation; a fault structure is identified as buried when all these four marks are met; and step 8: identifying exposed and buried fault structures: using a geographic information system (GIS) software platform to identify the fault structures as follows: identifying all exposed fault structures covered by the RS image based on the exposed fault structure identification marks constructed in step 6, and marking with solid lines of a particular color; identifying all buried fault structures covered by the RS image based on the buried fault structure identification marks constructed in step 7, and marking with dotted lines of a particular color, wherein all the marked solid lines and dotted lines of the particular color represent the identified exposed and buried fault structures.
2. The method for identifying exposed and buried fault structures in a granite-type uranium province according to claim 1, wherein the ORS data and the RRS data are acquired at noon, when the sky is cloud-free and the signal-to-noise ratio (SNR) is high; the ORS data are captured by the Landsat7 ETM' of the National Aeronautics and Space Administration (NASA), which have a maximum spatial resolution of 15 m and eight bands including visible (V), shortwave (SW) and thermal infrared (TIR); the RRS data are captured by a synthetic aperture radar (SAR, operating at C-band at frequency 5.4 GHZ) aboard the RadarSat-2 launched by the Canadian Space Agency (CSA), using a fully polarimetric (fine) mode and an 8 m nominal resolution.
3. The method for identifying exposed and buried fault structures in a granite-type uranium province according to claim 1, wherein in step 2, the radiation correction is performed by radiation regression analysis, the geometric correction is performed by polynomial correction, and the noise reduction is performed by median filtering.
4. The method for identifying exposed and buried fault structures in a granite-type uranium province according to claim 1, wherein in step 3, the focusing refers to a process performed on the obtained raw RSS data to directly output single-look complex (SLC) product data; the multi-look refers to a process performed in a transverse frequency domain to suppress speckle noise in the RRS data and improve the SNR of the image; the radiation correction is performed with lookup table data provided by the RRS data; the geometric correction is performed with a rational polynomial model and actual control point coordinates provided by the RRS data; the filtering is performed by a normalized Freeman decomposition method.
5. The method for identifying exposed and buried fault structures in a granite-type uranium province according to claim 1, wherein in step 4, the three-band color synthesis refers to color transformation performed on seventh, fifth and second bands of the ETM* ORS data to form a color image by contrast stretching; the data fusion refers to fusion of the synthesized three-band color image with an eighth band of the ETM* ORS data by principal component analysis (PCA) to obtain an ETM* color fused image with a spatial resolution of 15 m; the single-band texture information extraction refers to extraction of texture information of the second band of the ETM* ORS data by using a probability-based filtering method;
wherein in step 5, the texture information extraction refers to extraction of texture information of the RadarSat-2 RRS data by using afiltering method based on second-order probability; the data fusion includes: subjecting the ETM* color fused image obtained in step 4 to hue, saturation and brightness (HSB) conversion, replacing a B component after the HSB conversion with the obtained texture information data of the RadarSat-2 RRS data, and converting the HSB image after replacement into a red, green and blue (RGB) color image;
wherein in step 6, the ETM* three-band color image refers to a color synthesized image of the seventh, fifth and second bands of the ETM* ORS data; the particular linear shadow textures refer to shadow textures that have uneven width, break, and intermittently extend; the color fused image of the ORS/RRS data refers to an image formed by fusing the color synthesized image of the seventh, fifth and second bands of the ETM* ORS data with the texture information data of the RadarSat-2 RRS data; the particular terrain refers to a terrain shaped like a potato ridge;
wherein in step 7, the single-band texture information image of the ETM* ORS data refers to a texture information image of the second band of the ETM* ORS data; the intersection of the multiple tributaries with the main stream at obtuse angles indicates that acute angles at which the main stream intersects the tributaries point in opposite directions; the particular thickness refers to a thickness of less than 30 cm;
wherein in step 8, the GIS software refers to general geographic mapping software such as ARCGIS or MapGIS; the RS image refers to the color synthesized image of the seventh, fifth and second bands of the ETM* ORS data obtained in step 4, which has a spatial resolution of 15 m; the particular color refers to red; the solid lines represent exposed fault structures, and the dotted lines represent buried fault structures.
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| CN201911299408.2A CN111402194B (en) | 2019-12-17 | 2019-12-17 | Method suitable for identifying exposed and hidden fracture structure of granite uranium mining area |
| CN201911299408.2 | 2019-12-17 |
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| CN112465659A (en) * | 2020-11-24 | 2021-03-09 | 核工业北京地质研究院 | Method for constructing granite gneiss vault uranium mineralization mode |
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2019
- 2019-12-17 CN CN201911299408.2A patent/CN111402194B/en active Active
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| CN111402194B (en) | 2023-10-20 |
| CN111402194A (en) | 2020-07-10 |
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