CN114460554A - Karst cave three-dimensional imaging method based on ground penetrating radar multi-attribute fusion - Google Patents

Karst cave three-dimensional imaging method based on ground penetrating radar multi-attribute fusion Download PDF

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CN114460554A
CN114460554A CN202210090976.7A CN202210090976A CN114460554A CN 114460554 A CN114460554 A CN 114460554A CN 202210090976 A CN202210090976 A CN 202210090976A CN 114460554 A CN114460554 A CN 114460554A
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dimensional
attribute
data
ground penetrating
penetrating radar
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刘宗辉
刘毛毛
蓝日彦
徐一洲
高山
覃子秀
李建合
刘家庆
王红伟
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Guangxi University
Guangxi Xinfazhan Communications Group Co Ltd
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Guangxi Xinfazhan Communications Group Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G01S13/9094—Theoretical aspects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/418—Theoretical aspects

Abstract

A karst cave three-dimensional imaging method based on ground penetrating radar multi-attribute fusion comprises the following steps: (1) preprocessing ground penetrating radar data; (2) calculating the three-dimensional attributes of the ground penetrating radar of the instantaneous amplitude, energy and maximum amplitude spectrum of the data volume obtained by processing in the step (1); (3) normalizing the three-dimensional attribute bodies in the step (2) by using a range standardization method; (4) fusing the three attribute bodies subjected to range standardization treatment by using a principal component analysis method to obtain a fused attribute body; (5) extracting abnormal reflection region data in the fusion attribute body time slices in the step (4) at equal intervals, and performing three-dimensional reconstruction by using space coordinate information of each slice; (6) and (5) realizing three-dimensional visualization of the tunnel karst cave target body based on the reconstruction data obtained in the step (5) and a body visualization technology. The method can effectively perform accurate three-dimensional modeling on the underground karst cave through the ground penetrating radar, and provides guidance help for processing related problems of the underground karst cave in the construction process.

Description

Karst cave three-dimensional imaging method based on ground penetrating radar multi-attribute fusion
Technical Field
The invention relates to the field of tunnel geological detection, in particular to a karst cave three-dimensional imaging method based on ground penetrating radar multi-attribute fusion.
Background
When tunnel engineering construction is carried out in a karst area, unfavorable geology such as karst caves, underground rivers and the like is often encountered. The existence of the karst seriously restricts the construction period and the construction cost of the tunnel, and brings serious threats to the safety of constructors and collective property. The karst development has the characteristics of complexity, variability, various forms and the like, and has randomness and unpredictability in local parts although the macro aspect can be foreseen and presumed. The space position and the geometric form of the cave body and the surrounding karst cave are accurately detected in the tunnel construction process, and the method has an extremely important guiding function for evaluating disaster danger levels and making detailed construction schemes.
In recent years, geological advanced forecasting techniques during tunnel construction have been rapidly developed. Common methods include in-hole geological survey, advanced drilling, nondestructive testing, and the like. The advanced drilling has the advantages of accurate positioning, visual disclosure of properties of karst substances and the like, but the detection efficiency is low, the cost is high, and the complex and variable karst space morphology is difficult to accurately describe by one-hole observation. The tunnel nondestructive detection method mainly comprises ground penetrating radar, induced polarization and the like, and each method has respective advantages and applicable conditions. Among them, Ground Penetrating Radar (GPR) has the incomparable advantages of high resolution, intuitive result, fast scanning speed and other geophysical methods, and has become the most important nondestructive testing means for the poor geology of karst tunnels in recent years. However, the ground penetrating radar, as a nondestructive detection method based on the electromagnetic wave theory, also has the defect of multiple solutions, and the interference, electromagnetic attenuation, scattering and complexity of karst geology in the tunnel detection environment, so that the technology is always in a simple application stage. The prediction accuracy of the spatial position and geometry of the karst cavern in the tunnel is far from reaching the effect expected by the industry.
The ground penetrating radar imaging technology is an effective means for accurately acquiring the structural distribution of underground complex media, and is a research hotspot of a GPR application technology. A large number of scholars at home and abroad develop a great deal of research around three main imaging technologies, namely full waveform inversion, tomography and offset imaging, and a lot of achievements are achieved. However, most of the research is still in the theoretical research stage at present, and various imaging methods are easily influenced by factors such as medium dispersion, randomness and anisotropy when applied to the actual engineering case. The calculation precision and the calculation efficiency are still the bottlenecks which restrict the development of the ground penetrating radar imaging technology. Therefore, it is necessary to explore new fast and accurate imaging techniques for such extremely complex survey objects as karst caves.
Disclosure of Invention
In order to solve the technical problems encountered in the actual engineering and reduce the construction risk. The invention provides a karst cave three-dimensional imaging method based on ground penetrating radar multi-attribute fusion, which can extract three-dimensional attribute bodies based on different attributes from radar original three-dimensional grid data, and then fuse the three-dimensional attribute bodies to finally obtain comprehensive display of the whole outline of a karst cave.
In order to achieve the purpose, the invention adopts the technical scheme that: a karst cave three-dimensional imaging method based on ground penetrating radar multi-attribute fusion comprises the following steps:
(1) preprocessing ground penetrating radar data;
the preprocessing comprises track editing, oscillation-free filtering, zero-time correction, background removal, gain, band-pass filtering, offset and three-dimensional data synthesis;
the gain is a spherical diffusion compensation gain function;
(2) calculating the three-dimensional attributes of the ground penetrating radar of the instantaneous amplitude, energy and maximum amplitude spectrum of the data volume obtained by processing in the step (1);
the three-dimensional attribute of the ground penetrating radar is obtained by interpolating and synthesizing two-dimensional attribute data into a three-dimensional attribute body or directly calculating the three-dimensional attribute body on the basis of the interpolated three-dimensional radar data;
(3) normalizing the three ground penetrating radar three-dimensional attribute bodies in the step (2) by using a range standardization method;
the range standardization specific rule formula is as follows:
Figure BDA0003489166760000021
wherein: x is the number ofiIs the original data;
Figure BDA0003489166760000022
is the average of the ith variable; maxxiAnd minxiIs the maximum and minimum of the ith variable;
(4) fusing the three attribute bodies subjected to range standardization treatment by using a principal component analysis method to obtain a fused attribute body;
the principal component analysis, i.e., the fusion method in the linear dimension reduction method, first needs to convert the three-dimensional attribute body into a single-attribute one-dimensional matrix, each one-dimensional matrix contains spatial coordinate information, and further uses a two-dimensional matrix a to represent a plurality of attributes:
Figure BDA0003489166760000023
aiming at the matrix A, after a fused comprehensive attribute one-dimensional matrix is obtained by adopting a conventional principal component analysis method processing flow, the fused comprehensive attribute one-dimensional matrix needs to be restored into a three-dimensional comprehensive attribute body according to original coordinate information;
the conventional principal component analysis method comprises the following processing procedures: solving a zero mean matrix Z, solving a covariance matrix C, solving an eigenvalue and an eigenvector of the covariance matrix C, and constructing an eigen matrix P and data projection;
(5) extracting abnormal reflection region data in the fusion attribute body time slices in the step (4) at equal intervals, and performing three-dimensional reconstruction by using space coordinate information of each slice;
the method for acquiring the abnormal reflection region data mainly utilizes the characteristic that the fusion attribute body can highlight the abnormal reflection signal region, compares the abnormal reflection signal region with the original three-dimensional data body, and manually extracts the data of the reflection signal region of the karst cave target body in each time slice data of the fusion attribute body. Extracting slices from the fused attributes at equal time (space) intervals, then drawing a karst cave contour by picking the contour line of each slice in the three-dimensional attribute body, and finally associating the contour of each slice with a space coordinate;
(6) realizing three-dimensional visualization of the tunnel karst cave target body based on the reconstruction data obtained in the step (5) and a body visualization technology;
the body visualization technology mainly extracts an isosurface from three-dimensional body data by using an isosurface function.
The invention has the outstanding advantages that:
aiming at the unknown property of an underground karst cave in the tunnel construction process, a ground penetrating radar is used as a detection foundation, and a three-dimensional imaging method based on multi-attribute fusion is provided. The method fuses instantaneous amplitude attributes, spectral whitening attributes, spectral decomposition attributes, energy attributes and maximum amplitude spectral attributes and comprehensively analyzes and describes the detected underground karst cavern. Compared with the prior art that radar detection is analyzed and explained only by single attribute, the karst situation is comprehensively and comprehensively described from multiple attributes, the imaging accuracy is improved, and safer and more accurate guidance is provided for dangerous situations such as underground karst caves and the like which may be encountered in construction.
Drawings
Fig. 1 is a general flowchart of a three-dimensional imaging method based on ground penetrating radar multi-attribute fusion according to the present invention.
FIG. 2 is a flowchart illustrating a multi-attribute fusion process of the ground penetrating radar of the present invention.
FIG. 3 is a flow chart of a three-dimensional profile imaging method of a geological anomaly based on three-dimensional attribute slicing.
FIG. 4a shows the palm surface at YK364+ 193.
The right vertical fluid bowl at the palm surface YK364+193 in FIG. 4 b.
FIG. 4c at YK364+ 193.
FIG. 4d is a three-dimensional data volume after preprocessing.
FIG. 4e three-dimensional attribute body and slice images.
Fig. 4e (1), 4e (2), and 4e (3) show the instantaneous amplitude property, the energy property, and the maximum amplitude spectrum property obtained by calculation. Fig. 4e (4), 4e (5), and 4e (6) are slices of the individual animal body in fig. 4d at a depth of 8.0m, respectively.
FIG. 4f is a schematic diagram of extracting a geological anomaly contour from a three-dimensional attribute slice.
FIG. 4g three-dimensional imaging results of a karst cave.
Detailed Description
The technical solution of the present invention is further explained with reference to the drawings and the embodiments.
Example 1
As shown in fig. 1, the three-dimensional imaging method based on ground penetrating radar multi-attribute fusion, provided by the invention, comprises the following specific operation steps and control conditions:
(1) and preprocessing ground penetrating radar data. The main steps of the ground penetrating radar data preprocessing comprise channel editing, zero time correction, background removal, gain, band-pass filtering and three-dimensional data synthesis. After a plurality of data profiles are acquired by adopting three-dimensional grid detection on site, each two-dimensional data profile is processed according to the preprocessing method, the preprocessing flow and parameter setting of each profile data are consistent, wherein the band-pass filtering can be set to be 0.5-2.0 times of central frequency, and as the spherical diffusion compensation gain (SEC) can keep relative amplitude information, an SEC gain function is selected in the case. Furthermore, for locations where the diffraction hyperbola is significant, an F-K shift is used, and the wave velocity of the shift is obtained by fitting the diffraction hyperbola. And finally, synthesizing the three-dimensional data volume by interpolation.
(2) And (3) calculating the data in the step (1) to obtain a ground penetrating radar three-dimensional attribute body of an instantaneous amplitude attribute, a spectral whitening attribute, a spectral decomposition attribute, an energy attribute and a maximum amplitude spectral attribute. After a plurality of data profiles are acquired by adopting three-dimensional grid detection on site, each two-dimensional data profile is preprocessed according to the step (1), and the preprocessing flow and parameter setting of each profile data are consistent, such as uniformly adjusting the zero position, the gain function, the number of sampling tracks and the like. After the pretreatment of the single section is finished, according to the arrangement form of the field survey lines, the two-dimensional data is led out of the radial 7 software, and a three-dimensional data body is synthesized through interpolation. The three attributes of instantaneous amplitude, energy and maximum spectrum amplitude are most sensitive to the amplitude change of radar reflected waves, so the instant amplitude, energy and maximum spectrum amplitude attribute are selected to distinguish karst caves (high amplitude) from limestone (low amplitude).
(3) The three-dimensional attribute volume was normalized using the range normalization method. The specific rule formula is as follows:
Figure BDA0003489166760000041
wherein: x is the number ofiIs the original data;
Figure BDA0003489166760000042
is the average of the ith variable; maxxiAnd minxiIs the maximum and minimum of the ith variable;
(4) and fusing the three attribute bodies subjected to range standardization by using a principal component analysis method to obtain a fused attribute body. Since principal component analysis is initially implemented for a one-dimensional matrix, it is first necessary to convert three-dimensional attribute bodies into single-attribute one-dimensional matrices, each one-dimensional matrix contains spatial coordinate information, for example, n three-dimensional attribute bodies can be represented as n one-dimensional matrices (with the same dimension and magnitude) with length p, and further, a two-dimensional matrix a is used to represent a plurality of attributes:
Figure BDA0003489166760000043
for the matrix a, a conventional PCA processing procedure may be further employed, including: solving a zero mean matrix Z, solving a covariance matrix C, solving an eigenvalue and an eigenvector of the covariance matrix C, constructing an eigen matrix P, projecting data and the like. After the fused one-dimensional matrix of the comprehensive attributes is obtained, the fused one-dimensional matrix of the comprehensive attributes needs to be restored into a three-dimensional comprehensive attribute body according to the original coordinate information.
(5) And extracting abnormal reflection region data in the fusion attribute body time slice at equal intervals, and performing three-dimensional reconstruction by using the space coordinate information of each slice. Based on the current research situation, two methods are proposed to determine the isosurface of the three-dimensional attribute body and display the contour of the karst abnormality.
The first method is to regard the fused three-dimensional attribute body as a whole, directly take a threshold value for the three-dimensional attribute body to determine an isosurface, and influence of the change of the threshold value on the contour of the isosurface is large, so that some prior information such as drilling data, geological survey data and the like needs to be combined as much as possible in the process. The commonly used threshold value determination methods at present include K-Means clustering and the like. The method has the main advantages that the operation is simple, the effect of the medium is good corresponding to the uniform low loss, but if the ground penetrating radar signal is obviously attenuated along the depth direction, the result error is increased when the whole three-dimensional data body takes one threshold value.
The second method is to extract slices from the fused three-dimensional attribute body at equal time (space) intervals, then draw the contour of the karst cave by picking the contour line of each slice in the three-dimensional attribute body, and finally associate the contour of each slice with the space coordinates, thereby imaging the three-dimensional abnormal region. To prevent over-interpretation, each slice anomaly region contour should coincide with a strong reflective interface in the ground penetrating radar grayscale image. See the subsequent example 2 for the specific implementation process. The specific flow of the second method is shown in fig. 2.
(6) And (5) realizing three-dimensional visualization of the tunnel karst cave target body based on the reconstruction data obtained in the step (5) and a body visualization technology. And (3) extracting an isosurface from the three-dimensional data volume by using an isosurface function to obtain the contour line of each depth abnormal reflection area, and then performing interpolation and superposition on the contour lines to obtain a continuous three-dimensional curved surface.
Example 2
This embodiment is an application example of the method for three-dimensional imaging of a karst cave based on multi-attribute fusion of a ground penetrating radar,
the application effect of the invention is illustrated by taking a large karst cave disclosed at the bottom of the right line of a certain tunnel in Guangxi as an example:
in the construction process of the tunnel right-side tunnel face, a vertical dissolving tank is disclosed in the position of about 0.5 meter close to the right side wall in the sections YK364+ 200-YK 364+180 and in the direction parallel to the tunnel, the vertical dissolving tank has the length of about 10m, the width of about 2m and the depth of about 30m, and no filler is filled. The tunnel is continuously excavated to be near YK364+174, karst cavities are exposed on the left side and the right side of the tunnel face, the width of the karst on the left side is about 2 meters, the height of the karst on the right side is about 3 meters, the height of the karst on the right side is larger than that of the tunnel face, the cavities extend about 40 meters in the excavation direction, and the depth of the cavities is unknown. The tunnel YK364+ 202-YK 364+174 section has disclosed that the surrounding rock is slightly weathered limestone, the rock quality is hard, the rock mass is overall complete, the local joint cracks develop, the local part is broken, the right-side solution tank has the partial water seepage phenomenon, and the self-stability capability of the surrounding rock is relatively good. The field situation is shown in FIG. 4a at YK364+193, FIG. 4b at YK364+193, right vertical fluid bowl, FIG. 4c at YK364+ 193.
The inclined strong reflection interface can be obviously seen from the section views of three radar measuring lines on site, so that the upper reflection interface of the karst cave below the tunnel bottom plate can be well outlined. According to the reflection interface, the following judgment can be made: the more the tunnel face, the shallower the buried depth of the karst cave below the tunnel bottom plate. The two-dimensional detection section of the ground penetrating radar shows that obvious horizontal strong reflection signals exist in a part of the deep range when the time is 0-200ns, and joint cracks in the shallow range can be presumed to be relatively developed by combining with the field investigation condition.
After a plurality of data profiles are obtained by adopting three-dimensional grid detection on site, each two-dimensional data profile is preprocessed, the preprocessing flow and parameter setting of each profile data are consistent, and the embodiment further adopts shear wave transformation to remove noise on the basis of conventional preprocessing. After the pretreatment of a single section is completed, according to the arrangement form of field survey lines, the two-dimensional data is imported into the radial 7 software, and a three-dimensional data body is synthesized by interpolation, as shown in the pretreated three-dimensional data body in fig. 4d and the three-dimensional attribute body and slice diagram in fig. 4 e. It can be seen from the figure that the data of the transverse and longitudinal measuring lines can be well overlapped at the position of the cross point, which shows that the longitudinal and transverse data of the three-dimensional data can be mutually matched and the data is reliable.
Three-dimensional attribute body calculation was performed. In the embodiment, the three-dimensional attribute body is directly calculated on the basis of the three-dimensional radar data after interpolation, and the three-dimensional attribute can be directly calculated by adopting OpendTract software of dGB, Netherlands. Fig. 4e (1), 4e (2), and 4e (3) show the instantaneous amplitude property, the energy property, and the maximum amplitude spectrum property obtained by calculation. Fig. 4e (4), 4e (5), and 4e (6) are slices of the individual animal body in fig. 4d at a depth of 8.0m, respectively. From the three-dimensional attribute bodies and the two-dimensional slicing results, different attribute bodies can approximately represent the geometric form of the karst cave, but the karst forms displayed by the different attribute bodies have certain difference, and the instantaneous amplitude and energy attribute effects are relatively good.
In fig. 4e, the three attribute data volumes of the instantaneous amplitude, the energy and the maximum amplitude spectrum have the same dimension, and the magnitude is the same after range normalization, so that the three attribute data volumes are selected for fusion analysis in the present example. Fig. 4f is a process of extracting slices from different depths (time) of the fused three-dimensional attribute body by using the volume visualization technology in Matlab and mapping the contour of the abnormal reflection region, wherein the mapped karst cave contour in each slice should be consistent with the karst cave reflection interface in the ground penetrating radar gray level image, and arrows of 13.3m and 16.9m in fig. 4f indicate. And controlling the size of the karst cave outline mapped by each slice by adopting a method of adjusting the attribute color bars.
Fig. 4g shows three-dimensional imaging results of karst cave below the tunnel bottom plates of sections YK364+ 199-YK 364+184, and it can be seen from the figure that the spatial position and the geometric form of the karst cave can be well displayed.

Claims (4)

1. A karst cave three-dimensional imaging method based on ground penetrating radar multi-attribute fusion is characterized by comprising the following steps:
(1) preprocessing ground penetrating radar data;
the preprocessing comprises track editing, oscillation-free filtering, zero-time correction, background removal, gain, band-pass filtering, offset and three-dimensional data synthesis;
the gain is a spherical diffusion compensation gain function;
(2) calculating the three-dimensional attributes of the ground penetrating radar of the instantaneous amplitude, energy and maximum amplitude spectrum of the data volume obtained by processing in the step (1);
the three-dimensional attribute of the ground penetrating radar is obtained by interpolating and synthesizing two-dimensional attribute data into a three-dimensional attribute body or directly calculating the three-dimensional attribute body on the basis of the interpolated three-dimensional radar data;
(3) normalizing the three ground penetrating radar three-dimensional attribute bodies in the step (2) by using a range standardization method;
the range standardization specific rule formula is as follows:
Figure FDA0003489166750000011
wherein: x is the number ofiIs the original data;
Figure FDA0003489166750000012
is the average of the ith variable; maxxiAnd minxiIs the maximum and minimum of the ith variable;
(4) fusing the three attribute bodies subjected to range standardization treatment by using a principal component analysis method to obtain a fused attribute body;
the principal component analysis, i.e., the fusion method in the linear dimension reduction method, first needs to convert the three-dimensional attribute body into a single-attribute one-dimensional matrix, each one-dimensional matrix contains spatial coordinate information, and further uses a two-dimensional matrix a to represent a plurality of attributes:
Figure FDA0003489166750000013
aiming at the matrix A, after a fused comprehensive attribute one-dimensional matrix is obtained by adopting a conventional principal component analysis method processing flow, the fused comprehensive attribute one-dimensional matrix needs to be restored into a three-dimensional comprehensive attribute body according to original coordinate information;
the conventional principal component analysis method comprises the following processing procedures: solving a zero mean matrix Z, solving a covariance matrix C, solving an eigenvalue and an eigenvector of the covariance matrix C, and constructing an eigen matrix P and data projection;
(5) extracting abnormal reflection region data in the fusion attribute body time slices in the step (4) at equal intervals, and performing three-dimensional reconstruction by using space coordinate information of each slice;
the method for acquiring the abnormal reflection region data mainly utilizes the characteristic that the fusion attribute body can highlight the abnormal reflection signal region, compares the abnormal reflection signal region with the original three-dimensional data body, and manually extracts the data of the reflection signal region of the karst cave target body in each time slice data of the fusion attribute body. Extracting slices from the fused attributes at equal time (space) intervals, then drawing a karst cave contour by picking the contour line of each slice in the three-dimensional attribute body, and finally associating the contour of each slice with a space coordinate;
(6) realizing three-dimensional visualization of the tunnel karst cave target body based on the reconstruction data obtained in the step (5) and a body visualization technology;
the body visualization technology mainly extracts an isosurface from three-dimensional body data by using an isosurface function.
2. The method for three-dimensional imaging of the karst cave based on multi-attribute fusion of the ground penetrating radar as claimed in claim 1, wherein the preprocessing of the ground penetrating radar data is to acquire a plurality of data profiles by three-dimensional grid detection, and then, to process each two-dimensional data profile, the preprocessing flow and parameter setting of each profile data should be consistent, wherein the band-pass filtering is set to be 0.5-2.0 times of the central frequency, and for the position where the diffraction hyperbola is obvious, F-K shift is adopted, and the shifted wave speed is acquired by fitting the diffraction hyperbola.
3. The method for three-dimensional imaging of the karst cave based on multi-attribute fusion of the ground penetrating radar as claimed in claim 1, wherein the step (2) provides two methods for calculating the three-dimensional attributes of the ground penetrating radar of the instantaneous amplitude, energy and maximum amplitude spectrum of the data volume obtained by the step (1): the first method is that after a plurality of data profiles are obtained by adopting three-dimensional grid detection, each two-dimensional data profile is preprocessed according to the step (1), the preprocessing flow and parameter setting of each profile data are consistent, such as uniformly adjusting zero point position, gain function and sampling channel number, after the preprocessing of a single profile is finished, two-dimensional attributes of each two-dimensional data are calculated, and then the two-dimensional data are interpolated to form a three-dimensional data body; the second method is to interpolate the preprocessed two-dimensional data to form three-dimensional radar data, and directly calculate the three-dimensional attribute body on the basis of the three-dimensional radar data.
4. The method for three-dimensional imaging of the karst cave based on multi-attribute fusion of the ground penetrating radar according to claim 1, wherein the step (5) provides two methods for determining the isosurface of the three-dimensional attribute body: the first method is that the fused three-dimensional attribute body is regarded as a whole, and a threshold value is directly taken for the three-dimensional attribute body to determine an isosurface; the second method is to extract slices from the fused three-dimensional attribute body at equal time (space) intervals, then draw the contour of the karst cave by picking the contour line of each slice in the three-dimensional attribute body, and finally associate the contour of each slice with the space coordinates, thereby imaging the abnormal reflection region. To prevent over-interpretation, each slice anomaly region contour should coincide with a strong reflective interface in the ground penetrating radar grayscale image.
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CN115100363A (en) * 2022-08-24 2022-09-23 中国科学院地理科学与资源研究所 Underground abnormal body three-dimensional modeling method and device based on ground penetrating radar
CN115100363B (en) * 2022-08-24 2022-11-25 中国科学院地理科学与资源研究所 Underground abnormal body three-dimensional modeling method and device based on ground penetrating radar
CN116203557A (en) * 2023-03-06 2023-06-02 北京交通大学 Traditional stone wall internal damage and anomaly nondestructive identification method based on ground penetrating radar
CN116203557B (en) * 2023-03-06 2024-03-05 北京交通大学 Traditional stone wall internal damage and anomaly nondestructive identification method based on ground penetrating radar
CN116973914A (en) * 2023-09-06 2023-10-31 哈尔滨工业大学 Road hidden disease three-dimensional reconstruction method based on three-dimensional ground penetrating radar

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