Mineral resource exploration digital terrain model establishment method, system and equipment based on multi-source data fusion driving and storage medium

Abstract

The invention discloses a mineral resource exploration digital terrain model establishment method, system and device based on multi-source data fusion driving and a storage medium, and is applied to the technical field of mineral resource exploration, and the method comprises the steps: obtaining multi-source data, and converting the multi-source data to a same coordinate system in a unified manner; wherein the multi-source data comprises remote sensing data, ground measurement data, drilling data and mineral distribution data; the method comprises the following steps: fusing remote sensing data and ground measurement data, generating a digital elevation model by adopting a Kriging interpolation method, and superposing derivative terrain factors including gradient and slope orientation; based on the drilling data, a stratigraphic interface is constructed by adopting sequential Gaussian simulation, a stratigraphic structure model is constructed, mineral product distribution data is interpolated through a distance power inverse ratio method, and an ore body model is constructed; and integrating the digital elevation model, the stratigraphic structure model and the ore body model under a unified space framework to obtain a digital terrain model. The method effectively overcomes the defects of a traditional terrain model in the aspect of dynamic adaptability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mineral resources exploration, and more particularly to a method for establishing a digital terrain model for mineral resources exploration based on multi-source data fusion driving. BACKGROUND

[0002] Under the background of the continuous rise in global resource demand, mineral resources, as the key support for industrial development, have become increasingly important in exploration. As exploration activities continue to advance to deeper and more complex areas, the limitations of traditional terrain models have gradually emerged. Traditional models often rely on a single data source or static data from a specific period to construct, making it difficult to reflect the dynamic changes in the terrain in real time, as well as changes in geological conditions caused by mining activities, crustal movement, etc. This lag in data updates leads to discrepancies between reserve estimates and actual conditions, making it impossible to optimize mining plans in a timely manner based on real-time terrain and geological changes, resulting in wasted resources, increased unnecessary exploration costs, and even exacerbating environmental damage due to improper planning.

[0003] The emergence of digital terrain models provides a new approach to solving these problems. By integrating remote sensing, drilling, and ground surveying data, digital terrain models can create a three-dimensional visualization model that encompasses surface terrain, geological structures, and ore body distribution, among other multi-dimensional information. Remote sensing data, with its advantage of large-area and rapid acquisition of surface information, can be used to identify surface rock types, vegetation coverage, soil moisture, and other terrain features. It can also detect geological thermal anomaly areas through thermal infrared remote sensing and indirectly infer the approximate distribution of mineral resources using multispectral remote sensing. Ground surveying data provides elevation points and contour line information for accurate terrain surface construction. Drilling data reveals key information such as underground rock sequences, mineral content, and drilling depth. Integrating these multi-source data in a unified spatial reference framework not only allows for accurate depiction of the terrain and geological structure of the mining area, but also dynamically reflects changes in the mining area through real-time data updates. This provides a more accurate data foundation for mineral resource reserve estimation, helping exploration personnel better understand the spatial distribution and occurrence state of ore bodies, thereby optimizing mining plans, improving resource recovery rates, reducing mining risks, and reducing negative environmental impacts, achieving sustainable development and utilization of mineral resources.

[0004] Therefore, how to provide a method, system, device and storage medium for establishing a digital terrain model for mineral resources exploration based on multi-source data fusion driving, which can overcome the shortcomings of traditional terrain models in dynamic adaptability, is a problem that needs to be solved by those skilled in the art. SUMMARY

[0005] In view of this, the present application provides a kind of based on multi-source data fusion driving's mineral resources exploration digital terrain model establishment method, system, equipment and storage medium. Through the remote sensing data of mineral resources exploration area, ground survey data, borehole data and mineral resources distribution data are fully collected and integrated, different sources, different format data are unified to the same spatial reference framework. In the data integration process, using coordinate conversion, geometric correction and other technologies, eliminate the positional deviation and geometric deformation between data, ensure the accuracy and consistency of data. Based on the integrated multi-source data, using advanced three-dimensional modeling technology, construct the three-dimensional geological model that can truly reflect the topography of mining area, geological structure and ore body distribution. In the modeling process, the spatial correlation of data and geological regularity are fully considered, and suitable interpolation algorithm and modeling method are used to improve the accuracy and reliability of the model. Through visual analysis technology, three-dimensional geological model is presented in an intuitive way, to provide an interactive decision support platform for exploration personnel. On the platform, exploration personnel can observe the topography and geological features of mining area from different angles, perform topographic analysis, profile analysis, reserve calculation and other operations, so as to more accurately evaluate the mineral resources reserves, optimize the mining scheme, and provide strong technical support for efficient development of mineral resources.

[0006] In order to achieve the above object, the present application adopts the following technical scheme:

[0007] A kind of based on multi-source data fusion driving's mineral resources exploration digital terrain model establishment method, comprising:

[0008] Step 1: obtain the multi-source data of mineral resources to be surveyed, and unify the multi-source data to the same coordinate system;Wherein, multi-source data includes: remote sensing data, ground survey data, borehole data and mineral distribution data;

[0009] Step 2: fuse remote sensing data and ground survey data, generate digital elevation model using Kriging interpolation method, and superimpose derived terrain factors including slope and slope direction;

[0010] Step 3: based on borehole data, construct stratum interface using sequential Gaussian simulation, construct stratum structure model, and interpolate mineral distribution data through distance power inverse ratio method, to construct ore body model;

[0011] Step 4: integrate digital elevation model, stratum structure model and ore body model under the unified spatial framework, to obtain the digital terrain model of mineral resources to be surveyed.

[0012] Optionally, in step 1, the multi-source data is unified to the same coordinate system, specifically:

[0013] Based on the GIS platform, the multi-source data is converted to the national 2000 geodetic coordinate system by using a seven-parameter conversion model.

[0014] Optionally, in step 1, the remote sensing data is also radiometrically calibrated and geometrically corrected.

[0015] Optionally, in step 2, the remote sensing data and the ground survey data are fused, and a digital elevation model is generated by using the Kriging interpolation method, as follows:

[0016] Z(x,y)=Z tren d(x,y)+Z resi d ual (x,y);

[0017] Wherein, Z(x,y) is the elevation estimate of the unknown point (x,y) in the digital elevation model; Z tren d(x,y) is the trend surface analysis fitting value, which is generated based on the surface features extracted from the remote sensing data and the elevation points in the ground survey data, and reflects the macroscopic topographic trend; Z resi d ual (x,y) is a residual correction term, which is calculated based on the spatial correlation of the remote sensing data and the ground survey data, and is used to correct the difference between the trend surface analysis and the actual data.

[0018] Optionally, in step 3, based on the drilling data, a sequential Gaussian simulation is used to construct the stratigraphic interface and build a stratigraphic structure model, specifically as follows:

[0019] Based on the drilling rock sequence, a sequential Gaussian simulation is used to construct the stratigraphic interface:

[0020] The mineral resources to be surveyed are discretized into a grid system, and each grid node is sequentially processed; wherein the random variable at each grid node is subject to a conditional normal distribution, and the grid node value is determined by two parameters of mean and variance;

[0021] Solve the Kriging equation set to obtain the mean and variance of the grid node, determine the normal distribution of the variable at the node, and obtain a sample at the node by using the corresponding sampling method; wherein the conditional data for solving the Kriging equation set includes the original data and the values of all simulated grid nodes falling within the simulation neighborhood that have been simulated previously;

[0022] Combine the interlayer contact relationship constrained by the seismic profile data, determine the approximate stratification and interlayer contact relationship of the strata by analyzing the seismic reflection wave, and construct a stratigraphic structure model.

[0023] Optionally, in step 3, the mineral distribution data is interpolated by the distance power inverse ratio method to construct a mineral body model, specifically as follows:

[0024] The ore body grade data is interpolated by the distance power inverse ratio method:

[0025] The distance between the unknown point and the known data point is calculated, and the ore body grade of the known data point is weighted and averaged according to the power of the distance as the weight, so as to obtain the estimated value of the grade of the unknown point;

[0026] The structural elements including faults and folds are superimposed, so as to form a three-dimensional ore body model containing ore body boundaries and grade gradients.

[0027] Optionally, in step 4, a digital elevation model, a stratum structure model and an ore body model are integrated by using a platform including Unity and ArcGIS to obtain a digital terrain model of the mineral resources to be surveyed.

[0028] The application also provides a multi-source data fusion driven mineral resource exploration digital terrain model establishment system based on a multi-source data fusion driven mineral resource exploration digital terrain model establishment method, comprising:

[0029] The multi-source data acquisition and preprocessing module is used for acquiring multi-source data of the mineral resources to be surveyed, and converting the multi-source data to the same coordinate system; wherein the multi-source data comprises remote sensing data, ground survey data, drilling data and mineral distribution data;

[0030] The digital elevation model construction module is used for fusing the remote sensing data and the ground survey data, generating a digital elevation model by using Kriging interpolation method, and superimposing derived terrain factors including slope and slope direction;

[0031] The stratum structure model and the ore body model construction module is used for constructing a stratum interface based on the drilling data by using sequential Gaussian simulation, constructing a stratum structure model, and constructing an ore body model by interpolating the mineral distribution data by the distance power inverse ratio method;

[0032] The digital terrain model construction module is used for integrating the digital elevation model, the stratum structure model and the ore body model under the unified spatial framework to obtain the digital terrain model of the mineral resources to be surveyed.

[0033] The application also provides an electronic device, comprising:

[0034] The memory is used for storing the computer program;

[0035] The processor is used for executing the computer program to realize the steps of the multi-source data fusion driven mineral resource exploration digital terrain model establishment method.

[0036] The application further provides a computer readable storage medium, and the computer readable storage medium stores a computer program.

[0037] Compared with the prior art, the application provides a mineral resource exploration digital terrain model establishment method, system, device and storage medium based on multi-source data fusion driving. The application effectively solves the problem of poor dynamic adaptability of the conventional terrain model relying on a single data source or static data. By fusing remote sensing data, ground measurement data, drilling data and mineral distribution data, a digital elevation model, a stratum structure model and a ore body model are constructed and integrated in a unified coordinate system, which not only eliminates the positional deviation and geometric deformation of the multi-source data, ensures the data accuracy and consistency, but also improves the precision of the model in describing the terrain undulation, geological structure and ore body distribution by means of advanced algorithms such as Kriging interpolation, sequential Gaussian simulation and distance power inverse ratio method. Meanwhile, the model supports real-time data updating to dynamically reflect the changes in the mining area, realizes interactive analysis in combination with a visualization platform, provides more accurate data basis for reserve evaluation, helps to optimize the mining scheme, improve the resource recovery rate, reduce the mining risk, reduce the negative impact on the environment and realize sustainable development of mineral resources. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. 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 be obtained without creative labor on the basis of the provided drawings.

[0039] Figure 1 The method flowchart provided by the present application is shown. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0041] Embodiment 1

[0042] Embodiment 1 of the present application discloses a mineral resource exploration digital terrain model establishment method based on multi-source data fusion driving, as shown in Figure 1 , comprising:

[0043] Step 1: Obtain multi-source data of mineral resources to be surveyed, and convert the multi-source data to the same coordinate system. The multi-source data includes remote sensing data, ground survey data, drilling data, and mineral distribution data.

[0044] Remote sensing data: As an important means of obtaining macro information on the ground, it covers optical, thermal infrared and multispectral data. Optical remote sensing data captures visible light to near-infrared band of ground reflectance spectrum information through satellite or aerial photography, which can accurately identify surface rock types, vegetation coverage, soil moisture and terrain features. For example, different rocks have unique reflectivity at certain wave bands, and by analyzing these reflectivity differences, different lithology such as granite and sandstone can be distinguished, providing a basis for geological structure analysis. Thermal infrared remote sensing focuses on recording ground temperature information, which plays a key role in geological exploration and can sensitively capture geological thermal anomaly areas, which are often closely related to potential geological tectonic activity or geothermal resources, providing important clues for deep geological research. Multispectral remote sensing, with its ability to analyze the reflectivity or radiation intensity of ground objects at multiple specific wavelengths, has become a powerful tool for indirectly inferring the existence and approximate distribution of mineral resources. For example, certain metal mineralized areas will exhibit unique reflection characteristics at certain multispectral wave bands, and by identifying and analyzing these characteristics, the possible distribution range of ore bodies can be preliminarily delineated. Remote sensing data is mainly obtained through satellites (such as Landsat series satellites, which have medium-high resolution, wide coverage, and can provide rich ground spectral information) and unmanned aerial loads. In practical applications, for complex mountainous areas, unmanned aerial low-altitude remote sensing can be used to obtain high-resolution ground images, with flight heights that can be flexibly adjusted according to terrain and exploration needs, generally ranging from a few hundred meters to a thousand meters or more, which can clearly capture the small topographic changes and geological features of the ground, such as rock outcrops and small structures.

[0045] Ground survey data: Ground survey data is the key to accurately constrain the features of the terrain, mainly including elevation point data and terrain line data. Elevation point data is obtained by measuring equipment such as total station and GPS, which obtains the accurate elevation information of discrete points on the ground. These points are like the "skeleton" of the terrain surface, providing basic height information for terrain modeling. Terrain line data further refines the terrain features by measuring locations with obvious terrain changes, such as ridge lines and valley lines, making the construction of the terrain surface more accurate. In complex mountainous areas, dense elevation points and terrain line measurements can accurately reflect the true terrain of mountains and valleys, providing reliable basis for subsequent terrain analysis and mineral distribution inference. RTK-GPS (Real-Time Kinematic Global Positioning System) is used for point collection. During the measurement process, choose open and well-ventilated locations to set up the reference station and the mobile station. The reference station continuously receives satellite signals and sends differential correction information to the mobile station. The mobile station calculates its accurate position in real time according to the information, and the measurement accuracy can reach centimeter level. In a certain mountainous mineral exploration, RTK-GPS is used to measure the terrain control points. Each control point is measured for at least 5 minutes to ensure the accuracy of the measurement data.

[0046] Drilling data: Drilling data is the core data for understanding the underground geological stratification and the occurrence state of ore bodies, including drilling depth, rock sequence and mineral content data. Drilling depth determines the vertical range of exploration, providing scale information for studying geological structures at different depths. Rock sequence records the order and thickness of various rock layers encountered during drilling, reflecting the stratified structure of underground geology, which is of great significance for studying geological evolution and tectonic movement. Mineral content data directly reveals the mineral composition and content variation within the range of drilling, which is a key indicator for judging the existence of ore bodies and the grade. In a lead-zinc mine exploration project, through the analysis of multiple drilling data, the occurrence position and grade variation of lead-zinc ore bodies at different depths can be clearly determined, providing key data support for reserve estimation and mining plan formulation. The original records from exploration engineering record the position, depth, lithology, mineral content and other information of the drilling. During the drilling process, sampling and recording are strictly conducted according to the specifications to ensure the integrity and reliability of the data. For important drillings, multiple sampling and analysis will be conducted to verify the accuracy of the data.

[0047] Mineral distribution data: Mineral distribution data is the data that defines the core exploration targets of the model, covering ore body boundaries, grade changes, and structural information. The accurate delineation of ore body boundaries defines the spatial distribution of the ore body and is the basis for determining mineral resources reserves. Grade change data records the variation of useful mineral content in the ore body, which is crucial for assessing the economic value and mining feasibility of mineral resources. Structural information reveals geological structures such as faults and folds that affect the exploitation and utilization of the deposit. These structures not only affect the shape and distribution of the ore body, but also have a significant impact on safety and resource recovery rate during the mining process. In a large-scale copper mine exploration, detailed mineral distribution data can help exploration personnel accurately assess the reserves, grade distribution of copper mines, and the impact of surrounding geological structures on mining, thereby developing a reasonable mining plan.

[0048] The multi-source data is unified and converted to the same coordinate system, specifically:

[0049] In the process of multi-source data fusion, GIS spatial analysis technology plays an indispensable role, and the primary task is to realize the coordinate unification of multi-source data. Since different data sources may use different coordinate systems, in order to ensure the consistency of data in spatial position, a seven-parameter conversion model is used for coordinate conversion. Specifically, based on the GIS platform, the seven-parameter conversion model is used to unify multi-source data into the national 2000 geodetic coordinate system. This model unifies data in different coordinate systems into the target coordinate system through translation, rotation, scaling, and other operations, achieving seamless connection of data.

[0050] At the same time, the remote sensing image is radiometrically calibrated and geometrically corrected. Radiometric calibration is to convert the original digital quantization value recorded by the sensor into a physically meaningful radiance value or reflectivity, in order to eliminate the influence of sensor itself error and atmospheric factors. Geometric correction is to construct a geometric correction matrix containing sensor position, attitude and topographic parameters, and correct the remote sensing image, eliminate the geometric distortion and position deviation of the image, control the error within 0.5 pixels, and ensure that the features on the image correspond accurately to the actual geographical position.

[0051] Step 2: Fuse remote sensing data and ground survey data, use Kriging interpolation method to generate digital elevation model, and superimpose derived terrain factors including slope and aspect.

[0052] Kriging interpolation is a statistical method based on variogram theory and structural analysis, which assumes that spatial data has certain structural properties. By calculating the spatial correlation between different sample points (i.e., semi-variogram), the value of unknown points can be estimated. In practical applications, first, based on known ground elevation points and remote sensing extracted surface feature data, a semi-variogram model is constructed to determine the block effect, base value and range of parameters, to reflect the spatial structure characteristics of spatial autocorrelation. Then, using the constructed semi-variogram model, the elevation of unknown points is estimated to generate a digital elevation model (DEM).

[0053] Specifically, remote sensing data and ground measurement data are fused, and Kriging interpolation is used to generate a digital elevation model as follows:

[0054] Z(x,y) = Z tren d(x,y) + Z resi d ual (x,y);

[0055] where Z(x,y) is the elevation estimate of unknown point (x,y) in the digital elevation model; Z tren d(x,y) is the trend surface analysis fitting value, which is generated based on the fitting of surface features extracted from remote sensing data and elevation points in ground measurement data, reflecting the macroscopic topographic trend; Z resi d ual (x,y) is the residual correction term, which is calculated based on the spatial correlation (semi-variogram) of remote sensing data and ground measurement data, used to correct the difference between trend surface analysis and actual data.

[0056] Superimposed are derived topographic factors such as slope and aspect, highlighting the control of micro-topography such as ridges and valleys on ore body enrichment. Slope refers to the angle between the tangent of a point on the ground and the horizontal plane, reflecting the degree of terrain inclination; aspect refers to the direction in which the slope normal projects onto the horizontal plane, which has an important influence on environmental factors such as light and moisture. In the copper exploration of a certain mountainous area, through analysis of slope and aspect data, it is found that in areas with steep slope and south-facing aspect, the enrichment degree of copper ore bodies is higher, because such topographic conditions are conducive to the formation and preservation of copper ore. By superimposing these derived topographic factors, the relationship between topographic features and ore body distribution can be more intuitively displayed, providing more valuable information for mineral exploration.

[0057] Step 3: Based on drilling data, construct the stratigraphic interface using sequential Gaussian simulation, construct the stratigraphic structure model, and interpolate the mineral distribution data using the distance power inverse ratio method to construct the ore body model.

[0058] In mineral resource exploration, due to the limitation of drilling data, there is great uncertainty in key parameters such as ore body thickness and grade in the area where drilling is sparse. In order to quantify this uncertainty, the present application introduces a geostatistical method to conduct probability simulation in the area where drilling is sparse.

[0059] The sequential Gaussian simulation algorithm in geostatistics is used to generate the probability distribution model of the ore body thickness and grade. This algorithm is based on the theory of regionalized variables, assuming that the ore body parameters have certain correlation in space. First, according to the known drilling data, the variogram of the ore body parameters is constructed to describe their spatial variation characteristics. Then, through sequential simulation, at each node to be estimated, a value is randomly selected from the conditional probability distribution as the simulated value of the node according to the known data in its neighborhood and the variogram. After multiple simulations, a large number of simulation realizations are obtained, each of which represents a possible distribution of the ore body parameters.

[0060] Based on these simulation realizations, a confidence cloud map is generated. The confidence cloud map visually shows the uncertainty of the ore body parameters at different locations with different colors or transparencies. In high confidence areas, the estimation of the ore body parameters is reliable, and their distribution range is relatively narrow. In low confidence areas, the uncertainty is greater, and the parameter distribution range is wider. In the exploration of a certain lead-zinc mine, through the quantitative analysis of the uncertainty of the ore body grade, it is found that the grade confidence of some drilling sparse areas is low, which provides an important basis for the subsequent layout of the exploration drilling. By increasing the exploration drilling in these low confidence areas, the uncertainty of the ore body grade estimation can be effectively reduced, and the accuracy of the resource reserve evaluation can be improved.

[0061] Sequential Gaussian simulation requires that the original data field should follow a Gaussian distribution, or follow a Gaussian distribution after normal transformation. Based on drilling data, sequential Gaussian simulation is used to construct the stratigraphic interface and construct the stratigraphic structure model, which is:

[0062] Based on the drilling sequence of strata, sequential Gaussian simulation is used to construct the stratigraphic interface:

[0063] Discretize the mineral resources to be explored into a grid system, and sequentially process each grid node; wherein the random variable at each grid node is subject to a conditional normal distribution, and the grid node value is determined by the mean and variance of two parameters;

[0064] Solve the Kriging equation set to obtain the mean and variance of the grid node, determine the normal distribution of the variable at the node, and use the corresponding sampling method to obtain a sample at the node; wherein the conditional data for solving the Kriging equation set includes the original data and the values of all simulated grid nodes falling within the simulation neighborhood that have been simulated previously;

[0065] The seismic profile data can provide information of deep geological structure, and the contact relationship between layers can be determined by analyzing the seismic reflection wave, and a stratum structure model can be constructed.

[0066] The distance power inverse method is used to predict unknown data by weighted average of surrounding known data according to distance weight. The basic principle is that the data point closer to the unknown point has greater influence on the unknown point. The distance power inverse method is used to interpolate mineral distribution data to construct a mineral body model, specifically as follows:

[0067] The distance power inverse method is used to interpolate mineral body grade data as follows:

[0068] The distance between the unknown point and the known data point is calculated, and the grade of the mineral body of the known data point is weighted and averaged according to the power of the distance as the weight, so as to obtain the estimated value of the grade of the unknown point.

[0069] Structural elements including faults and folds are superimposed, which have important influence on the shape and distribution of the mineral body. In the exploration of a lead-zinc mine, by analyzing the position and shape of the faults and folds, it is found that the mineral body is often distributed along the structural belt of the faults and folds, and the shape of the mineral body is also controlled by these structures, thereby forming a three-dimensional mineral body model containing the boundary of the mineral body and the grade gradient.

[0070] Step 4: Integrate the digital elevation model, stratum structure model and mineral body model under the unified spatial framework to obtain the digital terrain model of the mineral resource to be explored.

[0071] Integrating the digital elevation model, stratum structure model and mineral body model under the unified spatial framework realizes seamless connection of the surface topography and underground geological structure. By unifying the data of different models to the same coordinate system and spatial reference framework, using the spatial analysis and data fusion function of GIS, the digital elevation model, stratum structure model and mineral body model are integrated, so that the surface topography and underground geological structure can be completely presented in one three-dimensional model. In the three-dimensional geological modeling of a coal mine area, by integrating the digital elevation model, stratum structure model and mineral body model, the distribution of the coal body underground and the relationship with the surface topography can be directly observed, which provides comprehensive information support for the mining planning of the coal mine.

[0072] The digital elevation model, stratum structure model and mineral body model are integrated by using platforms including Unity and ArcGIS to obtain the digital terrain model of the mineral resource to be explored.

[0073] The interactive display of terrain, geological structure and ore body distribution is realized through professional platforms such as Unity and ArcGIS. On the Unity platform, the built three-dimensional geological model is visualized by using the powerful graphics rendering capability and interactive function of Unity. Users can freely switch the perspective and observe the terrain, geological structure and ore body distribution from different angles. They can also zoom in, rotate and perform other operations to deeply understand the details of the geological features. The ArcGIS platform has rich geographic information processing functions. It can not only realize the visualization of the three-dimensional geological model, but also combine with spatial analysis tools to deeply analyze the geological data, such as terrain profile analysis and ore body reserve calculation, to provide comprehensive decision support for mineral resource exploration and development.

[0074] Specifically, in terms of terrain sectioning function, users can select a sectioning plane on the three-dimensional model by mouse operation to realize the profile display of terrain and geological structure. By observing the sectioned profile, the distribution of different strata, the thickness change of ore body and the characteristics of geological structure can be clearly seen. In terms of ore body transparent display, the transparency of the ore body model is adjusted by using the material rendering technology of Unity 3D, so that the internal structure can be clearly presented, which is convenient for researchers to observe the internal structure and grade distribution of the ore body. At the same time, the platform supports attribute query function. Users only need to click any object in the model, such as terrain, stratum and ore body, to pop up detailed attribute information including position, elevation, lithology and grade.

[0075] Through the space-time dynamic simulation function, the platform can display the terrain evolution and resource reserve change in different mining stages. In a coal mining project, the platform is used to simulate the terrain change and coal reserve reduction in different mining periods according to the mining plan and geological conditions. Through dynamic demonstration, managers can intuitively see the impact of mining activities on the terrain, plan land reclamation and ecological restoration in advance, and adjust the mining progress and equipment configuration according to the change of resource reserve to improve the mining efficiency and resource recovery rate.

[0076] Embodiment 2

[0077] Embodiment 2 of the present application discloses a specific implementation application of a mineral resource exploration digital terrain model establishment method based on multi-source data fusion driving, as follows:

[0078] The present application selects a certain polymetallic mining area in southwest China as a case area. The area of the mining area is about 50km 2, located in the southwest mountainous area, with severe topographic incision and large relief, the mountains and valleys crisscross, the relative height difference can reach more than 1000 meters. Such complex topographic conditions bring great difficulties to traditional mineral exploration work. At the same time, the ore body in this mining area is obviously controlled by fault structure, and the ore body shape is complex and widely distributed. It is difficult to accurately delineate the ore body boundary by traditional exploration means, resulting in low accuracy of resource assessment.

[0079] In the process of model construction, we integrated multiple data sources. First, we collected 12 scenes of high-resolution remote sensing images of different periods, covering optical, thermal infrared and multispectral data. These images can provide rich surface information, including rock types, vegetation coverage, topographic features, etc. At the same time, more than 500 ground elevation points were obtained through ground measurement, and the measurement accuracy was ensured to be centimeter level by using RTK-GPS technology. In addition, we collected 30 exploration drilling data, which recorded the location, depth, lithology, mineral content and other information of the drill holes in detail. Through the integration of these multi-source data, we provided comprehensive data support for the construction of high-precision digital terrain model.

[0080] Based on the integrated multi-source data, we first used Kriging interpolation method to fuse the ground elevation points and remote sensing surface features to generate a DEM with a resolution of 1m, which can accurately reflect the relief changes of the surface topography. On this basis, we used sequential Gaussian simulation algorithm to construct the stratigraphic interface according to the drilling lithology sequence, and combined with the seismic profile data to constrain the interlayer contact relationship, and constructed the stratigraphic structure model. Through distance power inverse ratio interpolation of ore body grade data, and superimposing faults, folds and other structural elements, we realized the spatial positioning of the ore body and constructed a 3D ore body model with a precision of 0.3m. Using this model, we successfully predicted and delineated 3 concealed ore bodies. After subsequent drilling verification, the ore body positioning accuracy reached 85%, which was 30% higher than traditional methods, significantly improving the accuracy of mineral exploration.

[0081] The traditional mineral resource exploration data processing process is tedious, which requires manual data sorting, analysis and drawing work, and the entire data processing cycle usually takes about 2 months. After using the digital terrain model construction method proposed in the invention, using advanced computer technology and automatic algorithm, the data processing period is greatly shortened from 2 months to 2 weeks, improving the efficiency and accuracy of data processing. At the same time, the digital model can quickly analyze and predict the position of the ore body, reducing unnecessary exploration work, and reducing the exploration cost by 25%, effectively improving the exploration efficiency and reducing the exploration cost.

[0082] In traditional mineral resource evaluation, due to the limitation of terrain model, the spatial distribution and reserve estimation of ore body are often not accurate enough, resulting in large errors in resource evaluation results. The digital terrain model constructed by the present application can more accurately reflect the true shape and distribution of the ore body, and through the accurate depiction of the ore body boundary and grade, the reserve estimation accuracy is improved by 15%. This provides a more reliable basis for the rational development and utilization of mineral resources, effectively avoids the misjudgment of resources caused by model lag, and improves the development efficiency of resources.

[0083] Environmental management improvement: In the production and operation process of the mining area, slope stability and geological disaster early warning are important contents of environmental management. The traditional monitoring method often has problems such as late monitoring and low precision, which is difficult to effectively prevent the occurrence of geological disasters. Using the digital terrain model, we can monitor the stability of the slope in real time, and through real-time analysis of the terrain changes, we can early warn 2 small-scale landslides, providing valuable time for timely protective measures, reducing disaster losses, and protecting the ecological environment and personnel safety of the mining area. At the same time, the digital model can also be used for optimizing the land use planning of the mining area, reducing the damage to the environment, and realizing the coordinated development of mineral resource development and environmental protection.

[0084] Example 3

[0085] The embodiment 3 of the present application discloses a multi-source data fusion driven mineral resource exploration digital terrain model establishment system based on a multi-source data fusion driven mineral resource exploration digital terrain model establishment method, comprising:

[0086] Multi-source data acquisition and preprocessing module: used for acquiring multi-source data of the mineral resource to be explored, and converting the multi-source data to the same coordinate system; wherein the multi-source data includes remote sensing data, ground measurement data, drilling data and mineral distribution data;

[0087] Digital elevation model construction module: used for fusing remote sensing data and ground measurement data, generating a digital elevation model by using Kriging interpolation method, and superimposing derived terrain factors including slope and slope direction;

[0088] Stratum structure model and ore body model construction module: used for constructing stratum interface based on drilling data by using sequential Gaussian simulation, constructing stratum structure model, and constructing ore body model by interpolating mineral distribution data by distance power inverse ratio method;

[0089] Digital terrain model construction module: used for integrating digital elevation model, stratum structure model and ore body model under the unified spatial framework to obtain the digital terrain model of the mineral resource to be explored.

[0090] Example 4

[0091] Embodiment 4 of the present application discloses an electronic device, comprising:

[0092] a memory for storing the computer program;

[0093] a processor for implementing the steps of the method for establishing a mineral resource exploration digital terrain model based on multi-source data fusion driving when executing the computer program.

[0094] Embodiment 5

[0095] Embodiment 5 of the present application discloses a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method for establishing a mineral resource exploration digital terrain model based on multi-source data fusion driving.

[0096] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the device disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0097] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for establishing a digital terrain model for mineral resource exploration based on multi-source data fusion, characterized in that, include: Step 1: Acquire multi-source data of the mineral resources to be explored, and convert the multi-source data to the same coordinate system; wherein, the multi-source data includes: remote sensing data, ground measurement data, borehole data, and mineral distribution data; Step 2: Integrate the remote sensing data and ground measurement data, generate a digital elevation model using the Kriging interpolation method, and overlay derived terrain factors including slope and aspect; Step 3: Based on borehole data, sequential Gaussian simulation is used to construct the stratigraphic interface and build a stratigraphic structure model. Mineral distribution data is then interpolated using the distance power inverse method to construct an ore body model. Step 4: Integrate the digital elevation model, stratigraphic structure model, and ore body model within a unified spatial framework to obtain a digital terrain model of the mineral resources to be explored.

2. The method for establishing a digital terrain model for mineral resource exploration based on multi-source data fusion as described in claim 1, characterized in that, In step 1, the multi-source data is uniformly transformed to the same coordinate system, specifically as follows: Based on the GIS platform, the multi-source data is uniformly converted to the National Geodetic Coordinate System 2000 using a seven-parameter conversion model.

3. The method for establishing a digital terrain model for mineral resource exploration based on multi-source data fusion as described in claim 1, characterized in that, Step 1 also includes: performing radiometric calibration and geometric correction on the remote sensing data.

4. The method for establishing a digital terrain model for mineral resource exploration based on multi-source data fusion as described in claim 1, characterized in that, In step 2, the remote sensing data and ground measurement data are fused together, and a digital elevation model is generated using the Kriging interpolation method, as follows: Z(x,y)=Z trend (x,y)+Z resi d ual (x,y); Where Z(x,y) is the elevation estimate of the unknown point (x,y) in the digital elevation model; Z trend (x, y) represents the fitted values ​​from the trend surface analysis, generated by fitting surface features extracted from the remote sensing data and elevation points from the ground measurement data, reflecting the macroscopic topographic trend; Z residual (x,y) is the residual correction term, which is calculated based on the spatial correlation between the remote sensing data and the ground measurement data, and is used to correct the difference between the trend surface analysis and the actual data.

5. The method for establishing a digital terrain model for mineral resource exploration based on multi-source data fusion as described in claim 1, characterized in that, Based on borehole data, a sequential Gaussian simulation was used to construct the formation interface and build a formation structure model, specifically as follows: Based on the borehole strata sequence, a sequential Gaussian simulation was used to construct the formation interface: The mineral resources to be explored are discretized into a grid system, and each grid node is processed sequentially; wherein, the random variable at each grid node follows a conditional normal distribution, and the grid node value is determined by two parameters: mean and variance. Solving the Kriging equations yields the mean and variance at the grid nodes, determines the normal distribution of the variables at the nodes, and uses the corresponding sampling method to obtain a sample at the node. The conditional data for solving the Kriging equations includes the original data and the values ​​at all simulated grid nodes that fall within the simulation neighborhood that have been previously simulated. By combining seismic profile data to constrain interlayer contact relationships, and analyzing seismic reflection waves, the approximate strata layering and interlayer contact relationships are determined, and a stratigraphic structure model is constructed.

6. The method for establishing a digital terrain model for mineral resource exploration based on multi-source data fusion as described in claim 1, characterized in that, A mineral distribution model is constructed by interpolating mineral distribution data using the inverse power-law distance method, specifically as follows: Ore body grade data was interpolated using the inverse power-law distance method: By calculating the distance between the unknown point and the known data point, and using the power of the distance as the weight, the grade of the ore body of the known data point is weighted and averaged to obtain the grade estimate of the unknown point. By superimposing structural elements, including faults and folds, a three-dimensional ore body model is formed, which includes the ore body boundary and grade gradient.

7. The method for establishing a digital terrain model for mineral resource exploration based on multi-source data fusion as described in claim 1, characterized in that, In step 4, the digital elevation model, stratigraphic structure model, and ore body model are integrated using platforms including Unity and ArcGIS to obtain a digital terrain model of the mineral resources to be explored.

8. A system for establishing a digital terrain model for mineral resource exploration based on multi-source data fusion, utilizing the method for establishing a digital terrain model for mineral resource exploration based on multi-source data fusion as described in any one of claims 1-8, characterized in that, include: Multi-source data acquisition and preprocessing module: used to acquire multi-source data of the mineral resources to be explored, and to uniformly convert the multi-source data to the same coordinate system; wherein, the multi-source data includes: remote sensing data, ground measurement data, borehole data and mineral distribution data; Digital elevation model construction module: used to fuse the remote sensing data and ground measurement data, generate a digital elevation model using the Kriging interpolation method, and superimpose derived terrain factors including slope and aspect; The stratigraphic structure model and ore body model construction module is used to construct the stratigraphic interface and build the stratigraphic structure model based on borehole data and sequential Gaussian simulation, and to construct the ore body model by interpolating mineral distribution data using the distance power inverse ratio method. Digital terrain model construction module: used to integrate the digital elevation model, stratigraphic structure model and ore body model under a unified spatial framework to obtain the digital terrain model of the mineral resources to be explored.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the method for establishing a digital terrain model for mineral resource exploration based on multi-source data fusion as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a method for establishing a digital terrain model for mineral resource exploration based on multi-source data fusion as described in any one of claims 1-8.

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