Gravity gradient deep constraint-based sandstone uranium mine excretion structure authenticity discrimination method

Abstract

The invention belongs to the technical field of uranium ore geological exploration, and particularly relates to a sandstone uranium ore excretion structure authenticity discrimination method based on gravity gradient deep constraint, which comprises the following steps: step 1, deep fracture ridge extraction based on gravity data; 2, constructing a'deep part-earth surface 'structure projection buffer area; 3, removing a micro-landform catchment area based on a DEM (Digital Elevation Model); 4, extracting earth surface remote sensing abnormity; and step five, space coupling and excretion zone authenticity discrimination are carried out. According to the method, by constructing a deep fracture-earth surface projection buffer area model and combining with micro-landform analysis, earth surface non-structural ponding and salt accumulation interference is effectively eliminated, and an effective discharge zone with a deep root is accurately locked.

Description

Technical Field

[0001] This invention belongs to the field of uranium geological exploration technology, specifically relating to a method for distinguishing the authenticity of sandstone uranium deposit discharge structures based on deep constraints of gravity gradient. Background Technology

[0002] Sandstone-type uranium deposits are typical "hydrated uranium deposits." Uranium-bearing, oxygen-rich water seeps into the basin's recharge area, migrates in the runoff area, and ultimately escapes through tectonic fractures or undergoes geochemical precipitate formation in the discharge area. Therefore, identifying local discharge zones (i.e., water-conducting structures that cut through the basement and connect to deeper layers) is crucial for mineral exploration.

[0003] Currently, the mainstream recognition methods are mainly divided into two categories:

[0004] 1. Surface extraction method based on pure remote sensing data: This method uses ASTER data to extract saline zones, water bodies and vegetation zones on the surface through band operations and density segmentation, thereby inferring drainage structures.

[0005] 2. Macroscopic prediction method based on multi-source data: using gravity, magnetic, and aerial data to overlay on a large scale to delineate favorable mineralization prospective areas or basement fault uplift structures.

[0006] The aforementioned existing technologies have the following serious problems in practical engineering applications, resulting in a low success rate for drilling verification:

[0007] 1. Severe interference from "false anomalies" (for pure remote sensing methods): Remote sensing technology can only identify whether the surface is "wet" or "salinized," but cannot identify the cause. In inland basins, many surface salinization zones are formed by the natural evaporation of rainwater collected in low-lying terrain ("evaporation pan effect"), rather than by the drainage of deep groundwater along geological structures. Current technologies lack a mechanism to distinguish between these two causes, easily leading to misjudgments.

[0008] 2. Unclear spatial correspondence between deep and shallow areas (for conventional gravity methods): The ore-controlling faults in sandstone uranium deposits are mostly normal faults or extensional-torsional faults with a certain dip angle. Traditional "overlay analysis" often ignores the spatial displacement caused by the fault dip angle, directly superimposing deep gravity anomalies with surface remote sensing anomalies vertically, leading to positioning errors.

[0009] 3. Lack of quantitative criteria for distinguishing authenticity: Existing methods rely heavily on the subjective visual interpretation of geologists and lack a quantitative screening process based on physical mechanisms (such as the geometric relationship between gravity gradient and surface projection). Summary of the Invention

[0010] The purpose of this invention is to provide a method for distinguishing the authenticity of sandstone uranium deposit discharge structures based on deep constraints of gravity gradient. This method constructs a "deep fault-surface projection buffer zone" model and combines it with micro-geomorphological analysis to effectively eliminate interference from non-structural surface water and salt accumulation, and accurately locate effective discharge zones with deep roots.

[0011] Technical solution to achieve the purpose of this invention:

[0012] A method for determining the authenticity of sandstone uranium deposit discharge structures based on deep gravity gradient constraints includes:

[0013] Step 1: Extraction of deep fracture ridges based on gravity data;

[0014] Step 2: Construct a deep-surface structural projection buffer zone;

[0015] Step 3: Removal of micro-topographic catchment areas based on DEM;

[0016] Step 4: Extraction of surface remote sensing anomalies;

[0017] Step 5: Spatial coupling and determination of the authenticity of the excretion zone.

[0018] Further, step one includes: acquiring Bouguer gravity anomaly data of the study area, performing gridding processing on the Bouguer gravity anomaly data, calculating the horizontal total gradient magnitude or the vertical second derivative, extracting the gradient band maxima connection line, and obtaining the gravity gradient ridge line L. gravity .

[0019] Furthermore, step two includes:

[0020] Step 2.1: Setting parameters: The average burial depth of the target layer in the study area is H, the estimated dip angle of the ore-controlling fault is α, and the dip direction of the gravity gradient zone ridge is D. dir ;

[0021] Step 2.2: Based on the average burial depth H of the target layer in the study area and the estimated dip angle of the ore-controlling fault α, calculate the projection offset W. offset ;

[0022] Step 2.3: Establish a buffer zone: Using the gravity gradient ridge extracted in Step 1 as a reference, extend the buffer zone in the dip direction D. dir Translation projection offset W offset Distance, to obtain the center line L of the fractured surface outcrop. surface And from this center line, the width δ is expanded to both sides to form a strip polygon, which is the construction projection buffer SPB.

[0023] Furthermore, in step 2.2, the projection offset W offset The calculation formula is:

[0024] W offset =H / tan(α).

[0025] Furthermore, step three includes:

[0026] Step 3.1: Calculate the topographic humidity index (TWI) and local elevation of the study area using the digital elevation model (DEM);

[0027] Step 3.2: Set a TWI threshold, extract areas where the TWI value is greater than the set TWI threshold and the local terrain is a closed low value, and generate a natural drainage depression vector V. sink .

[0028] Furthermore, in step 3.1, the formula for calculating the topographic humidity index (TWI) is:

[0029]

[0030] Where TWI is the topographic humidity index, δ is the catchment area per unit contour length, and β is the local slope.

[0031] Further, step four includes: using ASTER or Landsat remote sensing imagery, preprocessing the ASTER or Landsat remote sensing imagery data, and using methods such as band ratio, principal component analysis, or tasseled cap transformation to extract surface salinization zones, vegetation anomaly zones, and water anomaly zones, and merging them into a surface remote sensing anomaly vector V. RS .

[0032] Furthermore, step five includes:

[0033] Step 5.1: Determination of depth source: Calculate the surface remote sensing anomaly vector V RS The intersection with the construction projection buffer (SPB); remote sensing anomalies falling within the SPB are identified as having deep structural origins;

[0034] Step 5.2: Non-topographical determination: Based on the above intersection, eliminate vectors V that intersect with natural drainage depressions. sink Overlapping areas;

[0035] Step 5.3: Determination of the authenticity of the discharge zone: If salinization occurs on the surface of a certain place and it is located within the tectonic projection area, and at the same time it is shown as a non-low-lying area on the DEM, it is determined to be a tectonic discharge zone driven by strong pressure.

[0036] The beneficial technical effects of this invention are as follows:

[0037] 1. The present invention provides a method for distinguishing the authenticity of sandstone uranium deposit discharge structures based on deep gravity gradient constraints, which significantly improves the identification accuracy: by introducing dip projection correction of gravity gradient zones, the problem of "misalignment" between deep faults and surface appearance is solved, which is more in line with geological laws than simple vertical superposition.

[0038] 2. The present invention provides a method for distinguishing the authenticity of sandstone uranium deposit discharge structures based on deep gravity gradient constraints, which effectively eliminates false anomalies: by introducing a dual filtering mechanism of DEM micro-topography and gravity constraints, the interference of natural salt / water accumulation caused by low-lying terrain is eliminated, thus solving the problem of inefficient exploration caused by the existing remote sensing method of "drilling whenever salt is found".

[0039] 3. The present invention provides a method for distinguishing the authenticity of sandstone uranium deposit discharge structures based on deep constraints of gravity gradient. It is highly quantitative and operable: it provides a clear formula for calculating the offset and the discrimination logic, which changes the previous qualitative analysis mode that relied on geological intuition and facilitates computer automation. Detailed Implementation

[0040] The present invention will be further described in detail below with reference to the embodiments.

[0041] This invention provides a method for determining the authenticity of sandstone uranium deposit discharge structures based on deep constraints of gravity gradient, specifically including the following steps:

[0042] Step 1: Extraction of deep fracture ridges based on gravity data

[0043] Bouguer gravity anomaly data for the study area were acquired, and the data were gridded. The total horizontal gradient magnitude or the second vertical derivative was calculated to enhance the linear characteristics reflecting basement fracture. Gradient maxima were extracted and connected to obtain the gravity gradient ridge (L). gravity ).

[0044] Step 2: Construct a "deep-surface" structural projection buffer zone

[0045] Based on the burial depth of the target layer and the mechanical properties of the fracture in the study area, a spatial projection model was established to calculate the theoretical exposure range of deep fractures on the surface.

[0046] Step 2.1: Setting parameters: The average burial depth of the target layer in the study area is H, the estimated dip angle of the ore-controlling fault is α (usually taken as 60°-80°), and the dip direction of the gravity gradient zone ridge is D. dir .

[0047] Step 2.2: Calculate the projection offset W offset :

[0048] W offset =H / tan(α)

[0049] Step 2.3: Establish a buffer zone: Using the gravity gradient ridge extracted in Step 1 as a reference, extend the buffer zone in the dip direction D. dir Translation projection offset W offset Distance, to obtain the center line of the exposed fracture surface (L) surface ), and expand the width δ to both sides of this center line (the value of δ mainly takes into account the resolution error of gravity data, such as 500m-1000m), forming a strip polygon, which is the construction projection buffer (SPB).

[0050] Step 3: Micro-topographical drainage area removal based on DEM

[0051] Step 3.1: Calculate the topographic humidity index (TWI) and local elevation of the study area using a digital elevation model (DEM).

[0052] The formula for calculating the Terrain Humidity Index (TWI) is:

[0053]

[0054] Where TWI is the topographic humidity index, δ is the catchment area per unit contour length, and β is the local slope.

[0055] Step 3.2: Extract natural water catchment depressions: Set a TWI threshold, extract areas where the TWI value is greater than the set TWI threshold and the local terrain is a closed low value, and generate a natural water catchment depression vector V. sink These areas are highly susceptible to non-structural salinization.

[0056] Step 4: Extraction of surface remote sensing anomalies

[0057] Using ASTER or Landsat remote sensing imagery, the ASTER or Landsat remote sensing imagery data is preprocessed. Methods such as band ratio, principal component analysis, or tasseled cap transformation are employed to extract surface salinization zones, vegetation anomaly zones, and water anomaly zones, which are then merged into a surface remote sensing anomaly vector (V). RS ).

[0058] Step 5: Spatial Coupling and Authenticity Determination of Excretory Zones (Logical Filtering)

[0059] The results obtained in steps two through four are used to perform spatial topological analysis to identify truly advantageous areas within the excretory zone. The method for identifying advantageous areas within the excretory zone is as follows:

[0060] Step 5.1: Determination of depth source: Calculate the surface remote sensing anomaly vector (V RS The intersection of the remote sensing anomaly and the projection buffer (SPB). Remote sensing anomalies falling within the SPB are identified as having deep tectonic origins.

[0061] Step 5.2: Non-topographical determination: Based on the above intersection, eliminate vectors V that intersect with natural drainage depressions. sink Overlapping areas.

[0062] Step 5.3: Determination of the authenticity of the discharge zone: If salinization occurs on the surface and is located within the tectonic projection area, and is also shown as a non-low-lying area (or even a slight elevation / slope) on the DEM, it is determined to be a tectonic discharge zone driven by strong pressure.

[0063] Step Six: Output Results

[0064] Output a high-confidence vector distribution map of the discharge zone after authenticity verification to guide drilling deployment.

[0065] Example 1

[0066] This embodiment selects the edge of a typical Mesozoic-Cenozoic sedimentary basin in northern China as the test area. The target layer (Lower Cretaceous) in this area is buried at a depth of approximately 500-700 meters, with severe surface cover, mostly desert and Gobi, and features natural low-lying salt lakes and seasonal catchment areas. It is known that the ore-controlling structures in this area are mainly normal faults, dipping northwest (NW).

[0067] Taking the margin of a typical Mesozoic-Cenozoic sedimentary basin in northern China as an example, this embodiment provides a method for distinguishing the authenticity of sandstone uranium deposit discharge structures based on deep constraints of gravity gradient, specifically including the following steps:

[0068] Step 1: Extraction of deep fracture ridges based on the derivative of gravity

[0069] This step aims to identify the location of the basement fracture root that controls deep fluid transport.

[0070] Step 1.1: Grid the Bouguer gravity anomaly data, with a grid spacing of 50 meters.

[0071] Step 1.2: To eliminate the regional field background and highlight the local fault structure, the Vertical Second Derivative (VSD) algorithm is used. The calculation formula is as follows:

[0072]

[0073] Among them, g 2z denoted as the vertical second derivative, g is the gravitational anomaly value, and z is the vertical direction.

[0074] Step 1.3: On the vertical second derivative plot, extract the maxima of the gradient zone and connect them to obtain the gravity gradient ridge (L). gravity These consecutive maxima are connected and vectorized to generate a gravity gradient ridge vector layer.

[0075] Results: A set of linear structures trending mainly northeast (NE) was extracted, and these lines represent the central locations of basement faults.

[0076] Step 2: Construct a "deep-surface" structural projection buffer zone

[0077] This step is used to address the "misalignment" between the surface and deep locations caused by the fault dip angle.

[0078] Step 2.1: Parameter Setting

[0079] Based on borehole and seismic data, the average burial depth of the target layer in the study area is set to H = 600 meters.

[0080] Based on the regional tectonic geological background, the empirical dip angle α of the basin-controlling fault is set to 70°.

[0081] Determine the direction of the gravitational ridge line, D. dir Northwest (NW).

[0082] Step 2.2: Offset Calculation

[0083] Calculate the horizontal offset distance W of the deep fault extending to the surface. offset :

[0084] W offset =H / tan(α)=600 / tan(70°)≈218 meters

[0085] Step 2.3: Buffer Generation

[0086] The gravity gradient ridge line L extracted in step 1 gravity Using this as a reference, the fault surface outcrop centerline (L) is obtained by shifting 218 meters in the dip direction (NW). surface ).

[0087] Considering the resolution error of gravity data and the width of the fracture zone, with L surface A polygonal buffer zone is established by extending δ = 500 meters to both sides from the center.

[0088] The output result is denoted as the constructed projection buffer vector layer (Region). buffer This area represents the theoretically possible extent of deep fluid discharge to the surface.

[0089] Step 3: Removal of natural water catchment areas based on DEM micro-topography

[0090] This step aims to eliminate "false anomalies" by utilizing physical geomorphological features.

[0091] Step 3.1: Calculate the Topographic Moisture Index (TWI) using 30-meter resolution DEM data:

[0092]

[0093] Where TWI is the topographic humidity index, δ is the catchment area per unit contour length, and β is the local slope.

[0094] Step 3.2: Set the threshold:

[0095] The TWI values ​​of known natural lakes and low-lying areas in the statistical study area were used to set a TWI threshold T. sink (In this embodiment, T is set) sink >12 is a high-humidity catchment area.

[0096] Extracting interference areas: Regions with TWI > 12 and locally closed low-value terrain are extracted to generate a natural catchment depression vector layer (Region). sink Salinization in these areas is typically caused by surface runoff evaporation rather than tectonic discharge.

[0097] Step 4: Extraction of surface remote sensing anomaly information

[0098] This step can utilize existing, mature technologies to obtain surface characterization.

[0099] Step 4.1: Preprocess the ASTER data (radiative calibration, atmospheric correction).

[0100] Step 4.2: Calculation of the salinity index

[0101] The salinity index (SI) was used to extract information on surface salinization.

[0102]

[0103] SI stands for salinity index, Blue for the blue band, and Red for the red band.

[0104] Step 4.3: Vegetation and Water Body Extraction

[0105] Calculate NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index).

[0106] Step 4.4: Thresholding and Vectorization

[0107] High-value anomaly areas were extracted using density segmentation and converted into vector patches.

[0108] Step 4.5; The output result is recorded as the surface remote sensing anomaly patch layer (P). remote ).

[0109] Step 5: Spatial Coupling and Determination of the Authenticity of Excretory Zones

[0110] This step involves overlaying and analyzing the above layers to output the final favorable area.

[0111] Step 5.1; Primary Screening (Deep-Source Determination)

[0112] Calculate surface remote sensing anomaly patches (P remote ) and constructing the projection buffer (Region) buffer The spatial intersection of ).

[0113] Logic: If If the result is not empty, the patch is retained and recorded as a candidate patch (P). candidate ).

[0114] Physical significance: Surface anomalies supported by deep structural channels were screened out, while isolated anomalies far from fault zones were eliminated.

[0115] Step 5.2: Secondary screening (cause determination)

[0116] Candidate plaques (P) candidate ) and natural water catchment depressions (Region sink Overlay analysis was performed.

[0117] Judgment Rule A (True Excretory Zone): If the candidate plaque is located in the Region sink Outside (i.e.) ), or located on a slope / high part of a micro-topography.

[0118] Conclusion: This area is identified as a deep-source tectonic pressure relief zone (a first-level favorable area for mineral exploration).

[0119] Judgment Rule B (Pseudo / Mixed Anomalies): If the candidate plaque is mainly located in the Region sink Within.

[0120] Conclusion: The evaporation pan anomaly was determined to be topographically controlled and was therefore eliminated or downgraded.

[0121] The present invention has been described in detail above with reference to the embodiments. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention. All contents not described in detail in the present invention can be derived from existing technologies.

Claims

1. A method for determining the authenticity of sandstone uranium deposit discharge structures based on deep constraints of gravity gradient, characterized in that, include: Step 1: Extraction of deep fracture ridges based on gravity data; Step 2: Construct a "deep-surface" structural projection buffer zone; Step 3: Removal of micro-topographic catchment areas based on DEM; Step 4: Extraction of surface remote sensing anomalies; Step 5: Spatial coupling and determination of the authenticity of the excretion zone.

2. The method for determining the authenticity of sandstone uranium deposit discharge structures based on deep gravity gradient constraints according to claim 1, characterized in that, Step one includes: acquiring Bouguer gravity anomaly data of the study area, performing gridding processing on the Bouguer gravity anomaly data, calculating the total horizontal gradient magnitude or the second vertical derivative, extracting the gradient band maxima connection lines to obtain the gravity gradient ridge line L. gravity .

3. The method for determining the authenticity of sandstone uranium deposit discharge structures based on deep gravity gradient constraints according to claim 2, characterized in that, Step two includes: Step 2.1: Setting parameters: The average burial depth of the target layer in the study area is H, the estimated dip angle of the ore-controlling fault is α, and the dip direction of the gravity gradient zone ridge is D. dir ; Step 2.2: Based on the average burial depth H of the target layer in the study area and the estimated dip angle of the ore-controlling fault α, calculate the projection offset W. offset ; Step 2.3: Establish a buffer zone: Using the gravity gradient ridge extracted in Step 1 as a reference, extend the buffer zone in the dip direction D. dir Translation projection offset W offset Distance, to obtain the center line L of the fractured surface outcrop. surface And from this center line, the width δ is expanded to both sides to form a strip polygon, which is the construction projection buffer SPB.

4. The method for determining the authenticity of sandstone uranium deposit discharge structures based on deep gravity gradient constraints according to claim 3, characterized in that, In step 2.2, the projection offset W offset The calculation formula is: W offset =H / tan(α)。 5. The method for determining the authenticity of sandstone uranium deposit discharge structures based on deep gravity gradient constraints according to claim 3, characterized in that, Step three includes: Step 3.1: Calculate the topographic humidity index (TWI) and local elevation of the study area using the digital elevation model (DEM); Step 3.2: Set a TWI threshold, extract areas where the TWI value is greater than the set TWI threshold and the local terrain is a closed low value, and generate a natural drainage depression vector V. sink .

6. The method for determining the authenticity of sandstone uranium deposit discharge structures based on deep gravity gradient constraints according to claim 5, characterized in that, In step 3.1, the formula for calculating the topographic humidity index (TWI) is: Where TWI is the topographic humidity index, δ is the catchment area per unit contour length, and β is the local slope.

7. The method for determining the authenticity of sandstone uranium deposit discharge structures based on deep gravity gradient constraints according to claim 5, characterized in that, Step four includes: using ASTER or Landsat remote sensing imagery, preprocessing the ASTER or Landsat remote sensing imagery data, and using methods such as band ratio, principal component analysis, or tasseled cap transformation to extract surface salinization zones, vegetation anomaly zones, and water anomaly zones, and merging them into a surface remote sensing anomaly vector V. RS .

8. The method for determining the authenticity of sandstone uranium deposit discharge structures based on deep gravity gradient constraints according to claim 7, characterized in that, Step five includes: Step 5.1: Determination of depth source: Calculate the surface remote sensing anomaly vector V RS The intersection with the construction projection buffer (SPB); remote sensing anomalies falling within the SPB are identified as having deep structural origins; Step 5.2: Non-topographical determination: Based on the above intersection, eliminate vectors V that intersect with natural drainage depressions. sink Overlapping areas; Step 5.3: Determination of the authenticity of the discharge zone: If salinization occurs on the surface of a certain place and it is located within the tectonic projection area, and at the same time it is shown as a non-low-lying area on the DEM, it is determined to be a tectonic discharge zone driven by strong pressure.

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