Underground concealed engineering geological defect radar detection analysis system based on optical fiber sensing

Abstract

The application relates to the technical field of underground engineering geological exploration and safety monitoring, and discloses a radar detection and analysis system for underground concealed engineering geological defects based on optical fiber sensing, which comprises a distributed sensing network for sensing environmental vibration, a data acquisition module for acquiring original wave field data, a data preprocessing module for purifying the data, a virtual source reconstruction module for generating wideband virtual source response, a wave field frequency modulation module for decomposing the wideband virtual source response into multiple narrowband responses, an inverse time focusing imaging module for imaging each narrowband response to obtain a group of single-frequency energy focusing images, a resonance characteristic analysis module for identifying the physical properties of a target by analyzing the frequency differences of the focusing images, and a three-dimensional visualization module for generating an intuitive three-dimensional attribute label map. The application solves the technical problems of traditional methods, such as dependence on artificial seismic sources, high cost and difficulty in distinguishing defect properties, and has the significant advantages of an environmentally-friendly detection mode, low cost, high efficiency and wide analysis dimension.

Description

Technical Field

[0001] This invention relates to the field of underground engineering geological exploration and safety monitoring technology, specifically to a radar detection and analysis system for underground hidden engineering geological defects based on fiber optic sensing. Background Technology

[0002] In the process of bridge construction, roadbed filling and transportation infrastructure construction, timely and accurate detection and identification of hidden defects such as roadbed voids, loose interlayers, soil loss behind bridge abutments and water-rich soft zones are the key prerequisites for ensuring road structural safety and preventing collapse and settlement accidents.

[0003] Currently, conventional techniques used for this type of detection mainly include ground-penetrating radar, high-density electrical resistivity tomography, and active-source seismic exploration. These methods can detect the existence of discontinuities in the underground medium to a certain extent, thereby roughly delineating the anomalous area. However, these existing technologies generally face a deep-seated technical bottleneck in practical applications. They are usually good at answering the question of "where is the anomaly," that is, delineating the spatial location and approximate shape of the anomalous body by analyzing the propagation time of waves or differences in electromagnetic response. However, they are limited in answering the crucial question of "what is the anomaly." For example, traditional methods often cannot give a clear diagnosis of whether a detected velocity anomaly is a structural cavity that poses a major safety hazard or merely a harmless change in lithology or a local difference in density. This leads to a lack of sufficient basis for subsequent engineering decisions (such as whether grouting reinforcement is needed), and often requires the reliance on costly and destructive borehole sampling for final verification.

[0004] Furthermore, these traditional active detection methods heavily rely on artificial seismic sources (such as hammers, explosives, and controlled seismic source vehicles) or high-power electromagnetic wave transmitters to inject energy into the underground. This "active questioning" working mode not only significantly increases the implementation cost and logistical complexity of detection operations, but also greatly limits the application of the strong vibrations or electromagnetic radiation generated in urban built-up areas, environmental protection areas, or near operational critical infrastructure. This dependence on artificial sources also makes it impractical to achieve long-term, continuous, and unattended safety monitoring of target areas. Therefore, the industry urgently needs a new detection technology that can overcome the above limitations. It should not only be able to accurately locate underground defects, but also be able to deeply analyze their physical properties. Moreover, the entire detection process should be low-cost, environmentally friendly, and easy to deploy for the long term. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a radar detection and analysis system for underground hidden engineering geological defects based on fiber optic sensing. This system solves the limitations of existing underground geological defect detection technologies, which rely on active sources, are difficult to effectively detect static defects, or are unable to perform physical property analysis on detected defects.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a radar detection and analysis system for underground concealed engineering geological defects based on fiber optic sensing, comprising the following modules:

[0007] A distributed sensing network, which is deployed along the detection area, is used to sense the wave field generated by environmental vibrations;

[0008] A data acquisition module is used to acquire the wave field sensed by the distributed sensor network, thereby forming environmental vibration wave field data;

[0009] A virtual source reconstruction module is used to perform wavefield cross-correlation calculation on the environmental vibration wavefield data, thereby obtaining a broadband virtual source response characterizing the response of the subsurface medium;

[0010] A wave field frequency modulation module is used to filter the wideband virtual source response, thereby generating a set of narrowband virtual source responses corresponding to a scanning frequency.

[0011] A time-reverse focusing imaging module is used to perform time reversal and backpropagation calculations on each narrow-band virtual source response in the set of narrow-band virtual source responses, and generate a set of single-frequency energy focusing maps corresponding to the scanning frequencies respectively;

[0012] A resonance characteristic analysis module is used to identify defect regions in the set of single-frequency energy focusing maps, extract the focusing energy of the defect regions at different scanning frequencies, construct a resonance spectrum reflecting the scattering characteristics of the defect regions, and analyze the physical properties of geological defects based on the resonance spectrum.

[0013] Preferably, the distributed sensor network includes a standard single-mode communication optical fiber laid along the detection area, and a distributed acoustic sensor demodulator connected to the standard single-mode communication optical fiber.

[0014] The standard single-mode communication fiber is used to sense the wave field, and the distributed acoustic sensor demodulator, based on the coherent optical time-domain reflection principle, converts the signal sensed by the standard single-mode communication fiber into the environmental vibration wave field data.

[0015] In one specific embodiment, the virtual source reconstruction module selects a location on the distributed sensor network as... The first sensing point and location are The second sensing point, for the two points in time length Environmental vibration wave field data collected internally and Perform cross-correlation function The calculation is performed to obtain the broadband virtual source response, and the formula for calculating the cross-correlation function is as follows:

[0016] ;

[0017] in: This represents the cross-correlation function between the first sensing point and the second sensing point; Represents a time delay variable; Represents the position Environmental vibration wave field data collected at the first sensing point; Represents the position Data collected at the second sensing point and after a time delay Environmental vibration wave field data; This represents the time length used to calculate the cross-correlation function; Represents a time variable; Represents the time variable From 0 to Perform integration.

[0018] Preferably, the wavefield frequency modulation module is configured with a set of narrowband-pass digital filters, each with a different center frequency. The set of center frequencies of the narrowband-pass digital filters constitutes the set of scanning frequencies. The set of narrowband-pass digital filters is used to isolate the frequency components corresponding to the center frequencies from the wideband virtual source response, thereby generating the set of narrowband virtual source responses.

[0019] In one specific embodiment, the reverse-time focusing imaging module includes a storage unit and a computing unit, wherein the storage unit is used to store a preset acoustic velocity model of the subsurface medium. ,in This represents the spatial location vector. The computing unit is used to perform the reverse propagation calculation based on the acoustic velocity model of the underground medium by solving the acoustic wave equation, which is:

[0020] ;

[0021] in: Represents a vector in space and time Pressure wave field; Represents a vector in space Acoustic velocity of the underground medium at that location; Represents the Laplace operator; Represents time The second-order partial derivatives of .

[0022] Furthermore, the computing unit uses the wavefield energy calculated at time zero by the backpropagation as the imaging condition to generate the single-frequency energy focusing map. The formula for calculating the imaging conditions is:

[0023] ;

[0024] in: Represents the scanning frequency Below, in spatial position vector The energy value of the single-frequency energy focusing image at that location; Represents the corresponding scan frequency The pressure wave field at time zero in the reverse direction In spatial position vector The amplitude at that point; This represents the operation of squaring the magnitude.

[0025] Preferably, the resonance characteristic analysis module constructs the resonance spectrum by determining the functional correspondence between the focusing energy and the scanning frequency.

[0026] In one specific embodiment, the resonance characteristic analysis module further determines the physical properties of the geological defect by analyzing at least one of the peak frequency, peak intensity, and quality factor determined by the function correspondence.

[0027] In one embodiment, it also includes:

[0028] A data preprocessing module is provided, which is located between the data acquisition module and the virtual source reconstruction module, for performing bandpass filtering and amplitude normalization on the environmental vibration wave field data.

[0029] In one embodiment, it also includes:

[0030] A three-dimensional visualization module is used to fuse the location information of the defect area determined by the reverse time focusing imaging module with the physical properties analyzed by the resonance characteristic analysis module, thereby generating a three-dimensional attribute label map of the geological defect.

[0031] This invention provides a radar detection and analysis system for underground concealed engineering geological defects based on fiber optic sensing. It has the following beneficial effects:

[0032] 1. This invention passively collects all-weather environmental vibration wave field data by setting up a distributed sensor network, and uses a virtual source reconstruction module to perform wave field cross-correlation calculations on the data, transforming passive and disordered environmental noise signals into equivalent active virtual source responses that can be used for detection. This completely eliminates the dependence on artificial active seismic sources (such as hammers, explosives, and controllable seismic source vehicles), which not only significantly reduces the implementation cost of detection operations and the interference to the surrounding environment, but also enables long-term, low-cost, and uninterrupted automated monitoring of the detection area.

[0033] 2. This invention employs a reverse-time focusing imaging module. By performing time reversal and reverse propagation numerical calculations on the virtual source response within a preset acoustic velocity model of the underground medium, it utilizes the complete dynamic information of the wave field. This enables precise reconstruction of the propagation, diffraction, and scattering processes of waves in complex underground media, allowing the reverse-propagating energy to be automatically focused on the actual location of geological defects. Compared to traditional ray-based imaging methods that only utilize travel time information, the imaging results of this invention are clearer, the morphology of defects is more finely characterized, and the location is more accurate.

[0034] 3. This invention innovatively sets up a wave field frequency modulation module and a resonance characteristic analysis module, which decomposes the broadband virtual source response into multiple narrow-band single-frequency signals, enabling the system to detect the response of defects at different frequencies; the latter constructs a resonance spectrum reflecting the inherent scattering characteristics of defects by analyzing the energy focusing intensity of the defect region in each single-frequency imaging result. Based on the peak frequency, peak intensity and other characteristics of the resonance spectrum, the physical properties of defects such as size, shape or internal filling can be deduced, thereby distinguishing different types of geological defects such as cavities, water-filled areas or loose bodies, providing richer decision-making basis for the assessment and treatment of engineering hazards. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the detection process of the present invention;

[0036] Figure 2 This is a schematic diagram of the reverse-time focusing imaging process of the present invention. Detailed Implementation

[0037] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Please see the appendix Figure 1 This invention provides a radar detection and analysis system for underground concealed engineering geological defects based on fiber optic sensing, comprising:

[0039] The system includes a distributed sensor network, a data acquisition module, a virtual source reconstruction module, a wave field frequency modulation module, a reverse time focusing imaging module, and a resonance characteristic analysis module.

[0040] In one embodiment, it also includes:

[0041] The system includes a data preprocessing module and a 3D visualization module, with the data preprocessing module positioned between the data acquisition module and the virtual source reconstruction module.

[0042] In the system's workflow, the distributed sensor network is connected to the data acquisition module, the data output of the data acquisition module is connected to the data input of the data preprocessing module, and the data output of the data preprocessing module is connected to the data input of the virtual source reconstruction module.

[0043] The wideband virtual source response generated after processing by the virtual source reconstruction module is used as the input data of the wave field frequency modulation module, while a set of narrowband virtual source responses generated after processing by the wave field frequency modulation module are used as the input data of the reverse time focusing imaging module.

[0044] The set of single-frequency energy focusing images generated after processing by the reverse-time focusing imaging module serves as the input data for the resonance characteristic analysis module. The location information of the defect area determined by the reverse-time focusing imaging module, and the physical properties of the geological defects analyzed by the resonance characteristic analysis module, together serve as the input data for the three-dimensional visualization module.

[0045] In one specific embodiment, the distributed sensor network continuously senses environmental vibrations in the detection area and sends continuous sensing signals to the data acquisition module. The data acquisition module converts the received signals into digital environmental vibration wave field data containing time and spatial dimensions.

[0046] Environmental vibration wave field data is transmitted from the data acquisition module to the data preprocessing module. The data preprocessing module performs bandpass filtering and amplitude normalization on the received data and transmits the processed data to the virtual source reconstruction module.

[0047] The virtual source reconstruction module receives the preprocessed environmental vibration wave field data and generates a broadband virtual source response through wave field cross-correlation calculation. This broadband virtual source response is output as data and transmitted to the wave field frequency modulation module.

[0048] The wave field frequency modulation module receives the wideband virtual source response and processes it using a set of narrowband digital filters to generate a set of narrowband virtual source responses. Each of the narrowband virtual source responses corresponds to a specific scanning frequency. This set of data is transmitted to the reverse time focusing imaging module.

[0049] The reverse-time focusing imaging module receives a set of narrow-band virtual source responses and independently performs time inversion and backpropagation calculations on each response, ultimately generating a set of single-frequency energy focusing maps corresponding to the scanning frequencies. This set of image data is then transmitted to the resonance characteristic analysis module.

[0050] The resonance characteristic analysis module receives a set of single-frequency energy focusing images, identifies geological defect areas from them, extracts the focusing energy values ​​of the areas at different scanning frequencies, constructs the resonance spectrum, and finally analyzes and obtains the physical properties of the geological defects.

[0051] In one embodiment, the spatial location information of the defect area output by the reverse-time focusing imaging module and the physical attribute information of the geological defect output by the resonance characteristic analysis module are used together as input data and transmitted to the three-dimensional visualization module to generate a three-dimensional attribute label map.

[0052] In one specific embodiment, the distributed sensor network includes:

[0053] A standard single-mode communication optical fiber and a distributed acoustic sensor demodulator are used. The data acquisition module is integrated into the distributed acoustic sensor demodulator or works in conjunction with it.

[0054] Standard single-mode communication optical fiber is deployed along the detection area as a passive, continuously distributed sensing element.

[0055] In one embodiment, standard single-mode communication optical fiber can be shallowly buried along the surface of the detection area, or laid in a preset geometric shape such as a grid or linear array, to form effective coverage of the target area.

[0056] One or both ends of a standard single-mode communication fiber are connected to a distributed acoustic sensor demodulator, which operates based on the principle of phase-sensitive optical time-domain reflectometry, injecting highly coherent narrow-linewidth laser pulses into the standard single-mode communication fiber.

[0057] When the wave field generated by the vibration of the external environment propagates to the standard single-mode communication optical fiber, it will cause a small axial strain at the corresponding position of the optical fiber, which will in turn cause a change in the length and refractive index of the optical fiber at that point. This change will modulate the laser pulse propagating in the optical fiber and be reflected in the phase of the backscattered Rayleigh light generated at that point.

[0058] The distributed acoustic sensor demodulator receives the backscattered Rayleigh light signal returned along the entire standard single-mode communication optical fiber through a high-sensitivity photodetector, and performs coherent demodulation on the signal to demodulate the phase change at different positions along the optical fiber. The phase change is proportional to the strain acting on the corresponding position of the optical fiber.

[0059] The data acquisition module samples and digitizes the continuous signal demodulated by the distributed acoustic sensor demodulator at a preset time sampling rate and spatial resolution, ultimately generating an environmental vibration wave field data matrix with time as one dimension and distance along the optical fiber as another. This data matrix serves as the raw input for subsequent analysis and processing.

[0060] In one embodiment, the raw environmental vibration wave field data generated by the data acquisition module is first processed by the data preprocessing module.

[0061] The data preprocessing module receives environmental vibration wave field data and sequentially performs bandpass filtering and amplitude normalization processing on it.

[0062] The bandpass filtering process uses a digital bandpass filter to perform time-domain convolution on the environmental vibration wave field data for each channel. The passband range of the digital bandpass filter is set according to the geological characteristics of the target and the spectral analysis results of the ambient noise. It has a lower cutoff frequency and an upper cutoff frequency to suppress interference signals outside the effective signal frequency band.

[0063] Amplitude normalization is performed after bandpass filtering to balance the signal energy at different sensing locations and time periods, preventing strong amplitude noise from having a dominant influence in subsequent cross-correlation calculations.

[0064] In one embodiment, amplitude normalization employs a single-bit normalization method, the calculation formula of which is:

[0065] ;

[0066] in: Represents the position and time Wavefield data after bandpass filtering; Represents a symbolic function.

[0067] The data preprocessing module will process the data, i.e. The data is then transmitted to the virtual source reconstruction module.

[0068] The virtual source reconstruction module receives environmental vibration wave field data transmitted from the data preprocessing module. The function of the virtual source reconstruction module is to perform wave field cross-correlation calculations to generate a broadband virtual source response characterizing the response of the subsurface medium.

[0069] Wavefield cross-correlation calculation is based on the following principle:

[0070] For a medium surrounded by a uniform, random noise source, cross-correlation calculations are performed on wavefield data recorded at two different receiving points. The result is approximately equal to the Green's function between these two points. The Green's function is described as follows:

[0071] The wave field response that can be recorded at another receiving point when an instantaneous pulse source is applied at one of the receiving points.

[0072] In one specific embodiment, the virtual source reconstruction module selects a location on the distributed sensor network as... The first sensing point and location are The second sensing point, and the time length of the two points. Preprocessed environmental vibration wave field data recorded internally and The cross-correlation operation is performed, and the calculation formula is as follows:

[0073] ;

[0074] in: The cross-correlation function between the first and second sensing points is the broadband virtual source response. Represents a time delay variable; Represents the position The pre-processed environmental vibration wave field data at the first sensing point; Represents the position At the second sensing point, after preprocessing and time delay Environmental vibration wave field data; This represents the time length used to calculate the cross-correlation function; Represents a time variable; Represents the time variable From 0 to Perform integration.

[0075] Through calculation, the equivalent at position was obtained. A virtual seismic source is generated at the location, and at the location The earthquake records received at the location are the broadband virtual source response. The virtual source reconstruction module will generate the broadband virtual source response. Transmitted to the wave field frequency modulation module.

[0076] In one specific embodiment, the wave field frequency modulation module is configured with a set of narrowband pass digital filters, each with a different center frequency.

[0077] First, based on the expected resonant frequency range of the geological defects to be detected, a discrete set of scanning frequencies is pre-defined. Subsequently, for each scan frequency in this set... Design a corresponding narrowband pass digital filter. The passband center frequency of the filter is... .

[0078] The wave field frequency modulation module receives the same wideband virtual source response and convolves it with each of a set of narrowband digital filters to isolate the frequency components corresponding to each center frequency. For the first... The formula for calculating the narrowband virtual source response for each scanning frequency is:

[0079] ;

[0080] in: Represents the corresponding number Narrow-band virtual source response at a scanning frequency; A broadband virtual source response representing the input; The center frequency is represented by The impulse response function of a narrowband pass digital filter; Represents the convolution operator; Represents a time delay variable; This represents the integration operation performed on a function over its entire domain, from negative infinity to positive infinity.

[0081] By performing the above operations on all scanning frequencies, the wavefield frequency modulation module ultimately generates a set of narrowband virtual source responses. This set of data is transmitted as a whole to the reverse-time focusing imaging module for subsequent imaging calculations.

[0082] See attached document Figure 2 The reverse-time focusing imaging module receives a set of narrowband virtual source responses transmitted from the wave field frequency modulation module. The function of the reverse-time focusing imaging module is to independently perform time reversal and backpropagation calculations on the response of each narrow-band virtual source in the group, thereby generating a set of single-frequency energy focusing maps corresponding to the scanning frequencies.

[0083] In one specific embodiment, the reverse-time focusing imaging module includes a storage unit and a computing unit. The storage unit stores a preset two-dimensional or three-dimensional acoustic velocity model characterizing the wave velocity distribution of the subsurface medium in the detection area. The acoustic velocity model is a necessary basis for performing reverse propagation calculations. Narrowband virtual source response The computing unit performs the following steps:

[0084] First, response to narrowband virtual source Perform a time reversal operation to obtain the time-reversed signal. .

[0085] Secondly, the time-reversed signal As a wave field source, it is applied to the corresponding position of the sensor in the acoustic velocity model, and the reverse propagation calculation of the wave field is performed by solving the acoustic wave equation based on the acoustic velocity model.

[0086] In one embodiment, the acoustic wave equation is:

[0087] ;

[0088] in: Represents a vector in space and time Pressure wave field; Represents a vector in space Acoustic velocity of the underground medium at that location; Represents the Laplace operator; Represents time The second-order partial derivatives of .

[0089] The backpropagation calculation starts from the maximum inversion time and proceeds backward to time zero using discrete time steps. At time zero, the backpropagated wave field energy is focused at the location of the scatterer (i.e., geological defect) in the subsurface medium.

[0090] Finally, the wave field energy at time zero is used as the imaging condition to generate the corresponding scanning frequency. The single-frequency energy focusing pattern. Its calculation formula is:

[0091] ;

[0092] in: Represents the scanning frequency Below, in spatial position vector The energy value of the single-frequency energy focusing image at that location; Represents the corresponding scan frequency The pressure wave field at time zero in the reverse direction In spatial position vector The amplitude at that point; This represents the operation of squaring the magnitude.

[0093] The reverse-time focusing imaging module repeats the above calculations for all narrow-band virtual source responses, ultimately obtaining a set of single-frequency energy focusing maps. This set of image data is transmitted to the resonance characteristic analysis module.

[0094] The resonance characteristic analysis module receives a set of single-frequency energy focusing images transmitted from the reverse-time focusing imaging module. .

[0095] The functions of the resonance characteristic analysis module include:

[0096] First, the spatial regions corresponding to geological defects were identified in this set of single-frequency energy focusing images;

[0097] Then, the focusing energy of the region at different scanning frequencies is extracted, and a resonant spectrum reflecting the scattering characteristics of the region is constructed.

[0098] Finally, the physical properties of the geological defects were analyzed based on this resonance spectrum.

[0099] In one specific embodiment, the resonance characteristic analysis module performs the following steps:

[0100] A total energy map is generated by overlaying and averaging a set of single-frequency energy focusing maps. Connected regions with energy values ​​exceeding a preset threshold are identified on this total energy map; these regions are designated as geological defect areas and denoted as [missing information]. .

[0101] For each scan frequency Calculate the geological defect area Total focused energy within The calculation formula is:

[0102] ;

[0103] in: Represents the scanning frequency Below, geological defect area Total focused energy within;

[0104] To the scanning frequency Below, in spatial position vector The energy value of the single-frequency energy focusing image at that location; Representative in the region Integrating the space within the space.

[0105] Based on a series of calculated data points The resonant spectrum is constructed. The resonant spectrum is plotted with the scanning frequency on the x-axis and the total focusing energy on the y-axis.

[0106] Analyze the resonance spectrum and extract at least one spectral characteristic parameter.

[0107] In one embodiment, the extracted parameters include:

[0108] Peak frequency Peak intensity is the frequency at which the total focused energy reaches its maximum value. That is, the maximum value of the total focused energy; and the quality factor. Quality factor The calculation formula is:

[0109] ;

[0110] in: Full width at half maximum (FWHM) represents the peak value of the resonant spectrum.

[0111] The physical properties of geological defects are determined based on at least one extracted spectral feature parameter. For example, peak frequency. Peak intensity is related to the geometry of the geological defect. The quality factor is related to the difference in wave impedance between geological defects and the surrounding rock. Related to the energy dissipation characteristics of the filling medium inside geological defects, the type of geological defect, such as cavity, water-filled area or loose body, can be determined by comparing these parameters with a pre-set physical model database.

[0112] The 3D visualization module is used to integrate and graphically display the detection and analysis results.

[0113] The 3D visualization module receives two types of data as input: the first type is the spatial coordinates and boundary information of the geological defect area determined by the reverse time focusing imaging module and the resonance characteristic analysis module; the second type is the physical properties of the geological defects corresponding to the spatial area analyzed by the resonance characteristic analysis module.

[0114] In one specific embodiment, the 3D visualization module performs the following steps:

[0115] First, the 3D visualization module performs 3D geometric modeling on the spatial coordinates and boundary information of the received geological defect area in a unified 3D coordinate system, generating one or more 3D spatial objects representing geological defects.

[0116] Secondly, the 3D visualization module associates the received physical properties of geological defects (e.g., cavities, water-filled areas, or loose bodies) with the corresponding 3D spatial objects.

[0117] Then, the 3D visualization module performs 3D rendering operations, using preset visual encoding rules to display physical properties during the rendering process.

[0118] For example, different colors can be assigned to different types of physical properties (e.g., red for voids, blue for water-filled areas), or text labels can be displayed next to the corresponding 3D spatial objects.

[0119] In one embodiment, the 3D visualization module can also simultaneously render the 3D spatial path of the standard single-mode communication fiber in the distributed sensor network, thereby providing a geometric reference for the detection layout.

[0120] Finally, the 3D visualization module outputs a 3D attribute label map, which simultaneously displays the location, shape, and physical properties of geological defects in the underground space within the same 3D view.

Claims

1. A radar detection and analysis system for geological defects of underground concealed works based on optical fiber sensing, characterized in that, The method comprises the following steps: a distributed sensing network is laid along a detection area to sense a wave field generated by environmental vibration; a data acquisition module is used to acquire the wave field sensed by the distributed sensing network to form environmental vibration wave field data; a virtual source reconstruction module is used to perform wave field cross-correlation operation on the environmental vibration wave field data to obtain wideband virtual source response representing response of underground medium; a wave field frequency modulation module is used to filter the wideband virtual source response to generate a set of narrowband virtual source responses each corresponding to a scanning frequency; a reverse-time focusing imaging module is used to perform time reversal and reverse propagation calculation on each of the set of narrowband virtual source responses to generate a set of single-frequency energy focusing images each corresponding to the scanning frequency; a resonance characteristic analysis module is used to identify a defect area in the set of single-frequency energy focusing images and extract focusing energy of the defect area at different scanning frequencies to construct a resonance spectrum reflecting scattering characteristics of the defect area and analyze physical properties of the geological defect based on the resonance spectrum. The resonance characteristic analysis module determines a functional correspondence between the focusing energy and the scanning frequency to construct the resonance spectrum, and further determines the physical properties of the geological defect by analyzing at least one of a peak frequency, a peak intensity and a quality factor determined by the functional correspondence.

2. The optical fiber sensing based radar probing analysis system for geological defects of underground concealed works according to claim 1, characterized in that, The distributed sensing network comprises: a standard single-mode communication optical fiber laid along the detection area; a distributed acoustic sensing demodulator connected to the standard single-mode communication optical fiber; wherein the standard single-mode communication optical fiber is used to sense the wave field, and the distributed acoustic sensing demodulator is used to convert signals sensed by the standard single-mode communication optical fiber into the environmental vibration wave field data. 3.The optical fiber sensing based underground concealed engineering geological defect radar detection and analysis system according to claim 1, characterized in that, The virtual source reconstruction module obtains the wideband virtual source response by calculating a cross-correlation function of environmental vibration wave field data collected by a first sensing point and a second sensing point on the distributed sensing network within a period of time.

4. The optical fiber sensing based radar probing analysis system for geological defects of underground concealed works according to claim 1, characterized in that, The wave field frequency modulation module is configured with a set of narrowband digital filters each having a different center frequency, and a set of center frequencies of the set of narrowband digital filters constitutes a set of scanning frequencies; The set of narrowband digital filters is used to isolate frequency components corresponding to the center frequencies from the wideband virtual source response to generate the set of narrowband virtual source responses.

5. The optical fiber sensing based radar probing analysis system for geological defects of underground concealed works according to claim 1, characterized in that, The reverse-time focusing imaging module comprises: a storage unit used to store a preset underground medium acoustic velocity model; a calculation unit used to perform the reverse propagation calculation by solving an acoustic wave equation based on the underground medium acoustic velocity model.

6. The optical fiber sensing based radar probing analysis system for geological defects of underground concealed works according to claim 5, characterized in that, The calculation unit uses wave field energy at zero time of the reverse propagation calculation as an imaging condition to generate the single-frequency energy focusing image.

7. The optical fiber sensing based radar probing analysis system for geological defects of underground concealed works according to claim 1, characterized in that, Further comprising: A data preprocessing module is arranged between the data acquisition module and the virtual source reconstruction module, and is used for performing band-pass filtering and amplitude normalization processing on the environmental vibration wave field data. 8.The optical fiber sensing based underground engineering geological defect radar probing and analyzing system according to claim 1, characterized in that, Further comprising: A three-dimensional visualization module is used for fusing the position information of the defect area determined by the reverse time focusing imaging module and the physical properties analyzed by the resonance characteristic analysis module, and further generating a three-dimensional attribute label map of the geological defect.

Images