A method, system, and platform for deconstructing and separating P-wave, S-wave, and R-waves in tunnel blasting vibration based on multi-source information fusion.

Abstract

This invention discloses a method, system, and platform for deconstructing and separating P-waves, S-waves, and R-waves in tunnel blasting vibration based on multi-source information fusion. The solution achieves accurate identification and separation of P-waves, S-waves, and R-waves in tunnel blasting vibration by fusing particle motion trajectory analysis, polarization ellipsoid parameter calculation, and instantaneous phase difference analysis. This overcomes the technical bottleneck of traditional methods, which can only identify first-arrival waves but cannot effectively separate surface waves. Based on the constructed normalized dominant factor function, the dominant roles of different wave types in the propagation space are clearly defined, providing a direct scientific basis for targeted vibration reduction and protection in different sections of tunnel engineering, significantly improving the accuracy of blasting vibration safety assessment and the effectiveness of engineering protection.

Description

Technical Field

[0001] This invention belongs to the field of tunnel engineering and blasting vibration monitoring technology, specifically relating to a method, system and platform for deconstructing and separating P-wave, S-wave and R-wave of tunnel blasting vibration based on multi-source information fusion. Background Technology

[0002] In tunnel blasting construction, blasting vibrations can have varying degrees of impact on the surrounding rock mass and existing structures. Accurately assessing the impact of blasting vibrations on the tunnel's surrounding rock and adjacent structures is crucial for ensuring construction safety and project quality. Blasting vibration waves mainly include three basic types: longitudinal waves (P-waves), transverse waves (S-waves), and Rayleigh waves (R-waves), which differ significantly in their propagation characteristics, energy distribution, and impact on structures.

[0003] Currently, tunnel blasting vibration hazard assessment mainly relies on empirical formulas such as the Sadovsky formula for safety evaluation, and most methods employ uniform safety thresholds. Different wave types have different damage mechanisms and impacts on structures, and this traditional method cannot distinguish the contributions of different wave types and ignores the polarization characteristics of waves, making it difficult to accurately reflect the physical nature of the vibration waves. Furthermore, existing technologies (such as STA / LTA and AIC methods) can only pick up the first arrival times of P-waves and S-waves, without considering the R-wave acting on the surface, and cannot completely deconstruct and separate P-waves, S-waves, and R-waves. This results in the inability to specifically consider the impact of P-waves, S-waves, and R-waves on surrounding building structures when analyzing blasting vibration hazards. For example, underestimating the surface effect of R-waves or overestimating the deep impact of P-waves under complex geological conditions can lead to inadequate safety hazards due to inadequate protective measures. At the same time, the blasting vibration specifications of the International Society of Explosives Engineers (ISEE) mainly set vibration limits based on PPV (peak particle velocity) and frequency charts, without considering the weight of different wave types in blasting vibration hazards, which may lead to assessment biases in enclosed environments such as tunnels.

[0004] Therefore, in view of the above-mentioned technical problems and defects, there is an urgent need to design and develop a method, system and platform for deconstructing and separating P-wave, S-wave and R-wave of tunnel blasting vibration based on multi-source information fusion. Summary of the Invention

[0005] To overcome the shortcomings and difficulties of the existing technology, the present invention provides a method, system and platform for deconstructing and separating P-wave, S-wave and R-wave of tunnel blasting vibration based on multi-source information fusion, so as to solve the problem that the existing technology cannot accurately distinguish different wave types and is difficult to scientifically assess the impact of vibration.

[0006] The first objective of this invention is to provide a method for deconstructing and separating P-waves, S-waves, and R-waves in tunnel blasting vibrations based on multi-source information fusion; the second objective of this invention is to provide a system for deconstructing and separating P-waves, S-waves, and R-waves in tunnel blasting vibrations based on multi-source information fusion; and the third objective of this invention is to provide a platform for deconstructing and separating P-waves, S-waves, and R-waves in tunnel blasting vibrations based on multi-source information fusion.

[0007] The first objective of this invention is achieved as follows: the method includes the following steps: generating and simultaneously acquiring first data and second data corresponding to the tunnel blasting area; wherein, the first data is multi-component vibration data; the second data is multi-component displacement data; constructing a particle motion trajectory corresponding to the second data, and generating third data corresponding to the particle motion trajectory; wherein, the third data is polarization morphology feature data; generating and acquiring fourth data corresponding to the first data, and fusing the third data and the fourth data to identify and separate corresponding fifth data; wherein, the fourth data is polarization parameter data; the fifth data is blasting vibration wave waveform data, including P-wave waveform data, S-wave waveform data and R-wave waveform data; extracting and generating a sixth data corresponding to the fifth data, and dividing and generating a seventh data corresponding to the sixth data based on the sixth data and taking the blast center distance as a variable; wherein, the sixth data is vibration characteristic value data; the seventh data is the main action zone data of each wave type.

[0008] Furthermore, the step of generating and synchronously acquiring the first data and the second data corresponding to the tunnel blasting area further includes: generating and acquiring the first data and the second data corresponding to the tunnel blasting operation area and located in the horizontal radial direction; generating and acquiring the first data and the second data corresponding to the tunnel blasting operation area and located in the horizontal tangential direction; and generating and acquiring the first data and the second data corresponding to the tunnel blasting operation area and located in the vertical direction.

[0009] Furthermore, the construction of the particle motion trajectory corresponding to the second data and the generation of the third data corresponding to the particle motion trajectory further includes: generating and acquiring the eighth data corresponding to the tunnel blasting area, and based on the eighth data, dividing the received wave into an up-going wave, a down-going wave, or a wave at the same elevation; wherein the eighth data is the relative elevation data between the monitoring point and the blast source point; creating and generating a motion trajectory corresponding to the particle and located in the vertical-horizontal plane, and identifying and generating distribution quadrant data and polarization morphology data corresponding to the particle motion trajectory; wherein the polarization morphology data includes at least linear polarization data and elliptic polarization data.

[0010] Further, the step of generating and acquiring fourth data corresponding to the first data, fusing and processing the third data and the fourth data, and identifying and separating to generate corresponding fifth data further includes: constructing a covariance matrix corresponding to the first data, and generating corresponding ninth data based on the covariance matrix; wherein the ninth data is ellipsoidal parameter data characterizing polarization characteristics; generating and acquiring tenth data corresponding to the first data, and fusing and processing the third data, the ninth data, and the tenth data, while simultaneously generating corresponding eleventh data; wherein the tenth data is instantaneous phase difference data between vibration components in different directions; the eleventh data is vibration data after fusing and processing; creating a first model corresponding to tunnel blasting vibration, and based on the first model, combined with the eleventh data, extracting and generating corresponding twelfth data; wherein the first model is a vibration wave type identification model; the twelfth data is waveform data within a time window.

[0011] Furthermore, the step of constructing a covariance matrix corresponding to the first data and generating corresponding ninth data based on the covariance matrix further includes: constructing a covariance matrix corresponding to the three-component vibration velocity signal; wherein, the calculation formula for the covariance matrix is:

[0012] (1)

[0013] In the formula, For a moment The three-component vibration velocity vector, This represents the number of sampling points within the time window.

[0014] Further, the step of extracting and generating sixth data corresponding to the fifth data, and generating seventh data corresponding to the sixth data based on the sixth data and using the burst center distance as a variable, further includes: generating and acquiring thirteenth data corresponding to the fifth data, and constructing a normalized dominant factor relationship using the burst center distance as a variable; wherein, the thirteenth data is the peak vibration velocity data of each wave type; the expression of the normalized dominant factor relationship is:

[0015] (5)

[0016] In the formula, Indicates wave type Distance from the epicenter Peak particle velocity at that location;

[0017] Based on the normalized dominant factor relationship, the main partitions of P-wave, S-wave, and R-wave are divided and generated respectively.

[0018] The second objective of this invention is achieved as follows: the system is used to implement the method for deconstructing and separating P-waves, S-waves, and R-waves in tunnel blasting vibration based on multi-source information fusion. The system includes: a first data generation unit, used to generate and simultaneously acquire first data and second data corresponding to the tunnel blasting area; wherein the first data is multi-component vibration data; and the second data is multi-component displacement data; a data construction and generation unit, used to construct a particle motion trajectory corresponding to the second data and generate third data corresponding to the particle motion trajectory; wherein the third data is polarization morphology feature data; and a data fusion processing unit. The system is used to generate and acquire fourth data corresponding to the first data, and to fuse and process the third data and the fourth data to identify and separate corresponding fifth data; wherein, the fourth data is polarization parameter data; the fifth data is blasting vibration wave waveform data, including P-wave waveform data, S-wave waveform data and R-wave waveform data; the data extraction and division unit is used to extract and generate a sixth data corresponding to the fifth data, and to divide and generate a seventh data corresponding to the sixth data based on the sixth data and taking the blast center distance as a variable; wherein, the sixth data is vibration characteristic value data; the seventh data is the main action zone data of each wave type.

[0019] Furthermore, the first data generation unit further includes: a first generation module, used to generate and acquire first data and second data corresponding to the tunnel blasting operation area and located in the horizontal radial direction, respectively;

[0020] The second generation module is used to generate and acquire first data and second data corresponding to the tunnel blasting operation area and located in the horizontal tangential direction, respectively; the third generation module is used to generate and acquire first data and second data corresponding to the tunnel blasting operation area and located in the vertical direction, respectively.

[0021] And / or, the data construction and generation unit further includes: a first processing module, used to generate and acquire eighth data corresponding to the tunnel blasting area, and based on the eighth data, divide the received wave into an up-going wave, a down-going wave, or a wave at the same elevation; wherein, the eighth data is the relative elevation data between the monitoring point and the blast source point; a first construction module, used to create a motion trajectory corresponding to the particle and located in the vertical-horizontal plane, and identify and generate distribution quadrant data and polarization morphology data corresponding to the particle's motion trajectory; wherein, the polarization morphology data includes at least linear polarization data and elliptic polarization data;

[0022] And / or, the data fusion processing unit further includes: a second construction module, configured to construct a covariance matrix corresponding to the first data, and generate corresponding ninth data based on the covariance matrix; wherein the ninth data is ellipsoidal parameter data characterizing polarization characteristics; a second processing module, configured to generate and acquire tenth data corresponding to the first data, and fuse the third data, the ninth data, and the tenth data, while generating corresponding eleventh data; wherein the tenth data is instantaneous phase difference data between vibration components in different directions; the eleventh data is vibration data after fusion processing; a third construction module, configured to create a first model corresponding to tunnel blasting vibration, and based on the first model, combined with the eleventh data, extract and generate corresponding twelfth data; wherein the first model is a vibration wave type identification model; the twelfth data is waveform data within a time window;

[0023] And / or, the data extraction and partitioning unit further includes: a fourth construction module, used to generate and acquire thirteenth data corresponding to the fifth data, and construct a normalized dominant factor relationship using the burst center distance as a variable; wherein, the thirteenth data is the peak vibration velocity data of each wave type; the expression of the normalized dominant factor relationship is:

[0024] (5)

[0025] In the formula, Indicates wave type Distance from the epicenter Peak particle velocity at that location;

[0026] The fourth generation module is used to divide and generate the main partitions of P-wave, S-wave, and R-wave according to the normalized dominant factor relationship.

[0027] Furthermore, the second construction module further includes: a fifth construction module, used to construct a covariance matrix corresponding to the three-component vibration velocity signal; wherein the calculation formula for the covariance matrix is:

[0028] (1)

[0029] In the formula, For a moment The three-component vibration velocity vector, This represents the number of sampling points within the time window.

[0030] The third objective of this invention is achieved as follows: the platform includes a processor, a memory, and a control program for the deconstruction and separation processing of P-wave, S-wave, and R-wave of tunnel blasting vibration based on multi-source information fusion; wherein the control program for the deconstruction and separation processing of P-wave, S-wave, and R-wave of tunnel blasting vibration based on multi-source information fusion is executed on the processor, and the control program for the deconstruction and separation processing of P-wave, S-wave, and R-wave of tunnel blasting vibration based on multi-source information fusion is stored in the memory; the control program for the deconstruction and separation processing of P-wave, S-wave, and R-wave of tunnel blasting vibration based on multi-source information fusion implements the method for deconstructing and separating P-wave, S-wave, and R-wave of tunnel blasting vibration based on multi-source information fusion.

[0031] This invention generates and synchronously acquires first and second data corresponding to the tunnel blasting area using a method; wherein the first data is multi-component vibration data; the second data is multi-component displacement data; a particle motion trajectory corresponding to the second data is constructed, and third data corresponding to the particle motion trajectory is generated; wherein the third data is polarization morphology feature data; a fourth data corresponding to the first data is generated and acquired, and the third and fourth data are fused and processed to identify and separate corresponding fifth data; wherein the fourth data is polarization parameter data; the fifth data... The method generates blasting vibration wave waveform data, including P-wave, S-wave, and R-wave waveform data. A sixth data point corresponding to the fifth data point is extracted and generated. Based on the sixth data point and using the blast center distance as a variable, a seventh data point corresponding to the sixth data point is generated. The sixth data point represents vibration characteristic values. The seventh data point represents the main action zones for each wave type. The method also includes a corresponding system and platform, capable of accurately identifying and separating P-waves, S-waves, and R-waves. This provides a refined, zoned, and graded protection strategy for tunnel surrounding rock and support structures, effectively avoiding potential damage risks caused by insufficient protective measures.

[0032] In other words, this invention achieves accurate identification and separation of P-waves, S-waves, and R-waves in tunnel blasting vibrations by integrating particle motion trajectory analysis, polarization ellipsoid parameter calculation, and instantaneous phase difference analysis. This overcomes the technical bottleneck of traditional methods, which can only identify first arrival waves but cannot effectively separate surface waves. Based on the constructed normalized dominant factor function, the dominant role of different wave types in the propagation space is clearly defined, providing a direct scientific basis for targeted vibration reduction and protection in different sections of tunnel engineering. This significantly improves the accuracy of blasting vibration safety assessment and the effectiveness of engineering protection. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0034] Figure 1 This is a schematic diagram of the process steps of a method for deconstructing and separating P-wave, S-wave and R-wave of tunnel blasting vibration based on multi-source information fusion according to the present invention.

[0035] Figure 2 This is a schematic flowchart of an embodiment of the present invention, which describes a method for deconstructing and separating P-waves, S-waves, and R-waves in tunnel blasting vibration based on multi-source information fusion.

[0036] Figure 3 This is a schematic diagram of the polarization mode of tunnel blasting vibration waves in an embodiment of a method for deconstructing and separating P-waves, S-waves and R-waves of tunnel blasting vibration based on multi-source information fusion according to the present invention.

[0037] Figure 4 This is a schematic diagram of the P-wave, S-wave and R-wave action zone determination results in an embodiment of a tunnel blasting vibration P-wave, S-wave and R-wave deconstruction and separation processing method based on multi-source information fusion of the present invention.

[0038] Figure 5 This is a schematic diagram of the overall steps of an embodiment of the method for deconstructing and separating P-wave, S-wave and R-wave of tunnel blasting vibration based on multi-source information fusion according to the present invention.

[0039] Figure 6 This is a schematic diagram of the architecture of a tunnel blasting vibration P-wave, S-wave and R-wave deconstruction and separation processing system based on multi-source information fusion according to the present invention.

[0040] Figure 7 This is a schematic diagram of the device architecture of an embodiment of the tunnel blasting vibration P-wave, S-wave and R-wave deconstruction and separation processing system based on multi-source information fusion of the present invention;

[0041] Figure 8 This is a schematic diagram of the platform architecture for deconstructing and separating P-wave, S-wave, and R-wave of tunnel blasting vibration based on multi-source information fusion, according to the present invention. Detailed Implementation

[0042] To facilitate a clearer understanding of the objectives, technical solutions, and advantages of this invention, the invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of this invention from the content disclosed in this specification.

[0043] This invention can also be implemented or applied through other different specific examples, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of this invention.

[0044] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0045] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Secondly, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0046] Preferably, the method for deconstructing and separating P-waves, S-waves, and R-waves in tunnel blasting vibration based on multi-source information fusion is applied in one or more terminals or servers. The terminal is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0047] The terminal can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal can interact with the customer via a keyboard, mouse, remote control, touchpad, or voice control device.

[0048] This invention provides a method, system, and platform for deconstructing and separating P-waves, S-waves, and R-waves in tunnel blasting vibration based on multi-source information fusion.

[0049] like Figure 1 The diagram shown is a flowchart of a method for deconstructing and separating P-wave, S-wave, and R-wave of tunnel blasting vibration based on multi-source information fusion, provided in an embodiment of the present invention.

[0050] In this embodiment, the method for deconstructing and separating P-waves, S-waves and R-waves of tunnel blasting vibration based on multi-source information fusion can be applied to terminals or fixed terminals with display functions. The terminals are not limited to personal computers, smartphones, tablets, desktop computers or all-in-one computers with cameras, etc.

[0051] The method for deconstructing and separating P-waves, S-waves, and R-waves in tunnel blasting vibration based on multi-source information fusion can also be applied to a hardware environment consisting of a terminal and a server connected to the terminal via a network. The network includes, but is not limited to, a wide area network (WAN), a metropolitan area network (MAN), or a local area network (LAN). The method for deconstructing and separating P-waves, S-waves, and R-waves in tunnel blasting vibration based on multi-source information fusion in this embodiment can be executed by the server, by the terminal, or by both the server and the terminal.

[0052] For example, for terminals requiring multi-source information fusion-based deconstruction and separation processing of P-wave, S-wave, and R-wave tunnel blasting vibrations, the deconstruction and separation processing function based on multi-source information fusion of the present invention can be directly integrated onto the terminal, or a client application for implementing the method of the present invention can be installed. Alternatively, the method provided by the present invention can also run on servers or other devices in the form of a Software Development Kit (SDK). The SDK provides an interface for the multi-source information fusion-based deconstruction and separation processing function of P-wave, S-wave, and R-wave tunnel blasting vibrations, allowing terminals or other devices to implement this function through the provided interface. The present invention will be further described below with reference to the accompanying drawings.

[0053] like Figures 1-5As shown, this invention provides a method for deconstructing and separating P-wave, S-wave, and R-wave vibrations in tunnel blasting based on multi-source information fusion. The method includes the following steps: S01, generating and simultaneously acquiring first data and second data corresponding to the tunnel blasting area; wherein, the first data is multi-component vibration data; the second data is multi-component displacement data; S02, constructing a particle motion trajectory corresponding to the second data, and generating third data corresponding to the particle motion trajectory; wherein, the third data is polarization morphology feature data; S03, generating and acquiring fourth data corresponding to the first data, fusing the third data and the fourth data, identifying and separating corresponding fifth data; wherein, the fourth data is polarization parameter data; the fifth data is blasting vibration wave waveform data, including P-wave waveform data, S-wave waveform data, and R-wave waveform data; S04, extracting and generating sixth data corresponding to the fifth data, and dividing and generating seventh data corresponding to the sixth data based on the sixth data and taking the blast center distance as a variable; wherein, the sixth data is vibration characteristic value data; the seventh data is the main action zone data of each wave type.

[0054] The step of generating and synchronously acquiring first data and second data corresponding to the tunnel blasting area further includes: S011, generating and acquiring first data and second data corresponding to the tunnel blasting operation area and located in the horizontal radial direction; S012, generating and acquiring first data and second data corresponding to the tunnel blasting operation area and located in the horizontal tangential direction; S013, generating and acquiring first data and second data corresponding to the tunnel blasting operation area and located in the vertical direction.

[0055] The process of constructing the particle motion trajectory corresponding to the second data and generating the third data corresponding to the particle motion trajectory further includes: S021, generating and acquiring the eighth data corresponding to the tunnel blasting area, and based on the eighth data, dividing the received wave into an up-going wave, a down-going wave, or a wave at the same elevation; wherein, the eighth data is the relative elevation data between the monitoring point and the blast source point; S022, creating and generating a motion trajectory corresponding to the particle and located in the vertical-horizontal plane, and identifying and generating distribution quadrant data and polarization morphology data corresponding to the particle motion trajectory; wherein, the polarization morphology data includes at least linear polarization data and elliptic polarization data.

[0056] The process of generating and acquiring fourth data corresponding to the first data, fusing the third data and the fourth data, and identifying and separating the corresponding fifth data further includes: S031, constructing a covariance matrix corresponding to the first data, and generating corresponding ninth data based on the covariance matrix; wherein the ninth data is ellipsoidal parameter data characterizing polarization characteristics; S032, generating and acquiring tenth data corresponding to the first data, fusing the third data, the ninth data, and the tenth data, and simultaneously generating corresponding eleventh data; wherein the tenth data is instantaneous phase difference data between vibration components in different directions; the eleventh data is vibration data after fusing processing; S033, creating a first model corresponding to tunnel blasting vibration, and based on the first model and combined with the eleventh data, extracting and generating corresponding twelfth data; wherein the first model is a vibration wave type identification model; the twelfth data is waveform data within a time window.

[0057] The step of constructing a covariance matrix corresponding to the first data, and generating the corresponding ninth data based on the covariance matrix, further includes:

[0058] S0311. Construct the covariance matrix corresponding to the three-component vibration velocity signal; wherein, the formula for calculating the covariance matrix is:

[0059] (1)

[0060] In the formula, For a moment The three-component vibration velocity vector, This represents the number of sampling points within the time window.

[0061] The step of extracting and generating sixth data corresponding to the fifth data, and generating seventh data corresponding to the sixth data based on the sixth data and using the center distance as a variable, further includes:

[0062] S041. Generate and acquire the thirteenth data corresponding to the fifth data, and construct a normalized dominant factor relationship using the burst center distance as a variable; wherein, the thirteenth data is the peak vibration velocity data of each wave pattern; the expression of the normalized dominant factor relationship is:

[0063] (5)

[0064] In the formula, Indicates wave type Distance from the epicenter Peak particle velocity at that location;

[0065] S042. Based on the normalized dominant factor relationship, divide and generate the main partitions of P wave, S wave and R wave respectively.

[0066] Specifically, in this embodiment of the invention, a method for deconstructing and separating P-waves, S-waves, and R-waves of tunnel blasting vibration based on multi-source information fusion is provided, comprising the following steps:

[0067] Step 1: Deploy three-component vibration meters and three-axis displacement sensors in the tunnel blasting operation area to simultaneously collect vibration data and three-component displacement data in the horizontal radial, horizontal tangential, and vertical directions.

[0068] Step 2: Construct a particle motion trajectory diagram based on the three-component displacement data, divide the received wave into up-going wave, down-going wave or same-elevation wave according to the relative elevation of the measuring point and the explosion source, and identify the motion trajectory distribution quadrant and polarization morphology of each wave type.

[0069] Step 3: Construct a covariance matrix using the three-component vibration velocity data, and perform eigenvalue decomposition to obtain the principal axis direction and eigenvalue ratio of the polarization ellipsoid. Calculate the principal ellipsoid ratio and secondary ellipsoid ratio to distinguish between linear polarization and elliptic polarization. When both are close to 0, it is determined to be linear polarization; when they are between 0 and 1, it is determined to be elliptic polarization.

[0070] Step 4: Calculate the instantaneous phase difference between the horizontal composite component and the vertical component, and determine the P-wave, S-wave, and R-wave types based on the phase value range;

[0071] Step 5: The four types of features obtained in steps 2 to 4—quadrant distribution of particle trajectory, relative elevation relationship between the measuring point and the vibration source, polarization ellipsoid index, and instantaneous phase difference interval of horizontal and vertical components—are input into a pre-set multi-feature joint discrimination system for comprehensive identification. This discrimination system first classifies the received wave into upgoing waves, downgoing waves, or waves at the same elevation based on the elevation relationship. Then, it uses the quadrant distribution of the particle trajectory combined with the instantaneous phase difference interval to distinguish between P-waves and S-waves.

[0072] Step 6: Based on the identification results, the vibration data within the corresponding time window is extracted from the waveforms of the P-wave, S-wave, and R-wave in Step 5, and the waveforms of the P-wave, S-wave, and R-wave are separated from the original signal.

[0073] Step 7: Extract the peak vibration velocity of each waveform and use the distance from the burst center as the metric. Construct a normalized dominant factor function for the variable. Based on this, the main zones of P-wave, S-wave, and R-wave are divided.

[0074] Furthermore, the covariance matrix described in step 3 is composed of three vibrational velocities, and the ratio of the principal axis direction of the polarization ellipsoid to the eigenvalues ​​is extracted through eigenvalue decomposition to distinguish between linear and elliptic polarization states. Specifically, as follows...

[0075] Eigendecomposition of the covariance matrix yields:

[0076] (1)

[0077] In the formula, For a moment The three-component vibration velocity vector, This represents the number of sampling points within the time window.

[0078] The eigenvalues ​​are decomposed into:

[0079] (2)

[0080] In the formula, The eigenvector matrix, ,and ;

[0081] Therefore, the principal and secondary ellipsoidal indices are defined as follows:

[0082] (3)

[0083] (4)

[0084] when When it is determined to be linearly polarized, it corresponds to a P-wave; when When the polarization is determined to be elliptically polarized, it corresponds to either an S-wave or an R-wave.

[0085] The instantaneous phase difference is calculated as follows:

[0086] (5)

[0087] In the formula, This represents the instantaneous phase difference between the horizontal and vertical velocity signals; when The time period is determined to be a P wave; if Then the time period is determined to be an S wave; if Then the period is determined to be an R wave.

[0088] The normalized dominant factor function described in step 6 is defined as follows:

[0089]

[0090] In the formula, Indicates wave type Distance from the epicenter Peak particle velocity at that location.

[0091] If there is a center blast distance make and ,but Waveform With waveform The dominant transition point; the corresponding main control section Defined as:

[0092] (9)

[0093] In other words, during tunnel blasting construction, blasting vibrations can have varying degrees of impact on the surrounding rock mass and existing structures. Accurately assessing the impact of blasting vibrations on the tunnel's surrounding rock and adjacent structures is crucial for ensuring construction safety and project quality. Currently, peak blasting velocity is generally chosen as the assessment standard for vibration hazards. However, this method does not consider the contributions of P-waves, S-waves, and R-waves.

[0094] The polarization points of tunnel blasting vibration waves can be characterized based on time series, essentially being a set of polarization points arranged in a time sequence. However, the polarization paths of blasting vibration particles exhibit significant time-varying characteristics, with their trajectories dynamically changing over time. Therefore, the polarization characteristics of blasting vibration signals are primarily described by the geometric and directional parameters of their trajectories.

[0095] This invention provides a method for deconstructing and separating P-waves, S-waves, and R-waves of tunnel blasting vibration based on multi-source information fusion. By rationally deploying sensors in the tunnel blasting operation area and synchronously collecting data, the method achieves the deconstruction and separation of blasting vibration waves P-waves, S-waves, and R-waves.

[0096] like Figure 2 , Figure 5 As shown in the figure, this invention discloses a method for identifying P-wave, S-wave, and R-wave vibration waves in tunnel blasting, comprising the following steps:

[0097] Step S1: Acquire the original blasting vibration wave propagation data and displacement data of the template tunnel area recorded by the vibration meter and displacement sensor. In this embodiment, a three-component vibration meter and a three-axis displacement sensor are deployed in the tunnel blasting operation area to simultaneously collect vibration data and three-component displacement data in the horizontal radial, horizontal tangential, and vertical directions. When deploying the sensors, the tunnel cross-sectional characteristics and the relative position of the blasting area must be considered to ensure that complete wave propagation information can be captured.

[0098] Step S2: Construct a particle motion trajectory map based on the three-component displacement data. Divide the received wave into upgoing wave, downgoing wave, or wave at the same elevation according to the relative elevation of the measuring point and the explosion source, and identify the quadrants and polarization patterns of the motion trajectory of each wave type. Specifically, for upgoing waves, the P-wave particle trajectory is concentrated in the first and third quadrants with a phase difference close to 0°; the S-wave particle trajectory is concentrated in the second and fourth quadrants with a phase difference close to 180°; for downgoing waves, the P-wave particle trajectory is concentrated in the second and fourth quadrants with a phase difference close to 360°; the S-wave particle trajectory is concentrated in the first and third quadrants with a phase difference close to 0°; when the measuring point and the explosion source are at the same elevation, the P-wave trajectory is mainly horizontal, and the S-wave trajectory is mainly vertical; while the R-wave exhibits counterclockwise elliptical polarization at the ground surface in any of the above cases, and its horizontal-vertical phase is 180°.

[0099] Step S3: Construct a covariance matrix using the three-component vibration velocity data, and perform eigenvalue decomposition to obtain the principal axis direction and eigenvalue ratio of the polarization ellipsoid. Calculate the principal and secondary ellipsoidal indices to distinguish between linear and elliptic polarization states. In this embodiment, the covariance matrix C of the three-component vibration velocity signal is first constructed, and the calculation formula is as follows:

[0100] (1)

[0101] In the formula, For a moment The three-component vibration velocity vector, This represents the number of sampling points within the time window.

[0102] Then, eigenvalue decomposition was performed on the covariance matrix to obtain:

[0103] (2)

[0104] In the formula, The eigenvector matrix, It is an eigenvalue diagonal matrix, and satisfies ,like Figure 3 As shown;

[0105] Based on eigenvalues, the principal ellipsoid ratio is defined as follows:

[0106] (3)

[0107] Sum of sub-ellipsoidal indices:

[0108] (4)

[0109] Used to determine the polarization state of a wave. When the principal and secondary ellipsoidal indices are close to 0, it is determined to be linearly polarized, corresponding to P-waves and S-waves; when the principal and secondary ellipsoidal indices are between 0 and 1, it is determined to be elliptically polarized, corresponding to R-waves.

[0110] Step S4: The linearly polarized P-wave, the clockwise elliptical polarized S-wave, and the counterclockwise elliptical polarized R-wave can be identified by the elliptical characteristics of the particle trajectory. In this embodiment, based on the relative elevation of the measuring point and the vibration source, the received wave is divided into an ascending wave, a descending wave, or a wave at the same elevation, and the particle motion trajectory is plotted in the vertical-horizontal plane using three-component displacement data.

[0111] Specifically, for the upward wave, the P-wave particle trajectory is concentrated in the first and third quadrants with a phase difference close to 0°; the S-wave particle trajectory is concentrated in the second and fourth quadrants with a phase difference close to 180°; for the downward wave, the P-wave particle trajectory is concentrated in the second and fourth quadrants; the S-wave particle trajectory is concentrated in the first and third quadrants with a phase difference close to 0°; when the measuring point and the explosion source are at the same elevation, the P-wave trajectory is mainly horizontal, and the S-wave trajectory is mainly vertical; while the R-wave exhibits counterclockwise elliptical polarization in any of the above situations, and its horizontal-vertical phase difference is close to 90°.

[0112] Step S5, based on the elliptical characteristics of the particle trajectory, quadrant distribution, horizontal-vertical phase difference, and relative position information between the explosion source and the measuring point described in steps 1-4, can be determined under a multivariate discrimination rule.

[0113] The vibration waveforms of P-wave, S-wave, and R-wave are extracted from the vibration data to complete the deconstruction and separation of wave types. Furthermore, the elliptical characteristics of the particle motion trajectory, quadrant distribution, horizontal-vertical phase difference, and relative position information between the blast source and the measuring point described in steps 1-4 above cannot be used as a single criterion to identify the components of the blasting vibration wave. Instead, when all the discrimination rules of the above steps are met simultaneously within a certain time window, the corresponding wave type label (P-wave, S-wave, or R-wave) is automatically assigned to that time window.

[0114] The specific discrimination rules are as follows: (1) Wave direction classification rules based on elevation relationship

[0115] Based on wave direction, waves can be classified into ascending waves, descending waves, and moving waves. Details are as follows:

[0116] If the measuring point is higher than the vibration source, the received wave will be an upward wave;

[0117] If the measuring point is lower than the vibration source, the received wave will be a downward wave;

[0118] If the measuring point and the vibration source are at approximately the same elevation, then the received wave will be a wave at the same elevation.

[0119] (2) Discrimination rules for P-waves, S-waves and R-waves based on polarization ellipsoid index

[0120] when When it is determined to be linearly polarized, it corresponds to a P-wave; when The polarization is determined to be elliptically polarized, corresponding to either an S-wave or an R-wave. If the polarization is clockwise, it is determined to be an S-wave; if it is counterclockwise, it is determined to be an R-wave.

[0121] (3) Discrimination rules for P-wave, S-wave and R-wave based on particle trajectory and phase difference

[0122] For an upward wave, the particle trajectory of a P-wave is concentrated in the first and third quadrants, and the phase difference between the horizontal and vertical components is 0°; the particle trajectory of an S-wave is concentrated in the second and fourth quadrants, and the phase difference between the horizontal and vertical components is 180°.

[0123] For the downward wave, the particle trajectory of the P wave is concentrated in the second and fourth quadrants, and the phase difference between the horizontal and vertical components is 0°; the particle trajectory of the S wave is concentrated in the first and third quadrants, and the phase difference between the horizontal and vertical components is 180°.

[0124] For waves at the same elevation, the particle trajectory of a P-wave is dominated by the horizontal component, with a 0° phase difference between the horizontal and vertical components; the particle trajectory of an S-wave is concentrated in the first and third quadrants, with a 180° phase difference between the horizontal and vertical components. Under any of the above elevation conditions, the particle trajectory of an R-wave exhibits counterclockwise elliptical polarization, with a 90° phase difference between the horizontal and vertical components.

[0125] The following specific embodiment further illustrates step 5. During the construction of a highway tunnel in a mountainous area, in order to evaluate the propagation characteristics of blasting vibration waves and verify the effectiveness of the method of the present invention, five monitoring points were set up within 50m in front of the tunnel face. Each monitoring point was equipped with a three-component vibration meter and a triaxial displacement sensor. Among them, monitoring point M1 was located at the arch crown about 10m above the blast source, monitoring point M2 was at the same horizontal elevation as the blast source, monitoring point M3 was located near the arch foot about 8m below the blast source, and the remaining monitoring points M4 and M5 were located at different positions on the ground outside the tunnel.

[0126] During one detonation process, the three-component vibration velocity signal of measuring point M1 was acquired, and a covariance matrix was constructed within a time window. After eigenvalue decomposition, the principal ellipsoid ratio was found to be 0.05 and the secondary ellipsoid ratio to be 0.03, indicating that the motion trajectory within this time window exhibits approximately linear polarization. Simultaneously, the instantaneous phase difference between the horizontal and vertical components was calculated using Hilbert transform, yielding a result of 8°, and the particle's motion trajectory was mainly concentrated in the first and third quadrants. Based on the logical conditions set in step 5, since M1 is higher than the vibration source and satisfies the conditions of linear polarization, a phase difference close to 0°, and trajectory distribution, the signal within this time window is determined to be an upward P-wave.

[0127] Subsequently, another time window of M1 was analyzed, and the results showed that the principal ellipsoid ratio was 0.42, the trajectory was ellipticly polarized counterclockwise, and the instantaneous phase difference between the horizontal and vertical components was 172°. Based on the criterion in step 5, this time window was determined to be an R-wave.

[0128] The M2 measuring point, located at the same elevation, was processed. The result for a certain time window was: the trajectory was predominantly horizontal, with a phase difference of −5° and a polarization ellipsoid ratio <0.1. Based on the conditions in step 5, this signal segment was identified as a P-wave at the same elevation. Meanwhile, the M3 measuring point, within another time window, showed a trajectory concentrated in the first and third quadrants, with a phase difference of 183°, meeting the criteria for a "downward S-wave." Therefore, the output waveform label was "downward S-wave."

[0129] By performing the aforementioned discrimination and separation on the data from monitoring points M1-M5, the independent identification and decomposition of the P-wave, S-wave, and R-wave of blasting vibrations were ultimately achieved. Further statistical results show that within the region of 0-20m from the blast source, the peak velocity of the P-wave is dominant, while the S-wave gradually strengthens in the 20-40m range, and the R-wave dominates in the surface and far-field regions. These results provide a basis for the zonal protection design of tunnel surrounding rock and verify the feasibility and effectiveness of the method of this invention in actual engineering blasting monitoring.

[0130] Step S6: Extract the peak vibration velocity of different waveforms based on the distance from the burst center. Construct a normalized dominant factor function for the variable. Based on this, the main zones of P-wave, S-wave, and R-wave are divided.

[0131] In this embodiment, to analyze the spatial dominance of different waveforms, the present invention uses the burst center distance as a variable and constructs a normalized dominance factor function by combining the peak velocity and empirical attenuation coefficient of each waveform. Based on this, the main functional areas of P waves, S waves, and R waves are divided, such as... Figure 4 As shown in the figure. The results of this partitioning can be directly used for tunnel blasting vibration safety assessment and blasting parameter optimization.

[0132] Specifically, the normalized dominant factor function is defined as follows:

[0133] (5)

[0134] In the formula, Indicates wave type Distance from the epicenter Peak particle velocity at that location;

[0135] In this embodiment, a dynamic decay coefficient is proposed to enhance the adaptability of the dominant factor function. The real-time calibration method. Specifically, two points located at a distance from the explosion center are selected. and At adjacent measurement points, for waveform The peak vibration velocities are denoted as follows: and Based on the power-law decay model, local difference estimates can be quickly obtained:

[0136] (6)

[0137] The instantaneous estimate is then updated exponentially with the attenuation coefficient from the previous time period to obtain the real-time calibration value used for calculation.

[0138] (7)

[0139] In the formula, To smooth out the weights, For the update interval, This is the calibration result from the previous time period; using this method, the attenuation coefficient can be dynamically updated according to the blast propagation path and time-varying characteristics.

[0140] After calibration Substituting the normalized dominant factor function, we obtain the real-time expression for partition determination:

[0141] (8)

[0142] This determines the actual dominant range of each wave type in complex tunnel environments.

[0143] If there is a center blast distance make and ,but Waveform With waveform The dominant transition point; the corresponding main control section Defined as:

[0144] (9)

[0145] As can be seen, the method of the present invention includes: deploying a three-component vibration meter and a triaxial displacement sensor in the tunnel blasting operation area to synchronously collect blasting vibration velocity data and displacement data in the horizontal radial, horizontal tangential, and vertical directions, and controlling the acquisition time synchronization accuracy of the three-component blasting vibration signals to within 0.1 milliseconds through a time synchronization module; constructing a particle motion trajectory diagram from the three-component displacement data and analyzing the trajectory distribution quadrants based on the relative elevation difference between the measuring point and the blast source; simultaneously constructing a covariance matrix from the three-component vibration velocity data and performing eigenvalue decomposition to obtain the principal axis direction and eigenvalue ratio of the polarization ellipsoid. Based on this, the principal and secondary ellipsoidal indices are calculated, and the linear or elliptic polarization state is determined. Within the same time window, the Hilbert transform is performed on the horizontal composite component and the vertical component to obtain the instantaneous phase difference. The instantaneous phase difference criterion, the particle motion trajectory characteristics, and the polarization ellipsoid parameters are combined to form a multi-feature joint discrimination model to simultaneously determine the wave type attributes of P-waves, S-waves, and R-waves. Based on the discrimination results, the waveform signals of each wave type are extracted from the corresponding time windows, and their vibration velocity peak values ​​are calculated. A normalized dominant factor function is established with the burst center distance as a variable to determine the dominant role partition of different wave types.

[0146] To achieve the above objectives, the present invention also provides a tunnel blasting vibration P-wave, S-wave, and R-wave deconstruction and separation processing system based on multi-source information fusion, such as... Figure 6 As shown, the system is used to implement the method for deconstructing and separating P-wave, S-wave, and R-wave vibrations in tunnel blasting based on multi-source information fusion. The system specifically includes: a first data generation unit, used to generate and simultaneously acquire first data and second data corresponding to the tunnel blasting area; wherein the first data is multi-component vibration data; the second data is multi-component displacement data; a data construction and generation unit, used to construct the particle motion trajectory corresponding to the second data, and generate third data corresponding to the particle motion trajectory; wherein the third data is polarization morphology feature data; and a data fusion processing unit, used to generate and acquire fourth data corresponding to the first data, and fuse the third data and the fourth data to identify and separate corresponding fifth data; wherein the fourth data is polarization parameter data; and the fifth data is blasting vibration wave waveform data, including P-wave waveform data, S-wave waveform data, and R-wave waveform data.

[0147] The data extraction and partitioning unit is used to extract and generate a sixth data corresponding to the fifth data, and to partition and generate a seventh data corresponding to the sixth data based on the sixth data and with the burst center distance as a variable; wherein, the sixth data is vibration characteristic value data; and the seventh data is the main action zone data of each wave type.

[0148] The first data generation unit further includes: a first generation module, used to generate and acquire first data and second data corresponding to the tunnel blasting operation area and located in the horizontal radial direction; a second generation module, used to generate and acquire first data and second data corresponding to the tunnel blasting operation area and located in the horizontal tangential direction; and a third generation module, used to generate and acquire first data and second data corresponding to the tunnel blasting operation area and located in the vertical direction.

[0149] And / or, the data construction and generation unit further includes: a first processing module, used to generate and acquire eighth data corresponding to the tunnel blasting area, and based on the eighth data, divide the received wave into an up-going wave, a down-going wave, or a wave at the same elevation; wherein, the eighth data is the relative elevation data between the monitoring point and the blast source point; a first construction module, used to create a motion trajectory corresponding to the particle and located in the vertical-horizontal plane, and identify and generate distribution quadrant data and polarization morphology data corresponding to the particle's motion trajectory; wherein, the polarization morphology data includes at least linear polarization data and elliptic polarization data;

[0150] And / or, the data fusion processing unit further includes: a second construction module, configured to construct a covariance matrix corresponding to the first data, and generate corresponding ninth data based on the covariance matrix; wherein the ninth data is ellipsoidal parameter data characterizing polarization characteristics; a second processing module, configured to generate and acquire tenth data corresponding to the first data, and fuse the third data, the ninth data, and the tenth data, while generating corresponding eleventh data; wherein the tenth data is instantaneous phase difference data between vibration components in different directions; and the eleventh data is vibration data after fusion processing;

[0151] The third construction module is used to create a first model corresponding to tunnel blasting vibration, and based on the first model and combined with the eleventh data, to extract and generate the corresponding twelfth data; wherein, the first model is a vibration wave type identification model; and the twelfth data is waveform data within a time window;

[0152] And / or, the data extraction and partitioning unit further includes: a fourth construction module, used to generate and acquire thirteenth data corresponding to the fifth data, and construct a normalized dominant factor relationship using the burst center distance as a variable; wherein, the thirteenth data is the peak vibration velocity data of each wave type; the expression of the normalized dominant factor relationship is:

[0153] (5)

[0154] In the formula, Indicates wave type Distance from the epicenter Peak particle velocity at that location;

[0155] The fourth generation module is used to divide and generate the main partitions of P-wave, S-wave, and R-wave according to the normalized dominant factor relationship.

[0156] The second construction module further includes: a fifth construction module, used to construct a covariance matrix corresponding to the three-component vibration velocity signal; wherein the formula for calculating the covariance matrix is:

[0157] (1)

[0158] In the formula, For a moment The three-component vibration velocity vector, This represents the number of sampling points within the time window.

[0159] In the system solution embodiments of the present invention, such as Figure 7 As shown, this invention also discloses a device for deconstructing and separating the P-wave, S-wave, and R-wave components in tunnel blasting, comprising: a three-component vibration meter for acquiring three-component vibration velocity signals; a three-axis displacement sensor for acquiring three-component displacement signals; a data acquisition and synchronization module for integrating the three-component vibration meter and the three-axis displacement sensor into an integrated or separate structure, and connecting them to the data acquisition and synchronization module via wired or wireless means; a data processing and waveform recognition module for integrating the tunnel blasting vibration P-wave, S-wave, and R-wave deconstruction and separation processing method based on multi-source information fusion described in this invention, and equipped with an explosion-proof shell, a rechargeable battery, and a portable chassis, suitable for rapid deployment and real-time analysis in tunnel sites; and an output module for displaying or exporting the deconstruction results of the P-wave, S-wave, and R-wave, as well as information on the dominant section.

[0160] The three-component vibration meter and the triaxial displacement sensor adopt an integrated or separate structure, and are connected to the data acquisition and synchronization module via wired or wireless means.

[0161] In the system embodiment of the present invention, the specific details of the method steps involved in the deconstruction and separation of P-wave, S-wave and R-wave of tunnel blasting vibration based on multi-source information fusion have been described above. That is to say, the functional modules in the system are used to implement the steps or sub-steps in the above method embodiment, and will not be repeated here.

[0162] To achieve the above objectives, the present invention also provides a platform for deconstructing and separating P-waves, S-waves, and R-waves in tunnel blasting vibration based on multi-source information fusion, such as... Figure 8As shown, the system includes a processor, a memory, and a control program for a tunnel blasting vibration P-wave, S-wave, and R-wave deconstruction and separation processing platform based on multi-source information fusion. The processor executes the control program, which is stored in the memory. This control program implements the steps of the multi-source information fusion-based tunnel blasting vibration P-wave, S-wave, and R-wave deconstruction and separation processing method. Specific details of the steps have been described above and will not be repeated here.

[0163] In this embodiment of the invention, the built-in processor of the tunnel blasting vibration P-wave, S-wave, and R-wave deconstruction and separation processing platform based on multi-source information fusion can be composed of integrated circuits. For example, it can be composed of a single packaged integrated circuit, or it can be composed of multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor connects to various components through various interfaces and lines, and executes programs or units stored in the memory, and calls data stored in the memory to perform various functions and process data for tunnel blasting vibration P-wave, S-wave, and R-wave deconstruction and separation processing based on multi-source information fusion. The memory is used to store program code and various data, and is installed in the tunnel blasting vibration P-wave, S-wave, and R-wave deconstruction and separation processing platform based on multi-source information fusion, and realizes high-speed and automatic access to programs or data during operation. The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0164] This invention generates and synchronously acquires first and second data corresponding to the tunnel blasting area using a method; wherein the first data is multi-component vibration data; the second data is multi-component displacement data; a particle motion trajectory corresponding to the second data is constructed, and third data corresponding to the particle motion trajectory is generated; wherein the third data is polarization morphology feature data; a fourth data corresponding to the first data is generated and acquired, and the third and fourth data are fused and processed to identify and separate corresponding fifth data; wherein the fourth data is polarization parameter data; the fifth data... The method generates blasting vibration wave waveform data, including P-wave, S-wave, and R-wave waveform data. A sixth data point corresponding to the fifth data point is extracted and generated. Based on the sixth data point and using the blast center distance as a variable, a seventh data point corresponding to the sixth data point is generated. The sixth data point represents vibration characteristic values. The seventh data point represents the main action zones for each wave type. The method also includes a corresponding system and platform, capable of accurately identifying and separating P-waves, S-waves, and R-waves. This provides a refined, zoned, and graded protection strategy for tunnel surrounding rock and support structures, effectively avoiding potential damage risks caused by insufficient protective measures.

[0165] In other words, this invention achieves accurate identification and separation of P-waves, S-waves, and R-waves in tunnel blasting vibrations by integrating particle motion trajectory analysis, polarization ellipsoid parameter calculation, and instantaneous phase difference analysis. This overcomes the technical bottleneck of traditional methods, which can only identify first arrival waves but cannot effectively separate surface waves. Based on the constructed normalized dominant factor function, the dominant role of different wave types in the propagation space is clearly defined, providing a direct scientific basis for targeted vibration reduction and protection in different sections of tunnel engineering. This significantly improves the accuracy of blasting vibration safety assessment and the effectiveness of engineering protection.

[0166] In other words, this invention discloses a method for deconstructing and separating P-waves, S-waves, and R-waves in tunnel blasting vibration based on multi-source information fusion. The method includes: deploying a three-component vibration meter and a three-axis displacement sensor in the tunnel blasting operation area to simultaneously collect vibration data and three-component displacement data in the horizontal radial, horizontal tangential, and vertical directions; constructing a particle motion trajectory diagram using the three-component displacement data to identify the polarization characteristics of different waveband trajectories; calculating the covariance matrix and extracting the polarization ellipsoid parameters, while simultaneously calculating the instantaneous phase difference in the horizontal-vertical direction; identifying linearly polarized P-waves, laterally linearly polarized S-waves, and elliptically polarized R-waves based on the counterclockwise elliptical characteristics of the particle trajectories; extracting P-waves, S-waves, and R-waves from the vibration data according to the polarization characteristics of different wave types; extracting the peak vibration velocity of different wave types; and dividing the main action zones of P-waves, S-waves, and R-waves by constructing a normalized dominant factor function. This invention achieves accurate separation of P-waves, S-waves, and R-waves in tunnel blasting vibration, providing a scientifically feasible method for safety assessment of tunnel blasting vibration.

[0167] Specifically, compared with traditional methods, this invention determines the polarization characteristics of waves by combining polarization ellipsoid parameters and instantaneous phase difference, and identifies the motion characteristics of different wavebands by the inflection points of particle trajectories. By combining multiple criteria, the wave type is determined. This effectively overcomes the limitations of a single criterion in complex environments or under noise interference, especially when distinguishing signals with aliasing or unclear polarization states. It significantly improves the recognition accuracy of P-waves, S-waves, and R-waves, ensuring that the overall discrimination capability of the model is not severely affected. Therefore, this invention exhibits superior anti-interference performance in complex, high-noise underground or tunnel blasting environments.

[0168] Table 1 shows the statistical error of the analysis of the composition of blasting vibration waves by comparing the method of the present invention with two other different methods, based on the manual extraction of P-waves, S-waves, and R-waves.

[0169] Table 1: Error Analysis of Identification Results of Blasting Vibration Wave Composition under Different Processing Methods

[0170]

[0171] Furthermore, in this embodiment, to verify the effectiveness of the P-wave, S-wave and R-wave deconstruction and separation processing method based on multi-source information fusion described in this invention in blasting engineering, the following "prediction-control-verification" process was adopted for field testing: (1) Before the tunnel face blasting construction, multi-point three-component vibration meters and triaxial displacement sensors were set up in the planned blasting area according to the method of this invention, and several test blasting vibration velocity and displacement data were collected simultaneously. Based on the method of this invention, the P-wave, S-wave and R-wave dominant zones at different blast center distances of each measuring point were determined. (2) In the formal operation of the next cycle blasting, according to the determination results, monitoring points A and B were set up in pairs at the location identified as the P-wave dominant zone. Vibration isolation pads were arranged at point A in the area determined to be the P-wave dominant zone, and vibration isolation pads were arranged at point B in the same location and with the same installation method as a control. All monitoring points maintained the same sensor type, blast center distance, and sampling frequency before and after the blast. The separation process in steps 2 to 5 of this invention was used to deconstruct and separate the blast vibration wave components, and the peak particle velocity (PPV) of each wave type within the same time window was extracted as a comparison index. (3) Based on the data from the two blasts, the PPV decrease of points A and B on the identified P wave component was compared, and the changes of S wave and R wave components were observed at the same time to evaluate the differences before and after the implementation of vibration reduction and between the control point and the control point.

[0172] The results are shown in Table 2. At point A where vibration damping was implemented, the peak values ​​of the P-wave component in the radial, tangential, and vertical directions were 0.78 cm / s, 0.35 cm / s, and 1.95 cm / s, respectively, all significantly lower than the standard limit of 10-12 cm / s (GB6722-2014). However, at control point B (without vibration damping), the peak values ​​of the P-wave component in the same directions reached 9.24 cm / s, 13.18 cm / s, and 15.27 cm / s, respectively, with the tangential and vertical directions exceeding the standard limits. The comparison shows that the vibration damping measures had the most significant effect on reducing the P-wave component. Meanwhile, the peak values ​​of the S-wave and R-wave components at points A and B were between 3 and 8 cm / s, showing no decreasing trend with the same amplitude as the P-wave component. This indicates that the vibration damping measures mainly acted on the P-wave component, which was identified as the dominant component in this invention, while having limited suppression effects on the S-wave and R-wave components.

[0173] The results fully verify the effectiveness of the proposed method for deconstructing, separating, and identifying P-waves, S-waves, and R-waves. It not only accurately identifies the dominant wave types in different regions but also provides a reliable basis for targeted vibration reduction measures in blasting engineering practice, achieving a significant reduction in the dominant wave types and thus reducing the harm of blasting vibrations to surrounding structures.

[0174] Table 2: Comparison of Wave Pattern Dominant Zone Identification Results and Vibration Reduction Measures Based on the Method of the Invention

[0175]

[0176] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for deconstructing and separating P-waves, S-waves, and R-waves in tunnel blasting vibration based on multi-source information fusion, characterized in that, The method includes the following steps: First data and second data corresponding to the tunnel blasting area are generated and acquired synchronously, respectively; wherein, the first data is multi-component vibration data; and the second data is multi-component displacement data. Construct a particle motion trajectory corresponding to the second data, and generate third data corresponding to the particle motion trajectory; wherein, the third data is polarization morphology feature data; A fourth data corresponding to the first data is generated and acquired, and the third data and the fourth data are fused and processed to identify and separate the corresponding fifth data; wherein, the fourth data is polarization parameter data; the fifth data is blasting vibration wave waveform data, including P-wave waveform data, S-wave waveform data and R-wave waveform data; Extract and generate a sixth data corresponding to the fifth data, and based on the sixth data and taking the burst center distance as a variable, divide and generate a seventh data corresponding to the sixth data; wherein, the sixth data is vibration characteristic value data; and the seventh data is the main action zone data of each wave type.

2. The method for deconstructing and separating P-wave, S-wave, and R-wave of tunnel blasting vibration based on multi-source information fusion according to claim 1, characterized in that, The process of generating and synchronously acquiring first and second data corresponding to the tunnel blasting area also includes: First and second data, which correspond to the tunnel blasting operation area and are located in the horizontal radial direction, are generated and acquired respectively. First and second data, which correspond to the tunnel blasting operation area and are located in the horizontal tangential direction, are generated and acquired respectively. First and second data, which correspond to the tunnel blasting operation area and are located in the vertical direction, are generated and acquired respectively.

3. The method for deconstructing and separating P-wave, S-wave, and R-wave vibrations in tunnel blasting based on multi-source information fusion as described in claim 1, characterized in that, The step of constructing the particle motion trajectory corresponding to the second data and generating third data corresponding to the particle motion trajectory further includes: The eighth data corresponding to the tunnel blasting area is generated and acquired, and based on the eighth data, the received wave is divided into an up-going wave, a down-going wave, or a wave at the same elevation; wherein, the eighth data is the relative elevation data between the monitoring point and the blast source point; Create a motion trajectory corresponding to the particle and located in the vertical-horizontal plane, and identify and generate distribution quadrant data and polarization morphology data corresponding to the particle's motion trajectory; wherein, the polarization morphology data includes at least linear polarization data and elliptic polarization data.

4. The method for deconstructing and separating P-wave, S-wave, and R-wave of tunnel blasting vibration based on multi-source information fusion according to claim 1, characterized in that, The process of generating and acquiring fourth data corresponding to the first data, fusing and processing the third data and the fourth data, and identifying and separating to generate corresponding fifth data further includes: Construct a covariance matrix corresponding to the first data, and generate the corresponding ninth data based on the covariance matrix; wherein, the ninth data is ellipsoidal parameter data characterizing polarization characteristics; Generate and acquire tenth data corresponding to the first data, and fuse the third data, the ninth data, and the tenth data to generate corresponding eleventh data; wherein, the tenth data is the instantaneous phase difference data between vibration components in different directions; the eleventh data is the fused vibration data; A first model corresponding to tunnel blasting vibration is created, and based on the first model and the eleventh data, the corresponding twelfth data is extracted and generated; wherein, the first model is a vibration wave type identification model; and the twelfth data is waveform data within a time window.

5. The method for deconstructing and separating P-wave, S-wave, and R-wave of tunnel blasting vibration based on multi-source information fusion according to claim 4, characterized in that, The step of constructing a covariance matrix corresponding to the first data, and generating the corresponding ninth data based on the covariance matrix, further includes: Construct a covariance matrix corresponding to the three-component vibration velocity signal; wherein, the formula for calculating the covariance matrix is: （1） In the formula, For a moment The three-component vibration velocity vector, This represents the number of sampling points within the time window.

6. The method for deconstructing and separating P-wave, S-wave, and R-wave of tunnel blasting vibration based on multi-source information fusion according to claim 1, characterized in that, The step of extracting and generating sixth data corresponding to the fifth data, and generating seventh data corresponding to the sixth data based on the sixth data and using the center distance as a variable, further includes: Generate and acquire the thirteenth data corresponding to the fifth data, and construct a normalized dominant factor relationship using the burst center distance as a variable; wherein, the thirteenth data is the peak vibration velocity data of each wave pattern; the expression of the normalized dominant factor relationship is: （5） In the formula, Indicates wave type Distance from the epicenter Peak particle velocity at that location; Based on the normalized dominant factor relationship, the main partitions of P-wave, S-wave, and R-wave are divided and generated respectively.

7. A tunnel blasting vibration P-wave, S-wave, and R-wave deconstruction and separation processing system based on multi-source information fusion, characterized in that, The system is used to implement the tunnel blasting vibration P-wave, S-wave and R-wave deconstruction and separation processing method based on multi-source information fusion as described in any one of claims 1-6, the system comprising: The first data generation unit is used to generate and synchronously acquire first data and second data corresponding to the tunnel blasting area; wherein, the first data is multi-component vibration data; and the second data is multi-component displacement data. A data construction and generation unit is used to construct a particle motion trajectory corresponding to the second data and generate third data corresponding to the particle motion trajectory; wherein, the third data is polarization morphology feature data; A data fusion processing unit is used to generate and acquire fourth data corresponding to the first data, and to fuse and process the third data and the fourth data, and to identify and separate the corresponding fifth data; wherein, the fourth data is polarization parameter data; and the fifth data is blasting vibration wave waveform data, including P-wave waveform data, S-wave waveform data and R-wave waveform data; The data extraction and partitioning unit is used to extract and generate a sixth data corresponding to the fifth data, and to partition and generate a seventh data corresponding to the sixth data based on the sixth data and with the burst center distance as a variable; wherein, the sixth data is vibration characteristic value data; and the seventh data is the main action zone data of each wave type.

8. The tunnel blasting vibration P-wave, S-wave and R-wave deconstruction and separation processing system based on multi-source information fusion according to claim 7, characterized in that, The first data generation unit further includes: The first generation module is used to generate and acquire first data and second data that correspond to the tunnel blasting operation area and are located in the horizontal radial direction, respectively. The second generation module is used to generate and acquire first data and second data that correspond to the tunnel blasting operation area and are located in the horizontal tangential direction, respectively. The third generation module is used to generate and acquire first data and second data that correspond to the tunnel blasting operation area and are located in the vertical direction, respectively. And / or, the data construction and generation unit further includes: The first processing module is used to generate and acquire the eighth data corresponding to the tunnel blasting area, and based on the eighth data, divide the received wave into an up-going wave, a down-going wave, or a wave at the same elevation; wherein, the eighth data is the relative elevation data between the monitoring point and the blast source point; The first construction module is used to create a motion trajectory corresponding to the particle and located in the vertical-horizontal plane, and to identify and generate distribution quadrant data and polarization morphology data corresponding to the particle's motion trajectory; wherein, the polarization morphology data includes at least linear polarization data and elliptic polarization data; And / or, the data fusion processing unit further includes: The second construction module is used to construct a covariance matrix corresponding to the first data, and generate corresponding ninth data based on the covariance matrix; wherein, the ninth data is ellipsoidal parameter data characterizing polarization characteristics; The second processing module is used to generate and acquire tenth data corresponding to the first data, and to fuse and process the third data, the ninth data, and the tenth data, while generating corresponding eleventh data; wherein, the tenth data is the instantaneous phase difference data between vibration components in different directions; the eleventh data is the vibration data after fusion processing; The third construction module is used to create a first model corresponding to tunnel blasting vibration, and based on the first model and combined with the eleventh data, to extract and generate the corresponding twelfth data; wherein, the first model is a vibration wave type identification model; and the twelfth data is waveform data within a time window; And / or, the data extraction and partitioning unit further includes: The fourth construction module is used to generate and acquire the thirteenth data corresponding to the fifth data, and to construct a normalized dominant factor relationship using the burst center distance as a variable; wherein, the thirteenth data is the peak vibration velocity data of each wave pattern; the expression of the normalized dominant factor relationship is: （5） In the formula, Indicates wave type Distance from the epicenter Peak particle velocity at that location; The fourth generation module is used to divide and generate the main partitions of P-wave, S-wave, and R-wave according to the normalized dominant factor relationship.

9. The tunnel blasting vibration P-wave, S-wave and R-wave deconstruction and separation processing system based on multi-source information fusion according to claim 8, characterized in that, The second building module also includes: The fifth construction module is used to construct the covariance matrix corresponding to the three-component vibration velocity signal; wherein, the formula for calculating the covariance matrix is: （1） In the formula, For a moment The three-component vibration velocity vector, This represents the number of sampling points within the time window.

10. A platform for deconstructing and separating P-wave, S-wave, and R-wave vibrations in tunnel blasting based on multi-source information fusion, characterized in that, The system includes a processor, a memory, and a control program for a tunnel blasting vibration P-wave, S-wave, and R-wave deconstruction and separation processing platform based on multi-source information fusion. The processor executes the control program, which is stored in the memory. This control program implements the tunnel blasting vibration P-wave, S-wave, and R-wave deconstruction and separation processing method based on multi-source information fusion as described in any one of claims 1 to 6.

Images