Ecological sensitive area copper ore prospecting method based on lossless geophysical exploration

Abstract

The invention relates to the technical field of geological exploration, in particular to an ecological sensitive area copper ore prospecting method based on lossless geophysical exploration, which comprises the following steps of: 1, performing image acquisition on the earth surface of a target ecological sensitive area, and generating a cave entrance distribution vector diagram; 2, performing gravity detection on the target ecological sensitive area to generate a three-dimensional gravity detection image; step 3, carrying out confluence analysis on the distribution vector diagram of the cave entrance and the three-dimensional gravity detection image, identifying low-density false anomalies caused by the cave, and generating a correction scheme; and 4, performing data correction and geologic body marking on the gravity detection image in combination with the correction data and typical density characteristics of the copper ore. Aiming at the problems of gravitational exploration false negative anomalies caused by caverns of animals in ecological sensitive areas such as alpine meadow and the like and covering of real anomalies of copper ore, the problem of misjudgment of a gravitational exploration target area is solved by taking accurate recognition of the cave and targeted elimination of the false anomalies as the core, the positioning precision of the copper ore is improved, and the ecological disturbance is reduced.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, specifically to a method for copper prospecting in ecologically sensitive areas based on non-destructive geophysical exploration. Background Technology

[0002] Geophysical exploration is a core technology for copper prospecting. Its core principle is to use the inherent differences in physical properties (density, magnetism, conductivity, elastic wave propagation speed, etc.) between copper ore (such as chalcopyrite and bornite) and surrounding rocks to infer the distribution, depth and scale of underground ore bodies. It has been widely used in copper prospecting and detailed exploration in conventional geological areas.

[0003] For ecologically sensitive areas (such as alpine meadows), non-destructive testing is often employed. Non-destructive testing refers to techniques that infer internal information by measuring differences in the physical properties of geological bodies without damaging the original state of the target (soil, vegetation, strata). In copper prospecting in ecologically sensitive areas, the core methods can be categorized into six main types based on their physical principles: gravity exploration, magnetic exploration, electrical exploration, ground-penetrating radar (GPR), remote sensing, and seismic exploration (shallow layers). In ecologically sensitive areas such as alpine meadows, burrowing animals form large-scale burrow systems (e.g., marmot family burrow groups can reach depths of 0.5-3m and diameters of 0.2-1m, with a single group covering an area exceeding 100㎡). The interiors of these burrows are mostly filled with air or loose soil, creating a significant density difference compared to the surrounding compact alpine meadow soil and the target copper ore body. Gravity exploration, as a core method for deep copper ore prospecting in ecologically sensitive areas, works by capturing differences in strata density to infer the distribution of ore bodies. When gravity measuring points are mistakenly placed above cave systems, they will preferentially capture localized low-density "false negative anomalies" caused by the caves. These anomalies have amplitudes highly similar to those of underground karst caves and loose, mineral-free surrounding rock areas, making them easily misidentified as non-mineralized geological bodies. This directly masks or confuses the high-density positive anomalies generated by genuine copper ore bodies. Consequently, existing gravity exploration methods in alpine meadows and similar regions not only struggle to accurately delineate copper target areas but may also increase ineffective verification work due to misjudgments, wasting resources and potentially causing additional disturbance to sensitive ecosystems. Therefore, this invention proposes a non-destructive geophysical exploration method for copper prospecting in ecologically sensitive areas to address the aforementioned problems. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a copper prospecting method for ecologically sensitive areas based on non-destructive geophysical exploration. This method solves the problem of false negative anomalies in gravity exploration caused by burrowing animals in ecologically sensitive areas such as alpine meadows, and reduces ecological disturbance while achieving precise positioning of copper ore target areas.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows: A method for copper prospecting in ecologically sensitive areas based on non-destructive geophysical exploration, comprising the following steps: Step 1: Based on UAV image acquisition technology, images of the surface of the target ecologically sensitive area are acquired. The acquired surface images are then introduced into a preset burrow identification model to extract the coordinates and diameter of each burrow entrance in the surface images, generating a burrow entrance distribution vector map. Step 2: Based on gravity detection technology, the target ecologically sensitive area is detected to obtain raw gravity data, which is then preprocessed and converted into a three-dimensional gravity detection image. Step 3: Integrate and analyze the cave distribution information in the cave entrance distribution vector map and the three-dimensional gravity detection image, and then identify low-density false anomalies caused by the caves through the cave anomaly judgment strategy, and generate correction schemes. Step four: Combine the corrected data with the typical density characteristics of copper ore to perform data correction and geological body marking on the gravity detection images.

[0006] Furthermore, in the vector diagram of the cave entrances, the unique ID, center coordinates, and entrance diameter of each cave are marked.

[0007] Furthermore, in step one, visible light images and infrared imaging images of the target ecologically sensitive area are acquired based on UAV image acquisition technology; After preprocessing the visible light image, import the preset cave identification model and output the cave entrances identified in the visible light image; For infrared thermal imaging images, based on a preset temperature difference threshold, anomaly areas of surface temperature are extracted and designated as "suspected hidden cave areas". The infrared image segments of the "suspected hidden cave areas" are then superimposed with the visible light image segments of the corresponding locations and input into a preset cave identification model to verify whether they are real cave entrances. If the verification is successful, the center coordinates and diameter of the cave entrance are extracted and marked in the cave entrance distribution vector map, and distinguished from the cave entrances directly identified by the visible light images. At the same time, the temperature difference value of the cave is recorded to improve the cave information dimensions.

[0008] Furthermore, before extracting temperature anomaly areas from the infrared thermal imaging images, the cave data obtained from visible light image recognition is first called, and the overlap avoidance search radius is set based on the cave entrance diameter. In the coordinate system of the infrared thermal imaging images, the center coordinates of each visible light cave are used as the center and the overlap avoidance search radius is used as the radius to delineate the duplicate exclusion area. In the subsequent extraction of temperature anomaly areas from the infrared thermal imaging images, if the center coordinates of a certain temperature anomaly area fall into any duplicate exclusion area, or the overlap area with the duplicate exclusion area is ≥30%, then the temperature anomaly area is determined to be a visible light cave duplicate area and is directly removed from the infrared analysis process, not included in the screening range of "suspected hidden cave areas". Only for temperature anomaly areas that do not fall into the duplicate exclusion area, the identification and analysis operation continues.

[0009] Furthermore, the strategies for identifying anomalies in underground caverns include: A. Low-density region extraction: In the three-dimensional gravity detection image, the region with a gravity value less than the preset low-density value is defined as a low-density region. All low-density anomaly regions that meet the conditions are delineated, and the spatial range, amplitude and morphological characteristics of each low-density anomaly region are recorded. B. Connectivity and Coordinate Matching: Based on the vector map of the cave entrance distribution, the logic of "coordinate space association → three-dimensional path connectivity scanning → anomaly type classification and judgment" is used to distinguish between cave false anomalies and geological false anomalies. C. Verification and Correction: For false anomalies in the caves, secondary exploration using drones is used for verification. After confirmation, the interference amplitude and interference range of the false anomalies are calculated, and a "False Anomaly Correction Data Table for Caves" is generated. The False Anomaly Correction Data Table for Caves contains the false anomaly ID, the corresponding cave group ID, the interference amplitude, the interference range, and the correction method.

[0010] Furthermore, the coordinates of the cave entrance distribution vector map and the three-dimensional gravity detection image are unified. Taking the center coordinates of each cave entrance as the initial origin, a three-dimensional spatial tracking algorithm is used to scan the path along the channel path of the low-density area in the gravity detection image. Starting from the origin of the cave entrance, the spatial distribution path of the low-density area is tracked to determine whether there is a complete channel that extends continuously from the cave entrance on the surface to the underground. If so, it is determined to be a false anomaly of the cave. If a scan reveals a low-density area that exists in isolation at a certain depth underground and is not connected to the entrance of a surface cave, it is marked as a geological false anomaly.

[0011] Furthermore, in step four, combining the corrected data from step three with the typical density characteristics of copper ore, data correction and geological body marking are sequentially performed on the gravity detection images, as follows: First, import the correction values ​​recorded in the "Ventilation False Anomaly Correction Data Table" into the gravity detection image, and perform gravity value superposition correction on all areas identified as ventilation false anomalies. After completing the gravity data correction, determine the density conversion and labeling standards by referring to the density difference between copper ore and surrounding rock. Convert the gravity values ​​in the corrected gravity detection image into the density values ​​of the corresponding underground medium, and then set the labeling standards for three types of geological bodies based on the conversion results. High-density areas, corresponding to densities ≥3.8g / cm³, are marked as “suspected copper ore target areas” and indicated with a red solid line box; The normal density zone, corresponding to a density of 2.4-3.0 g / cm³, is marked as "surrounding rock zone" and filled with gray. Other abnormal areas, with a density of 3.0-3.8 g / cm³ or <2.4 g / cm³, are marked as "Abnormal Areas to be Verified" and indicated with a yellow dashed box. Finally, a distribution map of suspected copper ore target areas in ecologically sensitive areas is generated, and the center coordinates, three-dimensional range, and corresponding density value of each suspected copper ore target area are marked.

[0012] Furthermore, in the gravity data correction in step four, a spatial association between the "cavity false anomaly correction data table" and the three-dimensional gravity detection image is first established. The correction amount is matched with the corresponding cavity false anomaly area in the detection image through the false anomaly ID. If multiple cavity false anomalies overlap in a certain area, the arithmetic mean of the correction amounts of each false anomaly is taken as the final correction amount for that area.

[0013] Furthermore, the distribution map of suspected copper mine target areas in ecologically sensitive areas also marks the content of the target area credibility assessment; the assessment dimensions include the degree of matching between the target area density value and the typical density of copper mines, and the exclusion of caves and other non-mineral disturbances.

[0014] Furthermore, the density conversion process requires the use of a Bouguer gravity anomaly-density conversion formula adapted to the geological background of ecologically sensitive areas, and the surrounding rock density parameters must be set based on measured data from the target area.

[0015] The above approach has the following beneficial effects: 1. This solution can accurately eliminate false anomalies caused by underground caverns, improve the positioning accuracy of copper mine target areas in ecologically sensitive areas, and effectively solve the problem of misjudgment in traditional gravity exploration. The solution employs a multi-dimensional technical design to construct a precision assurance system encompassing "cavity identification - anomaly differentiation - data correction": Step one utilizes dual-source acquisition via visible light and infrared thermal imaging from a UAV, combined with a gravity avoidance search mechanism and a cavity identification model, to accurately extract the coordinates and diameter of the cavity entrance and prevent duplicate entry of cavity information, laying a data foundation for subsequent anomaly differentiation; Step three, through coordinate unification, 3D path connectivity scanning, and secondary verification by the UAV, efficiently distinguishes between false anomalies of cavities and geological false anomalies such as karst caves, achieving a false anomaly rejection rate of over 90%, reducing the possibility of misjudging low-density signals from cavities as non-mineral geological bodies; Step four employs a Bouguer gravity anomaly-density conversion formula adapted to the regional geological background, combined with measured surrounding rock density parameters, to reduce the target area density identification error to ±0.1 g / cm³ after gravity data correction. Simultaneously, high-confidence target areas are screened through credibility assessment (matching degree, interference elimination), reducing the target area misjudgment rate. Compared with traditional methods, this approach reduces the deviation in copper ore target area positioning and some invalid drilling verification work, thereby reducing resource waste caused by misjudgment and improving exploration efficiency and economy. 2. This plan adheres to the concept of non-destructive testing throughout the entire process, ensuring exploration accuracy while maximizing the protection of the fragile environment in ecologically sensitive areas, achieving a synergistic unity between "mineral exploration" and "ecological protection." Each stage of the plan is designed with "no disturbance" in mind: Step one utilizes low-altitude data collection using drones, with no ground contact throughout the process. This avoids human trampling and damage to alpine meadow vegetation (whose root recovery period exceeds 5 years) and avoids disturbing burrowing animals (such as marmots). Infrared thermal imaging identifies hidden burrows without the need for excavation, further reducing disturbance to the soil and vegetation. More importantly, through precise correction and target area selection, the number of target points for subsequent drilling verification is reduced, thereby minimizing ecological damage caused by ineffective drilling (such as soil erosion caused by drilling pits). For ecologically fragile areas such as alpine meadows and wetlands, this plan can control the vegetation damage rate during the exploration process to below 0.05% and the animal activity disturbance rate to below 5%, effectively reducing the vicious cycle of "ecological damage - decreased accuracy" in traditional exploration and meeting the environmental protection requirements for resource exploration in ecologically sensitive areas.

[0016] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an embodiment of the copper prospecting method for ecologically sensitive areas based on non-destructive geophysical exploration according to the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0020] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] The following detailed description illustrates the specific implementation method: Example 1:

[0022] As attached Figure 1 The following is a method for copper prospecting in ecologically sensitive areas based on non-destructive geophysical exploration, comprising the following steps: Step 1: Based on UAV image acquisition technology (selecting electric silent UAVs with noise ≤45dB to minimize disturbance to burrowing animals such as marmots and prevent them from abandoning their burrows or changing their activity trajectories, while also reducing UAV noise interference with migratory bird habitats in ecologically sensitive areas), images (including visible light images and infrared imaging images) of the surface of the target ecologically sensitive area are acquired. The acquired surface images are then introduced into a pre-set burrow identification model (the model is trained based on 5000+ sets of alpine meadow marmot burrow samples, covering burrow features under different vegetation coverage and lighting conditions, with an identification accuracy ≥95%). The coordinates and diameter of each burrow entrance in the surface images are extracted to generate a burrow entrance distribution vector map. At the same time, the unique ID, center coordinates (using the WGS84 coordinate system, accuracy ±0.3m), and entrance diameter of each burrow are marked in the burrow entrance distribution vector map.

[0023] After preprocessing the visible light image (performing Gaussian filtering for noise reduction and perspective transformation for geometric correction in sequence), the preset cave identification model is imported, and the cave entrances identified in the visible light image are output. Based on a preset temperature difference threshold (e.g., ≥2℃), the surface temperature anomaly area is extracted from the infrared thermal imaging image. This temperature anomaly area is designated as a "suspected hidden burrow area". Because the burrows in alpine meadows are filled with air or loose soil with low thermal conductivity, the temperature inside the burrow is 2-3℃ lower than the outside compacted soil in the early morning, forming a clear low temperature anomaly area. This can be used as a "suspected hidden burrow area" to cover hidden burrows that are completely covered by tall grass and have an entrance diameter of <0.3m, thus compensating for the blind spots in visible light image recognition. Next, the infrared image fragments of the "suspected hidden cave area" are overlaid with the corresponding visible light image fragments, and input into a preset cave identification model to verify whether they are real cave entrances. If the verification is successful, the cave is identified as a hidden cave. The center coordinates and diameter of the cave entrance are extracted and marked in the cave entrance distribution vector map, distinguishing it from the cave entrances directly identified by the visible light image. Simultaneously, the temperature difference value of the hidden cave (e.g., "2.5℃") is recorded—the temperature difference indirectly reflects the cave depth (the larger the temperature difference, the deeper the cave and the better the internal air circulation), providing a reference for subsequent judgment on whether the "cave-low density anomaly" has a continuous channel, further improving the cave information dimensions and enhancing the accuracy of subsequent anomaly identification. Secondly, in the cave entrance distribution vector map, the entrance location of the hidden cave is marked with "○ + infrared marker" symbols, and the cave entrances directly identified by the visible light image are marked with "solid circles" symbols for easy distinction.

[0024] Furthermore, before extracting temperature anomaly areas from infrared thermal imaging images, the cave data obtained from visible light image recognition is first called, and the overlap avoidance search radius is set based on the cave entrance diameter. In the coordinate system of the infrared thermal imaging image, the center coordinates of each visible light cave are used as the center and the overlap avoidance search radius is used as the radius to delineate the duplicate exclusion area. In the subsequent extraction of temperature anomaly areas from infrared thermal imaging images, if the center coordinates of a certain temperature anomaly area fall into any duplicate exclusion area, or the overlap area with the duplicate exclusion area is ≥30%, then the temperature anomaly area is determined to be a visible light cave duplicate area and is directly removed from the infrared analysis process, not included in the screening range of "suspected hidden cave areas". Only for temperature anomaly areas that do not fall into the duplicate exclusion area, the identification and analysis operation continues.

[0025] Step two involves using gravity detection technology (e.g., a lightweight, high-precision CG-6 gravimeter) to detect the target ecologically sensitive area, obtaining raw gravity data, and preprocessing it. The preprocessed gravity data is then imported into GMSYS gravity data processing software, and a three-dimensional gravity detection image is generated using Kriging interpolation. Gravity anomalies are visually presented using color gradients (e.g., red represents positive anomalies, corresponding to high-density bodies; blue represents negative anomalies, corresponding to low-density bodies), providing a visual data foundation for subsequent identification of false anomalies in the caves and real anomalies in copper mines.

[0026] Step 3: Integrate and analyze the cave distribution information in the cave entrance distribution vector map and the three-dimensional gravity detection image, and then identify low-density false anomalies caused by the caves through the cave anomaly judgment strategy, and generate correction schemes. The strategies for identifying anomalies in underground caverns include: A. Low-density region extraction: In the three-dimensional gravity detection image, the region with a gravity value less than the preset low-density value is defined as a low-density region. All low-density anomaly regions that meet the conditions are delineated, and the spatial range, amplitude and morphological characteristics of each low-density anomaly region are recorded. For example, combining the gravity anomaly characteristics of burrows and copper mines in the target ecologically sensitive area (a high-altitude meadow in the eastern part of a plateau), a low-density value of -0.3 mGal is preset. Gravity anomalies below this value are mostly caused by low-density bodies such as burrows and caves, and can effectively cover the amplitude range of pseudo-anomalies of marmot burrows (-0.5 to -2 mGal). In the three-dimensional gravity detection image, the "automatic anomaly area extraction function" of GMSYS software is used to delineate all low-density anomaly areas with gravity values ​​< -0.3 mGal, and record the core information of each area: spatial range, anomaly amplitude (minimum / maximum negative anomaly value, such as -0.8 to -1.5 mGal), and morphological characteristics (continuous sheet / isolated block / strip). Among them, "morphological characteristics" can preliminarily distinguish between burrow pseudo-anomalies (mostly continuous sheet, which is consistent with the cluster distribution characteristics of marmot burrow groups) and geological pseudo-anomalies (mostly isolated blocks, such as small caves).

[0027] B. Connectivity and Coordinate Matching: Based on the cave entrance distribution vector map, the system distinguishes between cave false anomalies and geological false anomalies through the logic of "coordinate space association → 3D path connectivity scanning → anomaly type classification and judgment". Specifically, the coordinates of the cave entrance distribution vector map and the 3D gravity detection image are first unified. Taking the center coordinates of each cave entrance as the initial origin, a 3D spatial tracking algorithm is used to scan the path along the channel path of the low-density area in the gravity detection image. Starting from the cave entrance origin, the spatial distribution path of the low-density area is tracked to determine whether there is a complete channel that extends continuously from the surface cave entrance to the underground. If so, it is determined to be a cave false anomaly. If the scan finds that the low-density area exists in isolation only at a certain depth underground and has no continuous channel connection with the surface cave entrance, it is marked as a geological false anomaly and labeled "non-cave interference".

[0028] C. Verification and Correction: For false anomalies in the caves, secondary exploration using drones is used for verification. After confirmation, the interference amplitude and interference range of the false anomalies are calculated, and a "False Anomaly Correction Data Table for Caves" is generated. The False Anomaly Correction Data Table for Caves contains the false anomaly ID, the corresponding cave group ID, the interference amplitude, the interference range, and the correction method.

[0029] Step four involves combining the corrected data with the typical density characteristics of copper ore to perform data correction and geological body labeling on the gravity survey images. Specifically, based on the corrected data from step three and the typical density characteristics of copper ore, the gravity survey images are sequentially subjected to data correction and geological body labeling as follows: First, import the correction values ​​recorded in the "Ventilation False Anomaly Correction Data Table" into the gravity detection image, and perform gravity value superposition correction on all areas identified as ventilation false anomalies. After completing the gravity data correction, determine the density conversion and labeling standards by referring to the density difference between copper ore and surrounding rock. Convert the gravity values ​​in the corrected gravity detection image into the density values ​​of the corresponding underground medium, and then set the labeling standards for three types of geological bodies based on the conversion results. High-density areas, corresponding to densities ≥3.8g / cm³, are marked as “suspected copper ore target areas” and indicated with a red solid line box; The normal density zone, corresponding to a density of 2.4-3.0 g / cm³, is marked as "surrounding rock zone" and filled with gray. Other abnormal areas, with a density of 3.0-3.8 g / cm³ or <2.4 g / cm³, are marked as "Abnormal Areas to be Verified" and indicated with a yellow dashed box. Finally, a distribution map of suspected copper ore target areas in ecologically sensitive areas was generated, and the center coordinates, three-dimensional range, and corresponding density value of each suspected copper ore target area were marked. Furthermore, the distribution map of suspected copper ore target areas in ecologically sensitive areas also included the content of the target area credibility assessment; the assessment dimensions included the degree of matching between the target area density value and the typical density of copper ore, and the exclusion of caverns and other non-mineral disturbances.

[0030] Secondly, in the gravity data correction, the spatial association between the "cavity false anomaly correction data table" and the three-dimensional gravity detection image is first established. The correction amount is matched with the corresponding cavity false anomaly area in the detection image through the false anomaly ID. If multiple cavity false anomalies overlap in a certain area, the arithmetic mean of the correction amounts of each false anomaly is taken as the final correction amount for that area.

[0031] Based on the above scheme, experimental verification was conducted, and some data are as follows: Table 1 - Relevant Parameters Core data metrics Experimental data Overall accuracy rate for burrow identification (primarily marmot burrows) 96.1% False anomaly removal rate of underground pits 92.9% Number of highly credible suspected copper mining target areas 8 Vegetation destruction rate during exploration (alpine meadow) 0.03% High confidence target area and typical density matching of copper mine 92% As shown in Table 1, the non-destructive geophysical exploration method of this invention can efficiently balance "copper ore exploration accuracy" and "alpine ecosystem protection" in alpine grassland ecological scenarios (characterized by dense marmot burrows and fragile meadow ecosystems), as detailed below: Burrow identification and false anomaly removal are accurately adapted to alpine grasslands: achieving a 96.1% identification accuracy rate for marmot burrows. By using "dual-source image acquisition + deduplication removal" to deal with the dense distribution of burrow clusters, the false anomaly removal rate reaches 92.9%, reducing the interference of burrows on gravity data and solving the problem of "false anomalies masking true anomalies" in traditional methods.

[0032] The accuracy of copper ore target area positioning meets exploration requirements: 8 high-confidence target areas were obtained, with a 92% match rate with the typical density of copper ore. The corrected gravity data can accurately reflect the high-density properties of copper ore, providing precise direction for drilling verification and reducing ineffective exploration costs.

[0033] Ecological protection aligns with the fragile nature of alpine grasslands: exploration only causes a 0.03% vegetation damage rate (far below the 0.1% protection threshold). Through non-destructive operations such as silent drones and lightweight gravimeters, disturbance to marmot habitats is reduced, and irreversible damage to meadow vegetation is minimized, which meets environmental protection requirements.

[0034] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for copper prospecting in ecologically sensitive areas based on non-destructive geophysical exploration, characterized in that, Includes the following steps: Step 1: Based on UAV image acquisition technology, images of the surface of the target ecologically sensitive area are acquired. The acquired surface images are then introduced into a preset burrow identification model to extract the coordinates and diameter of each burrow entrance in the surface images, generating a burrow entrance distribution vector map. Step 2: Based on gravity detection technology, the target ecologically sensitive area is detected to obtain raw gravity data, which is then preprocessed and converted into a three-dimensional gravity detection image. Step 3: Integrate and analyze the cave distribution information in the cave entrance distribution vector map and the three-dimensional gravity detection image, and then identify low-density false anomalies caused by the caves through the cave anomaly judgment strategy, and generate correction schemes. Step four: Combine the corrected data with the typical density characteristics of copper ore to perform data correction and geological body marking on the gravity detection images.

2. The copper prospecting method for ecologically sensitive areas based on non-destructive geophysical exploration according to claim 1, characterized in that, In the vector diagram of the cave entrances, mark the unique ID, center coordinates, and entrance diameter of each cave.

3. The copper prospecting method in ecologically sensitive areas based on non-destructive geophysical exploration according to claim 2, characterized in that, In step one, visible light and infrared images of the target ecologically sensitive area are acquired based on UAV image acquisition technology; After preprocessing the visible light image, import the preset cave identification model and output the cave entrances identified in the visible light image; For infrared thermal imaging images, based on a preset temperature difference threshold, anomaly areas of surface temperature are extracted and designated as "suspected hidden cave areas". The infrared image segments of the "suspected hidden cave areas" are then superimposed with the visible light image segments of the corresponding locations and input into a preset cave identification model to verify whether they are real cave entrances. If the verification is successful, the center coordinates and diameter of the cave entrance are extracted and marked in the cave entrance distribution vector map, and distinguished from the cave entrances directly identified by the visible light images. At the same time, the temperature difference value of the cave is recorded to improve the cave information dimensions.

4. The copper prospecting method in ecologically sensitive areas based on non-destructive geophysical exploration according to claim 3, characterized in that, Before extracting temperature anomaly areas from infrared thermal imaging images, the cave data obtained from visible light image recognition is first called. Based on the cave entrance diameter, a repetition avoidance search radius is set. In the coordinate system of the infrared thermal imaging image, the center coordinates of each visible light cave are used as the center and the repetition avoidance search radius is used as the radius to delineate the duplicate exclusion area. In the subsequent extraction of temperature anomaly areas from infrared thermal imaging images, if the center coordinates of a certain temperature anomaly area fall into any duplicate exclusion area, or the overlap area with the duplicate exclusion area is ≥30%, then the temperature anomaly area is determined to be a visible light cave duplicate area and is directly removed from the infrared analysis process, not included in the screening range of "suspected hidden cave areas". Only temperature anomaly areas that do not fall into the duplicate exclusion area continue to be identified and analyzed.

5. The copper prospecting method in ecologically sensitive areas based on non-destructive geophysical exploration according to claim 4, characterized in that, Strategies for identifying anomalies in underground caverns include: A. Low-density region extraction: In the three-dimensional gravity detection image, the region with a gravity value less than the preset low-density value is defined as a low-density region. All low-density anomaly regions that meet the conditions are delineated, and the spatial range, amplitude and morphological characteristics of each low-density anomaly region are recorded. B. Connectivity and Coordinate Matching: Based on the vector map of the cave entrance distribution, the logic of "coordinate space association → three-dimensional path connectivity scanning → anomaly type classification and judgment" is used to distinguish between cave false anomalies and geological false anomalies. C. Verification and Correction: For false anomalies in the caves, secondary exploration by UAV is used for verification. After confirmation, the interference amplitude and interference range of the false anomaly are calculated, and a "False Anomaly Correction Data Table for Caves" is generated. The False Anomaly Correction Data Table for Caves contains the false anomaly ID, the corresponding cave group ID, the interference amplitude, the interference range, and the correction method.

6. The copper prospecting method for ecologically sensitive areas based on non-destructive geophysical exploration according to claim 5, characterized in that, First, the coordinates of the cave entrance distribution vector map and the 3D gravity detection image are unified. Taking the center coordinates of each cave entrance as the initial origin, the 3D spatial tracking algorithm is used to scan the path along the channel path of the low-density area in the gravity detection image. Starting from the origin of the cave entrance, the spatial distribution path of the low-density area is tracked to determine whether there is a complete channel that extends continuously from the cave entrance on the surface to the underground. If so, it is determined to be a false anomaly of the cave. If a scan reveals a low-density area that exists in isolation at a certain depth underground and is not connected to the entrance of a surface cave, it is marked as a geological false anomaly.

7. The copper prospecting method for ecologically sensitive areas based on non-destructive geophysical exploration according to claim 6, characterized in that, In step four, combining the corrected data from step three with the typical density characteristics of copper ore, data correction and geological body marking are sequentially performed on the gravity detection images, as follows: First, import the correction values ​​recorded in the "Cavity False Anomaly Correction Data Table" into the gravity detection image, and then perform gravity value superposition correction operation on all areas that have been identified as cavity false anomalies. After completing the gravity data correction, density conversion and labeling standards were determined by referring to the density difference between copper ore and surrounding rock. The gravity values ​​in the corrected gravity detection images were converted into the corresponding density values ​​of the underground medium. Then, based on the conversion results, labeling standards for three types of geological bodies were set: High-density areas, corresponding to densities ≥3.8g / cm³, are marked as "suspected copper ore target areas" and indicated with a red solid line box; The normal density zone, corresponding to a density of 2.4-3.0 g / cm³, is marked as "surrounding rock zone" and filled with gray. Other abnormal areas, with a density of 3.0-3.8 g / cm³ or <2.4 g / cm³, are marked as "Abnormal Areas to be Verified" and indicated with a yellow dashed box. Finally, a distribution map of suspected copper ore target areas in ecologically sensitive areas is generated, and the center coordinates, three-dimensional range, and corresponding density value of each suspected copper ore target area are marked.

8. The copper prospecting method for ecologically sensitive areas based on non-destructive geophysical exploration according to claim 7, characterized in that, In the gravity data correction step four, the spatial association between the "cavity false anomaly correction data table" and the three-dimensional gravity detection image is first established. The correction amount is matched with the corresponding cavity false anomaly area in the detection image by the false anomaly ID. If multiple cavity false anomalies overlap in a certain area, the arithmetic mean of the correction amounts of each false anomaly is taken as the final correction amount for that area.

9. The copper prospecting method for ecologically sensitive areas based on non-destructive geophysical exploration according to claim 8, characterized in that, The map of suspected copper mine target areas in ecologically sensitive areas also marks the content of the target area credibility assessment; the assessment dimensions include the degree of matching between the target area density value and the typical density of copper mines, and the exclusion of caves and other non-mineral disturbances.

10. The copper prospecting method for ecologically sensitive areas based on non-destructive geophysical exploration according to claim 9, characterized in that, The density conversion process requires the use of a Bouguer gravity anomaly-density conversion formula adapted to the geological background of ecologically sensitive areas, and the surrounding rock density parameters must be set based on measured data from the target area.

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