CN111402194B - Method suitable for identifying exposed and hidden fracture structure of granite uranium mining area - Google Patents

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

A method suitable for identification of exposed and hidden fault structures in granite uranium mineralization areas

Technical field

The invention belongs to the technical field of geological information extraction, and specifically relates to a method suitable for identifying exposed and hidden fault structures in granite uranium mineralization areas.

Background technique

Granite uranium deposits refer to hydrothermal deposits that have a close spatial and genetic relationship with granite bodies. They occur within a certain range within the rock mass or its periphery. Granite uranium deposits are widely distributed in my country. According to relevant literature statistics, my country is the country with the most developed, most diverse and most widely distributed granite-type uranium deposits in the world. Granite uranium deposits can be said to be the main type of uranium deposits in my country.

Fault structure is one of the main factors controlling the formation and distribution of uranium deposits in granite. It not only provides migration channels and gathering places for ore-containing solutions, but also creates necessary physical and chemical conditions for the redistribution and enrichment of mineral-forming minerals. However, vegetation and Quaternary coverage in granite uranium mineralization areas are relatively high, with some fault structures exposed and some hidden. Therefore, accurate identification of exposed and hidden fault structures has important guiding significance for the analysis and prospect prediction of granite uranium mineralization environments.

Nowadays, the technical methods for identifying exposed and hidden fault structures mainly include conventional geological methods (such as petrology, mineralogy methods, field measurement methods, etc.), exploration geophysics (such as gravity, electromagnetic, etc.), and exploration geochemistry (such as geophysical methods). Gas, The labor and economic costs are high, the cycle is long, and the detection range is limited, which is not conducive to large-scale promotion and use. As an advanced technical method to modernize geological work in my country, traditional fault structure remote sensing identification technology has also played an active role in exposed and hidden fault structures. However, due to the low spatial resolution of remote sensing images, the interpretation is uncertain. Disadvantages such as high density and difficulty in identifying hidden fault structures have been questioned by many geoscientists in practical applications.

With the continuous improvement of the spatial resolution of remote sensing data, the improvement of technical means, and the deepening of the application of radar remote sensing, it has become possible to accurately identify exposed and hidden fault structures using multi-source remote sensing technology. Therefore, in view of the current situation that granite uranium mineralization areas have both exposed and hidden fault structures, it is very necessary to develop a method suitable for identifying exposed and hidden fault structures in granite uranium mineralization areas.

Contents of the invention

The technical problem to be solved by this invention is to provide a method suitable for identifying exposed and hidden fracture structures in granite uranium mineralization areas, which can quickly and accurately identify fracture structures and reduce work costs.

In order to solve the above technical problems, the present invention is a method suitable for identifying exposed and hidden fault structures in granite uranium mineralization areas, which in turn includes the following steps:

Step 1. Acquisition of optical and radar remote sensing data: Select ETM+ optical remote sensing data and RadarSat2 radar remote sensing data that cover a certain granite uranium mineralization area in my country and have high data collection quality;

Step 2. Optical remote sensing data preprocessing: Preprocess the ETM+ optical remote sensing data obtained in step 1, including radiation correction, geometric correction and noise removal, and obtain the preprocessed ETM+ optical remote sensing data;

Step 3. Preprocessing of radar remote sensing data: Preprocess the RadarSat-2 radar remote sensing data obtained in step 1, including focusing, multi-view, radiation correction, geometric correction, and filtering, and obtain the preprocessed RadarSat-2 radar remote sensing data. ;

Step 4. Optical remote sensing data processing and information extraction: perform three-band color synthesis, color and full-color data fusion on the ETM+ optical remote sensing data obtained in step 2, obtain color fusion images, and extract texture information from a single band of the ETM+ optical remote sensing data. , obtain the texture information image;

Step 5. Radar remote sensing data information extraction and fusion with optical remote sensing data: Extract texture information from the RadarSat-2 radar remote sensing data obtained in step 3, and perform data fusion with the three-band color composite image of the ETM+ optical remote sensing data obtained in step 4. , obtain optical and radar data fusion images that contain not only the electromagnetic reflection spectrum characteristics of different rocks, but also texture information such as topography and landforms;

Step 6. Construction of remote sensing identification marks for exposed fracture structures: When the following marks are met simultaneously on the three-band color image of ETM+ optical remote sensing data obtained in step 4, it can be determined as an exposed fracture structure. Sign 1: Specific linear shadow pattern characteristics are present. The rock mass and strata in the image are cut or staggered, and the stratigraphic shadow patterns are discontinuous and chaotic. Sign 2: Shown as a shadow on the color fusion image of optical and radar data obtained in step 5. Closely connected with the bright colors, the landform is in the shape of a broken mountain pass. The rocks are broken and scattered into ridges, partially forming one-sided mountains. Silicified zones develop on the top of the mountain where the fault passes, which has strong erosion resistance and forms a specific terrain. Mark 3: Along the mountain The fault surface has a thin soil covering layer, sparse vegetation, and undeveloped water systems.

Step 7. Construction of hidden fault structure remote sensing identification marks: The following marks appear in the single-band texture information extraction image of the ETM+ optical remote sensing data obtained in step 4: Mark 1: An obvious "barb-shaped" river system, with multiple water system characteristics. A tributary intersects the main stream at an obtuse angle. The existence of this abnormal river channel is a typical sign of fault control. Sign 2: There are obvious confluence concentrations on both sides of the main river; the following signs appear in the texture information extraction image of RadarSat-2 radar remote sensing data obtained in step five: Sign 1: Obvious meandering curves with dark tones are abnormal; Sign 2: The soil cover is generally less than a certain thickness and vegetation develops. When the above four signs are met at the same time, it can be determined to be a hidden fracture structure.

Step 8. Identification of exposed and hidden fault structures: Under the geographic information system software platform, based on the exposed fault structure identification mark constructed in step 6, identify all exposed fault structures within the range of remote sensing images, and use specific colors to identify them. Line marking; Based on the hidden fault structure identification mark constructed in step 7, all hidden fault structures within the range of the above remote sensing images are identified and marked with dotted lines of specific colors; all solid lines and dotted lines marked with specific colors are identified exposures and hidden fault structures.

The collection time of optical and radar remote sensing data is noon, when the sky is cloudless and the signal-to-noise ratio is high; the optical remote sensing data is transmitted by NASA, with a maximum spatial resolution of 15 meters and visible-shortwave-thermal infrared 8 Landsat7 ETM+ data of several bands; radar remote sensing data refers to the high-resolution RadarSat-2 synthetic aperture imaging radar data launched by the Canadian Space Agency equipped with C-band and frequency 5.4GHZ sensors. The data adopts full polarization fine mode and nominal resolution The rate is 8 meters.

In the second step, the radiation correction is completed using the radiation regression analysis method, the geometric correction is completed using the polynomial correction method, and the noise removal is completed using the median filtering method.

In the third step, focusing refers to processing the original data of the acquired radar data and directly outputting single-view complex product data; multi-view refers to suppressing the coherent speckle noise in the radar data and improving the signal-to-noise ratio of the image. The processing is carried out in the transverse frequency domain; the radiation correction is completed using the lookup table data provided by the radar data; the geometric correction is completed using the rational polynomial model and the actual coordinates of the control points provided by the radar data; the filtering is completed using the normalized Freeman decomposition method.

In the fourth step, three-band color synthesis refers to performing color transformation on the seventh, fifth, and second bands of ETM+ remote sensing data, and forming a color image through contrast stretching; data fusion refers to performing color transformation based on principal component analysis. The above synthesized three-band color image is fused with the eighth band of ETM+ remote sensing data to obtain an ETM+ color fusion image with a spatial resolution of 15 meters; single-band texture information extraction refers to the filtering method based on probability and statistics for the second band of ETM+ remote sensing data. Texture information is extracted.

In step five, texture information extraction refers to extracting texture information from RadarSat-2 radar data using a filtering method based on second-order probability statistics; data fusion refers to HSB transformation of the ETM+ color composite image obtained in step four, and then The RadarSat-2 radar texture information data obtained above is used to replace the B component after HSB transformation, and the replaced HSB image is converted into an RGB color image.

In step six, the ETM+ three-band color image refers to the color composite image of the seventh, fifth, and second bands of ETM+; the specific linear shadow pattern characteristics refer to the widening and narrowing, appearing and disappearing, and discontinuity. The continuously extending shadow pattern characteristics; the color fusion image of optical and radar data refers to the image that is the fusion of the color composite image of the seventh, fifth, and second bands of ETM+ and the RadarSat-2 radar texture information data; the specific terrain refers to the "potato" "Ridge" shaped terrain.

In the seventh step, the ETM+single-band texture information extraction image refers to the ETM+second-band texture information extraction image; multiple tributaries intersecting the main stream at an obtuse angle means that the acute angle between the main stream and the tributaries points in the opposite direction to the flow direction; a specific thickness refers to the thickness Less than 30cm.

In step eight, the geographic information system software refers to general geographic mapping software such as ARCGIS and MapGIS; the remote sensing image range refers to the ETM with a spatial resolution of 15 meters obtained in step four + the seventh, fifth, and second bands. Color fusion image range; the specific color refers to red, the solid line represents exposed fault structures, and the dotted line represents hidden fault structures.

The beneficial technical effects of the present invention are:

(1) The present invention provides a method suitable for identifying exposed and hidden fracture structures in granite uranium mineralization areas, which can quickly identify exposed and hidden fracture structures in granite uranium mineralization areas, greatly reducing the time required for geological surveys of fracture structures and geophysical and geochemical exploration methods. cost of detection;

(2) The present invention provides a method suitable for identifying exposed and hidden fault structures in granite uranium mineralization areas. It is of great significance for analyzing the uranium mineralization environment of granite uranium mineralization areas, and also provides information for the deployment of uranium exploration work in the area. important basis.

Detailed ways

The present invention will be further described in detail below with reference to examples.

The present invention is a method suitable for identifying outcrops and hidden fault structures in granite uranium mineralization areas, including the following steps:

Step 1. Obtain optical and radar remote sensing data. Select a granite uranium mineralization area covering a certain granite uranium deposit (spot) in my country, with high vegetation and Quaternary coverage and developed fault structures. The optical remote sensing data was launched by NASA with the maximum spatial resolution. Landsat7 ETM+ optical remote sensing data with a frequency of 15 meters and 8 bands of visible, shortwave and thermal infrared; the radar remote sensing data is captured by a C-band (frequency 5.4GHZ) sensor launched by the Canadian Space Agency, and adopts full polarization fine mode , RadarSat-2 synthetic aperture imaging radar remote sensing data with a nominal resolution of 8 meters; the data collection time is noon, the sky is cloudless and the signal-to-noise ratio is high;

Step 2: Optical remote sensing data preprocessing. For the ETM+ optical remote sensing data obtained in step 1, the radiation regression analysis method is used to complete the radiation correction, the polynomial correction method is used to complete the geometric correction, and the median filtering method is used to complete preprocessing such as noise removal, and the preprocessed ETM+ optical remote sensing data is obtained;

Step 3: Preprocessing of radar remote sensing data. Process the RadarSat-2 radar remote sensing data obtained in step 1 and directly output the single-view complex product data. In order to suppress the coherent speckle noise in the radar data and improve the signal-to-noise ratio of the image, further processing is performed in the transverse frequency domain; using The lookup table data provided by the radar data is used to complete the radiation correction, the rational polynomial model and the actual coordinates of the control points provided by the radar data are used to complete the geometric correction, the normalized Freeman decomposition method is used to complete the filtering process, and the preprocessed RadarSat-2 radar remote sensing data is obtained. ;

Step 4: Optical remote sensing data processing and information extraction. Color transform the seventh, fifth, and second bands of the ETM+ optical remote sensing data obtained in step 2, and form a color image through contrast stretching. Based on the principal component analysis method, the above synthesized three-band color image and ETM+ optical The eighth band of remote sensing data is fused to obtain an ETM+752 color fusion image with a spatial resolution of 15 meters. The filtering method based on probability statistics is used to extract the texture information of the second band of the ETM+ remote sensing data to obtain the texture information image;

Step 5: Extract radar remote sensing data information and fuse it with optical remote sensing data. The filtering method based on second-order probability statistics is used to extract texture information from the RadarSat-2 radar remote sensing data obtained in step three, and obtain the radar data texture information image. HSB transformation is performed on the ETM+752 color fusion image obtained in step four, and then The radar texture information image replaces the B component after HSB transformation, and the replaced HSB image is converted into an RGB color image to obtain an optical and radar data fusion image that contains not only the electromagnetic reflection spectrum characteristics of different rocks, but also texture information such as topography and landforms;

Step 6: Construction of remote sensing identification marks for exposed fault structures. The ETM+752 color fusion image obtained in step 4 shows the characteristics of shadow patterns that are wide and narrow, disappear and appear, and extend intermittently. The rock mass and formation images are cut or staggered, and the formation shadow patterns are discontinuous and chaotic; in The fused ETM+752 color image obtained in step five and the RadarSat-2 texture information image show that shadows and bright tones are closely connected. The landform is in the shape of a broken mountain pass. The rocks are broken and scattered into ridges, and some parts form single-sided mountains. , the top of the mountain where the fault passes develops a silicified zone, which has strong erosion resistance, forming a "potato ridge"-like terrain; there is a thin soil covering layer along the mountain fault surface, with sparse vegetation and undeveloped water systems; when the above signs are met at the same time, it can Identified as exposed fracture structures;

Step 7: Construction of remote sensing identification marks for hidden fault structures. On the ETM+ second band texture information extraction image obtained in step 4, there is an obvious "barb-shaped" river system. The water system is characterized by multiple tributaries intersecting the main stream at obtuse angles, that is, the acute angle where the main stream intersects the tributaries points in the opposite direction to the flow direction. , the existence of this abnormal river channel is a typical sign of fault control. In addition, there are obvious confluence phenomena on both sides of the main river channel, indicating that the fault zone has caused sharp folds in the strata; in the RadarSat-2 texture information extraction image obtained in step 5, there are obvious sinuous curves with dark tones. Abnormal; the thickness of the soil cover in this type of area is generally less than 30cm, and vegetation is developed. When the above signs are met at the same time, it can be identified as a hidden fracture structure;

Step 8: Identify exposed and hidden fault structures. Under general geographical information system software platforms such as ARCGIS and MapGIS, based on the exposed fault structure identification marks constructed in step six, all exposed fault structures in the ETM+752 color fusion image range with a spatial resolution of 15 meters are identified and used Marked by a red solid line; based on the hidden fault structure identification mark constructed in step 7, all hidden fault structures within the range of the above remote sensing images are identified and marked with a red dotted line; all marked red solid lines and red dotted lines are identified exposures and hidden fault structures.

The embodiments of the present invention have been described in detail above. The above-mentioned embodiments are only the best embodiments of the present invention. However, the present invention is not limited to the above-mentioned embodiments. Within the scope of knowledge possessed by those of ordinary skill in the art, other embodiments may also be used. Various changes can be made without departing from the spirit of the invention.

Claims (5)

1. A method suitable for identifying exposed and hidden fault structures in granite uranium mineralization areas, which is characterized by including the following steps: Step 1. Acquisition of optical and radar remote sensing data: Select ETM+ optical remote sensing data and RadarSat2 radar remote sensing data that cover a certain granite uranium mineralization area in my country and have high data collection quality; Step 2. Optical remote sensing data preprocessing: Preprocess the ETM+ optical remote sensing data obtained in step 1, including radiation correction, geometric correction and noise removal, and obtain the preprocessed ETM+ optical remote sensing data; Step 3. Preprocessing of radar remote sensing data: Preprocess the RadarSat-2 radar remote sensing data obtained in step 1, including focusing, multi-view, radiation correction, geometric correction, and filtering, and obtain the preprocessed RadarSat-2 radar remote sensing data. ; Step 4. Optical remote sensing data processing and information extraction: perform three-band color synthesis, color and full-color data fusion on the ETM+ optical remote sensing data obtained in step 2, obtain color fusion images, and extract texture information from a single band of the ETM+ optical remote sensing data. , obtain the texture information image; In the fourth step, three-band color synthesis refers to performing color transformation on the seventh, fifth, and second bands of ETM+ remote sensing data, and forming a color image through contrast stretching; data fusion refers to performing color transformation based on principal component analysis. The above synthesized three-band color image is fused with the eighth band of ETM+ remote sensing data to obtain an ETM+ color fusion image with a spatial resolution of 15 meters; single-band texture information extraction refers to the filtering method based on probability and statistics for the second band of ETM+ remote sensing data. Texture information is extracted; Step 5. Radar remote sensing data information extraction and fusion with optical remote sensing data: Extract texture information from the RadarSat-2 radar remote sensing data obtained in step 3, and perform data fusion with the three-band color composite image of the ETM+ optical remote sensing data obtained in step 4. , obtain optical and radar data fusion images that contain not only the electromagnetic reflection spectrum characteristics of different rocks, but also terrain and landform texture information; Step 6. Construction of remote sensing identification marks for exposed fracture structures: When the following marks are met simultaneously on the three-band color image of the ETM+ optical remote sensing data obtained in step 4, it is determined to be an exposed fracture structure; Mark 1: Shows a specific line shape Shadow pattern characteristics: the rock mass and strata in the image are cut or staggered, and the formation shadow patterns are discontinuous and chaotic; Mark 2: The color fusion image of optical and radar data obtained in step five shows that shadows and bright tones are closely connected, and the landform is In the form of a broken mountain pass, the rocks are broken and scattered into ridges and hills, partially forming a single-sided mountain. A silicified zone develops on the top of the mountain where the fault passes, which has strong erosion resistance and forms a specific terrain; sign 3: There is thin soil coverage along the mountain fault surface. layer, with sparse vegetation and undeveloped water systems; In step six, the ETM+ three-band color image refers to the color composite image of the seventh, fifth, and second bands of ETM+; the specific linear shadow pattern characteristics refer to the widening and narrowing, appearing and disappearing, and discontinuity. The continuously extending shadow pattern characteristics; the color fusion image of optical and radar data refers to the image that is the fusion of the color composite image of the seventh, fifth, and second bands of ETM+ and the RadarSat-2 radar texture information data; the specific terrain refers to the "potato" "Ridge" shaped terrain; Step 7. Construction of hidden fault structure remote sensing identification marks: The following marks appear in the single-band texture information extraction image of the ETM+ optical remote sensing data obtained in step 4: Mark 1: An obvious "barb-shaped" river system, with multiple water system characteristics. A tributary intersects with the main stream at an obtuse angle. The existence of this river system is a typical sign of fault control; Sign 2: There are obvious confluence concentrations on both sides of the main river channel; in the texture information extraction image of RadarSat-2 radar remote sensing data obtained in step 5 The following signs are present: Sign 1: Obvious meandering curves with abnormal dark tones; Sign 2: The soil covering layer is less than a certain thickness and vegetation is developed; when the above four signs are met at the same time, it is determined to be a hidden fracture structure; In the seventh step, the ETM+single-band texture information extraction image refers to the ETM+second-band texture information extraction image; multiple tributaries intersecting the main stream at an obtuse angle means that the acute angle between the main stream and the tributaries points in the opposite direction to the flow direction; a specific thickness refers to 30cm ; Step 8. Identification of exposed and hidden fault structures: Under the geographic information system software platform, based on the exposed fault structure identification mark constructed in step 6, identify all exposed fault structures within the range of remote sensing images, and use specific colors to identify them. Line marking; Based on the hidden fault structure identification mark constructed in step 7, all hidden fault structures within the range of the above remote sensing images are identified and marked with dotted lines of specific colors; all solid lines and dotted lines marked with specific colors are identified exposures and hidden fault structures; In step eight, the geographical information system software refers to ARCGIS and MapGIS general geographical mapping software; the remote sensing image range refers to the ETM with a spatial resolution of 15 meters obtained in step four + the color of the seventh, fifth and second bands. Fusion image range; the specific color refers to red, the solid line represents the exposed fault structure, and the dotted line represents the hidden fault structure. 2. A method suitable for identifying outcrops and hidden fault structures in granite uranium mineralization areas according to claim 1, characterized in that: the collection time of optical and radar remote sensing data is noon, and the sky is cloudless and signal-free. The noise ratio is high; the optical remote sensing data refers to the Landsat7 ETM+ data launched by NASA with a maximum spatial resolution of 15 meters and 8 bands of visible, shortwave and thermal infrared; the radar remote sensing data refers to the C Band, frequency 5.4GHZ sensor high-resolution RadarSat-2 synthetic aperture imaging radar data, the data uses full polarization fine mode, nominal resolution is 8 meters. 3. A method suitable for identifying outcrops and hidden fault structures in granite uranium mineralization areas according to claim 1, characterized in that: in the second step, radiation correction is completed using radiation regression analysis, and geometric correction uses polynomials. The correction method is completed, and the noise removal is completed using the median filtering method. 4. A method suitable for identifying outcrops and hidden fault structures in granite uranium mineralization areas according to claim 1, characterized in that: in step three, focusing refers to the original data of the acquired radar data. Processing, directly outputting single-view complex product data; multi-view refers to processing in the transverse frequency domain in order to suppress coherent patch noise in radar data and improve the signal-to-noise ratio of images; radiation correction uses lookup table data provided by radar data To complete; the geometric correction is completed using the rational polynomial model provided by the radar data and the actual coordinates of the control points; the filtering is completed using the normalized Freeman decomposition method. 5. A method suitable for identifying outcrops and hidden fault structures in granite uranium mineralization areas according to claim 1, characterized in that: in step five, texture information extraction refers to a filtering method based on second-order probability statistics. Extract the texture information of RadarSat-2 radar data; data fusion refers to HSB transformation of the ETM+ color composite image obtained in step 4, and then replace the B component after HSB transformation with the RadarSat-2 radar texture information data obtained above. Convert the replaced HSB image into an RGB color image.