A method and system for detecting and analyzing the vibration performance of a water conservancy foundation rock
By collecting multidimensional vibration characteristic parameters from the foundation rock mass of water conservancy projects, establishing a joint inversion equation set and performing iterative correction, the problem that multidimensional characteristics were not considered in the existing technology was solved, and the accuracy and reliability of rock mass vibration performance detection were improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG MINGCHEN QUALITY INSPECTION CO LTD
- Filing Date
- 2026-04-10
- Publication Date
- 2026-06-09
AI Technical Summary
Existing methods for detecting the vibration performance of foundation rock masses in water conservancy projects fail to fully consider multidimensional physical characteristics in their inversion models, resulting in insufficient ability to characterize the non-uniformity and anisotropy of the rock mass. Furthermore, traditional methods lack the ability to identify and correct abnormal data, affecting the accuracy and reliability of detection and analysis.
Vibration signals from the rock mass surface are collected by a sensor array. The slowness value, energy attenuation gradient and dispersion coefficient are extracted, a joint inversion equation set is established, and iterative correction is performed using the phase accumulation residual of the closed triangular path and the wave velocity spatial gradient. Damping constraint equations are constructed, and a rock mass vibration performance distribution map is generated.
It significantly improves the identification accuracy of heterogeneous regions such as fracture development zones and weak interlayers, enhances the engineering credibility and numerical stability of the inversion results, and shortens the iterative solution time.
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Figure CN122172299A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of geotechnical engineering testing technology, and relates to a method and system for testing and analyzing the vibration performance of foundation rocks in water conservancy projects. Background Technology
[0002] In hydraulic engineering, the assessment of the vibration performance of foundation rock masses is crucial, and its core relies on the inversion analysis of wave signals. However, existing mainstream inversion methods face challenges in both theoretical foundation and practical application, which limits the accuracy and reliability of detection and analysis.
[0003] First, the dynamic response of rock mass is essentially a complex process involving the coupling of multiple physical characteristics. However, most existing methods rely on a single feature to build an inversion model, failing to construct a multi-feature synergistic inversion framework. This simplification fails to fully capture the physical nature of wave propagation, resulting in insufficient ability to characterize the heterogeneity and anisotropy of rock mass, leading to biased assessment results.
[0004] Furthermore, in structurally complex regions, internal rock mass structural planes, joints, and dip angles can interfere with wave signals, leading to distortion of the signal propagation path. However, traditional single-dimensional inversion models lack cross-verification mechanisms between multiple parameters, making it difficult to effectively distinguish between the actual rock mass characteristics and anomalies introduced by tectonic interference. This results in spatial contradictions in the inversion results at key locations that violate geological laws, reducing their reliability.
[0005] More importantly, the aforementioned limitations at the data level are amplified during the inversion iteration process. Conventional methods typically lack automatic identification and targeted correction of anomalous data. When data distortion occurs at individual monitoring points due to measurement errors or local geological anomalies, these errors will propagate and spread during the inversion iteration, not only contaminating the overall inversion field and affecting the stability and reliability of the results, but also potentially leading to difficulties in computational convergence and limiting the efficiency of rock vibration performance detection and analysis. Summary of the Invention
[0006] In view of this, in order to solve the problems mentioned in the background technology, a method and system for detecting and analyzing the vibration performance of foundation rocks in water conservancy projects is proposed.
[0007] The first aspect of the present invention proposes a method for detecting and analyzing the vibration performance of foundation rock in water conservancy projects, comprising the following steps: S1, collecting vibration signals on the rock surface through a sensor array, extracting the slowness value, energy attenuation gradient and dispersion coefficient of the propagation path of each node, and establishing a joint inversion equation set accordingly.
[0008] S2. Traverse the closed triangular path formed by the propagation path and calculate the phase cumulative residual by accumulating the slowness values of its three sides.
[0009] S3. If the phase cumulative residual of the current closed triangular path is inconsistent with the sign of the previous iteration, then calculate the spatial gradient of the wave velocity in the triangular region based on the estimated wave velocity value.
[0010] S4. When the spatial gradient of wave velocity is opposite to the dip angle of the geological structure, lock the conflict node and extract the dispersion coefficient of the associated path of the node, construct the damping constraint equation and update the joint inversion equation set.
[0011] S5. Iterate through S2 to S4 until the cumulative phase residuals of all closed triangular paths remain unchanged for two consecutive rounds or the maximum number of iterations is reached. Output the rock mass dynamic parameter field obtained by solving the joint inversion equation set. Generate the rock mass vibration performance distribution map through spatial interpolation and vector superposition.
[0012] The second aspect of the present invention proposes a vibration performance detection and analysis system for foundation rocks of water conservancy projects, comprising the following modules: Feature extraction module: acquiring vibration signals from the rock surface through a sensor array, extracting the slowness value, energy attenuation gradient and dispersion coefficient of the propagation path of each node, and establishing a joint inversion equation set accordingly.
[0013] Residual calculation module: Traverses the closed triangular path formed by the propagation path and calculates the phase cumulative residual by accumulating the slowness values of its three sides.
[0014] Gradient calculation module: If the phase cumulative residual of the current closed triangular path is inconsistent with the sign of the previous iteration, the spatial gradient of the wave velocity in the triangular region is calculated based on the estimated wave velocity value.
[0015] Conflict Update Module: When the spatial gradient of wave velocity is opposite to the dip angle of the geological structure, the conflict node is locked and the dispersion coefficient of the associated path of the node is extracted. The damping constraint equation is constructed and the joint inversion equation set is updated.
[0016] Field map generation module: Iteratively executes residual calculation, gradient solution and conflict update modules until the phase cumulative residuals of all closed triangular paths remain unchanged for two consecutive rounds or the maximum number of iterations is reached. Outputs the rock mass dynamic parameter field obtained by solving the joint inversion equation set, and generates a rock mass vibration performance distribution map through spatial interpolation and vector superposition.
[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention constructs a three-parameter joint inversion equation set of slowness-attenuation-dispersion, and simultaneously solves the spatial distribution corresponding to the slowness value, energy attenuation gradient and dispersion coefficient, which solves the problem of insufficient characterization of multidimensional dynamic characteristics of rock mass by single parameter inversion, so that the inversion results can simultaneously reflect the rock mass stiffness, energy dissipation capacity and microstructural heterogeneity, and significantly improve the identification accuracy and spatial resolution of heterogeneous areas such as fracture development zone and weak interlayer.
[0018] (2) This invention solves the problem of the contradiction between the inverted parameter field and the physical law of the geological structure by using the dual criteria of monitoring the sign change of the phase accumulation residual of the closed triangular path and verifying the consistency of the wave velocity spatial gradient and the geological structure dip direction. It effectively identifies and corrects the non-physical interpretation of the conflict between the direction of wave velocity increase and the tectonic dip, so that the spatial distribution of the inverted parameters conforms to the control law of rock mass structure, and enhances the geological rationality and engineering credibility of the results in structurally complex areas.
[0019] (3) This invention solves the problem of abnormal data spreading and polluting the overall parameter field during iteration by adaptively constructing the damping constraint equation through the standard deviation of the dispersion coefficient of conflict nodes and integrating it into the joint inversion iteration process. This makes the wave velocity parameter update in the area with significant dispersion effect specifically suppressed, effectively blocking parameter oscillation caused by local measurement errors or geological anomalies, improving the numerical stability and convergence efficiency of the inversion process, and shortening the iteration solution time. Attached Figure Description
[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0021] Figure 1 This is a step diagram of a method for detecting and analyzing the vibration performance of foundation rock in a water conservancy project according to the present invention.
[0022] Figure 2 This is a flowchart illustrating the specific method S4 in this invention.
[0023] Figure 3 This is a schematic diagram showing the connection of each module in a hydraulic engineering foundation rock vibration performance detection and analysis system according to the present invention. Detailed Implementation
[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0025] Example 1.
[0026] Please see Figure 1As shown, this invention proposes a method for detecting and analyzing the vibration performance of foundation rocks in water conservancy projects, including the following steps: S1, collecting vibration signals from the rock surface through a sensor array, extracting the slowness value, energy attenuation gradient, and dispersion coefficient of the propagation path at each node, and establishing a joint inversion equation set accordingly.
[0027] Considering that the heterogeneity of the internal structure of the foundation rock mass of water conservancy projects leads to spatial differences in the propagation characteristics of vibration waves, and that traditional single-parameter inversion is difficult to accurately characterize the dynamic performance of the rock mass, three types of characteristic parameters are acquired simultaneously to provide a multi-dimensional data foundation for subsequent work.
[0028] In one specific embodiment, vibration sensors are deployed on the rock surface according to a grid of nodes. A unified clock triggers all sensors to synchronously acquire vibration signals. Each sensor is marked as a node, and the line connecting adjacent nodes is defined as the propagation path. The vibration signals can be excited by artificial sources such as hammering or environmental micro-vibrations.
[0029] The synchronous vibration signals collected at both ends of each propagation path are decomposed into a time-frequency matrix by short-time Fourier transform.
[0030] Search along the time axis for the time corresponding to the maximum energy amplitude as the arrival time of the first wave, calculate the difference between the arrival times of the first waves at the two ends of the same propagation path, and divide the difference by the propagation path length to obtain the slowness value.
[0031] It should be noted that the slowness value mentioned is essentially the average slowness of the vibration wave along the propagation path in wave theory. The larger the value, the slower the wave propagates and the softer the rock mass.
[0032] The signal envelope is extracted based on the time-frequency matrix. The peak amplitude of the envelope of each node is read sequentially along the propagation path. The ratio of the peak amplitude of the envelope of adjacent nodes is calculated. The energy attenuation gradient is obtained by taking the natural logarithm of the ratio and dividing it by the node spacing.
[0033] The energy attenuation gradient represents the rate attenuation of vibration wave energy along the propagation path, reflecting the rock mass's ability to absorb and scatter vibration energy. Areas with larger energy attenuation gradients usually correspond to fracture development zones, dense joint areas, or weak interlayers, while areas with smaller attenuation gradients represent hard rock masses with better integrity.
[0034] Multiple discrete frequency points are selected in the time-frequency matrix. The group velocity corresponding to the wavelength is calculated for each frequency point. The group velocity is used as the vertical axis and the frequency is used as the horizontal axis to construct the group velocity-frequency relationship curve. The curve is fitted with a least-squares quadratic polynomial, and the absolute value of the coefficient of the quadratic term of the fitted polynomial is taken as the dispersion coefficient.
[0035] The dispersion coefficient represents the degree of nonlinearity of the vibration wave group velocity with frequency, reflecting the scale effect and internal heterogeneity of the rock mass. When there is a heterogeneous structure in the rock mass with a wavelength scale, the propagation velocities of different frequency components are separated, the dispersion effect is enhanced, and the dispersion coefficient increases.
[0036] The above operations acquire the slowness value, energy decay gradient, and dispersion coefficient for each propagation path within the detection area. However, these parameters are observations of the integral or average effect of the path, while engineering evaluation requires attribute parameters at spatial points. Therefore, by establishing mathematical inversion equations, the path observations, i.e., the parameters, are inverted into a nodal parameter field covering the entire area.
[0037] Furthermore, the calculated observation values (slowness value T) along each propagation path p are then... p Energy decay gradient α p Dispersion coefficient β p ), which is regarded as the initial value of the attribute of the midpoint m of the path.
[0038] A discrete set of points covering the detection area is constructed using the midpoints of all paths, serving as the solution nodes for the inversion calculation. The total number of nodes is denoted as N. For each node i (i=1, 2, ..., N), three unknowns need to be solved: the wave velocity value V. i Attenuation coefficient value A i and the dispersion coefficient value B i .
[0039] Establish the physical relationship equation between path observations and nodal unknowns, forming a joint inversion equation set of slowness-decay-dispersion, specifically: for the slowness value T p : .
[0040] in, It is the set of nodes in the neighborhood of point m in path p. Let be the slowness of node i. The larger the value, the slower the wave propagates near that node. It is a weighting coefficient, representing the overall slowness T of path p from node i. p The contribution ratio can be determined by the inverse relationship between the vertical distance from node i to path p. The closer the distance, the greater the weight, and the following conditions must be met: ; This is the empirical value for the first observation error.
[0041] The summation term This represents the weighted average of the slowness of all nodes along path P. The larger the value, the slower the wave propagates along the path, the lower the overall wave velocity, and the more likely the rock mass is to be softer, more fragmented, or less dense. The smaller the value, the faster the wave propagates, the higher the overall wave velocity, and the more likely the rock mass is to be intact and harder.
[0042] For the energy decay gradient α p : .
[0043] in, It is the weighting coefficient for energy attenuation, and its determination method is similar to that of wave speed weighting; Let be the attenuation coefficient value of node i; This is the empirical value for the second observation error.
[0044] The summation term This represents the weighted average of the attenuation coefficients of all nodes along path P. The larger the value, the faster the vibration energy attenuates along the path, and the stronger the rock mass's absorption and scattering of energy. This usually corresponds to areas with well-developed fractures, dense joints, or weak interlayers. The smaller the value, the slower the energy attenuation and the better the integrity of the rock mass.
[0045] For the dispersion coefficient β p : .
[0046] in, These are the dispersion weighting coefficients, which can be determined using a distance-based weighting function similar to the one described above. Let be the dispersion coefficient value of node i; This is the empirical value for the third observation error.
[0047] The summation term This represents the weighted average of the dispersion coefficients of all nodes along path P. The larger the value, the more significant the difference in wave velocity among different frequency components as the wave propagates along this path, and the stronger the dispersion effect, reflecting the existence of heterogeneous structures within the rock mass, such as thin interlayers and microfractures, on a scale comparable to the wavelength. The smaller the value, the more homogeneous the medium, and the weaker the dispersion effect.
[0048] The three types of equations for all P paths within the region are combined to form a joint inversion equation system in matrix form: Gm=d, where m= It is a column vector containing all 3N unknown parameters; d= G is a column vector consisting of all path observations; G is a coefficient matrix of size 3P×3N.
[0049] This matrix describes the contribution weight of parameters (wave velocity, attenuation coefficient, dispersion coefficient) at each spatial node to each path observation, thereby linking discrete point parameters with line observations through spatial interpolation and providing a positive model for subsequent inversion solutions.
[0050] By solving the joint inversion equations, the current estimated values of the dynamic parameters at each node are obtained.
[0051] It should be noted that after the joint inversion equation set is established for the first time, initial estimated values need to be assigned to the parameters of each node. The initial value of wave velocity can be set based on experience; the initial values of attenuation coefficient and dispersion coefficient can be set to zero, and the implementer can set them according to the actual situation.
[0052] S2. Traverse the closed triangular path formed by the propagation path and calculate the phase cumulative residual by accumulating the slowness values of its three sides.
[0053] Since the wave velocity field inside the rock mass has spatial continuity, when the vibration wave propagates along any closed triangular path, its travel time disturbance should satisfy the path integral conservation relationship: traverse the three sides of the closed path in a unified direction, such as counterclockwise, and assign positive or negative signs to the slowness values of each side according to the consistency between the wave propagation direction and the traversal direction, and then perform algebraic accumulation. The theoretical result should approach zero.
[0054] The calculated phase cumulative residual is the actual value of the algebraic sum. The degree to which it deviates from zero quantitatively characterizes the local consistency of the current inversion parameter field within the triangular region. The larger the absolute value of the residual, the lower the degree of agreement between the spatial distribution of the parameter field and the actual wave propagation characteristics of the rock mass, providing a criterion for identifying unstable regions in the inversion process.
[0055] In one specific embodiment, the traversal direction of the closed triangular path is set to counterclockwise. For each edge, a sign is assigned to its slowness value according to the relative relationship between the propagation direction of the vibration wave of that edge and the traversal direction: when the propagation direction is consistent with the traversal direction, the slowness value contributed by that edge is positive; otherwise, it is negative.
[0056] The algebraic sum of the signed slowness values of the three sides is the phase cumulative residual of the closed triangular path.
[0057] S3. If the phase cumulative residual of the current closed triangular path is inconsistent with the sign of the previous iteration, then calculate the spatial gradient of the wave velocity in the triangular region based on the estimated wave velocity value.
[0058] Since the rock mass wave velocity field should gradually converge to a spatially continuous and physically self-consistent stable state during the iterative inversion process, the sign change of the phase cumulative residual of the closed triangular path reflects that the parameter field in this area has not yet reached local equilibrium and is still in the dynamic adjustment stage.
[0059] Therefore, unstable regions can be identified by monitoring whether the sign of the phase cumulative residual flips between adjacent iterations.
[0060] In one specific implementation, the phase cumulative residual sign state of all closed triangular paths in the previous iteration is stored and compared element by element with the residual sign of the corresponding path in the current iteration; when the phase cumulative residual sign of the same closed triangular path changes from positive to negative or from negative to positive in two adjacent iterations, the path is determined to be in an unstable state.
[0061] Based on the wave velocity values and their planar coordinates estimated by the current iteration of the three nodes of the triangular path, the planar wave velocity field within the triangular region is fitted using the least squares method, and the resulting planar gradient vector is the wave velocity spatial gradient vector.
[0062] Please see Figure 2 As shown in Figure S4, when the spatial gradient of wave velocity is opposite to the dip angle of the geological structure, the conflict node is locked and the dispersion coefficient of the associated path of the node is extracted. The damping constraint equation is constructed and the joint inversion equation set is updated.
[0063] Since the spatial distribution of rock mass wave velocity is usually physically consistent with the spatial distribution of geological structure, when the direction of the wave velocity spatial gradient obtained by inversion conflicts significantly with the dip direction of geological structure, it can reflect that the parameter field in the region is subject to local abnormal interference or indicate that the inversion process has not yet converged to a stable state in the local area.
[0064] Therefore, by identifying such conflict regions and utilizing the dispersion characteristics of these regions to construct damping constraint equations, regularization of the conflict regions can be achieved.
[0065] In one specific implementation, the wave velocity spatial gradient vector is multiplied by the dip angle vector of the geological structure corresponding to the triangular region. When the dot product result is negative, it indicates that the angle between the two vectors is greater than 90 degrees, that is, the direction of wave velocity increase is opposite to the direction of geological dip angle, which is determined to be a directional conflict. The three vertex nodes of the closed triangular path where the conflict occurs are marked as conflict nodes.
[0066] The dip direction of the geological structure is known prior information, which the implementer can obtain according to the specific circumstances.
[0067] For each conflict node, all propagation paths associated with that node are extracted. If the number of associated paths of a node is greater than or equal to 2, it indicates that the neighborhood observation information contained in the node can be used for statistical analysis of dispersion characteristics, and then the dispersion coefficients corresponding to each associated path are extracted.
[0068] The standard deviation of the dispersion coefficient is calculated and normalized to obtain the constraint weight factor of the node. Based on the constraint weight factor and the regularization coefficient, the damping constraint equation of the slowness parameter of the node is constructed.
[0069] Specifically, for each conflict node j, its damping constraint equation is: .
[0070] in, Let be the slowness of node j. The constraint weight factor for node j is the standard deviation of the dispersion coefficients of all associated paths of that node. After normalization, we get, that is , This represents the total number of node conflicts. The regularization coefficient is determined using the L-curve method; This is the slowness reference value for node j, which can be the slowness value of the previous iteration or the average slowness of its neighboring nodes. The implementer can set it according to the specific situation.
[0071] To prevent constraint failure, constraint weight factors are set. The minimum threshold is 0.01.
[0072] This indicates the constraint strength applied to node j; The larger the value, the slower the solution at that node. Closer ; The product of the square root of the constraint strength and the slowness reference value represents the objective of the constraint.
[0073] Write the damping constraint equations for all conflict nodes in matrix form: Hm=h.
[0074] Here, H is an M×3N matrix (M is the number of conflicting nodes), where each row corresponds to a conflicting node j, and the element in the j-th column of that row is... The remaining elements are 0; h is an M-dimensional column vector, and its j-th component is... .
[0075] The damping constraint equation is added as an additional constraint to the joint inversion equation system Gm=d, forming an updated equation system for the next iteration: (G T G+H T H)m=G T d+H T h, this equation utilizes the parameter vector m solved by the next-generation iterative solution, while simultaneously minimizing the observation fitting error and constraint violation, H T H is a diagonal matrix, which adds positions corresponding to conflict nodes on the diagonal. This allows for the regularization of node parameters during the solution process.
[0076] S5. Iterate through S2 to S4 until the cumulative phase residuals of all closed triangular paths remain unchanged for two consecutive rounds or the maximum number of iterations is reached. Output the rock mass dynamic parameter field obtained by solving the joint inversion equation set. Generate the rock mass vibration performance distribution map through spatial interpolation and vector superposition.
[0077] By setting iteration termination conditions to avoid infinite loops, and by converting the node parameters obtained after iteration convergence into a continuous field through spatial interpolation, and by integrating multi-parameter information with geological background, a visualized rock mass vibration performance distribution map is generated.
[0078] In one specific embodiment, the processing flow from S2 to S4 is called iteratively. In each iteration, the slowness value, phase cumulative residual, and wave velocity spatial gradient of each propagation path are recalculated based on the joint inversion equation set updated in the previous round.
[0079] After each iteration, the sign state of the phase cumulative residual of all closed triangular paths in the current iteration is compared with that in the previous iteration. When the sign of all paths remains unchanged in two consecutive iterations, it indicates that the relative time difference distribution in the triangular region has satisfied the path integral conservation relationship and the parameter adjustment direction tends to be stable. The inversion process is then determined to have reached the convergence state and the iteration is terminated.
[0080] If convergence is not achieved, the iteration continues. If convergence is still not achieved after the maximum number of iterations (50), an alarm message is output and the non-converged region is marked.
[0081] After convergence, extract the wave velocity values and attenuation coefficient values of each node obtained from solving the joint inversion equation set in the final round, and generate the wave velocity field and attenuation field by linear interpolation using the node spatial coordinates as control points.
[0082] The linear interpolation uses the spatial coordinates of each node as control points and the dynamic parameter values as interpolation variables to construct a linear relationship between adjacent nodes and generate a continuous parameter distribution on a regular grid.
[0083] The result of squaring the wave velocity values at each node in the wave velocity field is the dynamic stiffness distribution data, and the attenuation field is converted into energy attenuation distribution data.
[0084] Subsequently, the dynamic stiffness distribution data and energy attenuation distribution data are spatially aligned and superimposed according to the same spatial coordinates, and the geological structure strike vector field is integrated to generate a rock mass vibration performance distribution map.
[0085] Example 2.
[0086] Please see Figure 3As shown, the present invention provides a system for detecting and analyzing the vibration performance of foundation rocks in water conservancy projects, including: a feature extraction module, a residual calculation module, a gradient solution module, a conflict update module, and a field map generation module. The connection relationship between the modules is as follows: the feature extraction module is connected to the residual calculation module, the residual calculation module is connected to the gradient solution module, the gradient solution module is connected to the conflict update module, and the field map generation module is connected to the module that performs residual calculation, gradient solution, and conflict update.
[0087] Feature extraction module: Collects vibration signals from the rock surface through a sensor array, extracts the slowness value, energy attenuation gradient and dispersion coefficient of the propagation path of each node, and establishes a joint inversion equation set based on these.
[0088] Residual calculation module: Traverses the closed triangular path formed by the propagation path and calculates the phase cumulative residual by accumulating the slowness values of its three sides.
[0089] Gradient calculation module: If the phase cumulative residual of the current closed triangular path is inconsistent with the sign of the previous iteration, the spatial gradient of the wave velocity in the triangular region is calculated based on the estimated wave velocity value.
[0090] Conflict Update Module: When the spatial gradient of wave velocity is opposite to the dip angle of the geological structure, the conflict node is locked and the dispersion coefficient of the associated path of the node is extracted. The damping constraint equation is constructed and the joint inversion equation set is updated.
[0091] Field map generation module: Iteratively executes residual calculation, gradient solution and conflict update modules until the phase cumulative residuals of all closed triangular paths remain unchanged for two consecutive rounds or the maximum number of iterations is reached. Outputs the rock mass dynamic parameter field obtained by solving the joint inversion equation set, and generates a rock mass vibration performance distribution map through spatial interpolation and vector superposition.
[0092] In summary, this invention first deploys a sensor array on the rock surface to synchronously collect vibration signals, extracts the slowness value, energy attenuation gradient, and dispersion coefficient of each propagation path, and constructs a joint inversion equation set accordingly; secondly, by traversing all closed triangular paths and calculating their phase cumulative residuals, the internal self-consistency of the inversion parameter field is evaluated; furthermore, the wave velocity spatial gradient is calculated in the unstable region where the residual sign oscillates, and compared with the dip angle of the prior geological structure, locking the conflict node when there is a directional conflict.
[0093] Subsequently, a damping constraint equation is constructed based on the dispersion coefficient of the associated path of conflict nodes, and the joint inversion equation set is updated to suppress ill-conditioned solutions. The above consistency check and constraint enhancement steps are iteratively executed until convergence, and finally a highly reliable rock mass dynamic parameter field is obtained. After spatial interpolation and fusion of multi-source information, a vibration performance distribution map that comprehensively reflects the dynamic stiffness, energy attenuation characteristics and geological structure relationship of the rock mass is generated, thereby significantly improving the accuracy and reliability of vibration performance analysis of complex foundation rock masses.
[0094] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.
[0095] Those skilled in the art will recognize that the algorithmic steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.
[0096] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.
[0097] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
[0098] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for detecting and analyzing the vibration performance of foundation rock in hydraulic engineering projects, characterized in that, include: S1. Collect vibration signals on the rock surface through a sensor array, extract the slowness value, energy attenuation gradient and dispersion coefficient of the propagation path of each node, and establish a joint inversion equation set accordingly. S2. Traverse the closed triangular path formed by the propagation path and calculate the phase cumulative residual by summing the slowness values of its three sides. S3. If the phase cumulative residual of the current closed triangular path is inconsistent with the sign of the previous iteration, calculate the spatial gradient of the wave velocity in the triangular region based on the estimated wave velocity value. S4. When the spatial gradient of wave velocity is opposite to the dip angle of the geological structure, lock the conflict node and extract the dispersion coefficient of the associated path of the node, construct the damping constraint equation and update the joint inversion equation set. S5. Iterate through S2 to S4 until the cumulative phase residuals of all closed triangular paths remain unchanged for two consecutive rounds or the maximum number of iterations is reached. Output the rock mass dynamic parameter field obtained by solving the joint inversion equation set. Generate the rock mass vibration performance distribution map through spatial interpolation and vector superposition.
2. The method for detecting and analyzing the vibration performance of foundation rock in hydraulic engineering as described in claim 1, characterized in that, The methods for obtaining the slowness value, energy decay gradient, and dispersion coefficient are as follows: Vibration sensors are deployed on the rock mass surface according to a grid node, and each sensor is triggered synchronously by a unified clock to collect vibration signals. Each sensor is labeled as a node, and the line connecting adjacent nodes is defined as the propagation path; Short-time Fourier transform is performed on the synchronous vibration signals at both ends of each propagation path to obtain the time spectrum matrix. The time corresponding to the maximum energy amplitude along the time axis is used as the arrival time of the first wave. Calculate the difference in arrival times of the first wave at the two ends of the same propagation path, and divide the difference by the propagation path length to obtain the slowness value; The signal envelope is extracted based on the time-frequency matrix. The peak amplitude of the envelope of each node is read sequentially along the propagation path. The ratio of the peak amplitude of the envelope of adjacent nodes is calculated. The energy attenuation gradient is obtained by taking the natural logarithm of the ratio and dividing it by the node spacing. Multiple discrete frequency points are selected in the time-frequency matrix. The group velocity corresponding to the wavelength is calculated for each frequency point. The group velocity is used as the vertical axis and the frequency is used as the horizontal axis to construct the group velocity-frequency relationship curve. The curve is fitted with a least-squares quadratic polynomial, and the absolute value of the coefficient of the quadratic term of the fitted polynomial is taken as the dispersion coefficient.
3. The method for detecting and analyzing the vibration performance of foundation rock in hydraulic engineering as described in claim 1, characterized in that, The method for establishing the joint inversion equation set is as follows: The slowness value, energy decay gradient, and dispersion coefficient corresponding to each propagation path are respectively regarded as the observation values at the midpoint of the path; By using the discrete point set formed by the midpoints of all paths as solution nodes, a mapping relationship between node parameters and path observations is established, forming a joint inversion equation set.
4. The method for detecting and analyzing the vibration performance of foundation rock in hydraulic engineering as described in claim 1, characterized in that, The specific method of S2 is as follows: Set the traversal direction of the closed triangular path to counterclockwise; For each edge, a sign is assigned to its slowness value based on the relative relationship between the propagation direction of the vibration wave of that edge and the traversal direction: when the propagation direction is consistent with the traversal direction, the slowness value contributed by that edge is positive; otherwise, it is negative. The algebraic sum of the signed slowness values of the three sides is the phase cumulative residual of the closed triangular path.
5. The method for detecting and analyzing the vibration performance of foundation rock in hydraulic engineering as described in claim 1, characterized in that, The specific method of S3 is as follows: Compare the sign change of the phase cumulative residual of the same closed triangular path in the current iteration round with that in the previous iteration round. If the sign changes, the path is determined to be unstable. Based on the wave velocity values and their planar coordinates estimated by the current iteration of the three nodes of the triangular path, the planar wave velocity field within the triangular region is fitted using the least squares method, and the resulting planar gradient vector is the wave velocity spatial gradient vector.
6. The method for detecting and analyzing the vibration performance of foundation rock in hydraulic engineering as described in claim 1, characterized in that, The specific method of S4 is as follows: Perform a dot product operation between the wave velocity spatial gradient vector and the geological structure dip angle vector corresponding to the triangular region; When the dot product result is negative, it is determined to be a direction conflict, and the three nodes of the closed triangular path are marked as conflict nodes; For each conflict node, extract all propagation paths associated with that node. If the number of associated paths for that node is greater than or equal to 2, then extract its dispersion coefficient. Calculate the standard deviation of the dispersion coefficient and normalize it to obtain the constraint weight factor of the node; The damping constraint equation for the slowness parameter of the node is constructed based on the constraint weight factor and regularization coefficient, and then added to the joint inversion equation set.
7. The method for detecting and analyzing the vibration performance of foundation rock in hydraulic engineering as described in claim 1, characterized in that, The method for obtaining the dynamic parameter field of the rock mass is as follows: Iterate through S2 to S4 until the phase cumulative residual sign of all closed triangular paths remains unchanged in two consecutive iterations or the maximum number of iterations is reached. In the final round, the dynamic parameters of each node, which are solved by the joint inversion equations, are interpolated according to their spatial location to generate a continuously distributed rock mass dynamic parameter field.
8. The method for detecting and analyzing the vibration performance of foundation rock in hydraulic engineering as described in claim 1, characterized in that, The method for obtaining the rock mass vibration performance distribution map is as follows: Wave velocity and attenuation coefficient values of each node are extracted from the final rock mass dynamic parameter field and converted into dynamic stiffness distribution data and energy attenuation distribution data, respectively. The dynamic stiffness distribution data, energy attenuation distribution data, and geological structure strike vector field are superimposed and fused with the same spatial coordinates to generate a rock mass vibration performance distribution map.
9. A method for detecting and analyzing the vibration performance of foundation rock in hydraulic engineering as described in claim 8, characterized in that, The transformation employs a linear interpolation method, including: Using the spatial coordinates of each node in the rock mass dynamic parameter field as control points and its wave velocity value as the interpolation variable, linear interpolation is performed to obtain dynamic stiffness distribution data. Using the spatial coordinates of the same node as control points and their attenuation coefficients as interpolation variables, linear interpolation is performed to obtain energy attenuation distribution data.
10. A system for detecting and analyzing the vibration performance of foundation rock in hydraulic engineering projects, characterized in that, include: Feature extraction module: Collects vibration signals from the rock surface through a sensor array, extracts the slowness value, energy attenuation gradient and dispersion coefficient of the propagation path of each node, and establishes a joint inversion equation set based on these. Residual calculation module: Traverses the closed triangular path formed by the propagation path and calculates the phase cumulative residual by accumulating the slowness values of its three sides; Gradient calculation module: If the phase cumulative residual of the current closed triangular path is inconsistent with the sign of the previous iteration, the wave velocity spatial gradient of the triangular region is calculated based on the estimated wave velocity value. Conflict Update Module: When the spatial gradient of wave velocity is opposite to the dip angle of geological structure, lock the conflict node and extract the dispersion coefficient of the associated path of the node, construct the damping constraint equation and update the joint inversion equation set; Field map generation module: Iteratively executes residual calculation, gradient solution and conflict update modules until the phase cumulative residuals of all closed triangular paths remain unchanged for two consecutive rounds or the maximum number of iterations is reached. Outputs the rock mass dynamic parameter field obtained by solving the joint inversion equation set, and generates a rock mass vibration performance distribution map through spatial interpolation and vector superposition.