Tunnel face monitoring system and method

Abstract

The invention discloses a tunnel face monitoring system and method, and relates to the technical field of rock and soil geology monitoring, geological radar equipment is used for collecting face rock stratum reflection signals, wavelet transform denoising processing is carried out, a noise reduction data set D1 is generated, noise interference in data is effectively reduced, and the availability of original data is improved; secondly, radar signal feature extraction is carried out, a feature vector V is formed, and physical characteristics of different geological stratums can be accurately expressed; analysis is carried out through a support vector machine classification algorithm, a preliminary demarcation point set P1 is obtained, and preliminary recognition of the geologic stratum boundary is rapidly completed; according to the method, boundary errors possibly introduced by a classification algorithm are eliminated, the boundary errors are synchronously compared with a boundary stable state threshold value Tth, a geological stable state identifier S is generated, the geological stability in a tunnel face area is clarified, and a complete closed loop of boundary modeling and construction scheme generation is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of rock and soil geological monitoring, and in particular to a tunnel face monitoring system and method. Background Art

[0002] Tunnel construction is an important branch of civil engineering, involving the planning, design and construction of infrastructure such as tunnels, underground spaces and bridges. The safety and efficiency of the construction process are the core issues of engineering technology. During tunnel construction, the complexity of the surrounding rock geological conditions makes geological layer monitoring one of the key technologies to ensure construction safety. The tunnel face in tunnel construction is the working face that directly contacts the surrounding rock. The clarity of the boundaries between its geological layers not only determines the adjustment of the construction plan, but also directly affects the design and implementation of the support structure.

[0003] For example, at the junction of different geological layers, if the transition area between the soft layer and the hard layer is not accurately identified, it may cause support failure and surrounding rock instability, thus affecting the safety of tunnel construction.

[0004] In current tunnel construction practice, the identification of boundaries between geological layers mainly relies on geological radar detection technology, which analyzes rock boundaries through radar reflection wave signals. However, this method faces multiple challenges in practical applications. First, the noise interference of geological radar data is serious, resulting in unclear boundary recognition results, especially in areas with multiple layers of geological intersections. Secondly, traditional boundary detection algorithms have limited data processing capabilities and cannot fully utilize existing sensor data, resulting in low recognition accuracy. Finally, limited by the resolution of current monitoring technology, construction teams often need to combine manual experience to supplement data analysis, which not only increases time costs, but also increases the risk of misjudgment. These problems make it difficult for existing geological layer monitoring technology to fully meet the safety needs of construction under complex geological conditions. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a tunnel face monitoring system and method, which solve the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A tunnel face monitoring method, comprising the following steps:

[0007] S1, collect the reflected signal of the rock formation at the tunnel face through the geological radar equipment to form the original data set D0, and generate the de-noised data set D1 through wavelet transform denoising;

[0008] S2, extract radar signal features from the noise reduction data set D1, obtain rock material difference features, signal attenuation features and main frequency offset features, and form a feature vector V;

[0009] S3, using the support vector machine classification algorithm to perform preliminary identification of the address layer boundary on the feature vector V, and obtaining a preliminary demarcation point set P1;

[0010] S4, correcting the obtained preliminary demarcation point set P1 to obtain a corrected demarcation point set P2;

[0011] S5. Use the demarcation point set P2 to establish a three-dimensional model of the geological layer boundary, obtain the three-dimensional model M of the geological layer of the tunnel face, and simultaneously compare it with the boundary stability state threshold Tthe to generate a geological stability state mark S, and generate a construction support plan.

[0012] Preferably, said S1 includes S11 and S12;

[0013] S11. Use geological radar equipment to scan the tunnel face, obtain radar reflection signals, and record them as the original data set D0. Specifically, the geological radar transmits high-frequency electromagnetic waves into the rock formation. Different rock formations have different reflection intensities and propagation times for electromagnetic waves. The returned radar reflection signals contain the dielectric properties of the rock formation and the boundary information between geological layers, including the echo time T, the reflection signal intensity I and the signal main frequency Fp. The radar echo signal S = {T, I, Fp} constituting the scanning line i;

[0014] The original data set D0 is specifically D0={Si(t), i=1, 2, 3, ..., n}, wherein Si(t) represents the radar echo signal of the i-th scanning line at time t, and n represents the total number of scanning lines.

[0015] Preferably, S12, performing wavelet transform denoising processing on the acquired original data set D0, separating different frequency bands of the signal, removing the noise signal and retaining the high-frequency components of the geological information, to form a denoised data set D1.

[0016] Preferably, S2 includes S21 and S22;

[0017] S21, extract radar signal features from the noise reduction data set D1, obtain the rock material difference feature △Er, the signal attenuation feature A and the main frequency offset feature △Fp, and form a feature vector V = {△Er, A, △Fp};

[0018] Among them, the difference characteristic △Er of the rock material is obtained by extracting the dielectric constant change rate to reflect the response characteristics of the rock material to the electromagnetic wave. Specifically, by comparing the echo time T1 and the echo time T2 of different geological layers, the dielectric constant change rate between the two layers is calculated to obtain the difference characteristic △Er of the rock material;

[0019] The signal attenuation feature A is obtained by extracting the attenuation of the signal strength with the propagation distance to reflect the density and water content changes of the geological layer. Specifically, the signal attenuation state is compared between the signal strength of the reflection signal strength I1 and the reflection signal strength I2 at different geological interfaces to obtain the signal attenuation feature A;

[0020] The main frequency offset feature △Fp is obtained by analyzing the signal spectrum and extracting the main frequency change characteristics of different geological layer interfaces. Specifically, the main frequency offset value is obtained by calculating the main frequency peak value Fp1 and the main frequency peak value Fp2 of different geological interfaces to obtain the main frequency offset feature △Fp.

[0021] Preferably, said S3 includes S31 and S32;

[0022] S31, performing data standardization processing on the feature vector V to obtain a standardized standard feature vector Vn, and then using a support vector machine classification algorithm to preliminarily identify the boundary of the address layer, specifically finding a hyperplane through the support vector machine classification algorithm;

[0023] The hyperplane is obtained by the following target classification function:

[0024]

[0025] Wherein, f(F) represents the output of the target classification function, specifically represents the category of the Fth standard feature vector in the standard feature vector Vn, and the category is judged specifically according to the output f(F), sign represents the sign function, m represents the number of support vectors, Sj represents the jth support vector, wj represents the weight value of the jth support vector, K(F, Sj) represents the kernel function, b represents the bias term of the hyperplane, which is specifically used to adjust the position of the hyperplane, and m represents the number of support vectors.

[0026] Preferably, S32, classifying the standard feature vector Vn according to the found hyperplane, and simultaneously separating the demarcation points of different categories of geological layer data to obtain a preliminary demarcation point set P1;

[0027] The preliminary demarcation point set P1 is specifically formed by inferring the input standard feature vector Vn using the target classification function, and statistically analyzing the classification results of each demarcation point according to the output f(F) of the target classification function, and then marking the position of the boundary point to form the preliminary demarcation point set P1;

[0028] The preliminary demarcation point set P1 is obtained by the calculation formula P1={z|f(F)=0}, which specifically represents a collection of feature vector points that satisfy the target classification function output of 0, representing the boundary points of the geological layer.

[0029] Preferably, the S4 includes S41;

[0030] S41, modifying the obtained preliminary demarcation point set P1, including analyzing the distribution of neighboring points of the demarcation point position in the preliminary demarcation point set P1, including the occurrence of a neighboring point position that deviates by 10% from the average position of the neighboring points, determining that the current demarcation point needs to be modified, and re-substituting the modified demarcation point into the preliminary demarcation point set P1 to form a modified demarcation point set P2;

[0031] The correction includes using a neighboring point interpolation method to perform correction and smooth the position of the dividing point.

[0032] Preferably, said S5 includes S51 and S52;

[0033] S51, by using the demarcation point set P2 to establish a three-dimensional model of geological layer boundaries, obtain a three-dimensional model M of the geological layer of the tunnel face, and generate a continuous surface of the geological layer boundary by performing spatial interpolation fitting on the discrete demarcation points in the demarcation point set P2, including fitting the demarcation values ​​Ms (x, y, z) in the obtained three-dimensional interface models;

[0034] The demarcation value Ms(x, y, z) is obtained by the following calculation formula:

[0035]

[0036] Where Ms(x, y, z) represents the boundary value in the three-dimensional interface model, Wm represents the interpolation weight, which is related to the spatial distribution of the boundary point, Qm(x, y, z) represents the interpolation basis function of the mth boundary point, and M represents the total number of boundary points, specifically the total number of boundary points in the boundary point set P2.

[0037] Preferably, S52, according to a plurality of boundary values ​​Ms (x, y, z) in the three-dimensional model M of the geological layer of the tunnel face, are compared with the boundary stability state threshold value Tthe, and are associated with the tolerance range RC, to generate a geological stability state identifier S, and to generate a construction support plan, wherein the geological stability state identifier S is obtained by the following comparison method:

[0038]

[0039] When S=1, obtain the stable result of the demarcation value Ms(x, y, z), generate the construction support plan and maintain the original plan for support;

[0040] When S=0, the unstable result of the demarcation value Ms(x, y, z) is obtained, and a construction support plan is generated. While maintaining the original support plan, additional support measures related to anchor rods, concrete and steel mesh are planned for the distribution position of the unstable demarcation value Ms(x, y, z).

[0041] A tunnel face monitoring system includes a signal acquisition module, a signal feature extraction module, a preliminary identification module, a correction module and a modeling evaluation module;

[0042] The signal acquisition module collects the reflected signal of the rock formation on the face through the geological radar equipment to form the original data set D0, and generates the de-noised data set D1 through the wavelet transform denoising process;

[0043] The signal feature extraction module extracts radar signal features from the noise reduction data set D1, obtains rock material difference features, signal attenuation features and main frequency offset features, and forms a feature vector V;

[0044] The preliminary identification module performs preliminary identification of the address layer boundary by using the support vector machine classification algorithm on the feature vector V to obtain a preliminary demarcation point set P1;

[0045] The correction module corrects the obtained preliminary demarcation point set P1 to obtain a corrected demarcation point set P2;

[0046] The modeling and evaluation module establishes a three-dimensional model of geological layer boundaries by using the boundary point set P2, obtains a three-dimensional model M of the geological layer of the tunnel face, and simultaneously compares it with the boundary stability state threshold Tthe, generates a geological stability state mark S, and generates a construction support plan.

[0047] The present invention provides a tunnel face monitoring system and method, which have the following beneficial effects:

[0048] (1) The reflected signals of the face rock formations are collected by geological radar equipment and processed by wavelet transform to generate a denoised data set D1, which effectively reduces the noise interference in the data and improves the availability of the original data; secondly, the radar signal features are extracted to form a feature vector V, so that the physical characteristics of different geological layers can be accurately expressed; then the support vector machine classification algorithm is used for analysis to obtain the preliminary demarcation point set P1, and the preliminary identification of the geological layer boundary is quickly completed; and further correction is performed to obtain the corrected demarcation point set P2, eliminating the boundary error that may be introduced by the classification algorithm, and significantly improving the accuracy of the demarcation point. Finally, by constructing a three-dimensional model M of the geological layer boundary of the face, and comparing it with the boundary stability threshold Tthe, a geological stability state identifier S is generated to clarify the geological stability in the face area; at the same time, a scientific and reasonable construction support plan is provided, realizing a complete closed loop of boundary modeling and construction plan generation, solving the defects of insufficient accuracy in identifying geological layer demarcation points, delayed geological stability assessment, and lack of pertinence in support plans in traditional methods, and significantly improving the safety and construction efficiency during tunnel construction.

[0049] (2) By standardizing the data of the feature vector V, the standardized standard feature vector Vn is obtained, and the support vector machine classification algorithm is used to perform preliminary identification of the geological layer boundary and find the best classification hyperplane. The hyperplane is determined by the target classification function f(F) and accurately distinguishes the category of the standard feature vector Vn. The low-dimensional features are simultaneously mapped to the high-dimensional space, which effectively handles the nonlinear demarcation problem under complex geological conditions. The preliminary demarcation point set P1 is formed by counting and marking the classification boundary points. Each feature vector point that meets the classification boundary is processed in a set, which accurately characterizes the preliminary demarcation state of the geological layer. The boundary position of different geological layers on the face can be efficiently and accurately identified, overcoming the time-consuming and subjective problems of the traditional manual judgment process, providing accurate initial boundary data for subsequent boundary correction and modeling, and significantly improving the efficiency and reliability of geological layer classification.

[0050] (3) By analyzing and correcting the distribution of neighboring points of the preliminary demarcation point set P1, a more accurate corrected demarcation point set P2 was constructed, which effectively solved the problem of discontinuous distribution of demarcation points caused by the position deviation of the preliminary demarcation points. The correction process uses the neighboring point interpolation method to smooth the demarcation point positions to ensure that the spatial distribution of the demarcation points is more coherent and consistent with the actual boundary characteristics of the geological layer. Subsequently, the three-dimensional model M of the geological layer of the tunnel face was constructed through the demarcation point set P2, realizing the spatial interpolation fitting from discrete demarcation points to continuous demarcation surfaces, and generating a complete geological layer boundary model. Based on the comparison between several boundary values ​​Ms(x, y, z) in the three-dimensional model M and the boundary stability threshold Tthe, combined with the tolerance range RC, the geological stability mark S is generated. On the basis of the generated geological stability mark S, the support scheme further distinguishes between stable areas and unstable areas, and proposes additional support measures for unstable areas, which provides a more comprehensive basis for geological layer boundaries and support scheme optimization for tunnel construction, and significantly improves the accuracy of geological layer boundary modeling at the tunnel face and the safety of construction support. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of the steps of a tunnel face monitoring method according to the present invention;

[0052] Figure 2 The present invention is a schematic diagram of a block diagram of a tunnel face monitoring system. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0054] Example 1

[0055] The present invention provides a tunnel face monitoring method, please refer to Figure 1 , including the following steps:

[0056] S1, collect the reflected signal of the rock formation at the tunnel face through the geological radar equipment to form the original data set D0, and generate the de-noised data set D1 through wavelet transform denoising;

[0057] S2, extract radar signal features from the noise reduction data set D1, obtain rock material difference features, signal attenuation features and main frequency offset features, and form a feature vector V;

[0058] S3, using the support vector machine classification algorithm to perform preliminary identification of the address layer boundary on the feature vector V, and obtaining a preliminary demarcation point set P1;

[0059] S4, correcting the obtained preliminary demarcation point set P1 to obtain a corrected demarcation point set P2;

[0060] S5. Use the demarcation point set P2 to establish a three-dimensional model of the geological layer boundary, obtain the three-dimensional model M of the geological layer of the tunnel face, and simultaneously compare it with the boundary stability state threshold Tthe to generate a geological stability state mark S, and generate a construction support plan.

[0061] In this embodiment, the reflected signal of the rock formation of the face is collected by geological radar equipment and processed by wavelet transform denoising to generate a denoised data set D1, which effectively reduces the noise interference in the data and improves the availability of the original data; secondly, the radar signal feature is extracted to form a feature vector V, so that the physical characteristics of different geological layers can be accurately expressed; then the support vector machine classification algorithm is used for analysis to obtain the preliminary demarcation point set P1, and the preliminary identification of the geological layer boundary is quickly completed; and further correction is performed to obtain the corrected demarcation point set P2, eliminating the boundary error that may be introduced by the classification algorithm, and significantly improving the accuracy of the demarcation point. Finally, by constructing a three-dimensional model M of the geological layer boundary of the face, and synchronously comparing it with the boundary stability threshold Tthe, a geological stability state identifier S is generated to clarify the geological stability in the face area; at the same time, a scientific and reasonable construction support plan is provided, realizing a complete closed loop of demarcation modeling and construction plan generation, solving the defects of insufficient accuracy in identifying geological layer demarcation points, delayed geological stability assessment, and lack of pertinence in support plans in traditional methods, and significantly improving the safety and construction efficiency during tunnel construction.

[0062] Example 2

[0063] This embodiment is explained in Example 1, please refer to Figure 1, specifically: the S1 includes S11 and S12;

[0064] S11. Use geological radar equipment to scan the tunnel face, obtain radar reflection signals, and record them as the original data set D0. Specifically, the geological radar transmits high-frequency electromagnetic waves into the rock formation. Different rock formations have different reflection intensities and propagation times for electromagnetic waves. The returned radar reflection signals contain the dielectric properties of the rock formation and the boundary information between geological layers, including the echo time T, the reflection signal intensity I and the signal main frequency Fp. The radar echo signal S = {T, I, Fp} constituting the scanning line i;

[0065] The original data set D0 is specifically D0={Si(t), i=1, 2, 3, ..., n}, wherein Si(t) represents the radar echo signal of the i-th scanning line at time t, and n represents the total number of scanning lines.

[0066] S12, performing wavelet transform denoising processing on the original data set D0, separating different frequency bands of the signal, removing the noise signal and retaining the high-frequency components of the geological information, to form a denoised data set D1;

[0067] Among them, the wavelet transform denoising process is specifically processed by the following method:

[0068] Signal decomposition: The radar echo signal Si(t) in the original data set D0 is input into the wavelet transform model. According to the multi-resolution characteristics of the wavelet, the signal is decomposed into high-frequency components and low-frequency components in different frequency bands. The low-frequency components contain the overall trend information of the signal, and the high-frequency components include the boundary and geological layer interface information.

[0069] Threshold processing: The high-frequency components obtained by decomposition are filtered by using a preset processing threshold. The noise is concentrated in the high-frequency part, and its amplitude is small. By setting a threshold, the signal smaller than the threshold is set to zero, thereby retaining the main geological information and removing noise interference;

[0070] Signal reconstruction: The high-frequency components that have been threshold processed are recombined with the original low-frequency components to restore them to a noise-reduced signal. This process can preserve the boundary characteristics of the geological layer as much as possible while eliminating unnecessary noise components.

[0071] The S2 includes S21 and S22;

[0072] S21, extract radar signal features from the noise reduction data set D1, obtain the rock material difference feature △Er, the signal attenuation feature A and the main frequency offset feature △Fp, and form a feature vector V = {△Er, A, △Fp};

[0073] Among them, the difference characteristic △Er of the rock material is obtained by extracting the dielectric constant change rate to reflect the response characteristics of the rock material to the electromagnetic wave. Specifically, by comparing the echo time T1 and the echo time T2 of different geological layers, the dielectric constant change rate between the two layers is calculated to obtain the difference characteristic △Er of the rock material;

[0074] The rock material difference characteristic ΔEr is obtained by the following calculation formula:

[0075]

[0076] In the formula, c represents the propagation speed of electromagnetic waves in a vacuum, and d represents the spacing between ground radar antennas;

[0077] The signal attenuation feature A is obtained by extracting the attenuation of the signal strength with the propagation distance to reflect the density and water content changes of the geological layer. Specifically, the signal attenuation state is compared between the signal strength of the reflection signal strength I1 and the reflection signal strength I2 at different geological interfaces to obtain the signal attenuation feature A;

[0078] The signal attenuation characteristic A is obtained by the following calculation formula:

[0079]

[0080] In the formula, e represents the vertical spatial distance between different geological interfaces, and ln represents the natural logarithm;

[0081] The main frequency offset feature △Fp is obtained by analyzing the signal spectrum and extracting the main frequency change characteristics of different geological layer interfaces. Specifically, the main frequency offset value is obtained by calculating the main frequency peak value Fp1 and the main frequency peak value Fp2 of different geological interfaces to obtain the main frequency offset feature △Fp;

[0082] The main frequency offset characteristic ΔFp is obtained by the calculation formula ΔFp=|Fp1-Fp2|.

[0083] In this embodiment, the face is scanned by using geological radar equipment to obtain radar echo signals and record them as the original data set D0. The collected signals contain the dielectric properties of the rock layer and the boundary information between geological layers. The echo time T, the reflected signal intensity I and the signal main frequency Fp are recorded in detail, laying a high-resolution foundation for subsequent processing. Secondly, the original data set D0 is decomposed, thresholded and reconstructed by wavelet transform denoising to generate a denoised data set D1, which accurately retains the high-frequency components of the boundary characteristics of the geological layer, effectively removes the interference of environmental noise, and ensures the integrity of the geological information in the signal. In addition, by extracting radar signal features, the rock material difference feature △Er, the signal attenuation feature A and the main frequency offset feature △Fp are obtained to form a feature vector V, which accurately quantifies the dielectric constant change, density and water content difference and frequency characteristics of the geological layer, so that the physical property differences between geological layers can be accurately described, effectively solving the problems of severe noise interference and inaccurate physical property extraction in traditional geological radar data, greatly improving the accuracy and reliability of data processing, and providing a high-quality input basis for subsequent geological layer classification and boundary modeling.

[0084] Example 3

[0085] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: S3 includes S31 and S32;

[0086] S31, performing data standardization processing on the feature vector V to obtain a standardized standard feature vector Vn, and then using a support vector machine classification algorithm to preliminarily identify the boundary of the address layer, specifically finding a hyperplane through the support vector machine classification algorithm;

[0087] The hyperplane is obtained by the following target classification function:

[0088]

[0089] Wherein, f(F) represents the output of the target classification function, specifically represents the category of the Fth standard feature vector in the standard feature vector Vn, and the category is judged specifically according to the output f(F); sign represents the sign function, and specifically returns its sign information according to the positive and negative situation in the sign(*) brackets. When * in sign(*) is greater than 0, the sign function sign returns 1 to indicate a positive number, when * in sign(*) is equal to 0, the sign function sign returns 0 to indicate zero, and when * in sign(*) is less than 0, the sign function sign returns -1 to indicate a negative number. When the output f(F) is greater than 0, it indicates that the current standard feature vector Vn belongs to one category. When the output f(F) is less than 0, it indicates that the current standard feature vector Vn belongs to another category. When the output f(F) is equal to 0, it indicates that the current standard feature vector Vn belongs to another category. indicates that the current standard feature vector Vn belongs to the classification boundary, specifically the preliminary dividing point, m represents the number of support vectors, specifically represents the total number of samples actually involved in the classification in the target classification function, Sj is the jth support vector, specifically represents that the sample dividing point selected during the classification training process is automatically selected through the training process of the support vector machine, the support vector is the training sample point close to the classification boundary, and plays a major role in the shape and position of the classification hyperplane, wj represents the weight value of the jth support vector, K(F, Sj) represents the kernel function, including the Gaussian kernel, which is specifically used to map to the high-dimensional feature space, specifically used to measure the similarity between the Fth standard feature vector and the Sjth support vector, b represents the bias term of the hyperplane, which is specifically used to adjust the position of the hyperplane, and m represents the number of support vectors.

[0090] S32, classifying the standard feature vector Vn according to the found hyperplane, and simultaneously separating the demarcation points of different categories of geological layer data to obtain a preliminary demarcation point set P1;

[0091] The preliminary demarcation point set P1 is specifically formed by inferring the input standard feature vector Vn using the target classification function, and statistically analyzing the classification results of each demarcation point according to the output f(F) of the target classification function, and then marking the position of the boundary point to form the preliminary demarcation point set P1;

[0092] The preliminary demarcation point set P1 is obtained by the calculation formula P1={z|f(F)=0}, which specifically represents a collection of feature vector points that satisfy the target classification function output of 0, representing the boundary points of the geological layer.

[0093] In this embodiment, the data of the feature vector V is standardized to obtain the standardized standard feature vector Vn, and the support vector machine classification algorithm is used to perform preliminary identification of the geological layer boundary, and the best classification hyperplane is found. The hyperplane is determined by the target classification function f(F) and accurately distinguishes the category of the standard feature vector Vn; the low-dimensional features are simultaneously mapped to the high-dimensional space, and the nonlinear demarcation problem under complex geological conditions is effectively handled. By counting and marking the classification boundary points, a preliminary demarcation point set P1 is formed, and each feature vector point that meets the classification boundary is processed in a set, which accurately represents the preliminary demarcation state of the geological layer, and can efficiently and accurately identify the boundary position of different geological layers on the face, overcoming the problems of long time consumption and strong subjectivity in the traditional manual judgment process, providing accurate initial boundary data for subsequent boundary correction and modeling, and significantly improving the efficiency and reliability of geological layer classification.

[0094] Example 4

[0095] This embodiment is explained in Example 3, please refer to Figure 1 Specifically: S4 includes S41;

[0096] S41, modifying the obtained preliminary demarcation point set P1, including analyzing the distribution of neighboring points of the demarcation point position in the preliminary demarcation point set P1, including the occurrence of a neighboring point position that deviates by 10% from the average position of the neighboring points, determining that the current demarcation point needs to be modified, and re-substituting the modified demarcation point into the preliminary demarcation point set P1 to form a modified demarcation point set P2;

[0097] The correction includes using a neighboring point interpolation method to perform correction and smooth the position of the dividing point.

[0098] The S5 includes S51 and S52;

[0099] S51, by using the demarcation point set P2 to establish a three-dimensional model of geological layer boundaries, obtain a three-dimensional model M of the geological layer of the tunnel face, and generate a continuous surface of the geological layer boundary by performing spatial interpolation fitting on the discrete demarcation points in the demarcation point set P2, including fitting the demarcation values ​​Ms (x, y, z) in the obtained three-dimensional interface models;

[0100] The demarcation value Ms(x, y, z) is obtained by the following calculation formula:

[0101]

[0102] Wherein, Ms(x, y, z) represents the boundary value in the three-dimensional interface model, Wm represents the interpolation weight, which is related to the spatial distribution of the boundary point, Qm(x, y, z) represents the interpolation basis function of the mth boundary point, specifically represents the influence of the mth boundary point on the interpolation point, and M represents the total number of boundary points, specifically represents the total number of boundary points in the boundary point set P2.

[0103] S52, compare several boundary values ​​Ms (x, y, z) in the three-dimensional model M of the geological layer of the tunnel face with the boundary stability state threshold Tthe, and associate them with the tolerance range RC to generate a geological stability state mark S and a construction support plan, wherein the geological stability state mark S is obtained by the following comparison method:

[0104]

[0105] When S=1, obtain the stable result of the demarcation value Ms(x, y, z), generate the construction support plan and maintain the original plan for support;

[0106] When S=0, the unstable result of the demarcation value Ms(x, y, z) is obtained, and a construction support plan is generated. While maintaining the original support plan, additional support measures related to anchor rods, concrete and steel mesh are planned for the distribution position of the unstable demarcation value Ms(x, y, z).

[0107] In this embodiment, by analyzing and correcting the distribution of neighboring points of the preliminary demarcation point set P1, a more accurate corrected demarcation point set P2 is constructed, which effectively solves the problem of discontinuous distribution of demarcation points caused by the position deviation of the preliminary demarcation points. The correction process uses the neighboring point interpolation method to smooth the demarcation point positions to ensure that the spatial distribution of the demarcation points is more coherent and consistent with the actual boundary characteristics of the geological layer. Subsequently, the three-dimensional model M of the geological layer of the tunnel face is constructed through the demarcation point set P2, realizing the spatial interpolation fitting from discrete demarcation points to continuous demarcation surfaces, and generating a complete geological layer boundary model. Based on the comparison between several boundary values ​​Ms(x, y, z) in the three-dimensional model M and the boundary stability threshold Tthe, combined with the tolerance range RC, the geological stability mark S is generated. On the basis of the generated geological stability mark S, the support scheme further distinguishes between stable areas and unstable areas, and proposes additional support measures for unstable areas, which provides a more comprehensive basis for geological layer boundaries and support scheme optimization for tunnel construction, and significantly improves the accuracy of geological layer boundary modeling at the tunnel face and the safety of construction support.

[0108] Example 5

[0109] A tunnel face monitoring system, please refer to Figure 2,Specifically: including signal acquisition module, signal feature extraction module, preliminary identification module, correction module and modeling evaluation module;

[0110] The signal acquisition module collects the reflected signal of the rock formation on the face through the geological radar equipment to form the original data set D0, and generates the de-noised data set D1 through the wavelet transform denoising process;

[0111] The signal feature extraction module extracts radar signal features from the noise reduction data set D1, obtains rock material difference features, signal attenuation features and main frequency offset features, and forms a feature vector V;

[0112] The preliminary identification module performs preliminary identification of the address layer boundary by using the support vector machine classification algorithm on the feature vector V to obtain a preliminary demarcation point set P1;

[0113] The correction module corrects the obtained preliminary demarcation point set P1 to obtain a corrected demarcation point set P2;

[0114] The modeling and evaluation module establishes a three-dimensional model of geological layer boundaries by using the boundary point set P2, obtains a three-dimensional model M of the geological layer of the tunnel face, and simultaneously compares it with the boundary stability state threshold Tthe, generates a geological stability state mark S, and generates a construction support plan.

[0115] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A tunnel face monitoring method, characterized in that: The following steps are involved: S1, collect the reflected signal of the rock formation at the tunnel face through the geological radar equipment to form the original data set D0, and generate the de-noised data set D1 through wavelet transform denoising; S2, extract radar signal features from the noise reduction data set D1, obtain rock material difference features, signal attenuation features and main frequency offset features, and form a feature vector V; S3, using the support vector machine classification algorithm to perform preliminary identification of the address layer boundary on the feature vector V, and obtaining a preliminary demarcation point set P1; S4, correcting the obtained preliminary demarcation point set P1 to obtain a corrected demarcation point set P2; S5. Use the demarcation point set P2 to establish a three-dimensional model of the geological layer boundary, obtain the three-dimensional model M of the geological layer of the tunnel face, and simultaneously compare it with the boundary stability state threshold Tthe to generate a geological stability state mark S, and generate a construction support plan.

2. A tunnel face monitoring method according to claim 1, characterized in that: Said S1 includes S11 and S12; S11. Use geological radar equipment to scan the tunnel face, obtain radar reflection signals, and record them as the original data set D0. Specifically, the geological radar transmits high-frequency electromagnetic waves into the rock formation. Different rock formations have different reflection intensities and propagation times for electromagnetic waves. The returned radar reflection signals contain the dielectric properties of the rock formation and the boundary information between geological layers, including the echo time T, the reflection signal intensity I and the signal main frequency Fp. The radar echo signal S = {T, I, Fp} constituting the scanning line i; The original data set D0 is specifically D0={Si(t), i=1, 2, 3, ..., n}, wherein Si(t) represents the radar echo signal of the i-th scanning line at time t, and n represents the total number of scanning lines.

3. A tunnel face monitoring method according to claim 2, characterized in that: S12, performing wavelet transform denoising processing on the acquired original data set D0, separating different frequency bands of the signal, removing the noise signal and retaining the high-frequency components of the geological information, to form a denoised data set D1.

4. A tunnel face monitoring method according to claim 3, characterized in that: The S2 includes S21 and S22; S21, extract radar signal features from the noise reduction data set D1, obtain the rock material difference feature △Er, the signal attenuation feature A and the main frequency offset feature △Fp, and form a feature vector V = {△Er, A, △Fp}; Among them, the difference characteristic △Er of the rock material is obtained by extracting the dielectric constant change rate to reflect the response characteristics of the rock material to the electromagnetic wave. Specifically, by comparing the echo time T1 and the echo time T2 of different geological layers, the dielectric constant change rate between the two layers is calculated to obtain the difference characteristic △Er of the rock material; The signal attenuation feature A is obtained by extracting the attenuation of the signal strength with the propagation distance to reflect the density and water content changes of the geological layer. Specifically, the signal attenuation state is compared between the signal strength of the reflection signal strength I1 and the reflection signal strength I2 at different geological interfaces to obtain the signal attenuation feature A; The main frequency offset feature △Fp is obtained by analyzing the signal spectrum and extracting the main frequency change characteristics of different geological layer interfaces. Specifically, the main frequency offset value is obtained by calculating the main frequency peak value Fp1 and the main frequency peak value Fp2 of different geological interfaces to obtain the main frequency offset feature △Fp.

5. A tunnel face monitoring method according to claim 4, characterized in that: The S3 includes S31 and S32; S31, performing data standardization processing on the feature vector V to obtain a standardized standard feature vector Vn, and then using a support vector machine classification algorithm to preliminarily identify the boundary of the address layer, specifically finding a hyperplane through the support vector machine classification algorithm; The hyperplane is obtained by the following target classification function: Wherein, f(F) represents the output of the target classification function, specifically represents the category of the Fth standard feature vector in the standard feature vector Vn, and the category is judged specifically according to the output f(F), sign represents the sign function, m represents the number of support vectors, Sj represents the jth support vector, wj represents the weight value of the jth support vector, K(F, Sj) represents the kernel function, b represents the bias term of the hyperplane, which is specifically used to adjust the position of the hyperplane, and m represents the number of support vectors.

6. A tunnel face monitoring method according to claim 5, characterized in that: S32, classifying the standard feature vector Vn according to the found hyperplane, and simultaneously separating the demarcation points of different categories of geological layer data to obtain a preliminary demarcation point set P1; The preliminary demarcation point set P1 is specifically formed by inferring the input standard feature vector Vn using the target classification function, and statistically analyzing the classification results of each demarcation point according to the output f(F) of the target classification function, and then marking the position of the boundary point to form the preliminary demarcation point set P1; The preliminary demarcation point set P1 is obtained by the calculation formula P1={z|f(F)=0}, which specifically represents a collection of feature vector points that satisfy the target classification function output of 0, representing the boundary points of the geological layer.

7. A tunnel face monitoring method according to claim 6, characterized in that: The S4 includes S41; S41, modifying the obtained preliminary demarcation point set P1, including analyzing the distribution of neighboring points of the demarcation point position in the preliminary demarcation point set P1, including the occurrence of a neighboring point position that deviates by 10% from the average position of the neighboring points, determining that the current demarcation point needs to be modified, and re-substituting the modified demarcation point into the preliminary demarcation point set P1 to form a modified demarcation point set P2; The correction includes using a neighboring point interpolation method to perform correction and smooth the position of the dividing point.

8. A tunnel face monitoring method according to claim 7, characterized in that: The S5 includes S51 and S52; S51, by using the demarcation point set P2 to establish a three-dimensional model of geological layer boundaries, obtain a three-dimensional model M of the geological layer of the tunnel face, and generate a continuous surface of the geological layer boundary by performing spatial interpolation fitting on the discrete demarcation points in the demarcation point set P2, including fitting the demarcation values ​​Ms (x, y, z) in the obtained three-dimensional interface models; The demarcation value Ms(x, y, z) is obtained by the following calculation formula: Where Ms(x, y, z) represents the boundary value in the three-dimensional interface model, Wm represents the interpolation weight, which is related to the spatial distribution of the boundary point, Qm(x, y, z) represents the interpolation basis function of the mth boundary point, and M represents the total number of boundary points, specifically the total number of boundary points in the boundary point set P2.

9. A tunnel face monitoring method according to claim 8, characterized in that: S52, compare several boundary values ​​Ms (x, y, z) in the three-dimensional model M of the geological layer of the tunnel face with the boundary stability state threshold Tthe, and associate them with the tolerance range RC to generate a geological stability state mark S and a construction support plan, wherein the geological stability state mark S is obtained by the following comparison method: When S=1, obtain the stable result of the demarcation value Ms(x, y, z), generate the construction support plan and maintain the original plan for support; When S=0, the unstable result of the demarcation value Ms(x, y, z) is obtained, and a construction support plan is generated. While maintaining the original support plan, additional support measures related to anchor rods, concrete and steel mesh are planned for the distribution position of the unstable demarcation value Ms(x, y, z).

10. A tunnel face monitoring system, applied to a tunnel face monitoring method according to any one of claims 1 to 9, characterized in that: It includes signal acquisition module, signal feature extraction module, preliminary identification module, correction module and modeling evaluation module; The signal acquisition module collects the reflected signal of the rock formation on the face through the geological radar equipment to form the original data set D0, and generates the de-noised data set D1 through the wavelet transform denoising process; The signal feature extraction module extracts radar signal features from the noise reduction data set D1 to obtain rock material difference features, signal attenuation features and main frequency offset features. Constitute the feature vector V; The preliminary identification module performs preliminary identification of the address layer boundary by using the support vector machine classification algorithm on the feature vector V to obtain a preliminary demarcation point set P1; The correction module corrects the obtained preliminary demarcation point set P1 to obtain a corrected demarcation point set P2; The modeling and evaluation module establishes a three-dimensional model of geological layer boundaries by using the boundary point set P2, obtains a three-dimensional model M of the geological layer of the tunnel face, and simultaneously compares it with the boundary stability state threshold Tthe, generates a geological stability state mark S, and generates a construction support plan.

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