Mineral resource detection method and system based on remote sensing technology

Abstract

The invention provides a mineral resource detection method and system based on a remote sensing technology, and relates to the technical field of remote sensing. The method comprises the following steps: firstly, acquiring hyperspectral remote sensing data, synthetic aperture radar data, thermal infrared remote sensing data and ground geological survey data of corresponding time periods obtained in a target detection area including different seasons, different vegetation phenological periods and different detection heights to form a multi-source space-time remote sensing data body; performing space-time coupling processing on the dynamic response field to generate a metallogenic environment dynamic response field, analyzing to obtain a metallogenic potential gradient field, calibrating a metallogenic core target region according to gradient change characteristics, extracting dynamic metallogenic response characteristics, associating the dynamic metallogenic response characteristics with a standard model, and obtaining a mineral resource type inference result and occurrence dynamic parameters; and finally, generating a dynamic detection implementation scheme including a geographic coordinate range, mineral resource type possibilities, recommended detection technical means and detection depth adjustment rules according to mineral resource type inference results and occurrence dynamic parameters, thereby improving the efficiency and accuracy of mineral resource detection.

Description

Technical Field

[0001] This invention relates to the field of remote sensing technology, and more specifically, to a method and system for mineral exploration based on remote sensing technology. Background Technology

[0002] Traditional mineral exploration methods mainly rely on surface geological surveys and single remote sensing data. While surface geological surveys can obtain relatively detailed rock and mineral identification data and characteristic element occurrence data, their working range is limited, making it difficult to conduct comprehensive and rapid exploration of large areas. Furthermore, they are greatly limited by natural conditions such as topography and climate, resulting in low exploration efficiency and high costs.

[0003] The application of single remote sensing data also has many limitations. For example, while hyperspectral remote sensing data can provide rich spectral information, its detection of underground mineral deposits is easily interfered with by factors such as surface vegetation and soil. Synthetic aperture radar data has a certain ability to detect topographic deformation, but it is insufficient in identifying mineral deposit types. Thermal infrared remote sensing data can reflect the thermal radiation characteristics of the surface, but it is difficult to accurately determine information related to mineralization when used alone. Moreover, most existing remote sensing data processing methods only process data from a single data source or a simple combination of data, lacking the comprehensive utilization of multi-source spatiotemporal remote sensing data. They cannot fully consider the impact of multi-dimensional factors such as different seasons, different vegetation phenological periods, and different detection altitudes on the mineralization environment, making it difficult to accurately analyze the mineralization potential of the target detection area, resulting in low accuracy and efficiency in mineral exploration. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, the present invention provides a mineral deposit detection method based on remote sensing technology, the method comprising: Collect multi-source spatiotemporal remote sensing data of the target detection area. The multi-source spatiotemporal remote sensing data includes hyperspectral remote sensing data, synthetic aperture radar data, thermal infrared remote sensing data, and ground geological survey data of the corresponding time period, which includes rock and mineral identification data and characteristic element occurrence status data. The multi-source spatiotemporal remote sensing data volume is subjected to spatiotemporal coupling processing to generate a dynamic response field of the mineralization environment in the target detection area. The dynamic response field of the mineralization environment records the synergistic changes and correlation strengths of spectral response, thermal radiation response, and topographic deformation response related to mineralization under different spatiotemporal dimensions. Based on the analysis of the mineralization potential gradient field of the target detection area by the dynamic response field of the mineralization environment, the mineralization potential gradient field reflects the degree of difference and continuous change trend of different sub-regions in meeting the mineralization conditions in the spatiotemporal dimension, and includes information on the direction and rate of potential change. Based on the gradient change characteristics of the mineralization potential gradient field, the core mineralization target area is calibrated, the dynamic mineralization response characteristics of the core mineralization target area are extracted, and the dynamic mineralization response characteristics are hierarchically correlated with the standard mineralization response model of known mineral deposits to obtain the mineral type inference results and mineral occurrence dynamic parameters of the core mineralization target area. Based on the inference results of the mineral deposit type and the dynamic parameters of the mineral deposit occurrence, a dynamic detection implementation plan is generated. The dynamic detection implementation plan includes the geographical coordinate range of the core mineralization target area, the probability of mineral deposit type corresponding to each coordinate point, recommended detection techniques, and detection depth adjustment rules.

[0005] In another aspect, the present invention also provides a mineral exploration system based on remote sensing technology, including a processor and a machine-readable storage medium connected to the processor. The machine-readable storage medium is used to store programs, instructions or code, and the processor is used to execute the programs, instructions or code in the machine-readable storage medium to implement the above-described method.

[0006] Based on the above, by collecting multi-source spatiotemporal remote sensing data volumes of the target detection area and fusing them with corresponding ground geological survey data, this method integrates various remote sensing data acquired from different seasons, different vegetation phenological periods, and different detection altitudes, along with detailed ground geological information. Then, the multi-source spatiotemporal remote sensing data volumes are spatiotemporally coupled to generate a dynamic response field for the mineralization environment. This field can record the synergistic changes and correlation strengths of various responses related to mineralization under different spatiotemporal dimensions, effectively overcoming the limitations of a single data source and more comprehensively reflecting the dynamic characteristics of the mineralization environment. Next, based on the dynamic response field of the mineralization environment, the mineralization potential gradient field is analyzed, which can clearly reflect different sub-regions. The study examines the degree of difference and continuous trend of mineralization conditions in the spatiotemporal dimensions, including information on the direction and rate of potential changes. Then, it calibrates the core target area of ​​mineralization based on the mineralization potential gradient field, extracts dynamic mineralization response characteristics, and performs hierarchical correlation with the standard mineralization response model of known mineral deposits. This allows for accurate inference results of mineral deposit types and dynamic parameters of mineral occurrence. Finally, based on the inference results of mineral deposit types and dynamic parameters of mineral occurrence, a dynamic detection implementation plan is generated, including detailed geographical coordinate range, mineral deposit type probability, recommended detection techniques, and detection depth adjustment rules. This achieves dynamic optimization of the detection plan, improves the efficiency and accuracy of mineral exploration, and reduces detection costs. Attached Figure Description

[0007] Figure 1 This is a schematic diagram of the execution flow of the mineral exploration method based on remote sensing technology provided in an embodiment of the present invention.

[0008] Figure 2 This is a schematic diagram of exemplary hardware and software components of a mineral exploration system based on remote sensing technology provided in an embodiment of the present invention. Detailed Implementation

[0009] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a mineral exploration method based on remote sensing technology according to an embodiment of the present invention. The following is a detailed description of the mineral exploration method based on remote sensing technology.

[0010] Step S110: Collect multi-source spatiotemporal remote sensing data of the target detection area. The multi-source spatiotemporal remote sensing data includes hyperspectral remote sensing data, synthetic aperture radar data, thermal infrared remote sensing data, and ground geological survey data for the corresponding time period, which includes rock and mineral identification data and characteristic element occurrence status data.

[0011] In this embodiment, the target detection area is set as a mountainous region that may contain polymetallic mineral deposits. During the data acquisition phase, hyperspectral remote sensing data needs to be acquired using remote sensing equipment at different detection altitudes, such as 500 meters, 1000 meters, and 1500 meters, for this mountainous region during different seasons (spring, summer, autumn, and winter) and different vegetation phenological stages (such as budding, flourishing, and withering stages). Hyperspectral remote sensing data can capture the spectral information of ground objects in multiple continuous bands, playing a crucial role in identifying mineral types.

[0012] Simultaneously, synthetic aperture radar (SAR) data is acquired. SAR data is unaffected by weather and lighting conditions, can penetrate clouds and vegetation, and obtain information on the topography and surface structure of the target area. Thermal infrared remote sensing data is also collected during the corresponding time periods to detect the thermal radiation characteristics of the Earth's surface.

[0013] In addition, it is necessary to collect ground geological survey data for the corresponding time period. The ground geological survey data is obtained through field sampling and laboratory analysis. The rock and mineral identification data records in detail the types, contents and structural characteristics of minerals in the collected rock samples. The characteristic element occurrence status data describes the existence form, distribution and content changes of characteristic elements related to mineral deposits in the target area, such as copper, iron and zinc, in rocks and soil.

[0014] When collecting the above data, for cases involving privacy-sensitive data, such as some ground geological survey data that may involve the location information of specific survey personnel, data desensitization technology is used to process the sensitive parts of personal identity information and specific location coordinates, so that the above data will not leak relevant privacy during use.

[0015] Step S120: Perform spatiotemporal coupling processing on the multi-source spatiotemporal remote sensing data volume to generate a dynamic response field of the mineralization environment in the target detection area. The dynamic response field of the mineralization environment records the synergistic changes and correlation strengths of spectral response, thermal radiation response, and topographic deformation response related to mineralization under different spatiotemporal dimensions.

[0016] Step S121: Extract the spectral response factors of hyperspectral remote sensing data for each time period from the multi-source spatiotemporal remote sensing data volume. The spectral response factors include the reflectance variation trend of characteristic bands corresponding to characteristic minerals, the morphological variation of characteristic absorption valleys, and the slope variation characteristics of spectral curves. The characteristic bands are determined according to the spectral characteristics of the marker minerals corresponding to the mineral types that may exist in the target detection area.

[0017] Step S1211: Perform atmospheric correction processing on the hyperspectral remote sensing data for each time period to eliminate the interference of atmospheric scattering, atmospheric absorption and sensor noise on the spectral signal. The atmospheric correction processing adopts a correction process based on the atmospheric radiative transfer theory. The input parameters include atmospheric humidity, air pressure, solar altitude angle and sensor observation angle at the time of data acquisition.

[0018] When processing hyperspectral remote sensing data for the aforementioned mountainous target detection area, atmospheric correction is performed first. Atmospheric correction aims to eliminate the influence of the atmosphere on the spectral signal, as gas molecules and aerosols in the atmosphere scatter and absorb solar radiation, distorting the spectral signal received by the sensor. The correction process, based on atmospheric radiative transfer theory, requires inputting specific atmospheric parameters from the time of data acquisition. For example, for hyperspectral data acquired during a specific summer period, the atmospheric humidity over the target area during that period needs to be obtained. This humidity value is obtained through real-time monitoring by a meteorological station located near the data acquisition point; atmospheric pressure is also provided by the meteorological station; the solar altitude angle is calculated based on the data acquisition time and geographical location, specifically the angle between the sun's rays and the ground plane calculated using astronomical formulas based on the latitude and longitude of the mountainous area and the specific acquisition time; the sensor observation angle is the tilt angle of the sensor relative to the target area during data acquisition, recorded by the remote sensing equipment itself. By inputting these parameters into the atmospheric radiative transfer model, the influence of the atmosphere on the spectral signal is simulated, thereby correcting the original hyperspectral data and obtaining surface reflectance spectral data after eliminating atmospheric influence.

[0019] Step S1212: Divide the corrected hyperspectral remote sensing data into several characteristic band groups according to the mineral spectral characteristics. Each characteristic band group corresponds to the spectral response range of a type of marker mineral. The division of the characteristic band groups is based on the known mineralization-related mineral spectral database within the target detection area.

[0020] After atmospheric correction, the corrected hyperspectral remote sensing data is then grouped by band. Polymetallic deposits potentially existing within the target detection area have corresponding marker minerals with specific spectral characteristics. For example, for copper deposits, the marker mineral might be chalcopyrite, which exhibits significant spectral absorption characteristics within a specific wavelength range. Based on known mineralization-related mineral spectral databases, which store information on the reflectance and absorption characteristics of various minerals at different wavelengths, the characteristic spectral response range corresponding to chalcopyrite is identified. For instance, a low reflectance within a certain wavelength range creates a characteristic absorption peak. The bands covering this wavelength range in the corrected hyperspectral data are then grouped into a characteristic band group, corresponding to the marker mineral chalcopyrite. Similarly, for other potential marker minerals, such as hematite and magnetite corresponding to iron ore, corresponding characteristic band groups are determined based on their spectral characteristics. Each characteristic band group contains multiple consecutive bands that reflect the spectral response characteristics of this type of marker mineral.

[0021] Step S1213: Set up a uniformly distributed sampling grid in the image area corresponding to each feature band group. The density of the sampling grid is determined according to the spatial resolution of the hyperspectral remote sensing data. The number of pixels covered by each sampling grid unit can represent the spectral characteristics of the image area.

[0022] For each image region corresponding to a predefined feature band group, a sampling grid needs to be set up for spectral information extraction. The density of the sampling grid is related to the spatial resolution of the hyperspectral remote sensing data. Higher spatial resolution means a smaller actual ground area represented by each pixel in the image. To accurately reflect the spectral characteristics of the image region, the sampling grid density needs to be increased accordingly, meaning the grid cell size needs to be smaller. For example, when the spatial resolution of the hyperspectral data is 10 meters, setting the sampling grid cell size to 20 meters by 20 meters ensures that each sampling grid cell covers a certain number of pixels. The combined spectral information of these pixels can represent the spectral characteristics of the ground region corresponding to that grid cell. The sampling grid is evenly distributed within the image region to ensure comprehensive and uniform sampling of the entire image region corresponding to the feature band group.

[0023] Step S1214: Extract the reflectance values ​​of all pixels in each sampling grid cell for each band of the feature band group, calculate the average reflectance of each band, and form the average reflectance sequence of the sampling grid cell in the feature band group.

[0024] After setting up the sampling grid, for each sampling grid cell, the reflectance values ​​of all pixels within it for each band included in the characteristic band group are extracted. For example, if a characteristic band group includes band 1, band 2, band 3, ..., band n, then for each pixel in the sampling grid cell, there are reflectance values ​​corresponding to these n bands. Then, the average reflectance values ​​of all pixels in each band are calculated. That is, for band 1, the reflectance values ​​of all pixels in the grid cell for band 1 are added together and then divided by the number of pixels to obtain the average reflectance value of band 1. The average reflectance values ​​of bands 2 to n are calculated in the same way. The average reflectance values ​​of the above bands are arranged in band order to generate the average reflectance sequence of the sampling grid cell in the characteristic band group. This average reflectance sequence can reflect the overall spectral reflectance of the ground area corresponding to the grid cell in the characteristic band group.

[0025] Step S1215: Perform trend analysis on the average reflectance sequence to identify the continuous trend of reflectance value changing with waveband, and mark the location and shape of the characteristic absorption valleys that appear in the continuous trend. The shape of the characteristic absorption valleys is described by the width, depth and symmetry of the absorption valleys.

[0026] After obtaining the average reflectance sequence, trend analysis is performed. A curve showing reflectance variation with wavelength is plotted with wavelength bands on the horizontal axis and average reflectance on the vertical axis. By observing this curve, the continuous trend of reflectance values ​​with wavelength band variation is identified, such as a gradual increase, a gradual decrease, or a fluctuating change. Within this continuous trend, characteristic absorption valleys appear. These valleys are formed due to the absorption of specific wavelengths of light by minerals in the target area. The locations of these characteristic absorption valleys, i.e., the corresponding wavelength band ranges, are marked. Simultaneously, the morphology of the characteristic absorption valleys is described, where width refers to the number of wavelength bands spanned by the valley; depth refers to the difference between the average reflectance at the bottom of the valley and the average reflectance at the highest points on both sides; symmetry is judged by comparing the steepness of the reflectance changes on both sides of the valley. If the changes on both sides are similar, the symmetry is good; otherwise, the symmetry is poor. For example, a certain characteristic absorption valley may span up to five wavelength bands, and its depth is characterized by a significantly lower reflectance at the bottom than on the sides, with the reflectance changes on both sides being basically symmetrical.

[0027] Step S1216: Calculate the rate of change of reflectance on both sides of the characteristic absorption valley. The rate of change is described by the ratio of the reflectance difference between the starting point and the peak point of the absorption valley to the band difference.

[0028] After identifying the characteristic absorption valley, the rate of change of reflectance on both sides is calculated. First, the starting point and peak point of the absorption valley are determined. The starting point is the reflectance value corresponding to the band position where the absorption valley begins to decline, and the peak point is the reflectance value corresponding to the band position at the bottom of the absorption valley. For the left side of the absorption valley, the difference between the reflectance value at the starting point and the peak point is calculated, and then divided by the difference in the number of bands between the starting point and the peak point to obtain the rate of change of reflectance on the left side. Similarly, for the right side of the absorption valley, the difference between the reflectance value at the peak point and the reflectance value at the starting point on the right side (i.e., the reflectance value corresponding to the band position where the absorption valley ends to rise) is calculated, and then divided by the difference in the number of bands between the peak point and the starting point on the right side to obtain the rate of change of reflectance on the right side. Through this method, the slope variation characteristics of the spectral curves on both sides of the characteristic absorption valley can be quantified. These characteristics can reflect information such as the type and content of minerals.

[0029] Step S1217: Combine the average reflectance sequence, characteristic absorption valley location and shape, and reflectance change rate of each sampling grid unit to form the spectral response factor corresponding to the sampling grid unit; perform spatial interpolation on the spectral response factors of all sampling grid units to form a continuous spectral response factor distribution layer covering the target detection area. This continuous spectral response factor distribution layer serves as the spectral response base layer of the dynamic response field of the mineralization environment.

[0030] By combining information such as the average reflectance sequence, the location and morphological parameters (width, depth, symmetry) of characteristic absorption valleys, and the rate of reflectance change of each sampling grid cell, the spectral response factor corresponding to that sampling grid cell is constituted. Since there are intervals between sampling grid cells, spatial interpolation processing is required for the spectral response factors of all sampling grid cells to obtain continuous spectral response information covering the entire target detection area. Spatial interpolation processing employs a neighbor-based interpolation method. Based on the known spectral response factor values ​​of the sampling grid cells, the spectral response factor value at each location within the target detection area is estimated by calculating the distance weights between unknown locations and known sampling points, thus forming a continuous spectral response factor distribution layer. This spectral response factor distribution layer can intuitively display the spectral response characteristics of different locations within the target detection area and serves as the basic data layer for the spectral response portion of the dynamic response field of the mineralization environment.

[0031] Step S122: Extract the terrain deformation response factor of the synthetic aperture radar data for each time period from the multi-source spatiotemporal remote sensing data volume. The terrain deformation response factor includes the continuous change sequence of terrain slope, the spatial distribution and deformation trend of fault structures, and the dynamic change of terrain undulation. The spatial distribution of fault structures is obtained by identifying the texture variation region of the synthetic aperture radar data.

[0032] For the synthetic aperture radar (SAR) data of the aforementioned mountainous target detection area, preprocessing is first performed, including radiometric and geometric corrections, to eliminate noise and geometric distortion in the data. Then, a continuous sequence of terrain slope changes is extracted. The slope value of each pixel is calculated using SAR data, and the slope values ​​from different time periods are arranged chronologically to form a continuous sequence of terrain slope changes over time. Regarding the spatial distribution of fault structures, texture variation regions are identified by analyzing the texture features of the SAR data. These regions often correspond to the locations of fault structures. Specifically, texture analysis is performed on SAR images, calculating texture parameters such as the gray-level co-occurrence matrix. When texture parameters change significantly in a certain area, that area is identified as a texture variation region, thereby determining the spatial distribution characteristics of fault structures, such as their orientation, length, and distribution range. Simultaneously, by combining SAR data from different time periods, the deformation trend of fault structures is analyzed, i.e., determining whether the fault structures are in a relatively stable state or show signs of activity, as well as the direction and extent of such activity. The dynamic changes in topographic relief are obtained by calculating the standard deviation of topographic elevation in the target area at different time periods. The larger the standard deviation, the more severe the topographic relief. The topographic relief values ​​at different time periods are arranged in chronological order to obtain their dynamic changes. The information such as the continuous change sequence of topographic slope, the spatial distribution and deformation trend of fault structures, and the dynamic changes in topographic relief are combined to form the topographic deformation response factor.

[0033] Step S123: Extract thermal radiation response factors from the multi-source spatiotemporal remote sensing data volume for each time period. The thermal radiation response factors include the spatiotemporal distribution differences of surface temperature, the variation law of thermal radiation intensity, and the spatial migration trajectory of thermal anomaly areas. Thermal anomaly areas are identified by the difference between thermal radiation intensity and the surrounding areas.

[0034] For thermal infrared remote sensing data, radiometric calibration is first performed, converting the digitally quantized values ​​received by the sensor into thermal radiation intensity. Then, surface temperature is calculated based on this thermal radiation intensity. Methods such as the split-window algorithm, combined with atmospheric parameters and surface emissivity, are used to convert the thermal radiation intensity into a surface temperature value. The spatiotemporal distribution differences of surface temperature are obtained by analyzing surface temperature values ​​at different times and locations, such as comparing surface temperature differences at different altitudes in mountainous areas during the same period, and surface temperature changes at the same location in different seasons. The variation pattern of thermal radiation intensity is determined by statistically analyzing the thermal radiation intensity values ​​of the same location at different times, identifying patterns such as periodic or trend-based changes. Thermal anomaly areas are identified by comparing the thermal radiation intensity of each pixel with the average thermal radiation intensity of pixels within a certain surrounding range. When the thermal radiation intensity of a pixel is significantly higher than the surrounding average and exceeds a certain threshold, the area where that pixel is located is identified as a thermal anomaly area. By tracking the location changes of thermal anomaly areas at different times, the spatial migration trajectory of these areas is obtained. By combining information such as the spatiotemporal distribution differences of surface temperature, the variation pattern of thermal radiation intensity, and the spatial migration trajectory of thermal anomaly areas, a thermal radiation response factor is formed.

[0035] Step S124: Extract geological response factors from the multi-source spatiotemporal remote sensing data volume for each time period of ground geological exploration data. The geological response factors include the spatial distribution of rock and mineral assemblages, the changes in the occurrence state of characteristic elements, and the correlation between geological structures and mineral distribution.

[0036] Rock and mineral identification data from surface geological surveys provide information on the types and assemblages of minerals in rock samples from different locations. By spatially interpolating and gridding this data, the information on rock and mineral assemblages is distributed across the entire target exploration area, yielding the spatial distribution of these assemblages. Characteristic element occurrence state data records the existence forms and contents of characteristic elements in different samples. By analyzing this data over different time periods, changes in the occurrence state of characteristic elements, such as migration, enrichment, or depletion, are determined. The correlation between geological structures and mineral distribution is obtained by overlaying geological structural information, such as fault structures, with the spatial distribution of rock and mineral assemblages. This analysis determines whether there are differences in mineral assemblages at different locations within fault structures, and the control effect of these differences on mineral formation and distribution reflected by geological structures. Combining all this information forms a geological response factor.

[0037] Step S125: Establish the spatiotemporal correlation between various response factors, align the spatial locations of spectral response factors, topographic deformation response factors, thermal radiation response factors, and geological response factors in the same time period, and mark the synchronicity of changes of each response factor in the same spatial location.

[0038] Step S1251: Convert the spectral response factor distribution layer, topographic deformation response factor distribution layer, thermal radiation response factor distribution layer, and geological response factor distribution layer of the same time period into a unified geographic coordinate system, and adjust the spatial coordinate accuracy of each distribution layer to be consistent through coordinate transformation tools.

[0039] For the four response factor distribution layers mentioned above within the same time period, they first need to be transformed to a unified geographic coordinate system, such as the Gauss-Kruger projection coordinate system. Using a coordinate transformation tool, the coordinates of each distribution layer are transformed based on its original coordinate information and the parameters of the target coordinate system. During the transformation process, the parameter settings of the coordinate transformation tool are adjusted to ensure consistent spatial coordinate accuracy across all distribution layers, for example, keeping the coordinate errors of all distribution layers within a certain range to guarantee the accuracy of subsequent spatial alignment.

[0040] Step S1252: Set up common spatial sampling points under a unified coordinate system. The density of spatial sampling points is determined according to the resolution of each response factor distribution layer. Each spatial sampling point has corresponding response factor data in each distribution layer.

[0041] Under a unified geographic coordinate system, common spatial sampling points are established. The density of these sampling points needs to consider the resolution of each response factor distribution layer; the higher the resolution, the greater the sampling point density, ensuring that the detailed information of each distribution layer is fully reflected. For example, when the resolution of each distribution layer is 30 meters, the sampling point spacing can be set to 30 meters, forming a regular grid-like sampling point. Each spatial sampling point has a corresponding location in the four response factor distribution layers, so the response factor data corresponding to that sampling point can be extracted from each distribution layer, such as spectral response factor values ​​and terrain deformation response factor values.

[0042] Step S1253: Extract the response factor values ​​of each spatial sampling point in each response factor distribution layer to form a multi-response factor data group for that spatial sampling point.

[0043] For each designated spatial sampling point, specific numerical values ​​of the corresponding response factors are extracted from the spectral response factor distribution layer, topographic deformation response factor distribution layer, thermal radiation response factor distribution layer, and geological response factor distribution layer. For example, in the spectral response factor distribution layer, parameters related to the average reflectance sequence and characteristic absorption valleys are extracted; in the topographic deformation response factor distribution layer, parameters related to the topographic slope and fault structure are extracted; in the thermal radiation response factor distribution layer, parameters related to the surface temperature and thermal radiation intensity are extracted; and in the geological response factor distribution layer, parameters related to the rock and mineral assemblage and the occurrence state of characteristic elements are extracted. These response factor values ​​extracted from different distribution layers are then combined to generate a multi-response factor dataset for the spatial sampling point.

[0044] Step S1254: Analyze the changing trends of each response factor in the multi-response factor data set of each spatial sampling point, and determine whether the changing directions of the spectral response factor and the topographic deformation response factor are consistent, and whether the changing directions of the thermal radiation response factor and the geological response factor are consistent.

[0045] For each spatial sampling point's multi-response factor dataset, the changing trends of each response factor over time are analyzed. Taking the spectral response factor and topographic deformation response factor as examples, their values ​​at different time periods are arranged chronologically, and their changing directions are observed. If the value of the spectral response factor shows an increasing trend over time, and the value of the topographic deformation response factor also shows an increasing trend over time, then the two are judged to have the same changing direction; if one increases and the other decreases, then the changing directions are inconsistent. Similarly, the changing directions of the thermal radiation response factor and the geological response factor are determined.

[0046] Step S1255: Count the number of spatial sampling points with the same direction of change for each response factor, and calculate the first proportion of the number of such spatial sampling points to the total number of spatial sampling points.

[0047] After determining the direction of change of response factors at all spatial sampling points, the number of sampling points where the direction of change of spectral response factors is consistent with that of topographic deformation response factors, and the number of sampling points where the direction of change of thermal radiation response factors is consistent with that of geological response factors, are counted. Then, these two numbers are divided by the total number of spatial sampling points to obtain their respective first proportions. For example, if there are 1000 total spatial sampling points, and 600 of them have the same direction of change of spectral response factors as topographic deformation response factors, then the corresponding first proportion is 60%.

[0048] Step S1256: For spatial sampling points with consistent change directions, further analyze the change amplitude of each response factor, and determine whether the proportion of change amplitude is within a preset reasonable range. The reasonable range is determined by the proportion of change amplitude of response factors in a known ore-forming area.

[0049] For spatial sampling points with consistent directions of change, the variation amplitude of each response factor is further analyzed. The ratio of the change in the spectral response factor between two time periods to the change in the topographic deformation response factor between the same time periods is calculated to obtain the variation amplitude ratio. This ratio is compared with a preset reasonable range, which is determined through statistical analysis of the variation amplitude ratios of response factors in multiple known mineralized areas. For example, the reasonable range is set as a certain value range. If the calculated variation amplitude ratio falls within this range, the variation amplitude ratio is considered reasonable; otherwise, it is considered unreasonable.

[0050] Step S1257: Count the number of spatial sampling points whose change rate is within a reasonable range, and calculate the second proportion of the number of spatial sampling points with the same change direction to the number of spatial sampling points with the same change direction.

[0051] The second ratio is obtained by counting the number of spatial sampling points whose change rate falls within a reasonable range, and then dividing this number by the number of spatial sampling points with the same change direction. For example, if there are 600 spatial sampling points with the same change direction, and 400 of them have a change rate within a reasonable range, then the second ratio is 400 / 600 ≈ 66.7%.

[0052] Step S1258: Determine the synchronicity of changes of each response factor at the same spatial location based on the first ratio and the second ratio, and mark the synchronicity results on the corresponding spatial sampling points to form a spatial synchronicity distribution layer of each response factor. The higher the first ratio and the second ratio, the stronger the synchronicity of changes.

[0053] The synchronicity of changes in each response factor at the same spatial location is determined by combining the first and second proportions. When both the first and second proportions are high, it indicates good consistency in the direction and magnitude of change of the response factors, indicating strong synchronicity; conversely, when either the first or second proportion is low, the synchronicity is weak. The synchronicity results of each spatial sampling point are labeled, for example, using different numerical values ​​or levels to represent the strength of synchronicity. These labeled results are then displayed spatially to form a spatial synchronicity distribution layer for each response factor. This spatial synchronicity distribution layer can intuitively reflect the spatial differences in the synchronicity of response factor changes at different locations.

[0054] Step S126: By analyzing the consistency of the changing trends of different response factors in the same spatiotemporal dimension, determine the correlation strength between spectral response factors and topographic deformation response factors, the correlation strength between thermal radiation response factors and geological response factors, and the correlation strength between spectral response factors and thermal radiation response factors.

[0055] After obtaining the spatial synchronicity distribution layers of each response factor, the consistency of the changing trends of different response factors under the same spatiotemporal dimension is analyzed. For spectral response factors and topographic deformation response factors, the correlation coefficients of their changing trends at the same spatial sampling points and within the same time period are calculated. The larger the absolute value of the correlation coefficient, the higher the consistency of the changing trends of the two, and the stronger the correlation. Similarly, the correlation coefficients between thermal radiation response factors and geological response factors, and between spectral response factors and thermal radiation response factors, are calculated to determine their correlation strength. The numerical range of correlation strength can be divided according to the actual situation, such as strong correlation, moderate correlation, and weak correlation.

[0056] Step S127: Arrange the response factors and correlation strengths of each time period in chronological order to construct a spatiotemporally continuous sequence of response factor changes, and label the change magnitude and correlation strength of each response factor within each time period.

[0057] The spectral response factors, topographic deformation response factors, thermal radiation response factors, and geological response factors, along with their correlation strengths, are arranged chronologically. For each time period, the variation range of each response factor is marked, such as the change in the spectral response factor compared to the previous period, and the change in the topographic deformation response factor. The variation of each correlation strength during that time period is also marked, such as increases or decreases in the correlation strength values. This constructs a spatiotemporally continuous sequence of response factor changes, clearly demonstrating the dynamic changes of each response factor and correlation strength over time.

[0058] Step S128: Combine the response factor change sequence with the spatial coordinate system of the target detection area to form a dynamic response field of the ore-forming environment that can reflect the spatiotemporal dynamic changes of the ore-forming environment. Each spatial coordinate point in the dynamic response field of the ore-forming environment corresponds to a set of response factor change data and correlation strength data.

[0059] The constructed spatiotemporally continuous response factor variation sequence is combined with the spatial coordinate system of the target detection area. For each spatial coordinate point within the target detection area, corresponding response factor variation data is extracted from the response factor variation sequence based on its coordinate position. This includes spectral response factors, topographic deformation response factors, thermal radiation response factors, geological response factor values ​​for each time period, and the correlation strength data between them. These data are mapped one-to-one with the spatial coordinate points to form a three-dimensional data field containing both spatial and temporal information, which becomes the dynamic response field of the mineral environment. Through this response field, the changes in response factors and correlation strengths at any spatial coordinate point at different times can be viewed, comprehensively reflecting the spatiotemporal dynamic characteristics of the mineralization environment in the target detection area.

[0060] Step S130: Analyze the mineralization potential gradient field of the target detection area based on the dynamic response field of the mineralization environment. The mineralization potential gradient field reflects the degree of difference and continuous change trend of different sub-regions in meeting the mineralization conditions in the spatiotemporal dimension, and includes information on the direction and rate of potential change.

[0061] Step S131: Extract key response parameters related to mineralization from the dynamic response field of the mineralization environment. The key response parameters include the depth of characteristic absorption valleys in the spectral response factor, the density of fracture structures in the topographic deformation response factor, the intensity of thermal anomalies in the thermal radiation response factor, and the density of characteristic elements in the geological response factor.

[0062] Based on known metallogenic theories and the potential mineral deposit types in the target mountain area, key response parameters related to mineralization were determined for the dynamic response field of the metallogenic environment. For the spectral response factor, the depth of characteristic absorption valleys was selected as a key parameter because it reflects the content and purity of characteristic minerals within the target area; a greater depth indicates a higher probability of the presence of characteristic minerals. The characteristic absorption valley depth values ​​for each spatial coordinate point were extracted from the spectral response factor data. For the topographic deformation response factor, fault density is an important key parameter. Fault structures provide channels and space for mineral formation; a higher fault density indicates a higher probability of mineralization. The fault density was obtained by calculating the number or total length of fault structures per unit area. Thermal anomaly intensity is one of the key parameters in the thermal radiation response factor. Areas with high thermal anomaly intensity may have underground hydrothermal activity, which is closely related to the mineralization process. The thermal anomaly intensity is represented by the difference between the thermal radiation intensity and the surrounding area. The characteristic element occurrence density in the geological response factor, i.e., the content of characteristic elements per unit volume or unit mass of rock, is an important indicator for judging mineralization potential; this parameter was extracted from the geological response factor data.

[0063] Step S132: Combining the differences in mineralization among different mineral types, statistical analysis is conducted by collecting mineralization data from multiple known mineralization areas to obtain the degree of influence of each parameter on mineralization during the formation of known mineralization, so as to determine the mineralization contribution weight corresponding to each key response parameter.

[0064] Metallogenic data were collected from multiple known mineral deposit regions of different types. This data included the values ​​of key response parameters for each known mineral deposit region, as well as corresponding mineralization results such as reserves and grades. Statistical analysis was performed on this data, using methods such as multiple regression analysis to analyze the influence of each key response parameter on the formation process of known mineral deposits. For example, for copper deposit regions, the relationship between four parameters—characteristic absorption valley depth, fault density, thermal anomaly intensity, and characteristic element density—and copper reserves was analyzed to determine the contribution of each parameter to copper mineralization. Based on these contributions, a corresponding mineralization contribution weight was assigned to each key response parameter; the greater the contribution, the higher the weight value. For example, analysis revealed that characteristic element density has the greatest impact on mineralization, and its weight value might be set to 0.4. The weight values ​​for other parameters were, in descending order: fault density 0.3, characteristic absorption valley depth 0.2, thermal anomaly intensity 0.1, etc., and the sum of the mineralization contribution weights of all key response parameters was 1.

[0065] Step S133: For each sub-region within the target detection area, standardize the actual value of each key response parameter to obtain the standardized value of each key response parameter.

[0066] Step S1331: Based on the spatial resolution of the dynamic response field of the mineralization environment and the total area of ​​the target detection area, divide the target detection area into several sub-regions of equal area according to the preset spatial resolution.

[0067] Based on the spatial resolution of the dynamic response field of the ore-forming environment, such as 30 meters × 30 meters, and combined with the total area of ​​the target detection area, the number of sub-regions to be divided is calculated. The target detection area is then divided into several sub-regions of equal area. The size of each sub-region is determined according to the spatial resolution, for example, each sub-region is a 30-meter × 30-meter square area, ensuring that the division of sub-regions accurately reflects the spatial details of the dynamic response field of the ore-forming environment.

[0068] Step S1332: For each sub-region, extract the actual values ​​of each key response parameter of the sub-region during a set time period from the dynamic response field of the metallogenic environment: extract the average value of the characteristic absorption valley depth of all spatial sampling points in the sub-region from the spectral response factor distribution layer as the actual value of the characteristic absorption valley depth; extract the total length of the fracture structure per unit area in the sub-region from the topographic deformation response factor distribution layer as the actual value of the fracture structure density; extract the difference between the average value of the thermal radiation intensity of all spatial sampling points in the sub-region and the average value of the surrounding area from the thermal radiation response factor distribution layer as the actual value of the thermal anomaly intensity; extract the ratio of the total occurrence of characteristic elements in the sub-region to the area of ​​the sub-region from the geological response factor distribution layer as the actual value of the occurrence density of characteristic elements.

[0069] For each predefined sub-region, within a set time period, the actual values ​​of key response parameters are extracted from the distribution layers of response factors in the dynamic response field of the ore-forming environment. For the characteristic absorption valley depth, all spatial sampling points within the sub-region are located in the spectral response factor distribution layer, and the characteristic absorption valley depth value of each sampling point is extracted. The average of these values ​​is calculated as the actual value of the characteristic absorption valley depth for the sub-region. For the fault structure density, the lengths of all fault structures within the sub-region are statistically analyzed in the topographic deformation response factor distribution layer. The total length is divided by the area of ​​the sub-region to obtain the total length of fault structures per unit area, i.e., the actual value of the fault structure density. The actual value of the thermal anomaly intensity is obtained by first calculating the average thermal radiation intensity of all spatial sampling points within the sub-region, and then calculating the average thermal radiation intensity within a certain range around the sub-region (e.g., three times the area of ​​the sub-region). The difference between the two is the actual value of the thermal anomaly intensity. The actual value of the characteristic element occurrence density is obtained by extracting the total occurrence of characteristic elements within the sub-region from the geological response factor distribution layer and then dividing it by the area of ​​the sub-region.

[0070] Step S1333: Based on the actual values ​​of the key response parameters of all sub-regions in the entire target detection area, determine the numerical range of each key response parameter, including the minimum and maximum values.

[0071] Collect the actual values ​​of key response parameters for all sub-regions of the entire target detection area. For each key response parameter, identify the minimum and maximum values ​​to determine the parameter's numerical range. For example, the numerical range of the feature absorption valley depth is from the minimum feature absorption valley depth to the maximum feature absorption valley depth across all sub-regions.

[0072] Step S1334: For each key response parameter, standardize the actual value of the sub-region and obtain the mineralization contribution weight corresponding to each key response parameter. The values ​​of the mineralization contribution weight corresponding to the depth of the characteristic absorption valley, the mineralization contribution weight corresponding to the density of the fracture structure, the mineralization contribution weight corresponding to the intensity of the thermal anomaly, and the mineralization contribution weight corresponding to the density of the characteristic element are determined according to the known mineral deposit statistics. The sum of all mineralization contribution weights is the overall weight benchmark.

[0073] For each key response parameter, the min-max standardization method is used to standardize the actual values ​​of that sub-region. The formula for calculating the standardized value is: (actual value - minimum value) / (maximum value - minimum value). This formula converts the actual values ​​of each key response parameter to a standardized value between 0 and 1. Simultaneously, based on the mineralization contribution weights corresponding to each key response parameter determined in the previous steps, such as a weight of 0.2 for the depth of characteristic absorption valleys, 0.3 for the density of fracture structures, 0.1 for the intensity of thermal anomalies, and 0.4 for the density of characteristic elements, and with the sum of these weights being 1, the overall weight baseline is 1.

[0074] Step S134: Calculate the basic value of the mineralization potential of the sub-region in each time period. The basic value of the mineralization potential is the sum of the standardized values ​​of each key response parameter and the corresponding mineralization contribution weights.

[0075] Step S1341: Calculate the product of the normalized value of the characteristic absorption valley depth and the corresponding mineralization contribution weight to obtain the contribution value of the characteristic absorption valley depth to the mineralization potential.

[0076] Multiply the normalized value of the characteristic absorption valley depth of the sub-region by its corresponding mineralization contribution weight. For example, if the normalized value is 0.6 and the weight is 0.2, then the contribution value is 0.6 × 0.2 = 0.12.

[0077] Step S1342: Calculate the product of the normalized value of the fracture structure density and the corresponding mineralization contribution weight to obtain the contribution value of the fracture structure density to the mineralization potential.

[0078] Similarly, the normalized value of the fracture structure density is 0.5, the weight is 0.3, and the contribution value is 0.5 × 0.3 = 0.15.

[0079] Step S1343: Calculate the product of the standardized value of thermal anomaly intensity and the corresponding mineralization contribution weight to obtain the contribution value of thermal anomaly intensity to mineralization potential.

[0080] The standardized value of the thermal anomaly intensity is 0.3, the weight is 0.1, and the contribution value is 0.3 × 0.1 = 0.03.

[0081] Step S1344: Calculate the product of the standardized value of the occurrence density of characteristic elements and the corresponding mineralization contribution weight to obtain the contribution value of the occurrence density of characteristic elements to the mineralization potential.

[0082] The standardized value of the feature element storage density is 0.8, the weight is 0.4, and the contribution value is 0.8 × 0.4 = 0.32.

[0083] Step S1345: Add up all contribution values ​​to obtain the basic value of mineralization potential of the sub-region during the set time period; compare the calculated basic value of mineralization potential with the basic value of mineralization potential of the surrounding sub-regions. If the difference is greater than the set difference threshold, re-examine the extraction process of key response parameters and the value of mineralization contribution weight until the basic value of mineralization potential conforms to the spatial distribution law.

[0084] The contribution values ​​of the four key response parameters are added together: 0.12 + 0.15 + 0.03 + 0.32 = 0.62, yielding a base value of 0.62 for the mineralization potential of this sub-region during the specified time period. This base value is then compared with the base values ​​of neighboring sub-regions. If the difference is significant, exceeding a set difference threshold (e.g., 0.2), the extraction process of the key response parameters for this sub-region needs to be re-examined to ensure its accuracy and the reasonableness of the mineralization contribution weights. Problematic parts are corrected, and the base value of the mineralization potential is recalculated until the difference between it and the base values ​​of neighboring sub-regions is within a reasonable range, conforming to the gradual spatial distribution pattern of mineralization potential.

[0085] Step S135: Analyze the temporal variation characteristics of the basic value of mineralization potential in the sub-region across all time periods. Determine the temporal variation trend of the basic value of mineralization potential by the direction of change of the basic value in consecutive time periods, and determine the variation cycle by the time interval in which the basic value repeatedly shows the same variation trend.

[0086] Step S1351: Arrange the basic values ​​of mineralization potential of the sub-region in chronological order for all time periods to form a time series of basic values.

[0087] The basic mineralization potential values ​​calculated for this sub-region at different time periods (such as each quarter or each month) are arranged in chronological order to form a sequence of basic values ​​changing over time, for example [0.5, 0.55, 0.6, 0.58, 0.62, ...].

[0088] Step S1352: Calculate the difference between the base values ​​of two adjacent time periods in the base value time series to obtain the base value difference series.

[0089] Subtracting the base values ​​of two adjacent time periods in the base value time series yields the base value difference sequence. For example, the difference sequence corresponding to the above base value time series is [0.05, 0.05, -0.02, 0.04, ...].

[0090] Step S1353: Determine the time trend of the basic value of mineralization potential based on the positive and negative changes of the basic value difference sequence: when the basic value difference of multiple consecutive adjacent time periods is positive, the time trend is determined to be positive; when the basic value difference of multiple consecutive adjacent time periods is negative, the time trend is determined to be negative; when the basic value difference alternates between positive and negative, the time trend is determined to be stable.

[0091] Analyze the sign of the differences in the baseline value difference sequence. If the differences are positive for three or more consecutive adjacent time periods, it indicates that the baseline value of mineralization potential is continuously increasing, and the time trend is considered positive. If the differences are negative for multiple consecutive adjacent time periods, it indicates that the baseline value is continuously decreasing, and the time trend is considered negative. If positive and negative values ​​alternate in the difference sequence without a clear continuous positive or negative trend, the time trend is considered stable.

[0092] Step S1354: Analyze the time intervals in which the same trend of change recurs in the time series of base values ​​to determine the cycle of change of the base value of mineralization potential: When the same trend of change occurs continuously, calculate the time interval between adjacent trends of change; if the deviation of all consecutive time intervals from the average time interval is less than or equal to the first preset threshold, the cycle of change is determined to be stable; if the proportion of deviations greater than the second preset threshold exceeds the stable threshold, the cycle of change is determined to be unstable.

[0093] When a trend repeats itself over time, such as a positive trend followed by a negative trend, and then another positive trend, calculate the time interval between two adjacent identical trends (e.g., two positive trends). For example, if the first positive trend starts in period 1 and ends in period 3, and the second positive trend starts in period 5 and ends in period 7, then the time interval is period 5 - period 3 = 2 periods. Calculate the average of all these time intervals to obtain the average time interval. Compare each time interval with the average time interval and calculate the deviation. If all deviations are less than or equal to a first preset threshold (e.g., 10% of the average time interval), the cycle is considered stable. If any deviation exceeds a certain percentage (e.g., 30%) of the second preset threshold (e.g., 20% of the average time interval), the cycle is considered unstable.

[0094] Step S136: Based on the time change trend and cycle of the basic value of mineralization potential, the basic value of mineralization potential is corrected in the time dimension to obtain the time correction value of mineralization potential for the sub-region. When the change trend is positive and the cycle is stable, a positive correction is performed; when the change trend is negative and the cycle is unstable, a negative correction is performed.

[0095] Step S1361: Set the time correction coefficient: When the time change trend is positive and the change period is stable, the first type of time correction coefficient is used; when the time change trend is positive and the change period is unstable, the second type of time correction coefficient is used, and the second type of time correction coefficient is less than the first type of time correction coefficient; when the time change trend is stable, the third type of time correction coefficient is used, and the third type of time correction coefficient is 1; when the time change trend is negative and the change period is stable, the fourth type of time correction coefficient is used; when the time change trend is negative and the change period is unstable, the fifth type of time correction coefficient is used, and the fifth type of time correction coefficient is less than the fourth type of time correction coefficient.

[0096] Different time correction coefficients are set based on different combinations of time-varying trends and cycles. For example, the first type of time correction coefficient is set to 1.2, the second type to 1.1, the third type to 1, the fourth type to 0.9, and the fifth type to 0.8. The specific values ​​of these coefficients are determined based on the analysis of the variation of the basic value of the mineralization potential of known mineralized areas over time. When there is a positive trend and a stable cycle, the mineralization potential tends to increase continuously, so a larger positive correction coefficient is given; while when there is a negative trend and an unstable cycle, the mineralization potential is more uncertain, so a smaller negative correction coefficient is given.

[0097] Step S1362: Multiply the basic value of the mineralization potential of the sub-region by the corresponding time correction coefficient to obtain the time correction value of the mineralization potential of the sub-region; Calculate the average value and dispersion of the time correction values ​​of the mineralization potential of all sub-regions. If the difference between the correction value of a certain sub-region and the average value exceeds the reasonable range of dispersion, re-examine the judgment of the time change trend and the selection of the time correction coefficient of the sub-region, or recalculate the time correction value of the mineralization potential; Arrange the corrected time correction values ​​of the mineralization potential according to the spatial location of the sub-region to form a spatial distribution layer of the time correction value of the mineralization potential, which serves as the basic data layer of the mineralization potential gradient field.

[0098] Multiply the base value of the mineralization potential of the sub-region by the corresponding time correction factor. For example, if the base value is 0.6 and the corresponding time correction factor is 1.2, then the time correction value of the mineralization potential is 0.6 × 1.2 = 0.72. Statistically analyze the time correction values ​​of the mineralization potential of all sub-regions, calculating the mean and standard deviation (a representation of dispersion). If the absolute value of the difference between the correction value and the mean of a certain sub-region is greater than twice the standard deviation (within a reasonable range), then the correction value is considered to be potentially abnormal. In this case, it is necessary to re-examine whether the judgment of the time change trend of the sub-region is correct, whether the selection of the time correction factor is appropriate, or to recalculate the base value of the mineralization potential and the time correction value to ensure the rationality of the correction value. Finally, arrange the time correction values ​​of the mineralization potential of all sub-regions according to their spatial location to form a two-dimensional spatial distribution layer of the time correction values ​​of the mineralization potential. This spatial distribution layer of the time correction values ​​of the mineralization potential can intuitively show the spatial distribution of the mineralization potential within the target detection area and is the basic data layer for constructing the mineralization potential gradient field.

[0099] Step S137: Calculate the difference between the mineralization potential time correction value of this sub-region and the surrounding adjacent sub-regions, and determine the direction of change of mineralization potential in the spatial dimension by the positive or negative sign of the difference.

[0100] For each sub-region, select its neighboring sub-regions (e.g., sub-regions in the four directions: up, down, left, and right). Calculate the difference between the mineralization potential time correction value of this sub-region and the mineralization potential time correction value of each neighboring sub-region. If the difference is positive, it means that the mineralization potential time correction value of this sub-region is greater than that of its neighboring sub-regions, and the mineralization potential spatially points from the neighboring sub-regions towards this sub-region, i.e., the direction of change is towards this sub-region; if the difference is negative, the direction of change is away from this sub-region.

[0101] Step S138: Calculate the rate of change of the time correction value of mineralization potential between adjacent sub-regions. The rate of change is determined by the ratio of the difference to the distance between the sub-regions.

[0102] The distance between adjacent sub-regions is determined by the sub-region division method. For example, if a sub-region is a square with a side length of d, then the distance between the centers of adjacent sub-regions is d. The rate of change is obtained by dividing the difference in the mineralization potential time correction value calculated in step S137 by the distance between adjacent sub-regions. For example, if the difference is 0.1 and the distance is d, then the rate of change is 0.1 / d. The rate of change reflects how quickly the mineralization potential changes spatially.

[0103] Step S139: Integrate the time correction values, spatial change direction and change rate of the mineralization potential of all sub-regions according to their spatial location to form a continuous spatial distribution and change characteristics of mineralization potential. This spatial distribution and change characteristics of mineralization potential serve as the mineralization potential gradient field of the target detection area. Visualize the mineralization potential gradient field by using different colors to represent the magnitude of the mineralization potential, arrows to represent the direction of potential change, and arrow lengths to represent the rate of change, thus forming an intuitive distribution map of the mineralization potential gradient field.

[0104] The time correction values, spatial direction of change (indicated by the direction of the arrows), and rate of change (indicated by the length of the arrows) of the mineralization potential of all sub-regions are integrated according to their spatial location. Spatially, the information of adjacent sub-regions is connected to form a continuous overall description of the spatial distribution and variation characteristics of mineralization potential, which is the mineralization potential gradient field of the target detection area. To more intuitively display the mineralization potential gradient field, visualization processing is performed. In the visualization diagram, different colors are used to represent the magnitude of the time correction value of mineralization potential, such as darker colors indicating greater mineralization potential; the direction of change of mineralization potential in space is indicated by the direction of the arrows; and the length of the arrows represents the rate of change, with longer arrows indicating a greater rate of change. Through the above visualization processing, an intuitive distribution map of the mineralization potential gradient field is formed, which facilitates the subsequent calibration of the core mineralization target area.

[0105] Step S140: Based on the gradient change characteristics of the mineralization potential gradient field, the core mineralization target area is calibrated, the dynamic mineralization response characteristics of the core mineralization target area are extracted, and the dynamic mineralization response characteristics are hierarchically correlated with the standard mineralization response model of known mineral deposits to obtain the mineral type inference results and mineral occurrence dynamic parameters of the core mineralization target area.

[0106] Step S141: Based on the distribution characteristics of the time correction values ​​of the mineralization potential of all sub-regions in the mineralization potential gradient field of the target detection area, determine the mineralization potential screening threshold. The screening threshold is set as the lower limit of the high value range of the time correction values ​​of the mineralization potential.

[0107] Statistical analysis was performed on the time-corrected values ​​of the mineralization potential in all sub-regions of the mineralization potential gradient field of the aforementioned mountainous target detection area, and frequency distribution histograms or cumulative frequency curves were plotted. Based on the distribution characteristics of the time-corrected values ​​of the mineralization potential, a high-value interval was determined, and the sub-regions within this high-value interval have high mineralization potential. The lower limit of the high-value interval was used as the mineralization potential screening threshold. For example, if the analysis revealed that the region with a time-corrected value of mineralization potential greater than 0.7 was a high-value interval, then the screening threshold was set to 0.7.

[0108] Step S142: Divide the sub-regions in the mineralization potential gradient field where the mineralization potential time correction value is greater than the screening threshold into mineralization potential candidate regions, and mark the spatial boundary and mineralization potential change direction of each mineralization potential candidate region.

[0109] The process iterates through all sub-regions in the mineralization potential gradient field, selecting those with a mineralization potential time correction value greater than the screening threshold (0.7). These sub-regions are then combined to form mineralization potential candidate regions. For each mineralization potential candidate region, its spatial boundary is determined by the spatial coordinates of its contained sub-regions; the boundary can be a polygon formed by connecting the edges of the sub-regions. Simultaneously, based on the direction of mineralization potential variation of the sub-regions within the candidate region in the mineralization potential gradient field, the overall direction of mineralization potential variation of the entire candidate region is marked.

[0110] Step S143: Analyze the spatial correlation between adjacent mineralization potential candidate areas, merge adjacent candidate areas with a boundary distance less than a preset correlation threshold into a larger mineralization potential candidate area, and repeat this process until all adjacent candidate areas have been merged or determined to be unrelated.

[0111] Step S1431: Extract the boundary coordinates of each mineralization potential candidate region. The boundary coordinates are the vertex coordinates of the outer contour of the candidate region, and are arranged in clockwise order to form a boundary coordinate sequence.

[0112] For each candidate region with mineralization potential, the vertex coordinates of its outer contour are extracted. These vertex coordinates can be calculated from the boundary coordinates of the sub-regions, for example, the coordinates of the four vertices of the smallest bounding rectangle of the candidate region. The vertex coordinates are arranged in clockwise order to form a boundary coordinate sequence, such as [(x1, y1), (x2, y2), (x3, y3), (x4, y4)].

[0113] Step S1432: Calculate the straight-line distance between the two closest boundary points in the boundary coordinate sequence of any two adjacent mineralization potential candidate regions, and use this distance as the boundary distance between the two candidate regions.

[0114] For any two adjacent candidate regions with mineralization potential, find the two closest boundary points from their boundary coordinate sequences. For example, for point (xa, ya) in the boundary coordinate sequence of candidate region A and point (xb, yb) in the boundary coordinate sequence of candidate region B, calculate the straight-line distance between these two points, i.e., the distance value calculated using the distance formula between two points, and use this distance as the boundary distance between the two candidate regions.

[0115] Step S1433: Determine the preset association threshold based on the spatial continuity characteristics of the ore-forming area and the spatial resolution of the remote sensing data, and compare the calculated boundary distance with the preset association threshold.

[0116] Determining the preset association threshold requires considering the spatial continuity characteristics of the mineralized region and the spatial resolution of the remote sensing data. Mineralized regions typically possess a certain degree of spatial continuity, and adjacent potential mineralized candidate regions may belong to the same mineralization system. Higher spatial resolution of the remote sensing data allows for richer identification of details, enabling a smaller preset association threshold. For example, based on the mineralization characteristics and remote sensing data resolution of the target detection area in this mountainous region, the preset association threshold can be set to the sum of the side lengths of two sub-regions. The calculated boundary distance between the two candidate regions is then compared to the preset association threshold. If the boundary distance is less than the preset association threshold, the two candidate regions are considered to potentially have a spatial correlation.

[0117] Step S1434: When the boundary distance is less than the preset association threshold, further analyze the direction of change of the mineralization potential of the two mineralization potential candidate areas, and determine whether the direction of change points to the other area.

[0118] If the boundary distance between two candidate mineralization potential regions is less than a preset correlation threshold, further analysis is performed on the direction of their mineralization potential changes. Examining the overall direction of change in the mineralization potential of the two candidate regions, if the direction of change in candidate region A points towards candidate region B, and the direction of change in candidate region B also points towards candidate region A, it indicates that the directions of change in the mineralization potential of the two candidate regions are mutually pointing towards each other, indicating a strong spatial correlation.

[0119] Step S1435: If the direction of change is pointing to the other region, it is determined that the two mineralization potential candidate regions have spatial correlation, and they are merged into a larger mineralization potential candidate region. The boundary of the merged candidate region is the outer contour of the boundary of the two original candidate regions.

[0120] When the change directions of two candidate regions both point towards the other region, they are determined to have spatial correlation, and these two candidate regions are merged into a new, larger mineralization potential candidate region. The boundary of the merged candidate region is the circumscribed contour of the boundaries of the two original candidate regions, that is, the smallest polygon boundary containing the two original candidate regions.

[0121] Step S1436: If the direction of change does not point to the other region, calculate the difference between the average mineralization potential of the two candidate regions, set a preset difference threshold, if the difference is less than the preset difference threshold, determine that there is spatial correlation and merge them; if the difference is greater than or equal to the preset difference threshold, determine that there is no correlation.

[0122] If the change directions of two candidate regions do not point towards each other, calculate the average of the time-corrected values ​​of the mineralization potential of the two candidate regions. Subtract the two averages to obtain the difference, and set a preset difference threshold, which is determined based on the overall distribution of mineralization potential. If the difference is less than the preset difference threshold, it indicates that the mineralization potential levels of the two candidate regions are similar, and they may belong to the same mineralization belt, thus determining spatial correlation and merging them; if the difference is greater than or equal to the preset difference threshold, it indicates that the mineralization potential of the two candidate regions differs significantly, thus determining no correlation.

[0123] Step S1437: When the boundary distance is greater than or equal to the preset correlation threshold, it is directly determined that the two mineralization potential candidate areas have no spatial correlation; repeat the above process for all pairs of adjacent mineralization potential candidate areas to complete the merging of all candidate areas with spatial correlation; recalculate the average mineralization potential, average potential change rate, and consistency ratio of potential change direction for the merged mineralization potential candidate areas.

[0124] If the boundary distance between two candidate regions is greater than or equal to the preset correlation threshold, it indicates that they are spatially far apart, and they are directly judged to have no spatial correlation. All pairs of adjacent mineralization potential candidate regions within the target detection area are analyzed and judged according to steps S1432 to S1437 above, completing the merging of all candidate regions with spatial correlation. After merging, for each merged mineralization potential candidate region, its average mineralization potential (the average of the time correction values ​​of the mineralization potential of all contained sub-regions), average potential change rate (the average of the change rates between all adjacent sub-regions), and the proportion of consistency in potential change direction (the proportion of adjacent sub-region pairs with consistent change directions to the total number of adjacent sub-region pairs) are recalculated.

[0125] Step S144: Calculate the average mineralization potential, average potential change rate, and consistency ratio of potential change direction for each merged mineralization potential candidate region. Select the candidate region with the highest average mineralization potential, the largest average potential change rate, and the highest consistency ratio of potential change direction as the core mineralization target region.

[0126] For all merged candidate mineralization potential areas, their average mineralization potential, average rate of change of potential, and proportion of consistency in the direction of potential change are compared. First, the candidate area with the highest average mineralization potential is identified. If multiple candidate areas have the same average, the candidate area with the highest average rate of change of potential is selected. If multiple candidate areas still meet the criteria, the candidate area with the highest proportion of consistency in the direction of potential change is further selected and designated as the core mineralization target area. This core mineralization target area is the region within the target exploration area with the greatest mineralization potential and the most likely presence of mineral deposits.

[0127] Step S145: Extract the spectral response factors, topographic deformation response factors, thermal radiation response factors, and geological response factors of the core mineralization target area at all time periods from the dynamic response field of the mineralization environment, and combine them to form the dynamic mineralization response characteristics of the core mineralization target area. The dynamic mineralization response characteristics include the temporal variation sequence and spatial distribution characteristics of each response factor.

[0128] In the dynamic response field of the metallogenic environment, detailed data on spectral response factors, topographic deformation response factors, thermal radiation response factors, and geological response factors of the core metallogenic target area are extracted based on the spatial boundary coordinates of the area over all time periods. These data are then organized according to temporal order and spatial location to form dynamic metallogenic response characteristics. The temporal variation sequence of each response factor refers to the sequence formed by arranging the values ​​of each response factor in different time periods in chronological order, reflecting the dynamic changes of the response factors over time. The spatial distribution characteristics refer to the numerical distribution of each response factor at different spatial locations within the core metallogenic target area, represented by a two-dimensional distribution image or data matrix.

[0129] Step S146: Collect standard metallogenic response models for multiple known mineral deposits. Each standard metallogenic response model for a known mineral deposit includes typical spectral response sequences, typical topographic deformation response sequences, typical thermal radiation response sequences, and typical geological response sequences during the formation process of that known mineral deposit.

[0130] By consulting geological literature and mineral resource databases, mineralization data for multiple known mineral deposits of different types (such as copper, iron, and gold) were collected. Based on this data, standard mineralization response models were constructed. Each standard mineralization response model includes typical spectral response sequences of that type of mineral during its formation process, i.e., the spectral characteristic variation sequences of characteristic minerals at different mineralization stages; typical topographic deformation response sequences, such as the variation sequences of topographic slope and fault structures with the mineralization process; typical thermal radiation response sequences, reflecting the occurrence and change of thermal anomaly areas; and typical geological response sequences, including changes in the occurrence state of characteristic elements and rock-mineral assemblages. These typical sequences were obtained by analyzing and summarizing the mineralization history and related data of known mineral deposits.

[0131] Step S147: Divide the dynamic mineralization response characteristics of the core mineralization target area into four levels according to the response factor type: spectral response layer, topographic deformation response layer, thermal radiation response layer, and geological response layer. Similarly, divide the standard mineralization response model of known mineral deposits into the corresponding four levels.

[0132] The dynamic mineralization response characteristics of the core mineralization target area are stratified according to the type of response factors, forming four levels. The spectral response layer contains the temporal variation sequence and spatial distribution characteristics of the spectral response factors in the dynamic mineralization response characteristics; the topographic deformation response layer contains relevant information about the topographic deformation response factors; the thermal radiation response layer contains relevant information about the thermal radiation response factors; and the geological response layer contains information about the geological response factors. Similarly, the standard mineralization response model for each known mineral deposit is also divided into four corresponding levels: spectral response layer, topographic deformation response layer, thermal radiation response layer, and geological response layer. Each level contains the typical response sequence corresponding to that model.

[0133] Step S148: In the spectral response layer, compare the similarity between the spectral response sequence of the dynamic mineralization response characteristics and the typical spectral response sequence of the standard mineralization response model of each known mineral deposit, and mark the standard model with the highest similarity.

[0134] At the spectral response level, methods such as dynamic time warping or correlation coefficient analysis are used to calculate the similarity between the spectral response sequences of the core mineralization target area and the typical spectral response sequences of standard mineralization response models for each known mineral deposit. For example, the correlation coefficient between two sequences is calculated; the closer the correlation coefficient is to 1, the higher the similarity. This calculation is performed for each standard model, and then the standard model with the highest similarity is identified and marked.

[0135] Step S149: In the topographic deformation response layer, compare the similarity between the topographic deformation response sequence of the dynamic mineralization response characteristics and the typical topographic deformation response sequence of the standard mineralization response model of each known mineral deposit, and mark the standard model with the highest similarity.

[0136] In the topographic deformation response layer, methods similar to those used in the spectral response layer, such as calculating the Euclidean distance or dynamic time-warped distance between sequences, are employed to measure the similarity between the topographic deformation response sequences of the core mineralization target area and the typical topographic deformation response sequences of each standard model. The smaller the distance, the higher the similarity. The standard model with the highest similarity is then identified.

[0137] Step S1410: In the thermal radiation response layer, compare the similarity between the thermal radiation response sequence of the dynamic mineralization response characteristics and the typical thermal radiation response sequence of the standard mineralization response model of each known mineral deposit, and mark the standard model with the highest similarity.

[0138] For the thermal radiation response layer, a suitable similarity measurement method, such as cosine similarity, is used to compare the similarity between the thermal radiation response sequence of the ore-forming core target area and the typical thermal radiation response sequence of each standard model, and the standard model with the highest similarity is marked.

[0139] Step S1411: In the geological response layer, compare the similarity between the geological response sequence of the dynamic mineralization response characteristics and the typical geological response sequence of the standard mineralization response model of each known mineral deposit, and mark the standard model with the highest similarity.

[0140] In the geological response layer, the similarity between the sequence of characteristic element occurrence density changes, the sequence of rock and mineral assemblage changes, etc., and the typical geological response sequence in the standard model is compared. The similarity is calculated by methods such as the longest common subsequence length, and the standard model with the highest similarity is marked.

[0141] Step S1412: Statistically analyze the known mineralization response model with the most marking times in the four levels, and use the mineralization type corresponding to the known mineralization response model as the mineralization core target area inference result.

[0142] The standard models with the highest similarity across the four levels are statistically analyzed, and the number of times each standard model is labeled across the four levels is counted. The known mineralization response model with the most labels is identified, and the mineral type corresponding to this known mineralization response model is the inferred mineral type of the core mineralization target area. For example, if the copper standard mineralization response model is labeled three times across the four levels, which is the most labeled, then it is inferred that a copper deposit may exist in the core mineralization target area.

[0143] Step S1413: Combining the dynamic mineralization response characteristic parameters of the mineralization core target area, and based on the correlation between the mineral occurrence parameters and the response sequence in the known mineralization standard mineralization response model, determine the mineral occurrence dynamic parameters of the mineralization core target area. The mineral occurrence dynamic parameters include the burial depth variation trend, thickness distribution characteristics, and spatial continuity variation law of the mineral.

[0144] The standard mineralization response model for known mineral deposits includes the correlations between mineral occurrence parameters (such as burial depth and thickness) and various response sequences (such as spectral response sequences and geological response sequences). These correlations are statistical regularities or empirical formulas obtained through analysis of actual data from known mineral deposits. By substituting the dynamic mineralization response characteristic parameters of the mineralization core target area into these correlations, the dynamic parameters of the mineral occurrence are calculated. For example, based on the correlation between the depth of characteristic absorption valleys and the burial depth of the mineral deposit in the standard model, combined with the time variation sequence of the depth of characteristic absorption valleys in the mineralization core target area, the trend of the burial depth of the mineral deposit over time can be calculated; based on the correlation between the distribution of characteristic element occurrence density and the thickness of the mineral deposit in the geological response sequence, the thickness distribution characteristics of the mineral deposit can be determined; by analyzing the continuity of the spatial distribution characteristics of each response factor, the spatial continuity variation law of the mineral deposit can be determined, such as whether there are faults causing interruptions in the continuity of the mineral deposit.

[0145] Step S150: Generate a dynamic detection implementation plan based on the mineral deposit type inference results and the mineral deposit occurrence dynamic parameters. The dynamic detection implementation plan includes the geographical coordinate range of the mineral core target area, the probability of mineral deposit type corresponding to each coordinate point, recommended detection techniques, and detection depth adjustment rules.

[0146] For example, step S151: convert the boundary coordinates of the core mineralization target area into coordinate values ​​under a unified geographic coordinate system, determine the geographic coordinate range of the core mineralization target area, and form a coordinate range file.

[0147] The coordinates of the boundary vertices of the ore-forming core target area are converted from the coordinate system used in the dynamic response field of the ore-forming environment to a unified geographic coordinate system, such as the WGS-84 coordinate system. Using coordinate transformation software or algorithms, the parameters of the original coordinate system and the target coordinate system are input, and the coordinates of each boundary vertex are transformed. After the transformation, the boundary coordinate values ​​of the ore-forming core target area in the unified geographic coordinate system are obtained. These coordinate values ​​define the geographic coordinate range of the ore-forming core target area. This coordinate range information is then compiled into a coordinate range file, such as a KML or SHP format file, for loading and viewing in geographic information system software.

[0148] Step S152: Based on the thickness distribution characteristics in the dynamic parameters of mineral deposit occurrence, set detection points at preset intervals within the core target area of ​​mineralization.

[0149] Based on the thickness distribution characteristics of the mineral deposit dynamic parameters in the core mineralization target area, the principles for setting up detection points are determined. In areas with thicker mineral deposits, the interval between detection points can be appropriately reduced to obtain more detailed information about the mineral distribution; in areas with thinner deposits, the interval can be increased. The preset interval size is determined based on the required detection accuracy and actual detection capabilities, for example, set to an interval of 50 meters. Detection points are then set up uniformly or according to specific rules (such as along the strike direction of the mineral deposit) within the geographical coordinate range of the core mineralization target area according to the preset interval.

[0150] Step S153: Record the geographic coordinates of each detection point, and associate the probability of the mineral deposit type corresponding to the detection point with the similarity between the dynamic mineralization response characteristics and the standard mineralization response model.

[0151] For each designated detection point, its geographic coordinates (longitude and latitude) in a unified geographic coordinate system are accurately recorded. Based on the similarity between the dynamic mineralization response characteristics of the core mineralization target area and the standard mineralization response model at each level, a corresponding mineral deposit type probability is assigned to each detection point. For example, if the similarity is 0.8 in the spectral response layer, 0.7 in the topographic deformation response layer, 0.6 in the thermal radiation response layer, and 0.9 in the geological response layer, and considering these similarities, the mineral deposit type probability of this detection point is calculated to be 0.75 (range 0-1, with 1 indicating the highest probability) using a weighted average method.

[0152] Step S154: Determine the initial detection depth of each detection point based on the trend of burial depth change in the dynamic parameters of mineral occurrence.

[0153] By analyzing the burial depth variation trend in the dynamic parameters of mineral deposit occurrence, the burial depth of the deposit at different locations within the core mineralization target area can be determined. For each detection point, the initial detection depth is determined based on the burial depth variation trend at its location and the current predicted burial depth value. For example, if the predicted burial depth of the detection point is 200 meters and the variation trend is relatively stable, the initial detection depth is set to 200 meters; if the variation trend is gradually increasing, the initial detection depth can be appropriately increased with a certain margin, such as 210 meters.

[0154] Step S155: Based on the inference results of the mineral deposit type, select the appropriate technical means for the detection of the corresponding mineral deposit type. Different combination of technical means are selected for different detection points according to the mineral deposit occurrence characteristics.

[0155] Based on the inferred mineral deposit type (e.g., copper deposit), appropriate exploration techniques are selected for that type of deposit. Commonly used mineral exploration techniques include geological surveying, geophysical exploration (e.g., magnetic exploration, electrical exploration, seismic exploration), geochemical exploration (e.g., soil geochemical surveying, rock geochemical surveying), and remote sensing interpretation. For different exploration points, different combinations of techniques are selected based on the mineral occurrence characteristics of their location (e.g., burial depth, thickness, surrounding geological environment). For example, for exploration points with shallow burial and large thickness, geological surveying combined with soil geochemical surveying can be used; for points with deep burial, a combination of seismic exploration and electrical exploration can be used.

[0156] Step S156: Construct detection depth adjustment rules: When no mineral deposits are found during the initial detection of any detection point, the detection depth is adjusted according to the changing trend of the dynamic parameters of mineral deposit occurrence around the detection point. If the trend of the burial depth is an increase, the adjusted detection depth is greater than the initial detection depth; if the trend of the burial depth is a decrease, the adjusted detection depth is less than the initial detection depth.

[0157] Establish specific rules for adjusting the detection depth. If, after initial detection at a certain point, no mineral deposits are found (e.g., no characteristic minerals are detected, or the content of characteristic elements does not reach the threshold), the detection depth needs to be adjusted based on the changing trends of dynamic parameters of mineral deposits within a certain range around the detection point. If the trend of burial depth around the detection point is increasing, it indicates that the mineral deposit may be buried deeper, and the adjusted detection depth should be greater than the initial detection depth, for example, increasing it by 50 meters. If the trend of burial depth is decreasing, it indicates that the mineral deposit may be buried shallower, and the adjusted detection depth should be less than the initial detection depth, for example, decreasing it by 30 meters. The adjustment range can be determined based on the steepness of the trend; the steeper the trend, the larger the adjustment range.

[0158] Step S157: Determine the data acquisition frequency based on the time variation period of the dynamic parameters of the mineral deposit. The acquisition frequency is set higher for areas with shorter time variation periods than for areas with longer time variation periods.

[0159] Analyzing the time variation period of dynamic parameters of mineral deposits reveals how quickly these parameters change over time. For areas within the core mineralization target area with shorter time variation periods, it indicates rapid changes in the mineral deposit's state, requiring a higher data collection frequency, such as monthly collection. Conversely, for areas with longer time variation periods, the mineral deposit's state is relatively stable, allowing for a lower collection frequency, such as quarterly collection. This approach ensures data validity while enabling the rational allocation of exploration resources.

[0160] Step S158: Construct detection result feedback rules: Feed back the detection results of each detection point to the mineralization potential gradient field in real time. If the detection results are consistent with the gradient field prediction, maintain the original detection scheme. If they are inconsistent, re-analyze the mineralization potential gradient field and dynamic mineralization response characteristics, and adjust the detection scheme.

[0161] Establish a real-time feedback mechanism for detection results. The detection results from each detection point (such as whether mineral deposits were found, the type and content of the deposits, etc.) should be promptly fed back to the database of the mineralization potential gradient field. The detection results should be periodically compared with the predicted results of the mineralization potential gradient field. If the detection results are consistent with the predictions, the current detection plan is reasonable, and the original plan should be maintained. If the detection results are inconsistent with the predictions, for example, no mineral deposits were found in areas predicted as high-potential by the mineralization potential gradient field, or mineral deposits were found in low-potential areas, then it is necessary to re-analyze the calculation process of the mineralization potential gradient field, the extraction and analysis of dynamic mineralization response characteristics to identify any problems, and adjust the detection plan based on the new analysis results, such as adjusting the detection points, detection techniques, or detection depth.

[0162] Step S159: Organize the geographical coordinate range of the core target area of ​​the mineralization, the coordinates of the detection points and the probability of mineral types, the initial detection depth, recommended technical means, the rules for adjusting the detection depth, the data acquisition frequency, and the rules for result feedback to form a dynamic detection implementation plan.

[0163] The geographical coordinates of the identified core mineralization target area, the coordinates of each detection point and the corresponding mineral deposit type probability, the initial detection depth, the recommended combination of detection technologies, the rules for adjusting the detection depth, the data acquisition frequency, and the rules for feedback of detection results are compiled and summarized. This information is then organized into a dynamic detection implementation plan according to a specific format (such as a text report or a document combining charts and graphs). This dynamic implementation plan provides detailed guidance for actual mineral exploration work, including when, where, what technologies to use, at what depth to conduct detection, and how to adjust the plan based on the detection results, ensuring that the exploration work is carried out efficiently and accurately.

[0164] Figure 2 The illustration shows exemplary hardware and software components of a remote sensing-based mineral exploration system 100 that can implement the ideas of this application, according to some embodiments of this application. For example, a processor 120 can be used in the remote sensing-based mineral exploration system 100 and to perform the functions described in this application.

[0165] The mineral exploration system 100 based on remote sensing technology can be a general-purpose server or a special-purpose server; both can be used to implement the mineral exploration method based on remote sensing technology of this application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the load.

[0166] For example, a mineral exploration system 100 based on remote sensing technology may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and various forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the mineral exploration system 100 based on remote sensing technology may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The methods of this application can be implemented according to these program instructions. The mineral exploration system 100 based on remote sensing technology also includes an I / O interface 150 between the computer and other input / output devices.

[0167] For ease of explanation, only one processor is described in the remote sensing-based mineral exploration system 100. However, it should be noted that the remote sensing-based mineral exploration system 100 of this application may also include multiple processors, and therefore the steps performed by one processor described in this application may also be performed jointly or individually by multiple processors. For example, if the processor of the remote sensing-based mineral exploration system 100 performs steps A and B, it should be understood that steps A and B may also be performed jointly by two different processors or individually by one processor. For example, the first processor performs step A, the second processor performs step B, or the first processor and the second processor jointly perform steps A and B.

[0168] Furthermore, this embodiment of the invention also provides a readable storage medium, wherein computer-executable instructions are preset in the readable storage medium, and when the processor executes the computer-executable instructions, the above-mentioned mineral exploration method based on remote sensing technology is implemented.

[0169] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A method for detecting a mineral deposit based on remote sensing technology, characterized in that, The method comprises: Collecting multi-source spatio-temporal remote sensing data volume of a target detection area, the multi-source spatio-temporal remote sensing data volume containing hyperspectral remote sensing data, synthetic aperture radar data and thermal infrared remote sensing data obtained in different seasons, different vegetation phenological periods and different detection heights, and ground geological survey data corresponding to the time period, the ground geological survey data containing rock mineral identification data and characteristic element occurrence state data; Performing spatio-temporal coupling processing on the multi-source spatio-temporal remote sensing data volume to generate a dynamic response field of ore-forming environment of the target detection area, the dynamic response field of ore-forming environment recording the cooperative change relationship and correlation strength of spectral response, thermal radiation response and topographic deformation response in different spatio-temporal dimensions related to ore formation; Analyzing the dynamic response field of ore-forming environment to obtain a metallogenic potential gradient field of the target detection area, the metallogenic potential gradient field reflecting the degree difference and continuous change trend of different sub-regions in the spatio-temporal dimension satisfying the ore-forming conditions, and containing the direction and rate information of potential change; According to the gradient change characteristics of the metallogenic potential gradient field, a metallogenic core target area is marked, dynamic ore-forming response characteristics of the metallogenic core target area are extracted, the dynamic ore-forming response characteristics are hierarchically associated with a standard ore-forming response model of known mineral deposits to obtain a mineral deposit type inference result and a mineral deposit occurrence dynamic parameter of the metallogenic core target area; A dynamic detection implementation scheme is generated according to the mineral deposit type inference result and the mineral deposit occurrence dynamic parameter, the dynamic detection implementation scheme containing the geographic coordinate range of the metallogenic core target area, the mineral deposit type possibility corresponding to each coordinate point, the recommended detection technical means and the detection depth adjustment rules.

2. The method for detecting a mineral deposit based on remote sensing technology according to claim 1, characterized in that, The spatio-temporal coupling processing on the multi-source spatio-temporal remote sensing data volume to generate the dynamic response field of ore-forming environment of the target detection area comprises: Extracting spectral response factors of the hyperspectral remote sensing data in each time period from the multi-source spatio-temporal remote sensing data volume, the spectral response factors containing the characteristic waveband reflectivity change trend of the characteristic minerals, the morphological variation of the characteristic absorption valley and the slope change characteristics of the spectral curve, the characteristic waveband being determined according to the spectral characteristics of the marker minerals corresponding to the possible mineral deposit types in the target detection area; Extracting topographic deformation response factors of the synthetic aperture radar data in each time period from the multi-source spatio-temporal remote sensing data volume, the topographic deformation response factors containing the continuous change sequence of the topographic slope, the spatial distribution and deformation trend of the fault structure and the dynamic change of the topographic relief, the spatial distribution of the fault structure being obtained by identifying the texture variation area of the synthetic aperture radar data; Extracting thermal radiation response factors of the thermal infrared remote sensing data in each time period from the multi-source spatio-temporal remote sensing data volume, the thermal radiation response factors containing the spatio-temporal distribution difference of the surface temperature, the change rule of the thermal radiation intensity and the spatial migration track of the thermal anomaly area, the thermal anomaly area being obtained by identifying the difference between the thermal radiation intensity and the surrounding area; Extracting geological response factors of the ground geological survey data in each time period from the multi-source spatio-temporal remote sensing data volume, the geological response factors containing the spatial distribution of the rock mineral combination, the occurrence state change of the characteristic elements and the correlation relationship between the geological structure and the mineral distribution; The time-space correlation between the response factors is established, the spectral response factor, the topographic deformation response factor, the thermal radiation response factor and the geological response factor in the same time period are aligned in space, and the synchronization of the changes of the response factors in the same space position is marked; The correlation strength of the spectral response factor and the topographic deformation response factor, the correlation strength of the thermal radiation response factor and the geological response factor, and the correlation strength of the spectral response factor and the thermal radiation response factor are determined by analyzing the consistency of the change trends of different response factors in the same time-space dimension; The response factors and the correlation strength in each period are arranged in chronological order to construct a time-space continuous response factor change sequence, and the change amplitudes of the response factors and the change of the correlation strength in each period are marked; The response factor change sequence is combined with the spatial coordinate system of the target detection area to form a dynamic response field of the ore-forming environment which can reflect the time-space dynamic changes of the ore-forming environment, and each spatial coordinate point in the dynamic response field of the ore-forming environment corresponds to a group of response factor change data and correlation strength data.

3. The method for detecting a mineral deposit based on remote sensing technology according to claim 2, characterized in that, The spectral response factor of each period of hyperspectral remote sensing data is extracted from the multi-source time-space remote sensing data body, which includes: Atmospheric correction processing is performed on each period of hyperspectral remote sensing data to eliminate the interference of atmospheric scattering, atmospheric absorption and sensor noise on the spectral signal, and the atmospheric correction processing adopts a correction process based on atmospheric radiation transmission theory, and the input parameters include the atmospheric humidity, air pressure, solar elevation angle and sensor observation angle during data collection; The corrected hyperspectral remote sensing data is divided into several characteristic band groups according to the mineral spectral characteristics, each characteristic band group corresponds to the spectral response interval of a type of marker mineral, and the division of the characteristic band group is based on the known mineral spectral database of the target detection area; A uniformly distributed sampling grid is set in the image area corresponding to each characteristic band group, the density of the sampling grid is determined according to the spatial resolution of the hyperspectral remote sensing data, and the number of pixels covered by each sampling grid unit can represent the spectral characteristics of the image area; The reflectivity values of all pixel points in each sampling grid unit in each band of the characteristic band group are extracted, and the average reflectivity sequence of the sampling grid unit in the characteristic band group is formed by calculating the average reflectivity of each band; The average reflectivity sequence is analyzed to identify the continuous trend of the reflectivity values with the change of the band, and the characteristic absorption valley position and shape appearing in the continuous trend are marked, and the shape of the characteristic absorption valley is described by the width, depth and symmetry of the absorption valley; The change rate of the reflectivity on both sides of the characteristic absorption valley is calculated, and the change rate is described by the ratio of the reflectivity difference between the starting point and the peak point of the absorption valley to the band difference; The average reflectivity sequence of each sampling grid unit, the characteristic absorption valley position and shape, and the reflectivity change rate are combined to form the spectral response factor corresponding to the sampling grid unit; The spectral response factors of all sampling grid units are subjected to spatial interpolation processing to form a continuous spectral response factor distribution layer covering the target detection area, and the continuous spectral response factor distribution layer serves as the spectral response basic layer of the dynamic response field of the ore-forming environment.

4. The method for detecting a mineral deposit based on remote sensing technology according to claim 2, characterized in that, The spatial and temporal correlation between the response factors is established, the spectral response factor, the topographic deformation response factor, the thermal radiation response factor and the geological response factor in the same period are spatially aligned, and the synchronization of the changes of the response factors in the same spatial position is marked, including: The spectral response factor distribution layer, the topographic deformation response factor distribution layer, the thermal radiation response factor distribution layer and the geological response factor distribution layer in the same period are converted into a unified geographic coordinate system, and the spatial coordinate accuracy of each distribution layer is adjusted to be consistent through a coordinate conversion tool; A common spatial sampling point is set in the unified coordinate system, and the density of the spatial sampling point is determined according to the resolution of each response factor distribution layer. Each spatial sampling point has corresponding response factor data in each distribution layer; The response factor values of each spatial sampling point in each response factor distribution layer are extracted to form a multi-response factor data group of the spatial sampling point; The change trend of each response factor in the multi-response factor data group of each spatial sampling point is analyzed to determine whether the change directions of the spectral response factor and the topographic deformation response factor are consistent, and whether the change directions of the thermal radiation response factor and the geological response factor are consistent; The number of spatial sampling points with consistent change directions of each response factor is counted, and a first proportion of the number of spatial sampling points to the total number of spatial sampling points is calculated; For the spatial sampling points with consistent change directions, the change amplitudes of each response factor are further analyzed to determine whether the proportion of the change amplitudes is within a preset reasonable interval, and the reasonable interval is determined by the change amplitude proportion of the response factors in a known ore-forming area; The number of spatial sampling points with the change amplitude proportion within the reasonable interval is counted, and a second proportion of the number of spatial sampling points to the number of spatial sampling points with consistent change directions is calculated; The change synchronization of each response factor in the same spatial position is determined according to the first proportion and the second proportion, the synchronization result is marked on the corresponding spatial sampling point, and a spatial synchronization distribution layer of each response factor is formed. The higher the first proportion and the second proportion are, the stronger the change synchronization is.

5. The remote sensing based mineral deposit detection method of claim 1, wherein, The ore-forming potential gradient field of the target detection area is analyzed based on the dynamic response field of the ore-forming environment, including: Key response parameters related to ore formation are extracted from the dynamic response field of the ore-forming environment, including the feature absorption valley depth in the spectral response factor, the fault structure density in the topographic deformation response factor, the thermal anomaly intensity in the thermal radiation response factor, and the feature element occurrence density in the geological response factor; By collecting and statistically analyzing the ore-forming data of multiple known ore deposits, the influence of each parameter on ore formation during the formation of known ore deposits is obtained to determine the ore-forming contribution weight of each key response parameter according to the difference in ore formation of different ore deposits. For each sub-region divided in the target detection area, the actual values of each key response parameter are standardized to obtain the standardized values of each key response parameter; The ore-forming potential basic value of the sub-region in each period is calculated, which is the sum of the product of the standardized values of each key response parameter and the corresponding ore-forming contribution weight. analyzing time variation characteristics of the base values of the ore-forming potential of the sub-region in all time periods, determining a time variation trend of the base values of the ore-forming potential by variation directions of the base values in consecutive time periods, and determining a variation period by time intervals in which the base values repeatedly appear the same variation trend; correcting the base values of the ore-forming potential in the time dimension according to the time variation trend and the variation period of the base values of the ore-forming potential, to obtain time correction values of the ore-forming potential of the sub-region, and performing positive correction when the variation trend is positive and the period is stable, and performing negative correction when the variation trend is negative and the period is unstable; calculating difference values of the time correction values of the ore-forming potential of the sub-region and adjacent sub-regions, and determining variation directions of the ore-forming potential in the spatial dimension by positive and negative signs of the difference values; calculating variation rates of the time correction values of the ore-forming potential between adjacent sub-regions, the variation rates being determined by a ratio of the difference values to distances between the sub-regions; integrating the time correction values of the ore-forming potential, the variation directions and the variation rates of all the sub-regions according to spatial positions of the sub-regions, to form a continuous spatial distribution and variation characteristics of the ore-forming potential, and taking the spatial distribution and variation characteristics of the ore-forming potential as a gradient field of the ore-forming potential of the target detection region; performing visual processing on the gradient field of the ore-forming potential, using different colors to represent sizes of the ore-forming potential, using arrows to represent directions of the potential variation, and using lengths of the arrows to represent the variation rates, to form a distribution map of the gradient field of the ore-forming potential.

6. The remote sensing based mineral deposit detection method of claim 5, wherein, The method comprises the following steps: dividing the target detection region into a plurality of sub-regions according to a preset spatial resolution based on a spatial resolution of the dynamic response field of the ore-forming environment and a total area of the target detection region; extracting actual values of the key response parameters of the sub-region in a set time period from the dynamic response field of the ore-forming environment, including: extracting an average value of the characteristic absorption valley depth of all spatial sampling points in the sub-region from the spectral response factor distribution layer as an actual value of the characteristic absorption valley depth; extracting a total length of the fault structure per unit area in the sub-region from the topographic deformation response factor distribution layer as an actual value of the fault structure density; extracting a difference between an average value of the thermal radiation intensity of all spatial sampling points in the sub-region and an average value of the surrounding region from the thermal radiation response factor distribution layer as an actual value of the thermal anomaly intensity; and extracting a ratio between a total occurrence of the characteristic element in the sub-region and an area of the sub-region from the geological response factor distribution layer as an actual value of the characteristic element occurrence density; determining a value range of each key response parameter, including a minimum value and a maximum value, based on the actual values of the key response parameters of all the sub-regions of the target detection region; and For each key response parameter, the actual value of the sub-region is standardized, and the corresponding contribution weight of each key response parameter to mineralization is obtained. The values of the contribution weight of the characteristic absorption valley depth to mineralization, the contribution weight of the fault structure density to mineralization, the contribution weight of the thermal anomaly intensity to mineralization, and the contribution weight of the characteristic element occurrence density to mineralization are determined according to known mineral deposit statistical data, and the sum of all the contribution weights of mineralization is the overall weight reference; The product of the characteristic absorption valley depth standardized value and the corresponding contribution weight of mineralization is calculated to obtain the contribution value of the characteristic absorption valley depth to the mineralization potential; The product of the fault structure density standardized value and the corresponding contribution weight of mineralization is calculated to obtain the contribution value of the fault structure density to the mineralization potential; The product of the thermal anomaly intensity standardized value and the corresponding contribution weight of mineralization is calculated to obtain the contribution value of the thermal anomaly intensity to the mineralization potential; The product of the characteristic element occurrence density standardized value and the corresponding contribution weight of mineralization is calculated to obtain the contribution value of the characteristic element occurrence density to the mineralization potential; All the contribution values are added to obtain the mineralization potential basic value of the sub-region in the set period; The calculated mineralization potential basic value is compared with the mineralization potential basic value of the surrounding sub-region. If the difference is greater than the set difference threshold, the extraction process of the key response parameter and the value of the contribution weight of mineralization are rechecked until the mineralization potential basic value meets the spatial distribution rule.

7. The remote sensing based mineral deposit detection method of claim 5, wherein, The time dimension of the mineralization potential basic value is corrected according to the time variation trend and the change period of the mineralization potential basic value to obtain the time correction value of the mineralization potential of the sub-region, which includes: The mineralization potential basic values of the sub-region in all periods are arranged in chronological order to form a basic value time sequence; The difference between the basic values of two adjacent periods in the basic value time sequence is calculated to obtain a basic value difference sequence; The time variation trend of the mineralization potential basic value is determined according to the positive and negative changes of the basic value difference sequence: when the basic value differences of multiple consecutive adjacent periods are all positive, it is determined that the time variation trend is positive; when the basic value differences of multiple consecutive adjacent periods are all negative, it is determined that the time variation trend is negative; when the basic value differences appear alternately, it is determined that the time variation trend is stable; The change period of the mineralization potential basic value is determined by analyzing the time interval of the repeated occurrence of the same variation trend in the basic value time sequence: when the same variation trend continuously appears, the time interval between adjacent variation trends is calculated; if the deviation of all continuous time intervals from the average time interval is less than or equal to a first preset threshold, it is determined that the change period is stable; if the proportion of the deviation greater than a second preset threshold exceeds a stable threshold, it is determined that the change period is unstable; The time correction coefficient is set: when the time change trend is positive and the change period is stable, a first type of time correction coefficient is adopted; when the time change trend is positive and the change period is unstable, a second type of time correction coefficient is adopted, the second type of time correction coefficient is smaller than the first type of time correction coefficient; when the time change trend is stable, a third type of time correction coefficient is adopted, the third type of time correction coefficient is 1; when the time change trend is negative and the change period is stable, a fourth type of time correction coefficient is adopted; when the time change trend is negative and the change period is unstable, a fifth type of time correction coefficient is adopted, the fifth type of time correction coefficient is smaller than the fourth type of time correction coefficient; The metallogenic potential time correction value of the sub-region is obtained by multiplying the metallogenic potential basic value of the sub-region by the corresponding time correction coefficient; The average value and the dispersion degree of the metallogenic potential time correction values of all sub-regions are counted, if the difference between the correction value of a certain sub-region and the average value exceeds the reasonable range of the dispersion degree, the time change trend judgment and the time correction coefficient selection of the sub-region are rechecked, or the metallogenic potential time correction value is recalculated; The corrected metallogenic potential time correction values are arranged according to the spatial positions of the sub-regions to form a metallogenic potential time correction value spatial distribution layer, which is used as a basic data layer of the metallogenic potential gradient field.

8. The remote sensing based mineral deposit detection method of claim 1, wherein, The gradient change characteristic of the metallogenic potential gradient field is used to mark a metallogenic core target area, the dynamic metallogenic response characteristic of the metallogenic core target area is extracted, the dynamic metallogenic response characteristic is hierarchically associated with a standard metallogenic response model of a known mineral deposit to obtain a mineral deposit type inference result and a mineral deposit occurrence dynamic parameter of the metallogenic core target area, including: A metallogenic potential screening threshold is determined according to the distribution characteristic of the metallogenic potential time correction values of all sub-regions in the metallogenic potential gradient field of the target detection area, and the screening threshold is the lower limit of the high value interval of the metallogenic potential time correction value; Sub-regions with a metallogenic potential time correction value greater than the screening threshold in the metallogenic potential gradient field are divided into metallogenic potential candidate regions, and the spatial boundary and the metallogenic potential change direction of each metallogenic potential candidate region are marked; The spatial correlation of adjacent metallogenic potential candidate regions is analyzed, adjacent candidate regions with a boundary distance less than a preset correlation threshold are merged into larger metallogenic potential candidate regions, and the merging is continued until all adjacent candidate regions are merged or determined to be irrelevant; The metallogenic potential average value, the potential change rate average value and the potential change direction consistency proportion of each merged metallogenic potential candidate region are calculated, and the candidate region with the highest metallogenic potential average value, the largest potential change rate average value and the highest potential change direction consistency proportion is selected as the metallogenic core target area; The spectral response factor, the topographic deformation response factor, the thermal radiation response factor and the geological response factor of the metallogenic core target area at all time periods are extracted from the metallogenic environment dynamic response field to form a dynamic metallogenic response characteristic of the metallogenic core target area, and the dynamic metallogenic response characteristic includes the time change sequence and the spatial distribution characteristic of each response factor. collecting standard ore-forming response models of a plurality of known deposits, each standard ore-forming response model of a known deposit comprising a typical spectral response sequence, a typical topographic deformation response sequence, a typical thermal radiation response sequence and a typical geological response sequence during the formation of the known deposit; dividing the dynamic ore-forming response characteristics of the ore-forming core target area into four levels of spectral response layer, topographic deformation response layer, thermal radiation response layer and geological response layer according to the response factor types, and dividing the standard ore-forming response models of the known deposits into corresponding four levels; in the spectral response layer, comparing the similarity degree of the spectral response sequence of the dynamic ore-forming response characteristics with the typical spectral response sequence of each standard ore-forming response model of the known deposits, and marking the standard model with the highest similarity degree; in the topographic deformation response layer, comparing the similarity degree of the topographic deformation response sequence of the dynamic ore-forming response characteristics with the typical topographic deformation response sequence of each standard ore-forming response model of the known deposits, and marking the standard model with the highest similarity degree; in the thermal radiation response layer, comparing the similarity degree of the thermal radiation response sequence of the dynamic ore-forming response characteristics with the typical thermal radiation response sequence of each standard ore-forming response model of the known deposits, and marking the standard model with the highest similarity degree; in the geological response layer, comparing the similarity degree of the geological response sequence of the dynamic ore-forming response characteristics with the typical geological response sequence of each standard ore-forming response model of the known deposits, and marking the standard model with the highest similarity degree; counting the known deposit standard ore-forming response model with the most marked times in the four levels, and taking the deposit type corresponding to the known deposit standard ore-forming response model as the deposit type inference result of the ore-forming core target area; combining the dynamic ore-forming response characteristic parameters of the ore-forming core target area, and determining the deposit occurrence dynamic parameters of the ore-forming core target area according to the correlation between the deposit occurrence parameters and the response sequence in the known deposit standard ore-forming response model, the deposit occurrence dynamic parameters comprising the burial depth variation trend, the thickness distribution characteristics and the spatial continuity variation law of the deposit.

9. The remote sensing based mineral deposit detection method of claim 8, wherein, The spatial correlation of adjacent ore-forming potential candidate areas is analyzed, and adjacent candidate areas with a boundary distance less than a preset correlation threshold are merged into a larger ore-forming potential candidate area, including: extracting the boundary coordinates of each ore-forming potential candidate area, the boundary coordinates being the vertex coordinates of the peripheral contour of the candidate area, and the boundary coordinates being arranged in a clockwise order to form a boundary coordinate sequence; calculating the straight-line distance between the two closest boundary points in the boundary coordinate sequence of any two adjacent ore-forming potential candidate areas as the boundary distance of the two candidate areas; determining the preset correlation threshold according to the spatial continuity characteristics of the ore-forming area and the spatial resolution of the remote sensing data, and comparing the calculated boundary distance with the preset correlation threshold; when the boundary distance is less than the preset correlation threshold, further analyzing the ore-forming potential variation direction of the two ore-forming potential candidate areas, and judging whether the variation direction is directed to the other area; if the variation direction is directed to the other area, it is determined that the two ore-forming potential candidate areas have spatial correlation, and they are merged into a larger ore-forming potential candidate area, and the boundary of the merged candidate area is the circumscribed contour of the boundaries of the two original candidate areas; If the change direction is not directed to the opposite region, the difference between the average values of the two candidate regions is calculated, a preset difference threshold is set, if the difference is less than the preset difference threshold, it is determined that there is spatial correlation and merging is performed; if the difference is greater than or equal to the preset difference threshold, it is determined that there is no correlation; When the boundary distance is greater than or equal to the preset correlation threshold, it is directly determined that the two metallogenic potential candidate regions have no spatial correlation; The above process is repeated for all pairs of adjacent metallogenic potential candidate regions to complete the merging of all candidate regions with spatial correlation; The average value of the metallogenic potential, the average value of the potential change rate and the consistency proportion of the potential change direction are recalculated for the merged metallogenic potential candidate regions.

10. A mineral deposit detection system based on remote sensing technology, characterized in that, The mineral exploration system based on remote sensing technology includes a processor and a memory, the memory and the processor are connected, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to realize the mineral exploration method based on remote sensing technology in any one of claims 1-9.

Images