A method, equipment, and medium for delineating gold prospecting target areas in areas with high vegetation cover.

Abstract

This application provides a method for delineating gold prospecting target areas in areas with high vegetation cover, relating to the field of mineralization prediction. The method includes: selecting poisoning information, geomorphic indices, tectonic buffer zones, tectonic density, lithological classification, and geochemistry as mineralization influencing factors based on the mineralization characteristics of high vegetation cover areas; then using the analytic hierarchy process (AHP) to determine the weights of each mineralization influencing factor layer; and finally delineating the mineralization target area based on the calculated mineralization potential values. This invention fully utilizes remote sensing, geological, and geochemical data, and employs the AHP for comprehensive analysis, improving the efficiency and accuracy of mineralization prediction.

Description

Technical Field

[0001] This application relates to the field of mineralization prediction, and in particular to a method, equipment and medium for delineating gold prospecting target areas in areas with high vegetation cover. Background Technology

[0002] Currently, mineral exploration in China has shifted from shallow to deep exploration, from searching for outcrops to prospecting in covered areas, and from low mountainous regions to mid-to-high mountain and deeply dissected areas. This has increased the difficulty of mineral exploration and necessitates more scientific and effective prospecting methods. Mineral exploration in vegetated areas faces numerous technical, data, and environmental challenges, requiring the comprehensive application of knowledge and techniques from multiple fields, including remote sensing, geological surveys, and environmental science, to overcome these difficulties.

[0003] Existing related technologies and patents:

[0004] Chinese patent "Metallogenic Prediction Method Based on Analytic Hierarchy Process (AHP)," application number CN201710939942.X, provides a metallogenic prediction method based on the AHP, specifically including: interpreting geological features based on remote sensing image data; delineating alteration anomaly zones based on remote sensing image data; delineating geochemical anomaly zones within alteration zones based on existing geological data and geochemical sediment measurements; using the geological feature interpretation results, alteration anomaly zone delineation results, and geochemical anomaly zone delineation results as metallogenic influencing factors, and calculating the weight of each metallogenic influencing factor using the AHP; overlaying the geological feature interpretation result map, alteration anomaly zone delineation result map, and geochemical anomaly zone delineation result map, calculating the comprehensive weight value of each pixel in the overlaid map according to the weight of each metallogenic influencing factor, and delineating the metallogenic prediction area based on the comprehensive weight value.

[0005] Chinese patent application number CN202310182665.8, entitled "A Method for Gold Exploration Controlled by Fault Structures in High-Altitude Permafrost Covered Areas," discloses a method for gold exploration in high-altitude permafrost covered areas where fault structures control gold deposits. First, based on the stratigraphic environment and well-developed fault structures favorable for gold mineralization, a favorable gold-forming section is selected within the high-altitude permafrost covered area to obtain a prospective exploration area. Then, sampling is conducted within the prospective area to compile geochemical anomaly maps of elements closely related to gold mineralization, thus obtaining geochemical anomaly zones for these elements. Remote sensing imagery is used to interpret the linear structures of the prospective exploration area. The obtained chemical anomaly maps and structural interpretation maps are overlaid, and the overlapping area between the geochemical anomaly zones and the remote sensing-interpreted linear structures is selected as the prospective target area. Finally, after obtaining the prospective target area, a field geological survey is conducted, employing detailed geophysical and geochemical exploration methods to determine the most favorable location for gold mineralization.

[0006] In densely vegetated high-altitude areas, the entire region is basically covered by vegetation, with very few outcrops in the field, making it extremely difficult to conduct on-site exploration and rock sampling. This is not conducive to the research of prospectors on the metallogenic tectonic background, genesis, and ore-controlling factors. The vegetation cover may also hinder personnel from entering designated locations, making it difficult to obtain geological samples, which greatly hinders subsequent mineral exploration information and mineralization prediction, and makes it impossible to locate mineral deposits.

[0007] While remote sensing can overcome sampling difficulties, it still has limitations. First, vegetation can obscure the surface, making it difficult for remote sensing data to penetrate below the surface. Furthermore, remote sensing images of vegetated areas may contain a large amount of interfering information, such as vegetation reflections and shadows, making it difficult to interpret the structural information of highly vegetated areas. Second, when using spectral remote sensing to extract alteration information, vegetation cover prevents the acquisition of spectral information from direct rock strata. Moreover, the presence of ecological water in vegetation overlaps with the spectral absorption characteristics of hydroxyl alteration minerals being explored, making remote sensing methods that perform well in exposed areas unsuitable for high-vegetation-coverage areas. Summary of the Invention

[0008] The purpose of this invention is to solve the problems mentioned in the background art above, and to provide a method, equipment and medium for delineating gold prospecting target areas in highly vegetated areas with low prospecting costs and high accuracy.

[0009] The above-mentioned objective of this application is achieved through the following technical solution:

[0010] S1: Collect data on the mining area; the data includes: regional geological maps, multispectral images, radar images, and DEM remote sensing data of the mining area;

[0011] S2: Measure the spectral curves of polluted vegetation and healthy vegetation, preprocess the spectral curves according to the red edge effect, and use the magnitude of the red edge blue shift of the spectral curves to replace the alteration information of the mining area for classification, as the first mineralization influencing factor layer.

[0012] S3: The multispectral image and the radar image are preprocessed to generate a fused image;

[0013] S4: Obtain water system information and watershed information within the mining area using DEM remote sensing data; calculate the river channel steepness index and fracture point distribution based on the water system information and watershed information; determine the second mineralization influencing factor layer based on the river channel steepness index.

[0014] S5: Based on the water system information, the distribution of fracture points, the fused image, and the regional geological map, a comprehensive visual interpretation is performed to generate a fracture structure interpretation map of the mining area; based on the fracture structure interpretation map, a buffer zone analysis is performed, and the buffer zone is divided into three levels as the third mineralization influencing factor layer;

[0015] S6: Perform linear extraction, visual correction, and density analysis on the fused image, and divide it into three levels according to the standard deviation as the fourth mineralization influencing factor layer;

[0016] S7: Based on the lithology and distribution of intrusive bodies in the mining area, determine the metallogenic potential map and use it as the fifth metallogenic influencing factor layer;

[0017] S8: Conduct soil geochemical measurements in the mining area, determine the geochemical gold element anomaly map and classify its levels. The geochemical anomaly in the mining area is used as the sixth metallogenic influencing factor layer.

[0018] S9: Based on the known data and geochemical data of the mining area, establish a judgment matrix using the analytic hierarchy process (AHP) and calculate the weights of the six metallogenic influencing factor layers; calculate the comprehensive weight value of each point based on the weights of the six metallogenic influencing factor layers, and delineate the metallogenic prediction area based on the comprehensive weight value.

[0019] Optionally, step S2 includes:

[0020] S21: Identify the characteristic vegetation of the mining area;

[0021] S22: Distinguish between polluted and unpolluted areas within a mining region;

[0022] S23: Using an ASD FieldSpec3 spectrometer, determine the spectral curves of characteristic plants in the polluted area to identify the spectral curves of the polluted vegetation; determine the spectral curves of characteristic plants in the unpolluted area to identify the spectral curves of the healthy vegetation.

[0023] S24: Perform Savitzky-Golay smoothing on the spectral curve to remove the water vapor band;

[0024] S25: Take the maximum value point corresponding to the first derivative of the spectral curve in the 690nm and 750nm range as the red edge position, and count the red edge positions of the polluted vegetation and the healthy vegetation respectively to determine the red edge statistics results.

[0025] S26: In the hyperspectral image of the data, extract the vegetation cover area by normalized vegetation index;

[0026] S27: Based on the red edge statistics, extract the contaminated area, overlay the contaminated area and the vegetation coverage area to determine the vegetation poisoning area;

[0027] S28: Based on the magnitude of the red-edge blue shift of the spectral curve in the vegetation poisoning area, the mining area is divided into three levels to determine the first mineralization influencing factor layer.

[0028] Optionally, step S3 includes:

[0029] S31: Perform median filtering on the radar image, and then perform geometric calibration on the filtered radar image;

[0030] S32: The PCA image fusion method is used to fuse the calibrated radar image with the multispectral image to generate the fused image.

[0031] Optionally, step S4 includes:

[0032] S41: Use ALOS PALSAR 12.5m DEM remote sensing data to extract water system and watershed information within the mining area;

[0033] S42: Calculate the river steepness index and fracture point distribution of the river system using the water system information and watershed information, and then perform normalization and difference processing; based on the standard deviation, divide the river steepness index into three levels to determine the second mineralization influencing factor layer.

[0034] Optionally, step S5 includes:

[0035] The three-level buffer zone consists of: a 0-100m buffer zone, a 100-200m buffer zone, and a 200-300m buffer zone.

[0036] Optionally, step S6 includes:

[0037] The fused image was linearly extracted using PCI Geomatica software, and the extracted linear structures were visually interpreted. Density analysis was performed on the visually interpreted linear structures, and they were divided into three levels according to the standard deviation, serving as the fourth mineralization influence factor layer.

[0038] Optionally, step S7 includes:

[0039] Based on the regional geological map of the mining area, obtain the lithology and distribution of intrusive bodies;

[0040] Based on lithology and intrusive bodies, the mineralization potential map is divided into three levels, which serves as the fifth mineralization influencing factor layer.

[0041] The lithology and the intrusive bodies are classified into three levels according to their mineralization potential: the areas near the intrusive bodies and metamorphic rock belts have the highest gold mineralization potential and are set as level 3; mineral occurrences are also distributed in Silurian limestone, Devonian limestone, and Carboniferous limestone and are set as level 2; the remaining strata have lower mineralization potential and are set as level 1.

[0042] Optionally, step S8 includes:

[0043] According to the geochemical gold anomaly map, the gold content is classified into three levels: Level 3 has the highest mineralization potential with a gold content greater than 0.3 ppm, Level 2 has a gold content between 0.1 and 0.3 ppm, and Level 1 has a gold content less than 0.1 ppm.

[0044] An electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform a method for delineating gold prospecting target areas in areas with high vegetation cover.

[0045] A computer-readable storage medium storing instructions that, when executed, perform a method for delineating a gold prospecting target area in a highly vegetated area.

[0046] The beneficial effects of the technical solution provided in this application are:

[0047] Due to the extensive vegetation cover in high-vegetation mining areas, accurate extraction of mineral alteration information is difficult. This application utilizes the red-edge effect of vegetation to extract vegetation toxicity / stress information, inverting mineral alteration information, and classifying it using the magnitude of red-edge blue shift. This classification replaces alteration information as a mineralization influencing factor layer, which to some extent avoids the problem of unextractable alteration information and helps delineate mineralization target areas. Based on the characteristics of regional water system development, water system information and watershed information are introduced and identified as mineralization influencing factors, greatly improving the accuracy of mineral exploration prediction in high-vegetation-coverage areas. Known mining area data and geochemical data are processed and integrated. A judgment matrix is ​​established using the analytic hierarchy process (AHP), and the weights of the six mineralization influencing factor layers are calculated. Verification is then performed using existing mineral occurrences and veins, improving gold exploration efficiency while significantly reducing exploration costs. Attached Figure Description

[0048] The present application will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0049] Figure 1 This is a flowchart of the gold prospecting target area delineation method in a high vegetation cover area according to an embodiment of this application;

[0050] Figure 2This is a regional geological map of the Daping area, which is a method for delineating gold prospecting target areas in high vegetation cover areas according to the embodiments of this application.

[0051] Figure 3 This is a plan view of soil measurement anomalies in the Daping gold mine area, which is a method for delineating gold prospecting target areas in high vegetation cover areas according to the embodiments of this application.

[0052] Figure 4 This is an engineering layout diagram of the Daping mining area, which is a method for delineating gold prospecting target areas in high vegetation cover areas according to embodiments of this application.

[0053] Figure 5 This is a spectral curve of characteristic plants measured in the Daping mining area, which is a gold prospecting target area delineation method in a high vegetation cover area according to an embodiment of this application.

[0054] Figure 6 This is a vegetation pollution classification anomaly map of the Daping mining area, which is a method for delineating gold prospecting target areas in high vegetation cover areas according to the embodiments of this application.

[0055] Figure 7 This is a fracture structure interpretation diagram of the Daping mining area, which is a gold prospecting target area delineation method in a high vegetation cover area according to the embodiments of this application.

[0056] Figure 8 This is a fault classification anomaly map of the Daping mining area, which is a gold prospecting target area delineation method in a high vegetation cover area according to the embodiments of this application.

[0057] Figure 9 This is an automatically extracted linear structural map of the gold prospecting target area delineation method in high vegetation cover areas in the embodiments of this application;

[0058] Figure 10 This is a linear structural density classification anomaly map of the gold prospecting target area delineation method in high vegetation cover areas in the embodiments of this application;

[0059] Figure 11 This is a lithological anomaly classification map of the Daping mining area, which is a method for delineating gold prospecting target areas in high vegetation cover areas according to embodiments of this application.

[0060] Figure 12 This is a soil gold element anomaly classification map of the Daping mining area, which is a method for delineating gold prospecting target areas in high vegetation cover areas according to the embodiments of this application.

[0061] Figure 13 This is a map showing the predicted results of the metallogenic target area of ​​the gold prospecting target area delineation method in high vegetation cover areas in this application embodiment;

[0062] Figure 14 This is a schematic diagram of the electronic device structure of the gold prospecting target area delineation method in a high vegetation cover area according to an embodiment of this application. Detailed Implementation

[0063] To provide a clearer understanding of the technical features, objectives, and effects of this application, the specific embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0064] The embodiments of this application provide a method for delineating gold prospecting target areas in areas with high vegetation cover.

[0065] Please refer to Figure 1 , Figure 1 This is a flowchart of a method for delineating gold prospecting target areas in areas with high vegetation cover, as described in this application, including:

[0066] S1: Collect data on the mining area; the data includes: regional geological maps, multispectral images, radar images, and DEM remote sensing data of the mining area;

[0067] S2: Measure the spectral curves of polluted vegetation and healthy vegetation, preprocess the spectral curves according to the red edge effect, and use the magnitude of the red edge blue shift of the spectral curves to replace the alteration information of the mining area for classification, as the first mineralization influencing factor layer.

[0068] S3: The multispectral image and the radar image are preprocessed to generate a fused image;

[0069] S4: Obtain water system information and watershed information within the mining area using DEM remote sensing data; calculate the river channel steepness index and fracture point distribution based on the water system information and watershed information; determine the second mineralization influencing factor layer based on the river channel steepness index.

[0070] S5: Based on the water system information, the distribution of fracture points, the fused image, and the regional geological map, a comprehensive visual interpretation is performed to generate a fracture structure interpretation map of the mining area; based on the fracture structure interpretation map, a buffer zone analysis is performed, and the buffer zone is divided into three levels as the third mineralization influencing factor layer;

[0071] S6: Perform linear extraction, visual correction, and density analysis on the fused image, and divide it into three levels according to the standard deviation as the fourth mineralization influencing factor layer;

[0072] S7: Based on the lithology and distribution of intrusive bodies in the mining area, determine the metallogenic potential map and use it as the fifth metallogenic influencing factor layer;

[0073] S8: Conduct soil geochemical measurements in the mining area, determine the geochemical gold element anomaly map and classify its levels. The geochemical anomaly in the mining area is used as the sixth metallogenic influencing factor layer.

[0074] S9: Based on the known data and geochemical data of the mining area, establish a judgment matrix using the analytic hierarchy process (AHP) and calculate the weights of the six metallogenic influencing factor layers; calculate the comprehensive weight value of each point based on the weights of the six metallogenic influencing factor layers, and delineate the metallogenic prediction area based on the comprehensive weight value.

[0075] Step S2 includes:

[0076] S21: Identify the characteristic vegetation of the mining area;

[0077] S22: Distinguish between polluted and unpolluted areas within a mining region;

[0078] S23: Using an ASD FieldSpec3 spectrometer, determine the spectral curves of characteristic plants in the polluted area to identify the spectral curves of the polluted vegetation; determine the spectral curves of characteristic plants in the unpolluted area to identify the spectral curves of the healthy vegetation.

[0079] S24: Perform Savitzky-Golay smoothing on the spectral curve to remove the water vapor band;

[0080] S25: Take the maximum value point corresponding to the first derivative of the spectral curve in the 690nm and 750nm range as the red edge position, and count the red edge positions of the polluted vegetation and the healthy vegetation respectively to determine the red edge statistics results.

[0081] S26: In the hyperspectral image of the data, extract the vegetation cover area by normalized vegetation index;

[0082] S27: Based on the red edge statistics, extract the contaminated area, overlay the contaminated area and the vegetation coverage area to determine the vegetation poisoning area;

[0083] S28: Based on the magnitude of the red-edge blue shift of the spectral curve in the vegetation poisoning area, the mining area is divided into three levels to determine the first mineralization influencing factor layer.

[0084] Step S3 includes:

[0085] S31: Perform median filtering on the radar image, and then perform geometric calibration on the filtered radar image;

[0086] S32: The PCA image fusion method is used to fuse the calibrated radar image with the multispectral image to generate the fused image.

[0087] Step S4 includes:

[0088] S41: Use ALOS PALSAR 12.5m DEM remote sensing data to extract water system and watershed information within the mining area;

[0089] S42: Calculate the river steepness index and fracture point distribution of the river system using the water system information and watershed information, and then perform normalization and difference processing; based on the standard deviation, divide the river steepness index into three levels to determine the second mineralization influencing factor layer.

[0090] Step S5 includes:

[0091] The three-level buffer zone consists of: a 0-100m buffer zone, a 100-200m buffer zone, and a 200-300m buffer zone.

[0092] Step S6 includes:

[0093] The fused image was linearly extracted using PCI Geomatica software, and the extracted linear structures were visually interpreted. Density analysis was performed on the visually interpreted linear structures, and they were divided into three levels according to the standard deviation, serving as the fourth mineralization influence factor layer.

[0094] Step S7 includes:

[0095] Based on the regional geological map of the mining area, obtain the lithology and distribution of intrusive bodies;

[0096] Based on lithology and intrusive bodies, the mineralization potential map is divided into three levels, which serves as the fifth mineralization influencing factor layer.

[0097] The lithology and the intrusive bodies are classified into three levels according to their mineralization potential: the areas near the intrusive bodies and metamorphic rock belts have the highest gold mineralization potential and are set as level 3; mineral occurrences are also distributed in Silurian limestone, Devonian limestone, and Carboniferous limestone and are set as level 2; the remaining strata have lower mineralization potential and are set as level 1.

[0098] Step S8 includes:

[0099] According to the geochemical gold anomaly map, the gold content is classified into three levels: Level 3 has the highest mineralization potential with a gold content greater than 0.3 ppm, Level 2 has a gold content between 0.1 and 0.3 ppm, and Level 1 has a gold content less than 0.1 ppm.

[0100] Key Point 1: Targeting the characteristics of areas with high vegetation cover, the use of ALOS-2 imagery with higher resolution and stronger penetration is beneficial for obtaining structural information hidden by vegetation in mining areas. The resolution of ALOS-2 imagery in spotlight mode is about 1m, and the L-band synthetic aperture radar it carries has stronger penetration ability than the C-band, which can penetrate low canopies to a certain extent and obtain structural information hidden by vegetation.

[0101] Key Point Two: Leveraging the abundant rainfall and well-developed river systems in high-vegetation-coverage areas, indirect interpretation of faults in the mining area is performed based on the river system's response to faults. Specifically, this includes: extracting the river system from DEM remote sensing data; identifying faults based on the river system's morphology (e.g., large-angle course changes, fault control); calculating the river channel steepness index; and interpreting the faults based on the distribution of fracture points along the river system. Indirect interpretation requires ALOS-2 imagery as a base map, and the interpreted faults must show corresponding linear traces in the radar imagery to be considered the final interpretation result.

[0102] Key Point 3: The obtained mineralization influencing factor layers are fused using AHP (Analytic Hierarchy Process), and a judgment matrix is ​​constructed using the 1-9 scaling method. Each influencing factor needs to be scored by mineral exploration experts based on existing mineral deposits and veins, combined with geological knowledge. The final evaluation result also needs to be adjusted for weights based on existing mineral deposits and veins.

[0103] Regional geological data were collected and organized to conduct regional tectonic evolution studies, delineate tectonic units, and select favorable mineralization strata and intrusive bodies. High-precision DEM data was used to extract the river system of the study area and calculate the channel steepness index (Ksn). High-resolution L-band ALOS-2 radar imagery was used as the base map for tectonic interpretation. By integrating river geomorphological indices and the textural features of the radar imagery, a comprehensive visual interpretation of the study area was conducted to obtain the comprehensive interpretation results of the fault structures in the study area. The spectra of characteristic plants in the mining area were measured, and hyperspectral data was used to extract vegetation toxicity / stress information based on the measurement data. Mineral alteration information was inverted through vegetation toxicity information to obtain vegetation toxicity anomaly areas in the study area. Based on existing geochemical data or soil measurements of the study area, anomaly groups dominated by gold were delineated, and a soil measurement anomaly planar map was obtained. Integrating geological, remote sensing, and geochemical data, a analytic hierarchy process (AHP) was used to establish a judgment matrix, calculate the weights of each data layer, and finally obtain the predicted gold mineralization target area.

[0104] The specific experimental steps are as follows:

[0105] The selected Yuanyang Daping gold mining area is located in southern Yunnan Province, administratively belonging to Daping Township, Yuanyang County, Honghe Prefecture, Yunnan Province, bordering Jinping County to the south, covering an area of ​​approximately 105 square kilometers. Geographical coordinates: 103°04′—103°09′30″ E, 22°50′—22°56′01″ N. The mining area is situated in the southern section of the Ailao Mountains, characterized by high mountains and deeply dissected river valleys, with an altitude ranging from 688 to 2575 meters. The Jinzi River serves as the lowest erosion base level in the mining area, flowing from northwest to southeast, traversing the entire area. The mining area has a typical vertical climate, with a rainy season from May to October, characterized by dampness, rain, and fog, with an annual rainfall of approximately 2282 mm.

[0106] ALOS-2 (Advanced Land Observing Satellite-2), also known as radar imagery, is a synthetic aperture radar (SAR) satellite launched by the Japan Aerospace Exploration Agency (JAXA) in 2014. ALOS-2's onboard SAR sensor can observe the Earth's surface, providing high-resolution radar imagery data.

[0107] The L-band is one of the main operating bands of ALOS-2. The L-band is a microwave band used in radar sensors, typically operating between 1 and 2 GHz. ALOS-2's L-band radar imagery has the following characteristics and applications: The longer wavelength of the L-band gives ALOS-2 greater penetration capability over the Earth's surface. It can penetrate clouds, rain, and some vegetation, providing the ability to acquire surface information under various meteorological and environmental conditions. L-band imagery is highly sensitive to detailed features, morphological changes, and textural variations on the Earth's surface. It has advantages in identifying surface features such as rivers, roads, buildings, and lakes.

[0108] The parameters of the ALOS-2 radar imagery are shown in the table below:

[0109] Table 1 ALOS-2 Radar Image Parameter Table

[0110]

[0111] The Zhuhai-1 hyperspectral image, OHS sensor: spatial resolution 10m, altitude 500km, mass 67kg, imaging range 150km×2500km, 32 spectral bands, spectral resolution 2.5nm, spectral range 400nm-1000nm, orbit 98°.

[0112] Table 2. Parameters of Zhuhai-1 Hyperspectral Image

[0113]

[0114] ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar) is a satellite mission launched by the Japan Aerospace Exploration Agency (JAXA) to observe the Earth's surface using L-band synthetic aperture radar (SAR) technology. The radar sensors onboard ALOS PALSAR can acquire high-quality radar imagery data, of which the ALOS PALSAR DEM is a component. The ALOS PALSAR DEM provides high-resolution surface elevation information, with a resolution of up to 12.5 meters. This allows it to capture detailed terrain features, including mountains, canyons, and rivers. The ALOS PALSAR DEM can be used to extract surface features such as ridgelines, river tracks, estuaries, and lakes, making it highly valuable for Geographic Information Systems (GIS) and geomorphological studies.

[0115] Spectral measurement data: Using an ASD FieldSpec3 spectrometer, the spectral curves of characteristic plants were measured in both polluted and unpolluted areas of the Daping mining area.

[0116] Mining area data includes: geological maps of the Daping gold mining area (such as...) Figure 2 (as shown), soil anomaly measurement map (such as) Figure 3 As shown), engineering layout drawing (such as) Figure 4 (As shown).

[0117] The experimental steps are as follows:

[0118] Anomaly Extraction: Based on the mineralization characteristics of gold mining areas with high vegetation cover, mineralization influencing factors were identified, namely, toxicity information, geomorphic index, tectonic buffer zone, linear tectonic density, lithology, and geochemistry (soil concentration of gold element). These influencing factors were classified into three levels based on their degree of favorability to gold mineralization, with level three being the most favorable, followed by level two and level one.

[0119] Vegetation toxicity information extraction: Based on the spectral curves of characteristic plants measured in the Daping mining area. Figure 5 The measured spectra were preprocessed to remove the water vapor band, and the spectral curves were smoothed using Savitzky-Golayconvolution (SG). The maximum points corresponding to the first derivatives of the spectra in the 690nm and 750nm intervals were used as the red edge positions, and the red edge positions of polluted and healthy vegetation were statistically analyzed (Table 3). Based on the red edge statistics, polluted areas were extracted, and the degree of vegetation toxicity was determined by the magnitude of the blue shift in the spectral red edge and the statistically analyzed red edge positions. Figure 6The red edge position is divided into three levels: Level 3 (red edge position less than 705nm), Level 2 (red edge position between 705-718nm), and Level 1 (red edge position greater than 720nm).

[0120] Table 3. Statistics on the location of red edges on plants

[0121]

[0122] Geomorphic index: The water system and watershed in the mining area were extracted using ALOS PALSAR 12.5m DEM data. The river steepness index (Ksn) and the distribution of crack points were calculated. After normalization, the difference was calculated. The obtained Ksn index was classified into three levels according to the standard deviation.

[0123] Structural buffer zones: Based on the extracted water system morphology and fracture point distribution, ALOS-2 radar images are overlaid and combined with regional geological maps for comprehensive visual interpretation to obtain the fracture structure interpretation map of the mining area. Buffer zone analysis is performed on the interpreted structures, and the corresponding 0-50m buffer zone, 50-150m buffer zone, and 150-300m buffer zone are divided into three levels.

[0124] Linear structure density: Linear structures were extracted from the fused image using PCI Geomatica software. The extracted linear structures were visually corrected to remove overlapping and erroneous lines. Density analysis was performed, and the structures were classified into three levels according to their standard deviation.

[0125] Lithology: Based on the regional geological map of the mining area, and according to the lithology and distribution of intrusive bodies, the mineralization potential is divided into three levels. Areas close to intrusive bodies and metamorphic rock belts have the highest gold mineralization potential, classified as level 3; mineral occurrences are also distributed in Silurian, Devonian, and Carboniferous limestone, classified as level 2; the remaining strata have lower mineralization potential, classified as level 1.

[0126] Table 4. Lithological Classification of Mineralization Potential

[0127]

[0128] Geochemistry: Based on the classification of gold anomalies measured in soil, the highest mineralization potential is classified as level 3, with gold content greater than 0.3 ppm, level 2 is between 0.1 and 0.3 ppm, and level 1 is less than 0.1 ppm.

[0129] Metallogenic target area delineation: Based on the mineral occurrences, veins and structures of existing mining areas, the analytic hierarchy process (AHP) is used, and an expert review is conducted to establish a judgment matrix (Table 5) to confirm the weights of the above six metallogenic factor layers. Based on the weights of each layer (Table 6), the comprehensive weight value of each point in the figure is calculated, and the metallogenic prediction area is delineated based on the comprehensive weight value.

[0130] Table 5 Ore-forming factor judgment matrix

[0131]

[0132] Table 6 Weights of mineralization influencing factors

[0133]

[0134] Specifically, the location of tunnels and mineral deposits in the engineering layout map of the mining area are used to verify the results of the prospecting target area delineation in this application.

[0135] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided above belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0136] This application also discloses an electronic device. (See reference...) Figure 14 , Figure 14 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0137] The communication bus 502 is used to enable communication between these components.

[0138] The user interface 503 may include a display screen and a camera. Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0139] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0140] The processor 501 may include one or more processing cores. The processor 501 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 505, and by calling data stored in memory 505. Optionally, the processor 501 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 501 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 501 and may be implemented as a separate chip.

[0141] The memory 505 may include random access memory (RAM) or read-only memory.

[0142] Optionally, the memory 505 includes a non-transitory computer-readable storage medium. The memory 505 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. The memory 505 may also optionally include, but is not limited to, at least one storage device located remotely from the aforementioned processor 501. (Refer to...) Figure 14 The memory 505, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a method of delineating gold prospecting target areas in areas with high vegetation cover.

[0143] exist Figure 14In the illustrated electronic device 500, the user interface 503 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 501 can be used to call an application program stored in the memory 505 that describes a method for delineating gold prospecting target areas in areas with high vegetation cover. When executed by one or more processors 501, the electronic device 500 performs one or more methods as described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0144] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0145] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed include, but are not limited to, indirect couplings or communication connections between apparatuses or units through some service interfaces, including but not limited to electrical or other forms.

[0146] The units described as separate components include, but are not limited to, physically separate units. Components shown as units include, but are not limited to, physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of these units can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or may exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0148] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0149] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will readily conceive of those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0150] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for delineating target areas for gold prospecting in areas with high vegetation cover, characterized in that, The method includes the following steps: S1: Collect data on the mining area; the data includes: regional geological maps, multispectral images, radar images, and DEM remote sensing data of the mining area; S2: Measure the spectral curves of polluted vegetation and healthy vegetation, preprocess the spectral curves according to the red edge effect, and use the magnitude of the red edge blue shift of the spectral curves to replace the alteration information of the mining area for classification, as the first mineralization influencing factor layer. S3: Preprocess the multispectral image and the radar image to generate a fused image; Step S3 includes: S31: Perform median filtering on the radar image, and then perform geometric calibration on the filtered radar image; S32: The PCA image fusion method is used to fuse the calibrated radar image with the multispectral image to generate the fused image; S4: Obtain water system information and watershed information within the mining area using DEM remote sensing data; calculate the river channel steepness index and fracture point distribution based on the water system information and watershed information; determine the second mineralization influencing factor layer based on the river channel steepness index. S5: Based on the water system information, the distribution of fracture points, the fused image, and the regional geological map, a comprehensive visual interpretation is performed to generate a fracture structure interpretation map of the mining area; based on the fracture structure interpretation map, a buffer zone analysis is performed, and the buffer zone is divided into three levels as the third mineralization influencing factor layer; S6: Perform linear extraction, visual correction, and density analysis on the fused image, and divide it into three levels according to the standard deviation as the fourth mineralization influencing factor layer; S7: Based on the lithology and distribution of intrusive bodies in the mining area, determine the metallogenic potential map and use it as the fifth metallogenic influencing factor layer; S8: Conduct soil geochemical measurements in the mining area, classify the soil gold anomaly based on the soil gold anomaly measurement plan of the mining area, and use the geochemical anomaly of the mining area as the sixth metallogenic influencing factor layer. S9: Based on the known data and geochemical data of the mining area, establish a judgment matrix using the analytic hierarchy process (AHP) and calculate the weights of the six metallogenic influencing factor layers; calculate the comprehensive weight value of each point based on the weights of the six metallogenic influencing factor layers, and delineate the metallogenic prediction area based on the comprehensive weight value.

2. The method for delineating gold prospecting target areas in high vegetation cover areas as described in claim 1, characterized in that, Step S2 includes: S21: Identify the characteristic vegetation of the mining area; S22: Distinguish between polluted and unpolluted areas within a mining region; S23: Using an ASD FieldSpec3 spectrometer, determine the spectral curves of characteristic plants in the polluted area to identify the spectral curves of the polluted vegetation; determine the spectral curves of characteristic plants in the unpolluted area to identify the spectral curves of the healthy vegetation. S24: Perform Savitzky-Golay smoothing on the spectral curve to remove the water vapor band; S25: Take the maximum value point corresponding to the first derivative of the spectral curve in the 690nm and 750nm range as the red edge position, and count the red edge positions of the polluted vegetation and the healthy vegetation respectively to determine the red edge statistics results. S26: In the hyperspectral image of the data, extract the vegetation cover area by normalized vegetation index; S27: Based on the red edge statistics, extract the contaminated area, overlay the contaminated area and the vegetation coverage area to determine the vegetation poisoning area; S28: Based on the magnitude of the red-edge blue shift of the spectral curve in the vegetation poisoning area, the mining area is divided into three levels to determine the first mineralization influencing factor layer.

3. The method for delineating gold prospecting target areas in high vegetation cover areas as described in claim 1, characterized in that, Step S4 includes: S41: Use ALOS PALSAR 12.5m DEM remote sensing data to extract water system and watershed information within the mining area; S42: Calculate the river steepness index and fracture point distribution of the river system using the water system information and watershed information, and then perform normalization and difference processing; based on the standard deviation, divide the river steepness index into three levels to determine the second mineralization influencing factor layer.

4. The method for delineating gold prospecting target areas in high vegetation cover areas as described in claim 1, characterized in that, Step S5 includes: The three-level buffer zone consists of: a 0-100m buffer zone, a 100-200m buffer zone, and a 200-300m buffer zone.

5. The method for delineating gold prospecting target areas in high vegetation cover areas as described in claim 1, characterized in that, Step S6 includes: The fused image was linearly extracted using PCI Geomatica software, and the extracted linear structures were visually interpreted. Then, density analysis was performed on the visually interpreted linear structures, which were divided into three levels according to the standard deviation, serving as the fourth mineralization influence factor layer.

6. The method for delineating gold prospecting target areas in high vegetation cover areas as described in claim 1, characterized in that, Step S7 includes: Based on the regional geological map of the mining area, obtain the lithology and distribution of intrusive bodies; Based on lithology and intrusive bodies, the mineralization potential map is divided into three levels, which serves as the fifth mineralization influencing factor layer. The lithology and the intrusive bodies are classified into three levels according to their mineralization potential: the areas near the intrusive bodies and metamorphic rock belts have the highest gold mineralization potential and are set as level 3; mineral occurrences are also distributed in Silurian limestone, Devonian limestone, and Carboniferous limestone and are set as level 2; the remaining strata have lower mineralization potential and are set as level 1.

7. The method for delineating gold prospecting target areas in high vegetation cover areas as described in claim 1, characterized in that, Step S8 includes: According to the geochemical gold anomaly map, the gold content is classified into three levels: Level 3 has the highest mineralization potential with a gold content greater than 0.3 ppm, Level 2 has a gold content between 0.1 and 0.3 ppm, and Level 1 has a gold content less than 0.1 ppm.

8. An electronic device, characterized in that, The device includes a processor (501), a memory (505), a user interface (503), and a network interface (504). The memory (505) is used to store instructions. The user interface (503) and the network interface (504) are used to communicate with other devices. The processor (501) is used to execute the instructions stored in the memory (505) to cause the electronic device (500) to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the steps of the method as described in any one of claims 1-7.

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