CN114742937B - Three-dimensional geological analysis method and device - Google Patents
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Abstract
The invention discloses a three-dimensional geological analysis method and a device, which belong to the field of design and construction of underground space shield tunnels, wherein the method comprises the following steps: s1: acquiring a data frame D corresponding to the subdivided geological survey drilling data; d is 1, D is 2 and D is 3 is three-dimensional coordinate data of the geological survey borehole; d is that, 4 is soil layer data; s2: carrying out drilling point cloud modeling, drawing a geological shield section model and grid division by utilizing three-dimensional coordinate data to obtain a grid model M dm'; calculating the minimum distance between the coordinates of non-repeated grid nodes in M dm' and the drilling point cloud model, analyzing the soil layer types and calculating the thickness of each soil layer so as to obtain the distribution of the section soil layers; s3: analyzing soil layer data D [: 4] to obtain soil layer types and assigning values to obtain D [: 5]; training the interpolation algorithm model by taking the three-dimensional coordinate data and D [: 5] as training samples; and predicting soil layer parameter distribution of each grid node by using the trained model. The method provided by the invention can improve the comprehensiveness and accuracy of geological analysis.
Description
Technical Field
The invention belongs to the field of design and construction of underground space shield tunnels, and particularly relates to a three-dimensional geological analysis method and device.
Background
Shield tunnels in an underground space are distributed over long distances, typically across very long soft and hard composite formations, including: clay, sand, strong wind mudstone, gravel layer, bad rock burst, dense joint zone, altered rock breaking zone, etc., so that a plurality of adverse factors are easily generated in the construction of tunnels. For example: aiming at the problems of difficult tunneling, easy instability, difficult tunneling parameter control and the like of a shield machine in a stratum combining high-strength (more than 100 Mpa) rock and silt; when unexpected hard rock is encountered, the shield machine has a series of problems of difficult rock breaking, low tunneling efficiency, short service life of the main bearing, serious cutter abrasion and the like; when shuttling in plate areas with strong activity or areas with high ground stress soft rock large deformation and hard rock strong rock burst, tunnel soft rock deformation control and hard rock burst control need to be reinforced especially. Therefore, the shield tunnel is usually required to fully know the structure of the composite stratum in the region in advance and take countermeasures in time during design and construction.
Geological surveys typically use deterministic methods such as drilling, sampling, etc. to recognize the geological environment and use borehole samples and survey data as the only accurate basis. However, due to the limited drilling density and variability of the rock and soil mass in the construction process, geologists often have inaccurate analysis on geology, and serious errors in design and construction and even safety accidents are caused. In fact, many scholars at home and abroad at present use DSI, stochastic simulation, geostatistics and other prediction methods to calculate the stratum boundary and elevation, and put forward a Pancritic gold (Universal Kriging) method to infer the uncertainty of the physical property index of the rock and soil. Although the prediction results improve the accuracy of geological evaluation, the relevant surveyors still abstract and project the three-dimensional space of the geologic body into a two-dimensional plane. Personnel involved in shield design and construction can only understand the geological environment based on the abstracted two-dimensional plan.
Therefore, it is necessary to improve the comprehensiveness and accuracy of geological analysis and analyze out bad or unfavorable geological distribution for shield engineering.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a three-dimensional geological analysis method and a three-dimensional geological analysis device, and aims to analyze three-dimensional geology based on geological exploration drilling data so as to design and construct a shield tunnel, thereby solving the technical problems of single geological analysis data and low accuracy in the existing shield tunnel design and construction process.
To achieve the above object, according to one aspect of the present invention, there is provided a three-dimensional geological analysis method for shield tunnel design and construction, comprising:
S1: acquiring a data frame D corresponding to the subdivided geological survey drilling data; the first column D of D is that 0 is the serial number of the geological survey drilling hole; the second column D of D is 1, the three columns D is 2 and the four columns D is 3 are three-dimensional coordinate data of the geological survey borehole; the fifth column D [: 4] of the D is soil layer data of the geological survey drill hole, is used for analyzing the types of soil layers, and assigns values for rock-soil parameters of each soil layer to obtain a sixth column D [: 5];
S2: carrying out drilling point cloud modeling by utilizing the three-dimensional coordinate data { D [: 1], D [: 2], D [: 3] } of the geological survey drilling hole to obtain a drilling point cloud model P; drawing a geological and shield section model M dm based on the drilling point cloud model P, and carrying out grid division on the model M dm to obtain a new grid model M dm'; extracting the vertex coordinates (x i yi zi) of the ith grid node of the ith behavior of the ith grid node in the coordinate data frames V and V of all non-repeated grid nodes in the M dm', and calculating the minimum distance between the coordinates of each grid node and the drilling point cloud model P; analyzing the maximum likelihood soil layer types SoilType of each grid node by taking the minimum distance as an index to generate a soil layer type information data frame V' = (V SoilType); calculating the average thickness of each soil layer contained in the geological and shield section model M dm by utilizing the soil layer type information data frame V' so as to obtain section soil layer distribution;
s3: training the interpolation algorithm model by taking the three-dimensional coordinate data { D [: 1, D [: 2, D [: 3] } of the geological survey drill hole and the rock-soil parameters D [: 5] of each soil layer as training samples to obtain a target model; and predicting soil layer parameter distribution of each grid node by using the target model.
In one embodiment, the analyzing the soil layer data D [: 4] of the geological survey hole in the S1 obtains the kind of the soil layer, including:
Carrying out soil layer division on soil layer data D [: 4] of the geological survey drilling hole according to a division scale to obtain soil layer types; the dividing scale is smaller than the elevation difference of the interface between the upper soil layer and the lower soil layer; the average thickness of each soil layer is the average value of more than three thickness values corresponding to the same soil layer.
In one embodiment, the profile soil layer distribution in S2 is used for segment design of the shield tunnel.
In one embodiment, the geological and shield section model M dm in S2 includes a Solid object Solid and a boundary object Brep.
In one embodiment, in S2, the analyzing the maximum likelihood soil layer types SoilType to which each grid node belongs with the minimum distance as an index to generate the soil layer type information data frame V' = (V SoilType) includes:
And analyzing the maximum likelihood soil layer types SoilType of each grid node by using a KNN algorithm by taking the minimum distance of each grid node as an evaluation index, and generating a soil layer type information data frame V'.
In one embodiment, the soil layer parameter distribution in S3 is used for predicting the rock-soil parameter distribution of the face in the shield construction.
In one embodiment, the interpolation algorithm model in S3 is a three-dimensional Universal Kriging interpolation algorithm model.
In one embodiment, the method further comprises:
S4: and carrying out visual treatment on the profile soil layer distribution and the soil layer parameter distribution.
In one embodiment, the S4 includes: and sequentially carrying out vertex coloring and fuzzy rendering to carry out three-dimensional visualization processing on the profile soil layer distribution and the rock-soil parameter distribution.
According to another aspect of the invention, a three-dimensional geological analysis device is provided, which is applied to shield tunnel design and construction, and is used for executing the three-dimensional geological analysis method.
In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art: 1. based on mileage parameters, the stratum distribution of the tunnel surrounding rock can be automatically analyzed by using a program command flow; 2. based on mileage parameters, the soil property parameter distribution of the excavated face can be automatically analyzed by using a program command flow; 3. the graphic rendering method can be utilized to visually display the results and bring more visual data display effect to design and constructors
Drawings
FIG. 1 is a flow chart of a three-dimensional geological analysis method according to an embodiment of the invention;
FIG. 2 is a horizontal distribution diagram of a borehole in accordance with one embodiment of the present invention;
FIG. 3 is a schematic diagram of a model of a point cloud of a borehole in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram of a cross-section model of a shield after meshing in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of a meshing geological section model according to an embodiment of the present invention;
FIG. 6 is a chart of a parsed geological profile according to an embodiment of the present invention;
FIG. 7 is a spatial distribution diagram of rock and soil parameters of a parsed shield section according to an embodiment of the present invention;
FIG. 8 is a graph showing probability distribution of rock and soil parameters of a parsed shield cross-section in an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
The invention provides a three-dimensional geological analysis method, which is used for shield tunnel design and construction and comprises the following steps:
S1: acquiring a data frame D corresponding to the subdivided geological survey drilling data; the first column D of D is that 0 is the serial number of the geological survey drilling hole; the second column D of D is 1, the three columns D is 2 and the four columns D is 3 are three-dimensional coordinate data of the geological survey borehole; the fifth column D [: 4] of the D is soil layer data of a geological survey borehole, is used for analyzing the types of soil layers, and assigns values to the rock-soil parameters of each soil layer to obtain the sixth column D [: 5];
S2: carrying out drilling point cloud modeling by utilizing three-dimensional coordinate data { D [: 1, D [: 2, D [: 3] } of the geological drilling to obtain a drilling point cloud model P; drawing a geological and shield section model M dm based on the drilling point cloud model P, and carrying out grid division on the model M dm to obtain a new grid model M dm'; extracting the vertex coordinates (x i yi zi) of the ith grid node of the ith behavior of the V and the ith grid node of the coordinate data frames V and V of all non-repeated grid nodes in the M dm', and calculating the minimum distance between the coordinates of each grid node and the drilling point cloud model P; analyzing the maximum likelihood soil layer types SoilType of each grid node by taking the minimum distance as an index to generate a soil layer type information data frame V' = (V SoilType); calculating the average thickness of each soil layer contained in the geological and shield section model M dm by using the soil layer type information data frame V' to obtain section soil layer distribution;
S3: training an interpolation algorithm model by taking three-dimensional coordinate data { D [: 1, D [: 2, D [: 3] } of a geological exploration drilling hole and rock-soil parameters D [: 5] of each soil layer as training samples to obtain a target model; and predicting soil layer parameter distribution of each grid node by using the target model.
As shown in fig. 1, the three-dimensional geological analysis method of the present invention is specifically as follows:
1) Drilling data are finely surveyed in equal intervals according to the elevation, and a data frame D is formed through information arrangement;
2) Extracting three-dimensional coordinate points (D [: 1], D [: 2], D [: 3 ]) of the finely divided geological survey drill hole, and importing the three-dimensional coordinate points into three-dimensional modeling software to carry out drill hole point cloud modeling P;
3) Extracting finely divided soil layer data D [: 4] of the geological survey drill holes, analyzing the total number of soil layer seeds, and carrying out label setting on each soil layer;
4) Based on soil layer parameters in the survey report (e.g.: natural severity γ) gives corresponding parameters as D [: 5] for each soil layer;
5) Drawing a corresponding geological and shield section model M dm on the existing drilling point cloud model P according to requirements;
6) Performing grid division on the established model M dm to obtain a new grid model M dm';
7) Extracting all non-repeated grid node coordinates V of a grid model M dm';
8) Sequentially carrying out minimum distance calculation on each grid node V and the drilling point cloud model P, deducing the maximum likelihood soil layer type of each grid node through a KNN algorithm, giving corresponding labels, and generating V' with soil layer information labels;
9) If the segment design is used for the shield tunnel, calculating the elevation range of Z coordinates V '[: 3] under each soil layer in V', and finally outputting the average thickness and the arrangement sequence of each soil layer under the section;
10 If the model is used for predicting the rock-soil parameter distribution of the face in shield construction, extracting drilling data (D [: 1], D [: 2], D [: 3], D [: 5 ]) as training samples, then importing UniversalKriging into an interpolation algorithm model, and using the trained model for predicting the rock-soil mechanical parameter distribution of the section;
11 Three-dimensional display is carried out on the profile soil layer and the physical property parameter distribution by using a visual means.
In step 1), the acquisition format of the original survey data is as follows:
table 1 format of borehole data
Wherein D zk [: 0] represents the data of all rows and columns 1 in D zk, D zk [: n ] represents the data of all rows and columns n-1 in D zk, and the same applies below. For D zk, the same geological survey data drill holes have different soil layer distributions, the elevations and soil layer codes of the upper interface and the lower interface of each soil layer under each drill hole are sequentially filled from top to bottom, and parameters in table 1 finally form n rows and 5 columns of data frames (DATAFRAME) D after the units are finely divided:
The subdivision of the original geological survey borehole data D zk is to generate points which are distributed more regularly and densely and are provided with soil layer codes in the borehole, and finally form new geological survey borehole data D. D differs from D zk mainly in the number of rows, k < < n. The specific algorithm is shown in Table 2:
TABLE 2
In the step 2), D [:,1], D [:,2], D [:,3] are the 2 nd, 3 rd and 4 th column data of all rows in the data frame D, namely each group of x, y and z coordinates, and the three-dimensional modeling software is any three-dimensional modeling software with command stream drawing or programming drawing functions. The point cloud modeling P is a point cloud model automatically generated by directly inputting x, y and z coordinates, wherein the x, y and z are stored in the model in a format of n rows and 3 columns of data frames, namely:
in the step 3), the soil layer seed number analysis and the soil layer type calibration algorithm are shown in table 3:
TABLE 3 Table 3
In step 4), the algorithm for assigning the design reference value of the corresponding soil layer parameter to each soil layer type is shown in table 4:
TABLE 4 Table 4
In step 5), the geological and shield section model M dm is a three-dimensional digital model object represented by the Brep boundary in the modeling software.
In step 6), the Mesh model M dm' is a Mesh model object represented by Mesh in modeling software.
In step 7), the grid node coordinates V are derived from coordinates with the grid vertices Vertex in the Mesh object,
In step 8), the V' given the label is an n-row 4-column data frame based on the soil layer type information added after the grid node V, namely:
In step 8), a KNN algorithm for analyzing the type of the maximum likelihood soil layer to which each grid node belongs is specifically described as shown in table 5:
TABLE 5
In step 9), the algorithm for determining the thickness of the section soil layer is shown in table 6:
TABLE 6
In step 10), training the three-dimensional Universal Kriging interpolation algorithm model requires the sequential input of parameters D [: 1, D [: 2, D [: 3, D [: 5), variogram _model = "hole-effect", drift_terms = [ "regional _linear" ]. After training the above model, the distribution prediction of the geotechnical parameters in the section is performed by the algorithm shown in table 7:
TABLE 7
In step 11), the visual presentation means is visual presentation with graphics rendering software having command stream operations.
In one embodiment, the profile soil layer distribution in S2 is used to perform segment design of the shield tunnel.
In one embodiment, the geological and shield section model M dm in S2 includes a Solid object Solid and a boundary object Brep.
In one embodiment, in S2, the maximum likelihood soil layer category SoilType to which each grid node belongs is analyzed by using the minimum distance as an index to generate a soil layer category information data frame V' = (V SoilType), including:
And analyzing the maximum likelihood soil layer types SoilType of each grid node by using a KNN algorithm by taking the minimum distance of each grid node as an index, and generating a soil layer type information data frame V'.
In one embodiment, the soil layer parameter distribution in S3 is used for performing rock-soil parameter distribution prediction of the face in the shield construction.
In one embodiment, S1 analyzes soil layer data D [: 4] of the earth borehole to obtain a soil layer type, including:
Soil layer division is carried out on soil layer data D [: 4] of the geological survey drilling holes according to the division scale, and soil layer types are obtained; the dividing scale is smaller than the elevation difference of the interface between the upper soil layer and the lower soil layer; the average thickness of each soil layer is the average value of more than three thickness values corresponding to the same soil layer.
In one embodiment, the interpolation algorithm model in S3 is a three-dimensional Universal Kriging interpolation algorithm model.
In one embodiment, the method further comprises:
s4: and carrying out visual treatment on the profile soil layer distribution and the soil layer parameter distribution.
In one embodiment, S4 comprises: and sequentially carrying out vertex coloring and fuzzy rendering to carry out three-dimensional visualization treatment on the profile soil layer distribution and the rock-soil parameter distribution.
For example, the geological survey data is derived from a geological survey report of a shield engineering of an underground space of a city, 62 drilling data of a shield segment between a provincial Bose originating well and Qin Yuandong receiving wells are selected as shown in table 8, and the horizontal arrangement of the drilling holes is shown in fig. 2.
Table 8 raw earth boring data (unit: meters) after dividing the raw earth boring data into units: (assuming that the size of the dividing unit is 0.5)
Table 9: parameters in the table above the geological survey drilling data (unit: meter) after dividing the units finally form n rows and 5 columns of data frames D, namely:
step 2: the data columns of D [: 1], D [: 2], D [: 3] of the data frame D are imported into modeling software and a drilling point cloud model P is automatically built through a command stream, as shown in FIG. 3.
Step 3: after reading the 62-land survey borehole data of the dividing unit, and analyzing the total occurrence of stratum types, the calibrated soil layer labels are shown in the following table:
table 10: dividing the data of the earth's boring (unit: meter)
Step 4: and according to the information of the geological survey report, each soil layer in the table is endowed with a rock-soil parameter design reference value. The present example takes natural severe gamma (kN/m 3) as an example, and specific assignment is shown in the following table:
Table 11: design reference value of natural severe gamma of each soil layer
Step 5-6: the invention takes 100 meters of shield planning mileage as an example, and a specific section model M dm' is shown in figures 4 and 5.
Step 7-8: extracting all non-repeated node coordinates in each grid model M dm ', analyzing the type of the maximum likelihood soil layer to which each grid node belongs through a KNN algorithm, giving corresponding labels, and generating V' giving the soil layer labels. In this example, the parameter k of the KNN algorithm is taken to be 7, through repeated testing and comparison. Wherein V' of the shield section is as follows:
the data frame V' of the geological section is as follows:
Step 9: calculating the elevation range of V '[: 2] under each soil layer label according to the V' data frame of the geological section, and calculating the average thickness and distribution condition of each soil layer under the geological section from high to low, wherein the result is shown in the following table:
Table 12 shield mileage 100m geological section soil layer distribution
Step 10: the trained Universal Kriging interpolation algorithm model is used for predicting the rock-soil parameters (taking natural severe gamma as an example) of each node in the shield section, and the prediction results are shown in the following table:
Table 13 soil layer parameters (natural severe gamma) for each node in shield section
Step 11: and carrying out numerical visualization display on the geological and shield grid section model through vertex coloring (VertexColor). Because the geological section needs to distinguish different types of soil layers, gray level rendering is adopted; for a shield segment surface used as construction, a threshold value of a rock-soil parameter needs to be displayed, so that a gray-scale parameter during rendering is in a linear relation with the rock-soil parameter. The two visual effects are shown in fig. 6 to 8.
The geological distribution condition of the shield mileage 100m can be clearly resolved by the table 12 and the figure 6, and 1-5 in the figure 6 sequentially represents five soil layers 1-1, 1-2, 10-1, 10-4 and 20a-2 in the fifth table. The result can feed back the soil layer burial depth parameter of the section and can be used for design and calculation of a duct piece structure; the rock-soil parameter spatial distribution and the probability distribution of the working face at the position of 100m of the shield mileage can be analyzed by the table 13, the figure 7 and the figure 8, and the corresponding construction management decision can be adopted in advance during tunneling construction.
According to the invention, drilling data in a geological survey report are subdivided, and drilling point cloud modeling is performed based on spatial data. Then, carrying out unit division on each node of the grid by establishing a grid model of the section, and predicting soil layer distribution of the section grid by using a KNN algorithm based on point cloud drilling data; and predicting the distribution condition of the rock-soil parameters by applying Universal Kriging interpolation algorithm based on the point cloud drilling data. The invention finally visualizes the result in a color rendering mode. The invention is executed by the pre-coding program, overcomes the problems of repeated manual operation and low efficiency of manual calculation, and can automatically and repeatedly carry out the whole process. The invention automatically executes the script based on the computer, the feedback numerical value result is more accurate and faster, and the result can be more intuitively displayed from the three-dimensional view. The invention helps the design and constructors to master the geological conditions more accurately and more timely.
According to another aspect of the invention, a three-dimensional geological analysis device is provided, which is applied to shield tunnel design and construction and is used for executing a three-dimensional geological analysis method.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. The three-dimensional geological analysis method is characterized by being used for shield tunnel design and construction and comprising the following steps:
S1: acquiring a data frame D corresponding to the subdivided geological survey drilling data; the first column D of D is that 0 is the serial number of the geological survey drilling hole; the second column D of D is 1, the three columns D is 2 and the four columns D is 3 are three-dimensional coordinate data of the geological survey borehole; the fifth column D [: 4] of the D is soil layer data of the geological survey drill hole, is used for analyzing the types of soil layers, and assigns values for rock-soil parameters of each soil layer to obtain a sixth column D [: 5];
S2: carrying out drilling point cloud modeling by utilizing the three-dimensional coordinate data { D [: 1], D [: 2], D [: 3] } of the geological survey drilling hole to obtain a drilling point cloud model P; drawing a geological and shield section model M dm based on the drilling point cloud model P, and carrying out grid division on the model M dm to obtain a new grid model M dm'; extracting the vertex coordinates (x i yi zi) of the ith grid node of the ith behavior of the ith grid node in the coordinate data frames V and V of all non-repeated grid nodes in the M dm', and calculating the minimum distance between the coordinates of each grid node and the drilling point cloud model P; analyzing the maximum likelihood soil layer types SoilType of each grid node by taking the minimum distance as an index to generate a soil layer type information data frame V' = (V SoilType); calculating the average thickness of each soil layer contained in the geological and shield section model M dm by utilizing the soil layer type information data frame V' so as to obtain section soil layer distribution;
s3: training the interpolation algorithm model by taking the three-dimensional coordinate data { D [: 1, D [: 2, D [: 3] } of the geological survey drill hole and the rock-soil parameters D [: 5] of each soil layer as training samples to obtain a target model; and predicting soil layer parameter distribution of each grid node by using the target model.
2. The three-dimensional geological analysis method of claim 1, wherein the analyzing of the soil layer data D [: 4] of the geological borehole in S1 to obtain the kind of soil layer comprises:
Carrying out soil layer division on soil layer data D [: 4] of the geological survey drilling hole according to a division scale to obtain soil layer types; the dividing scale is smaller than the elevation difference of the interface between the upper soil layer and the lower soil layer; the average thickness of each soil layer is the average value of more than three thickness values corresponding to the same soil layer.
3. The three-dimensional geological analysis method according to claim 1, wherein the profile soil layer distribution in the step S2 is used for segment design of a shield tunnel.
4. The three-dimensional geologic analysis method of claim 1, wherein the geologic and shield cross-sectional model M dm in S2 comprises Solid objects Solid and boundary objects Brep.
5. The three-dimensional geological analysis method according to claim 1, wherein the step S2 of analyzing the maximum likelihood soil layer type SoilType to which each grid node belongs by using the minimum distance as an index to generate the soil layer type information data frame V' = (V SoilType) includes:
And analyzing the maximum likelihood soil layer types SoilType of each grid node by using a KNN algorithm by taking the minimum distance of each grid node as an index, and generating a soil layer type information data frame V'.
6. The three-dimensional geological analysis method according to claim 1, wherein the soil layer parameter distribution in the step S3 is used for predicting the rock-soil parameter distribution of the face in the shield construction.
7. The three-dimensional geologic analysis method of claim 1, wherein the interpolation algorithm model in S3 is a three-dimensional Universal Kriging interpolation algorithm model.
8. The three-dimensional geologic resolution method of claim 1, further comprising:
S4: and carrying out visual treatment on the profile soil layer distribution and the soil layer parameter distribution.
9. The three-dimensional geologic resolution method of claim 8, wherein S4 comprises: and sequentially carrying out vertex coloring and fuzzy rendering to carry out three-dimensional visualization processing on the profile soil layer distribution and the rock-soil parameter distribution.
10. A three-dimensional geological analysis device, applied to shield tunnel design and construction, for performing the method of any one of claims 1-9.
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