CN115018986B - Strong-structure active region rock microstructure information interpretation and three-dimensional modeling method - Google Patents
Strong-structure active region rock microstructure information interpretation and three-dimensional modeling method Download PDFInfo
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Abstract
The invention discloses a strong-structure active area rock microstructure information interpretation and three-dimensional modeling method which comprises the steps of collecting rock samples, manufacturing orthogonal slices in different directions, extracting and counting azimuth angles and lengths of microstructures in the plane direction under a polarizing microscope, dividing the microstructures into dominant groups according to the counted angles, calculating the space dominant appearance of the microstructures, and statistically analyzing a probability density distribution model and a volume density of the lengths of the microstructures of the dominant groups, thereby constructing a three-dimensional model reflecting the rock microstructures. According to the invention, the three-dimensional attitude information of the rock polarized light microscopic slice in the strong-structure active area is interpreted by utilizing the two-dimensional microstructure image in the rock polarized light microscopic slice, so that a three-dimensional model is constructed to represent the spatial distribution state of the microstructure, and effective references are provided for analyzing the correlation between the microstructure and the microstructure in a rock body, the diagenesis mechanism of the strong-structure active area, the evaluation of environmental geological disasters and the like.
Description
Technical Field
The invention belongs to the field of intelligent extraction of rock mass structure information, and particularly relates to a method for interpretation and three-dimensional modeling of rock microstructure information in a strong-structure active region.
Background
The complex geological environment is the root cause of a large number of engineering problems in the current underground engineering construction, especially in the conditions of strong tectonic activity area, large burial depth and high ground stress. The rock mass structure is taken as a key factor for controlling the engineering stability, the mechanical property of the complete rock is fundamentally changed, and the development and expansion of the microstructure can continuously deteriorate the mechanical property of the rock mass, thereby causing adverse effect on the aging stability of the rock mass. Many studies have shown that the presence of microstructures in rock significantly affects the deformation behavior of the rock and causes a deterioration in the strength of the rock. The influence of the microstructure on the stress distribution in the rock is even larger than the heterogeneity of the mineral, and particularly under the action of a strong structure moving area or complex stress, the microstructure in the rock is easy to develop and evolve, and finally, macroscopic damage can be caused. Therefore, the macroscopic mechanical behavior of the rock-soil mass is closely related to the microscopic structure. The damage deformation damage process of the rock is researched, and the influence of the microstructure is not negligible.
At present, the prior art about rock microstructure has at least the following problems: most researches mainly focus on two-dimensional plane image recognition and acquisition of microstructure information, and the real distribution state of the microstructure in a three-dimensional space cannot be intuitively reflected and represented. In addition, in the aspects of microstructure identification and three-dimensional modeling, the three-dimensional core visualization method based on the CT picture is disclosed in Chinese patent with application number CN103971410A which is mainly researched by an X-ray CT scanning method. There are problems in that the conventional CT scan is limited by penetration energy, limited in the ability to identify a micro-scale fracture, 100 μm in accuracy, and expensive in the CT scan test.
The polarizing microscope is commonly used for observing images of a representative region of a rock microstructure, the identification precision of the microstructure can reach 10 micrometers, and is improved by about 10 times compared with the precision of a CT (computed tomography) identification microstructure, but no good topological relation exists among microscopic pictures, so that the current technical research is only limited to the identification and extraction of two-dimensional plane information, the good relation with three-dimensional space information is difficult to establish, and a three-dimensional model of the microstructure cannot be established.
Disclosure of Invention
Aiming at the defects, the invention provides a method for interpreting and three-dimensional modeling of rock microstructure information in a strong structure active region, which comprises the following steps:
the invention is realized by the following technical scheme:
a strong structure active region rock microstructure information interpretation and three-dimensional modeling method is characterized by comprising the following steps:
collecting a representative rock sample in a strong structure moving area, manufacturing orthogonal slices of the rock sample in different directions, and observing the slices of the rock sample under a polarizing microscope;
the rock sample is respectively made into slices according to the north-south, east-west and horizontal orthogonal directions, and a complete image of a view area covering rock slice sample is obtained through a polarizing microscope, so that the microstructure in the rock slice can be better observed, and the development characteristics and the distribution rule of the microstructure are analyzed.
Optimizing an orthogonal image observed by a polarizing microscope in the step (1) based on a region growing algorithm;
(1) The image of the rock microstructure acquired by the polarization microscope can be represented in the form of a matrix (1):
in the formula, x and y are coordinates of pixel points in the image; the function f (x, y) is the gray value at the pixel point (x, y) and takes the value of 0-255.
And processing the image by adopting a region growing algorithm and extracting points approximate to the gray value of the seed points. If the gray value f (x, y) of the point to be analyzed satisfies the formula (2), it is classified as a microstructure area. And when the pixel point coordinates in the image are traversed and no pixel point meeting the requirement is added into the microstructure area after the detection, the image segmentation is finished.
|f(x,y)-g(x,y)|≥t(2)
Wherein f (x, y) is the gray value of the point to be analyzed, and g (x, y) is the average gray value of the microstructure whose segmentation is completed.
Extracting and counting the azimuth angle and the length of the microstructure in the plane direction, and dividing the microstructure into dominant groups according to the counted angle;
(1) And marking each separated microstructure in the two-dimensional microstructure according to the difference of the gray values of the microstructure and the rock grains, and measuring the angle and length attributes of the microstructure in the marking matrix.
(2) And determining the inclination direction epsilon and the inclination angle zeta of the microstructure according to the direction of the sheet where the microstructure is located. Specifically, the inclination direction of the slice in the north-south direction is 180 ° or 360 ° (specifically, depending on the spatial direction of the microstructure), and the inclination angle is the azimuth angle of the plane in which the microstructure is located; the dip direction of the east-west direction slice is 90 degrees or 270 degrees (specifically, the dip angle is the angle of the plane of the microstructure); the inclination direction of the horizontal slice is the angle of the plane of the microstructure, and the inclination angle is 0.1 degrees for convenient calculation. Then, the microstructures on each slice are divided into three dominant groups J according to the inclination direction and inclination angle of the counted microstructures 1 、J 2 、J 3 And classifying the microstructure into the same advantageous group according to the spatial occurrence of each group of microstructures of the three sections.
Acquiring space advantage occurrence of the microstructure by utilizing mathematical analysis, and statistically analyzing probability distribution models of the volume density and the length of each advantage group microstructure;
(1) If the actual tendency of the microstructure dominant set in the three-dimensional space is α and the inclination angle is β (unknown parameter), the direction cosine { l, m, n } of the microstructure normal n in the three-dimensional space is:
the predominant set of microstructures had a dip on a two-dimensional sheet profile of ε and a dip angle of ζ (known parameters). Then the direction cosine { l ', m ', n ' } is
(2) The geometric relationship between the direction cosine of the normal n of the microstructure in the three-dimensional space and the direction cosine of the microstructure on the section of the sheet is obtained, and the cosine of the included angle between the normal n of the microstructure and the trace of the microstructure on the section of the sheet is as follows:
cosθ＝ll′+mm′+nn′ (5)
according to the direction cosine { l, m, n } of the microstructure normal line n and the direction cosine { l ', m ', n ' } on the sheet section in three-dimensional space, the two are approximately vertical, and theta is approximately equal to 90 degrees. It is noted that the microstructure is affected by in-situ structural stresses, and the direction of the microstructure may be slightly deflected along the rock grain boundaries, so that the above-mentioned perpendicular relationship is considered to be satisfied when θ is in the range of 70 to 110 ° (| cos θ | < 0.342). An equation can be constructed according to the information distribution conditions of the same advantage group of the microstructures in the three section directions, and then the actual occurrence (inclination alpha and inclination beta) of the advantage group of each microstructure in a three-dimensional space is solved.
And (4) obtaining a corresponding probability distribution model through statistical analysis according to the length information of the microstructure advantage group. Meanwhile, the total length d (mum) of the microstructure obtained by the identification and statistics in the two-dimensional plane is used for calculating the surface density lambda s (μm -2 ) And then solving the volume density lambda of the microstructure in the three-dimensional space according to the statistical rock mechanics theory v (μm -3 )
Wherein
Respectively, the average length and radius of the microstructure, S is the area of the two-dimensional microflake image, λ s 、λ v Respectively, the linear density, the areal density, and the bulk density of the microstructure in the target region.
Utilizing Monte Carlo theory to simulate and construct a three-dimensional structure network model reflecting spatial distribution characteristics, quantity and scale characteristics of the rock microstructure;
and then according to the basic principle of rock mass structure network simulation, representing the orientation of the microstructure by using the attitude, representing the data volume of the microstructure by using the volume density and representing the size of the microstructure by using the length. Based on the probability distribution characteristics of the parameters, random variables obeying the microstructure occurrence (inclination alpha, inclination angle beta) and length are generated, and a three-dimensional model which visually reflects the actual situation of the rock microstructure is generated by taking the volume density as a termination condition.
Compared with the prior art, the invention has the following advantages:
1. the method comprises the steps of firstly processing a rock microscopic picture under a polarizing microscope through a region growing intelligent algorithm, then extracting and counting the geometrical information of the microstructure such as the azimuth angle, the length and the like on an orthogonal plane, then interpreting the three-dimensional occurrence information of the microstructure by using limited two-dimensional sample data, counting the characteristic parameters of the microstructure such as the scale, the volume density and the like by using a statistical theory, and finally reconstructing a three-dimensional model reflecting the development condition and the distribution characteristic of the microstructure. Compared with the prior CT image three-dimensional information identification and model reconstruction technology, the method mainly adopts a polarizing microscope instrument, can identify the microstructure of the rock with higher precision (the precision of the microstructure is improved by about 10 times compared with the precision of the CT identification), has relatively lower price of the microstructure test, and has better applicability and popularization.
2. The method establishes the relationship between the spreading state of the rock microstructure in a two-dimensional picture and three-dimensional spatial information under the polarizing microscope, constructs an equation set according to the relationship between the spreading state and the three-dimensional spatial information, obtains the spatial distribution state of the rock micro-scale fracture by using mathematical analysis, can accurately and quickly interpret the three-dimensional geometrical information characteristics of the rock microstructure, and further reconstructs a three-dimensional model to represent the spatial distribution state of the microstructure. The advancement of the rock microstructure informatization recognition technology is realized. Powerful support is provided for the disaster prevention and reduction research of rock engineering in strong construction areas.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic view showing the orientation of an orthogonal section of a specimen.
FIG. 3 is a distribution diagram of the cutting directions and microstructures of the north-south section, the east-west section and the horizontal section of the rock sample: (a) observing the microstructure of the slice under a polarizing microscope; (b) A microstructure profile map identified and extracted from fig. 3a and (c) a rosette of different micro-section microcracks.
Fig. 4 is a schematic view of a microstructure in a three-dimensional spatial coordinate system.
FIG. 5 is a model diagram of the probability distribution of microstructure lengths.
FIG. 6 is a schematic representation of a three-dimensional model of a rock microstructure.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, the specific implementation steps of the present invention are as follows:
step 1: collecting a representative rock sample in a strong-structure moving area, manufacturing orthogonal slices of the rock sample in different directions, and observing the slices of the rock sample under a polarization microscope;
collecting rock samples at a strong structure moving area, processing the rock samples into cylindrical samples, and respectively manufacturing the rock samples into thin slices in the north-south, east-west and horizontal orthogonal directions. A complete image of a sample of the rock slice covered by the view area is obtained by a Leica DM4P intelligent polarizing microscope so as to better observe the microstructure in the rock slice and analyze the development characteristics and the distribution rule of the microstructure. The slice cut direction and microstructure development state are shown in fig. 2 and fig. 3a, respectively.
And 2, step: optimizing and processing an image observed by a polarizing microscope by using Matlab software in combination with a region growing algorithm;
(1) The microstructure image of the rock mass obtained by the polarization microscope can be represented by a matrix (1):
in the formula, x and y are coordinates of pixel points in the image; the function f (x, y) is the gray value at the pixel point (x, y) and takes the value of 0-255.
And selecting initial seed points in the image by using a function [ y, x ] = getpts interactive mode in Matlab, processing the image by adopting a region growing algorithm, and extracting points approximate to the gray value of the seed points. A threshold t =25 (i.e., similarity) is set, and if the point gray-scale value f (x, y) to be analyzed satisfies equation (2), it is classified as being in the microstructure area.
|f(x,y)-g(x,y)|≥t(2)
Wherein f (x, y) is the gray value of the point to be analyzed, g (x, y) is the average gray value of the microstructure whose segmentation is completed, and t is the threshold value of the gray value approximate point of the seed point.
When the coordinates of the pixel points in the image are traversed and no more pixel points meeting the requirements are added into the microstructure area after the detection, the image segmentation is completed, a corresponding microstructure contour map is obtained, as shown in fig. 3b, and the microstructure distribution state of the rock can be clearly observed.
And 3, step 3: extracting and counting the azimuth angle and the length of the microstructure in the plane direction, and dividing the microstructure into dominant groups according to the counted angle;
(1) Marking each separated microstructure in the two-dimensional microstructure image by using a Bwlabel function in MATLAB software according to the difference of the gray values of the microstructure and the rock grains, and searching and discriminating a microstructure area; then, the attributes of the microstructure region in the labeling matrix are measured by a RegionProps function, such as an "organization" command to count the intersection angle of the microstructure and the x-axis, and a "Majora AxisLength" command to count the length of the microstructure, so as to automatically count the azimuth angle (0-180 ℃) and the length of each microstructure in the plane in the graph of FIG. 3 b.
And determining the inclination direction epsilon and the inclination angle zeta of the sheet according to the direction of the sheet with the microstructure. Specifically, the inclination direction of the slice in the north-south direction is 180 ° or 360 ° (specifically, depending on the spatial direction of the microstructure), and the inclination angle is the azimuth angle of the plane in which the microstructure is located; the slice inclination angle in the east-west direction is 90 degrees or 270 degrees (specifically, according to the space direction of the microstructure), and the inclination angle is the angle of the plane of the microstructure; the inclination direction of the horizontal slice is the angle of the plane of the microstructure, and the inclination angle is 0.1 degrees for convenient calculation.
(2) The orientation and angle of inclination of the microstructure counted in FIG. 3b divides the microstructure on each slice into three dominant groups J 1 、J 2 、J 3 . And classifying the microstructure into the same advantageous group according to the spatial occurrence of each group of microstructures of the three sections. As shown in Table 1The following steps:
TABLE 1 plane angles of microstructures in orthogonal sections of rock samples
And 4, step 4: acquiring space advantage occurrence of the microstructure by utilizing mathematical analysis, and statistically analyzing probability distribution models of the volume density and the length of each advantage group microstructure;
as shown in fig. 4, a three-dimensional rectangular space coordinate system is established with the south (S) being positive in the x-axis direction, the north (N) being negative in the x-axis direction, the east (E) being positive in the y-axis direction, the west (W) being negative in the y-axis direction, the upper side being positive in the z-axis direction, and the lower side being negative in the z-axis direction. If the actual tendency of the microstructure dominance set in the three-dimensional space is α and the inclination angle is β (unknown parameter), the direction cosine { l, m, n } of the microstructure normal n in the three-dimensional space is:
the predominant set of microstructures had a dip on a two-dimensional sheet profile of ε and a dip angle of ζ (known parameters), as shown in Table 1. The direction cosines { l ', m ', n ' } are
The direction cosine of the normal n of the microstructure in the three-dimensional space and the direction cosine of the microstructure on the section of the sheet are mutually perpendicular, and the cosine of an included angle between the normal n of the microstructure and the trace of the microstructure on the section of the sheet is obtained as follows:
cosθ＝ll′+mm′+nn′ (5)
according to the direction cosine { l, m, n } of the normal n of the microstructure and the direction cosine { l ', m ', n ' } of the microstructure on the section plane of the sheet in the three-dimensional space, the two are approximately vertical, and theta is approximately equal to 90 degrees. Notably, the microstructure is affected by in situ structural stresses, which may be directed in the directionSlight deflection can occur along the rock grain boundaries, so that the above-mentioned perpendicular relationship is considered to be satisfied when θ is in the range of 70 to 110 ° (| cos θ | < 0.342). An equation can be constructed according to the information distribution conditions of the same advantage group of the microstructures in the three section directions, and then the actual occurrence (inclination alpha and inclination beta) of the advantage group of each microstructure in a three-dimensional space is solved. In the fracture group J 1 For example:
wherein alpha is 1 、β 1 ，α 2 、β 2 ，α 3 、β 3 Are respectively fracture group J 1 Inclination and dip angle in the north-south, east-west and horizontal directions; epsilon 1 、ζ 1 ，ε 2 、ζ 2 ，ε 3 、ζ 3 Are respectively fracture group J 1 The dip angle in the north-south, east-west and horizontal directions.
Two unknowns exist, so that the equations are combined in pairs to obtain three equation sets to be solved, namely north-south-east-west, north-south-horizontal and east-west-horizontal, and the values of alpha and beta corresponding to three sets of direction combinations are obtained by the formula (7-9).
(1) North-south-east-west combination:
(2) north-south-horizontal combination:
(3) east-west-horizontal direction combination:
by using the above formulaRespectively solving, if the absolute errors of the three groups of tendencies and the inclination angles are within 10 degrees, taking the three groups of average values as a microstructure advantage group J 1 Inclination α, inclination β. The microstructure dominant group J is obtained by the same solution of the method 2 And J 3 Inclination, inclination angle. Subsequently, the microstructure J is subjected to 1 、J 2 、J 3 The inclination α, the inclination β is taken as the data center of the attitude obeying the Fisher distribution.
Dominance group J by microstructure 1 、J 2 、J 3 The length information of (2) is statistically analyzed to obtain corresponding probability distribution models, which are all subject to negative exponential distribution, see equation (10) and fig. 5.
Referring to the statistical theory of rock mechanics in the thesis, see Wu, F.Q., wang, S.J.,2002.Statistical model for structure of joint rock mass. G. otechnique 52 (2), 137-140. By the method, the total length d (mum) of the microstructure is obtained through recognition and statistics in a two-dimensional plane, the area density lambdas (mum-2) is calculated, and then the volume density lambdas (mum-2) of the microstructure in a three-dimensional space is solved according to the statistical theory of rock mechanics v (μm -3 ) See formula (11):
wherein
Respectively, the average length and radius of the microstructure, S is the area of the two-dimensional microflake image, λ s 、λ v Respectively, the linear density, the areal density, and the bulk density of the microstructure in the target region.
And 5: utilizing Monte Carlo theory to simulate and construct a three-dimensional structure network model reflecting spatial distribution characteristics, quantity and scale characteristics of the rock microstructure;
according to the basic principle of rock mass structure network simulation, the orientation of the microstructure is represented by utilizing the attitude, the data volume of the microstructure is represented by utilizing the volume density, the size of the microstructure is represented by the trace length, and an algorithm and a flow of three-dimensional microstructure network simulation are realized by adopting a Matlab programming procedure. Firstly, determining the size range of a simulated rock region, inputting three groups of microstructure advantage states obtained by interpretation and microstructure volume density parameters and a length probability distribution model obtained by statistical analysis. Then, based on the probability distribution characteristics of the parameters, random variables complying with the advantages, shapes (inclination angle alpha and inclination angle beta) and lengths of the microstructure are generated by a Monte Carlo simulation method, the volume density is taken as a termination condition, and a three-dimensional model which visually reflects the actual situation of the rock microstructure is generated as shown in figure 6, wherein the image space resolution is 10 mu m.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made to the embodiment of the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A strong-structure active region rock microstructure information interpretation and three-dimensional modeling method is characterized by comprising the following steps:
【1】 Collecting a representative rock sample in a strong structure moving area, manufacturing orthogonal slices of the rock sample in different directions, and observing the slices of the rock sample under a polarizing microscope;
【2】 Optimizing and processing the image observed by the polarizing microscope by adopting a region-based growth algorithm;
【3】 Extracting and counting the azimuth angle and the length of the microstructure in the plane direction, and dividing the microstructure into advantage groups according to the counted angle;
【4】 The method comprises the following steps of obtaining the space advantage occurrence of the microstructure by utilizing mathematical analysis, and statistically analyzing the probability distribution parameters of the length and the volume density of each advantage group microstructure, and comprises the following specific steps:
let the actual tendency of the microstructure dominance group in the three-dimensional space be α and the inclination angle be β, the direction cosine { l, m, n } of the microstructure normal n in the three-dimensional space is:
the dip direction of the microstructure dominance group on the two-dimensional sheet cross section is epsilon, the dip angle is zeta, and the direction cosines { l ', m ', n ' } are:
according to the mutual perpendicular relation between the direction cosine of the normal n of the microstructure in the three-dimensional space and the direction cosine of the microstructure above the slice cross section, the cosine of the included angle between the microstructure normal n and the trace line of the microstructure on the slice cross section is obtained as follows:
cosθ＝ll′+mm′+nn′
wherein theta is an included angle between a microstructure normal n and a sheet section trace line, and the value range of theta is 70-110 degrees;
solving the actual tendency alpha and inclination angle beta of the advantage groups of each microstructure in the three-dimensional space, and taking the tendency alpha and the inclination angle beta as data centers of which the production states are subjected to Fisher distribution;
(4.3) obtaining a corresponding probability distribution model through statistical analysis according to the length information of the microstructure dominant group, obtaining the total length d (mum) of the microstructure through recognition and statistics in a two-dimensional plane, and solving the volume density lambda (lambda) of the microstructure in a three-dimensional space according to a statistical rock mechanics theory v (μm -3 )：
Wherein
Respectively, the average length and radius of the microstructure, S is the area of the two-dimensional microflake image, λ s 、λ v Respectively the linear density, the surface density and the volume density of the microstructure in a target area;
【5】 And (3) utilizing Monte Carlo theory to simulate and construct a three-dimensional structure network model reflecting the spatial distribution characteristics, quantity and scale characteristics of the rock microstructure.
2. The method for interpreting and three-dimensional modeling of rock microstructure information of strong tectonic activity zones as claimed in claim 1, wherein: the step [ 1 ] is specifically as follows:
the rock sample is respectively made into slices according to the north-south, east-west and horizontal orthogonal directions, and a complete image of the rock slice sample is obtained through a polarizing microscope so as to observe the development characteristics and the distribution rule of the rock slice sample.
3. The method for interpreting and three-dimensional modeling of rock microstructure information in a strongly constructed active region according to claim 1, wherein: the step [ 2 ] is specifically as follows:
[ 2.1 ] images of the rock microstructure taken by a polarizing microscope are represented as:
in the formula, x and y are coordinates of pixel points in the image; the function f (x, y) is the gray value of the pixel point (x, y) and the value is 0-255;
(2.2) processing the image by adopting a region growing algorithm and extracting points approximate to the gray value of the seed points, if the gray value f (x, y) of the point to be analyzed meets | f (x, y) -g (x, y) | is more than or equal to t, classifying the point to be analyzed into a microstructure region, and finishing image segmentation when the coordinates of pixel points in the image are traversed and no pixel points meeting the requirements are added into the microstructure region after the inspection;
wherein f (x, y) is the gray value of the point to be analyzed, g (x, y) is the average gray value of the microstructure whose segmentation is completed, and t is the threshold value of the gray value approximate point of the seed point.
4. The method for interpreting and three-dimensional modeling of rock microstructure information in a strongly constructed active region according to claim 1, wherein: the step [ 3 ] is specifically as follows:
marking each separated microstructure in the two-dimensional microstructure according to the difference of the gray values of the microstructure and the rock grains, and measuring the angle and length attributes of a microstructure area in a marking matrix;
(3.2) determining the inclination direction epsilon and the inclination angle zeta of the microstructure according to the direction of the slice in which the microstructure is positioned; the microstructures on each sheet are divided into three dominance groups J according to the dip direction epsilon and dip angle zeta of the counted microstructures 1 、J 2 、J 3 And classifying the microstructures in each group into the same dominant group according to the spatial occurrence of the microstructures in each group of the three sections.
5. The method for interpreting and three-dimensional modeling of rock microstructure information of strong tectonic activity zones as claimed in claim 1, wherein: the step [ 5 ] is specifically as follows:
according to the rock mass structure network simulation principle, the orientation of the microstructure is represented by utilizing the attitude, the data volume of the microstructure is represented by utilizing the volume density, the size of the microstructure is represented by the trace length, random variables obeying the attitude and the length of the microstructure are generated based on the probability distribution characteristics of the parameters, and a three-dimensional model which visually reflects the actual situation of the rock microstructure is generated by taking the volume density as a termination condition.
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| CN113945175A (en) * | 2021-10-14 | 2022-01-18 | 中国地质科学院地质力学研究所 | Rock three-dimensional data acquisition method and device |
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