A real-time three-dimensional imaging ground collapse detection and early warning scheme

Abstract

The application discloses a ground collapse detection and early warning scheme based on real-time three-dimensional imaging, and a method thereof comprises the following steps: acquiring background noise data of a target underground structure through a seismometer array installed at a target underground structure detection site; then, surface wave extraction and dispersion curve calculation are carried out based on the background noise data measured by the seismometer; imaging of the underground three-dimensional structure is carried out through a seismic interference imaging technology, the change of the wave velocity of the underground medium is monitored, so that the abnormal area of the velocity structure is identified and positioned, and a reliable basis is provided for identifying the precursor information of the ground collapse. The technical scheme provided by the application can realize real-time monitoring and early warning of the ground collapse risk which is prone to occur in cities in the field of city safety risk monitoring, early warning and management and control, improve the city ground collapse early warning and emergency disposal capacity, and greatly reduce the collapse risk hidden danger and guarantee the safety of people's lives and property.

Description

Technical Field

[0001] This invention relates to the field of ground subsidence detection and early warning technology, and in particular to a ground subsidence detection and early warning method and system based on real-time three-dimensional imaging. Background Technology

[0002] Ground subsidence is a phenomenon in which surface rock and soil collapse downwards due to natural factors or human engineering activities, forming a landslide on the ground. Unlike earthquakes and dangerous slopes, ground subsidence is the destruction and imbalance of the stability of underground rock and soil masses. Before it causes a surface collapse, its development process takes place inside the rock and soil mass, and the surface precursors are subtle and difficult to detect, making the timing and location of ground subsidence highly concealed. Ground subsidence is mainly caused by damage to urban infrastructure (such as pipelines and culverts) and improper construction, exhibiting human factors. Secondly, rainwater erosion is an important triggering factor for ground subsidence accidents. Cities with rainy seasons may experience ground subsidence every year, exhibiting a cyclical characteristic. After repeated damage to the overburden soil, when the collapse force exceeds a certain critical state, a subsidence will occur, exhibiting the characteristics of suddenness and difficulty in prevention. Ground subsidence often does not exist in isolation but occurs in a series, forming a planar distribution of subsidence clusters, exhibiting characteristics of cluster occurrence. The seven main causes of ground subsidence and related typical accidents are as follows: (1) leakage or rupture of culverted waterways; (2) leakage or rupture of water supply and drainage pipes; (3) ground subsidence caused by settlement and deformation during underground tunnel construction; (4) ground subsidence caused by damage to the foundation pit support during deep foundation pit construction, resulting in pipe rupture and seepage, leading to soil erosion; (5) settlement of soft soil in reclamation areas, where soft soil is prevalent and its compression settlement causes ground deformation, leading to damage and breakage of water supply and drainage pipes, forming underground cavities, and thus causing ground subsidence; (6) engineering quality problems, such as foundation treatment, backfilling construction, pipe material quality, and construction technology in some urban construction projects and municipal engineering projects, which are also causes of ground subsidence; (7) other unreasonable use, such as long-term overloading and unreasonable use of facilities like roads and culverted waterways, leading to ground subsidence and damage to pipes, thus causing ground subsidence. The number of ground subsidence accidents has been increasing year by year, and in recent years, these accidents have continued to occur frequently, happening throughout the country. Ground subsidence accidents are mainly distributed on municipal roads and sidewalks, posing a significant threat to vehicles and pedestrians, and seriously endangering people's lives and property. Therefore, the detection and early warning of underground cavities and ground subsidence can effectively grasp the development process of ground subsidence, promptly report subsidence warning information to relevant management departments, and take preventive measures in subsidence areas, effectively avoiding casualties and economic losses caused by subsidence. To achieve this goal, choosing an economical, efficient, environmentally friendly, and convenient method is crucial.

[0003] Currently, the prevention of ground subsidence accidents mainly relies on regular manual inspections. Traditional methods for detecting ground subsidence include ground-penetrating radar (GPR), active source detection (seismic vehicle or explosive blasting), and coring. However, these traditional methods are costly, environmentally unfriendly, and often involve only one-time detection with poor comparability between multiple detections. Traditional methods cannot provide real-time early warning of ground subsidence and have many drawbacks. For example, GPR has a shallow detection depth and is easily affected by urban environments; active source detection is costly, has a shallow detection depth, and has a significant impact on the surrounding environment; coring is inefficient, costly, and cannot be repeated. Furthermore, ground subsidence accidents are often insidious and sudden, making prevention difficult. To improve the initiative, accuracy, and effectiveness of risk assessment and prevention efforts, it is necessary to adopt other advanced technologies to achieve real-time monitoring and early warning of ground subsidence.

[0004] In the past decade or so, the internationally developed three-dimensional imaging detection method for background noise has provided a new approach for real-time detection of ground subsidence. Background noise (micro-vibration signal sources of parameters such as Earth solid tides, seismic waves, ocean tides, typhoons, volcanic activity, mechanical operation, and human activities) is a continuous and stable natural source containing rich information about the subsurface medium. Using background noise imaging technology, we can gain insight into the subsurface velocity structure of the study area, making it an effective method for detecting and imaging subsurface faults, Earth structures, and seafloor structures. Currently, this method is mainly applied to geophysical observation and research in deep earth and deep sea areas, and rarely applied to the detection of shallow soil structures in urban areas. Moreover, most existing related algorithms do not have real-time analysis capabilities. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] Traditional ground subsidence detection methods are costly and mostly involve one-time detection, which presents challenges such as difficulty in real-time monitoring and early warning.

[0007] (II) Technical Solution

[0008] To overcome the shortcomings of existing technologies, this invention aims to achieve real-time monitoring and early warning of the risk of frequent and prone ground subsidence in cities in the field of urban safety risk monitoring, early warning and control, thereby improving the city's ground subsidence early warning and emergency response capabilities and mitigating the risk of subsidence.

[0009] A real-time 3D imaging method for detecting and warning of ground subsidence is a new technology based on background noise for rapid imaging, precise positioning, and real-time warning of subsidence areas at low cost. Its key feature is that it records background noise using a seismograph array, calculates and superimposes the Green's function through cross-correlation, and then uses the Green's function to invert the 3D shear velocity structure underground. This yields high-resolution real-time 3D imaging results and sediment layer thickness of the shallow underground layer (within 100 meters) in urban areas. This method can effectively detect developing underground cavities or risk factors leading to ground subsidence and provide timely risk warnings. Specific steps include:

[0010] S1: Real-time background noise data of the target underground structure is acquired by a seismograph array installed at the measuring points of the target underground structure;

[0011] S2: By repeatedly performing real-time cross-correlation calculations on the background noise signals recorded by two or more seismograph arrays, surface wave extraction is performed on the background noise data to obtain the noise surface wave ellipticity of a single seismograph array.

[0012] S3: Calculate the dispersion curve of the background noise data;

[0013] S4: Based on the wave velocity changes caused by ground subsidence, the three-dimensional velocity structure and sediment layer thickness of the target underground structure are inverted in real time by combining the noise dispersion curve and the noise surface wave ellipticity.

[0014] S5: Based on the three-dimensional velocity structure and sediment layer thickness of the underground structure, identify and locate abnormal areas of the target underground structure in real time, detect the risk of ground subsidence, and issue early warning signals.

[0015] A real-time three-dimensional imaging ground subsidence detection and early warning system, the system being used to execute the real-time three-dimensional imaging ground subsidence detection and early warning method described above, and comprising:

[0016] Based on the cloud platform, a unified data layer, model layer, evaluation algorithm layer, and decision support layer are formed, along with a B / S architecture software system deployed in the cloud. The main modules and their functions are as follows:

[0017] (1) Real-time data acquisition and transmission module: The seismometer used for hole and ground subsidence risk detection performs 24-hour unattended continuous data acquisition, and transmits the monitoring data back to the management in real time via 4G / 5G or dedicated network. The sensor status and related parameters can be remotely viewed and set. When the transmission is interrupted, the data resume function is provided and the on-duty personnel are reminded.

[0018] (2) Data storage and management module: For a large amount of real-time monitoring data from multiple seismometers, a high-performance database with dynamic expansion of storage capacity and dynamic hierarchical management of data is established based on cloud storage technology;

[0019] (3) Data analysis and ground subsidence safety assessment module: assesses the risk of ground subsidence and embeds an analysis algorithm based on real-time three-dimensional imaging for identifying and locating ground subsidence risk points;

[0020] (4) Ground subsidence risk early warning and early warning information sending module: to issue early warnings based on standard limits and ground subsidence assessment results;

[0021] (5) System visualization module: Provides a user-friendly system visualization interface based on B / S architecture.

[0022] (III) Beneficial Effects

[0023] This invention provides a real-time 3D imaging-based ground subsidence detection and early warning scheme, which facilitates the application of real-time online monitoring and early warning technologies and systems for urban ground subsidence. By identifying potential hazards early and taking corresponding engineering measures in advance, subsidence accidents can be nipped in the bud, effectively mitigating the risk of subsidence and safeguarding urban construction. A real-time 3D imaging-based ground subsidence detection and early warning method and system is an important component of smart cities, providing data support for the development and utilization of urban underground space and for understanding the underground environment.

[0024] The advantages of this invention in the field of real-time detection and early warning of ground subsidence are mainly reflected in:

[0025] (1) The data acquisition method is convenient. The array only needs to be buried 30-40cm deep in the ground of the detection area. There is no need to set up a fixed base. It has no impact on the surrounding environment and is easy to carry out monitoring in urban areas.

[0026] (2) Based on background noise, shallow structures within 100 meters can be detected and imaged in real time. The results of long-term detection are highly consistent. By comparing the results of imaging at different times, information on the dynamic changes of underground structures can be obtained.

[0027] Background noise imaging technology utilizes a joint inversion method of dispersion curves and surface wave ellipticity to achieve high-precision rapid imaging of three-dimensional shear wave velocities in underground structures. By monitoring seismic wave velocity disturbances in the underground medium using background noise cross-correlation functions, it enables real-time velocity disturbance imaging of underground spaces within the coverage area of ​​seismic arrays, depicting the location and contour of the disturbed areas, improving the temporal and spatial resolution of imaging, and effectively enabling real-time monitoring and early warning of underground spaces. The ground subsidence identification technology proposed in this invention uses real-time imaging of underground structures based on the background noise of seismic arrays. By detecting disturbances in seismic wave velocity on the order of one-thousandth to one-hundredth caused by changes in the underground medium, it achieves rapid identification and location of subsidence. Attached Figure Description

[0028] Figure 1This is a schematic diagram of a real-time three-dimensional imaging ground collapse detection and early warning method according to an embodiment of the present invention;

[0029] Figure 2 This is a flowchart of a real-time three-dimensional imaging ground collapse detection and early warning method according to an embodiment of the present invention;

[0030] Figure 3 This is a schematic diagram illustrating an application example of a real-time three-dimensional imaging ground collapse detection and early warning scheme in an embodiment of the present invention;

[0031] Figure 4 This is a schematic diagram illustrating the implementation of a real-time three-dimensional imaging ground collapse detection and early warning system in an embodiment of the present invention;

[0032] Figure 5 This is a software system architecture diagram of a real-time three-dimensional imaging ground collapse detection and early warning system according to an embodiment of the present invention. Detailed Implementation

[0033] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0034] A real-time 3D imaging-based ground subsidence detection and early warning scheme comprises two parts: a method and a system. The system primarily utilizes a seismograph array to record background noise data in real time, followed by surface wave extraction and dispersion curve calculation. By imaging the subsurface 3D structure, changes in the wave velocity of the subsurface medium are monitored, thereby identifying and locating abnormal areas of velocity structure, providing a basis for identifying precursory information of ground subsidence. To obtain high-precision subsurface 3D imaging results, this method utilizes a combined imaging technique of background noise dispersion curves and surface wave ellipticity to invert the subsurface medium velocity structure and sedimentary layer thickness. Based on the wave velocity changes caused by ground subsidence, subsurface structure inversion calculations are performed, enabling early detection of the development of underground cavities and timely identification of ground subsidence risks, as well as the issuance of early warning signals.

[0035] Specifically, such as Figure 2As shown, a real-time 3D imaging method for ground subsidence detection and early warning is provided. Based on the background noise of a seismic array, real-time 3D imaging of underground structures is performed. By detecting disturbances in the seismic wave velocity on the order of one-thousandth to one-hundredth caused by changes in the underground medium, rapid identification and location of subsidence can be achieved. The method involves performing cross-correlation analysis on the background noise signals recorded by two or more seismometers. Assuming the noise source remains constant, when the properties of the underground medium between the two seismometers change, the propagation velocity of vibrations caused by the background noise in the soil layer will also change. Repeated cross-correlation calculations are performed on the background noise data from two or more seismometers. The resulting Green's function shows a time delay in the tailwave signal. Then, through inversion calculations, the disturbance of soil wave velocity can be extracted, ultimately achieving a map of the shallow shear wave velocity structure and the thickness distribution of sedimentary layers. The specific technical route and calculation process are as follows: Figure 1 , Figure 2 The detailed process is as follows:

[0036] Step 1: Extracting the Empirical Green's Function from Background Noise. Assuming the noise sources in space are randomly distributed, cross-correlation calculations of background noise data over a sufficiently long period between two seismometers can yield the empirical Green's function for that pair. When the noise signal in the scattered wave field is randomly distributed, cross-correlation calculations of the noise received at any two measuring points (seismometers) yield the cross-correlation function between the two seismometers. ,Should With seismometer pair Green's function They share some similarities, differing only in amplitude. Taking mode equipartition theory as an example, the principle of extracting the empirical Green's function from the background noise surface wave is explained:

[0037] Internal wave field of any bounded elastic body It can be represented as:

[0038] (1)

[0039] In formula (1), t represents time. Representative mode activation function, Represents position, The eigenfunctions (i.e., modes) of an elastic body. These are its eigenfrequency. Expanding equation (1) reveals that each mode... The amplitude and time are completely random and uncorrelated variables, that is:

[0040] < (2)

[0041] In formula (2) The energy density spectrum of the scattered wave field is represented by, where The range of values ​​is <> indicates the averaging operation of measurement data over a sufficient time period. Combining formulas (1) and (2), it can be seen that when time t is sufficiently large, under the premise of averaging over a sufficiently long measurement time, the cross-correlation function of the waveform records at any two uncorrelated measurement points (seismometers) x and y in the scattered wave field is... It can be represented as:

[0042] (3)

[0043] The corresponding theoretical Green's function between x and y for:

[0044] (4)

[0045] Comparing formulas (3) and (4), we can find the cross-correlation function between any two seismometers x and y. and its theoretical Green's function There is only amplitude between them The difference lies in the frequency distribution. In background noise imaging, the dispersion information (velocity and time information) of surface waves is mainly utilized, while the amplitude information is normalized. Therefore, in practical data processing, it is only necessary to perform cross-correlation calculations on sufficiently long noise data between seismometers to obtain the corresponding empirical Green's function.

[0046] Step 2: Rapidly extract the ellipticity of a single noise surface wave. Using polarization analysis, obtain transient Rayleigh surface wave signals from the environmental noise recorded by a single seismometer. Rotate the three-component record (ZNE) to the radial and vertical components (ZR) of the transient noise surface wave, calculate the Rayleigh surface wave elliptic polarizability curve, and use the ellipticity curve to perform a nonlinear Monte Carlo inversion method to construct the three-dimensional velocity structure and overburden thickness distribution of the shallow subsurface. The rapid inversion of the noise surface wave ellipticity, theoretically calculated in the inversion, can be obtained from the following formula:

[0047] (5)

[0048] in and The sum of Rayleigh surface wave energies from 0 to the m-th order can be given by the theoretical noise energy spectra of the vertical and horizontal components:

[0049] (6)

[0050] (7)

[0051] in, h is the soil attenuation coefficient. and These are horizontal and vertical point sources randomly distributed on the Earth's surface. The proportion of point sources. The energy ratio of Rayleigh wave R and Love wave L is obtained. Generally, the stable distribution is within the frequency band of 0.1-5s, and the stable distribution is between 0.4-1.0. and For wave number, and The amplification factor of the medium for Rayleigh and Love waves is denoted by . Let be the ellipticity of the m-th order Rayleigh wave on the Earth's surface.

[0052] Step 3: Joint inversion of shallow shear wave velocity structure using surface wave phase velocity and single-station surface wave ellipticity. The surface wave phase velocity obtained through background noise imaging and the single-station noisy Rayleigh surface wave ellipticity are jointly inverted. The model is divided into soil and bedrock layers. For each layer, three B-spline functions are used for interpolation to invert the shear wave velocity structure of the subsurface medium. The density and P-wave velocity structures are constructed using experimental formulas for soil. Finally, the shallow surface velocity structure is rapidly obtained through nonlinear inversion using a hybrid density neural network.

[0053] Real-time background noise imaging obtains soil wave velocity disturbances by calculating the relative travel time offset of the overlapping portions of two waveforms within the moving-window cross spectrum (MWCS) after cross-correlation calculation and superposition of all seismograph pairs. and These represent the Green's function obtained after cross-correlation calculation and the reference Green's function, respectively, assuming the relative seismic wave velocity of the subsurface medium between any two seismometers ( The wave velocity is uniformly varied with space; in this case, the change in the relative wave velocity between seismometers can be measured. and Relative travel time offset ( To calculate, that is:

[0054] (8)

[0055] The moving window cross-spectral method calculates the relative travel time offset in the frequency domain. First, it... and The time window is divided into many partially overlapping time windows, and then the time offset between the two is calculated sequentially within each time window. At this point, within the corresponding time window... and Cross-correlation spectrum for:

[0056] (9)

[0057] and They represent and The Fourier transform of , where * denotes the complex conjugate operation. It refers to frequency.

[0058] Transforming formula (9) into the relationship between amplitude and phase, we get:

[0059] (10)

[0060] Phase expansion of the cross-correlation spectrum in formula (10) yields:

[0061] , (11)

[0062] From formula (11), we can see that the phase of the cross-correlation spectrum is... With frequency They are linearly related, with a proportionality constant of 2. .

[0063] Therefore, the slope of the phase spectrum of the cross-correlation function can be used to calculate... ,Right now

[0064] (12)

[0065] According to formula (12), each is calculated within a series of small windows. Then, the overall relative travel time offset is obtained through linear fitting. Its measurement error is:

[0066] (13)

[0067] in, Finally, the change in relative velocity is obtained using formula (8). .

[0068] The above method is used for real-time ground collapse detection based on real-time 3D imaging. The specific calculation results are as follows: Figure 3As shown, three-dimensional imaging of underground soil layers can be performed. The imaging result is the three-dimensional shear wave velocity structure of the soil layers. Different colors of shear wave velocity represent soil layers or pores of different materials. By observing the cross-sections and longitudinal sections of the underground three-dimensional structure, different soil layers and pores can be intuitively distinguished. Observing the changes in the three-dimensional shear wave velocity structure over different time periods can reveal the incubation process of underground pores and ground subsidence risks. When the three-dimensional shear wave velocity structure is stable, it means that the soil layer structure is in a stable state and no alarm is needed. When the three-dimensional shear wave velocity structure changes abruptly or continuously, it means that the soil layer structure is in an unstable state and an alarm is needed.

[0069] like Figure 4 As shown in Figure 5, a real-time 3D imaging ground subsidence detection and early warning system is provided. This system utilizes a cloud platform to form a unified data layer, model layer, evaluation algorithm layer, and auxiliary decision-making layer, ultimately creating a cloud-deployed B / S architecture software system. This system enables integrated and efficient management of intelligent monitoring, assessment, early warning, and auxiliary decision-making for underground cavities and ground subsidence risks. When events such as leakage or rupture in culverted waterways, leakage or rupture in water supply and drainage pipes, ground subsidence due to settlement deformation during underground tunnel construction, pipeline rupture and seepage leading to soil erosion due to deep foundation pit construction and damage to foundation pit support, or soft soil settlement in reclaimed areas occur, the system automatically triggers and records event data. The main modules and their functions are as follows:

[0070] (1) Real-time data acquisition and transmission module: The seismometer used for hole and ground subsidence risk detection can perform 24-hour unattended continuous data acquisition. The monitoring data can be transmitted back to the management in real time via 4G / 5G or dedicated network. The status of the sensor and related parameters can be viewed and set remotely. When the transmission is interrupted, the data resume function is provided and the duty personnel are reminded. In addition to using the seismometer to collect background noise, distributed optical fiber can also be used as a sensor. The high sensitivity of optical fiber to strain can be used to measure background noise in real time. The dense distribution of optical fiber can comprehensively record the background noise wave field along the line, providing high spatiotemporal resolution data acquisition for real-time automated ground subsidence detection practice.

[0071] (2) Data storage and management module: For a large amount of real-time monitoring data from multiple seismometers, a high-performance database with dynamically expandable storage capacity and dynamically hierarchical data management is established based on cloud storage technology to solve the problems of limited storage space and low reading efficiency of traditional monitoring data storage;

[0072] (3) Data analysis and ground subsidence safety assessment module: It assesses the risk of ground subsidence and embeds an analysis algorithm based on real-time three-dimensional imaging for identifying and locating ground subsidence risk points. The process does not require manual intervention and achieves automated and efficient ground subsidence risk assessment and automatic alarm.

[0073] (4) Ground subsidence risk warning and warning information sending module: Based on the standard limit and ground subsidence assessment results, the warning will be issued and the warning information can be automatically pushed to the management personnel in various forms such as email, WeChat and SMS as needed;

[0074] (5) System visualization module: Provides a user-friendly system visualization interface based on B / S architecture, realizing dynamic visualization display of structural information management, sensor management, real-time data and analysis results.

[0075] The above is a description of a real-time three-dimensional imaging ground collapse detection and early warning scheme of the present invention, which is used to help understand the present invention; however, the implementation of the present invention is not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the principle of the present invention shall be considered as equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for ground collapse detection and early warning by real-time three-dimensional imaging, characterized in that, The present application is based on the background noise recorded by the seismometer array, and the Green function is obtained by cross-correlation calculation and stacking. Then, the three-dimensional shear velocity structure of the underground is inverted based on the Green function, so as to obtain the real-time three-dimensional imaging result of the shallow layer within 100 meters of the city underground and the thickness of the sedimentary layer, find the risk factors of the developing hole in the underground or the occurrence of ground subsidence, and give timely risk warning. The specific steps include: S1: Real-time acquisition of background noise data of the target underground structure by the seismometer array installed at the target underground structure measuring point; S2: Surface wave extraction of the background noise data by repeatedly performing real-time cross-correlation calculation on the background noise signals recorded by two or more seismometer arrays; S3: Frequency dispersion curve calculation of the background noise data; S4: Real-time inversion of the three-dimensional velocity structure and the thickness of the sedimentary layer of the target underground structure based on the wave velocity change caused by ground subsidence, combined with the noise frequency dispersion curve and the noise surface wave ellipticity; S5: Real-time identification and positioning of the abnormal area of the target underground structure according to the three-dimensional velocity structure and the thickness of the sedimentary layer of the underground structure, finding the risk of ground subsidence and issuing a warning signal; Wherein, the S4 includes: S41: The model is divided into soil layer and bedrock layer, each layer is interpolated by 3 B-spline functions, the shear wave velocity structure of the underground medium is inverted, and the density and P wave velocity structure is constructed by the experimental formula of the soil, and finally the shallow surface velocity structure is quickly obtained by mixed density neural network nonlinear inversion; S42: The real-time imaging of the background noise is obtained by cross-correlation calculation and stacking of all seismometers, and the relative travel time offset of the overlapping part of the two waveforms in the moving time window is calculated by the moving window cross spectrum method to obtain the disturbance of the soil layer wave velocity.

2. A real-time three-dimensional imaging ground collapse detection and warning method according to claim 1, characterized in that, In S1, the acquired background noise data of the target underground structure is preprocessed, and the data preprocessing includes band-pass filtering, time domain normalization and frequency spectrum white noise.

3. A real-time 3D imaging ground collapse detection and warning method as claimed in claim 1, wherein, The S2 includes: S21: Extracting the empirical Green function of the background noise; assuming that the noise source is randomly distributed, the background noise data of a long period between two seismometers is cross-correlated to obtain the empirical Green function between the seismometer pair; wherein the empirical Green function is similar to the theoretical Green function, and the difference lies in the amplitude; S22: Quickly extracting the single noise surface wave ellipticity; the transient Rayleigh surface wave signal is obtained from the ambient noise recorded by a single seismometer by using the polarization analysis method, the three-component record is rotated to the radial and vertical components of the transient noise surface wave, and the Rayleigh surface wave ellipticity curve is calculated; and the nonlinear Monte Carlo inversion method is used to construct the three-dimensional velocity structure of the shallow underground and the thickness distribution of the overburden layer.

4. A real-time 3D imaging ground collapse detection and warning method as claimed in claim 3, characterized in that, In S4: Let and denote the cross-correlation computed Green's function and the reference Green's function, respectively, assuming that the relative seismic wave velocity of the subsurface medium between any two seismometers is spatially uniform, in which case the variation of the relative wave velocity between the seismometers is computed by measuring the relative travel-time shifts of and ​​ （8） The moving window cross-spectrum method is to calculate the relative travel time shift in the frequency domain, first dividing and into a plurality of partially overlapping time windows, and then calculating the time shift of each time window in turn; at this time, the cross-correlation spectrum and of the corresponding time window is ​ （9） and denote the Fourier transform of and denote the Fourier transform of is the frequency; The formula (9) is transformed into the relationship between amplitude and phase: （10） The phase of the cross-correlation spectrum in formula (10) is expanded to: ， （11） According to equation (11), the phase of the cross-correlation spectrum is linearly related to the frequency with a proportionality coefficient of 2 ; Slope calculation from phase spectrum of cross-correlation function i.e. （12） According to equation (12), the relative travel time offset of each small window is calculated Then the overall relative travel time offset is obtained by linear fitting The measurement error is （13） wherein, ; and finally the relative velocity change is obtained from equation (8), i.e. .

5. A real-time 3D imaging ground subsidence detection and warning method according to claim 3, characterized in that, The S21 includes: The internal wave field of an arbitrary bounded elastic body is represented as: （1） t represents time in equation (1), represents the mode excitation function, represents the position, represents the eigenfunction of the elastic body, is its eigenfrequency; after expanding equation (1), the amplitude of each mode is a completely random and mutually independent variable with respect to time, i.e.: < （2） In formula (2) represents the energy density spectrum of the scattered wave field, wherein The value range of ; < > represents the mean operation of the measured data for a sufficient length of time. Combining equations (1) and (2), when time t is large enough, under the premise of meeting the sufficient long measurement time to take the average, the cross-correlation function of the waveform records at any two uncorrelated measurement points x and y in the scattered wave field is represented as: （3） the corresponding theoretical Green function between x and y is: （4） According to equations (3) and (4), the cross-correlation function between any two seismometers x and y only differs in amplitude between the theoretical Green's function and its theoretical Green's function 6. A real-time three-dimensional imaging method for ground collapse detection and warning as claimed in claim 3, wherein, The S22 includes: The noise Rayleigh surface wave ellipticity is quickly inverted, and the theoretical calculation of the noise Rayleigh surface wave ellipticity is obtained by the following formula: （5） wherein and are the Rayleigh surface wave energy from 0 to mth order and are given by the theoretical noise energy spectrum of the vertical and horizontal components: （6） （7） wherein h is the attenuation coefficient of the soil, and is a point source randomly distributed on the surface horizontally and vertically, is the proportion of point sources; is obtained by the energy ratio of Rayleigh wave R and Love wave L, wherein is stable in the frequency band range of 0.2-10 Hz, and is between 0.4-1.0; is the amplification coefficient of the medium to the Rayleigh wave, is the wave number, is the ellipticity of the mth order Rayleigh wave on the surface.

7. A real-time 3D imaging method for ground collapse detection and warning according to any of claims 1 to 6, characterized in that, The S5 includes: S51: three-dimensional imaging of the underground soil layer, wherein the result of the imaging is a three-dimensional shear wave velocity structure of the soil layer, and different colors of shear wave velocity represent different material soil layers or cavities; S52: by observing the cross section and longitudinal section of the underground three-dimensional structure, different soil layers and cavities can be directly distinguished, and by observing the change of the three-dimensional shear wave velocity structure in different time periods, the incubation process of underground cavities and ground collapse risks can be found; wherein, when the three-dimensional shear wave velocity structure is stable and unchanged, it represents that the soil layer structure is in a stable state, and no alarm is needed; when the three-dimensional shear wave velocity structure has a sudden change or a continuous change, it represents that the soil layer structure is in an unstable state, and an alarm is needed.

8. A ground subsidence detection and warning system using real-time three-dimensional imaging, characterized by, The system is used to perform a real-time three-dimensional imaging ground collapse detection and early warning method according to any one of claims 1 to 6, and comprises: A unified data layer, model layer, evaluation algorithm layer and auxiliary decision layer based on a cloud platform, and a B / S architecture software system deployed on the cloud, the main modules and their functions are as follows: (1) Data real-time acquisition and transmission module: the hole and ground collapse risk detection seismometer is used for 24-hour unattended continuous data acquisition, the monitoring data is transmitted back to the management party in real time through 4G / 5G or a special network, the state and related parameters of the sensor are remotely viewed and set, data continuation function is provided when transmission is interrupted, and the on-duty personnel are reminded to pay attention; (2) Data storage and management module: for a large amount of real-time monitoring data of multiple seismometers, a high-performance database with dynamic expansion of storage capacity and dynamic hierarchical management of data is established based on cloud storage technology; (3) Data analysis and ground collapse safety evaluation module: for ground collapse risk evaluation, embedded analysis algorithm for ground collapse risk point identification and positioning based on real-time three-dimensional imaging; (4) Ground collapse risk early warning and early warning information sending module: early warning according to specification standard limit value and ground collapse evaluation result; (5) System visualization module: provides a user-friendly system visualization interface based on B / S architecture.

9. A real-time three-dimensional imaging ground subsidence detection and warning system as claimed in claim 8, characterized in that, In addition to collecting background noise with a seismometer, a distributed optical fiber is also used as a sensor to measure background noise in real time by using the high sensitivity of the optical fiber to strain. The dense distribution of optical fibers can comprehensively record the background noise wave field along the line, providing high spatiotemporal resolution data acquisition for underground detection practice.

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