Optical fiber acoustic wave comprehensive monitoring and detection method and system

Abstract

The present invention discloses a fiber optic acoustic wave comprehensive monitoring and detection method and system. A plurality of optical cables and DAS optical fiber acoustic wave sensor devices are arranged in a tunnel. Ultrasonic waves and infrasonic waves are emitted in the tunnel environment by the DAS optical fiber acoustic wave sensor devices. The ultrasonic waves are used to monitor the tunnel wall contour, and the infrasonic waves are used to monitor the geological anomalies outside the tunnel. Various acoustic wave signals in the tunnel environment are sensed by the optical cable and cause the optical cable to vibrate. The optical cable transmits the vibration signal to the DAS optical fiber acoustic wave sensor device, and the DAS optical fiber acoustic wave sensor device transmits the vibration signal to a cloud platform. The received optical cable vibration signal is processed and analyzed by the cloud platform to realize the monitoring of the train running status, the tunnel wall contour and the geological anomalies outside the tunnel and abnormal alarm. The optical cable is cheap and will not be affected by the kicking of people in the tunnel to affect normal monitoring, so it can be used for long-term and large-scale monitoring in the tunnel.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel operation monitoring, and in particular to an optical fiber acoustic wave comprehensive monitoring and detection method and system. Background Art

[0002] With the increase of urban population, the pressure of ground traffic has also increased, and underground traffic is an important channel to alleviate the pressure of ground traffic. In the construction and operation of underground traffic tunnels, the monitoring of the geological conditions outside the tunnel and its side walls has a vital impact on driving safety and personnel safety. In the existing technology, there is a method of obtaining the sound field distribution data in the entire tunnel by applying optical fiber acoustic wave detection technology and combining it with three-dimensional mapping imaging technology, and identifying different foreign objects in the tunnel and the corridor through data analysis, but this method can only identify the invasion of foreign objects, and cannot scan the inner surface contour of the tunnel and the corridor, and cannot identify the geological conditions such as precipitation and rock cavities outside the tunnel, and cannot monitor the running status of the train in the tunnel; in addition, there is a method of using distributed optical fiber sensing technology (Distributed Acoustic Sensing, DAS) combined with current artificial intelligence algorithms and big data processing technology to monitor and obtain the running status of the train in real time, but it can only realize the single monitoring of the running status of the train, and cannot simultaneously and comprehensively monitor the contour of the tunnel wall and the geological changes outside the tunnel, and there is a problem of inadequate monitoring. Summary of the invention

[0003] The present application provides a fiber optic acoustic wave comprehensive monitoring and detection method and system to solve the problem that the existing technology cannot achieve comprehensive monitoring of the running status of trains in tunnels, tunnel wall contours, and geological anomalies outside the tunnel.

[0004] According to the first aspect, an embodiment provides a fiber optic acoustic wave comprehensive monitoring and detection method, the method comprising:

[0005] Multiple optical cables and DAS fiber optic acoustic wave sensor devices are laid in the tunnel, and ultrasonic and infrasound waves are emitted in the tunnel environment through the DAS fiber optic acoustic wave sensor devices. The ultrasonic waves are used to monitor the contour of the tunnel wall, and the infrasound waves are used to monitor the geological anomalies outside the tunnel.

[0006] The optical cable senses various acoustic signals in the tunnel environment and causes the optical cable to vibrate. The optical cable transmits the vibration signal to the DAS optical fiber acoustic wave sensor device, and the DAS optical fiber acoustic wave sensor device transmits the vibration signal to the cloud platform;

[0007] The received optical cable vibration signals are processed and analyzed through the cloud platform to monitor and alarm the train operation status, tunnel wall contours, and geological anomalies outside the tunnel.

[0008] Furthermore, multiple optical cables and DAS optical fiber acoustic wave sensor equipment are laid in the tunnel, including:

[0009] An optical cable is laid on the arch of the tunnel, an optical cable is laid on each side of the tunnel, two optical cables are laid at the bottom of the tunnel, and a DAS fiber optic acoustic wave sensor device is laid next to the optical cable at the tunnel arch.

[0010] Furthermore, various acoustic signals in the tunnel environment are sensed through the optical cable and cause the optical cable to vibrate, including:

[0011] The various sound wave signals in the tunnel environment include: the sound wave signals generated by the running train, the rebound signals of ultrasonic waves on the tunnel wall, and the rebound signals of infrasound waves on the strata outside the tunnel after passing through the tunnel wall.

[0012] Furthermore, the received optical cable vibration signal is processed and analyzed through the cloud platform, including:

[0013] Perform wavelet transform on the detected signal to remove noise;

[0014] The frequency range of different events is obtained through statistical analysis, and the signal within a specific frequency range is filtered and enhanced using an adaptive filter to extract the individual signal spectra under different events.

[0015] The probability model and time-frequency conversion processing are carried out on the individual signal spectra under different events to extract the key spectrum features. The event types corresponding to different spectrum features are identified through spectrum feature analysis or by using pre-trained machine learning models, including train operation, tunnel wall reflection and various geological anomalies outside the tunnel. Various geological anomalies outside the tunnel include geological hollow layers, water level changes and foreign object intrusion.

[0016] Furthermore, the monitoring of the train running status specifically includes:

[0017] By sensing the change in the phase of the optical signal in the optical cable, the train position can be located, and by demodulating the reflected signal containing phase information, a series of train status parameters can be obtained to monitor the train's operating status.

[0018] Furthermore, monitoring of the tunnel wall profile includes:

[0019] When there is no train running in the tunnel, the ultrasonic positioning algorithm is used to measure the tunnel wall profile, including:

[0020] When the ultrasonic sound source starts to actively emit ultrasonic signals, set t 1The ultrasonic signal emitted by the sound source first reaches a certain position on the optical fiber located on the tunnel wall. Since the frequency of the sound wave signal in different media remains unchanged but the speed changes, the propagation speed c of the sound wave in the tunnel is measured through additional experiments, and then y 1 =c×t 1 Get the distance y between the tunnel wall and the sound source at the corresponding position 1 Similarly, the same processing is performed on the next position within the spatial resolution range of the DAS fiber optic acoustic sensor device to obtain the distance y between the tunnel wall and the sound source at the next position. 2 , and so on, the distance between the tunnel wall and the sound source at other locations is obtained, thereby constructing the approximate range and spatial model of the tunnel wall.

[0021] Furthermore, the monitoring of geological abnormalities outside the tunnel includes:

[0022] If a geological hollow layer is identified outside the tunnel, the infrasound positioning algorithm is used to monitor the cavities in the geological layer outside the tunnel wall when no train is running in the tunnel, including:

[0023] When the infrasound source starts to actively emit infrasound signals, set t 1 The infrasound signal emitted by the time sound source first reaches a certain position on the optical fiber located on the tunnel wall and partially transmits and continues to propagate in the dielectric layer outside the tunnel. When encountering a cavity, the infrasound wave will be significantly reflected at the geological layer-cavity interface. Assume that the reflected signal is delayed for a certain period of time at t 2 If the time is returned to the same position of the optical fiber, the propagation time of the infrasound signal in the corresponding process is Δt=t 2 -t 1 ;

[0024] Since the frequency of the sound wave signal in different media remains unchanged but the speed changes, the propagation speed c of the sound wave in the geological layer is measured through additional experiments, and then x 1 =c×Δt / 2 to obtain the distance x between the geological layer and the cavity interface around the corresponding position 1 Similarly, the same processing is performed on the next position within the spatial resolution range of the DAS fiber optic acoustic sensor device to obtain the geological layer-cavity interface position distance x around the next position. 2 , and so on, the distance between the geological layer and the cavity interface at other locations is obtained, thereby constructing the approximate range and spatial model of the cavity.

[0025] Furthermore, the monitoring of geological abnormalities outside the tunnel includes:

[0026] If foreign objects are detected outside the tunnel, the location of the foreign objects will be located using time difference-based positioning technology based on the time difference between the monitored infrasound wave emission time and the return time.

[0027] Furthermore, the monitoring of geological abnormalities outside the tunnel includes:

[0028] If water is detected outside the tunnel, the water level is estimated by inversion based on the observed infrasound signal intensity, based on the signal intensity variation and propagation model of the infrasound in the tunnel and surrounding media.

[0029] According to the second aspect, an embodiment provides a fiber optic acoustic wave comprehensive monitoring and detection system, the system comprising:

[0030] The DAS monitoring module includes a plurality of optical cables and DAS optical fiber acoustic wave sensor devices arranged in the tunnel. The DAS optical fiber acoustic wave sensor devices emit ultrasonic waves and infrasound waves in the tunnel environment. The ultrasonic waves are used to monitor the tunnel wall profile, and the infrasound waves are used to monitor the geological abnormalities outside the tunnel. Various acoustic wave signals in the tunnel environment are sensed by the optical cables and cause the optical cables to vibrate. The optical cables transmit the vibration signals to the DAS optical fiber acoustic wave sensor devices, and the vibration signals are transmitted to the cloud platform by the DAS optical fiber acoustic wave sensor devices.

[0031] The cloud platform is used to process and analyze the received optical cable vibration signals, and realize the monitoring and abnormal alarm of the train operation status, tunnel wall contour and geological anomalies outside the tunnel.

[0032] The present application provides a fiber optic acoustic wave comprehensive monitoring and detection method and system, multiple optical cables and DAS optical fiber acoustic wave sensor equipment are arranged in the tunnel, and ultrasonic and infrasound waves are emitted in the tunnel environment by the DAS optical fiber acoustic wave sensor equipment, and the ultrasonic wave is used to monitor the geological abnormality outside the tunnel; various acoustic wave signals in the tunnel environment are sensed by the optical cable and cause the optical cable to vibrate, and the optical cable transmits the vibration signal to the DAS optical fiber acoustic wave sensor equipment, and the vibration signal is transmitted to the cloud platform by the DAS optical fiber acoustic wave sensor equipment; the received optical cable vibration signal is processed and analyzed by the cloud platform, so as to realize the monitoring and abnormal alarm of the train running state, the tunnel wall contour and the geological abnormality outside the tunnel. The present invention only needs to lay out the optical cable and the DAS monitoring equipment, and perform acoustic wave emission and analysis, so as to monitor and abnormal alarm the geological abnormality outside the tunnel such as the train running state, the tunnel wall contour and the geological hollow layer, water level change, foreign body intrusion, etc. The optical cable is cheap and will not affect the normal monitoring due to the kicking of the personnel in the tunnel, and can be monitored in the tunnel for a long time and in large quantities. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A flowchart of a fiber optic acoustic wave comprehensive monitoring and detection method provided by one embodiment of the present invention;

[0034] Figure 2 A schematic diagram of the layout of optical cables and equipment in a fiber optic acoustic wave comprehensive monitoring and detection method provided by one embodiment of the present invention;

[0035] Figure 3 A schematic diagram of the logical structure of a fiber optic acoustic wave comprehensive monitoring and detection system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, this is to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0037] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0038] The first embodiment of the present invention provides a fiber optic acoustic wave comprehensive monitoring and detection method, which is combined with Figure 1 Provide detailed explanation.

[0039] like Figure 1 As shown, in step S100, multiple optical cables and DAS fiber optic acoustic wave sensor devices are laid in the tunnel, and ultrasonic waves and infrasound waves are emitted in the tunnel environment by the DAS fiber optic acoustic wave sensor devices, wherein the ultrasonic waves are used to monitor the tunnel wall profile, and the infrasound waves are used to monitor geological anomalies outside the tunnel.

[0040] Specifically, Figure 2 As shown, an optical cable ( Figure 2 The optical fiber in the tunnel is installed with an optical cable on each side of the tunnel arch, two optical cables are installed at the tunnel arch bottom, and a DAS optical fiber acoustic wave sensor device is installed next to the optical cable at the tunnel arch. Figure 2By laying five optical cables in the tunnel vault, waist and bottom, and a DAS fiber optic acoustic wave sensor device in the tunnel waist, the tunnel wall profile can be obtained through acoustic wave scanning, and the normal operation of the train in the tunnel and the geological hollow layer outside the tunnel, water level changes, foreign body intrusion, etc. can be monitored in real time.

[0041] like Figure 1 As shown, in step S200, various sound wave signals in the tunnel environment are sensed by the optical cable and cause the optical cable to vibrate. The optical cable transmits the vibration signal to the DAS optical fiber acoustic wave sensor device, and the vibration signal is transmitted to the cloud platform through the DAS optical fiber acoustic wave sensor device.

[0042] Specifically, the various sound wave signals in the tunnel environment include: sound wave signals generated by train operation, rebound signals of ultrasonic waves on the tunnel wall, and rebound signals of infrasound waves on the ground outside the tunnel after passing through the tunnel wall.

[0043] The normal operation of the train will cause all the optical fibers in the tunnel to vibrate. When the train runs in the tunnel, it will generate corresponding acoustic vibration signals and propagate in the form of mechanical waves. The optical fibers laid nearby will generate forced damped vibrations along with the acoustic vibrations.

[0044] The infrasound waves emitted by the DAS fiber optic acoustic sensor device have a long wavelength and are good at penetrating the strata. They usually travel to a very far distance and then bounce back. After the infrasound waves hit the side wall of the tunnel on the other side, they break through the gap in the side wall into the strata outside the tunnel, and bounce back to the optical fiber on the other side of the tunnel (vault, waist, and bottom optical fiber) at a very distant stratum, causing the optical fiber here to vibrate.

[0045] The wavelength of the ultrasonic wave emitted by the DAS fiber optic acoustic wave sensor device is relatively short. When it encounters the side wall of the tunnel on the other side, it will rebound along the same path and return to the optical fiber on the other side, causing the optical fiber here to vibrate.

[0046] The optical cable senses various acoustic signals in the tunnel environment and causes the optical cable to vibrate. The optical cable transmits the vibration signal to the DAS optical fiber acoustic wave sensor device, and then transmits the vibration signal to the cloud platform through the DAS optical fiber acoustic wave sensor device.

[0047] like Figure 1 As shown, in step S300, the received optical cable vibration signal is processed and analyzed by the cloud platform to realize the monitoring and abnormal alarm of the train running status, tunnel wall contour and geological abnormalities outside the tunnel.

[0048] Specifically, the cloud platform processes and analyzes the received optical cable vibration signals, including:

[0049] (1) Perform wavelet transform on the detected signal to remove noise.

[0050] The sound waves are emitted through the DAS fiber optic acoustic wave sensor device. The fiber optic vibration signals such as train running sound waves, tunnel wall reflected sound waves, foreign object intrusion waves, external cavity reflected sound waves, etc. sensed by the optical fiber can be decomposed into wavelet coefficients of different scales and positions. The wavelet coefficients representing noise can be selectively removed or attenuated. Its advantage is that it can retain the details of the signal and is sensitive to local signal changes, which is suitable for noise reduction of complex signals.

[0051] (2) The frequency range of different events is obtained through statistical analysis, and the signal within a specific frequency range is filtered and enhanced using an adaptive filter to extract the individual signal spectra under different events.

[0052] A large number of cases are summarized and analyzed, and the sound wave frequency ranges in various cases are compared. Then an adaptive filter is used to enhance the signal within a specific frequency range, while suppressing the signals of other frequencies, thereby achieving noise reduction and signal enhancement. The adaptive filter can dynamically adjust its parameters (such as filter coefficients) according to the characteristics of the input signal to achieve the best filtering effect. Adaptive filters are more suitable when the noise characteristics change over time, or the frequency of the signal and the noise overlap, and the filter needs to be able to adapt to these changes. In the end, separate frequency spectra can be drawn for various situations such as train operation, rock and soil cavities outside the tunnel, foreign body intrusion, water level changes, tunnel wall contours, etc.

[0053] (3) Construct a probability model and perform time-frequency conversion processing on the individual signal spectra under different events to extract key spectral features. Then, identify the event types corresponding to different spectral features through spectral feature analysis or by using a pre-trained machine learning model, including train operation, tunnel wall reflection, and various geological anomalies outside the tunnel. The geological anomalies outside the tunnel include geological hollow layers, water level changes, and foreign object intrusion.

[0054] According to the signal characteristics (such as frequency and intensity), the signal is classified into train sound waves, tunnel wall reflection waves, foreign object intrusion waves, etc. A probability model is constructed for each type of signal, such as Gaussian distribution model and Poisson distribution model. The model parameters include mean (μ) and standard deviation (σ). These parameters can be used as one of the input features of the machine learning model to help the model learn the spectral characteristics of different event types. The cloud platform uses fast Fourier transform (FFT) to convert time signals (optical fiber vibration intensity and frequency signals that change dynamically over time) into frequency domain signals, and analyzes the spectral characteristics of the signal to identify different situations. Key features such as spectrum peaks, energy distribution, spectral entropy, and phase information can be extracted from the FFT results.

[0055] Use machine learning to train the model to identify event types corresponding to different spectral features, such as train operation, rock and soil cavities outside tunnels, and foreign body intrusion. First, use the extracted features as input and train the model using labeled data (i.e. known event types). The goal is to let the model learn the mapping relationship between different spectral features and event types. Secondly, evaluate the performance of the model through methods such as cross-validation, using indicators such as accuracy, recall, and F1 score. Adjust the model parameters or try different model structures based on the evaluation results to improve the accuracy of recognition.

[0056] Based on the cloud platform, a graph is drawn showing the relationship between the sound wave spectrum and intensity over time. If there are abnormal changes in the frequency and intensity in the monitoring spectrum or new intensity or new frequency components are added, an alarm value is set. If the spectrum and intensity waveform exceeds 15% of the normal value, an alarm is issued and monitoring is strengthened.

[0057] Based on the above contents, the monitoring and abnormal alarm of train running status, tunnel wall profile and geological abnormalities outside the tunnel are as follows:

[0058] (1) Monitoring of train operation status

[0059] The normal operation of the train causes all optical fibers in the tunnel to vibrate. The external vibration changes the phase of the optical signal transmitted in the optical fiber. By sensing the change in the phase of the optical signal in the optical cable, the train position is located, and by demodulating the reflected signal containing phase information, a series of train status parameters are obtained to monitor the train's running status.

[0060] Specifically, assume that the incident light is an ideal pulse light E(0), which satisfies the Dirac equation, that is, there is no pulse light with a pulse width. The energy loss coefficient of the optical pulse when it is transmitted forward in the optical fiber is (np / m), the relationship between the transmitted light energy and the transmission distance in the optical fiber is:

[0061]

[0062] When a light pulse travels a tiny distance dz from position z, the scattered light energy is:

[0063]

[0064] in, is the Rayleigh scattering loss coefficient. Define B(z) as the Rayleigh scattered light energy that is transmitted backward from point z in the optical fiber. The energy of the Rayleigh scattered light returned to the input is:

[0065]

[0066] Then the time taken for the backscattered Rayleigh light from the end of the optical fiber to return to the receiving end is ,in, is the group velocity of light in the optical fiber, is the pulse width, so:

[0067]

[0068] When the pulse width of the optical pulse is W, the backward Rayleigh scattered light in the optical fiber is:

[0069]

[0070] From the above formula, we can know that the optical power of the backscattered Rayleigh light has a one-to-one correspondence with the distance. Therefore, when the train generates a corresponding acoustic vibration signal during operation, the train positioning is achieved through the change in the intensity of the optical signal caused by it.

[0071] Specifically, the DAS fiber optic acoustic wave sensor device continuously emits laser pulses to the connected sensor cable. Due to the presence of uneven scatterers inside the optical fiber, part of the incident pulse light will be scattered. Due to the photoelastic effect, when a certain position of the optical fiber is disturbed by the outside world and produces strain, the refractive index of the optical fiber at that position will change, where the refractive index change is:

[0072]

[0073] In the formula is the change in refractive index, is the strain parameter of the refractive index, is the change in the dependent variable, and They are the Poisson's ratio and the elastic-optic tensor coefficient of the optical fiber, and the phase change between the two scattering points caused by the change in spacing and the change in the refractive index of the optical fiber. for:

[0074]

[0075] =

[0076] in is the phase constant, is the length of the optical fiber, is the wavelength of light. According to the above formula, when the optical fiber is disturbed by external factors, the refractive index and length changes produced are converted into the phase change of the optical signal transmitted in the optical fiber. The backscattered optical signal carrying the phase information returns to the DAS optical fiber acoustic wave sensor device terminal after Rayleigh scattering. The two ends of this section of optical fiber are located by optical time domain reflection technology, and the phase change between the two ends is solved to obtain the external vibration information, thereby obtaining the state parameters such as the running speed of the train. Combined with the above positioning technology, the optical phase information at any position of the entire section of optical fiber can be demodulated to realize the monitoring of the train running status information in the tunnel.

[0077] (2) Monitoring of tunnel wall profile

[0078] When there is no train running in the tunnel, the ultrasonic positioning algorithm is used to measure the tunnel wall profile, including:

[0079] When the ultrasonic sound source starts to actively emit ultrasonic signals, set t 1 The ultrasonic signal emitted by the sound source first reaches a certain position on the optical fiber located on the tunnel wall. Since the frequency of the sound wave signal in different media remains unchanged but the speed changes, the propagation speed c of the sound wave in the tunnel is measured through additional experiments, and then y 1 =c×t 1 Get the distance y between the tunnel wall and the sound source at the corresponding position 1 Similarly, the same processing is performed on the next position within the spatial resolution range of the DAS fiber optic acoustic sensor device to obtain the distance y between the tunnel wall and the sound source at the next position. 2 , and so on to obtain the distance y from the tunnel wall to the sound source at other locations 3 ,y 4 ,y 5 …, thereby constructing the approximate range and spatial model of the tunnel wall.

[0080] (3) Monitoring of geological abnormalities outside the tunnel

[0081] a. If a geological hollow layer is identified outside the tunnel, when there is no train running in the tunnel, the infrasound positioning algorithm is used to monitor the cavities in the geological layer outside the tunnel wall, including:

[0082] When the infrasound source starts to actively emit infrasound signals, set t 1 The infrasound signal emitted by the time sound source first reaches a certain position on the optical fiber located on the tunnel wall and partially transmits and continues to propagate in the dielectric layer outside the tunnel. When encountering a cavity, the infrasound wave will be significantly reflected at the geological layer-cavity interface. Assume that the reflected signal is delayed for a certain period of time at t 2 If the time is returned to the same position of the optical fiber, the propagation time of the infrasound signal in the corresponding process is Δt=t 2 -t1 ;

[0083] Since the frequency of the sound wave signal in different media remains unchanged but the speed changes, the propagation speed c of the sound wave in the geological layer is measured through additional experiments, and then x 1 =c×Δt / 2 to obtain the distance x between the geological layer and the cavity interface around the corresponding position 1 Similarly, the same processing is performed on the next position within the spatial resolution range of the DAS fiber optic acoustic sensor device to obtain the geological layer-cavity interface position distance x around the next position. 2 , and so on to obtain the geological layer-cavity interface distance x at other locations 3 、x 4 、x 5 …, thereby constructing the approximate range and spatial model of the void.

[0084] b. If foreign objects are detected outside the tunnel, the location of the foreign objects will be located using the time difference-based positioning technology based on the time difference between the monitored infrasound wave emission time and the return time.

[0085] Specifically, the calculation formula is as follows:

[0086] △t i =

[0087] Where r represents the position vector of the target point (such as a hole, anomaly, etc.), that is, the specific position of the point you want to locate in space. Here r is a three-dimensional vector, representing the position in the (x, y, z) coordinate system in three-dimensional space. ri represents the position vector of the i-th position of the optical fiber. r0 represents the position vector of the reference point or benchmark point. and They represent the distance from the i-th sensor to the target point and from the reference point to the target point respectively. By calculating these distances and combining the propagation speed c of signals such as sound waves or electromagnetic waves, the time difference △t of the signal arriving at different positions of the optical fiber can be determined i According to the △t measured by DAS fiber optic acoustic sensor equipment i , and reversely calculate r to obtain the position where the foreign object invades the tunnel.

[0088] c. If water is detected outside the tunnel, the water level is estimated by inversion based on the observed infrasonic signal intensity changes and propagation model in the tunnel and surrounding media.

[0089] Specifically, the signal propagation model is constructed as follows: First, a propagation function model of infrasound in the tunnel and surrounding media (including water) needs to be established. This function model should take into account the acoustic properties of the medium (such as sound velocity, attenuation coefficient, etc.), the geometry of the tunnel, and the positional relationship between the signal source (DAS fiber optic acoustic sensor device) and the receiver (optical fiber). For example, for a simple case, assuming that the propagation of infrasound in a homogeneous medium can be expressed using the formula To describe, where I is the signal strength received at a distance r from the signal source, I 0 is the initial intensity emitted by the signal source, and α is the attenuation coefficient. When there is water, the attenuation coefficient α will change due to the acoustic properties of the water, and the signal may be reflected or refracted at the water interface, which all need to be considered in the propagation function model.

[0090] Inversion principle: Inversion technology is the process of inferring water level related parameters based on known observation data (i.e., the received infrasonic signal strength) and propagation function model. Assuming that the water level height h is the parameter to be estimated, when the water level changes, the medium distribution in the signal propagation path (such as water and surrounding rock and soil) will change, causing the received signal strength I to change. By establishing a functional model relationship between the signal strength I and the water level height h, for example, I=f(h), the water level height can be inferred based on the observed signal strength.

[0091] Corresponding to the above-disclosed optical fiber acoustic wave comprehensive monitoring and detection method, the embodiment of the present invention also discloses an optical fiber acoustic wave comprehensive monitoring and detection system, such as Figure 3 As shown, it specifically includes:

[0092] The DAS monitoring module includes a plurality of optical cables and DAS optical fiber acoustic wave sensor devices arranged in the tunnel. The DAS optical fiber acoustic wave sensor devices emit ultrasonic waves and infrasound waves in the tunnel environment. The ultrasonic waves are used to monitor the tunnel wall profile, and the infrasound waves are used to monitor the geological abnormalities outside the tunnel. Various acoustic wave signals in the tunnel environment are sensed by the optical cables and cause the optical cables to vibrate. The optical cables transmit the vibration signals to the DAS optical fiber acoustic wave sensor devices, and the vibration signals are transmitted to the cloud platform by the DAS optical fiber acoustic wave sensor devices.

[0093] The cloud platform is used to process and analyze the received optical cable vibration signals, and realize the monitoring and abnormal alarm of the train operation status, tunnel wall contour and geological anomalies outside the tunnel.

[0094] It should be noted that for the detailed description of an optical fiber acoustic wave comprehensive monitoring and detection system provided in an embodiment of the present invention, reference can be made to the relevant description of an optical fiber acoustic wave comprehensive monitoring and detection method provided in an embodiment of the present application, which will not be repeated here.

[0095] The above specific examples are used to illustrate the present invention, which is only used to help understand the present invention and is not intended to limit the present invention. For those skilled in the art, according to the concept of the present invention, some simple deductions, modifications or substitutions can be made.

Claims

1. A fiber optic acoustic wave comprehensive monitoring and detection method, characterized in that: The method comprises: Multiple optical cables and DAS fiber optic acoustic wave sensor devices are laid in the tunnel, and ultrasonic and infrasound waves are emitted in the tunnel environment through the DAS fiber optic acoustic wave sensor devices. The ultrasonic waves are used to monitor the contour of the tunnel wall, and the infrasound waves are used to monitor the geological anomalies outside the tunnel. The various acoustic signals in the tunnel environment are sensed through the optical cable and cause the optical cable to vibrate. The optical cable transmits the vibration signal to the DAS optical fiber acoustic sensor device, and the DAS optical fiber acoustic sensor device transmits the vibration signal to the cloud platform. The various acoustic signals in the tunnel environment include: acoustic signals generated by train operation, ultrasonic rebound signals on the tunnel wall, and infrasound rebound signals on the ground outside the tunnel after passing through the tunnel wall; The received optical cable vibration signals are processed and analyzed through the cloud platform to monitor and give abnormal alarms for the train running status, tunnel wall profile, and geological abnormalities outside the tunnel. The received optical cable vibration signal is processed and analyzed through the cloud platform, including: Perform wavelet transform on the detected signal to remove noise; The frequency range of different events is obtained through statistical analysis, and the signal within a specific frequency range is filtered and enhanced using an adaptive filter to extract the individual signal spectra under different events. The probability model and time-frequency conversion processing are performed on the individual signal spectra under different events to extract the key spectrum features. The event types corresponding to different spectrum features are identified through spectrum feature analysis or by using pre-trained machine learning models, including train operation, tunnel wall reflection, and various geological anomalies outside the tunnel. Various geological anomalies outside the tunnel include geological hollow layers, water level changes, and foreign body intrusion; Monitoring of tunnel wall profiles, including: When there is no train running in the tunnel, the ultrasonic positioning algorithm is used to measure the contour of the tunnel wall, including: when the ultrasonic sound source starts to actively emit ultrasonic signals, it is assumed that after t1 time, the ultrasonic signal emitted by the sound source first reaches a certain position on the optical fiber located on the tunnel wall. Since the frequency of the acoustic wave signal in different media remains unchanged but the speed changes, the propagation speed c of the acoustic wave in the tunnel is measured through additional experiments, and the distance y1 from the tunnel wall to the sound source at the corresponding position is obtained by y1=c×t1. Similarly, the same processing is performed on the next position within the range allowed by the spatial resolution of the DAS optical fiber acoustic wave sensor device to obtain the distance y2 from the tunnel wall to the sound source at the next position. The distances from the tunnel wall to the sound source at other positions are obtained by analogy, thereby constructing the approximate range and spatial model of the tunnel wall; Monitoring of geological abnormalities outside the tunnel includes: If a geological hollow layer is identified outside the tunnel, the infrasound positioning algorithm is used to monitor the cavities in the geological layer outside the tunnel wall when no train is running in the tunnel, including: When the infrasound source starts to actively emit infrasound signals, it is assumed that after time t1, the infrasound signal emitted by the sound source reaches a certain position on the optical fiber located on the tunnel wall for the first time and partially transmits and continues to propagate in the dielectric layer outside the tunnel. When encountering a cavity, the infrasound wave will be significantly reflected at the geological layer-cavity interface. It is assumed that the reflected signal returns to the same position of the optical fiber at time t2 after a certain time delay, then the propagation time of the infrasound signal in the corresponding process is Δt=t2-t1; Since the frequency of the sound wave signal in different media remains unchanged but the speed changes, the propagation speed c of the sound wave in the geological layer is measured through additional experiments. The geological layer-void interface position distance x1 around the corresponding position is obtained by x1=c×Δt / 2. Similarly, the next position is processed in the same way within the range allowed by the spatial resolution of the DAS fiber optic acoustic sensor equipment to obtain the geological layer-void interface position distance x2 around the next position. The geological layer-void interface position distances at other positions are obtained by analogy, thereby constructing the approximate range and spatial model of the cavity.

2. The optical fiber acoustic wave comprehensive monitoring and detection method according to claim 1, characterized in that: Multiple optical cables and DAS fiber optic acoustic wave sensor equipment are laid in the tunnel, including: An optical cable is laid on the arch of the tunnel, an optical cable is laid on each side of the tunnel, two optical cables are laid at the bottom of the tunnel, and a DAS fiber optic acoustic wave sensor device is laid next to the optical cable at the tunnel arch.

3. The optical fiber acoustic wave comprehensive monitoring and detection method according to claim 1, characterized in that: Monitoring of train operation status includes: By sensing the change in the phase of the optical signal in the optical cable, the train position can be located, and by demodulating the reflected signal containing phase information, a series of train status parameters can be obtained to monitor the train's operating status.

4. The optical fiber acoustic wave comprehensive monitoring and detection method according to claim 1, characterized in that: Monitoring of geological abnormalities outside the tunnel includes: If foreign objects are detected outside the tunnel, the location of the foreign objects will be located using time difference-based positioning technology based on the time difference between the monitored infrasound wave emission time and the return time.

5. The optical fiber acoustic wave comprehensive monitoring and detection method according to claim 1, characterized in that: Monitoring of geological abnormalities outside the tunnel includes: If water is detected outside the tunnel, the water level is estimated by inversion based on the observed infrasound signal intensity, based on the signal intensity variation and propagation model of the infrasound in the tunnel and surrounding media.

6. An optical fiber acoustic wave comprehensive monitoring and detection system, characterized in that: The system comprises: The DAS monitoring module includes a plurality of optical cables and DAS optical fiber acoustic wave sensor devices arranged in the tunnel. The DAS optical fiber acoustic wave sensor devices emit ultrasonic waves and infrasound waves in the tunnel environment. The ultrasonic waves are used to monitor the contour of the tunnel wall, and the infrasound waves are used to monitor the geological abnormalities outside the tunnel. Various acoustic wave signals in the tunnel environment are sensed by the optical cable and cause the optical cable to vibrate. The optical cable transmits the vibration signal to the DAS optical fiber acoustic wave sensor device, and the DAS optical fiber acoustic wave sensor device transmits the vibration signal to the cloud platform. The various acoustic wave signals in the tunnel environment include: acoustic wave signals generated by train operation, ultrasonic wave rebound signals on the tunnel wall, and infrasound wave rebound signals on the strata outside the tunnel after passing through the tunnel wall; The cloud platform is used to process and analyze the received optical cable vibration signals, and monitor and alarm the train running status, tunnel wall profile, and geological abnormalities outside the tunnel; The cloud platform processes and analyzes the received optical cable vibration signals, including: Perform wavelet transform on the detected signal to remove noise; The frequency range of different events is obtained through statistical analysis, and the signal within a specific frequency range is filtered and enhanced using an adaptive filter to extract the individual signal spectra under different events. The probability model and time-frequency conversion processing are performed on the individual signal spectra under different events to extract the key spectrum features. The event types corresponding to different spectrum features are identified through spectrum feature analysis or by using pre-trained machine learning models, including train operation, tunnel wall reflection, and various geological anomalies outside the tunnel. Various geological anomalies outside the tunnel include geological hollow layers, water level changes, and foreign body intrusion; Monitoring of tunnel wall profiles, including: When there is no train running in the tunnel, the ultrasonic positioning algorithm is used to measure the contour of the tunnel wall, including: when the ultrasonic sound source starts to actively emit ultrasonic signals, it is assumed that after t1 time, the ultrasonic signal emitted by the sound source first reaches a certain position on the optical fiber located on the tunnel wall. Since the frequency of the acoustic wave signal in different media remains unchanged but the speed changes, the propagation speed c of the acoustic wave in the tunnel is measured through additional experiments, and the distance y1 from the tunnel wall to the sound source at the corresponding position is obtained by y1=c×t1. Similarly, the same processing is performed on the next position within the range allowed by the spatial resolution of the DAS optical fiber acoustic wave sensor device to obtain the distance y2 from the tunnel wall to the sound source at the next position. The distances from the tunnel wall to the sound source at other positions are obtained by analogy, thereby constructing the approximate range and spatial model of the tunnel wall; Monitoring of geological abnormalities outside the tunnel includes: If a geological hollow layer is identified outside the tunnel, the infrasound positioning algorithm is used to monitor the cavities in the geological layer outside the tunnel wall when no train is running in the tunnel, including: When the infrasound source starts to actively emit infrasound signals, it is assumed that after time t1, the infrasound signal emitted by the sound source reaches a certain position on the optical fiber located on the tunnel wall for the first time and partially transmits and continues to propagate in the dielectric layer outside the tunnel. When encountering a cavity, the infrasound wave will be significantly reflected at the geological layer-cavity interface. It is assumed that the reflected signal returns to the same position of the optical fiber at time t2 after a certain time delay, then the propagation time of the infrasound signal in the corresponding process is Δt=t2-t1; Since the frequency of the sound wave signal in different media remains unchanged but the speed changes, the propagation speed c of the sound wave in the geological layer is measured through additional experiments. The geological layer-void interface position distance x1 around the corresponding position is obtained by x1=c×Δt / 2. Similarly, the next position is processed in the same way within the range allowed by the spatial resolution of the DAS fiber optic acoustic sensor equipment to obtain the geological layer-void interface position distance x2 around the next position. The geological layer-void interface position distances at other positions are obtained by analogy, thereby constructing the approximate range and spatial model of the cavity.

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