CN114297929A - Machine learning-fused radial basis function curved surface complex ore body modeling method and device - Google Patents
Machine learning-fused radial basis function curved surface complex ore body modeling method and device Download PDFInfo
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Abstract
The invention provides a machine learning-fused radial basis function curved surface complex ore body modeling method and device, wherein the method comprises the following steps: resampling three-dimensional profile data and giving characteristic attributes to the ore body; training a stacking machine learning model; interpolation encryption of a sparse part with a complex section shape; extracting boundary points of the profile set and corresponding normal vectors; establishing an implicit field by a Hermite radial basis function; and visualizing the three-dimensional ore body model based on a traveling tetrahedral algorithm. The invention realizes the technical effect of establishing a continuous, reliable and smooth-surface three-dimensional ore body model for a curved surface complex ore body by utilizing the characteristic of learning and mining three-dimensional profile data by a stacking machine.
Description
Technical Field
The invention relates to the technical field of three-dimensional ore body modeling, in particular to a machine learning-fused radial basis function curved surface complex ore body modeling method.
Background
With the rise of digital mines and intelligent mining, the construction and expression of underground mineral resource models become a research hotspot. The establishment of the three-dimensional ore body model is the basis of digital mines, and the accurate and reasonable three-dimensional ore body model can three-dimensionally and visually express the spatial form and attribute distribution of the ore body, thereby providing reliable basis for the prediction, reserve evaluation and other aspects of underground resources for professionals.
The traditional three-dimensional ore body modeling adopts an artificial explicit modeling method, and the three-dimensional ore body model established by the method is unreasonable in shape distribution, unsmooth in surface and low in model quality. While some modeling cases use implicit modeling methods, most of their research is directed to borehole data, modeling of profile image-like data is less studied, and information mining of attribute features of ore body profiles is lacking. In addition, artificial intelligence algorithms such as machine learning play a great role in various fields at present, but application research of machine learning on ore body profiles is lacked at present. In addition, the existing three-dimensional ore body modeling method lacks the utilization of the attribute characteristics and the geometric characteristics of the profile data, and the quality of the established three-dimensional ore body model is limited.
Disclosure of Invention
The invention provides a radial base curve ore body modeling scheme fused with a stacking machine learning algorithm based on ore body profile data, and the method is particularly suitable for ore body modeling of complex profiles.
According to one aspect of the invention, a machine learning fused radial basis function surface complex ore body modeling method is provided, which comprises the following steps:
step 1, resampling profile data, and converting the profile data into three-dimensional discrete point data, wherein the three-dimensional discrete point data comprises information extracted from a corresponding layer after a profile is layered according to attributes;
step 2, training a stacking machine learning model by using three-dimensional discrete point data generated after section data is resampled;
step 3, carrying out interpolation encryption on the profile data by using the trained stacking machine learning model to construct a new profile set;
step 4, extracting boundary points and corresponding normal vectors from the new profile set, and analyzing Hermite radial basis function coefficients by using the boundary points and the corresponding normal vectors;
step 5, establishing a modeling area implicit field: determining the boundary of the modeling area, establishing three-dimensional grid nodes in the whole modeling area, and calculating the three-dimensional grid node values in the whole modeling area by using the analyzed Hermite radial basis function.
Step 6, visualization model: and carrying out visual operation on the implicit field of the modeling area by utilizing a traveling tetrahedron algorithm to establish a three-dimensional ore body model.
Preferably, in step 1, the resampling process on the profile data includes:
selecting section data by using a rectangular frame, extracting points at equal intervals in the rectangle, and acquiring x, y and z coordinate values of the points;
using the section boundary line as a boundary line, giving an ore body attribute value to a point inside the boundary line, and giving a non-ore body attribute value to a point outside the boundary line;
and adding corresponding attribute values to the extracted point data to form a three-dimensional discrete point data set.
Preferably, in step 2, the three-dimensional discrete point data set is divided into a training set and a test set, three basis classifiers of RF, KNN, and XGBoost in the first layer in the stacking machine learning model are trained first, and then the output of the three basis classifiers is used as training data of the second layer XGBoost to train the second layer XGBoost.
Preferably, in step 3, a plane to be interpolated in the space is selected, a range is selected by using a rectangle, the selected range of the plane is discretized into three-dimensional discrete points, the three-dimensional discrete points are predicted by using a trained stacking model, then a point set predicted as an ore body attribute is converted into profile data, and the converted profile data and the original profile data are fused into a new profile data set.
Preferably, step 5 comprises:
s51, determining the boundary of the whole modeling range, and establishing a bounding box capable of accommodating the whole modeling area;
s52, dividing the regular grids according to the precision requirement, and filling the bounding boxes of the whole modeling area with grid nodes;
s53, calculating a function value of a grid node of the modeling area by using the analyzed Hermite radial basis function, wherein the node with the function value less than 0 is in the ore body model, the node equal to 0 is on the boundary of the ore body model, and the node greater than 0 is outside the ore body model, so that the grid node is divided into an ore body node and a non-ore body node, and an implicit field of the modeling area is established.
Preferably, in step 6, each three-dimensional mesh is divided into 6 tetrahedrons, the mesh of the whole modeling area is divided according to the method, and the implicit field of the whole modeling area is visualized by using a traveling tetrahedron algorithm.
Compared with the prior art, the invention has the beneficial effects that:
the three-dimensional ore body modeling method is based on the stacking machine learning model, the profile data of the ore body is fully utilized, the attribute data and the geometric data of the ore body are simultaneously used in the stacking machine learning modeling, and the three-dimensional ore body model which is reliable, smooth in surface and high in model quality can be quickly established. Not only can ensure the quality of the model, but also can save manpower.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic diagram of dividing a rectangular discrete point into an ore body internal point and an ore body external point according to a section boundary in the embodiment of the invention.
FIG. 2 is a schematic diagram of discrete points having structures x, y, z and label according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a method for modeling a complex ore body with a radial basis function curved surface in combination with machine learning according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of performing interpolation encryption on a sparse place with a complex cross-sectional shape according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of an implicit field of a modeling area established in the embodiment of the present invention.
FIG. 6 is a schematic diagram of a three-dimensional ore body model established in an embodiment of the invention.
Detailed Description
Various exemplary embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
The first embodiment provides a machine learning-fused radial basis function surface complex ore body modeling method, which is shown in fig. 3 and specifically includes the following steps:
step 1: and (3) a data preprocessing stage. Resampling and normalizing an original profile of a curved surface complex ore body, and converting the original profile into three-dimensional discrete point data, wherein the three-dimensional discrete point data comprises information of corresponding layers extracted after the profile is layered according to attributes;
step 2: and a Stacking model training phase. Training a stacking machine learning model by using three-dimensional discrete point data generated after resampling of original profile data, wherein the stacking machine learning model comprises two layers of single machine learning models, the first layer of machine learning model comprises three base classifiers of RF, KNN and XGBoost, and the second layer of machine learning model comprises XGBost;
establishing a three-dimensional model stage by using HRBF (Hermite radial basis function):
and step 3: carrying out interpolation encryption on original profile data by using a trained stacking machine learning model, selecting a complex and sparse position of the original profile data, extracting a plane positioned between any two profiles, converting the plane into three-dimensional discrete points, predicting by using the stacking machine learning model, fusing the predicted profile data (namely a virtual profile) and the original profile data, and constructing a new profile set;
and 4, step 4: extracting boundary points and normal vectors corresponding to the boundary points from the new profile set, wherein the normal vectors corresponding to the profile boundary points point to the interior of the profile, and analyzing Hermite type radial basis function coefficients by using the boundary points and the corresponding normal vectors;
and 5: establishing a modeling area implicit field: determining the boundary of a modeling area, dividing a three-dimensional grid according to the precision requirement, wherein the grid length is the modeling precision, establishing three-dimensional grid nodes in the whole modeling area, and calculating the three-dimensional grid node values in the whole modeling area by using the analyzed Hermite radial basis function so as to establish an implicit field of the modeling area;
step 6: visualization model: and carrying out visual operation on the implicit field of the modeling area by utilizing a marching tetrahedron algorithm (Marchingtetrahedrons), and establishing a three-dimensional ore body model.
In step S1, the section data is resampled, and the extracting information by attribute hierarchy specifically includes:
firstly, statistical analysis is carried out on all the cross section sizes, a rectangle which can be used for frame selection of the maximum-shaped cross section in the same plane is selected, and frame selection is carried out on each cross section according to the size of the rectangle. Then, discretizing all rectangles, extracting a section boundary, and dividing the rectangular discrete points into ore body internal points and ore body external points according to the section boundary, as shown in fig. 1.
Adding an ore body attribute value to each three-dimensional discrete point according to the division condition to form discrete points with the structures of x, y, z and label, as shown in FIG. 2.
In step S2, the specific process steps of training the stacking machine learning model using the three-dimensional discrete point data are as follows:
s21, dividing an original data set (discrete three-dimensional point data) into a training set (accounting for 80%) and a testing set (accounting for 20%);
s22, dividing the training set into 5 parts, during each training, taking 1 part as a test set and the other 4 parts as the training set without repetition, and obtaining 5 classifiers according to the classification method;
s23, for each classification method in S22, 5 classifiers are used for predicting the extracted corresponding test set, and each prediction result (namely, classification probability) is sequentially and vertically spliced to serve as the feature of the meta classifier; predicting the test set divided in the step S21, and obtaining another characteristic of the meta classifier based on the prediction result;
s24, obtaining two features provided by each classifier through S23, wherein the 3 base classifiers have 6 features in total, the 6 features are used as training features of a meta classifier, a corresponding real class is used as a training label, and a test set of the meta classifier is a real label corresponding to a prediction result of the test set divided by S21 in S23;
and S25, training the meta classifier by using the training set and the test set obtained in the S24 to obtain a final stacking machine learning model.
In step S3, performing interpolation encryption on the profile data by using the trained stacking machine learning model, specifically including:
as shown in fig. 4, interpolation encryption is performed on a place where the profile is more complex and sparse. Firstly, selecting a place with complex and sparse profile data form, taking one plane in the middle of two original profiles as a plane to be interpolated in a selected area, selecting a rectangle at the same position in the step S1 for frame selection on the plane to be interpolated, converting the rectangle after frame selection into three-dimensional discrete points, predicting the three-dimensional discrete points by using a trained stacking machine learning model, extracting the three-dimensional discrete points with the predicted attributes of an ore body, converting the three-dimensional discrete points into new profile data, and fusing the new profile data and the original profile data to form a new profile set.
In step S4, establishing the hidden field of the modeling area is realized by the process shown in fig. 5, and the specific steps are as follows:
s41, determining the range of the modeling area, and establishing a maximum surrounding rule three-dimensional grid;
s42, determining the length, width and height of the three-dimensional grid of the unit according to the modeling precision requirement, wherein the length, width and height respectively correspond to the modeling precision in the horizontal direction and the modeling precision in the vertical direction; filling the whole modeling area with the unit three-dimensional grids, and extracting and storing node coordinate values of all the three-dimensional grids;
s43, extracting the coordinates of the boundary points of the profile set and normal vectors corresponding to the boundary points at equal intervals, and analyzing a Hermite radial basis function coefficient by using the coordinates and the normal vectors, wherein the formula is as follows:
where ψ (x) is a radial basis function to the third power of distance;
the number of the ith and the jth spatial discrete points are respectively, and i and j are the number of the discrete points; alpha is alphai，βiThe implicit function coefficients to be solved are respectively solved by the following formula:
wherein n isjNormal vectors corresponding to boundary points, denoted nx,ny,nz，
For the gradient operator, H is the Hessian operator, which can be calculated by the following formula:
wherein HijIs xi、xjPartial differential value of (d).
And S44, inputting the three-dimensional grid node coordinates extracted in S42 into the analyzed Hermite radial basis function to obtain a function value of each three-dimensional grid node, dividing the grid nodes into ore body nodes and non-ore body nodes according to the rule that the node with the node function value smaller than 0 is in the ore body model, the node with the node value equal to 0 is in the boundary of the ore body model, and the node with the node value larger than 0 is outside the ore body model, and establishing a modeling area implicit field.
In step S5, the hidden field of the region created in step S4 is visualized by using a marching tetrahedron algorithm, and the step of creating a three-dimensional ore body model is:
using a traveling tetrahedral algorithm to perform tetrahedral subdivision on all unit three-dimensional grids in the modeling area, then triangulating and rendering each surface of a tetrahedron according to function values of tetrahedral nodes, so that an implicit field of the whole modeling area is visualized as an explicit model, and finally establishing a three-dimensional ore body model, as shown in fig. 6.
The invention provides a machine learning-fused radial basis curved surface complex ore body modeling method, which has the following advantages:
1) establishing a three-dimensional ore body model based on the three-dimensional profile, fusing expert experience modeling, and being different from most of modeling methods utilizing original drilling data;
2) the method comprises the steps that a three-dimensional ore body model is built based on a stacking machine learning algorithm and a Hermite radial basis function, on one hand, attribute characteristics of a three-dimensional section can be mined by the stacking machine learning algorithm, a modeling data source is fully mined, the problem that the Hermite radial basis function is lack of data constraint is solved, and on the other hand, the three-dimensional ore body model can be automatically and quickly built by the Hermite radial basis function;
according to the method for modeling the complex ore body with the radial base curve surface and the machine learning integrated, provided by the invention, according to information provided by a three-dimensional profile, a Hermite type radial basis function is taken as a core, the attribute characteristics of the three-dimensional profile are mined by using a stacking machine learning algorithm, more constraints are provided for establishing an implicit field for the Hermite type radial basis function, and a three-dimensional ore body model with a smooth surface is quickly established by using the Hermite type radial basis function. The invention combines various algorithms, utilizes different functions of different algorithms on modeling data, fully utilizes the attribute characteristics and the geometric characteristics of the upper three-dimensional profile data, quickly establishes a reliable model and provides a new thought for three-dimensional ore body modeling.
According to a second aspect of the present application, the present invention further provides a machine-learning-fused radial basis function curved surface complex ore body modeling apparatus, where the machine-learning-fused radial basis function curved surface complex ore body modeling apparatus includes a memory, a processor, and a machine-learning-fused radial basis function ore body modeling program stored in the memory and executable on the processor, and when the machine-learning-fused radial basis function ore body modeling program is executed by the processor, the steps of the machine-learning-fused radial basis function curved surface complex ore body modeling method in any of the foregoing embodiments are implemented.
The radial basis function curved surface complex ore body modeling device integrating machine learning can operate in computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud server. The device for modeling the complex ore body with the radial basis function curved surface and fused with the machine learning can be operated by comprising a processor and a memory.
It will be understood by those skilled in the art that the example is merely an example of a fused machine-learned radial basis function surface complex ore body modeling apparatus, and does not constitute a limitation of a fused machine-learned radial basis function surface complex ore body modeling apparatus, and may include more or less components than a scale, or combine certain components, or different components, for example, the fused machine-learned radial basis function surface complex ore body modeling apparatus may further include an input-output device, a network access device, a bus, and the like. The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific-Integrated-Circuit (ASIC), a Field-Programmable Gate array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor, etc., the processor is a control center of the radial basis function curved surface complex ore body modeling device for fusion machine learning, and various interfaces and lines are utilized to connect various parts of the whole operational device of the radial basis function curved surface complex ore body modeling device for fusion machine learning. The memory can be used for storing the computer program and/or the module, and the processor realizes various functions of the radial basis function surface complex ore body modeling device fusing machine learning by running or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory may primarily include a program storage area and a data storage area, which may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart-Media-Card (SMC), a Secure-Digital (SD) Card, a flash-Card (flash-Card), at least one magnetic disk storage device, a flash memory device, or other volatile solid state storage device.
The above description is only exemplary of the present invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and scope of the present invention should be included in the present invention.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
Claims (8)
1. A modeling method for a complex ore body with a radial basis function curved surface, which is integrated with machine learning, comprises the following steps:
resampling the original profile data, and converting the processed data into three-dimensional discrete point data;
training a stacking machine learning model by using the three-dimensional discrete point data;
carrying out interpolation encryption on the original profile data by using a trained stacking machine learning model to construct a new profile set;
extracting boundary points and normal vectors corresponding to the boundary points from the profile set, and analyzing a Hermite radial basis function by using the boundary points and the normal vectors;
determining a modeling area boundary, establishing three-dimensional grid nodes in the whole modeling area, calculating the three-dimensional grid node values by utilizing an analyzed Hermite radial basis function, and establishing a modeling area implicit field based on the three-dimensional grid node values;
and carrying out visual operation on the implicit field of the modeling area by utilizing a traveling tetrahedron algorithm to establish a three-dimensional ore body model.
2. The method for modeling the complex ore body with the radial basis function curved surface fused with machine learning according to claim 1, wherein the resampling processing is carried out on the section data, and the method comprises the following steps:
selecting profile data by using a rectangular frame, extracting data points at equal intervals in the rectangle, and acquiring coordinate values of the data points in the x, y and z directions;
using the section boundary line as a boundary line, giving an ore body attribute value to a point inside the boundary line, and giving a non-ore body attribute value to a point outside the boundary line;
and giving the data points of the ore body attribute value and the non-ore body attribute value to form a three-dimensional discrete data set.
3. The method for modeling the radial basis function curved surface complex ore body fused with machine learning according to claim 1, wherein the step of training the stacking machine learning model by using the three-dimensional discrete point data comprises the following steps:
training three base classifiers of RF, KNN and XGboost in a first layer in a stacking machine learning model by using the three-dimensional discrete point data;
and (4) taking the output of the three base classifiers as training data of a second layer XGboost in the stacking machine learning model, and training the second layer XGboost to obtain the stacking machine learning model.
4. The method for modeling the complex ore body with the radial basis function curved surface by fusing machine learning according to claim 1, wherein the method for interpolating and encrypting the original profile data by using the trained stacking machine learning model to construct a new profile set comprises the following steps:
taking the middle plane of any two original sections as a plane to be interpolated in a section area where original section data is complex in shape and sparse, and converting the plane to be interpolated into new three-dimensional discrete point data;
predicting the new three-dimensional discrete point data by using a trained stacking machine learning model, and extracting the prediction attribute of the prediction result as the three-dimensional discrete point of the ore body;
and converting the three-dimensional discrete points of the ore body into new profile data, and fusing the new profile data and the original profile data to construct a new profile set.
5. The method for modeling a complex ore body with a curved surface based on radial basis functions fused with machine learning according to claim 1, wherein the method for modeling the complex ore body with the curved surface based on radial basis functions fused with machine learning comprises the steps of determining the boundary of a modeling area, establishing three-dimensional grid nodes in the whole modeling area, calculating the node values of the three-dimensional grid nodes by using the resolved radial basis functions of Hermite type, and establishing an implicit field of the modeling area based on the node values of the three-dimensional grid nodes, and comprises the following steps:
determining the boundary of the whole modeling range, and establishing a bounding box capable of containing the whole modeling area;
dividing a regular grid according to the precision requirement, and filling the bounding boxes of the whole modeling area with grid nodes;
calculating a function value of a grid node of the modeling area by using the analyzed Hermite radial basis function;
dividing the grid nodes into ore body nodes and non-ore body nodes based on the function values;
and establishing a modeling area implicit field based on the divided ore body nodes and the non-ore body nodes.
6. The method according to claim 5, wherein the criterion for dividing the mesh nodes into the mineral nodes and the non-mineral nodes based on the function values comprises:
the node with the function value smaller than 0 is positioned in the ore body model;
the node with the function value of 0 is positioned on the boundary of the ore body model;
nodes with function values greater than 0 are outside the ore body model.
7. The method for modeling the complex ore body with the radial basis function curved surface fused with machine learning according to claim 1, wherein the hidden field of the modeling area is visualized by using a traveling tetrahedron algorithm, and the building of the three-dimensional ore body model comprises the following steps:
dividing each three-dimensional grid into 6 tetrahedrons, dividing the grid of the whole modeling area according to the method, and visualizing the hidden field of the whole modeling area by utilizing a traveling tetrahedron algorithm, thereby forming a three-dimensional ore body model.
8. A fusion machine-learned radial basis function surface complex ore body modeling apparatus comprising a memory, a processor, and a fusion machine-learned radial basis function ore body modeling program stored on the memory and executable on the processor, the fusion machine-learned radial basis function ore body modeling program when executed by the processor implementing the steps of the fusion machine-learned radial basis function surface complex ore body modeling method of any one of claims 1 to 7.
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