CN117671160A

CN117671160A - Multi-source data collaborative coal seam modeling method, device, equipment and storage medium

Info

Publication number: CN117671160A
Application number: CN202410125020.5A
Authority: CN
Inventors: 温立文; 李建兵; 李健; 高一凡; 余优生; 袁金国; 吴�琳; 张嗣亮; 王露; 薛世伟; 林春蕾; 苏晓斐
Original assignee: Beijing Xingtiandi Information Technology Co Ltd
Current assignee: Beijing Xingtiandi Information Technology Co Ltd
Priority date: 2024-01-30
Filing date: 2024-01-30
Publication date: 2024-03-08
Anticipated expiration: 2044-01-30
Also published as: CN117671160B

Abstract

In the method, a correlation between high-precision data and low-precision contour data is established to cooperate with a kriging Jin Jitong equation to recalculate the height of the contour. Due to the participation of high-precision data, the systematic error of the contour line data is corrected to reach the precision required by modeling, so that the system can restrict the coal seam. And finally, independently modeling the top and bottom plates of the high-definition coal layer model by dividing the top and bottom plates into two parts during modeling, and supplementing the constrained coal layer top plate by using the elevation obtained by calculating the thickness data, so that the thickness change of the high-definition coal layer model is more accurate.

Description

Multi-source data collaborative coal seam modeling method, device, equipment and storage medium

Technical Field

The application relates to the technical field of interaction methods, in particular to a multi-source data collaborative coal seam modeling method, device, equipment and storage medium.

Background

Along with the rapid development of the three-dimensional coal seam modeling technology, the method is widely applied to the fields of urban construction, mining, geotechnical engineering and the like.

The patent CN116152461B discloses a multisource geological data modeling method based on drilling and high-precision point cloud data, firstly, a high-precision roadway three-dimensional model and a kilometer drilling coal seam layer model are constructed by utilizing the high-precision laser point cloud data, and the stratum model can be constrained by constructing the high-precision roadway model and the high-precision coal seam model based on a large number of high-precision roadway layer sites and coal seam roof and floor data points serving as virtual drilling, so that the rapid modeling of a real three-dimensional geological body is realized.

In addition to traditional geological data, high-precision roadway data are additionally introduced to carry out additional constraint on stratum data during modeling, and the overall precision of the model is effectively improved. However, the high-precision data has a limited radiation range, no data coverage area exists, and the model precision is reduced along with the increase of the distance from roadway data.

Disclosure of Invention

The application provides a multi-source data collaborative coal seam modeling method, device, equipment and storage medium, wherein in the method, on the basis of utilizing laser point cloud data, contour data of a coal seam bottom plate with limited precision but larger range and uniform distribution is additionally introduced, and the contour data is corrected by utilizing a collaborative kriging method, so that high-precision contour data which can be used for modeling is obtained to restrict areas without high-precision data coverage, and the overall precision of a model is greatly improved. The coverage of the data is covered by the full work area, and the correction of the data is based on statistical analysis of the known real data.

In a first aspect, an embodiment of the present application provides a method for modeling a coal seam with multi-source data collaboration, including:

acquiring laser point cloud data and geological data, wherein the geological data comprises drilling data, horizon data, contour data, coal seam thickness data and fault data;

correcting the contour line data by utilizing the laser point cloud data to obtain corrected contour line data;

obtaining virtual drilling data by utilizing the laser point cloud data and the corrected contour line data;

correcting the coal seam thickness data by utilizing the laser point cloud data to obtain corrected coal seam thickness data;

constructing a layer model according to the virtual drilling data, the horizon data and the corrected coal seam thickness data;

constructing a fault model by adopting the fault data, and generating stratum frame grids by utilizing the fault model and the layer model;

and inserting the layer model into the stratum frame grid to obtain a coal bed model, and adjusting the coal bed model by using the fault data.

In some embodiments, the contour data includes contour elevation data, and the step of correcting the contour data by using laser point cloud data to obtain corrected contour data includes:

Taking the laser point cloud data as first main data, taking the contour line data as first auxiliary data, and calculating the respective spatial variability and the spatial relationship between the two;

based on the respective spatial variability and the spatial relationship between the two, a cooperative kriging Jin Jitong equation is established, and the contour data is recalculated based on the cooperative kriging system equation so as to correct the contour data.

In some embodiments, before the step of calculating the respective spatial variability and the spatial relationship between the two using the laser point cloud data as the first main data and the contour line data as the first auxiliary data, the method further includes:

and preprocessing the laser point cloud data and the contour line data.

In some embodiments, the step of correcting the coal seam thickness data by using the laser point cloud data to obtain corrected coal seam thickness data includes:

taking the laser point cloud data as second main data, taking the coal seam thickness data as second auxiliary data, and calculating the respective spatial variability and the spatial relationship between the two;

based on the respective spatial variability and the spatial relationship between the two, a cooperative kriging Jin Jitong equation is established, and the coal seam thickness data is recalculated based on the cooperative kriging system equation so as to correct the coal seam thickness data.

In some embodiments, the step of inserting the bedding plane model into the stratigraphic framework grid to obtain a coal seam model comprises:

importing the layer model into the stratum frame grid to generate a rough three-dimensional grid;

and constructing the coal bed model by using the rough three-dimensional grid, the corrected coal bed thickness data and the model precision.

In a second aspect, an embodiment of the present application provides a multi-source data collaborative coal seam modeling apparatus, including:

the acquisition unit is used for acquiring laser point cloud data and geological data, wherein the geological data comprises drilling data, horizon data, contour data, coal seam thickness data and fault data;

the first correction unit is used for correcting the contour line data by utilizing the laser point cloud data to obtain corrected contour line data;

the first construction unit is used for obtaining virtual drilling data by utilizing the laser point cloud data and the corrected contour line data;

the second correction unit is used for correcting the coal seam thickness data by utilizing the corrected contour line data and the laser point cloud data to obtain corrected coal seam thickness data;

The second construction unit is used for constructing a layer model according to the virtual drilling data, the horizon data and the corrected coal seam thickness data;

the generation unit is used for constructing a fault model by adopting the fault data and generating stratum frame grids by utilizing the fault model and the layer model;

and the adjusting unit is used for inserting the layer model into the stratum frame grid to obtain a coal bed model, and adjusting the coal bed model by using the fault data to obtain the coal bed model.

In some embodiments, the contour data includes contour elevation data, and the first correction unit includes:

the first calculation unit is used for taking the laser point cloud data as first main data, taking the contour line data as first auxiliary data and calculating the respective space variability and the space relation between the two;

the first establishing unit is used for establishing a cooperative kriging Jin Jitong equation based on the respective space variability and the space relation between the two, and recalculating the contour elevation data based on the cooperative kriging system equation to correct the contour elevation data.

In some embodiments, the second correction unit includes:

the second calculation unit is used for taking the laser point cloud data as second main data, the coal seam thickness data as second auxiliary data, and calculating the respective space variability and the space relation between the two;

and the second establishing unit is used for establishing a cooperative kriging Jin Jitong equation based on the respective spatial variability and the spatial relationship between the two, and recalculating the coal seam thickness data based on the cooperative kriging system equation so as to correct the coal seam thickness data.

In a third aspect, embodiments of the present application provide a computer device, including a memory storing a computer program and a processor implementing the steps of the multi-source data collaborative coal seam modeling method when the computer program is executed.

In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the multi-source data collaborative coal seam modeling method.

In the above embodiment, a method, an apparatus, a device, and a storage medium for modeling a coal seam with cooperation of multi-source data are provided, where the method establishes a cooperative krill Jin Jitong equation for recalculating the elevation of a contour line according to the correlation between high-precision data and low-precision contour line data. Due to the participation of high-precision data, the systematic error of the contour line data is corrected to reach the precision required by modeling, so that the system can restrict the coal seam. And finally, independently modeling the top and bottom plates of the high-definition coal layer model by dividing the top and bottom plates into two parts during modeling, and supplementing the constrained coal layer top plate by using the elevation obtained by calculating the thickness data, so that the thickness change of the high-definition coal layer model is more accurate. The method comprises the following steps: acquiring laser point cloud data and geological data, wherein the geological data comprises drilling data, horizon data, contour data, coal seam thickness data and fault data; correcting the contour line data by utilizing the laser point cloud data to obtain corrected contour line data; obtaining virtual drilling data by utilizing the laser point cloud data and the corrected contour line data; correcting the coal seam thickness data by utilizing the laser point cloud data to obtain corrected coal seam thickness data; constructing a layer model according to the virtual drilling data, the horizon data and the corrected coal seam thickness data; constructing a fault model by adopting the fault data, and generating stratum frame grids by utilizing the fault model and the layer model; and inserting the layer model into the stratum frame grid to obtain a coal bed model, and adjusting the coal bed model by using the fault data.

Drawings

FIG. 1 illustrates a flow chart of a method of multi-source data collaborative coal seam modeling provided in accordance with some embodiments;

FIG. 2 shows a ZL028 synthetic histogram provided by an embodiment of the present application;

FIG. 3 illustrates a mining area large fault map provided by an embodiment of the present application;

FIG. 4 shows a schematic diagram of a fault roof and floor line provided by an embodiment of the present application;

FIG. 5 illustrates a corrected contour data map provided by an embodiment of the present application;

FIG. 6 illustrates a corrected coal seam thickness map provided by an embodiment of the present application;

FIG. 7 is a schematic diagram of virtual drilling data provided by an embodiment of the present application;

FIG. 8 illustrates a schematic diagram of corrected coal seam thickness data;

FIG. 9 is a schematic diagram of a planar fault digitizing result provided by an embodiment of the present application;

FIG. 10 illustrates a schematic architecture of a fault three-dimensional model provided by an embodiment of the present application;

FIG. 11 illustrates a schematic architecture of a stratigraphic framework grid provided in an embodiment of the present application;

FIG. 12 illustrates a schematic diagram of a three-dimensional coal seam model provided in an embodiment of the present application;

FIG. 13 is a schematic diagram of a model accuracy result provided by an embodiment of the present application;

FIG. 14 is a diagram showing a precision improvement value result provided by an embodiment of the present application;

FIG. 15 is a schematic diagram showing the overall effect comparison between a conventional method model and a model of the present application provided in an embodiment of the present application;

FIG. 16 is a schematic diagram showing a model partial comparison provided in an embodiment of the present application;

FIG. 17 is a schematic structural diagram of a multi-source data collaborative coal seam modeling apparatus according to an embodiment of the present disclosure;

fig. 18 shows a schematic device structure of a computer device according to an embodiment of the present application.

Detailed Description

For purposes of clarity and implementation of the present application, the following description will make clear and complete descriptions of exemplary implementations of the present application with reference to the accompanying drawings in which exemplary implementations of the present application are illustrated, it being apparent that the exemplary implementations described are only some, but not all, of the examples of the present application.

It should be noted that the brief description of the terms in the present application is only for convenience in understanding the embodiments described below, and is not intended to limit the embodiments of the present application. Unless otherwise indicated, these terms should be construed in their ordinary and customary meaning.

The terms "first," second, "" third and the like in the description and in the claims and in the above-described figures are used for distinguishing between similar or similar objects or entities and not necessarily for limiting a particular order or sequence, unless otherwise indicated. It is to be understood that the terms so used are interchangeable under appropriate circumstances.

The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a product or apparatus that comprises a list of elements is not necessarily limited to all elements explicitly listed, but may include other elements not expressly listed or inherent to such product or apparatus.

Along with the rapid development of the three-dimensional coal seam modeling technology, the method is widely applied to the fields of urban construction, mining, geotechnical engineering and the like. The patent CN116152461B discloses a multisource geological data modeling method based on drilling and high-precision point cloud data, firstly, a high-precision roadway three-dimensional model and a kilometer drilling coal seam layer model are constructed by utilizing the high-precision laser point cloud data, and the stratum model can be constrained by constructing the high-precision roadway model and the high-precision coal seam model based on a large number of high-precision roadway layer sites and coal seam roof and floor data points serving as virtual drilling, so that the rapid modeling of a real three-dimensional geological body is realized.

In order to solve the technical problems, the embodiment of the application provides a multi-source data collaborative coal seam modeling method, which additionally introduces the contour data of a coal seam bottom plate with limited precision but larger range and uniform distribution on the basis of utilizing laser point cloud data, and corrects the contour data by utilizing a collaborative kriging method to obtain high-precision contour data which can be used for modeling so as to restrict areas without high-precision data coverage, thereby greatly improving the overall precision of the model. The coverage of the data is covered by the full work area, and the correction of the data is based on statistical analysis of the known real data.

FIG. 1 illustrates a flow chart of a method of coal seam modeling that provides multi-source data collaboration in accordance with some embodiments. The method includes S100-S700.

S100, acquiring laser point cloud data and geological data, wherein the geological data comprise drilling data, horizon data, contour data, coal seam thickness data and fault data.

In the embodiment of the application, the GeoSLAM horizontal (three-dimensional laser scanner) hand-held mobile three-dimensional laser scanner can be used for realizing the direct acquisition of the three-dimensional information of the coal mine tunnel and the three-dimensional modeling of the fast replication entity target. In the embodiment of the application, the geological modeling system responds to the data scanning instruction and operates the scanner according to the data scanning instruction, wherein the scanner can be a GeoSLAM horizontal handheld mobile three-dimensional laser scanner, and can acquire underground laser point cloud data through the scanner to provide high-precision data for the geological modeling system. The data scanning instruction can be issued by a staff through a front-end application provided by the geological modeling system, and the staff can bind the scanner or access the scanner into the geological modeling system through the front-end application in advance, so that when the staff issues the data scanning instruction, the geological modeling system can operate the scanner to acquire laser point cloud data obtained by scanning of the scanner in response to the data scanning instruction.

In some embodiments, the geological data includes contour data, coal seam thickness data, and fault data, as well as borehole data and horizon data.

And S200, correcting the contour line data by utilizing the laser point cloud data to obtain corrected contour line data.

In the embodiment of the application, the contour line data of the coal seam floor with limited precision but larger range and uniform distribution is additionally introduced on the basis of utilizing laser point cloud data, and the contour line data is corrected by utilizing the collaborative kriging method, so that the contour line data with high precision, which can be used for modeling, is obtained to restrict the area without high-precision data coverage, thereby greatly improving the overall precision of the model.

And S300, obtaining virtual drilling data by using the laser point cloud data and the corrected contour line data.

In order to better improve the accuracy of the whole coal seam model, the virtual drilling data are determined by introducing modified contour line data based on laser point cloud data. The virtual well drilling is the well drilling with single geological attribute as the real well drilling, so that the coal bed model can be more attached to the geologic body. Thus, the geological modeling system can utilize the virtual drilling, so that the coal seam is restrained, and the influence caused by the quality and distribution of drilling data is reduced.

And S400, correcting the coal seam thickness data by using the corrected contour line data and the laser point cloud data to obtain corrected coal seam thickness data.

In the embodiment of the application, the corrected contour line data and the laser point cloud data are utilized to determine the top plate elevation of the coal bed, and further determine the coal bed thickness data. The top plate and the bottom plate of the high-definition coal layer model are divided into two parts for independent modeling, and the constraint coal layer top plate is supplemented by the elevation obtained by calculating the thickness data, so that the thickness change of the high-definition coal layer model is more accurate. The coal seam thickness data is the vertical distance between the roof of the coal seam and the floor of the coal seam.

S500, constructing a layer model according to the virtual drilling data, the horizon data and the corrected coal seam thickness data.

In the embodiment of the application, the geological modeling system utilizes a roadway model constructed by laser point cloud data to establish a plurality of virtual drilling wells, and utilizes the plurality of virtual drilling wells and coal seam related data to construct a layer model.

S600, constructing a fault model by adopting the fault data, and generating a stratum frame grid by utilizing the fault model and the layer model.

In order to fuse drilling, fault and other data more accurately, a geological modeling system digitizes a geological map to obtain fault data, builds a fault model according to the fault data, and then builds a corner grid model according to a given grid size to form a three-dimensional stratum frame. In the embodiment of the application, a geological modeling system acquires a fault map, and fault data in the fault map is adopted to construct a fault model. By processing the fault map, fault data information such as a fault top line, a fault bottom line, a coal seam top line, a coal seam bottom line and the like can be obtained from the fault map, and a fault model is constructed by using the fault data information, so that the follow-up geological modeling system can integrate high-precision fault data into the coal seam model. Then, the geological modeling system generates stratum frame grids by using the fault model, so that the geological modeling system can construct the fault model by using fault data, further construct a corner grid model to obtain three-dimensional stratum frame grids, reduce human intervention in a modeling process, simplify the construction flow of the coal bed model, and further improve the accuracy of the whole coal bed model.

And S700, inserting the layer model into the stratum frame grid to obtain a coal bed model, and adjusting the coal bed model by using the fault data.

According to the method, the layer model is inserted into the three-dimensional stratum frame grid, the vertical grid is built according to the stratum thickness and the model precision, the preliminary construction of the three-dimensional coal seam model is completed, and then the three-dimensional coal seam model with reasonable structure is adjusted and constructed for strata on two sides of the fault according to the breaking distance of the coal seam model. In the embodiment of the application, the geological modeling system guides the layer model into the stratum framework grid to obtain the coal bed model. The geologic modeling system then uses the fault distance information in the fault map to adjust the model of the coal seam.

Further, as a refinement and extension of the foregoing embodiment, in order to fully describe a specific implementation procedure of the embodiment, another method for modeling a coal seam is provided in the embodiment of the present application, where the method includes:

s1: laser point cloud data and geological data are acquired.

In the embodiment of the present application, the process of acquiring laser point cloud data is the same as that of step S100. The acquisition of the geological data specifically includes the following steps S101 to S104.

S101: and (5) digitizing the drilling well position to obtain drilling data.

In the embodiment of the application, the geological modeling system reads the drilling related information from the drilling histogram, and sorts the drilling related information according to the mapping relation between each item of data in the drilling related information and a plurality of preset items. For example, the name of the well, the plane coordinates of the well, the bottom depth of the well, the heart rate of the well are obtained in a well histogram, and a well site file is created in a well name, an X-chord, a Y-chord, a bottom depth, and a KB (heart rate of the well, and the well site file is in a text format.

S102: and digitizing the drilling horizon to obtain horizon data.

Because horizon data are important parameters for modeling a three-dimensional coal seam of a coal field, and accurate layering and layering digitization are important preconditions for good modeling display, the method and the device provide more comprehensive data support for model construction by reading top depth information in a drilling histogram. In an embodiment of the present application, a geologic modeling system obtains a drilling histogram, such as a ZL028 synthetic histogram. A ZL028 synthetic histogram is described as follows:

as shown in fig. 2, the application uses actual geological data of the Zholin mountain coal mine as an illustration of the drilling horizon digitizing process, and the mining area has 21 drilling comprehensive bar charts, and totally comprises six horizons: the bottom of the No. 15 coal bed, namely the top of the upper stone coal system Taiyuan group, namely the top of the C3t, the top of the lower two stacks of Shanxi groups, namely the top of the P1s, the top of the lower two stacks of lower stone boxes, namely the top of the P1x, and the top of the upper two stacks of upper stone boxes, namely the top of the P2s. Thus, the geologic modeling system reads the top depths of groups of layers in each well from the well synthetic columns. Specifically, taking a ZL028 synthetic histogram in actual geological data of a bamboo forest mountain coal mine as an example, the geological modeling system determines that the top depth of the P1x layer is at a first line mark, namely 218.35m, and the top depth of the P1s layer is at a second line mark, namely 251.9m, and the top and bottom depths of the coal seam No. 3 and the coal seam No. 15 are respectively named as 3top, 3bottom, 15top and 15bottom. Thus, the coal seam is a black rectangular area, with a depth of 3top corresponding to 267.5m and a depth of 3bottom corresponding to 271.05m. Thus, the geologic modeling system sorts the top depths of the groups of layers in a tabular form, generating a horizon file, and the horizon file is in the format of b.txt (text format). It should be noted that the original ZL028 synthetic columnar image is too large and is not completely displayed.

With the above-described drilling histogram, the geologic modeling system determines a plurality of horizons in the drilling histogram. And then, the geological modeling system reads the top depth information corresponding to each horizon in the drilling histogram to obtain a plurality of top depth information. Finally, the geologic modeling system collates the plurality of top-depth information into a table.

S103: and (5) performing fault digitization to obtain fault data.

In order to effectively improve the accuracy of the whole coal seam model, the embodiment of the application fuses fault data with data such as drilling holes and horizons. Thus, in embodiments of the present application, a geologic modeling system obtains a fault map and coordinates-corrects the fault map. The tomographic map is corrected, that is, the positions in the tomographic map are calibrated to actual coordinates. A description of a large fault map of a mine is provided below:

as shown in fig. 3, the mining area large fault map comprises Bai Zhuang syncline, north slope ditch anticline and urban rear waist fault. The geological modeling system calibrates the positions of the white village syncline, the north slope ditch anticline and the urban and posturban waist fault to determine the actual coordinates.

Through the fault map, the geological modeling system reads the fault dip angle and the elevation range in the corrected fault map. And then, the geological modeling system calculates the projection positions of the coal seam roof line and the coal seam floor line in the fault chart by using the fault inclination angle and the elevation range, and determines parallel lines of the fault line at the projection positions of the fault chart, wherein the parallel lines comprise the fault roof line, the fault floor line, the coal seam roof line and the coal seam floor line. A schematic representation of a fault roof line is described below:

As shown in fig. 4, the geological modeling system reads out the fault inclination angle and the elevation range according to the fault map, calculates the projection positions of the TOP and BOTTOM lines of the coal seam on the plane, and draws parallel lines of the fault lines at the corresponding positions, namely a fault TOP line F1304-TOP and a fault BOTTOM line F1304-BOTTOM.

Then, the geological modeling system acquires a preset processing algorithm, the corrected fault diagram is subjected to vectorization processing, the background color and the target color of the corrected fault diagram are set, and pixel automatic tracking is performed on fault lines and parallel lines of the fault lines, so that dense pixel point coordinates are obtained. And filtering the coordinate data of the pixel points, removing unnecessary pixel points except the head and the tail on the straight line, and properly reducing the density of the arc pixel points. The geological modeling system acquires a preset pixel point filtering rule, performs thinning filtering on the dense pixel point coordinates by using the preset pixel point filtering rule, generates fault data by using the thinned and filtered sparse pixel point coordinates, and collates the filtered fault coordinate point data to generate a fault file c.txt.

S104: and digitizing the bottom plate contour line data graph and the coal seam thickness graph to obtain contour line data and coal seam thickness data.

Specifically, the data map of the bottom plate contour line is subjected to coordinate correction, each position in the picture is calibrated to be an actual coordinate, the corrected picture is shown in fig. 5, the coal seam thickness map is subjected to coordinate correction, each position in the picture is calibrated to be an actual coordinate, and the corrected picture is shown in fig. 6. Specifically, the corrected tomogram is vectorized by using an algorithm, pixel coordinates are obtained by setting background color, target color, contour line data and equal thickness lines and automatically tracking pixels, and the pixel density of partial simple arcs is properly reduced according to specific line conditions. And (3) sorting the processed bottom plate contour line data graph and the coordinate point data of the coal seam thickness graph to generate a contour line data file c.txt and a thickness graph file d.txt.

S2: and correcting the contour line data by utilizing the laser point cloud data to obtain corrected contour line data.

The contour line data obtained by the geophysical prospecting method has large data range and even distribution, but the accuracy is far lower than that of laser point cloud data and drilling data, and the stratum cannot be directly restrained in the modeling process and needs to be corrected.

In some embodiments, the step of correcting the contour data using laser point cloud data to obtain corrected contour data includes:

and taking the laser point cloud data as first main data, taking the contour line data as first auxiliary data, and calculating the respective space variability and the space relation between the two, wherein the specific calculation method is shown in a formula (1) -a formula (3).

Based on the respective spatial variability and the spatial relationship between the two, a cooperative kriging Jin Jitong equation is established, and the contour data is recalculated based on the cooperative kriging system equation so as to correct the contour data. The specific calculation method is shown in a formula (4) and a formula (5).

And preprocessing the laser point cloud data and the contour line data.

The following describes the contour data correction specifically:

first, data collection and preprocessing are performed

Data selection: the laser point cloud data is the primary variable and the contour data is the secondary variable.

Pretreatment: including data cleansing, normalization, etc., to ensure data quality.

Then, structural analysis is performed

Establishing a semi-variance model:

the variability of spatial data across different distances is quantified according to equation (1).

Wherein:is a variable Z ₁ Distance->Half variance of the upper>Is a variable Z ₂ Distance->Half variance of the upper>Is distance->The upper pair of observation points, Z (>) And->Is the data value of paired observation points, +.>And->The laser point cloud data are data with higher precision, the contour line data are data with lower precision, the contour line data can be corrected to be data with higher precision, and the area without high-precision coverage is restrained. It should be noted that x does not represent an x value in the coordinates, but represents a position.

Constructing a covariance model:

the degree of correlation between the spatial data points is described by equation (2).

Wherein:is the covariance over the distance h, +.>Variance->Is the half variance.

Constructing a cross semi-variance model:

and determining the spatial relationship between the laser point cloud data and the contour line data, as shown in the formula (3).

(3)

Wherein:is a variable->And->At a distance->Cross half variance on ∈ ->Is a variable->And->At a distance->Cross half variance over. />And->Is a variable->In position->And->Two observations at. />And->Is a variable->In position->And->Two observations at.

Construction of the synergistic krey Jin Jitong equation

A mathematical model of multivariable spatial interpolation is established, and the equation set is solved to obtain optimal weights for predicting the values of unknown points.

(4)

Wherein:and->Is the optimal weight solved by least square method,/->、/>、/>And->Is a half variance and cross half variance function, +.>And->Is a covariance function>Is the position to be estimated, +.>Is the location of a known data point. Interpolation calculation:

and (5) carrying out interpolation calculation on the coordinate vectorized by the contour data according to a formula (5) by utilizing the optimized cooperative kriging Jin Fangcheng. This involves a weighted average of the observations of the known points, the weights being determined by the parameters of the collaborative kriging equation. And replacing the corresponding elevation value in the contour data file c.txt obtained in the step S1 with the calculated elevation value.

(5)

Wherein Z is%) Is the location point +.>Is a value of (2). />Is the position to be estimated, i.e. the contour data coordinates that need to be corrected, < >>, _… ,/>N sample data, which is the initial variable, +.>, _… ,/>M sample data, which are secondary variables, < +.>, _… ,/>And, _… ,/>Is a cooperative kriging weighting system that needs to be determined.

S3: and obtaining virtual drilling data by using the laser point cloud data and the corrected contour line data.

Specifically, a roadway model is created using laser point cloud data. And establishing a plurality of virtual wells by using the roadway model. And acquiring a plurality of virtual drilling data corresponding to the plurality of virtual drilling in the roadway model, acquiring a plurality of preset projects, and sorting the plurality of virtual drilling data according to the mapping relation between each item of data in the plurality of virtual drilling data and the plurality of preset projects.

Because the influence of drilling data quality and distribution can be to the precision of coal seam model, virtual drilling is generated to this application embodiment, can increase the quantity, quality and the deviation that the distribution caused to the model like this, and this application increases the data of virtual drilling simultaneously for stratum data resolution is high. Further, the virtual drilling is the same as the real drilling, and the drilling with single geological attribute after preprocessing and expert judgment by using the geological information which is actually disclosed can restrict the coal bed model. In the embodiment of the application, the geological modeling system acquires a roadway model, acquires a preset interval, such as 100 meters, and sets virtual drilling according to the preset interval in the roadway model to obtain a plurality of virtual drilling. The preset distance can be used for arranging uniformly distributed characteristic points on the roadway model bottom plate at certain intervals by the geological modeling system to serve as virtual drilling. In this way, the geological modeling system establishes a plurality of virtual wells through the high-precision roadway model so as to acquire more accurate well drilling data from the plurality of subsequent virtual wells.

As shown in fig. 7, the drilling data of the real drilling is limited, and richer data can be provided by the constructed virtual drilling data, so that the high-precision laser point cloud coordinates and the corrected contour line data coordinates are written into the horizon data according to the format of the real drilling data. And for convenience in calculation, the geologic modeling system uniformly sets the format to a Bottom depth value and a KB value.

S4: and correcting the coal seam thickness data by utilizing the laser point cloud data to obtain corrected coal seam thickness data.

In the embodiment of the present application, the method for correcting the data of the thickness of the coal seam is the same as the method for correcting the contour data, and the methods from the formula (1) to the formula (5) can be adopted, but only the parameters need to be changed into the data related to the thickness of the coal seam.

In the embodiment of the application, the basis for calculating the thickness of the coal layer is a coal layer thickness chart, and the thickness chart d.txt obtained in the step S1 is used for correcting the coal layer thickness data by adopting a kriging interpolation method. The calculated coal seam thickness data after the coal seam number 3 correction is added to the corresponding file according to the format described in S102. Fig. 8 illustrates a schematic diagram of corrected coal seam thickness data.

During modeling, the top plate and the bottom plate of the high-definition coal layer model are divided into two parts for independent modeling, and the top plate of the constrained coal layer is supplemented by the elevation calculated by the thickness data, so that the thickness change of the high-definition coal layer model is more accurate.

S5: and constructing a layer model according to the virtual drilling data, the horizon data and the corrected coal seam thickness data.

Specifically, a kriging interpolation algorithm is obtained, and target horizon data is calculated by using the kriging interpolation algorithm to obtain a layer model, wherein the horizon data generated by S3 and S4 are the target horizon data.

The method adopts the Kriging interpolation method to process the target horizon data, thereby establishing a top surface model of each layer. The principle of the kriging interpolation method is that no bias and minimum variance condition exists in the probability statistics estimation theory, and the spatial correlation of the described object is considered in the interpolation process, so that the interpolation is more scientific and is closer to the actual situation. In the embodiment of the application, for each horizon, the geological modeling system extracts a plurality of horizon coordinates of the horizon and horizon coordinate values corresponding to each horizon coordinate from target horizon data, calculates horizon distance values and half variances of any two horizon coordinates in the plurality of horizon coordinates by using the plurality of horizon coordinate values, and obtains a plurality of horizon distance values and a plurality of half variances, namely, calculates distances and half variances for coordinate data of the same horizon in pairs. And then, the geological modeling system establishes a relation function by adopting a plurality of horizon distance values and a plurality of half variances, acquires an index model, and fits the relation function by utilizing the index model to obtain a fitting function, so that the corresponding half variances can be calculated according to any distance. Then, the geological modeling system calculates a plurality of horizon distance values by using a fitting function to obtain a plurality of first half variances, and calculates a half variance coefficient matrix by using the plurality of first half variances. Then, the geological modeling system obtains unknown horizon coordinates of the horizon, calculates half variances from the unknown horizon coordinates to the plurality of horizon coordinates, obtains a plurality of second half variances, and calculates a coefficient equation by using the plurality of second half variances. And then, the geological modeling system calculates a half variance coefficient matrix and a coefficient equation to obtain a weighting coefficient, acquires a plurality of attribute values corresponding to a plurality of horizon coordinates in the target horizon data, and performs weighted summation calculation on the attribute values by using the weighting coefficient to obtain an estimated value of the unknown horizon coordinates. And finally, respectively calculating a plurality of horizon coordinates of each horizon by using the geological modeling system to obtain a plurality of estimated values, and constructing a layer model by using the target horizon data and the estimated values.

S6: and constructing a fault model by adopting the fault data.

The fault is the projection of the actual fault on a certain plane, so the fault normal vector is calculated according to the inclination angle and the tendency of the fault, thereby calculating the fault plane equation and the coordinates of points on the plane, and further constructing a fault model. A planar tomographic image is described below:

as shown in fig. 3, the fault is a projection of an actual fault on a certain plane, and in a planar fault scan, the inclination angle and the tendency of the fault can be obtained, the inclination angle of the fault with the fault number F1 is 70 degrees, the fault drop H is 8m, the inclination angle of the fault with the fault number F1304 is 75 degrees, the fault drop H is 2.5m-6.0m, the inclination angle of the fault with the fault number F1408 is 70 degrees, the fault drop H is 5m-9m, the inclination angle of the urban lumbar fault is 75 degrees, and the fault drop H comprises 260m, 269m, 318m, 328m and 364m.

In the embodiment of the application, a geological modeling system acquires preset intervals, and performs digital processing on fault data according to the preset intervals to obtain a plurality of discrete line segments. A planar fault digitization result is described as follows:

as shown in fig. 9, the geologic modeling system digitizes fault data into discrete strings of points, i.e., a plurality of line segments, at a given spacing. Specifically, the fault numbers F1, F1304, F1408 and the city rear waist fault are digitalized according to preset intervals to obtain corresponding line segments.

Then, for each discrete line segment, the geological modeling system obtains the end point coordinates of the discrete line segment to obtain a first end point coordinate and a second end point coordinate, for example, the first end point coordinate isThe second end point coordinates are->. Subsequently, the geologic modeling system calculates a plane vector, a vertical vector, for example, the plane vector is +.>The vertical vector is +.>And determining the fault plane corresponding to the fault line. The geologic modeling system then calculates a target vector perpendicular to the discrete line segments and parallel to the fault plane using the planar vector, the perpendicular vector, e.g., the target vector +.>And calculating the plane vector and the target vector to obtain a fault plane normal vector n, wherein the calculation formula of the fault plane normal vector is shown in the following formula 6:

equation 6:

wherein v is a plane vector and the coordinates are，/>Is a target vector and the coordinates are +.>。

Then, the geological modeling system acquires an initial plane equation, adds the normal vector of the fault plane and the first endpoint coordinate into the initial plane equation to obtain a fault plane equation, and the calculation formula of the fault plane equation is shown in the following formula 7:

equation 7:

wherein,is the abscissa of normal vector of fault plane, +.>Is the ordinate of the normal vector of the fault plane, +. >For the height of the normal vector of the fault plane, +.>Is the abscissa of the first endpoint coordinate, +.>Is the ordinate of the first endpoint coordinate, +.>Is the height of the first endpoint coordinate.

And finally, constructing a fault model by using a fault plane equation and an elevation range by using the geological modeling system. Thus, the geological modeling system utilizes fault data to construct a fault model, and can better reflect the spatial distribution conditions of complex structures such as underground folds, fractures and the like. An architectural diagram of a fault three-dimensional model is described below:

as shown in fig. 10, the geologic modeling system builds a fault three-dimensional model from the cross-sectional plane equations and elevation ranges. Where elevation refers to the distance of a point from the absolute base in the direction of the plumb line.

S7: and generating stratum frame grids by using the fault model and the layer model.

And determining a model boundary according to the drilling horizon information and fault data, and generating a frame grid by using a corner grid model. Specifically, the angular point grid model is a structured grid type widely applied at present, grid positions can be defined by i, j and k, the length and width of unit grids are variable, grid faces vertically connected with top and bottom grid points can be inclined, grids can be twisted, and fault lines, boundaries or tip-extinguishing lines can be conveniently simulated. Therefore, the method and the device determine the model boundary according to the drilling horizon information and the fault data, and generate the framework grid by using the corner grid model so as to construct the coal bed model better. In the embodiment of the application, the geological modeling system extracts intermediate shape points in the fault model, obtains the model boundary of the fault model, and obtains the preset coordinate direction and the preset grid number corresponding to the preset coordinate direction, wherein the intermediate shape points are the intersection points of three planes and a fault plane, which are divided into by the geological body corresponding to the fault model in the elevation direction. And then, the geological modeling system generates a plane quadrilateral grid by utilizing the intermediate shape points, the model boundaries, the preset coordinate directions and the preset grid numbers corresponding to the preset coordinate directions. Then, the geological modeling system acquires fault trend, fault top and fault bottom in the fault model, builds a planar quadrilateral grid to the fault top and the fault bottom according to the fault trend to obtain a columnar three-dimensional frame network for connecting the fault top, the fault middle and the fault bottom, and simultaneously generates stratum frame grids by using the layer model, so that the geological modeling system builds the fault model according to fault data, builds a corner grid model according to given grid size to form a three-dimensional stratum frame, simplifies the modeling process, avoids investment of a large amount of manpower, material resources and time, and improves the efficiency of building the coal bed model.

Therefore, the architecture schematic diagram of the stratigraphic framework grid provided in the embodiment of the application is as follows:

as shown in FIG. 11, the geologic modeling system extracts intermediate shape points of the fault model, combines the model boundaries, and generates planar quadrilateral meshes according to a given number of meshes along the X, Y directions. Wherein, the geologic body of the region is divided into three surface and fault surface intersection points in the elevation direction as intermediate shape points. Then, the geological modeling system pushes the generated plane grid points to the top and bottom of the fault model along the fault trend, and a columnar three-dimensional framework grid connecting the top, the middle and the bottom is generated.

S8: and inserting the layer model into the stratum framework grid to obtain a coal bed model.

Specifically, the layer model generated in the step S5 is imported into the stratum frame grid model, the intersection points between all grids and layers become one node of the three-dimensional grid, grid cells in the Z direction are defined at the same time, and a rough three-dimensional grid is generated. And constructing finer vertical grids according to the corrected coal seam thickness data, the model precision and the rough three-dimensional grids, namely dividing the coal seam thickness data, the model precision and the rough three-dimensional grids into a plurality of vertical grids among stratum layers of each layer, and optimizing and adjusting the model by utilizing drilling horizon points.

S9: and adjusting the coal bed model by using the fault data.

After the coal bed model is generated, the generated faults of the coal bed model are only restrained by trend, trend and dip angle under the influence of geological condition complexity, the fault break distance of the model is required to be adjusted by utilizing the model editing function of modeling software according to the break distance and fault influence range marked by the fault map, so that the coal bed model becomes reasonable and is matched with the actual situation, the three-dimensional high-definition coal bed model is shown in fig. 12, and the arrow points to the fault surface indication of the urban and lumbar faults.

S10: accuracy verification

10 known high-precision data coordinate points are randomly selected from a high-precision coal seam model to serve as check points 1-10, the elevation of the output model of the application at the check points is read to be compared with the elevation of the known coordinate points, interpolation of the two is calculated to obtain a model precision result of the application, the model precision result is shown in a figure 13, and the result shows that the error of the application in a data coverage area is less than 0.2%.

10 contour data coordinate points are randomly selected from the data uncovered area as check points 11-20, the elevation in the read model is compared with the original contour data elevation value, two interpolation values are calculated to obtain the accuracy improvement value result of the model in the data uncovered area, the result is shown in fig. 14, and the result shows that the error average correction elevation of the model in the data uncovered area is 7.96 meters. The overall effect pair of the conventional method model and the model of the application is shown in fig. 15, the upper side is the conventional method model, the lower side is the model of the application, and the two models are partially shown in fig. 16. The model in the area of the conventional method is a straight line which is not consistent with the actual situation, the coal seam morphology of the data blank area of the model on the left side of the model is obviously improved, and the elevation change accords with the elevation change rule of the contour line data.

According to the technical scheme, in the first embodiment of the application, the laser point cloud data with high precision is adopted to correct the low-precision contour line data by adopting the cooperative kriging method, and the corrected data is used for additionally restraining the blank area of the coal seam data; secondly, calculating the elevation of a No. 3 coal seam roof at the cloud coordinates and the contour coordinates of the high-precision laser point by adopting a Kriging interpolation method, and filling the coal seam thickness data of the data blank area; thirdly, using well position, horizon and fault data obtained digitally, introducing high-precision laser point cloud data and contour line data as virtual drilling to construct a stratum layer model, and constructing a fault model by using the fault data so as to construct a corner grid model to obtain a three-dimensional stratum frame; fourthly, inserting the layer model into a three-dimensional stratum frame, and constructing a vertical grid according to the thickness of the coal bed and the model precision to finish the preliminary construction of the three-dimensional coal bed model; and finally, adjusting stratum at two sides of the fault to construct a reasonable three-dimensional coal bed model according to the size of the breaking distance.

The method and the device have the advantages that the construction of the domestic geological high-precision coal layer model of a certain mine is finished, the available data range is greatly increased due to the cooperative processing of data, the constraint data density is greatly increased, the data coverage of a full working area is realized, and the problems of insufficient drilling data and uneven distribution in actual production are solved by utilizing the technical means.

In the first embodiment of the application, the laser point cloud data with high precision is adopted to correct the low-precision contour line data by adopting a cooperative kriging method, and the corrected data is used for additionally restraining the blank area of the coal seam data; secondly, calculating the elevation of a No. 3 coal seam roof at the cloud coordinates and the contour data coordinates of the high-precision laser point by adopting a Kriging interpolation method, and filling the coal seam thickness data of the data blank area; thirdly, using well position, horizon and fault data obtained digitally, introducing high-precision laser point cloud data and contour line data as virtual drilling to construct a stratum layer model, and constructing a fault model by using the fault data so as to construct a corner grid model to obtain a three-dimensional stratum frame; fourthly, inserting the layer model into a three-dimensional stratum frame, and constructing a vertical grid according to the stratum thickness and the model precision to finish the preliminary construction of the three-dimensional coal bed model; and finally, adjusting stratum at two sides of the fault to construct a reasonable three-dimensional coal bed model according to the size of the breaking distance.

In the embodiment, a multi-source data collaborative coal seam modeling method is provided, in which a collaborative krill Jin Jitong equation is established according to a correlation between high-precision data and low-precision contour data to recalculate the elevation of the contour. Due to the participation of high-precision data, the systematic error of the contour line data is corrected to reach the precision required by modeling, so that the system can restrict the coal seam. And finally, independently modeling the top and bottom plates of the high-definition coal layer model by dividing the top and bottom plates into two parts during modeling, and supplementing the constrained coal layer top plate by using the elevation obtained by calculating the thickness data, so that the thickness change of the high-definition coal layer model is more accurate. The method comprises the following steps: acquiring laser point cloud data and geological data, wherein the geological data comprises drilling data, horizon data, contour data, coal seam thickness data and fault data; correcting the contour line data by utilizing the laser point cloud data to obtain corrected contour line data; obtaining virtual drilling data by utilizing the laser point cloud data and the corrected contour line data; correcting the coal seam thickness data by utilizing the laser point cloud data to obtain corrected coal seam thickness data; constructing a layer model according to the virtual drilling data, the horizon data and the corrected coal seam thickness data; constructing a fault model by adopting the fault data, and generating stratum frame grids by utilizing the fault model and the layer model; and inserting the layer model into the stratum frame grid to obtain a coal bed model, and adjusting the coal bed model by using the fault data.

Further, as a specific implementation of the method of fig. 1, an embodiment of the present application provides a structural schematic diagram of a multi-source data collaborative coal seam modeling apparatus, as shown in fig. 17, including:

an acquisition unit 1701 for acquiring laser point cloud data and geological data, wherein the geological data includes borehole data, horizon data, contour data, coal seam thickness data, and fault data;

a first correction unit 1702 configured to correct the contour line data by using the laser point cloud data, so as to obtain corrected contour line data;

a first construction unit 1703, configured to obtain virtual drilling data by using the laser point cloud data and the modified contour line data;

a second correction unit 1704, configured to correct the coal seam thickness data by using the corrected contour line data and the laser point cloud data, to obtain corrected coal seam thickness data;

a second construction unit 1705, configured to construct a layer model according to the virtual drilling data, the horizon data, and the corrected coal seam thickness data;

a generating unit 1706, configured to construct a fault model using the fault data, and generate a stratigraphic framework grid using the fault model and the layer model;

And the adjusting unit 1707 is configured to insert the bedding plane model into the stratum frame grid to obtain a coal seam model, and adjust the coal seam model by using the fault data.

In a specific application scenario, the first correction unit includes:

In a specific application scenario, the apparatus further includes:

and the preprocessing module is used for preprocessing the laser point cloud data and the contour line data.

In a specific application scenario, the second correction unit includes:

In a specific application scene, the adjusting unit is used for importing the layer model into the stratum frame grid to generate a rough three-dimensional grid; and constructing the coal bed model by using the rough three-dimensional grid, the corrected coal bed thickness data and the model precision.

It should be noted that, other corresponding descriptions of each functional unit related to the multi-source data collaborative coal seam modeling apparatus provided in the embodiments of the present application may refer to corresponding descriptions in fig. 1, and are not described herein again.

In an exemplary embodiment, referring to fig. 18, there is also provided a computer device including a bus, a processor, a memory, and a communication interface, and may further include an input-output interface and a display device, wherein the functional units may communicate with each other through the bus. The memory stores a computer program and a processor for executing the program stored in the memory to perform the multi-source data collaborative coal seam modeling method in the above embodiment.

A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the multi-source data collaborative coal seam modeling method.

From the above description of the embodiments, it will be apparent to those skilled in the art that the present application may be implemented in hardware, or may be implemented by means of software plus necessary general hardware platforms. Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.), and includes several instructions for causing a computer device (may be a personal computer, a server, or a network device, etc.) to perform the methods described in various implementation scenarios of the present application.

Those skilled in the art will appreciate that the drawings are merely schematic illustrations of one preferred implementation scenario, and that the modules or flows in the drawings are not necessarily required to practice the present application.

Those skilled in the art will appreciate that modules in an apparatus in an implementation scenario may be distributed in an apparatus in an implementation scenario according to an implementation scenario description, or that corresponding changes may be located in one or more apparatuses different from the implementation scenario. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.

The foregoing application serial numbers are merely for description, and do not represent advantages or disadvantages of the implementation scenario.

The foregoing disclosure is merely a few specific implementations of the present application, but the present application is not limited thereto and any variations that can be considered by a person skilled in the art shall fall within the protection scope of the present application.

Claims

1. The multi-source data collaborative coal seam modeling method is characterized by comprising the following steps of:

2. The method of claim 1, wherein the contour data comprises contour elevation data, and wherein the step of correcting the contour data using laser point cloud data to obtain corrected contour data comprises:

3. The method of claim 2, further comprising, prior to the step of calculating the respective spatial variability and the spatial relationship between the two using the laser point cloud data as first primary data and the contour data as first secondary data:

And preprocessing the laser point cloud data and the contour line data.

4. The method of claim 1, wherein the step of correcting the coal seam thickness data using the laser point cloud data to obtain corrected coal seam thickness data comprises:

5. The method of claim 1, wherein the step of inserting the bedding plane model into the stratigraphic framework grid to obtain a coal seam model comprises:

6. A multi-source data collaborative coal seam modeling apparatus, comprising:

7. The apparatus of claim 6, wherein the contour data comprises contour elevation data, the first correction unit comprising:

8. The apparatus of claim 6, wherein the second correction unit comprises:

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 5 when the computer program is executed.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 5.