Rock mass failure precursor identification method and system based on das-dss joint perception

Abstract

The application relates to a rock mass failure precursor identification method based on DAS-DSS joint sensing. The method comprises the following steps: collecting a phase change signal through a distributed acoustic wave sensing system and collecting a frequency offset signal through a distributed strain sensing system; performing feature extraction on the phase change signal to obtain a DAS sensing feature parameter and performing feature extraction on the frequency offset signal to obtain a DSS sensing feature parameter; mapping the DAS sensing feature parameter and the DSS sensing feature parameter to the same grid and correlating them to establish a multi-physical field joint feature matrix and extract a precursor sensitive factor; correcting the phase change signal and temperature / creep / zero drift, determining a fusion early warning index according to the corrected phase change signal and calibrated real strain; and according to the DAS sensing feature parameter, the DSS sensing feature parameter, the fusion early warning index and the precursor sensitive factor, the rock mass stability grade is demarcated, so that the accuracy and reliability of rock mass failure precursor identification are improved.

Description

Technical Field

[0001] This application relates to the field of rock mass engineering safety monitoring technology, and in particular to a method and system for identifying precursors of rock mass failure based on DAS-DSS joint sensing. Background Technology

[0002] During excavation or operation, rock mass engineering projects experience progressive damage accumulation due to stress redistribution and external disturbances, eventually leading to instability and failure. Identifying precursors to rock mass failure is crucial for the safety of rock mass engineering. Current rock mass stability monitoring mainly consists of acoustic / microseismic monitoring and strain / displacement monitoring. The former measures elastic wave signals induced by microfractures in the rock mass, thus enabling the perception of internal damage evolution; however, it struggles to reflect the degree of deformation, and its wave velocity model lacks accuracy. The latter monitors the deformation field evolution using strain gauges or distributed optical fibers, directly acquiring strain concentration characteristics; however, it cannot detect the early initiation process of microfractures and is susceptible to temperature drift. Some researchers have attempted to jointly analyze these two types of data, but most studies only involve data superposition and fail to achieve mutual calibration and quantitative fusion at the mechanistic level of acoustic, strain, stress, and temperature fields. Therefore, the accuracy and reliability of current rock mass failure precursor identification methods are relatively low. Summary of the Invention

[0003] Therefore, it is necessary to provide a method and system for identifying precursors of rock mass failure based on DAS-DSS joint sensing, which can improve the efficiency and accuracy of fault sealing assessment, in order to address the above-mentioned technical problems.

[0004] A method for identifying precursors of rock mass failure based on DAS-DSS joint sensing, the method comprising: Step S1: Deploy a multi-physics joint sensing system: Deploy a distributed acoustic wave sensing system and a distributed strain sensing system on the surface or inside the target rock mass. The distributed acoustic wave sensing system collects the phase change signal of the back Rayleigh scattering light, and the distributed strain sensing system collects the frequency shift signal of the back Brillouin scattering light. Step S2: Feature parameter extraction: Perform feature extraction on the phase change signal to obtain DAS sensing feature parameters, and perform feature extraction on the frequency offset signal to obtain DSS sensing feature parameters. The DSS sensing feature parameters include DSS strain feature parameters, temperature-compensated true strain parameters, and stress feature parameter set. Step S3: Construct a spatiotemporal multiphysics synchronous mapping: Map the DAS sensing feature parameters and the DSS sensing feature parameters to the same grid, and associate the DAS sensing feature parameters and the DSS sensing feature parameters under a unified time reference and spatial coordinate system to establish a multiphysics joint feature matrix; Step S4: Extraction of Precursor Sensitive Factors for Rock Mass Failure: Based on the multi-physics joint feature matrix, extract the precursor sensitive factors, which include the entropy change index of acoustic wave emission evolving from disorder to order, the strain localization factor of strain field evolving from uniform to non-uniform, the stress field precursor index, and the synergistic change rate of multi-physics fields in time and space. Step S5: Multiphysics mutual calibration and fusion early warning index analysis: The phase change signal is corrected according to the wave velocity model of DSS constrained DAS to obtain the corrected phase change signal. The temperature / creep / zero drift correction of DSS constrained by DAS is used to obtain the calibrated true strain. The fusion early warning index is determined according to the corrected phase change signal and the calibrated true strain. Step S6: Dynamic stability classification and early warning: Determine the rock mass stability level based on the DAS sensing characteristic parameters, the DSS sensing characteristic parameters, the fused early warning index, and the precursor sensitivity factor.

[0005] In one embodiment, the step of extracting features from the phase change signal to obtain DAS sensing feature parameters includes: The phase change signal is filtered, denoised, and subjected to event detection and localization to obtain a preprocessed phase change signal; Feature extraction is performed on the preprocessed phase change signal to obtain DAS microseismic characteristic parameters characterizing rock mass microfracture events. : ; in, Unit time window The number of microseismic events within the area; Unit time window Microseismic energy release rate within; Unit time window The core frequency shift characteristics within the system; Unit time window The slope of the magnitude-frequency relationship within the earthquake; Unit time window The number of related dimensions within; Unit time window Information entropy within; Unit time window The seismic moment within.

[0006] In one embodiment, the step of extracting features from the frequency offset signal to obtain DSS sensing feature parameters includes: The frequency shift signal is demodulated to obtain a linear relationship between the Brillouin frequency shift and temperature and strain: ; in, The frequency offset of the signal at position x along the fiber optic axis at time t; The temperature coefficient representing the frequency shift of the backscattered Brillouin light. The strain coefficient is the frequency shift of the back-scattered Brillouin light. Let x be the change in strain in the external environment at position x along the fiber optic axis at time t. This represents the temperature change at position x along the fiber optic axis at time t. Based on the linear relationship between Brillouin frequency shift and temperature and strain, preliminary DSS strain characteristic parameters are extracted from the frequency shift signal. : ; in, For strain rate; For axial strain; The original strain value is obtained by demodulating the frequency offset signal after being measured by the distributed strain sensing system. This represents the maximum shear strain. It is the strain localization factor; The strain gradient condensation index; The displacement field non-uniformity coefficient; Based on the linear relationship between Brillouin frequency shift and temperature and strain, and the strain obtained after demodulation of the frequency shift signal, temperature field compensation parameters are extracted to obtain the true strain parameters after temperature compensation. : ; in, The actual strain after temperature compensation; The optical fiber thermo-optic coefficient; This represents the temperature change at position x along the fiber optic axis at time t. The preliminary DSS strain characteristic parameters are updated to obtain the DSS strain characteristic parameters. : ; The measured strain field is obtained based on the linear relationship between Brillouin frequency shift and temperature and strain. Inverse the stress characteristic parameter set by the constitutive equation of the rock mass. : ; in, These are the components of the stress tensor; This is the first stress invariant; It is the second invariant of deviatoric stress; Von Mises equivalent stress; These are the first, second, and third principal stresses, respectively. The angle between the maximum principal stress and the x-axis; For damage variables; The stress concentration factor is... This refers to the three-dimensional spatial location of a point within the rock mass.

[0007] In one embodiment, the method of mapping the DAS sensing feature parameters and the DSS sensing feature parameters to the same raster is as follows: Establish a unified spatiotemporal coordinate system ,in, These are the unified X-axis, Y-axis, and Z-axis spatial coordinates after spatial coordinate transformation for the corresponding distributed acoustic wave sensing system and distributed strain sensing system, respectively. To provide a unified time coordinate after time synchronization between the distributed acoustic wave sensing system and the distributed strain sensing system; Map the DAS sensing feature parameters and the DSS sensing feature parameters to the same raster: ; in: These represent the i-th discrete point in unified X-axis spatial coordinates, the j-th discrete point in unified Y-axis spatial coordinates, the k-th discrete point in unified Z-axis spatial coordinates, and the l-th time point in unified time coordinates, respectively. This represents discretizing a continuous four-dimensional spacetime into grid points. Each grid point stores the DAS sensing feature parameters and the DSS sensing feature parameters at that location and time. For the temperature field.

[0008] In one embodiment, the method for associating the DAS sensing feature parameters and the DSS sensing feature parameters under a unified time reference and spatial coordinate system is as follows: Through spatial transformation matrix Achieve DAS and DSS coordinate alignment: ; Among them, DAS spatial positioning: ; Among them, DSS spatial positioning: ; in, Spatial location measured by a distributed acoustic wave sensing system; The spatial position measured by the distributed strain sensing system; The speed of light in an optical fiber; The time of reflection in the optical time domain; The speed of light in a vacuum; This represents the time delay difference between pulsed light and continuous light. The refractive index of the optical fiber; The step size for the Brillouin frequency shift; Using a sliding window Perform feature time synchronization alignment between DAS and DSS: ; ; ; in: The DAS microseismic characteristic parameters are obtained by averaging at the l-th unified time point. The DSS strain characteristic parameters are obtained by window averaging at the l-th unified time point; This is the set of stress characteristic parameters after time window averaging at the l-th unified time point; These are the instantaneous values ​​of the high-sampling-rate DAS microseismic characteristic parameters at time t; Here are the characteristic parameters of the DSS strain at time t; Let be the set of stress characteristic parameters at time t; This is the width of the window when sliding.

[0009] In one embodiment, the multiphysics joint feature matrix is: ; in: These are the DAS microseismic characteristic parameters after time window averaging; These are the DSS strain characteristic parameters after time window averaging; This is the set of stress characteristic parameters after time window averaging; For temperature field; This is the joint characteristic matrix of the multiphysics field.

[0010] In one embodiment, the corrected phase change signal is: , ; in, This is the corrected phase change signal. It is a phase change signal. To calibrate the P-wave velocity, The initial longitudinal wave velocity; The volumetric strain-wave velocity coupling coefficient; The stress-wave velocity coupling coefficient; The change in volumetric strain; This represents the change in the first stress invariant.

[0011] In one embodiment, the expression for the temperature / creep / zero drift correction of the DAS-constrained DSS is: ; in: This represents the actual strain after calibration. This is a zero-drift correction term; This represents the temperature change at position x along the fiber optic axis at time t. The optical fiber thermo-optic coefficient; The damage-strain coupling coefficient; For damage variables; The reset condition for the zero-drift correction item is as follows: ,when and ; in: Count the instantaneous microseismic events at position x and time t0 along the fiber optic axis; The silent threshold for microseismic events; The original strain value measured by the distributed strain sensing system at time t0 when the conditions are met; Install the initial reference strain for the distributed strain sensing system; It is the second invariant of deviatoric stress; This is the deviatoric stress threshold.

[0012] In one embodiment, the fusion early warning index includes an energy-strain-stress comprehensive index and a comprehensive index change rate, the expressions for which are respectively: , ; in: It is a comprehensive index of energy, strain, and stress; The rate of change of the comprehensive indicator; This represents the actual strain after calibration. This refers to the micro-vibration energy after calibration. The equivalent uniaxial stress is Von Mises stress, which takes into account the equivalent uniaxial stress of all stress components.

[0013] In one embodiment, the step of determining the rock mass stability level based on the DAS sensing feature parameters, the DSS sensing feature parameters, the fused early warning index, and the precursor sensitivity factor includes: If the DAS sensing feature parameters, the DSS sensing feature parameters, the fusion early warning index, and the precursor sensitivity factor satisfy the following conditions: and and If so, the rock mass stability level is stable. If the DAS sensing feature parameters, the DSS sensing feature parameters, the fusion early warning index, and the precursor sensitivity factor satisfy the following conditions: and and and If so, the rock mass stability level is abnormal. If the DAS sensing feature parameters, the DSS sensing feature parameters, the fusion early warning index, and the precursor sensitivity factor satisfy the following conditions: and and and and and and and The rock mass stability level is at the pre-slip stage; in: The rate of change of damage; Damage acceleration rate; The peak value of the strain gradient condensation index; To integrate early warning indicators The first threshold; To integrate early warning indicators The second threshold; The threshold for the rate of change of coordination; The rate of change of the stress concentration factor; The peak value of the stress concentration factor. Unit time window The rate of change of information entropy within; The rate of change of multiple physical fields in space and time; The rate of change of the strain localization factor; The background strain localization factor; This is the strain gradient concentration index.

[0014] The aforementioned method for identifying precursors of rock mass failure based on DAS-DSS joint sensing involves deploying distributed acoustic sensing systems and distributed strain sensing systems on or inside the target rock mass. The distributed acoustic sensing system acquires the phase change signal of backscattered Rayleigh light, while the distributed strain sensing system acquires the frequency shift signal of backscattered Brillouin light. Feature extraction is performed on the phase change signal to obtain DAS sensing feature parameters, and feature extraction is performed on the frequency shift signal to obtain DSS sensing feature parameters. These DSS sensing feature parameters include DSS strain feature parameters, temperature-compensated true strain parameters, and a set of stress feature parameters. The DAS and DSS sensing feature parameters are mapped to the same grid and correlated under a unified time reference and spatial coordinate system to establish a multi-physics joint feature matrix. Based on the... A multi-physics joint feature matrix is ​​described, and precursor sensitivity factors are extracted. These precursor sensitivity factors include the entropy change index of acoustic wave emission evolving from disorder to order, the strain localization factor of strain field evolving from uniform to non-uniform, the stress field precursor index, and the spatiotemporal coordinated change rate of the multi-physics fields. The phase change signal is corrected according to the wave velocity model of DSS constrained DAS to obtain the corrected phase change signal. The temperature / creep / zero drift correction of DSS constrained by DAS is used to obtain the calibrated true strain. The fusion early warning index is determined based on the corrected phase change signal and the calibrated true strain. The rock mass stability level is determined based on the DAS sensing feature parameters, the DSS sensing feature parameters, the fusion early warning index, and the precursor sensitivity factors. Thus, the joint sensing and mutual calibration of acoustic wave, strain, stress, and temperature data are realized, significantly improving the accuracy and reliability of rock mass failure precursor identification. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a method for identifying precursors of rock mass failure based on DAS-DSS joint sensing in one embodiment. Figure 2 This is a schematic diagram of the framework for extracting feature parameters in a rock mass failure precursor identification method based on DAS-DSS joint sensing in one embodiment. Figure 3 This is a schematic diagram of the framework for dynamic stability classification and early warning of a rock mass failure precursor identification method based on DAS-DSS joint sensing in one embodiment. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0017] In one embodiment, such as Figures 1-3 As shown, a method for identifying precursors of rock mass failure based on DAS-DSS joint sensing is provided. Taking the application of this method to a terminal as an example, the method includes the following steps: Step S1: Deploy a multi-physics joint sensing system: Deploy a distributed acoustic wave sensing system and a distributed strain sensing system on the surface or inside the target rock mass. The distributed acoustic wave sensing system collects the phase change signal of the back Rayleigh scattering light, and the distributed strain sensing system collects the frequency shift signal of the back Brillouin scattering light.

[0018] Among them, the Distributed Acoustic Sensing (DAS) system transforms an ordinary optical fiber into tens of thousands of continuously arranged virtual microphones, enabling real-time and accurate monitoring of sound and vibration events over a range of tens of kilometers.

[0019] Among them, the Distributed Strain Sensing (DSS) system is a cutting-edge technology that can transform ordinary optical fibers into neural networks. It can continuously sense the force and deformation of every tiny interval along the optical fiber path, acquiring strain data from tens of kilometers and tens of thousands of measuring points, thereby solving the problem of blind spots in the monitoring of traditional point sensors.

[0020] It should be understood that the combined use of a distributed acoustic sensing system and a distributed strain sensing system yields multiphysics spatiotemporal evolution data, including acoustic / vibration field, strain / displacement field, temperature field, and inverted stress field. Specifically, the acoustic / vibration field contains elastic wave signals induced by microfractures in the rock mass, i.e., the phase change signal of backscattered Rayleigh light acquired by the distributed acoustic sensing system; the strain / displacement field contains information on rock mass deformation, strain concentration, and displacement evolution, i.e., the frequency shift signal of backscattered Brillouin light acquired by the distributed strain sensing system; the temperature field is acquired either as an accessory to the DSS system or by an independent temperature sensing unit, and is used for strain-temperature compensation; the stress field is obtained through constitutive inversion or boundary condition transformation.

[0021] Step S2: Feature Parameter Extraction: Perform feature extraction on the phase change signal to obtain DAS sensing feature parameters, and perform feature extraction on the frequency offset signal to obtain DSS sensing feature parameters. The DSS sensing feature parameters include DSS strain feature parameters, temperature-compensated true strain parameters, and stress feature parameter set.

[0022] The phase change signal is filtered, denoised, and the event is detected and located to extract DAS microseismic characteristic parameters that characterize rock mass microfracture events.

[0023] Specifically, the frequency offset signal is demodulated to extract DSS strain characteristic parameters that characterize the localization of rock mass deformation; the temperature field is acquired by the DSS system or an independent temperature sensing unit for strain-temperature compensation; and the stress field is obtained through constitutive inversion or boundary condition transformation.

[0024] Step S3: Construct a spatiotemporal multiphysics synchronous mapping: Map the DAS sensing feature parameters and the DSS sensing feature parameters to the same raster, and associate the DAS sensing feature parameters and the DSS sensing feature parameters under a unified time reference and spatial coordinate system to establish a multiphysics joint feature matrix.

[0025] Step S4: Extraction of Precursor Sensitive Factors for Rock Mass Failure: Based on the multi-physics joint feature matrix, extract the precursor sensitive factors, which include the entropy change index of acoustic wave emission evolving from disorder to order, the strain localization factor of strain field evolving from uniform to non-uniform, the stress field precursor index, and the coordinated change rate of multi-physics fields in time and space.

[0026] Among them, the precursor indicators of the stress field include the rate of change of stress concentration factor and the rate of damage acceleration.

[0027] In one embodiment, the entropy change index of the acoustic wave emission evolving from disorder to order is expressed as: ; in, Unit time window The rate of change of information entropy within; Unit time window Information entropy within.

[0028] In one embodiment, the strain localization factor for the evolution of the strain field from uniform to non-uniform is expressed as: ; in, The rate of change of the strain localization factor; This is the strain localization factor.

[0029] In one embodiment, the stress field precursor indicators include the rate of change of stress concentration factor and the damage acceleration rate.

[0030] The rate of change of the stress concentration factor is expressed as: ; in: This represents the rate of change of the stress concentration factor.

[0031] The damage acceleration rate is expressed as: ; in: This represents the damage acceleration rate.

[0032] In one embodiment, the spatiotemporal rate of change of the multiphysics fields is expressed as: ; in, The rate of change of multiple physical fields in space and time; In spatial location Place Monitoring time DAS microseismic characteristic parameters at that time; In spatial location Monitoring time Strain characteristic parameters at time; In spatial location Monitoring time The set of stress characteristic parameters at time; Let be the coordinates of a discrete point in space, representing the position of the i-th spatial point. This refers to the area range of the monitoring region.

[0033] Step S5: Multiphysics Mutual Calibration and Fusion Early Warning Index Analysis: The phase change signal is corrected according to the wave velocity model of DSS constrained DAS to obtain the corrected phase change signal. The true strain after calibration is obtained according to the temperature / creep / zero drift correction of DSS constrained by DAS. The fusion early warning index is determined according to the corrected phase change signal and the true strain after calibration.

[0034] Step S6: Dynamic stability classification and early warning: Determine the rock mass stability level based on the DAS sensing characteristic parameters, the DSS sensing characteristic parameters, the fused early warning index, and the precursor sensitivity factor.

[0035] In one embodiment, the step of extracting features from the phase change signal to obtain DAS sensing feature parameters includes: The phase change signal is filtered, denoised, and subjected to event detection and localization to obtain a preprocessed phase change signal; Feature extraction is performed on the preprocessed phase change signal to obtain DAS microseismic characteristic parameters characterizing rock mass microfracture events. : ; in, Unit time window The number of microseismic events within the area; Unit time window Microseismic energy release rate within; Unit time window The core frequency shift characteristics within the system; Unit time window The slope of the magnitude-frequency relationship within the earthquake; Unit time window The number of related dimensions within; Unit time window Information entropy within; Unit time window The seismic moment within.

[0036] In one embodiment, the step of extracting features from the frequency offset signal to obtain DSS sensing feature parameters includes: The frequency shift signal is demodulated to obtain a linear relationship between the Brillouin frequency shift and temperature and strain: ; in, The frequency offset of the signal at position x along the fiber optic axis at time t; The temperature coefficient representing the frequency shift of the backscattered Brillouin light. The strain coefficient is the frequency shift of the back-scattered Brillouin light. Let x be the change in strain in the external environment at position x along the fiber optic axis at time t. This represents the temperature change at position x along the fiber optic axis at time t. Based on the linear relationship between Brillouin frequency shift and temperature and strain, preliminary DSS strain characteristic parameters are extracted from the frequency shift signal. : ; in, For strain rate; For axial strain; The original strain value is obtained by demodulating the frequency offset signal after being measured by the distributed strain sensing system. This represents the maximum shear strain. It is the strain localization factor; The strain gradient condensation index; The displacement field non-uniformity coefficient; Based on the linear relationship between Brillouin frequency shift and temperature and strain, and the strain obtained after demodulation of the frequency shift signal, temperature field compensation parameters are extracted to obtain the true strain parameters after temperature compensation. : ; in, The actual strain after temperature compensation; The optical fiber thermo-optic coefficient; This represents the temperature change at position x along the fiber optic axis at time t. The preliminary DSS strain characteristic parameters are updated to obtain the DSS strain characteristic parameters. : ; The measured strain field is obtained based on the linear relationship between Brillouin frequency shift and temperature and strain. Inverse the stress characteristic parameter set by the constitutive equation of the rock mass. : ; in, These are the components of the stress tensor; This is the first stress invariant; It is the second invariant of deviatoric stress; Von Mises equivalent stress; These are the first, second, and third principal stresses, respectively. The angle between the maximum principal stress and the x-axis; For damage variables; The stress concentration factor is... This refers to the three-dimensional spatial location of a point within the rock mass.

[0037] The strain gradient lumped index can be expressed as: ; in, The strain gradient condensation index; The rate of change of strain in space; These represent the horizontal and vertical coordinates of the monitoring plane, and the monitoring time, respectively. This refers to the area range of the monitoring region.

[0038] In one embodiment, the method of mapping the DAS sensing feature parameters and the DSS sensing feature parameters to the same raster is as follows: Establish a unified spatiotemporal coordinate system ,in, These are the unified X-axis, Y-axis, and Z-axis spatial coordinates after spatial coordinate transformation for the corresponding distributed acoustic wave sensing system and distributed strain sensing system, respectively. To provide a unified time coordinate after time synchronization between the distributed acoustic wave sensing system and the distributed strain sensing system; Map the DAS sensing feature parameters and the DSS sensing feature parameters to the same raster: ; in: These represent the i-th discrete point in unified X-axis spatial coordinates, the j-th discrete point in unified Y-axis spatial coordinates, the k-th discrete point in unified Z-axis spatial coordinates, and the l-th time point in unified time coordinates, respectively. This represents discretizing a continuous four-dimensional spacetime into grid points. Each grid point stores the DAS sensing feature parameters and the DSS sensing feature parameters at that location and time. For the temperature field.

[0039] In one embodiment, the method for associating the DAS sensing feature parameters and the DSS sensing feature parameters under a unified time reference and spatial coordinate system is as follows: Through spatial transformation matrix Achieve DAS and DSS coordinate alignment: ; Among them, DAS spatial positioning: ; Among them, DSS spatial positioning: ; in, Spatial location measured by a distributed acoustic wave sensing system; The spatial position measured by the distributed strain sensing system; The speed of light in an optical fiber; The time of reflection in the optical time domain; The speed of light in a vacuum; This represents the time delay difference between pulsed light and continuous light. The refractive index of the optical fiber; The step size for the Brillouin frequency shift; Using a sliding window Perform feature time synchronization alignment between DAS and DSS: ; ; ; in: The DAS microseismic characteristic parameters are obtained by averaging at the l-th unified time point. The DSS strain characteristic parameters are obtained by window averaging at the l-th unified time point; This is the set of stress characteristic parameters after time window averaging at the l-th unified time point; These are the instantaneous values ​​of the high-sampling-rate DAS microseismic characteristic parameters at time t; Here are the characteristic parameters of the DSS strain at time t; Let be the set of stress characteristic parameters at time t; This is the width of the window when sliding.

[0040] In one embodiment, the multiphysics joint feature matrix is: ; in: These are the DAS microseismic characteristic parameters after time window averaging; These are the DSS strain characteristic parameters after time window averaging; This is the set of stress characteristic parameters after time window averaging; For temperature field; This is the joint characteristic matrix of the multiphysics field.

[0041] It should be understood that the multiphysics joint feature matrix stores four types of feature parameters: DAS sensing feature parameters, DSS strain feature parameters, temperature-compensated true strain parameters, and stress feature parameter sets.

[0042] In one embodiment, the corrected phase change signal is: , ; in, This is the corrected phase change signal. It is a phase change signal. To calibrate the P-wave velocity, The initial longitudinal wave velocity; The volumetric strain-wave velocity coupling coefficient; The stress-wave velocity coupling coefficient; The change in volumetric strain; This represents the change in the first stress invariant.

[0043] In one embodiment, the expression for the temperature / creep / zero drift correction of the DAS-constrained DSS is: ; in: This represents the actual strain after calibration. This is a zero-drift correction term; This represents the temperature change at position x along the fiber optic axis at time t. The optical fiber thermo-optic coefficient; The damage-strain coupling coefficient; For damage variables; The reset condition for the zero-drift correction item is as follows: ,when and ; in: Count the instantaneous microseismic events at position x and time t0 along the fiber optic axis; The silent threshold for microseismic events; The original strain value measured by the distributed strain sensing system at time t0 when the conditions are met; Install the initial reference strain for the distributed strain sensing system; It is the second invariant of deviatoric stress; This is the deviatoric stress threshold.

[0044] In one embodiment, the fusion early warning index includes an energy-strain-stress comprehensive index and a comprehensive index change rate, the expressions for which are respectively: , ; in: It is a comprehensive index of energy, strain, and stress; The rate of change of the comprehensive indicator; This represents the actual strain after calibration. This refers to the micro-vibration energy after calibration. The equivalent uniaxial stress is Von Mises stress, which takes into account the equivalent uniaxial stress of all stress components.

[0045] In one embodiment, determining the rock mass stability level based on the DAS sensing feature parameters, the DSS sensing feature parameters, the fused early warning index, and the precursor sensitivity factor includes: If the DAS sensing feature parameters, the DSS sensing feature parameters, the fusion early warning index, and the precursor sensitivity factor satisfy the following conditions: and and If so, the rock mass stability level is stable. If the DAS sensing feature parameters, the DSS sensing feature parameters, the fusion early warning index, and the precursor sensitivity factor satisfy the following conditions: and and and If so, the rock mass stability level is abnormal. If the DAS sensing feature parameters, the DSS sensing feature parameters, the fusion early warning index, and the precursor sensitivity factor satisfy the following conditions: and and and and and and and The rock mass stability level is at the pre-slip stage; in: The rate of change of damage; Damage acceleration rate; The peak value of the strain gradient condensation index; To integrate early warning indicators The first threshold; To integrate early warning indicators The second threshold; The threshold for the rate of change of coordination; The rate of change of the stress concentration factor; The peak value of the stress concentration factor. Unit time window The rate of change of information entropy within; The rate of change of multiple physical fields in space and time; The rate of change of the strain localization factor; The background strain localization factor; This is the strain gradient concentration index.

[0046] The aforementioned method for identifying precursors of rock mass failure based on DAS-DSS joint sensing involves deploying distributed acoustic sensing systems and distributed strain sensing systems on or inside the target rock mass. The distributed acoustic sensing system acquires the phase change signal of backscattered Rayleigh light, while the distributed strain sensing system acquires the frequency shift signal of backscattered Brillouin light. Feature extraction is performed on the phase change signal to obtain DAS sensing feature parameters, and feature extraction is performed on the frequency shift signal to obtain DSS sensing feature parameters. These DSS sensing feature parameters include DSS strain feature parameters, temperature-compensated true strain parameters, and a set of stress feature parameters. The DAS and DSS sensing feature parameters are mapped to the same grid and correlated under a unified time reference and spatial coordinate system to establish a multi-physics joint feature matrix. Based on the... A multi-physics joint feature matrix is ​​described, and precursor sensitivity factors are extracted. These precursor sensitivity factors include the entropy change index of acoustic wave emission evolving from disorder to order, the strain localization factor of strain field evolving from uniform to non-uniform, the stress field precursor index, and the spatiotemporal coordinated change rate of the multi-physics fields. The phase change signal is corrected according to the wave velocity model of DSS constrained DAS to obtain the corrected phase change signal. The temperature / creep / zero drift correction of DSS constrained by DAS is used to obtain the calibrated true strain. The fusion early warning index is determined based on the corrected phase change signal and the calibrated true strain. The rock mass stability level is determined based on the DAS sensing feature parameters, the DSS sensing feature parameters, the fusion early warning index, and the precursor sensitivity factors. Thus, the joint sensing and mutual calibration of acoustic wave, strain, stress, and temperature data are realized, significantly improving the accuracy and reliability of rock mass failure precursor identification.

[0047] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0048] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0049] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for identifying precursors of rock mass failure based on DAS-DSS joint sensing, characterized in that, The rock mass failure precursor identification method based on DAS-DSS joint sensing includes: Step S1: Deploy a multi-physics joint sensing system: Deploy a distributed acoustic wave sensing system and a distributed strain sensing system on the surface or inside the target rock mass. The distributed acoustic wave sensing system collects the phase change signal of the back Rayleigh scattering light, and the distributed strain sensing system collects the frequency shift signal of the back Brillouin scattering light. Step S2: Feature parameter extraction: Perform feature extraction on the phase change signal to obtain DAS sensing feature parameters, and perform feature extraction on the frequency offset signal to obtain DSS sensing feature parameters. The DSS sensing feature parameters include DSS strain feature parameters, temperature-compensated true strain parameters, and stress feature parameter set. Step S3: Construct a spatiotemporal multiphysics synchronous mapping: Map the DAS sensing feature parameters and the DSS sensing feature parameters to the same grid, and associate the DAS sensing feature parameters and the DSS sensing feature parameters under a unified time reference and spatial coordinate system to establish a multiphysics joint feature matrix; Step S4: Extraction of Precursor Sensitive Factors for Rock Mass Failure: Based on the multi-physics joint feature matrix, extract the precursor sensitive factors, which include the entropy change index of acoustic wave emission evolving from disorder to order, the strain localization factor of strain field evolving from uniform to non-uniform, the stress field precursor index, and the synergistic change rate of multi-physics fields in time and space. Step S5: Multiphysics mutual calibration and fusion early warning index analysis: The phase change signal is corrected according to the wave velocity model of DSS constrained DAS to obtain the corrected phase change signal. The temperature / creep / zero drift correction of DSS constrained by DAS is used to obtain the calibrated true strain. The fusion early warning index is determined according to the corrected phase change signal and the calibrated true strain. Step S6: Dynamic stability classification and early warning: Determine the rock mass stability level based on the DAS sensing characteristic parameters, the DSS sensing characteristic parameters, the fused early warning index, and the precursor sensitivity factor.

2. The method for identifying precursors of rock mass failure based on DAS-DSS joint sensing according to claim 1, characterized in that, The step of extracting features from the phase change signal to obtain DAS sensing feature parameters includes: The phase change signal is filtered, denoised, and subjected to event detection and localization to obtain a preprocessed phase change signal; Feature extraction is performed on the preprocessed phase change signal to obtain DAS microseismic characteristic parameters characterizing rock mass microfracture events. : ； in, Unit time window The number of microseismic events within the area; Unit time window Microseismic energy release rate within; Unit time window The core frequency shift characteristics within the system; Unit time window The slope of the magnitude-frequency relationship within the earthquake; Unit time window The number of related dimensions within; Unit time window Information entropy within; Unit time window The seismic moment within.

3. The method for identifying precursors of rock mass failure based on DAS-DSS joint sensing according to claim 2, characterized in that, The step of extracting features from the frequency offset signal to obtain DSS sensing feature parameters includes: The frequency shift signal is demodulated to obtain a linear relationship between the Brillouin frequency shift and temperature and strain: ； in, The frequency offset of the signal at position x along the fiber optic axis at time t; The temperature coefficient representing the frequency shift of the backscattered Brillouin light. The strain coefficient is the frequency shift of the back-scattered Brillouin light. Let x be the change in strain in the external environment at position x along the fiber optic axis at time t. This represents the temperature change at position x along the fiber optic axis at time t. Based on the linear relationship between Brillouin frequency shift and temperature and strain, preliminary DSS strain characteristic parameters are extracted from the frequency shift signal. : ； in, For strain rate; For axial strain; The original strain value is obtained by demodulating the frequency offset signal after being measured by the distributed strain sensing system. This represents the maximum shear strain. It is the strain localization factor; The strain gradient condensation index; The displacement field non-uniformity coefficient; Based on the linear relationship between Brillouin frequency shift and temperature and strain, and the strain obtained after demodulation of the frequency shift signal, temperature field compensation parameters are extracted to obtain the true strain parameters after temperature compensation. : ； in, The actual strain after temperature compensation; The optical fiber thermo-optic coefficient; This represents the temperature change at position x along the fiber optic axis at time t. The preliminary DSS strain characteristic parameters are updated to obtain the DSS strain characteristic parameters. : ； The measured strain field is obtained based on the linear relationship between Brillouin frequency shift and temperature and strain. Inverse the stress characteristic parameter set by the constitutive equation of the rock mass. : ； in, These are the components of the stress tensor; This is the first stress invariant; It is the second invariant of deviatoric stress; Von Mises equivalent stress; These are the first, second, and third principal stresses, respectively. The angle between the maximum principal stress and the x-axis; For damage variables; The stress concentration factor is... This refers to the three-dimensional spatial location of a point within the rock mass.

4. The method for identifying precursors of rock mass failure based on DAS-DSS joint sensing according to claim 3, characterized in that, The method for mapping the DAS sensing feature parameters and the DSS sensing feature parameters to the same raster is as follows: Establish a unified spatiotemporal coordinate system ,in, These are the unified X-axis, Y-axis, and Z-axis spatial coordinates after spatial coordinate transformation for the corresponding distributed acoustic wave sensing system and distributed strain sensing system, respectively. To provide a unified time coordinate after time synchronization between the distributed acoustic wave sensing system and the distributed strain sensing system; Map the DAS sensing feature parameters and the DSS sensing feature parameters to the same raster: ； in: These represent the i-th discrete point in unified X-axis spatial coordinates, the j-th discrete point in unified Y-axis spatial coordinates, the k-th discrete point in unified Z-axis spatial coordinates, and the l-th time point in unified time coordinates, respectively. This represents discretizing a continuous four-dimensional spacetime into grid points. Each grid point stores the DAS sensing feature parameters and the DSS sensing feature parameters at that location and time. For the temperature field.

5. The method for identifying precursors of rock mass failure based on DAS-DSS joint sensing according to claim 4, characterized in that, The method for associating the DAS sensing feature parameters and the DSS sensing feature parameters under a unified time reference and spatial coordinate system is as follows: Through spatial transformation matrix Achieve DAS and DSS coordinate alignment: ； Among them, DAS spatial positioning: ； Among them, DSS spatial positioning: ； in, Spatial location measured by a distributed acoustic wave sensing system; The spatial position measured by the distributed strain sensing system; The speed of light in an optical fiber; The time of reflection in the optical time domain; The speed of light in a vacuum; This represents the time delay difference between pulsed light and continuous light. The refractive index of the optical fiber; The step size for the Brillouin frequency shift; Using a sliding window Perform feature time synchronization alignment between DAS and DSS: ； ； ； in: The DAS microseismic characteristic parameters are obtained by averaging at the l-th unified time point. The DSS strain characteristic parameters are obtained by window averaging at the l-th unified time point; This is the set of stress characteristic parameters after time window averaging at the l-th unified time point; These are the instantaneous values ​​of the high-sampling-rate DAS microseismic characteristic parameters at time t; Here are the characteristic parameters of the DSS strain at time t; Let be the set of stress characteristic parameters at time t; This is the width of the window when sliding.

6. The method for identifying precursors of rock mass failure based on DAS-DSS joint sensing according to claim 5, characterized in that, The joint characteristic matrix of the multiphysics field is: ； in: These are the DAS microseismic characteristic parameters after time window averaging; These are the DSS strain characteristic parameters after time window averaging; This is the set of stress characteristic parameters after time window averaging; For temperature field; This is the joint characteristic matrix of the multiphysics field.

7. The method for identifying precursors of rock mass failure based on DAS-DSS joint sensing according to claim 6, characterized in that, The corrected phase change signal is: ， ； in, This is the corrected phase change signal. It is a phase change signal. To calibrate the P-wave velocity, The initial longitudinal wave velocity; The volumetric strain-wave velocity coupling coefficient; The stress-wave velocity coupling coefficient; The change in volumetric strain; This represents the change in the first stress invariant.

8. The method for identifying precursors of rock mass failure based on DAS-DSS joint sensing according to claim 7, characterized in that, The expression for the temperature / creep / zero drift correction of the DAS-constrained DSS is as follows: ； in: This represents the actual strain after calibration. This is a zero-drift correction term; This represents the temperature change at position x along the fiber optic axis at time t. The optical fiber thermo-optic coefficient; The damage-strain coupling coefficient; For damage variables; The reset condition for the zero-drift correction item is as follows: ,when and ; in: Count the instantaneous microseismic events at position x and time t0 along the fiber optic axis; The silent threshold for microseismic events; The original strain value measured by the distributed strain sensing system at time t0 when the conditions are met; Install the initial reference strain for the distributed strain sensing system; It is the second invariant of deviatoric stress; This is the deviatoric stress threshold.

9. The method for identifying precursors of rock mass failure based on DAS-DSS joint sensing according to claim 8, characterized in that, The fusion early warning index includes an energy-strain-stress comprehensive index and a comprehensive index change rate, the expressions for which are respectively: ， ； in: It is a comprehensive index of energy, strain, and stress; The rate of change of the comprehensive indicator; This represents the actual strain after calibration. This refers to the micro-vibration energy after calibration. The equivalent uniaxial stress is Von Mises stress, which takes into account the equivalent uniaxial stress of all stress components.

10. The method for identifying precursors of rock mass failure based on DAS-DSS joint sensing according to claim 9, characterized in that, The process of determining the rock mass stability level based on the DAS sensing characteristic parameters, the DSS sensing characteristic parameters, the fused early warning index, and the precursor sensitivity factor includes: If the DAS sensing feature parameters, the DSS sensing feature parameters, the fusion early warning index, and the precursor sensitivity factor satisfy the following conditions: and and If so, the rock mass stability level is stable. If the DAS sensing feature parameters, the DSS sensing feature parameters, the fusion early warning index, and the precursor sensitivity factor satisfy the following conditions: and and and If so, the rock mass stability level is abnormal. If the DAS sensing feature parameters, the DSS sensing feature parameters, the fusion early warning index, and the precursor sensitivity factor satisfy the following conditions: and and and and and and and The rock mass stability level is at the pre-slip stage; in: The rate of change of damage; Damage acceleration rate; The peak value of the strain gradient condensation index; To integrate early warning indicators The first threshold; To integrate early warning indicators The second threshold; The threshold for the rate of change of coordination; The rate of change of the stress concentration factor; The peak value of the stress concentration factor. Unit time window The rate of change of information entropy within; The rate of change of multiple physical fields in space and time; The rate of change of the strain localization factor; The background strain localization factor; This is the strain gradient concentration index.

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