Method for identifying salt-alkali land as an indicator of sandstone-type uranium mineralization

Abstract

This invention belongs to the field of remote sensing geology and mineral exploration technology, specifically relating to a method for identifying indicative saline-alkali lands for sandstone-type uranium mineralization. The method includes: Step 1: preliminary identification and spatiotemporal characteristic analysis of saline-alkali lands based on remote sensing imagery; Step 2: in-depth identification based on spatial morphology and structural coupling; Step 3: evaluation of mineralization indices based on the identification results; and Step 4: comprehensive prediction of mineralization potential and selection of favorable areas. This method can distinguish between surface hydrologically formed and deep hydrologically formed saline-alkali lands, accurately identify effective saline-alkali lands that are indicative of sandstone-type uranium mineralization, and ultimately serve the comprehensive evaluation of uranium mineralization potential and the selection of favorable exploration areas.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing geology and mineral exploration technology, specifically relating to a method for identifying indicative saline-alkali lands for sandstone-type uranium deposits. Background Technology

[0002] The formation and enrichment of sandstone-type uranium deposits is a geological process in which uranium-bearing oxidizing groundwater encounters reducing barriers during its migration and precipitates into minerals. In sedimentary basins of arid to semi-arid climate zones, the distribution of surface saline-alkali lands is a direct surface response to groundwater activity; therefore, saline-alkali lands are considered an important indirect remote sensing indicator for finding sandstone-type uranium deposits. However, current technologies for utilizing saline-alkali land information in uranium exploration suffer from a significant technical flaw: conflating saline-alkali lands of different origins, severely impacting the accuracy of mineral exploration predictions.

[0003] In arid regions, the formation of saline-alkali lands is complex and can be mainly divided into two categories. The first category is of surface hydrological origin, formed by seasonal rainfall, surface runoff, or rapid evaporation after river flooding. The extent and intensity of this type of saline-alkali land vary drastically with the seasons, primarily reflecting the surface hydrological cycle. Therefore, its intrinsic connection with deep uranium mineralization is weak, and it is considered "background noise" or "interference" in mineral exploration evaluation. The second category is of deep hydrological origin, formed by the stable and continuous discharge and evaporation of mineral-rich deep groundwater to the surface along faults and other water-conducting structures. This type of saline-alkali land serves as a "window" into the deep hydrological system; its distribution directly indicates the discharge channels and locations of groundwater, making it "effective" mineral exploration information for finding interlayer oxidation zones and reduction barriers. Current technologies typically use single-phase remote sensing images and spectral index methods (such as MSI and SI) to extract saline-alkali lands. However, this method cannot effectively distinguish between the two types of saline-alkali lands, leading to a high false positive rate in mineral exploration predictions and unclear geological significance.

[0004] Furthermore, saline-alkali lands of different origins exhibit significant differences in their behavioral characteristics over time. Deep-seated saline-alkali lands, due to their relatively stable water supply, demonstrate high stability in their spatial location, morphology, and salinity accumulation levels across interannual and seasonal variations. In contrast, surface-seated saline-alkali lands exhibit significant dynamism and instability. Current technologies largely rely on "snapshot" analysis of single-temporal images, neglecting this crucial temporal dimension information, thus losing an important basis for identifying the origins of saline-alkali lands and assessing the stability of hydrological systems.

[0005] Meanwhile, the spatial morphology and distribution pattern of deep hydrologically formed saline-alkali lands are often strictly controlled by deep water-conducting structures (such as faults and fracture zones), frequently exhibiting beaded, banded, or tectonic-line-oriented characteristics. Current techniques, after extracting saline-alkali land, mostly perform simple spatial overlay analysis, lacking quantitative analysis of the morphology of the saline-alkali patches themselves and quantitative evaluation of their coupling tightness with structures, making it difficult to effectively establish the genetic link between "surface saline-alkali morphology and deep structural channels."

[0006] Therefore, there is an urgent need in this field for a new method that can accurately identify "effective" saline-alkali lands closely related to sandstone-type uranium mineralization from a large amount of saline-alkali land information, and scientifically evaluate their mineral exploration indication significance. Summary of the Invention

[0007] The purpose of this invention is to provide a method for identifying indicative saline-alkali lands for sandstone-type uranium mineralization. This method can distinguish between surface hydrological saline-alkali lands and deep hydrological saline-alkali lands, accurately identify effective saline-alkali lands that are indicative of sandstone-type uranium mineralization, and ultimately serve the comprehensive evaluation of uranium mineralization potential and the selection of favorable prospecting areas.

[0008] Technical solution to achieve the purpose of this invention:

[0009] A method for identifying indicative saline-alkali lands for sandstone-type uranium deposits, the method comprising:

[0010] Step 1: Preliminary identification and spatiotemporal feature analysis of saline-alkali land based on remote sensing imagery;

[0011] Step 2: In-depth identification based on the coupling degree between spatial morphology and structure;

[0012] Step 3: Evaluation of mineralization indices based on the screening results;

[0013] Step 4: Comprehensive prediction of mineralization potential and selection of favorable areas.

[0014] Further, step 1 includes:

[0015] Step 1.1 Acquisition and preprocessing of multi-temporal remote sensing data;

[0016] Landsat 8 / 9OLI remote sensing images of the study area during the dry and arid seasons were collected to obtain multiple images from different periods; SRTM DEM data with a spatial resolution of 30 meters were acquired, and the images from each period were preprocessed.

[0017] Step 1.2 Preliminary identification of saline-alkali land based on multi-index fusion;

[0018] After calculating three independent single-band grayscale images—Improved Salinity Index (MSI), Salinity Index (SI), and Normalized Differential Salinity Index (NDSI)—from the preprocessed images for each period, a pixel-by-pixel weighted linear combination operation was performed on the three raster layers (MSI, SI, and NDSI) using a raster calculator or band operation tool in remote sensing image processing software to generate a CSI image map. The generated CSI image map was then subjected to OTSU adaptive threshold segmentation to extract the preliminary saline-alkali land distribution map for each period.

[0019] Step 1.3 Initial screening of causes based on spatiotemporal dynamic characteristics;

[0020] The preliminary saline-alkali land distribution map is converted into a preliminary saline-alkali land binary map. The preliminary saline-alkali land binary maps of all periods are overlaid in the geographic information system software to construct a spatiotemporal data cube. For each pixel in the cube, its spatiotemporal dynamic index is calculated. A screening threshold is set for the spatiotemporal dynamic index. Based on the screening threshold, a preliminary stable saline-alkali land map layer is selected.

[0021] Furthermore, the preprocessing in step 1.1 includes: radiometric calibration, FLAASH atmospheric correction, geometric fine correction, and DEM-based topographic correction of the remote sensing image.

[0022] Furthermore, the spatiotemporal dynamic indicators in step 1.3 include:

[0023] Saline-alkali land stability index (SSI): Calculates the ratio of the number of times a pixel is identified as saline-alkali land across all periods to the total number of periods.

[0024] Annual Variation Rate of Saline-Alkali Land (AYV): Calculates the number of times the saline-alkali land condition of a pixel changes in the dry season images of consecutive years during the study period, and then divides it by (number of consecutive years in the study period - 1).

[0025] Further, step 2 includes:

[0026] Step 2.1 Quantification of morphological characteristics of stable saline-alkali land patches;

[0027] The selected stable saline-alkali map layers are converted into vector polygon layers; for each individual polygon patch in the layer, its morphological parameters are calculated using spatial analysis tools.

[0028] Step 2.2 Coupling analysis with deep structures;

[0029] Load the 1:250,000 scale fault structure interpretation layer of the study area; calculate the coupling index between each stable saline-alkali patch and the fault structure.

[0030] Step 2.3 Final identification of mineralization indicator saline-alkali land;

[0031] Based on morphological parameters and coupling degree indices, a comprehensive identification rule based on geological significance was established to query and screen stable saline-alkali map layers, and finally identify mineralized indicative saline-alkali lands.

[0032] Furthermore, the morphological parameters in step 2.1 include: elongation, which is the aspect ratio of the smallest bounding rectangle of the patch; and orientation, which is the angle between the main extension direction of the patch and the due north direction.

[0033] Furthermore, the coupling index in step 2.2 includes:

[0034] Directional Consistency (DA): Calculate the absolute value of the angle between the main axis direction of the patch and the strike of the most important fault segment within a 1 km effective influence range;

[0035] Spatial proximity SP: Calculates the Euclidean distance from the centroid of the patch to the nearest fracture.

[0036] Further, step 3 includes:

[0037] Step 3.1 Calculation of the Mineralization Indicator Score (SMI);

[0038] For the finally identified mineralization indicative saline-alkali land patches, the mineralization indicative score SMI is calculated by weighted summation of the normalized mineralization indicative score SMI, saline-alkali land stability index SSI, elongation, spatial proximity SP, and area.

[0039] Step 3.2 Spatial mapping of mineralization indicators;

[0040] The mineralization indices of all mineralized indicative saline-alkali patches are used to generate a raster map of the mineralization indices of the saline-alkali land in the study area through spatial interpolation or direct assignment.

[0041] Further, step 4 includes:

[0042] Step 4.1 Multi-factor information fusion and potential evaluation;

[0043] In geographic information system software, the saline-alkali land mineralization indicator raster layer is used as the core hydrogeological condition indicator layer. It is then weighted and overlaid with other key mineral exploration element layers to calculate and generate the final mineralization potential index map.

[0044] Step 4.2 Mineralization potential zoning and optimal selection of favorable areas;

[0045] The mineralization potential index map was statistically classified using the natural discontinuity classification method, dividing it into three levels of potential zones: high, medium, and low, thus identifying the most promising areas for mineral exploration.

[0046] Furthermore, other key mineral exploration element layers in step 4.1 include: a buffer layer of uranium source rock mass, a layer of favorable ore-bearing sand body distribution, and a layer reflecting tectonic channels and reducing environments.

[0047] The beneficial technical effects of this invention are as follows:

[0048] 1. Solved the core problem of identifying the genesis of saline-alkali land: Proposed and implemented a quantitative identification method based on spatiotemporal dynamic characteristics and spatial morphology analysis, which can effectively distinguish between saline-alkali land with surface hydrological genesis and deep hydrological genesis, fundamentally improving the effectiveness and reliability of remote sensing mineral exploration information.

[0049] 2. Significantly reduced false positive rate in mineral exploration prediction: By eliminating a large number of "interfering" saline-alkali lands that are unrelated to deep mineralization, the subsequent evaluation and analysis of mineralization potential is based on more reliable geological information. Compared with existing technologies, this invention effectively reduces the interference of non-mineralization factors by identifying the genesis of saline-alkali lands, thereby improving the reliability and accuracy of delineating favorable mineral exploration areas and making the prediction targets more focused.

[0050] 3. It provides a new approach to evaluate the stability of hydrological systems: By calculating the Stability Index (SSI) and Annual Variation Rate (AYV) of saline-alkali land, this invention indirectly but effectively evaluates the long-term stability of groundwater discharge systems, providing an important basis for determining whether the interlayer oxidation zone is in a stable or slowly advancing favorable mineralization state.

[0051] 4. A direct link between surface morphology and deep structure was established: Through quantitative analysis of the coupling degree between saline-alkali land morphology and structure, the genetic link between the distribution of surface saline-alkali land and deep water-conducting faults was scientifically established, providing intuitive evidence for understanding the regional tectonic water and mineral control mechanisms.

[0052] 5. Improved scientific rigor of comprehensive mineralization prediction: The “mineralization indicative saline-alkali land” and its “mineralization indicator (SMI)” produced by this invention are new, comprehensive and quantifiable mineral exploration evaluation indicators. Introducing them into the mineralization prediction model greatly improves the scientific rigor and prediction accuracy of the model. Detailed Implementation

[0053] The present invention will be further described in detail below with reference to the embodiments.

[0054] This invention provides a method for identifying indicative saline-alkali lands of sandstone-type uranium deposits, specifically including the following steps:

[0055] Step 1: Preliminary identification and spatiotemporal feature analysis of saline-alkali land based on remote sensing imagery;

[0056] Step 1.1 Acquisition and preprocessing of multi-temporal remote sensing data;

[0057] Landsat 8 / 9OLI remote sensing images of the study area during the dry and arid seasons were collected to obtain multiple images from different periods; SRTM DEM data with a spatial resolution of 30 meters were acquired, and the images from each period were preprocessed.

[0058] Preprocessing of images from different periods includes radiometric calibration, FLAASH atmospheric correction, geometric fine correction, and DEM-based topographic correction of remote sensing images to eliminate errors caused by sensors, atmosphere, and topography, and to ensure that the image sequence has good consistency and comparability in time and space.

[0059] Step 1.2 Preliminary identification of saline-alkali land based on multi-index fusion;

[0060] After calculating three independent single-band grayscale images—Improved Salinity Index (MSI), Salinity Index (SI), and Normalized Differential Salinity Index (NDSI)—from the preprocessed images for each period, a cell-by-cell weighted linear combination operation is performed on the three raster layers (MSI, SI, and NDSI) using a raster calculator or band math tool in remote sensing image processing software (such as ENVI or ArcGIS) to generate a CSI image map.

[0061] The specific generation process is as follows: taking the three raster layers MSI, SI, and NDSI as input, and according to the set weight coefficients (0.5, 0.3, and 0.2 in this embodiment of the invention), the following cell-level operation expression is executed:

[0062] CSI=0.5×MSI+0.3×SI+0.2×NDSI.

[0063] In the formula, CSI is the comprehensive salinity index; MSI is the improved salinity index; SI is the salinity index; and NDSI is the normalized differential salinity index.

[0064] The output of this operation is a new, single-band floating-point raster data file, namely a CSI image map. Each pixel value in this image map is a comprehensive salinity index value calculated using the weighting formula described above. The value reflects the degree of salinization at that pixel location.

[0065] The generated CSI image map was segmented using the OTSU (Otsu method) adaptive thresholding method to extract the preliminary distribution map of saline-alkali land for each period.

[0066] Step 1.3 Initial screening of causes based on spatiotemporal dynamic characteristics;

[0067] The "preliminary saline-alkali land distribution map" is converted into a "preliminary saline-alkali land binary map" through data format conversion or reclassification, aiming to provide standardized binary (0 or 1) input data for subsequent spatiotemporal cube construction and statistical analysis.

[0068] The specific implementation is as follows: After processing the CSI image map using the OTSU adaptive threshold segmentation method in step 1.2, its direct output result is itself a binary classification map, that is, all pixels are divided into two categories: "saline-alkali land" and "non-saline-alkali land". To generate a standardized binary map, the following operations need to be performed: In the Geographic Information System (GIS) software, use the Reclassify tool to assign values ​​to the preliminary saline-alkali land distribution map generated by OTSU segmentation. All pixels or regions identified as "saline-alkali land" are uniformly assigned a pixel value of 1; all pixels or regions identified as "non-saline-alkali land" are uniformly assigned a pixel value of 0.

[0069] After this step, each preliminary saline-alkali land distribution map is converted into a raster layer containing only two pixel values, 0 and 1, namely the "preliminary saline-alkali land binary map". This standardized binary map can be directly used for subsequent construction of spatiotemporal data cubes and calculation of indicators such as stability index (SSI).

[0070] Preliminary binary maps of saline-alkali land from all periods are overlaid in Geographic Information System (GIS) software to construct a spatiotemporal data cube. For each pixel in the cube, its spatiotemporal dynamic index is calculated, and a screening threshold is set for the spatiotemporal dynamic index. Based on the screening threshold, a preliminary saline-alkali land map layer is selected.

[0071] Spatiotemporal dynamic indicators include:

[0072] Saline-alkali land stability index (SSI): Calculates the ratio of the number of times a pixel is identified as saline-alkali land across all periods to the total number of periods.

[0073] Annual Variation Rate of Saline-Alkali Land (AYV): Calculates the number of times the saline-alkali land status (0 or 1) of a pixel changes in the dry season imagery of consecutive years during the study period, and then divides by (number of consecutive years in the study period - 1).

[0074] The Annual Variation Rate (AYV) of saline-alkali land aims to calculate the average frequency of change in the state of saline-alkali land between years. For a time series containing observation data of n consecutive years (in this example, n=7), there are a total of n-1 time intervals in which the state can change. For example, the data of 7 years contains 6 time intervals: (Year 1-2), (Year 2-3), (Year 3-4), (Year 4-5), (Year 5-6), and (Year 6-7).

[0075] Therefore, dividing the total number of changes in the state of saline-alkali land by the number of time intervals (i.e., "number of consecutive years in the study period - 1") yields the normalized annual average rate of change. This indicator can objectively measure the interannual stability of saline-alkali land. A low AYV value means that the saline-alkali land has been stable over many years with minimal changes. This is one of the key quantitative indicators used in this invention to identify saline-alkali land with deep, stable water source recharge.

[0076] Step 2: In-depth identification based on the coupling degree between spatial morphology and structure;

[0077] Step 2.1 Quantification of morphological characteristics of stable saline-alkali land patches;

[0078] The selected stable saline-alkali map layers are converted into vector polygon layers; for each individual polygon patch in the layer, its morphological parameters are calculated using spatial analysis tools.

[0079] The main morphological parameters include: elongation, which is the aspect ratio of the smallest bounding rectangle of the patch; and orientation, which is the angle between the main extension direction of the patch and the due north direction.

[0080] Step 2.2 Coupling analysis with deep structures;

[0081] Load the 1:250,000 scale fault structure interpretation layer for the study area. For each stable saline-alkali patch, calculate its coupling index with the fault structure.

[0082] Coupling indices include:

[0083] Directional Consistency (DA): Calculate the absolute value of the angle between the main axis direction of the patch and the strike of the most important fault segment within a 1 km effective influence range.

[0084] Spatial proximity (SP): Calculates the Euclidean distance from the centroid of the patch to the nearest fracture.

[0085] Step 2.3 Final identification of mineralization indicator saline-alkali land;

[0086] Based on morphological parameters and coupling degree indices, a comprehensive identification rule based on geological significance was established to query and screen stable saline-alkali map layers, and finally identify mineralized indicative saline-alkali lands.

[0087] Step 3: Evaluation of mineralization indices based on the screening results;

[0088] Step 3.1 Calculation of the Mineralization Indicator Score (SMI);

[0089] For the finally identified mineralized indicative saline-alkali land patches, the mineralization indicative score (SMI) is calculated to quantify the strength of their mineral exploration indication significance.

[0090] SMI is obtained by weighted summation of multiple normalized parameters. The parameters include: mineralization indicator score (SMI), saline-alkali land stability index (SSI), elongation, spatial proximity (SP), and area (area refers to the geometric area of ​​each independent, contiguous mineralization indicator saline-alkali land patch).

[0091] Specifically, in step 2.1, the "Stable Saline-Alkali Land" raster layer has been converted into a vector polygon layer. After the final verification in step 2.3, a "Mineral-Indicating Saline-Alkali Land" layer containing several independent polygons is obtained. Here, "Area" refers to the geographic space area occupied by each polygon patch, automatically calculated by the GIS software, typically in square meters (m²). 2 ) or square kilometers (km) 2 ).

[0092] The introduction of the area parameter in the calculation of mineralization indicator (SMI) has the geological significance that, under similar conditions (such as stability, morphology, and tectonic coupling), a larger area of ​​"mineralization indicator saline-alkali land" patch may indicate that the underlying deep groundwater drainage system is larger in scale, has a wider range of influence, or has a more persistent drainage effect, and therefore its mineralization indicator significance may be stronger.

[0093] Step 3.2 Spatial mapping of mineralization indicators;

[0094] The SMI values ​​of all mineralized indicative saline-alkali patches were used to generate a saline-alkali mineralization indicator raster map of the study area through spatial interpolation or direct assignment.

[0095] Step 4: Comprehensive prediction of mineralization potential and selection of favorable areas;

[0096] Step 4.1 Multi-factor information fusion and potential evaluation;

[0097] In geographic information system software, the saline-alkali land mineralization indicator raster layer is used as the core hydrogeological condition indicator layer. It is then weighted and overlaid with other key mineral exploration element layers to calculate and generate the final mineralization potential index map.

[0098] Other key mineral exploration element layers include: a buffer zone layer for uranium source rock bodies, a distribution layer of favorable ore-bearing sand bodies (obtained from geological maps and sedimentary facies analysis), and layers reflecting tectonic channels and reducing environments, which may specifically include a fault density layer and / or a layer of anomalous oil and gas micro-leakage zones.

[0099] Step 4.2 Mineralization potential zoning and optimal selection of favorable areas;

[0100] The mineralization potential index map was statistically classified using the "Natural Discontinuity Classification Method (Jenks)" to divide it into three levels of potential zones: high, medium, and low, thus identifying the most promising areas for mineral exploration.

[0101] Example

[0102] Taking sandstone-type uranium deposit prospecting prediction in a certain area of ​​southwestern Tarim Basin as an example, this invention provides a method for identifying indicative saline-alkali lands for sandstone-type uranium deposits, specifically including the following steps:

[0103] Step 1: Preliminary identification and spatiotemporal feature analysis of saline-alkali land based on remote sensing imagery

[0104] Step 1.1: Data Acquisition and Preprocessing

[0105] Landsat 8 / 9 OLI imagery for the study area from 2018 to 2024 was collected for seven years. To capture seasonal hydrological changes, images with cloud cover below 5% were selected each year during April-May (dry season) and August-September (wet season), totaling 14 images. SRTM DEM data with a spatial resolution of 30 meters were also acquired. Radiometric calibration, FLAASH atmospheric correction, geometric fine correction, and DEM-based topographic correction were performed on all Landsat imagery using ENVI 5.6 software to eliminate errors caused by sensors, atmosphere, and topography, ensuring good spatiotemporal consistency and comparability of the image sequence.

[0106] Step 1.2 Preliminary identification of saline-alkali land

[0107] For each preprocessed image, the Improved Salinity Index (MSI), Salinity Index (SI), and Normalized Differential Salinity Index (NDSI) are calculated simultaneously. To improve the robustness of identification, a weighted fusion method is used to generate a Comprehensive Salinity Index (CSI). In this embodiment, the weighting calculation expression is as follows:

[0108] CSI=0.5×MSI+0.3×SI+0.2×NDSI.

[0109] In the formula, CSI is the comprehensive salinity index; MSI is the improved salinity index; SI is the salinity index; and NDSI is the normalized differential salinity index.

[0110] The rationale for this weighting is based on the diagnostic capabilities of each index in the specific arid environment of the Tarim Basin:

[0111] MSI utilizes the shortwave infrared band, which is sensitive to soil minerals and moisture. It is the most robust for distinguishing saline soil from background features such as high-albedo sandy land, and therefore assigns it the highest weight (0.5) as the basis for identifying the main body of saline-alkali land.

[0112] SI is sensitive to severe salinization features such as salt frost and salt crust on the surface, which are strong evidence of long-term stable groundwater discharge. Therefore, it is assigned a weight of 0.3 to enhance its ability to capture strong anomaly centers.

[0113] NDSI is sensitive to changes in soil texture and moisture content, which helps delineate the boundary transition zone of saline-alkali land. However, it is easily affected by instantaneous moisture, so it is given the lowest weight (0.2) as an auxiliary correction to limit the noise it may introduce. For the generated CSI image map, OTSU (Otsu method) adaptive threshold segmentation is used. This method can automatically determine the optimal segmentation threshold based on the statistical characteristics of the image gray-level histogram, thereby objectively extracting the preliminary saline-alkali land distribution map for each period. This operation is repeated for all 14 image scenes.

[0114] Step 1.3 Initial screening of causes based on spatiotemporal dynamic characteristics

[0115] All preliminary binary maps of saline-alkali land from 14 periods (with saline-alkali land pixels assigned a value of 1 and non-saline-alkali land pixels assigned a value of 0) were overlaid in ArcGIS Pro to construct a spatiotemporal data cube. For each pixel in the cube, its spatiotemporal dynamic index was calculated:

[0116] Saline-alkali land stability index (SSI): Calculates the ratio of the number of times a pixel is identified as saline-alkali land in 14 periods to the total number of periods, 14.

[0117] Annual Variation Rate of Saline-Alkali Land (AYV): This calculates the number of times the saline-alkali land status (0 or 1) of a pixel changes during dry season imagery across seven consecutive years, and then divides by 6. In this embodiment, the screening threshold is set to SSI > 0.75 and AYV < 0.2. This threshold is set based on the following: SSI > 0.75 represents the upper quartile of the statistical distribution, indicating that the pixel exhibits saline-alkali land status for the vast majority of periods (at least 11 periods), ensuring high temporal continuity; AYV < 0.2 requires that the interannual variation within the seven years does not exceed one instance, excluding unstable saline-alkali land that repeatedly appears and disappears between years. Through this step, a layer of "stable saline-alkali land" initially considered to be replenished by deep, stable water sources is selected.

[0118] Step 2: In-depth identification based on the coupling degree of spatial morphology and structure

[0119] Step 2.1 Quantification of morphological characteristics of stable saline-alkali land patches

[0120] Convert the "Stable Saline-Alkali Land" raster layer into a vector polygon layer. For each individual polygon patch in the layer, calculate its morphological parameters using spatial analysis tools, mainly including: elongation, which is the aspect ratio of the patch's smallest bounding rectangle; and orientation, which is the angle between the patch's main extension direction and true north.

[0121] Step 2.2 Coupling analysis with deep structures

[0122] Load the 1:250,000 scale fracture structure interpretation layer for the study area. For each "stable saline-alkali land" patch, calculate its coupling index with the fracture structure:

[0123] Directional Consistency (DA): Calculate the absolute value of the angle between the main axis direction of the patch and the strike of the most important fault segment within a 1 km effective influence range.

[0124] Spatial proximity (SP): Calculates the Euclidean distance from the centroid of the patch to the nearest fracture.

[0125] Step 2.3 Final identification of "mineralization indicator saline-alkali land"

[0126] A comprehensive identification rule based on geological significance is established to query and filter the "stable saline-alkali land" layer. In this embodiment, the identification rule is set as ("Elongation" > 3.0 AND "DA" < 20°) OR "SP" < 500 meters. The rationale for setting this rule is as follows:

[0127] The combination of "Elongation" > 3.0 and "DA" < 20° aims to screen for saline-alkali landforms that exhibit distinctly elongated strip-like morphology and whose orientation is highly consistent with the direction of the regional tectonic stress field. This strongly suggests that their formation is controlled by deep linear structures (faults). Elongation > 3.0 is a commonly used empirical threshold for distinguishing between strip-like and nodal features, while DA < 20° ensures the genetic correlation between morphology and tectonic formation.

[0128] The condition "SP" < 500 meters, used as a parallel OR condition, aims to identify saline-alkali lands that, while irregularly shaped, are located adjacent to known main fault distributions. The 500-meter distance is an estimate based on regional geological experience of the typical width of secondary fracture zones or permeation influence zones adjacent to main faults. Through this step, the ultimately selected patches are identified as "mineralization indicator saline-alkali lands."

[0129] Step 3: Evaluation of mineralization indices based on screening results

[0130] Step 3.1 Calculation of Metallogenic Indicator (SMI)

[0131] For each ultimately identified "mineralized indicative saline-alkali land" patch, a comprehensive mineralization indicative score (SMI) is calculated to quantify the strength of its mineral exploration indication. The SMI is obtained by weighted summation of multiple normalized parameters. In this embodiment, the calculation formula is as follows:

[0132] SMI=0.4×SSI_norm+0.3×Elongation_norm+0.2×(1 / SP_norm)+0.1×Area_norm.

[0133] In the formula, SMI is the mineralization indicator score, SSI is the saline-alkali land stability index; Elongation is the elongation rate; SP is the spatial proximity; and Area is the area (i.e., the geographic space area occupied by each polygonal patch automatically calculated by GIS software, usually in square meters (m²)). 2 ) or square kilometers (km) 2 )).

[0134] The weighting is based on the following criteria: Stability (SSI) best reflects the sustainability of water sources, so it has the highest weight (0.4); Elongation and Structural Proximity (SP) are key factors in determining whether a water source is controlled by tectonics, and have the next highest weights (0.3 and 0.2); Area, as a scale indicator, has a relatively low weight (0.1).

[0135] Step 3.2 Spatial mapping of mineralization indicators

[0136] The SMI values ​​of all mineralized indicative saline-alkali patches were used to generate a saline-alkali mineralization indicator raster map of the study area through spatial interpolation or direct assignment.

[0137] Step 4: Comprehensive prediction of mineralization potential and selection of favorable areas

[0138] Step 4.1 Multi-factor information fusion and potential evaluation

[0139] In the ArcGIS Pro geographic information system platform, the saline-alkali land mineralization indicator (SMI) raster layer is used as the core hydrogeological condition indicator layer, and weighted overlay analysis is performed with other key mineral exploration feature layers. These feature layers include: a uranium source rock buffer layer, a favorable ore-bearing sand body distribution layer (obtained from geological maps and sedimentary facies analysis), and layers reflecting tectonic channels and reducing environments, specifically including fault density layers and / or oil and gas micro-permeability anomaly zones layers.

[0140] Based on the regional metallogenic geological background and expert knowledge, weights were assigned to each element layer. In this embodiment, since the study area is rich in oil and gas resources and oil and gas micro-permeability is a key ore-controlling factor, the oil and gas micro-permeability anomaly zone layer was taken as one of the core elements. The weights were set as follows: SMI layer (0.4), oil and gas micro-permeability anomaly zone layer (0.3), uranium source rock buffer zone layer (0.15), and favorable ore-bearing sand body distribution layer (0.15). The SMI layer was given the highest weight because it integrates the stability of the hydrological system and tectonic channel information, and is the most direct surface response to mineralization. The final metallogenic potential index map was calculated and generated using the "weighted overlay" tool.

[0141] Step 4.2 Mineralization Potential Zoning and Optimization of Favorable Areas

[0142] Finally, the mineralization potential index map was statistically classified using the Jenks method (natural discontinuity classification method), dividing it into three levels of potential zones: high, medium, and low, thus identifying the most promising areas for mineral exploration.

[0143] 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 identifying indicative saline-alkali lands of sandstone-type uranium deposits, characterized in that, The method includes: Step 1: Preliminary identification and spatiotemporal feature analysis of saline-alkali land based on remote sensing imagery; Step 2: In-depth identification based on the coupling degree between spatial morphology and structure; Step 3: Evaluation of mineralization indices based on the screening results; Step 4: Comprehensive prediction of mineralization potential and selection of favorable areas; Step 1 includes: Step 1.1 Acquisition and preprocessing of multi-temporal remote sensing data; Landsat 8 / 9 OLI remote sensing images of the study area during the dry and arid seasons were collected to obtain multiple images from different periods; SRTM DEM data with a spatial resolution of 30 meters were acquired, and the images from each period were preprocessed. Step 1.2 Preliminary identification of saline-alkali land based on multi-index fusion; After calculating three independent single-band grayscale images—Improved Salinity Index (MSI), Salinity Index (SI), and Normalized Differential Salinity Index (NDSI)—from the preprocessed images for each period, a pixel-by-pixel weighted linear combination operation was performed on the three raster layers (MSI, SI, and NDSI) using a raster calculator or band operation tool in remote sensing image processing software to generate a CSI image map. The generated CSI image map was then subjected to OTSU adaptive threshold segmentation to extract the preliminary saline-alkali land distribution map for each period. Step 1.3 Initial screening of causes based on spatiotemporal dynamic characteristics; The preliminary saline-alkali land distribution map is converted into a preliminary saline-alkali land binary map. The preliminary saline-alkali land binary maps of all periods are overlaid in the geographic information system software to construct a spatiotemporal data cube. For each pixel in the cube, its spatiotemporal dynamic index is calculated. A screening threshold is set for the spatiotemporal dynamic index. Based on the screening threshold, a stable saline-alkali map layer is initially selected. The spatiotemporal dynamic indicators in step 1.3 include: Saline-alkali land stability index (SSI): Calculates the ratio of the number of times a pixel is identified as saline-alkali land across all periods to the total number of periods. Annual Variation Rate of Saline-Alkali Land (AYV): Calculates the number of times the saline-alkali land condition of a pixel changes in the dry season images of consecutive years during the study period, and then divides it by the number of time intervals, where the number of time intervals = the number of consecutive years during the study period - 1. Step 2 includes: Step 2.1 Quantification of morphological characteristics of stable saline-alkali land patches; The selected stable saline-alkali map layers are converted into vector polygon layers; for each individual polygon patch in the layer, its morphological parameters are calculated using spatial analysis tools. Step 2.2 Coupling analysis with deep structures; Load the 1:250,000 scale fault structure interpretation layer of the study area; calculate the coupling index between each stable saline-alkali patch and the fault structure. Step 2.3 Final identification of mineralization indicator saline-alkali land; Based on morphological parameters and coupling degree indices, a comprehensive identification rule based on geological significance was established to query and screen stable saline-alkali map layers, and finally identify mineralized indicative saline-alkali lands. The coupling degree index in step 2.2 includes: Directional Consistency (DA): Calculate the absolute value of the angle between the main axis direction of the patch and the strike of the most important fault segment within a 1 km effective influence range; Spatial proximity SP: Calculates the Euclidean distance from the centroid of the patch to the nearest fracture.

2. The method for identifying sandstone-type uranium deposits as indicative saline-alkali lands according to claim 1, characterized in that, The preprocessing in step 1.1 includes: radiometric calibration, FLAASH atmospheric correction, geometric fine correction, and DEM-based topographic correction of the remote sensing image.

3. The method for identifying sandstone-type uranium deposits as indicative saline-alkali lands according to claim 1, characterized in that, The morphological parameters in step 2.1 include: elongation, which is the aspect ratio of the smallest bounding rectangle of the patch; and orientation, which is the angle between the main extension direction of the patch and the due north direction.

4. The method for identifying sandstone-type uranium deposits as indicative saline-alkali lands according to claim 1, characterized in that, Step 3 includes: Step 3.1 Calculation of the Mineralization Indicator Score (SMI); For the finally identified mineralization indicative saline-alkali land patches, the mineralization indicative score SMI is calculated by weighted summation of the normalized saline-alkali land stability index SSI, elongation, spatial proximity SP, and area. Step 3.2 Spatial mapping of mineralization indicators; The mineralization indices of all mineralized indicative saline-alkali patches are used to generate a raster map of the mineralization indices of the saline-alkali land in the study area through spatial interpolation or direct assignment.

5. The method for identifying sandstone-type uranium deposits as indicative saline-alkali lands according to claim 4, characterized in that, Step 4 includes: Step 4.1 Multi-factor information fusion and potential evaluation; In geographic information system software, the saline-alkali land mineralization indicator raster layer is used as the core hydrogeological condition indicator layer. It is then weighted and overlaid with other key mineral exploration element layers to calculate and generate the final mineralization potential index map. Step 4.2 Mineralization potential zoning and optimal selection of favorable areas; The mineralization potential index map was statistically classified using the natural discontinuity classification method, dividing it into three levels of potential areas: high, medium, and low, thus identifying the most promising areas for mineral exploration.

6. The method for identifying sandstone-type uranium deposits as indicative saline-alkali lands according to claim 5, characterized in that, Other key mineral exploration element layers in step 4.1 include: a buffer layer of uranium source rock mass, a layer showing the distribution of favorable ore-bearing sand bodies, and a layer reflecting tectonic channels and reducing environments.