Method and system for constructing metallogenic mode of wollastonite ore based on space-time constraint

Abstract

The invention relates to the technical field of metallogenic research of wollastonite ore, in particular to a method and a system for constructing a metallogenic mode of wollastonite ore based on space-time constraint. The method comprises the following steps: acquiring multi-source basic data in a target area by taking a contact zone of an invading body and carbonate rock and a peripheral buffer range as the target area; extracting an initial geographic feature data set from the multi-source basic data; the method comprises the following steps: performing time sequence stability analysis on observation characteristics of a mineral marginal zone corresponding to wollastonite contact excursion mineralization, and determining a target time scale and a spatial data block in combination with spatial continuity constraints of wollastonite contact excursion mineralization; based on the target time scale and the spatial data block, screening a space-time coupling feature data set; and performing multiple linear regression analysis on the time-space coupling characteristic data set, screening target characteristic parameters related to the wollastonite mineralization process, and constructing a wollastonite mineralization mode based on the target characteristic parameters. According to the method, the geological credibility of the metallogenic characteristics can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ore-forming research of wollastonite deposits, and in particular to a method and system for constructing an ore-forming model of wollastonite deposits based on space-time constraints. BACKGROUND

[0002] Wollastonite deposits are an important non-metallic mineral resource, which is widely used in the fields of ceramics, metallurgy, building materials and new materials. Wollastonite deposits are mainly of contact metasomatic type, and their formation process is usually closely related to the contact between acidic intrusions and carbonate rocks. The ore-forming process is controlled by multiple geological factors such as hydrothermal activity, lithological combination and tectonic conditions, and has obvious spatial continuity and temporal stability characteristics.

[0003] The existing ore-forming research and exploration methods of wollastonite deposits mainly rely on field geological investigation, drilling sampling and geochemical and geophysical data analysis. Although these methods can reveal the ore-forming regularity in a local area, they have problems such as long working period, high cost and limited spatial coverage. With the development of remote sensing technology and geographic information technology, multi-temporal and multi-scale remote sensing data have been gradually introduced into mineral resource investigation, providing a new data source for regional-scale ore-forming information extraction.

[0004] Currently, when constructing an ore-forming model of wollastonite deposits by analyzing remote sensing data, feature extraction needs to be performed on the obtained geographic data. A key problem is to focus on which factors are related to ore formation during the construction of the ore-forming model. However, actual ore-forming model construction requires feature extraction on the obtained geographic data. Due to the existence of a large amount of feature data in geographic data, irrelevant feature data is introduced, which reduces the geological reliability of ore-forming features and further reduces the accuracy of the construction of the ore-forming model of wollastonite deposits. SUMMARY

[0005] The present application aims to provide a method and system for constructing an ore-forming model of wollastonite deposits based on space-time constraints, which solves the technical problem of low accuracy of the construction of the ore-forming model of wollastonite deposits caused by the existence of a large amount of feature data in geographic data, which reduces the geological reliability of ore-forming features.

[0006] In a first aspect, an embodiment of the present application provides a method for constructing an ore-forming model of wollastonite deposits based on space-time constraints, which comprises: The contact zone between the intrusion and the carbonate rock and the peripheral buffer range are taken as the target area, and multi-source basic data in the target area are obtained. The multi-source basic data includes multi-temporal optical remote sensing images, digital elevation models and derived topographic parameters, basic geological data and historical mineral data. extracting an initial geographic feature dataset from multi-source basic data; the initial geographic feature dataset includes spectral statistical features, topography and structure related features, and spatial statistical and texture features; determining a target time scale and a spatial data block matched with the target time scale by performing time series stability analysis on the observed features of the mineral edge zone corresponding to the wollastonite contact metasomatic mineralization, in combination with the spatial continuity constraint of the wollastonite contact metasomatic mineralization; filtering out a spatiotemporal coupling feature dataset from the initial geographic feature dataset based on the target time scale and the spatial data block; the spatiotemporal coupling feature dataset is used to represent the corresponding relationship between a spatiotemporal coupling feature vector and a mineralization intensity level vector; the spatiotemporal coupling feature vector is used to represent a geographic feature vector related to wollastonite mineralization; performing multiple linear regression analysis on the spatiotemporal coupling feature dataset, filtering out a target feature parameter related to the wollastonite mineralization process, and constructing a wollastonite mineralization model based on the target feature parameter.

[0007] In one embodiment, the determining the target time scale by performing time series stability analysis on the observed features of the mineral edge zone corresponding to the wollastonite contact metasomatic mineralization comprises: extracting mineral edge zone information of each time node based on the mineralization influence area; the mineral edge zone information includes the spatial range of the mineral edge zone and the observed features in the transition zone; the spatial range is used to represent the transition zone between the mineralization area and the non-mineralization area; based on the mineral edge zone information, calculating the number of overlapping grids and the total number of covered grids of the mineral edge zone of adjacent time nodes in different time windows by using a grid indication function; the grid indication function is used to indicate whether each grid in the target region belongs to the edge zone of the wollastonite mine; based on the number of overlapping grids and the total number of covered grids, calculating a spatial overlap rate, and determining the target time scale based on the spatial overlap rate; the spatial overlap rate is used to represent the ratio of the number of overlapping grids to the total number of covered grids.

[0008] In one embodiment, the determining the spatial data block matched with the target time scale in combination with the spatial continuity constraint of the wollastonite contact metasomatic mineralization comprises: starting from the mineral edge zone stable in the target time scale, constructing a profile along the contact metasomatic direction of the intrusive body-surrounding rock; the contact metasomatic direction is the hydrothermal migration direction of the intrusive body to the surrounding rock; establishing a local coordinate system with the strike of the contact zone as the x-axis and the normal of the contact zone as the y-axis, and dividing the space into units according to different spatial sampling intervals; calculating the wollastonite mine zoning index of each spatial unit and the continuous transition index of the target region; determining the spatial data block matched with the target time scale based on the wollastonite mine zoning index and the continuous transition index.

[0009] In one embodiment, the calculating the wollastonite ore zoning index of each spatial unit comprises: For any spatial unit, obtaining a silicification comprehensive characteristic value and a carbonate rock comprehensive characteristic value; the silicification comprehensive characteristic value is used to represent the intensity and distribution characteristics of silicification in the ore-forming area; the carbonate rock comprehensive characteristic value is used to represent the distribution and reaction degree of carbonate rock in the ore-forming area; According to the silicification comprehensive characteristic value and the carbonate rock comprehensive characteristic value, the wollastonite ore zoning index of the spatial unit is determined.

[0010] In one embodiment, the calculating the continuous transition index of the target area comprises: Calculating the wollastonite ore zoning index difference value of adjacent spatial units; Based on a preset threshold, the wollastonite ore zoning index difference value is binarized to obtain the marking information; According to the marking information, the total number of continuous spatial units of all profiles is determined, and the continuous transition index of the target area is determined based on the total number of continuous spatial units and the total number of profiles.

[0011] In one embodiment, the performing multiple linear regression analysis on the spatiotemporal coupling feature data set to screen the target feature parameter related to the wollastonite mineralization process comprises: Calculating the variance inflation factor of the spatiotemporal coupling feature vector, and based on a preset variance inflation factor threshold, performing preliminary screening on the spatiotemporal coupling feature vector to obtain the preliminary screened spatiotemporal coupling feature vector; Determining the regression coefficient corresponding to the preliminary screened spatiotemporal coupling feature vector, and constructing a regression function based on the regression coefficient; Calculating the probability that the regression coefficient is 0; According to the preset judgment threshold and the probability, the target feature parameter related to the wollastonite mineralization process is determined from the preliminary screened spatiotemporal coupling feature vector.

[0012] In one embodiment, the extracting the initial geographic feature data set from the multi-source basic data comprises: Based on multi-temporal optical remote sensing images, spectral statistical features and spatial statistical and texture features are extracted; the spectral statistical features include band mean, variance and band ratio features, and the spatial statistical and texture features include gray level co-occurrence matrix texture features and local variation intensity index; Based on digital elevation model and derived terrain parameters, basic geological data, terrain and structure related features are extracted; the terrain and structure related features include slope, slope direction, relief and structure density; Based on the spectral statistical features, the terrain and structure related features, and the spatial statistical and texture features, the initial geographic feature data set is constructed.

[0013] In one embodiment, the regression function is constructed based on the regression coefficients, comprising: Based on the mineralization intensity level, each spatiotemporal coupling feature vector and its corresponding regression coefficient are used to construct a regression function.

[0014] In a second aspect, another embodiment of the present application provides a spatiotemporal constraint-based wollastonite ore-forming model construction system, comprising: A data acquisition module is configured to acquire multi-source basic data in a target area including a contact zone of an intrusive body and carbonate rocks and a peripheral buffer range; the multi-source basic data includes multi-temporal optical remote sensing images, digital elevation models and derived terrain parameters, basic geological data, and historical mineral data; an initial geographic feature data set is extracted from the multi-source basic data; the initial geographic feature data set includes spectral statistical features, terrain and structure-related features, and spatial statistical and texture features; A conditional constraint module is configured to determine a target time scale and a spatial data block matched with the target time scale by performing time series stability analysis on observed features of a mineral edge zone corresponding to wollastonite contact metasomatic mineralization, and combining spatial continuity constraints of wollastonite contact metasomatic mineralization; A target feature parameter determination module is configured to filter a spatiotemporal coupling feature data set from the initial geographic feature data set based on the target time scale and the spatial data block; the spatiotemporal coupling feature data set is used to represent the correspondence between a spatiotemporal coupling feature vector and a mineralization intensity level vector; the spatiotemporal coupling feature vector is used to represent a geographic feature vector related to wollastonite mineralization; a multivariate linear regression analysis is performed on the spatiotemporal coupling feature data set to filter a target feature parameter related to the wollastonite mineralization process, and a wollastonite ore-forming model is constructed based on the target feature parameter.

[0015] In one embodiment, the conditional constraint module comprises: A calculation module is configured to extract mineral edge zone information of each time node based on a mineralization influence area; the mineral edge zone information includes a spatial range of the mineral edge zone and observed features in a transition zone; the spatial range is used to represent a transition zone between a mineralized area and a non-mineralized area; based on the mineral edge zone information, a grid indication function is used to count the number of overlapping grids and the total number of covered grids of adjacent time nodes in the mineral edge zone in different time windows; the grid indication function is used to indicate whether each grid in the target area belongs to the edge zone of the wollastonite ore; based on the number of overlapping grids and the total number of covered grids, a spatial overlap rate is calculated, and the target time scale is determined based on the spatial overlap rate; the spatial overlap rate is used to represent the ratio of the number of overlapping grids to the total number of covered grids.

[0016] In a third aspect, the present application provides an electronic device including a processor, a memory, and a computer program stored in the memory and executable on the processor, which, when executed by the processor, implements the steps of the method of the first aspect.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of the first aspect.

[0018] The present application has the following advantages: In the process of constructing the ore-forming model of the wollastonite mine, the embodiment of the present application takes the contact zone and the peripheral buffer range of the intrusive body and the carbonate rock as the target area, obtains multi-source basic data in the target area, extracts an initial geographic feature data set from the multi-source basic data, determines a target time scale and a spatial data block matched with the target time scale through time series stability analysis of the observed features of the wollastonite contact metasomatic ore-forming mineral edge zone and combining the spatial continuity constraint of the wollastonite contact metasomatic mineralization, screens out a spatio-temporal coupling feature data set from the initial geographic feature data set based on the target time scale and the spatial data block, performs multivariate linear regression analysis on the spatio-temporal coupling feature data set, screens out a target feature parameter related to the wollastonite ore-forming process, and constructs the wollastonite ore-forming model based on the target feature parameter. The embodiment of the present application introduces the spatial continuity constraint of the wollastonite contact metasomatic mineralization process and the time series stability analysis of the observed features in the ore-forming target feature parameter construction stage, avoiding the problem of feature selection distortion caused by the pure data-driven method for ore-forming analysis. Under the time scale constraint, the size of the spatial data block is determined, so that the time scale and the spatial scale are mutually constrained, thereby ensuring that the constructed feature data set can truly reflect the zoning structure characteristics of the wollastonite ore-forming, and through multivariate linear regression, the correlation between the spatio-temporal coupling features and the changes in the mineral is statistically analyzed, the target feature parameter having a significant correlation with the wollastonite ore-forming process is effectively screened out, and the accuracy of the wollastonite ore-forming model construction is improved. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0020] Figure 1 is a flowchart of a wollastonite ore-forming model construction method based on spatio-temporal constraints provided by the embodiment of the present application. Figure 2 is a structure schematic diagram of a wollastonite ore mineralization model construction system based on space-time constraints provided by an embodiment of the present application. Figure 3 is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined object, the specific implementation, structure, features and effects of the mineralization model construction method and system based on space-time constraints of wollastonite ore according to the present application are described in detail as follows in combination with the drawings and preferred embodiments. Different "one embodiment" or "another embodiment" in the following description do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0023] The specific scheme of the mineralization model construction method of wollastonite ore based on space-time constraints provided by the present application is specifically described below in combination with the drawings.

[0024] The present application proposes a mineralization model construction method of wollastonite ore based on space-time constraints. Please refer to Figure 1 , which shows a schematic flowchart of a mineralization model construction method of wollastonite ore based on space-time constraints provided by an embodiment of the present application. The method comprises the following steps: Step S1, taking the contact zone of intrusive body and carbonate rock and the peripheral buffer range as the target area, obtaining multi-source basic data in the target area; the multi-source basic data includes multi-temporal optical remote sensing image, digital elevation model and derived terrain parameters, basic geological data and historical mineral data.

[0025] The contact zone of intrusive body and carbonate rock refers to the core area of wollastonite mineralization, which is the interface of mutual contact between acidic intrusive body (such as granite) and carbonate rock (such as limestone), and hydrothermal metasomatism occurs violently here, which is the key place for the formation of wollastonite ore body.

[0026] The peripheral buffer range refers to the extended area within a certain distance around the contact zone, which is used as the radiation range of mineralization influence, to avoid missing the potential mineralization related area around the contact zone and ensure the integrity of the analysis range.

[0027] The target area refers to the contact zone of intrusive body and carbonate rock and the peripheral buffer range, which is the spatial boundary of data acquisition, feature extraction and analysis in the whole mineralization model construction process.

[0028] Multi-temporal optical remote sensing image refers to optical band (visible light, near infrared, etc.) remote sensing data covering different seasons and different years, which is a digital image recording the ground surface reflection or radiation information, and has time continuity and spatial consistency, which can avoid short-term ground interference.

[0029] Digital Elevation Model (DEM) refers to a three-dimensional digital terrain model constructed by limited ground elevation sampling points, which stores the elevation of each position on the ground. Derived terrain parameters refer to indexes derived from DEM through spatial analysis algorithm, including slope, slope direction, relief, etc., which are used to reflect the geomorphic background of the ore-forming area.

[0030] Basic geological data refers to basic and objective data representing the geological background of the study area, which can include stratigraphic lithology distribution map, intrusive body boundary information, structure line information, etc., and is mostly from long-term geological survey and exploration results, which is used to determine the core space of mineralization and analysis direction.

[0031] Historical mineral data refers to targeted data recording the mineralization, distribution and resource changes of wollastonite at different periods, including ore body distribution records, resource quantity change records, mineralization intensity grade data (0-no ore, 1-weak mineralization, 2-industrial ore body) and other data, which is a reference to reflect the actual situation of mineralization.

[0032] It should be noted that in the embodiments of the present application, by limiting the target area to the contact zone of intrusive body and carbonate rock and the peripheral buffer range, it can be ensured that the analysis focuses on the core of mineralization and the potential impact area; and by obtaining four types of core multi-source basic data, including multi-temporal optical remote sensing image, DEM and derived terrain parameters, basic geological data and historical mineral data, a data system of remote sensing-terrain-geology-mineral change can be formed, which provides data for subsequent feature extraction, time and space constraints and ore-forming pattern construction.

[0033] Step S2, extracting an initial geographic feature data set from the multi-source basic data; the initial geographic feature data set includes spectral statistical features, terrain and structure related features, and spatial statistical and texture features.

[0034] Among them, the initial geographic feature data set refers to a feature set covering multi-dimensional ore-forming related information extracted from multi-source basic data.

[0035] Spectral statistical features refer to features extracted based on multi-temporal optical remote sensing image, including mean, variance, band ratio of each band, and mineral sensitive index such as silicification related index and carbonate rock related index, which can reflect the mineral composition and lithology characteristics of the ground surface.

[0036] The terrain and structure related features refer to features extracted from DEM and basic geological data, including terrain parameters derived from DEM, such as slope, slope direction, relief, and structure density and distance to main structure line calculated based on structure line data, for representing the geomorphic background and structure control condition of the ore-forming area.

[0037] The spatial statistics and texture features refer to features extracted from multi-temporal optical remote sensing images, mainly including Gray Level Co-occurrence Matrix (GLCM) texture features and local variation intensity indicators, which can reflect the spatial heterogeneity of lithological contact zone and alteration zone and can assist in identifying the spatial distribution of ore-related.

[0038] Further, the initial geographic feature data set is extracted from the multi-source basic data, including: Based on multi-temporal optical remote sensing images, spectral statistical features and spatial statistics and texture features are extracted; the spectral statistical features include band mean, variance and band ratio features, and the spatial statistics and texture features include Gray Level Co-occurrence Matrix (GLCM) texture features and local variation intensity indicators.

[0039] The band mean refers to the average value of all pixel gray values in a certain band of the remote sensing image, which is used to reflect the overall intensity of the surface reflection or radiation corresponding to the band.

[0040] The variance is used to describe the dispersion degree of the pixel gray values in a certain band of the remote sensing image, which can reflect the uniformity or difference degree of the ground cover.

[0041] The band ratio feature can be obtained by calculating the ratio of the gray values of different bands of the remote sensing image, which can enhance the spectral difference of minerals and lithology and facilitate the identification of ore-related mineral assemblages.

[0042] The GLCM texture feature can be obtained by statistically analyzing the co-occurrence relationship of adjacent pixel gray values in the image, which can represent the texture features such as fineness, density and uniformity of the image, and can reflect the spatial distribution structure of the lithology.

[0043] The local variation intensity indicator is used to quantify the variation amplitude of the pixel gray values in the local area of the remote sensing image, which can highlight the spatial mutation areas such as lithological contact zone and alteration zone, and assist in positioning the key areas related to mineralization.

[0044] Based on digital elevation model and derived terrain parameters and basic geological data, terrain and structure related features are extracted; the terrain and structure related features include slope, slope direction, relief and structure density.

[0045] The derived terrain parameter refers to a terrain feature index derived based on DEM data through a spatial analysis algorithm, is an application extension of DEM data, and can accurately reflect the topographic details of a mineralization region.

[0046] The slope refers to the inclination of the ground surface, can reflect the steepness of the terrain, and affects the hydrothermal migration path and ore body occurrence conditions.

[0047] The slope direction refers to the direction of the ground surface inclination, is related to environmental factors such as illumination and precipitation, and indirectly affects the hydrothermal activity in the mineralization process.

[0048] The relief degree refers to the relief of the terrain in a local region, can reflect the topographic fragmentation degree of the mineralization region, and provides a basis for structural analysis.

[0049] The structural density refers to a characteristic index calculated based on the structural line information in the basic geological data, refers to the number or length of geological structures (such as faults and folds) per unit area, can reflect the regional tectonic activity intensity, and is an important control factor for mineralization.

[0050] It should be noted that in the embodiments of the present application, DEM and derived terrain parameters and basic geological data are used as joint data sources, and four types of key features (slope, slope direction, relief degree, and structural density) are extracted. The slope, slope direction, and relief degree reflect the topographic background of the mineralization region, and the structural density reflects the regional tectonic activity intensity. The two types of information jointly constitute the terrain and structure related features.

[0051] The initial geographic feature dataset is constructed based on spectral statistical features, terrain and structure related features, spatial statistical and texture features.

[0052] The initial geographic feature dataset refers to a set formed by integrating spectral statistical features, terrain and structure related features, spatial statistical and texture features, and is extracted from multi-source basic data by existing methods. It covers multi-dimensional information related to the mineralization process, such as minerals, topography, structure, and spatial distribution, and lays a foundation for subsequent time and space constraint screening and target feature extraction.

[0053] It should be noted that in the embodiments of the present application, by integrating the three types of core features, the initial geographic feature dataset covering the multi-dimensional information of mineralization is formed, and the conversion from multi-source basic data to mineralization related features is realized.

[0054] In step S3, the time series stability of the observed features of the wollastonite contact metasomatic mineralization corresponding to the mineral edge zone is analyzed, and the target time scale and the spatial data block matched with the target time scale are determined in combination with the spatial continuity constraint of wollastonite contact metasomatic mineralization.

[0055] The wollastonite contact metasomatism mineralization refers to the main mineralization type of wollastonite ore, which is formed at the contact interface of acidic intrusive bodies and carbonate rocks, and is jointly controlled by hydrothermal activity, lithological combination, and tectonic conditions, and is the basis for mineralization constraints.

[0056] The observation characteristics of the mineral edge zone refer to a set of multi-dimensional characteristics that can reflect the geological and mineral-related properties of the region in the transition zone (i.e., the mineral edge zone) between the wollastonite ore mineralization area and the non-mineralization area, which are extracted based on multi-temporal optical remote sensing images, digital elevation models and derived terrain parameters, basic geological data, and historical mineral data.

[0057] The time series stability analysis refers to an analysis process that takes the observation characteristics of the wollastonite-related mineral edge zone as the analysis object, and based on multi-temporal optical remote sensing images and historical mineral data, quantifies the spatial consistency of the observation characteristics in different time windows, eliminates short-term disturbances caused by surface cover (vegetation, snow cover, soil moisture) and imaging conditions, and selects the best observation time window that truly reflects the static geological structure of the ore body.

[0058] For example, the time series stability analysis can be based on the geological nature of wollastonite formed in the geological history period, and its physical location and form remain static within the remote sensing observation window. According to multi-temporal optical remote sensing images, combined with the known ore body distribution range in historical mineral data, the observation characteristics (such as spectral statistical characteristics, spatial texture characteristics) of the mineral edge zone at each time node are extracted. By converting the mineral edge zone at different time nodes into a binary mask grid, the spatial overlap rate (Cumulative Spatial Overlap Rate, CT) of adjacent nodes in different time windows is calculated, the consistency and anti-interference ability of the observation characteristics of the mineral edge zone in the time series are analyzed, and short-term disturbances caused by surface cover (vegetation, snow cover, soil moisture) and imaging conditions are eliminated. The analysis process selects the best observation time window that truly reflects the static geological structure of the ore body.

[0059] The spatial continuity constraint refers to a data selection criterion based on the geological law that wollastonite is distributed in zonal distribution along the contact zone of intrusive bodies and country rocks. It needs to ensure that the feature extraction conforms to the hydrothermal migration and mineralization zoning law of intrusive bodies to country rocks, and avoid spatial analysis from deviating from the mineralization mechanism.

[0060] For example, the target time scale refers to the effective time window selected by the time series stability analysis, i.e., the time range with the highest spatial continuity (overlap rate) of the mineral edge zone, which can avoid short-term interference and provide stable time dimension support for subsequent feature extraction.

[0061] The spatial data block refers to a spatial analysis range matched with a target time scale, determined by a spatial continuity constraint, and sized to fit the wollastonite mineralization zoning law, to ensure that the spatial dimension feature extraction corresponds to the mineralization region.

[0062] It should be noted that in the embodiments of the present application, through the double constraints of the mineralization mechanism transformation, the optimal time and space range of mineralization analysis is determined, that is, the geological characteristics of the time stability and spatial continuity of wollastonite contact metasomatic mineralization are transformed into constraint conditions for data screening; the target time scale reflecting the real mineralization structure is locked through the time series stability constraint, and the spatial data block matched with the time scale and fitting the mineralization zoning law is determined through the spatial continuity constraint, so that the mutual constraint of time and space scale is finally realized, which lays a foundation for subsequent screening of anti-interference features and construction of time and space coupling data set, and ensures the geological reliability of the mineralization features from the source.

[0063] Further, the time series stability analysis of the observation features of the mineral product edge zone corresponding to the wollastonite contact metasomatic mineralization to determine the target time scale comprises: Based on the mineralization influence area, the mineral product edge zone information of each time node is extracted, and the mineral product edge zone information includes the spatial range of the mineral product edge zone and the observation features in the transition zone; the spatial range is used to represent the transition zone between the mineralization area and the non-mineralization area.

[0064] The mineralization influence area refers to the area around the known ore body affected by the mineralization (such as hydrothermal activity, element migration, etc.), which may have weak mineralization or mineralization related geological conditions, and is the reference range for extracting the mineral product edge zone, and its boundary is determined based on historical mineral exploration and geological survey results.

[0065] The time node refers to the specific time point (covering different seasons and years) of obtaining multi-temporal optical remote sensing images and geographic data, which needs to be arranged according to a unified time axis to form a continuous time window or a fixed time period aggregation window, to provide a basis for analyzing the time stability of the mineral product edge zone.

[0066] The mineral product edge zone information refers to a comprehensive information set extracted based on the wollastonite contact metasomatic mineralization mechanism, integrating multi-temporal optical remote sensing images, DEM and derived terrain parameters, basic geological data, historical mineral data and other multi-source data, which can include the spatial range determined based on the distribution of known ore bodies in historical mineral data, combined with the basic geological data such as intrusive body boundary, and the multi-dimensional observation features extracted in the spatial range, including spectral statistical features, spatial texture features, topographic and structural related features, etc.

[0067] It should be noted that the observation features of the mineral edge zone are a subset of the initial geographic feature data set, which refers to the geographic features within the spatial range of the mineral edge zone (the transition zone between the mineralized area and the non-mineralized area), and the feature categories are consistent with the initial geographic feature data set, but the spatial range is constrained within the mineral edge zone.

[0068] Illustratively, the process of obtaining mineral edge zone information at each time node includes: first, based on the known ore body distribution in the historical mineral data, in combination with the contact zone of the intrusive body and the carbonate rock and the peripheral buffer range specified in the basic geological data, the fixed spatial range of the transition zone between the mineralized area and the non-mineralized area is delimited; then, for each time node of multi-temporal optical remote sensing image, DEM and derived topographic parameters and other data, within the spatial range, the existing method is used to extract spectral statistical features, topographic and structural related features, spatial texture features and other observation features; finally, through 3*3 grid neighborhood analysis to remove isolated noise points, combined with historical mineral data and basic geological data comparison correction, the complete mineral edge zone information of fixed spatial range and dimensional observation features at each time node is formed, and the consistency of data in the same spatial coordinate system needs to be ensured in the extraction process to avoid spatial deviation between time nodes.

[0069] The mineralized area refers to an area where wollastonite mineralization (including weak mineralization and industrial ore body) has been confirmed, and its range is delimited according to historical mineral data (such as ore body distribution map, resource quantity record) and geological exploration results, and is the core object of ore-forming model analysis.

[0070] The non-mineralized area refers to an area in the study area that is not affected by wollastonite mineralization or does not have ore-forming geological conditions (such as being far away from the intrusive body-carbonate rock contact zone, no tectonic fracture control, etc.), and forms a clear spatial boundary with the mineralized area.

[0071] The transition zone refers to the essential attribute of the mineral edge zone, which is the spatial transition area between the mineralized area and the non-mineralized area, and its spatial position and form can reflect the extension limit of the wollastonite ore body, and it has stability within the effective time scale, which is controlled by the ore-forming mechanism and is not affected by short-term vegetation, snow cover and other surface disturbances.

[0072] It should be noted that in the embodiments of the present application, the mineralized influence area is taken as the reference range, and for each time node of the obtained data, the mineral edge zone information capable of representing the spatial transition relationship between the mineralized area and the non-mineralized area is extracted, which provides data for subsequent analysis of the time continuity of the mineral edge zone and screening of the effective time scale, and thus avoids the influence of short-term surface disturbance on the extraction of ore-forming characteristics.

[0073] Based on the mineral edge zone information, the number of overlapping grids and the total number of covered grids in the mineral edge zone of adjacent time nodes in different time windows are counted through a grid indication function; the grid indication function is used to indicate whether each grid in the target region belongs to the edge zone of the wollastonite mine.

[0074] The grid indication function refers to a binary function for identifying the attribute of a spatial grid, which can determine whether each grid in the target region belongs to the edge zone of the wollastonite mine, and the output result is only 1 or 0 (1 represents that the grid belongs to the edge zone, and 0 represents that it does not belong), which is stored in the form of a binary mask and is a tool for quantifying the spatial position relationship of the mineral edge zone.

[0075] The time window refers to a collection of multi-temporal data arranged according to a unified time axis, which is divided into continuous time windows (such as N-year sliding windows) and fixed time period aggregation windows (such as dry season and low vegetation period windows), and is used for concentrated analysis of the spatial continuity of the mineral edge zone in a specific time range, and is the core unit for screening effective time scales.

[0076] The adjacent time nodes refer to two data acquisition time points (such as different seasons and different years of remote sensing image acquisition time) connected in sequence in the time window, which need to ensure that the data at the same spatial position are consistent, and are the comparison objects for calculating the spatial overlap rate of the edge zone.

[0077] The mineral edge zone refers to the spatial transition zone between the mineralized area and the non-mineralized area, which reflects the extension limit of the wollastonite ore body, and the spatial position and shape thereof have stability in the effective time scale, which can be extracted from remote sensing data by artificial or neural network method.

[0078] The number of overlapping grids refers to the total number of grids that are simultaneously determined to belong to the edge zone (the value of the grid indication function is 1) in the mineral edge zone of adjacent time nodes, which is a quantitative indicator for measuring the spatial continuity of the edge zone, and the more the number of overlapping grids, the better the continuity of the edge zone.

[0079] The total number of covered grids refers to the total number of grids that are determined to belong to the edge zone (at least one value of the grid indication function is 1) in the mineral edge zone of adjacent time nodes, which is used for normalizing the number of overlapping grids and calculating the CT value.

[0080] It should be noted that in the embodiments of the present application, whether each grid belongs to the edge zone of the wollastonite mine is identified through the grid indication function, and then the number of overlapping grids and the total number of covered grids in the edge zone of adjacent time nodes in different time windows are counted, and finally the ratio of the two (the spatial overlap rate CT value) can be used to screen out the effective time scale with high continuity, and eliminate the non-mineralization changes caused by short-term disturbances such as vegetation and snow, thereby providing a time constraint basis for subsequent mineralization feature extraction.

[0081] Based on the number of overlapping grids and the total number of covered grids, the spatial overlap rate is calculated, and the target time scale is determined based on the spatial overlap rate; the spatial overlap rate is used to represent the ratio of the number of overlapping grids to the total number of covered grids.

[0082] Wherein, the spatial overlap rate (CT value) refers to the ratio of the number of overlapping grids to the total number of covered grids (range 0~1), the closer the ratio is to 1, the more stable the spatial position of the adjacent time node of the mineral edge zone is, and the better the continuity is, which is a quantitative index for screening effective time scale.

[0083] The target time scale refers to the optimal time window selected based on the spatial overlap rate, that is, the time range with the highest spatial overlap rate, which can avoid short-term surface disturbances such as vegetation, snow cover, soil moisture, and truly reflect the long-term stability of the wollastonite mineralization structure.

[0084] It should be noted that in the embodiments of the present application, based on the grid statistics of the mineral edge zone, the spatial overlap rate is obtained by calculating the ratio of the number of overlapping grids to the total number of covered grids, and the time window with the highest spatial overlap rate is selected as the target time scale; the spatial overlap rate directly quantifies the time continuity of the mineral edge zone, ensures that the target time scale can eliminate short-term interference and match the time stability characteristics of wollastonite mineralization, and provides reliable time dimension support for subsequent spatial data block matching and feature selection.

[0085] Further, the spatial continuity constraint combined with the contact metasomatism of wollastonite is determined to match the spatial data block of the target time scale, comprising: The stable mineral edge zone in the target time scale is taken as the starting point, and the profile is constructed along the contact metasomatism direction of the intrusive body-surrounding rock; the contact metasomatism direction is the hydrothermal migration direction of the intrusive body pointing to the surrounding rock.

[0086] Wherein, the stable mineral edge zone refers to the transition zone between the mineralized area and the non-mineralized area with consistent spatial position and morphology within the target time scale, which is a spatial identifier reflecting the real mineralization structure and provides a reliable starting point for subsequent spatial analysis.

[0087] The contact metasomatism direction of the intrusive body-surrounding rock refers to the key spatial direction of wollastonite contact metasomatism, specifically the extension direction from the acidic intrusive body to the carbonate rock surrounding rock, which is consistent with the hydrothermal migration path and is the main extension direction of the mineralization zoning.

[0088] The profile refers to the longitudinal or transverse analysis section constructed along the contact metasomatism direction of the intrusive body-surrounding rock, which is used to capture the element migration, mineral phase change and mineralization zoning characteristics near the contact zone, and replaces the traditional arbitrary two-dimensional grid analysis.

[0089] The contact metasomatism direction refers to the hydrothermal migration direction of the intrusive body pointing to the wall rock, is the path direction of the mineral matter carried by the hydrothermal fluid diffusing from the intrusive body to the wall rock and generating metasomatism reaction, and determines the spatial distribution law of the mineralization zoning.

[0090] The intrusive body refers to an acidic rock mass (such as granite) intruding into the wall rock such as carbonate rock, is the hydrothermal fluid and material source of the wollastonite mineralization, and the contact interface thereof and the wall rock is the core area of the mineralization.

[0091] The wall rock refers to the rock (for example: carbonate rock) around the intrusive body, is the carrier of the wollastonite ore formation, and forms the wollastonite ore body after the metasomatism reaction with the hydrothermal fluid released by the intrusive body.

[0092] The hydrothermal migration direction refers to the direction of the hydrothermal fluid moving and diffusing from the intrusive body to the wall rock, the mineral matter such as silica carried by the hydrothermal fluid in the process reacts with the carbonate rock in the wall rock, and finally forms the wollastonite ore, and the direction is the pointing direction of the spatial analysis.

[0093] It should be noted that in the embodiments of the present application, the stable mineral product edge zone in the target time scale is taken as the reliable spatial starting point, and the hydrothermal migration direction (contact metasomatism direction) of the intrusive body pointing to the wall rock is used to construct a targeted profile; the profile can fit the spatial law of the wollastonite contact metasomatism mineralization, provide a spatial analysis framework for subsequent division of spatial units, calculation of zoning index (B value), and determination of the size of the spatial data block, and ensure that the spatial analysis is highly adapted to the mineralization mechanism.

[0094] A local coordinate system is established, with the strike of the contact zone as the x-axis and the normal of the contact zone as the y-axis, and the spatial units are divided according to different spatial sampling intervals.

[0095] The local coordinate system refers to a special coordinate system established to adapt to the spatial distribution law of the wollastonite contact metasomatism mineralization, rather than a general geographic coordinate system, which is used to focus on the contact zone of the intrusive body and the wall rock and the mineralization related area, simplify the spatial analysis dimension, and ensure that the subsequent spatial unit division, zoning index calculation and other operations fit the geological mineralization mechanism.

[0096] The contact zone refers to the boundary area between the acidic intrusive body and the carbonate rock, is the core place of the wollastonite contact metasomatism mineralization, and the hydrothermal activity, element migration and mineral phase change all occur in this area, and the spatial form and strike thereof directly determine the distribution characteristics of the mineralization zoning.

[0097] The strike of the contact zone refers to the extension direction of the contact zone in the horizontal plane (such as east-west strike, north-south strike), is the basis for establishing the x-axis of the local coordinate system, and selecting this direction as the x-axis can be parallel to the extension track of the mineralization zoning, facilitating the acquisition of the change of the geological characteristics along the zoning direction.

[0098] The contact zone normal refers to the direction perpendicular to the contact zone and pointing towards the dominant direction of hydrothermal migration, that is, the direction from the intrusive body to the surrounding rock. As the y-axis of the local coordinate system, it can reflect the spatial progression of element migration and metasomatism during the mineralization process.

[0099] Spatial sampling interval refers to the distance (e.g., Δd) between two adjacent spatial sampling points (or adjacent spatial units) when dividing spatial units along the hydrothermal migration direction (perpendicular to the normal direction of the intrusive-wall rock contact zone) during the construction of a wollastonite contact metasomatic profile.

[0100] For example, multiple different Δd values ​​can be set first (such as 10 meters, 50 meters, 100 meters, etc., with specific candidate values ​​calibrated according to the target area's metallogenic zoning width and remote sensing image spatial resolution), and then the continuous transition index of the zoning index (B value) can be used. (Value) to filter for the optimal interval.

[0101] By comparing the continuity index corresponding to different candidate Δd ( (value), filter out The sampling interval with the highest value (i.e. the best continuous transition between bands) is taken as the optimal spatial sampling interval that matches the target time scale, and spatial data blocks are finally divided based on this interval.

[0102] A spatial unit refers to the smallest analytical unit (e.g., ...) that is divided within a profile constructed based on a local coordinate system according to a preset spatial sampling interval. , representing the i-th unit on the k-th metasomatic profile, is the basic spatial carrier for calculating the wollastonite zoning index (B value) and summarizing geographical features.

[0103] It should be noted that, in the embodiments of the present invention, in order to conform to the spatial laws of contact metasomatic mineralization, a local coordinate system is established with the contact zone orientation as the x-axis and the contact zone normal (hydrothermal migration direction) as the y-axis. Then, the mineralization-related profile is divided into several spatial units according to different spatial sampling intervals. Finally, by selecting the optimal sampling interval (matching the continuous transition of mineralization zoning), a data foundation is provided for subsequent zoning index calculation and spatiotemporal coupling feature extraction, ensuring that spatial analysis is highly compatible with the mineralization mechanism.

[0104] Calculate the wollastonite zoning index for each spatial unit and the continuous transition index for the target region.

[0105] Here, a spatial unit refers to the smallest analytical unit (denoted as Δd) divided according to different spatial sampling intervals (Δd) in a mineralization-related profile constructed based on a local coordinate system. , representing the i-th unit on the k-th metasomatic profile, is the basic spatial carrier for calculating the wollastonite zoning index and summarizing geographical features. Its division conforms to the mineralization spatial law of intrusive body-wall rock contact metasomatism.

[0106] The wollastonite zoning index (B value) is an indicator used to quantify the relative equilibrium between silicification and carbonate rock characteristics within a spatial unit. Its value is fixed in the range of [-1, +1]. A value closer to 0 indicates that the ratio of silicon (Si) to calcium (Ca) is more consistent with the chemical reaction conditions for wollastonite formation. It can reflect the core characteristics of mineralization zones.

[0107] For example, the zoning index of wollastonite can be expressed as: ; In the formula, In the section element The comprehensive eigenvalues ​​related to silicification at the location, In the section element At the same location, the composite characteristic value is related to the characteristics of carbonate rocks (Ca). To prevent the use of small positive numbers with a denominator of zero (such as 0.001).

[0108] The target area refers to the mineralization analysis area constructed along the contact metasomatic direction of the intrusive body and the surrounding rock (the hydrothermal migration direction), including the stable mineralized edge zone and the gradually expanding spatial range. It is the area for assessing the continuous transition of mineralization zones.

[0109] The continuous transition index is a quantitative indicator used to determine whether the wollastonite mineralization zones within a target area are continuously distributed. It is obtained by calculating whether the difference between the zoning indices (B values) of adjacent spatial units is less than a preset threshold (0.5 times the standard deviation of the zoning index). The value is 0 or 1. The higher the cumulative mean, the better the continuity of the mineralization zones. It can be used to determine the optimal spatial data block size.

[0110] For example, the continuous transition index can be expressed as: ; ; In the formula, M represents the number of cross-sections. Indicates the first In the cross section, the set of indices determined to be intermediate zones is based on... Compare with a preset threshold to obtain , The value can be 0 or 1. This preset threshold is determined based on the statistical distribution characteristics of the zoning index within the region. The preset threshold can be 0.5 times the standard deviation of the zoning index. When the difference is greater than the threshold, When it is 0, less than the threshold The value is 1.

[0111] It should be noted that, in order to avoid If there is an order-of-magnitude issue, a maximum-minimum normalization function can be used. After normalization, its mapping interval is [0,1].

[0112] When expanding spatial units along the cross-sectional direction, the maximum expansion distance Max(D) (5km) is set, or expansion is stopped when the rate of change of the zoning index is less than a preset threshold of 0.3.

[0113] Based on the wollastonite zoning index and continuous transition index, spatial data blocks matching the target time scale are identified.

[0114] For example, the target time scale refers to the effective time window selected through time series stability analysis, that is, the time range with the highest spatial overlap rate (CT value) of the mineral edge zone, which can avoid short-term surface disturbances and ensure the long-term stability of the mineralization structure.

[0115] Spatial data blocks refer to the spatial analysis range that matches the target time scale. Their size is determined by spatial continuity constraints and conforms to the zonal distribution pattern of wollastonite along the intrusive-surrounding rock contact zone. They are the basic spatial units for subsequent feature statistical summarization.

[0116] It should be noted that, in the embodiments of the present invention, the mineralization potential of spatial units is characterized by calculating the wollastonite zoning index (B value) based on the target time scale, and by using the continuous transition index (…). The spatial continuity of mineralization zones was verified by the value; finally, the spatial range with the best zonation index balance and continuous transition was selected as the spatial data block that matches the target time scale, so as to realize the mutual constraint between the time scale and the spatial scale, and ensure that the subsequent feature extraction fits the mineralization mechanism, laying the spatial foundation for the construction of a spatiotemporally coupled dataset.

[0117] Furthermore, the calculation of the wollastonite zoning index for each spatial unit includes: For any spatial unit, the comprehensive characteristic values ​​of silicification and carbonate rocks are obtained. The comprehensive characteristic value of silicification is used to characterize the intensity and distribution characteristics of silicification in the mineralization area. The comprehensive characteristic value of carbonate rocks is used to characterize the distribution and reaction degree of carbonate rocks in the mineralization area.

[0118] Among them, the comprehensive characteristic value of silicification ( This refers to an index that quantifies the intensity and spatial distribution of silicification within a mineralized area, calculated based on the thermal infrared bands of remote sensing images (such as ASTER data). The band ratio can effectively enhance the spectral response of siliceous rocks and silicified zones, reflecting the strength and coverage of silicification.

[0119] Comprehensive characteristic value of carbonate rocks ( This refers to an index characterizing the distribution range and reaction degree of carbonate rocks within a mineralization area, calculated based on shortwave infrared bands of remote sensing images (such as ASTER data). (Band combination) can highlight the spectral differences between carbonate rocks and other lithologies, reflecting the degree to which they participate in hydrothermal metasomatic reactions.

[0120] The mineralization area refers to the core area within the target area (the contact zone between the intrusive body and the carbonate rock and the surrounding buffer zone) that has mineralization potential. It is the main area where silicification and carbonate rock metasomatism occur.

[0121] Silicification refers to the geological process during mineralization in which hydrothermal fluids carry siliceous minerals and react with the surrounding rocks, enriching the surrounding rocks with siliceous materials. It is a preliminary process for the formation of wollastonite deposits, and its intensity and distribution directly affect the formation and scale of the ore body.

[0122] Carbonate rocks refer to the core host rock type of mineralization (such as limestone), which are the material carriers for the formation of wollastonite deposits and undergo metasomatic reactions with hydrothermal fluids released by intrusive bodies. This eventually forms a wollastonite ore body.

[0123] The degree of reaction refers to the thoroughness of the metasomatic reaction between carbonate rocks and minerals such as silica in hydrothermal fluids. The higher the degree of reaction, the easier it is to form industrial-grade wollastonite ore bodies, which is an important basis for judging mineralization potential.

[0124] It should be noted that, in the embodiments of the present invention, for each spatial unit divided by the cross-section, two types of characteristic values ​​are obtained: a comprehensive characteristic value of silicification that characterizes the intensity and distribution of silicification. ), and the comprehensive characteristic values ​​of carbonate rocks that characterize the distribution and degree of metasomatic reaction of carbonate rocks ( The two types of characteristic values ​​support the quantitative calculation of the zoning index, providing data for subsequent judgment of the continuity of mineralization zoning and determination of the size of spatial data blocks, ensuring that spatial analysis conforms to the mineralization mechanism of wollastonite contact metasomatism.

[0125] Based on the comprehensive characteristic values ​​of silicification and carbonate rocks, the wollastonite zoning index of the spatial unit is determined.

[0126] The wollastonite ore zoning index (B value) refers to a quantitative index calculated by a silicification comprehensive characteristic value and a carbonate rock comprehensive characteristic value, is used for quantifying the relative balance state of silicon (Si) and calcium (Ca) in a spatial unit, and is fixed in a value range of [-1, +1]. The closer the value is to 0, the stronger the relative balance state of the silicification and the response intensity of the remote sensing characteristics of the carbonate rock, and the stronger the transition region indicating the contact metasomatism.

[0127] It should be noted that in the embodiments of the present application, the spatial unit is taken as a basic analysis carrier, the silicification comprehensive characteristic value and the carbonate rock comprehensive characteristic value in the unit are extracted by remote sensing data, and the zoning index (B value) is calculated by substituting the values into the formula. Finally, the silicium-calcium balance state is quantified by the index, which provides a basis for judging the continuous transition of the mineralization zoning and determining the optimal spatial data block size, and ensures that the spatial analysis is highly adapted to the wollastonite contact metasomatic mineralization mechanism.

[0128] Further, the continuous transition index of the target region comprises: Calculating the difference value of the wollastonite ore zoning index of adjacent spatial units.

[0129] The adjacent spatial units refer to the minimum analysis units (denoted as and represent two adjacent units on the kth metasomatic profile) which are connected in space after being divided by the same spatial sampling interval (Δd) in the ore-forming profile constructed based on a local coordinate system (taking the strike of the contact zone as the x-axis and the normal of the contact zone as the y-axis).

[0130] The difference value refers to the absolute difference value (denoted as ) of the wollastonite ore zoning index of the adjacent two spatial units, which is used for judging whether the mineralization zoning is in a continuous transition state and is an intermediate parameter for calculating the continuous transition index.

[0131] The difference value of the wollastonite ore zoning index is converted into binary by a preset threshold to obtain the marking information.

[0132] The preset threshold refers to a quantitative standard for determining whether the difference value of the wollastonite ore zoning index meets the continuous transition characteristics, which is determined according to the statistical distribution characteristics of the zoning index in the study area. For example, the preset threshold can be 0.5 times the standard deviation of the zoning index, which is the basis for binary conversion.

[0133] The binarization conversion refers to a processing manner of converting continuous zoning index difference (GB value) into discrete values containing only 0 or 1, for example, a rule that 1 is taken when the GB value is less than a preset threshold value, and 0 is taken when the threshold value is greater than the threshold value, which is a quantitative operation of simplifying data and highlighting continuous transition characteristics.

[0134] The marking information refers to discrete values (0 or 1) obtained after binarization conversion, 1 represents that the mineralization zoning of the adjacent spatial unit is in a continuous transition state, and 0 represents that the zoning has a jump, which is the basis data for calculating the continuous transition index (C).

[0135] Exemplarily, in the embodiment of the present application, 0.5 times of the standard deviation of the zoning index is taken as the preset threshold value, the wollastonite mineral zoning index difference of the adjacent spatial unit is binarized and converted, and whether the zoning is continuous is converted into intuitive marking information (0 or 1); the operation realizes the discretization processing of continuous data, provides standardized data for subsequent calculation of the continuous transition index and judgment of the spatial continuity of the mineralization zoning, and ensures that the spatial scale screening fits the mineralization mechanism.

[0136] According to the marking information, the total number of continuous spatial units of all profiles is determined, and the continuous transition index of the target region is determined based on the total number of continuous spatial units and the total number of profiles.

[0137] The total number of continuous spatial units refers to the total sum of the number of adjacent spatial units with the marking information of 1 in all profiles, that is, the total number of combinations of spatial units in a continuous transition state along the hydrothermal migration direction, which can reflect the continuous range of the mineralization zoning.

[0138] The total number of profiles (M) refers to the total number of targeted profiles constructed along the contact metasomatism direction of the intrusive body-surrounding rock, which is a parameter for balancing the continuous transition characteristics of different profiles and calculating the overall index of the region.

[0139] The continuous transition index (C) refers to a quantitative index for measuring the spatial continuity of the wollastonite mineralization zoning in the target region, which is calculated by the total number of continuous spatial units, the total number of profiles and the effective index set in the profile, and the closer the value is to 1, the more continuous the mineralization zoning along the hydrothermal migration direction, and the more consistent with the mineralization mechanism. It should be noted that, in the embodiment of the present application, based on the binarized marking information, the total number of spatial units in a continuous transition state in all profiles is counted, and then the continuous transition index of the target region is calculated by combining the total number of profiles and the intermediate zoning index set in the profile; the index quantifies the spatial continuity of the mineralization zoning, provides a basis for subsequent determination of the spatial data block size matched with the target time scale, and ensures that the spatial scale screening fits the zoning rule of wollastonite contact metasomatism mineralization.

[0140] ​​

[0141] Step S4: Based on the target time scale and spatial data blocks, select the spatiotemporal coupling feature dataset from the initial geographic feature dataset; the spatiotemporal coupling feature dataset is used to characterize the correspondence between the spatiotemporal coupling feature vector and the mineralization intensity level vector; the spatiotemporal coupling feature vector is used to characterize the geographic feature vector related to wollastonite mineralization.

[0142] Among them, the spatiotemporal coupled feature dataset refers to the structured dataset formed by statistically summarizing the features in the initial geographic feature dataset under the dual constraints of the target time scale and spatial data blocks. It is used to establish the correspondence between the spatiotemporal coupled feature vector and the mineralization intensity level vector.

[0143] Spatiotemporal coupled feature vectors refer to the vector form after integrating geographical features (such as silicification index, tectonic density, slope, etc.) related to wollastonite mineralization within a spatial data block. They centrally represent the comprehensive state of mineralization-related geographical features within a specific spatiotemporal range.

[0144] The mineralization intensity level vector refers to a vector composed of mineralization intensity levels (divided into 0-no mineralization, 1-weak mineralization, and 2-industrial ore bodies according to mineralization potential) in historical mineral data. It serves as the dependent variable (Y) in multiple linear regression analysis to verify the correlation between the spatiotemporal coupling feature vector and the mineralization process.

[0145] Geographic feature vectors refer to vectors composed of one or more geographic features (such as spectral features, topographic parameters, tectonic features, etc.) related to wollastonite mineralization. They are the basic data form that characterizes the geological, topographic, and spectral attributes of the mineralization area. Spatiotemporal coupled feature vectors are their integrated form under specific spatiotemporal constraints.

[0146] It should be noted that, in the embodiments of the present invention, the selected target time scale and the matching spatial data block are used as dual constraints to extract and integrate geographical features related to wollastonite mineralization from the massive initial geographical feature dataset, forming a spatiotemporal coupled feature dataset. This dataset establishes the correspondence between the spatiotemporal coupled feature vector representing the mineralization-related geographical features and the mineralization intensity level vector representing the actual mineralization situation, providing structured and highly reliable data support for subsequent screening of target mineralization feature parameters and construction of mineralization models through multiple linear regression analysis.

[0147] Step S5: Perform multiple linear regression analysis on the spatiotemporal coupling feature dataset, screen out target feature parameters related to the wollastonite mineralization process, and construct a wollastonite mineralization model based on the target feature parameters.

[0148] The multivariate linear regression analysis refers to a statistical analysis method, taking a space-time coupling feature vector as an independent variable (X) and a mineralization intensity grade as a dependent variable (Y), constructing a regression model, solving regression coefficients by a least square method, quantifying the correlation strength of each feature and a mineralization process, and being a tool for screening target feature parameters.

[0149] The wollastonite mineralization process refers to a formation process of wollastonite ore mainly in the form of contact metasomatism, mainly related to the contact action of an acidic intrusive body and a carbonate rock, controlled by geological factors such as hydrothermal activity, lithological combination and tectonic conditions, and having the characteristics of time stability (long-term stability of ore body position) and spatial continuity (zoned distribution along the contact zone).

[0150] For example, the target feature parameter refers to a key feature significantly related to the wollastonite mineralization process screened out by multivariate linear regression analysis and significance test (t test, p value < 0.05), such as silicification index, carbonate rock index, tectonic density, slope and the like. Such features not only conform to statistical rules, but also have clear geological interpretation, and can truly reflect the ore-controlling factors.

[0151] For example, the wollastonite ore mineralization model refers to a model reflecting the wollastonite contact metasomatism mineralization mechanism, constructed based on the target feature parameter by using existing methods (such as rule-based induction, weight superposition analysis, expert knowledge driven ore-favorability evaluation and the like). It is used for reducing the mineralization law, providing reliable data support for delineation of a prospecting target area and deployment of mineral exploration.

[0152] It should be noted that in the embodiments of the present application, the space-time coupling feature data set is taken as the basis for analysis, the correlation strength of geographical features and the mineralization process is quantified by multivariate linear regression analysis, the target feature parameters with statistical reliability and geological significance are screened out by combining significance test, and finally the wollastonite contact metasomatism mineralization mechanism is accurately reflected by using existing mineralization analysis methods to integrate these key parameters, so that the problem of unclear mechanism and low prediction accuracy of the traditional method can be solved, and scientific support is provided for mineral resource exploration.

[0153] Further, the multivariate linear regression analysis on the space-time coupling feature data set and the screening of the target feature parameters related to the wollastonite mineralization process include: The variance inflation factor of the space-time coupling feature vector is calculated, the space-time coupling feature vector is preliminarily screened based on a preset variance inflation factor threshold, and the preliminarily screened space-time coupling feature vector is obtained.

[0154] It should be noted that the variance inflation factor (VIF) is a core indicator that measures the strength of multicollinearity among spatiotemporally coupled feature vectors. Its value is ≥1. The larger the value, the stronger the multicollinearity among features, which will lead to distortion of regression coefficients.

[0155] For example, the preset variance inflation factor threshold refers to a quantitative standard used to screen low collinearity features. If VIF ≤ 10, features exceeding this threshold are judged as high collinearity features and need to be removed.

[0156] Initial screening refers to the feature preprocessing step before multiple linear regression analysis. It is used to remove highly collinear features, optimize the dataset structure, avoid the interference of multicollinearity on the regression analysis results, and lay the foundation for accurate calculation of regression coefficients in the subsequent process.

[0157] The spatiotemporal coupling feature vectors after initial screening refer to the set of feature vectors that retain VIF≤10 (or retain them after compression by Lasso regression) after the variance inflation factor test. This set eliminates redundant high collinear features and retains only mutually independent and non-overlapping mineralization-related features.

[0158] It should be noted that, in the embodiments of the present invention, by calculating the variance inflation factor (VIF) of the spatiotemporal coupled feature vector, the features are initially screened with a VIF≤10 as a preset threshold to remove highly collinear features (or Lasso regression is used to compress irrelevant features), and finally a low-redundancy, highly independent feature vector is obtained after initial screening. This process solves the problem of regression model distortion caused by information overlap between features, optimizes the dataset structure, and provides reliable input data for subsequent calculation of regression coefficients and screening of key mineralization features using the least squares method.

[0159] Determine the regression coefficients corresponding to the spatiotemporal coupling feature vectors after initial screening, and construct a regression function based on the regression coefficients.

[0160] Among them, the regression coefficient ( In a multiple linear regression model, the parameter denoted as is a parameter that measures the strength and direction of the influence of each spatiotemporal coupling feature vector after initial screening on the dependent variable (mineral change). It is obtained by solving the least squares method. The larger its absolute value, the stronger the explanatory power of the corresponding feature on the mineralization process; the sign reflects the positive / negative correlation between the feature and the mineralization intensity, and is a quantitative basis for judging the correlation between the feature and mineralization.

[0161] The regression function refers to the function based on the spatiotemporal coupling feature vector (independent variable X) and regression coefficients after initial screening. ) and constant term ( The mathematical model is constructed to quantify the linear relationship between geographical features and mineral resource changes.

[0162] For example, the regression function can be expressed as: ; in, This represents the change in mineral resources (mineralization intensity level vector). to These are the spatiotemporal coupling feature vectors after each initial screening. For constant terms, to These are the regression coefficients for the corresponding feature vectors.

[0163] It should be noted that, in the embodiments of the present invention, the regression coefficients corresponding to each feature are first obtained by using the least squares method on the initial screening spatiotemporally coupled feature vectors after spatiotemporal constraints and high collinearity elimination. Then, by integrating the regression coefficients, eigenvectors, and constant terms, a regression function is constructed to quantify the linear relationship between geographical features and mineral resource changes. This function forms the basis for subsequent t-tests to screen key mineralization features (determining significant features with p-values ​​< 0.05), providing support for accurately identifying target feature parameters closely related to the wollastonite mineralization process.

[0164] Calculate the probability that the regression coefficient is 0.

[0165] For example, regression coefficients The statistical probability of a value equal to 0 (i.e., the p-value) is a quantitative indicator calculated through a t-test. It is used to determine whether the regression coefficient is significantly different from 0 and to reflect the reliability of the correlation between the corresponding feature and the mineralization process.

[0166] It should be noted that calculating the probability (p-value) that the regression coefficient is equal to 0 is essentially to verify the statistical significance of the coefficient through a t-test. If the p-value is less than the preset threshold, it means that the regression coefficient is significantly different from 0, and the corresponding feature has a reliable correlation with the wollastonite mineralization process; otherwise, it means that the feature has a weak correlation with mineralization and should be eliminated, ultimately supporting the accurate screening of key mineralization feature parameters.

[0167] Based on preset judgment thresholds and probabilities, target feature parameters related to the wollastonite mineralization process are determined from the spatiotemporal coupling feature vectors after initial screening.

[0168] Among them, the preset judgment threshold is used as a quantitative standard to determine whether the feature is significantly related to the mineralization process. For example, the preset judgment threshold can be 0.05, which is the basis for judging the target feature parameters.

[0169] For example, the statistical probability (i.e., p-value) that the regression coefficient is significantly different from 0 is calculated through a t-test, reflecting the reliability of the association between the corresponding spatiotemporal coupling feature vector and the mineralization intensity level (dependent variable Y).

[0170] Wollastonite mineralization process refers to a mineralization process with time stability and spatial continuity, which is characterized by acid intrusive body and carbonate rock contact metasomatism as the core, and is controlled by hydrothermal activity, lithological combination, tectonic conditions and other factors.

[0171] The target feature parameter refers to a key feature (such as silicification index, carbonate rock index, tectonic density, etc.) that has significant statistical correlation with the wollastonite mineralization process after significant screening, and has geological interpretation and statistical reliability, and is the core input of the mineralization model construction.

[0172] It should be noted that in the embodiments of the present application, the preset significance level threshold (p value < 0.05) is used as the determination standard, and the probability (p value) calculated by t test is used to perform secondary screening on the time and space coupled feature vectors after preliminary screening; only the features with p value less than the threshold and significantly related to the mineralization process are retained, and the target feature parameter is finally determined, which realizes the double control of geological constraint and statistical verification, ensures that the selected features are consistent with the mineralization mechanism and have reliable statistical correlation, and provides high-quality data support for subsequent mineralization model construction.

[0173] Further, the regression function is constructed based on the regression coefficient, comprising: Based on the mineralization intensity grade, each time and space coupled feature vector and its corresponding regression coefficient are used to construct a regression function.

[0174] It should be noted that in the embodiments of the present application, the mineralization intensity grade representing the actual mineralization is used as the dependent variable, the mineralization related geographical features (time and space coupled feature vectors) under the space-time constraint are used as the independent variable, and the regression coefficients of each feature are solved by the least square method. The regression function quantitatively linearly correlates the two. The function is the basis for extracting key mineralization feature parameters by t test (selecting significant features with p value < 0.05) subsequently, and provides a mathematical tool for subsequent construction of a wollastonite mineralization model with geological credibility and statistical reliability.

[0175] Overall, in the process of constructing the ore-forming model of wollastonite mine, the embodiment of the present application takes the contact zone of intrusive body and carbonate rock and the peripheral buffer range as the target area, obtains multi-source basic data in the target area; extracts an initial geographic feature data set from the multi-source basic data; determines a target time scale and a spatial data block matched with the target time scale by performing time sequence stability analysis on the observed features of the mineral edge zone corresponding to the wollastonite contact metasomatic mineralization, in combination with the spatial continuity constraint of the wollastonite contact metasomatic mineralization; filters out a spatio-temporal coupling feature data set from the initial geographic feature data set based on the target time scale and the spatial data block; performs multiple linear regression analysis on the spatio-temporal coupling feature data set, filters out a target feature parameter related to the wollastonite mineralization process, and constructs a wollastonite ore-forming model based on the target feature parameter. In the mineralization target feature parameter construction stage, the embodiment of the present application introduces the spatial continuity constraint of the wollastonite contact metasomatic mineralization process and the time sequence stability analysis of the observed features, avoiding the problem of feature selection distortion caused by the pure data-driven method for mineralization analysis. Under the time scale constraint, the size of the spatial data block is determined, so that the time scale and the spatial scale are mutually constrained, thereby ensuring that the constructed feature data set can truly reflect the zoning structure characteristics of wollastonite mineralization, and through multiple linear regression, the correlation between the spatio-temporal coupling features and the mineral changes is statistically analyzed, the target feature parameter significantly associated with the wollastonite mineralization process is effectively filtered out, and the accuracy of the wollastonite ore-forming model construction is improved.

[0176] The present application proposes a wollastonite ore-forming model construction system based on spatio-temporal constraints, please refer to Figure 2 , which shows a structure schematic diagram of a wollastonite ore-forming model construction system 200 based on spatio-temporal constraints provided by one embodiment of the present application, and the system comprises: The data acquisition module 201 is used to take the contact zone of intrusive body and carbonate rock and the peripheral buffer range as the target area, and obtain multi-source basic data in the target area; the multi-source basic data comprises multi-temporal optical remote sensing images, digital elevation models and derived terrain parameters, basic geological data and historical mineral data; an initial geographic feature data set is extracted from the multi-source basic data; the initial geographic feature data set comprises spectral statistical features, terrain and structure related features, and spatial statistical and texture features; The conditional constraint module 202 is used to perform time sequence stability analysis on the observed features of the mineral edge zone corresponding to the wollastonite contact metasomatic mineralization, in combination with the spatial continuity constraint of the wollastonite contact metasomatic mineralization, to determine a target time scale and a spatial data block matched with the target time scale; The target feature parameter determination module 203 is used to select a spatiotemporal coupled feature dataset from the initial geographic feature dataset based on the target time scale and spatial data blocks. The spatiotemporal coupled feature dataset is used to characterize the correspondence between the spatiotemporal coupled feature vector and the mineralization intensity level vector. The spatiotemporal coupled feature vector is used to characterize the geographic feature vector related to wollastonite mineralization. Multiple linear regression analysis is performed on the spatiotemporal coupled feature dataset to select target feature parameters related to the wollastonite mineralization process, and a wollastonite mineralization model is constructed based on the target feature parameters.

[0177] In one embodiment, the calculation module is used to extract mineral edge zone information at each time point based on the mineralization influence zone, whereby the mineral edge zone is used to characterize the transition zone between the mineralized and unmineralized areas; to count the number of overlapping graticles and the total number of covered graticles of mineral edge zones at adjacent time points within different time windows using a raster indicator function; the raster indicator function is used to indicate whether each graticle in the target area belongs to the edge zone of wollastonite ore; to calculate the spatial overlap rate based on the number of overlapping graticles and the total number of covered graticles, and to determine the target time scale based on the spatial overlap rate; the spatial overlap rate is used to characterize the ratio of the number of overlapping graticles to the total number of covered graticles.

[0178] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system for constructing a wollastonite mineralization model based on spatiotemporal constraints and the embodiment of a method for constructing a wollastonite mineralization model based on spatiotemporal constraints provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0179] This invention also provides an electronic device. Please refer to [link to relevant documentation]. Figure 3 The electronic device may include a processor 301, a memory 302, and a program 3021 stored in the memory 302 and capable of running on the processor 301.

[0180] When program 3021 is executed by processor 301, it can achieve the following: Figure 1 Any steps in the corresponding method embodiments and the achievement of the same beneficial effects will not be repeated here.

[0181] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by hardware related to program instructions, and the program can be stored in a readable medium.

[0182] The embodiment of the present application further provides a readable storage medium, wherein the readable storage medium stores a computer program, and the computer program can realize the above-mentioned method when executed by a processor. Figure 1 Any step in the corresponding method embodiment can be achieved, and the same technical effects can be achieved, to avoid repetition, which will not be described here.

[0183] The computer readable storage medium of the embodiment of the present application can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, device or apparatus.

[0184] The computer readable signal medium can include a data signal propagating in a baseband or as part of a carrier wave propagating through a transmission medium, in which the computer readable program code is embodied. Such a propagating data signal can take many forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate or transport program for use by or in connection with an instruction execution system, apparatus or device.

[0185] The program code contained on the storage medium can be transmitted in any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination thereof.

[0186] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0187] The embodiment of the present application further provides a computer program product, which, when running on a computer, enables the computer to execute the above related steps to realize the method for constructing a mineralization model of a wollastonite deposit based on space-time constraints provided by the above embodiment.

[0188] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0189] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the difference from other embodiments.

Claims

1. A method for constructing a mineralization model of wollastonite based on spatiotemporal constraints, characterized in that, The method includes: The target area is the contact zone between the intrusive body and the carbonate rock and the surrounding buffer zone. Multi-source basic data within the target area are acquired. The multi-source basic data includes multi-temporal optical remote sensing images, digital elevation models and derived topographic parameters, basic geological data and historical mineral data. An initial geographic feature dataset is extracted from the multi-source basic data; the initial geographic feature dataset includes spectral statistical features, topographic and structural related features, and spatial statistical and textural features. By performing time series stability analysis on the observation characteristics of the mineralized edge zone corresponding to wollastonite contact metasomatic mineralization, and combining the spatial continuity constraints of the wollastonite contact metasomatic mineralization, the target time scale and the spatial data block matching the target time scale are determined. Based on the target time scale and the spatial data block, a spatiotemporal coupled feature dataset is selected from the initial geographic feature dataset; the spatiotemporal coupled feature dataset is used to characterize the correspondence between the spatiotemporal coupled feature vector and the mineralization intensity level vector; the spatiotemporal coupled feature vector is used to characterize the geographic feature vector related to wollastonite mineralization; Multiple linear regression analysis was performed on the spatiotemporal coupling feature dataset to screen target feature parameters related to the wollastonite mineralization process, and a wollastonite mineralization model was constructed based on the target feature parameters.

2. The method for constructing a mineralization model of wollastonite based on spatiotemporal constraints according to claim 1, characterized in that, The method involves performing time-series stability analysis on the observational characteristics of the mineralized edge zone corresponding to wollastonite contact metasomatism to determine the target time scale, including: Based on the mineralization impact zone, mineral edge zone information is extracted at each time point. The mineral edge zone information includes the spatial range of the mineral edge zone and the observation characteristics within the transition zone. The spatial range is used to characterize the transition zone between the mineralized area and the non-mineralized area. Based on the mineral edge zone information, the number of overlapping grids and the total number of covered grids of mineral edge zones at adjacent time nodes within different time windows are counted using a grid indicator function; the grid indicator function is used to indicate whether each grid in the target area belongs to the edge zone of wollastonite ore. Based on the number of overlapping graticles and the total number of covered graticles, the spatial overlap rate is calculated, and the target time scale is determined based on the spatial overlap rate; the spatial overlap rate is used to characterize the ratio of the number of overlapping graticles to the total number of covered graticles.

3. The method for constructing a mineralization model of wollastonite based on spatiotemporal constraints according to claim 2, characterized in that, The determination of spatial data blocks matching the target timescale, based on the spatial continuity constraints of wollastonite contact metasomatism mineralization, includes: Starting from a stable mineral margin zone within the target timescale, a profile is constructed along the contact metasomatic direction between the intrusive body and the surrounding rock; the contact metasomatic direction is the hydrothermal migration direction of the intrusive body towards the surrounding rock. Establish a local coordinate system with the contact zone direction as the x-axis and the contact zone normal as the y-axis, and divide the spatial units according to different spatial sampling intervals; Calculate the wollastonite zoning index of each of the spatial units and the continuity transition index of the target region; Based on the wollastonite zoning index and the continuous transition index, spatial data blocks matching the target time scale are determined.

4. The method for constructing a mineralization model of wollastonite based on spatiotemporal constraints according to claim 3, characterized in that, The calculation of the wollastonite zoning index for each of the spatial units includes: For any spatial unit, a comprehensive characteristic value of silicification and a comprehensive characteristic value of carbonate rocks are obtained; the comprehensive characteristic value of silicification is used to characterize the intensity and distribution characteristics of silicification within the ore-forming region; the comprehensive characteristic value of carbonate rocks is used to characterize the distribution and reaction degree of carbonate rocks within the ore-forming region. Based on the comprehensive characteristic values ​​of silicification and carbonate rocks, the wollastonite zoning index of the spatial unit is determined.

5. The method for constructing a mineralization model of wollastonite based on spatiotemporal constraints according to claim 4, characterized in that, The calculation of the continuous transition index of the target region includes: Calculate the difference in wollastonite zoning index between adjacent spatial units; Based on a preset threshold, the difference in the zoning index of the wollastonite ore is binarized to obtain the marking information; Based on the marking information, the total number of continuous spatial units in all profiles is determined, and the continuity transition index of the target region is determined based on the total number of continuous spatial units and the total number of profiles.

6. The method for constructing a mineralization model of wollastonite based on spatiotemporal constraints according to claim 1, characterized in that, The step of performing multiple linear regression analysis on the spatiotemporal coupling feature dataset to screen target feature parameters related to the wollastonite mineralization process includes: Calculate the variance inflation factor of the spatiotemporal coupling feature vector, and perform initial screening on the spatiotemporal coupling feature vector based on a preset variance inflation factor threshold to obtain the initial screening spatiotemporal coupling feature vector. Determine the regression coefficients corresponding to the spatiotemporal coupling feature vectors after the initial screening, and construct a regression function based on the regression coefficients; Calculate the probability that the regression coefficient is 0; Based on the preset judgment threshold and the probability, target feature parameters related to the wollastonite mineralization process are determined from the spatiotemporal coupling feature vector after the initial screening.

7. The method for constructing a mineralization model of wollastonite based on spatiotemporal constraints according to claim 1, characterized in that, The extraction of the initial geographic feature dataset from the multi-source basic data includes: Based on the multi-temporal optical remote sensing images, the spectral statistical features and the spatial statistical and texture features are extracted; the spectral statistical features include the mean, variance and band ratio features of each band, and the spatial statistical and texture features include gray-level co-occurrence matrix texture features and local change intensity index. Based on the digital elevation model and derived topographic parameters, and the basic geological data, the topographic and structural features are extracted; the topographic and structural features include slope, aspect, relief, and structural density. An initial geographic feature dataset is constructed based on the spectral statistical features, the topographic and structural features, and the spatial statistics and texture features.

8. The method for constructing a mineralization model of wollastonite based on spatiotemporal constraints according to claim 6, characterized in that, The construction of the regression function based on the regression coefficients includes: Based on the mineralization intensity level, a regression function is constructed using each of the spatiotemporal coupled feature vectors and their corresponding regression coefficients.

9. A system for constructing a mineralization model of wollastonite based on spatiotemporal constraints, characterized in that, The system includes: The data acquisition module is used to acquire multi-source basic data within the target area, which includes the contact zone between the intrusive body and the carbonate rock and the surrounding buffer zone. The multi-source basic data includes multi-temporal optical remote sensing images, digital elevation models and derived topographic parameters, basic geological data, and historical mineral data. An initial geographic feature dataset is extracted from the multi-source basic data. The initial geographic feature dataset includes spectral statistical features, topographic and structural correlation features, and spatial statistical and textural features. The condition constraint module is used to perform time series stability analysis on the observation characteristics of the mineralized edge zone corresponding to wollastonite contact metasomatic mineralization, and combine the spatial continuity constraint of the wollastonite contact metasomatic mineralization to determine the target time scale and the spatial data block that matches the target time scale. The target feature parameter determination module is used to filter out a spatiotemporal coupled feature dataset from the initial geographic feature dataset based on the target time scale and the spatial data block; the spatiotemporal coupled feature dataset is used to characterize the correspondence between the spatiotemporal coupled feature vector and the mineralization intensity level vector; the spatiotemporal coupled feature vector is used to characterize the geographic feature vector related to wollastonite mineralization; multiple linear regression analysis is performed on the spatiotemporal coupled feature dataset to filter out target feature parameters related to the wollastonite mineralization process, and a wollastonite mineralization model is constructed based on the target feature parameters.

10. The system for constructing a spatiotemporally constrained mineralization model of wollastonite deposits according to claim 9, characterized in that, The condition constraint module includes: The calculation module is used to extract mineral edge zone information at various time points based on the mineralization influence zone. The mineral edge zone information includes the spatial range of the mineral edge zone and the observation characteristics within the transition zone. The spatial range is used to characterize the transition zone between the mineralized and unmineralized areas. Based on the mineral edge zone information, the module uses a raster indicator function to count the number of overlapping rasters and the total number of covered rasters of adjacent time points within different time windows. The raster indicator function is used to indicate whether each raster in the target area belongs to the edge zone of wollastonite. Based on the number of overlapping rasters and the total number of covered rasters, the module calculates the spatial overlap rate and determines the target time scale based on the spatial overlap rate. The spatial overlap rate is used to characterize the ratio of the number of overlapping rasters to the total number of covered rasters.

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