Optimization method for geological modeling construction suitable for open-pit mine geological disaster exploration

Abstract

The present application relates to the technical field of geological modeling, in particular to a geological modeling construction optimization method suitable for open-pit mine geological disaster exploration. The method comprises the following steps: collecting and integrating geological data and geophysical data, determining reasonable drilling positions by using scientific geological inversion analysis method, combining drilling sampling data to construct ore body model, and using GIS system to construct geological profile map to obtain accurate contour parameters, and then constructing original geological three-dimensional model. At the same time, the Bayesian method is introduced to calculate the probability distribution of stratum, and the original model is labeled and compiled to obtain the final high-precision three-dimensional geological model. The method can effectively overcome the shortcomings of traditional methods, improve the accuracy, reliability and scientificity of geological modeling, and provide more accurate and comprehensive geological information support for the prediction, evaluation and prevention of open-pit mine geological disasters.

Description

Technical Field

[0001] This invention relates to the field of geological modeling technology, and specifically to a geological modeling construction and optimization method applicable to geological hazard exploration in open-pit mines. Background Technology

[0002] In the process of open-pit mining, geological modeling, as an important means of visualizing and quantifying geological information, can intuitively present the spatial morphology, interrelationships and attribute characteristics of underground geological bodies in the mine, and provide a scientific basis for the prediction, assessment and prevention of geological disasters in the mine.

[0003] In traditional geological modeling methods, on the one hand, the determination of the location of engineering geological boreholes or exploration boreholes is usually based on experience or simple geological inference. This subjective decision-making approach can easily lead to unreasonable borehole layout, with some key geological areas not being effectively explored while some non-critical areas are over-drilled. This not only increases exploration costs but also fails to obtain comprehensive and accurate geological information, thus affecting the accuracy of the geological model. On the other hand, there are currently many 3D geological modeling software programs on the market. When dealing with complex geological structures, the automated modeling functions of these software programs often cannot meet actual needs, requiring a lot of manual intervention and adjustments. This not only increases the workload of modeling but also easily introduces human error, resulting in low accuracy of the 3D geological model.

[0004] Therefore, there is an urgent need to apply geological modeling and optimization methods for open-pit mine geological hazard exploration to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide an optimized geological modeling method applicable to open-pit mine geological hazard exploration. This method aims to overcome the shortcomings of traditional methods, improve the accuracy, reliability, and scientific nature of geological modeling, and provide more accurate and comprehensive geological information support for the prediction, assessment, and prevention of open-pit mine geological hazards. It has significant theoretical and practical application value.

[0006] Geological modeling construction and optimization methods applicable to open-pit mine geological hazard exploration include:

[0007] Collect geological and geophysical data of the open-pit mine to be modeled, perform geological inversion analysis on the selected engineering geological borehole locations or the selected exploration borehole locations based on the geological and geophysical data, generate analysis results, and determine the engineering geological borehole locations or exploration borehole locations based on the analysis results.

[0008] Drilling is carried out based on the location of engineering geological boreholes or exploration boreholes, and geological sampling data is collected in the vertical direction of the boreholes. Based on the geological sampling data, a ore body model is constructed.

[0009] Based on the GIS system, construct a cross section perpendicular to the orientation of the open-pit mine slope to be modeled and a longitudinal section parallel to the orientation of the open-pit mine slope to be modeled. Obtain the contour parameters of the cross section and the longitudinal section. Based on the contour parameters, determine the relative position parameters between the ore body model and the ground surface contour line. Based on the ore body model, the relative position parameters, the cross section and the longitudinal section, construct the original geological three-dimensional model.

[0010] The probability distribution of the surface layer of the open-pit mine to be modeled is calculated based on the Bayesian method.

[0011] The original three-dimensional geological model is annotated based on the probability distribution of the ground strata to obtain an elementary three-dimensional geological model. The elementary three-dimensional geological model is then compiled and drawn in combination with geological mapping data to obtain the final three-dimensional geological model.

[0012] Furthermore, the geological data includes the structural features, stratigraphic distribution patterns, and fault system characteristics of the area where the open-pit mine to be modeled is located, and the geophysical data includes the seismic reflection standard layer of the open-pit mine to be modeled. The main seismic reflection wave groups are manually identified and tracked for comparison to determine the geological properties of the reflection standard layer of the open-pit mine to be modeled.

[0013] Furthermore, based on geological and geophysical data, geological inversion analysis is performed on the selected engineering geological borehole locations or the selected exploration borehole locations to generate analysis results. The specific process includes the following steps:

[0014] A reservoir sensitivity simulation experiment was conducted to construct the stratigraphic interface based on structural features, stratigraphic distribution patterns, fault system characteristics, and geological attributes. When acid enters the reservoir, it reacts with certain minerals in the reservoir, producing gels, precipitates, or releasing particles, leading to a decrease in reservoir permeability. When alkaline working fluid enters the reservoir, it reacts with reservoir rocks or reservoir fluids, producing precipitates and reducing reservoir permeability. The reservoir permeability ST was determined based on the simulation experiment results.

[0015] Constrained sparse pulse inversion is performed on the selected engineering geological borehole locations or the selected exploration borehole locations to obtain the initial model for geostatistical inversion, as well as the wavelet and signal-to-noise ratio required for inversion, and the seismic data weights used to set the lateral variation function; stochastic inversion parameters are set, including histogram analysis, curves describing the distribution range of reservoir variation, probability distribution functions describing the distribution range of specific rock elastic parameters, lateral variation functions describing the formation size obtained from the deterministic inversion results, and vertical variation functions describing the formation thickness;

[0016] The probability distribution function of a specific range of rock elastic parameters is compared with a preset probability distribution function to determine the rock elastic parameter TX;

[0017] The reservoir permeability (ST) and rock elastic parameter (TX) are dimensionless, and their numerical values ​​are used as inputs into the correlation formula to calculate the drilling difficulty index of the candidate engineering geological borehole location or the candidate exploration borehole location. And the drilling difficulty index The analysis result is recorded, and the correlation formula is as follows:

[0018] ;

[0019] in, , For weights.

[0020] Furthermore, determining the location of engineering geological boreholes or exploration boreholes based on the analysis results specifically includes the following processes:

[0021] Load the drilling difficulty index threshold, which is set based on historical drilling experience. Determine whether the drilling difficulty index of the candidate engineering geological drilling location or the candidate prospective drilling location exceeds the drilling difficulty index threshold. If it does, discard the candidate engineering geological drilling location or the candidate prospective drilling location. If not, determine the candidate engineering geological drilling location or the candidate prospective drilling location as the engineering geological drilling location or the prospective drilling location.

[0022] Furthermore, the specific process of constructing a ore body model based on geological sampling data includes the following:

[0023] Step 1: Based on the geometric shape characteristics of the geological sampling data, construct sampling points for the upper and lower layers of the ore body respectively.

[0024] Step 2: An implicit modeling method based on radial basis function interpolation is used to obtain interpolation constraints for the upper and lower layers of the ore body;

[0025] Step 3: By solving the interpolation equations separately, the implicit functions of the upper and lower layers are obtained. The implicit functions of the upper and lower layers are then combined to construct modeling rules that satisfy the geometric characteristics of the ore body.

[0026] Step 4: Based on the Boolean combination constraints based on the symbolic distance field, construct the ore body thickness constraints and ore body boundary constraints, and adjust the modeling results according to the prior geological rules;

[0027] Step 5: Using the implicit surface reconstruction method, extract the isosurfaces of the upper and lower bedding planes of the ore body, respectively.

[0028] Step six: After combining the upper and lower layers using polygon Boolean operations, a complete ore body model is obtained.

[0029] Furthermore, determining the relative positional parameters between the ore body model and the surface contour line based on the contour parameters specifically includes the following process:

[0030] A rectangular coordinate system is established with the centroid of the polygon corresponding to the outline of the stratum where the ore body model is located as the origin. The stratum where the ore body model is located is projected to obtain the projection plane. The position of the projection point of the ore body model on the projection plane is obtained. The minimum distance between the projection point position and the outline of the stratum is calculated, and the minimum distance is used as the relative position parameter.

[0031] Furthermore, the process of calculating the probability distribution of the surface strata of the open-pit mine to be modeled based on the Bayesian method includes the following steps:

[0032] make Let be the set of stratigraphic boundary elevation values ​​for the location, where This represents the total number of elevation values. This represents the stratigraphic boundary elevation value at the j-th location;

[0033] Update random variable : ;

[0034] in, Indicates by Provided about The posterior probability density function; To be independent The proportionality coefficient; Representing the prior probability density function for quantization Prior information; Let be the likelihood function, representing the given... Occasionally The possibility;

[0035] ;

[0036] in, , For position estimation error coefficient of variation, location estimation error express and The difference between them This represents the average elevation value obtained by averaging all the stratigraphic boundary elevation values ​​in the set of stratigraphic boundary elevation values ​​for a given location.

[0037] Calculate the posterior failure probability of the leaf node. :

[0038] ;

[0039] in, The function is used to evaluate the stability of the stratigraphic boundary. Indicates when hour, Take 1, otherwise Set to 0;

[0040] posterior failure probability Let denot be the probability distribution of the surface layer of the open-pit mine to be modeled.

[0041] Furthermore, the preliminary three-dimensional geological model is compiled by combining geological mapping data to obtain the final three-dimensional geological model. The specific process includes the following steps:

[0042] Geological mapping data includes surface geological information, magnetotelluric sounding data, and two-dimensional geology. Firstly, the deep information reflected in the two-dimensional geology and magnetotelluric sounding data is used as the primary basis. Combined with the zoning and grading principles of surface geological information, a holistic approach to probing the regional fault system is constructed. Based on the surface geological information, the surface attitude and strike of the fault are determined, and its apparent dip angle on the profile is determined according to the angle between the profile line and the fault strike. Then, based on the deep information reflected in the two-dimensional geology and magnetotelluric sounding data, the cutting depth of the fault is determined. Finally, the deep borehole information adjacent to the fault is used for verification and calibration, refining the final three-dimensional geological model.

[0043] Compared to existing solutions, the beneficial effects achieved by this invention are:

[0044] This invention, through the comprehensive collection and integration of geological and geophysical data, employs scientific geological inversion analysis methods to determine reasonable borehole locations, constructs an orebody model based on borehole sampling data, and utilizes a GIS system to build geological profile maps to obtain accurate contour parameters, thereby constructing an original three-dimensional geological model. Simultaneously, it introduces Bayesian methods to calculate the probability distribution of strata, annotating and compiling the original model to obtain a final high-precision three-dimensional geological model. This effectively overcomes the shortcomings of traditional methods, improves the accuracy, reliability, and scientific rigor of geological modeling, and provides more accurate and comprehensive geological information support for the prediction, assessment, and prevention of geological hazards in open-pit mines.

[0045] Furthermore, by obtaining the contour parameters of the cross-section and longitudinal section, and determining the relative position parameters between the ore body model and the ground stratum contour line based on these parameters, the relative positional relationship between the ore body and the surrounding strata in three-dimensional space can be accurately described, avoiding the model deviation caused by inaccurate estimation of the relative positional relationship in traditional methods.

[0046] Finally, the probability distribution of the ground strata in the open-pit mine to be modeled is calculated based on the Bayesian method. The Bayesian method can comprehensively consider prior information and observation data, and quantify the uncertainty of the ground strata through probability statistics. It calculates the probability distribution of the ground strata appearing in different locations, which overcomes the limitation of treating geological parameters as deterministic values ​​in traditional methods and can more realistically reflect the actual range of changes in underground geological conditions. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0048] Figure 1 This is a flowchart illustrating a geological modeling construction and optimization method applicable to open-pit mine geological hazard exploration according to an embodiment of the present invention.

[0049] Figure 2 This is a flowchart illustrating another geological modeling construction and optimization method applicable to open-pit mine geological hazard exploration according to an embodiment of the present invention.

[0050] Figure 3 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more exemplary embodiments. Numerous specific details are provided in the following description to give a full understanding of exemplary embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, steps, etc., can be employed. In other instances, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0053] This embodiment provides a geological modeling construction and optimization method applicable to geological hazard exploration in open-pit mines. Figure 1This is a flowchart illustrating the workflow of a geological modeling construction and optimization method applicable to open-pit mine geological hazard exploration, as described in this invention. Figure 1 As shown, the method includes the following steps:

[0054] Step S101: Collect geological and geophysical data of the open-pit mine to be modeled, perform geological inversion analysis on the selected engineering geological borehole locations or the selected exploration borehole locations based on the geological and geophysical data, generate analysis results, and determine the engineering geological borehole locations or the exploration borehole locations based on the analysis results.

[0055] The geological data includes the structural features, stratigraphic distribution patterns, and fault system characteristics of the area where the open-pit mine to be modeled is located. Data acquisition can include: designing a reasonable survey route to cover the area of ​​the open-pit mine to be modeled; and having investigators observe and record the rock's attitude (strike, dip, and dip angle), fold morphology, and joint and fracture development along the route. For example, in mountainous open-pit mines, arranging survey routes along ridges and valleys allows for better observation of fold changes in the strata and the collection of geological reports compiled by previous geological exploration and mineral development studies of the area. These reports typically contain detailed geological structural analyses, maps, and data, such as regional geological structural outline maps and tectonic evolution histories.

[0056] Geophysical data includes seismic reflection standard layers of the open-pit mine to be modeled. Major seismic reflection wave groups were manually identified and compared to determine the geological properties of the reflection standard layers of the open-pit mine to be modeled.

[0057] Lithological comparison: The seismic reflection standard layer is compared with the lithology of the formations revealed by drilling. Based on drilling core and logging data, the lithology of the formations corresponding to the reflection standard layer is determined. For example, if a reflection standard layer is found to correspond to a thick sandstone layer revealed in the well, then the geological attribute of the reflection standard layer can be determined to be sandstone.

[0058] Lithological assemblage analysis: This considers the lithological assemblage relationship between the strata above and below the reflection standard layer. Different lithological assemblages produce different reflection characteristics. By analyzing the characteristics of the reflection wave group and the lithological assemblage in the drilling data, the geological properties of the reflection standard layer can be determined more accurately. For example, when the reflection standard layer is surrounded by interbedded sandstone and mudstone, its reflection characteristics differ from those of a single lithological stratum. Combining this with drilling data, it can be determined that this is a specific reflection interface within the interbedded sandstone and mudstone.

[0059] Thickness determination: A time-depth curve is established using drilling data to convert the time thickness of the seismic reflection standard layer into its actual depth thickness. By measuring the time interval of the reflection standard layer on the seismic profile and combining it with the time-depth curve, the formation thickness of the reflection standard layer can be accurately calculated.

[0060] Step S102: Drill holes based on the engineering geological borehole location or the exploration borehole location, and collect geological sampling data in the vertical direction of the boreholes. Construct an ore body model based on the geological sampling data.

[0061] Step S103: Based on the GIS system, construct a cross section perpendicular to the orientation of the open-pit mine slope to be modeled and a longitudinal section parallel to the orientation of the open-pit mine slope to be modeled, obtain the contour parameters of the cross section and the longitudinal section, determine the relative position parameters between the ore body model and the ground surface contour line based on the contour parameters, and construct the original geological three-dimensional model based on the ore body model, the relative position parameters, the cross section and the longitudinal section.

[0062] The process of constructing cross-sectional and longitudinal profiles perpendicular to the slope of the open-pit mine to be modeled, based on a GIS (Geographic Information System), includes: drawing cross-sectional lines perpendicular to the slope's direction, and determining the spacing and number of these lines based on the mine's actual conditions and modeling requirements. Generally, cross-sectional lines are densely distributed at key locations on the slope (such as the slope crest, toe, and transition points) and in areas with significant geological variations. For the longitudinal profiles, lines are drawn parallel to the slope's direction. Similarly, the location and number of longitudinal profiles are determined based on the mine's characteristics and modeling requirements to ensure a comprehensive reflection of the geological and topographical features along the slope's direction. Using the GIS system's profile analysis function, relevant information is extracted from DEM data, geological map data, and borehole data along the determined cross-sectional and longitudinal profiles. Extracted information includes surface elevation, stratigraphic interface elevation, lithological codes, and orebody boundaries. Profile drawing: The extracted data is drawn into cross-sections and longitudinal sections according to a certain scale and map specifications. The stratigraphy, lithology, ore body location, topographic elevation and other information are accurately marked on the map so that the profile can clearly reflect the geological and topographic features along the profile line.

[0063] The process involves inputting the ore body model, relative location parameters, cross-sections, and longitudinal sections into the Surpac software to construct the original three-dimensional geological model.

[0064] Step S104: Calculate the probability distribution of the surface layer of the open-pit mine to be modeled based on the Bayesian method;

[0065] Step S105: Based on the probability distribution of the geological strata, the original three-dimensional geological model is annotated to obtain a preliminary three-dimensional geological model. The preliminary three-dimensional geological model is then compiled and drawn in combination with geological mapping data to obtain the final three-dimensional geological model.

[0066] In summary, by fully collecting and integrating geological and geophysical data, employing scientific geological inversion analysis methods to determine reasonable borehole locations, constructing an orebody model based on borehole sampling data, and utilizing a GIS system to build geological profile maps to obtain accurate contour parameters, a preliminary 3D geological model is constructed. Simultaneously, the Bayesian method is introduced to calculate the probability distribution of strata, and the preliminary model is labeled and mapped to obtain a final high-precision 3D geological model. This approach effectively overcomes the shortcomings of traditional methods, improves the accuracy, reliability, and scientific rigor of geological modeling, and provides more accurate and comprehensive geological information support for the prediction, assessment, and prevention of geological hazards in open-pit mines.

[0067] In some embodiments, Figure 2 This is a flowchart illustrating another geological modeling construction and optimization method applicable to open-pit mine geological hazard exploration according to an embodiment of the present invention, as shown below. Figure 2 As shown, based on geological and geophysical data, a geological inversion analysis is performed on the selected engineering geological borehole locations or the selected exploration borehole locations. The specific analysis results include the following processes:

[0068] Step S201: Construct a reservoir sensitivity simulation experiment of the stratigraphic interface based on structural features, stratigraphic distribution patterns, fault system characteristics, and geological attributes, and determine the reservoir permeability ST based on the simulation experiment results;

[0069] Specifically, when acid enters the reservoir in the simulation experiment, it reacts with certain minerals in the reservoir to produce gels, precipitates, or release particles, resulting in a decrease in reservoir permeability. When alkaline working fluid enters the reservoir, it reacts with reservoir rocks or reservoir fluids to produce precipitates and reduce the reservoir's seepage capacity.

[0070] Step S202: Perform constrained sparse pulse inversion on the selected engineering geological borehole location or the selected exploration borehole location to obtain the initial model for geostatistical inversion, as well as the wavelet and signal-to-noise ratio required for inversion, and the seismic data weights used to set the transverse variation function;

[0071] Step S203: Set stochastic inversion parameters, including histogram analysis, curves describing the distribution range of reservoir variation, probability distribution functions describing the distribution range of specific rock elastic parameters, lateral variation functions describing the formation size obtained from deterministic inversion results, and vertical variation functions describing the formation thickness.

[0072] Step S204: Compare the probability distribution function of the specific rock elastic parameter distribution range with the preset probability distribution function to determine the rock elastic parameter TX;

[0073] The process involves calculating the probability density function (PDF) based on specific rock elastic parameter data and plotting the curve. Similarly, a curve representing a preset probability distribution function is also plotted. The curves are then visually compared: observing the shape, peak position, and distribution range of the two curves. If the two curves have similar shapes, close peak positions, and significant overlap in their distribution ranges, it indicates that the probability distribution of the specific rock elastic parameter closely matches the preset distribution.

[0074] Step S205: Dedimensionalize the reservoir permeability ST and rock elastic parameter TX, and use their numerical values ​​to calculate the drilling difficulty index of the candidate engineering geological borehole location or the candidate exploration borehole location using the correlation formula. And the drilling difficulty index The analysis result is recorded, and the correlation formula is as follows:

[0075] ;

[0076] in, , As weight;

[0077] Preferably, the rock elastic parameter (TX) reflects the rock hardness; the higher the value, the more difficult it is to drill.

[0078] Reservoir permeability (ST): reflects the fluidity of formation fluids. The higher the value, the easier it is to drill (e.g., loose sandstone is easier to drill than hard granite).

[0079] The following criteria can be used as a reference for setting weights: The value is 0.6 (with a higher weighting for permeability): because permeability directly affects the risk of drilling fluid loss and drilling efficiency, and has a significant impact on the difficulty. 0.3 (lower weight for elasticity parameter): While rock hardness is important, modern drill bit technology can partially offset its influence.

[0080] Furthermore, a drilling difficulty index threshold is loaded, which is set based on historical drilling experience. It is determined whether the drilling difficulty index of the candidate engineering geological drilling location or the candidate prospective drilling location exceeds the drilling difficulty index threshold. If so, the candidate engineering geological drilling location or the candidate prospective drilling location is discarded. If not, the candidate engineering geological drilling location or the candidate prospective drilling location is determined as the engineering geological drilling location or the prospective drilling location.

[0081] In some embodiments, constructing a ore body model based on geological sampling data specifically includes the following process:

[0082] Step 1: Based on the geometric shape characteristics of the geological sampling data, construct sampling points for the upper and lower layers of the ore body respectively.

[0083] Specifically, based on borehole sampling data, sampling points are distinguished between the top (upper) and bottom (lower) layers of the ore body, and outliers (such as erroneous data points caused by drilling deviation) are removed to ensure data quality.

[0084] Step 2: An implicit modeling method based on radial basis function interpolation is used to obtain interpolation constraints for the upper and lower layers of the ore body;

[0085] Specifically, the choice of interpolation method

[0086] Radial basis functions (such as Gaussian functions or multiple quadratic functions) are used as interpolation kernel functions to adapt to the nonlinear variation characteristics of the ore body interface.

[0087] Set interpolation smoothing parameters to balance fitting accuracy and model smoothness, and avoid overfitting.

[0088] Constraint Definition

[0089] Treat the sampling points as hard constraints (points that the interpolation must pass through precisely).

[0090] A geological trend surface (such as the dip angle of regional strata) is introduced as a soft constraint to guide the interpolation direction.

[0091] Step 3: By solving the interpolation equations separately, the implicit functions of the upper and lower layers are obtained. The implicit functions of the upper and lower layers are then combined to construct modeling rules that satisfy the geometric characteristics of the ore body.

[0092] Specifically, implicit function generation involves obtaining implicit function expressions for the upper and lower layers of the ore body through least squares or matrix solving. The function output is a scalar field, with positive values ​​representing the outside of the ore body, negative values ​​representing the inside, and zero values ​​corresponding to the ore body interface.

[0093] Combination rule design

[0094] The functions of the upper and lower levels are combined through logical operations (such as "AND" and "OR") to ensure the geometric closure of the ore body, and geological rules such as faults or lithological contact zones are added to correct the local morphology of the functions.

[0095] Step 4: Based on the Boolean combination constraints based on the symbolic distance field, construct the ore body thickness constraints and ore body boundary constraints, and adjust the modeling results according to the prior geological rules;

[0096] Thickness constraint construction

[0097] Calculate the symbolic distance field between the upper and lower bedding planes to ensure that the ore body thickness conforms to the exploration data (such as the minimum mineable thickness).

[0098] Automatically shrink or expand areas that do not meet thickness requirements.

[0099] Boundary constraint optimization

[0100] Based on geological maps or geophysical interpretation results, manually define the boundary control line of the ore body.

[0101] Buffer analysis limits the outward extension of the ore body, preventing unreasonable extrapolation.

[0102] Artificial intervention adjustment

[0103] Geological engineers manually correct complex local areas (such as lenticular ore bodies) based on their experience.

[0104] Ensure that the model conforms to the regional metallogenic regularity (such as the integrated contact relationship of stratabound deposits).

[0105] Step 5: Using the implicit surface reconstruction method, extract the isosurfaces of the upper and lower bedding planes of the ore body, respectively.

[0106] Isosurface Extraction Algorithm

[0107] The MarchingCubes algorithm is used to extract the upper and lower triangular mesh surfaces from the implicit function scalar field.

[0108] Set a threshold for isosurface extraction (usually zero surface) and optimize the mesh to reduce redundant triangles.

[0109] Surface smoothing

[0110] Apply Laplacian smoothing or Taubin filtering to eliminate jagged edges on the mesh.

[0111] Preserve key feature points (such as the pinch-out points of ore bodies) to avoid excessive smoothing that leads to loss of detail.

[0112] Step six: After combining the upper and lower layers using polygon Boolean operations, a complete ore body model is obtained.

[0113] Layer-based combined operations

[0114] Import the upper and lower triangular meshes into 3D modeling software (such as Blender or MineSight).

[0115] Perform a Boolean difference operation (subtract the lower level from the upper level) to generate a closed ore body shell model.

[0116] Model Validation and Repair

[0117] Check the model for self-intersection or broken surfaces, and use mesh repair tools (such as MeshLab) to automatically stitch up the gaps.

[0118] Verify the rationality of the volume calculation (e.g., the error with the amount of explored resources is ≤5%).

[0119] Output standardization

[0120] Export to a common format (such as STL or DXF) that is compatible with mainstream mining software (Surpac, Leapfrog).

[0121] Additional attribute information (such as grade distribution and lithology code) is provided for subsequent analysis.

[0122] In some embodiments, determining the relative positional parameters between the ore body model and the ground stratum contour line based on the contour parameters specifically includes the following process:

[0123] A rectangular coordinate system is established with the centroid of the polygon corresponding to the outline of the stratum where the ore body model is located as the origin. The stratum where the ore body model is located is projected to obtain the projection plane. The position of the projection point of the ore body model on the projection plane is obtained. The minimum distance between the projection point position and the outline of the stratum is calculated, and the minimum distance is used as the relative position parameter.

[0124] In some embodiments, calculating the probability distribution of the surface layer of the open-pit mine to be modeled based on the Bayesian method specifically includes the following process:

[0125] make Let be the set of stratigraphic boundary elevation values ​​for the location, where This represents the total number of elevation values. This represents the stratigraphic boundary elevation value at the j-th location;

[0126] Update random variable : ;

[0127] in, Indicates by Provided about The posterior probability density function; To be independent The proportionality coefficient; Representing the prior probability density function for quantization Prior information; Let be the likelihood function, representing the given... Occasionally The possibility;

[0128] ;

[0129] in, , For position estimation error coefficient of variation, location estimation error express and The difference between them This represents the average elevation value obtained by averaging all the stratigraphic boundary elevation values ​​in the set of stratigraphic boundary elevation values ​​for a given location.

[0130] Calculate the posterior failure probability of the leaf node. :

[0131] ;

[0132] in, The function is used to evaluate the stability of the stratigraphic boundary. Indicates when hour, Take 1, otherwise Set to 0;

[0133] posterior failure probability Let denot be the probability distribution of the surface layer of the open-pit mine to be modeled.

[0134] Among them, the functional function for evaluating the stability of stratigraphic boundaries The acquisition process may include: determining the parameters in the function, which requires collecting geological, mechanical, and environmental parameters of the strata, as well as the corresponding stratum boundary stability (e.g., stable, basically stable, unstable, etc.); and function parameter calibration: based on the collected and processed data, appropriate methods are used to calibrate the parameters in the function. Common parameter calibration methods include: statistical analysis methods, for linear combination function, regression analysis and other methods can be used to determine the weight coefficients of each factor based on actual data. By establishing a regression equation, the error between the predicted value and the actual observed value is minimized, thereby obtaining the optimal parameter values. Machine learning methods, for nonlinear combination function, such as neural network models, training algorithms (e.g., backpropagation) can be used to train and optimize the model parameters. The actual data is divided into training and test sets. By continuously adjusting the model parameters, the prediction error of the model on the training set is minimized, and the model is validated on the test set to ensure its generalization ability. Based on the above process, a linear function can be obtained. When the stability of the stratum boundary is stable, its value is positive; when the stability of the stratum boundary is basically stable, its value is 0; and when the stability of the stratum boundary is unstable, its value is negative.

[0135] In some embodiments, annotating the original three-dimensional geological model based on the probability distribution of strata to obtain an elementary three-dimensional geological model includes: formulating reasonable annotation rules based on the results of probability distribution analysis. For example, a probability threshold can be set; when the probability of a stratum occurring at a certain location is greater than the threshold, that location is marked as an area where the stratum exists; when the probability is less than the threshold, it is marked as an uncertain area or an area where the stratum does not exist.

[0136] In some embodiments, the elementary three-dimensional geological model is compiled by combining geological mapping data to obtain the final three-dimensional geological model. The geological mapping data includes surface geological information, magnetotelluric sounding data, and two-dimensional geology. First, the deep information reflected by the two-dimensional geology and magnetotelluric sounding data is used as the main basis, and the overall depth-extending guideline for the regional fault system is constructed by dividing and classifying the surface geological information. Based on the surface geological information, the surface attitude and strike of the fault are determined, and the apparent dip angle on the profile is determined according to the angle between the profile line and the fault strike. Then, based on the deep information reflected by the two-dimensional geology and magnetotelluric sounding data, the cutting depth of the fault is determined. Finally, the deep borehole information adjacent to the fault is used to verify and calibrate it, and the final three-dimensional geological model is improved.

[0137] The above formulas are all dimensionless calculations, and the preset parameters in the formulas should be set by those skilled in the art according to the actual situation.

[0138] In some embodiments, Figure 3 This is a structural block diagram of an electronic device according to an embodiment of the present invention, such as... Figure 3 As shown, the electronic device includes a memory 301 and a processor 302. The memory 301 stores a computer program. When the computer program is executed by the processor 302, the processor 302 executes a geological modeling construction optimization method applicable to open-pit mine geological hazard exploration as described in any of the above embodiments.

[0139] The memory 301 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. The memory 301 has storage space 303 for program code 313 for performing any of the method steps described above. For example, the storage space 303 for program code may include individual program codes 313 for implementing the various steps in the methods described above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When this code is run by a computing device, it causes the device to perform the various steps in the geological modeling construction optimization method applicable to open-pit mine geological hazard exploration described above.

[0140] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0141] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0142] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0144] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0145] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A geological modeling construction and optimization method applicable to geological hazard exploration in open-pit mines, characterized in that the method... include: Collect geological and geophysical data of the open-pit mine to be modeled, perform geological inversion analysis on the selected engineering geological borehole locations or the selected exploration borehole locations based on the geological and geophysical data, generate analysis results, and determine the engineering geological borehole locations or exploration borehole locations based on the analysis results. Drilling is carried out based on the location of engineering geological boreholes or exploration boreholes, and geological sampling data is collected in the vertical direction of the boreholes. Based on the geological sampling data, a ore body model is constructed. Based on the GIS system, construct a cross section perpendicular to the orientation of the open-pit mine slope to be modeled and a longitudinal section parallel to the orientation of the open-pit mine slope to be modeled. Obtain the contour parameters of the cross section and the longitudinal section. Based on the contour parameters, determine the relative position parameters between the ore body model and the ground surface contour line. Based on the ore body model, the relative position parameters, the cross section and the longitudinal section, construct the original geological three-dimensional model. The probability distribution of the surface layer of the open-pit mine to be modeled is calculated based on the Bayesian method. The original three-dimensional geological model is annotated based on the probability distribution of the ground strata to obtain an elementary three-dimensional geological model. The elementary three-dimensional geological model is then compiled and drawn in combination with geological mapping data to obtain the final three-dimensional geological model.

2. The geological modeling construction and optimization method applicable to open-pit mine geological hazard exploration according to claim 1, characterized in that, Geological data includes the structural features, stratigraphic distribution patterns, and fault system characteristics of the area where the open-pit mine to be modeled is located. Geophysical data includes the seismic reflection standard layer of the open-pit mine to be modeled. The main seismic reflection wave groups are manually identified and tracked for comparison to determine the geological properties of the reflection standard layer of the open-pit mine to be modeled.

3. The geological modeling construction and optimization method applicable to open-pit mine geological hazard exploration according to claim 2, characterized in that, Based on geological and geophysical data, a geological inversion analysis is performed on the selected engineering geological borehole locations or the selected exploration borehole locations to generate analysis results. The specific process includes the following steps: A reservoir sensitivity simulation experiment was constructed based on structural features, stratigraphic distribution patterns, fault system characteristics, and geological properties to determine the reservoir permeability ST based on the simulation results. Constrained sparse pulse inversion is performed on the selected engineering geological borehole locations or the selected exploration borehole locations to obtain the initial model for geostatistical inversion, as well as the wavelet and signal-to-noise ratio required for inversion, and the seismic data weights used to set the transverse variation function; Set stochastic inversion parameters, including histogram analysis, curves describing the distribution range of reservoir variation, probability distribution functions describing the distribution range of specific rock elastic parameters, lateral variation functions describing the formation size obtained from deterministic inversion results, and vertical variation functions describing the formation thickness. The probability distribution function of a specific range of rock elastic parameters is compared with a preset probability distribution function to determine the rock elastic parameter TX; The reservoir permeability (ST) and rock elastic parameter (TX) are dimensionless, and their numerical values ​​are used as inputs into the correlation formula to calculate the drilling difficulty index of the candidate engineering geological borehole location or the candidate exploration borehole location. And the drilling difficulty index The analysis result is recorded, and the correlation formula is as follows: ； in, , For weights.

4. The geological modeling construction and optimization method applicable to open-pit mine geological hazard exploration according to claim 3, characterized in that, Determining the location of engineering geological boreholes or prospecting boreholes based on the analysis results includes the following process: Load the drilling difficulty index threshold, which is set based on historical drilling experience. Determine whether the drilling difficulty index of the candidate engineering geological drilling location or the candidate prospective drilling location exceeds the drilling difficulty index threshold. If it does, discard the candidate engineering geological drilling location or the candidate prospective drilling location. If not, determine the candidate engineering geological drilling location or the candidate prospective drilling location as the engineering geological drilling location or the prospective drilling location.

5. The geological modeling construction and optimization method applicable to open-pit mine geological hazard exploration according to claim 1, characterized in that, The process of constructing a ore body model based on geological sampling data includes the following steps: Step 1: Based on the geometric shape characteristics of the geological sampling data, construct sampling points for the upper and lower layers of the ore body respectively. Step 2: An implicit modeling method based on radial basis function interpolation is used to obtain interpolation constraints for the upper and lower layers of the ore body; Step 3: By solving the interpolation equations separately, the implicit functions of the upper and lower layers are obtained. The implicit functions of the upper and lower layers are then combined to construct modeling rules that satisfy the geometric characteristics of the ore body. Step 4: Based on the Boolean combination constraints based on the symbolic distance field, construct the ore body thickness constraints and ore body boundary constraints, and adjust the modeling results according to the prior geological rules; Step 5: Using the implicit surface reconstruction method, extract the isosurfaces of the upper and lower bedding planes of the ore body, respectively. Step six: After combining the upper and lower layers using polygon Boolean operations, a complete ore body model is obtained.

6. The geological modeling construction and optimization method applicable to open-pit mine geological hazard exploration according to claim 1, characterized in that, Determining the relative positional parameters between the ore body model and the ground stratum contour line based on contour parameters specifically includes the following process: A rectangular coordinate system is established with the centroid of the polygon corresponding to the outline of the stratum where the ore body model is located as the origin. The stratum where the ore body model is located is projected to obtain the projection plane. The position of the projection point of the ore body model on the projection plane is obtained. The minimum distance between the projection point position and the outline of the stratum is calculated, and the minimum distance is used as the relative position parameter.

7. The geological modeling construction and optimization method applicable to open-pit mine geological hazard exploration according to claim 1, characterized in that, The process of calculating the probability distribution of the ground strata in an open-pit mine to be modeled based on the Bayesian method includes the following steps: make Let be the set of stratigraphic boundary elevation values ​​for the location, where This represents the total number of elevation values. This represents the stratigraphic boundary elevation value at the j-th location; Update random variable : ; in, Indicates by Provided about The posterior probability density function; To be independent The proportionality coefficient; Representing the prior probability density function for quantization Prior information; Let be the likelihood function, representing the given... Occasionally The possibility; ； in, , For position estimation error coefficient of variation, location estimation error express and The difference between them This represents the average elevation value obtained by averaging all the stratigraphic boundary elevation values ​​in the set of stratigraphic boundary elevation values ​​for a given location. Calculate the posterior failure probability of the leaf node. : ； in, The function is used to evaluate the stability of the stratigraphic boundary. Indicates when hour, Take 1, otherwise Set to 0; posterior failure probability Let denot be the probability distribution of the surface layer of the open-pit mine to be modeled.

8. The geological modeling construction and optimization method applicable to open-pit mine geological hazard exploration according to claim 1, characterized in that, The process of compiling a preliminary three-dimensional geological model based on geological mapping data to obtain the final three-dimensional geological model includes the following steps: Geological mapping data includes surface geological information, magnetotelluric sounding data, and two-dimensional geology. Firstly, the deep information reflected in the two-dimensional geology and magnetotelluric sounding data is used as the primary basis. Combined with the zoning and grading principles of surface geological information, a holistic approach to probing the regional fault system is constructed. Based on the surface geological information, the surface attitude and strike of the fault are determined, and its apparent dip angle on the profile is determined according to the angle between the profile line and the fault strike. Then, based on the deep information reflected in the two-dimensional geology and magnetotelluric sounding data, the cutting depth of the fault is determined. Finally, the deep borehole information adjacent to the fault is used for verification and calibration, refining the final three-dimensional geological model.

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