Ultrahigh-density background noise deeply-buried pipeline imaging method, device and equipment

Abstract

The invention relates to an ultra-high-density background noise deeply-buried pipeline imaging method, device and equipment. The method comprises the following steps: acquiring ultra-high density background noise data and active source detection data; extracting Loff wave signal data and Rayleigh wave signal data based on the ultra-high density background noise data and the active source detection data, and obtaining a Loff wave empirical Green function and a Rayleigh wave empirical Green function; performing frequency-wavenumber domain analysis on the Loff wave empirical Green function and the Rayleigh wave empirical Green function to generate a Loff wave frequency dispersion curve and a Rayleigh wave frequency dispersion curve; and based on the Loff wave frequency dispersion curve and the Rayleigh wave frequency dispersion curve, using a conjugate gradient method combined with an inversion algorithm to calculate and generate deeply buried pipeline imaging result data of each sensor of the detection matrix sensor. By adopting the method, ultrahigh-density background noise data and active source detection data can be effectively utilized, the imaging precision and reliability of the deeply-buried pipeline are improved, and clearer and more accurate results are provided for detection of the deeply-buried pipeline.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of engineering detection technology, and particularly relates to a method, device and equipment for imaging a deep-buried pipeline with ultrahigh density background noise. BACKGROUND

[0002] The deep-buried pipeline is generally formed by the pipe-jacking construction technology in trenchless construction. Such a pipeline has the following characteristics: 1. large burial depth, generally more than 3 meters; 2. weak electromagnetic induction signals, such as a deep-buried precast concrete pipeline formed by a small amount of reinforced concrete pouring without obvious electromagnetic signals, or a steel pipeline with too great depth and distance, so that the signal is often weak when directly detected by a conventional pipeline detector; and 3. the pipeline has a certain deviation or bending due to the characteristics of the pipe-jacking construction technology. With the development of engineering detection technology, underground pipeline detection technology has emerged, which is of great significance to ensure the safety of urban infrastructure and the smooth progress of engineering construction. In the traditional technology, the position of the underground pipeline is detected by pipeline detectors, ground penetrating radars, high-density electrical methods, etc. to analyze and determine the approximate position of the underground pipeline, which has a great safety hazard.

[0003] The current traditional method has many problems: 1. the detection depth of the pipeline detector is generally suitable for metal pipelines with a depth of less than 3 meters; 2. the ground penetrating radar method is greatly affected by environmental interference, and the detection depth is reduced in areas with high water levels, generally less than 5 meters; 3. the magnetic gradient method is greatly affected by environmental interference and needs to be punched, which is high in cost and low in efficiency, and is only suitable for metal pipelines; 4. the charging method is only suitable for metal pipelines and needs to have a dew point; 5. the shallow seismic method is easy to miss when the pipe diameter is small, and the effect is poor when there is a thick roadbed on the surface; 6. the borehole radar needs to be punched, which is high in cost; and the inertial gyroscope is suitable for non-closed buried pipes, and is not suitable for existing operating pipelines, and is difficult to position and high in cost when the pipe opening is in a deep well. In addition, the current method for detecting the position or depth of a buried object based on Rayleigh wave mainly uses artificial source Rayleigh wave exploration, which needs a shocker, a vibration pickup or a Rayleigh wave seismic exploration device. When the artificial source excites the Rayleigh wave, environmental noise vibration will affect the collection of the artificial source Rayleigh wave signal, so that the artificial source Rayleigh wave exploration cannot be accurately implemented in a site environment with great interference, such as large noise vibration.

[0004] In the prior art with the authorization announcement No. CN108924651B, a method for identifying pipeline position based on micro-motion information is disclosed. The method does not need to artificially excite Rayleigh wave signals, has low requirements for exploration environment, can normally detect the position of deep buried pipelines under general environmental conditions, has no requirements for pipeline materials, and can realize the detection of metal pipelines and non-metal pipelines. However, the method only relies on Rayleigh wave signals in natural noise for analysis, resulting in incomplete acquisition of underground medium information, especially under complex geological conditions, due to the lack of auxiliary analysis of Love wave signals, which will affect the comprehensiveness and accuracy of velocity structure inversion. Therefore, the method still has defects such as single signal type, weak inversion algorithm performance, low data collection density, and simple data processing method. SUMMARY

[0005] Therefore, it is necessary to provide an ultra-high density background noise deep buried pipeline imaging method, device and equipment capable of improving data inversion accuracy and environmental adaptability through multi-signal fusion, joint inversion and high-density data collection.

[0006] In a first aspect, the present application provides an ultra-high density background noise deep buried pipeline imaging method, comprising:

[0007] Obtaining original sensing data collected by a detection matrix sensor, and preprocessing the original sensing data to obtain ultra-high density background noise data and active source detection data;

[0008] Extracting Love wave signal data and Rayleigh wave signal data based on the ultra-high density background noise data and the active source detection data corresponding to each sensor in the detection matrix sensor, and respectively performing cross-correlation calculation on the Love wave signal data and the Rayleigh wave signal data to obtain Love wave empirical Green function and Rayleigh wave empirical Green function;

[0009] Performing frequency-wavenumber domain analysis on the Love wave empirical Green function and the Rayleigh wave empirical Green function to generate Love wave dispersion curves and Rayleigh wave dispersion curves;

[0010] Calculating and constructing the velocity structure of the detection area of the detection matrix sensor based on the Love wave dispersion curves and the Rayleigh wave dispersion curves using a conjugate gradient joint inversion algorithm to generate deep buried pipeline imaging result data of each sensor of the detection matrix sensor.

[0011] In one embodiment, the Love wave dispersion curves and the Rayleigh wave dispersion curves are calculated and constructed based on the velocity structure of the detection area of the detection matrix sensor using a conjugate gradient joint inversion algorithm to generate deep buried pipeline imaging result data of each sensor of the detection matrix sensor, comprising:

[0012] An initial conjugate gradient method joint inversion model is initialized to obtain initial parameter data of the conjugate gradient method joint inversion model parameters of the detection region;

[0013] Based on the Love wave dispersion curve and the Rayleigh wave dispersion curve, a conjugate gradient method is used to combine a Love wave-Rayleigh wave joint objective function, a Love wave-Rayleigh wave joint preconditioning operator and a Love wave-Rayleigh wave joint gradient operator to iteratively update the conjugate gradient method joint inversion model parameters;

[0014] If the iteration end condition is met, the velocity structure of the detection region is constructed based on the conjugate gradient method joint inversion model parameters to generate the deep buried pipeline imaging result data.

[0015] In one embodiment, the expression of the conjugate gradient method combined with the Love wave-Rayleigh wave joint objective function is as follows:

[0016]

[0017] In the formula, f is the frequency of the dispersion curve at different frequency points, is the conjugate gradient method combined with the Love wave-Rayleigh wave joint objective function, α L , α R and γ are respectively the Love wave residual weight coefficient, the Rayleigh wave residual weight coefficient and the cross gradient constraint weight coefficient, and Φ C are respectively the Love wave residual term, the Rayleigh wave residual term and the cross gradient constraint term, f is the frequency of the dispersion curve at different frequency points, is the conjugate gradient method joint inversion model parameter of the kth iteration, is the observed Love wave phase velocity at frequency f, is the Love wave phase velocity at frequency f based on the conjugate gradient method joint inversion model parameter of the kth iteration, J is the total number of orders of Rayleigh waves, ω j is the weight coefficient of the jth order Rayleigh wave, is the observed Rayleigh wave phase velocity of the jth order Rayleigh wave at frequency f, is the Rayleigh wave phase velocity of the jth order Rayleigh wave at frequency f based on the conjugate gradient method joint inversion model parameter of the kth iteration, Ω is the detection region, is the S-wave velocity based on the Rayleigh wave dispersion curve, is the S-wave velocity based on the Love wave dispersion curve, ρ is the horizontal direction and z is the vertical depth direction.

[0018] In one embodiment, the expression of the Love wave-Rayleigh wave joint preconditioning operator corresponding to the conjugate gradient method joint inversion model parameter of the kth iteration is as follows:

[0019]

[0020] The conjugate gradient method of the kth iteration is combined with the expression of the Love-Rayleigh joint gradient scalar operator corresponding to the model parameter:

[0021]

[0022] The conjugate gradient method of the kth iteration is combined with the expression of the Love-Rayleigh joint gradient direction operator corresponding to the model parameter:

[0023]

[0024] In the formula, λ R , λ L and λ δ are respectively a Rayleigh wave Jacobian weight coefficient, a Love wave Jacobian weight coefficient and a regularization coefficient, and are respectively a Rayleigh wave Jacobian matrix, a Love wave Jacobian matrix and a unit matrix, is the gradient matrix of the conjugate gradient method combined with the Love-Rayleigh joint objective function corresponding to the model parameter of the kth iteration of the conjugate gradient method.

[0025] In one of the embodiments, the Love wave signal data includes noise Love wave signal data, active Love wave signal data and comprehensive calibration Love wave signal data, the Rayleigh wave signal data includes noise Rayleigh wave signal data, active Rayleigh wave signal data and comprehensive calibration Rayleigh wave signal data, the detection matrix sensor includes a center sensor and a peripheral sensor, the Love wave signal data and the Rayleigh wave signal data are extracted based on the ultra-high density background noise data and the active source detection data corresponding to each sensor in the detection matrix sensor, and the cross-correlation calculation is performed on the Love wave signal data and the Rayleigh wave signal data respectively to obtain the Love wave empirical Green function and the Rayleigh wave empirical Green function, comprising:

[0026] The noise Rayleigh wave signal data is generated based on the ultra-high density background noise data of each peripheral sensor in the detection matrix sensor in the tangential direction of the circumference of the center sensor as the center and the peripheral sensor as a point on the circumference, and the noise Love wave signal data is generated based on the ultra-high density background noise data of each sensor in the detection matrix sensor in the vertical direction of the ground.

[0027] The active Rayleigh wave signal data is generated based on the active source detection data of each peripheral sensor in the detection matrix sensor in the tangential direction, and the active Love wave signal data is generated based on the active source detection data of each sensor in the detection matrix sensor in the vertical direction of the ground.

[0028] The comprehensive calibration Love wave signal data is generated according to noise Love wave signal data and active Love wave signal data, and the comprehensive calibration Rayleigh wave signal data is generated according to noise Rayleigh wave signal data and active Rayleigh wave signal data;

[0029] The Love wave empirical Green function and the Rayleigh wave empirical Green function are obtained by respectively performing cross-correlation calculation on the comprehensive calibration Love wave signal data and the comprehensive calibration Rayleigh wave signal data.

[0030] In one of the embodiments, the super-high-density background noise deep pipeline imaging method further comprises:

[0031] The position matching and data calibration are performed on the deep pipeline imaging result data based on the position information of each sensor, so as to obtain calibrated deep pipeline imaging result data;

[0032] The deep pipeline imaging profile is arranged according to the calibrated deep pipeline imaging result data of each sensor, and the deep pipeline imaging profile is used to represent the velocity structure of the detection area.

[0033] The deep pipeline position data of the deep pipeline is identified based on the deep pipeline imaging profile, and the deep pipeline position data comprises horizontal position data and vertical position data.

[0034] In one of the embodiments, the detection matrix sensor adopts a nested triangular array structure, and the midpoint of the edge of the outer triangular shape constructed by the peripheral sensors is the vertex of the inner triangular shape constructed by the peripheral sensors, and each layer of triangular shape constructed by the peripheral sensors is an equilateral triangle with the center sensor as the center.

[0035] In one of the embodiments, the detection matrix sensor adopts a concentric multi-circle array structure, and each layer of circle constructed by the peripheral sensors is a concentric circle with the center sensor as the center, and each layer of concentric circle constructed by the peripheral sensors forms a radial sensor layout with the center sensor as the origin and covering three equal angles.

[0036] In a second aspect, the application further provides a super-high-density background noise deep pipeline imaging device, comprising:

[0037] The sensor data acquisition module is configured to acquire original sensor data collected by the detection matrix sensor, and to pre-process the original sensor data to obtain super-high-density background noise data and active source detection data.

[0038] The Green function generation module is configured to extract Love wave signal data and Rayleigh wave signal data based on the super-high-density background noise data and the active source detection data corresponding to each sensor in the detection matrix sensor, and perform cross-correlation calculation on the Love wave signal data and the Rayleigh wave signal data respectively to obtain a Love wave empirical Green function and a Rayleigh wave empirical Green function.

[0039] The dispersion curve generation module is configured to perform frequency-wavenumber domain analysis on the Love wave empirical Green function and the Rayleigh wave empirical Green function to generate a Love wave dispersion curve and a Rayleigh wave dispersion curve.

[0040] The imaging result generation module is configured to calculate and construct a velocity structure of a detection area of the detection matrix sensor based on the Love wave dispersion curve and the Rayleigh wave dispersion curve by using a conjugate gradient method joint inversion algorithm to generate deep buried pipeline imaging result data of each sensor of the detection matrix sensor.

[0041] In a third aspect, the present application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method according to any one of the noise aspects of the present application when executing the computer program.

[0042] The above-mentioned super-high-density background noise deep buried pipeline imaging method, device and equipment can improve spatial resolution and signal-to-noise ratio, and more accurately capture weak signals caused by deep buried pipelines through super-high-density sensor layout and background noise separation technology; can more accurately invert underground velocity structures and improve detection capability of deep buried pipelines, thereby more accurately positioning and identifying deep buried pipelines and improving reliability of inversion results through multi-wave signal separation, cross-correlation calculation and complementary design of multi-wave dispersion curves; can improve inversion efficiency, more quickly and accurately generate deep buried pipeline imaging results, and improve speed and accuracy of deep buried pipeline imaging through design of joint inversion algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the embodiments or the related art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0044] Figure 1 An application environment schematic diagram of a super-high-density background noise deep buried pipeline imaging method provided by an embodiment of the present application;

[0045] Figure 2 A flowchart of a super-high-density background noise deep buried pipeline imaging method provided by an embodiment of the present application;

[0046] Figure 3 Another flowchart of a method for imaging a deep-buried pipeline with ultra-high density background noise is provided for an embodiment of the present application.

[0047] Figure 4 A structural diagram of a detection matrix sensor with a nested triangular array configuration is provided for an embodiment of the present application.

[0048] Figure 5 A structural diagram of a detection matrix sensor with a concentric multi-circle array configuration is provided for an embodiment of the present application.

[0049] Figure 6 A structural diagram of an imaging device for a deep-buried pipeline with ultra-high density background noise is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the objectives, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] The method for imaging a deep-buried pipeline with ultra-high density background noise provided by the embodiments of the present application can be applied in an application environment as shown in Figure 1 . The computing control platform 101 can communicate with the server 104 through a communication channel. The data storage system can store data required to be processed by the server 104. The data storage system can be integrated on the server 104, or can be placed on a cloud or other network server. The server 104 can provide data support and computing power support for the computing control platform 101. The computing control platform 101 can collect sensing data of the deep-buried pipeline through the sensing device 102, and send the sensing data to the computing control platform 101. The computing control platform 101 can calculate and generate positioning data of the deep-buried pipeline based on the sensing data of the deep-buried pipeline collected by the sensing device, and can control the display device 103 to display the positioning data of the deep-buried pipeline. The computing control platform 101 can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things device can be a smart speaker, a smart television, a smart air conditioner, a smart vehicle device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0052] In an exemplary embodiment, as shown in Figure 2 , a method for imaging a deep-buried pipeline with ultra-high density background noise is provided. The method is applied in Figure 1The calculation control platform 101 in the device is taken as an example for illustration, including the following steps S201 to S204. Among them:

[0053] In step S201, the original sensing data collected by the detection matrix sensor is acquired, and the original sensing data is preprocessed to obtain the ultrahigh-density background noise data and the active source detection data.

[0054] Specifically, the calculation control platform 101 can acquire the original sensing data of the detection region collected by the detection matrix sensor composed of a plurality of detection sensors, and preprocess the original sensing data to obtain the ultrahigh-density background noise data and the active source detection data of the detection region.

[0055] Optionally, the ultrahigh-density background noise data can be data collected for a long time without an active vibration source; and the active source detection data can be data collected for a shorter time than the ultrahigh-density background noise data and excited by the active vibration source in the detection region.

[0056] Optionally, the preprocessing can include, but is not limited to, de-instrument response, de-meaning, de-linear trend, band-pass filtering, data segmentation and interception, time domain normalization and frequency domain spectrum whitening.

[0057] In step S202, the Love wave signal data and the Rayleigh wave signal data are extracted based on the ultrahigh-density background noise data and the active source detection data corresponding to each sensor in the detection matrix sensor, and the cross-correlation calculation is performed on the Love wave signal data and the Rayleigh wave signal data respectively to obtain the Love wave empirical Green function and the Rayleigh wave empirical Green function.

[0058] Specifically, the calculation control platform 101 can extract the Love wave data based on the data records in the tangential direction of the detector, extract the Rayleigh wave data based on the data records in the vertical direction of the detector, and perform cross-correlation calculation on the Love wave signal data and the Rayleigh wave signal data respectively based on the ultrahigh-density background noise data and the active source detection data of the medium in the detection region corresponding to each sensor in the detection matrix sensor, to obtain the Love wave empirical Green function and the Rayleigh wave empirical Green function.

[0059] Illustratively, the Love wave (Love wave) is a kind of seismic wave propagating near the surface of the earth, which was first proposed by British mathematician A. E. H. Love in 1911, hence the name. It is the interference result of many shear waves (S waves) guided by elastic layer, the particle of Love wave does shear motion in the horizontal direction perpendicular to the wave propagation direction, that is, the ground moves horizontally, and it vibrates in an anticlockwise ellipse in the vertical plane, without vertical component, similar to transverse wave, but the lateral vibration amplitude will decrease with the increase of depth.

[0060] Illustratively, Rayleigh wave, named after the British physicist Rayleigh who first predicted its existence mathematically, is a polarized wave propagating along the free surface of a semi-infinite elastic medium, the particle motion of which traces a counterclockwise ellipse with the major axis perpendicular to the direction of wave propagation and the minor axis parallel to the direction of wave propagation, having components both parallel and perpendicular to the direction of wave propagation, its motion is similar to that of ocean waves, but the particle motion is opposite to that of ocean waves. The propagation speed of Rayleigh wave is between that of transverse wave and longitudinal wave, and in a homogeneous elastic body space, the wave speed is independent of frequency.

[0061] Step S203, frequency-wavenumber domain analysis is performed on the Love wave empirical Green's function and the Rayleigh wave empirical Green's function to generate Love wave dispersion curves and Rayleigh wave dispersion curves.

[0062] Specifically, the calculation control platform 101 can perform frequency-wavenumber domain analysis on the Love wave empirical Green's function and the Rayleigh wave empirical Green's function through Fourier transform to generate Love wave dispersion curves and Rayleigh wave dispersion curves.

[0063] Optionally, the Fourier transform can be, but is not limited to, two-dimensional Fourier transform.

[0064] Step S204, the velocity structure of the detection area of the detection matrix sensor is calculated and constructed based on the Love wave dispersion curves and the Rayleigh wave dispersion curves using the conjugate gradient joint inversion algorithm to generate deep buried pipeline imaging result data of each sensor of the detection matrix sensor.

[0065] Optionally, the calculation control platform 101 can calculate and construct the velocity structure of the detection area of the detection matrix sensor based on the Love wave dispersion curves and the Rayleigh wave dispersion curves using the conjugate gradient joint inversion algorithm, and generate deep buried pipeline imaging result data of each sensor of the detection matrix sensor based on the velocity structure data of the detection area of the detection matrix sensor.

[0066] Illustratively, the deep buried pipeline imaging result data can be used to represent the velocity distribution of the transverse wave in the medium of the detection area.

[0067] In the above method for imaging deep-buried pipelines based on super-high-density background noise, super-high-density background noise data and active source detection data are obtained, and Love wave and Rayleigh wave signals are extracted therefrom, so that underground pipeline information contained in the data can be more meticulously mined, the underground pipeline situation can be more comprehensively and accurately reflected, the position, trend and depth of the deep-buried pipelines can be clearly presented, the imaging precision is improved, and omission and misjudgment are reduced. By using the conjugate gradient method combined with the joint inversion algorithm to construct the velocity structure of the detection region, faster convergence speed and higher stability can be achieved when processing large-scale complex data, the velocity structure of the underground medium can be more accurately calculated, and more reliable deep-buried pipeline imaging result data can be generated, thereby providing more solid data support for subsequent pipeline evaluation, maintenance and management based on the imaging data.

[0068] In an optional embodiment of the present application, referring to Figure 3 , the conjugate gradient method combined with the joint inversion algorithm is used to calculate and construct the velocity structure of the detection region of the detection matrix sensor based on the Love wave dispersion curve and the Rayleigh wave dispersion curve, and deep-buried pipeline imaging result data of each sensor of the detection matrix sensor are generated, including:

[0069] In step S307, the conjugate gradient method combined with the joint inversion model is initialized to obtain initial parameter data of the conjugate gradient method combined with the joint inversion model parameters of the detection region.

[0070] In step S308, the conjugate gradient method combined with the joint inversion model parameters is iteratively updated based on the Love wave dispersion curve and the Rayleigh wave dispersion curve, the Love wave-Rayleigh wave joint objective function, the Love wave-Rayleigh wave joint preprocessing operator and the Love wave-Rayleigh wave joint gradient operator.

[0071] In step S309, if the iteration end condition is met, the velocity structure of the detection region is constructed based on the conjugate gradient method combined with the joint inversion model parameters, and the deep-buried pipeline imaging result data are generated.

[0072] In an optional embodiment of the present application, the expression of the conjugate gradient method combined with the Love wave-Rayleigh wave joint objective function is:

[0073]

[0074] In the formula, is the conjugate gradient method combined with the Love wave-Rayleigh wave joint objective function, α L , α R and γ are respectively the Love wave residual weight coefficient, the Rayleigh wave residual weight coefficient and the cross-gradient constraint weight coefficient, and Φ Crespectively, f is the frequency of the frequency dispersion curve at different frequency points, is the conjugate gradient method joint inversion model parameter of the kth iteration, is the observed Love wave phase velocity at frequency f, is the Love wave phase velocity at frequency f based on the conjugate gradient method joint inversion model parameter of the kth iteration, J is the total number of Rayleigh wave orders, ω j is the weight coefficient of the jth order Rayleigh wave, is the observed Rayleigh wave phase velocity of the jth order Rayleigh wave at frequency f, is the Rayleigh wave phase velocity of the jth order Rayleigh wave at frequency f based on the conjugate gradient method joint inversion model parameter of the kth iteration, Ω is the detection area, is the S-wave velocity based on the Rayleigh wave dispersion curve, is the S-wave velocity based on the Love wave dispersion curve, ρ is the horizontal direction, and z is the vertical depth direction.

[0075] In the above high-density background noise deep buried pipeline imaging method, by constructing a conjugate gradient method joint inversion model, and combining Love wave-Rayleigh wave joint objective function, Love wave-Rayleigh wave joint preprocessing operator and Love wave-Rayleigh wave joint gradient operator for iterative updating, the information of Love wave and Rayleigh wave can be comprehensively analyzed and processed, so that the inversion result can more accurately reflect the true velocity structure of the underground detection area, thereby generating more accurate deep buried pipeline imaging result data, which can effectively reduce the inversion error and improve the imaging accuracy.

[0076] In an optional embodiment of the present application, the Love wave-Rayleigh wave joint gradient operator includes a Love wave-Rayleigh wave joint gradient scalar operator and a Love wave-Rayleigh wave joint gradient direction operator.

[0077] The expression of the Love wave-Rayleigh wave joint preprocessing operator corresponding to the conjugate gradient method joint inversion model parameter of the kth iteration is:

[0078]

[0079] The expression of the Love wave-Rayleigh wave joint gradient scalar operator corresponding to the conjugate gradient method joint inversion model parameter of the kth iteration is:

[0080]

[0081] The expression of the Love wave-Rayleigh wave joint gradient direction operator corresponding to the conjugate gradient method joint inversion model parameter of the kth iteration is:

[0082]

[0083] where λ R , λ L and λ δ are Rayleigh wave, Love wave and regularization coefficients respectively, and are Rayleigh wave, Love wave and identity matrices respectively, is the gradient matrix of the conjugate gradient method combined with the Love wave-Rayleigh wave joint objective function corresponding to the conjugate gradient method joint inversion model parameters of the kth iteration.

[0084] In an optional embodiment of the present application, the Love wave signal data includes noise Love wave signal data, active Love wave signal data and comprehensive calibration Love wave signal data, the Rayleigh wave signal data includes noise Rayleigh wave signal data, active Rayleigh wave signal data and comprehensive calibration Rayleigh wave signal data, and the detection matrix sensor includes a center sensor and a peripheral sensor, please refer to Figure 3 Love wave signal data and Rayleigh wave signal data are extracted based on the ultra-high density background noise data and active source detection data corresponding to each sensor in the detection matrix sensor, and cross-correlation calculation is performed on the Love wave signal data and Rayleigh wave signal data respectively to obtain Love wave empirical Green's function and Rayleigh wave empirical Green's function, including:

[0085] In step S302, noise Rayleigh wave signal data is generated based on the ultra-high density background noise data of each peripheral sensor in the detection matrix sensor in the tangential direction of the circumferential of the circle with the center sensor as the center and the peripheral sensor as a point on the circumference, and noise Love wave signal data is generated based on the ultra-high density background noise data of each sensor in the detection matrix sensor in the vertical direction of the ground.

[0086] In step S303, active Rayleigh wave signal data is generated based on the active source detection data of each peripheral sensor in the detection matrix sensor in the tangential direction, and active Love wave signal data is generated based on the active source detection data of each sensor in the detection matrix sensor in the vertical direction of the ground.

[0087] In step S304, comprehensive calibration Love wave signal data is generated according to the noise Love wave signal data and the active Love wave signal data, and comprehensive calibration Rayleigh wave signal data is generated according to the noise Rayleigh wave signal data and the active Rayleigh wave signal data.

[0088] In step S305, cross-correlation calculation is performed on the comprehensive calibration Love wave signal data and the comprehensive calibration Rayleigh wave signal data respectively to obtain Love wave empirical Green's function and Rayleigh wave empirical Green's function.

[0089] The empirical Green's function is a key information for describing the wave propagation characteristics in the underground medium, and the accuracy thereof directly affects the generation of subsequent dispersion curves and the accuracy of velocity structure inversion.

[0090] In the above-mentioned high-density background noise deep pipeline imaging method, the cross-correlation calculation on the integrated and calibrated Love wave and Rayleigh wave signal data can more accurately obtain the Love wave and Rayleigh wave empirical Green's function, help to improve the accuracy of the imaging result, and clearly present the details such as the position, depth and morphology of the deep pipeline.

[0091] In an optional embodiment of the present application, referring to Figure 3 , the high-density background noise deep pipeline imaging method further comprises:

[0092] In step S310, the position matching and data calibration are performed on the deep pipeline imaging result data based on the position information of each sensor, and the calibrated deep pipeline imaging result data is obtained.

[0093] In step S311, the deep pipeline imaging profile is arranged according to the calibrated deep pipeline imaging result data of each sensor, and the deep pipeline imaging profile is used to represent the velocity structure of the detection area.

[0094] In step S312, the deep pipeline position data of the deep pipeline is identified based on the deep pipeline imaging profile, and the deep pipeline position data includes horizontal position data and vertical position data.

[0095] In the above-mentioned high-density background noise deep pipeline imaging method, the position matching and data calibration are performed on the deep pipeline imaging result data based on the position information of each sensor, which can effectively correct the inaccurate imaging result caused by the position deviation of the sensor, the data acquisition error and other factors, and further improve the reliability and usability of the imaging result. The generated deep pipeline imaging profile can directly represent the velocity structure of the detection area, and can provide a clear and easy-to-understand detection result for the engineering and technical personnel, so that they can quickly understand the distribution of the underground pipeline and the corresponding geological structure characteristics, thereby better guiding the actual engineering activities and improving the work efficiency and decision accuracy.

[0096] In an optional embodiment of the present application, as Figure 4 shown, the detection matrix sensor adopts a nested triangular array structure, the midpoint of the edge of the outer triangular shape constructed by the peripheral sensors is the vertex of the inner triangular shape constructed by the peripheral sensors, and each layer of triangular shape constructed by the peripheral sensors is an equilateral triangle with the center sensor as the center.

[0097] Illustratively, the nested triangular array structure can form a high-density sensor layout in a limited area, improve the resolution of local anomalies, and reduce the error accumulation of the sensor layout through the regular triangular geometry, thereby improving the consistency of data acquisition in complex environments.

[0098] In an optional embodiment of the present application, as shown in Figure 5 The detection matrix sensor adopts a concentric multi-circle array structure, and each layer of circles formed by the peripheral sensors are concentric circles with the center of the circle coinciding with the center sensor, and each layer of concentric circles formed by the peripheral sensors forms a radial sensor layout with the center sensor as the origin and covering three equal angles.

[0099] Illustratively, the concentric multi-circle array structure can uniformly capture background noise and active source signals from different directions, avoid detection blind areas in a single direction, and is suitable for full-area scanning of unknown pipeline routes. Moreover, the ring-symmetric layout of the concentric multi-circle array structure can facilitate dispersion curve analysis and the implementation of joint inversion algorithms, thereby enabling the rapid construction of the velocity structure of the detection area and shortening the imaging time.

[0100] In an exemplary embodiment of the present application, as shown in Figure 3 A super-high-density background noise deep-buried pipeline imaging method is provided, comprising:

[0101] Step S301: Obtain the original sensing data collected by the detection matrix sensor, and pre-process the original sensing data to obtain super-high-density background noise data and active source detection data.

[0102] Step S302: Generate noise Rayleigh wave signal data based on the super-high-density background noise data of each peripheral sensor in the detection matrix sensor in the tangential direction of the circumference with the center sensor as the center and the peripheral sensor as a point on the circumference, and generate noise Love wave signal data based on the super-high-density background noise data of each sensor in the detection matrix sensor in the vertical direction of the ground.

[0103] Step S303: Generate active Rayleigh wave signal data based on the active source detection data of each peripheral sensor in the detection matrix sensor in the tangential direction, and generate active Love wave signal data based on the active source detection data of each sensor in the detection matrix sensor in the vertical direction of the ground.

[0104] Step S304: Generate comprehensive calibration Love wave signal data according to the noise Love wave signal data and the active Love wave signal data, and generate comprehensive calibration Rayleigh wave signal data according to the noise Rayleigh wave signal data and the active Rayleigh wave signal data.

[0105] Step S305, respectively, the integrated calibration Love wave signal data and the integrated calibration Rayleigh wave signal data are subjected to cross-correlation calculation to obtain Love wave empirical Green function and Rayleigh wave empirical Green function.

[0106] Step S306, the Love wave empirical Green function and the Rayleigh wave empirical Green function are subjected to frequency-wavenumber domain analysis to generate Love wave dispersion curve and Rayleigh wave dispersion curve.

[0107] Step S307, the conjugate gradient method joint inversion model is initialized to obtain initial parameter data of the conjugate gradient method joint inversion model parameter of the detection region.

[0108] Step S308, based on the Love wave dispersion curve and the Rayleigh wave dispersion curve, the conjugate gradient method is used to combine the Love wave-Rayleigh wave joint objective function, the Love wave-Rayleigh wave joint preprocessing operator and the Love wave-Rayleigh wave joint gradient operator to iteratively update the conjugate gradient method joint inversion model parameter.

[0109] Step S309, if the iteration end condition is met, the velocity structure of the detection region is constructed based on the conjugate gradient method joint inversion model parameter to generate deep buried pipeline imaging result data.

[0110] Step S310, the position matching and data calibration are performed on the deep buried pipeline imaging result data based on the position information of each sensor to obtain calibrated deep buried pipeline imaging result data.

[0111] Step S311, the deep buried pipeline imaging profile is arranged according to the calibrated deep buried pipeline imaging result data of each sensor.

[0112] Step S312, the deep buried pipeline position data of the deep buried pipeline are identified based on the deep buried pipeline imaging profile.

[0113] In the above-mentioned super-high-density background noise deep buried pipeline imaging method, through systematic design of the whole process, intelligent automation of the whole link from data acquisition to pipeline position identification can be realized, and thus the detection efficiency of the deep buried pipeline can be ensured; through preprocessing and integrated calibration, the signal quality and anti-interference ability can be improved, and thus the imaging precision and accuracy can be enhanced; through generation of intuitive and clear imaging profile, the visualization and interpretability of the deep buried pipeline detection data can be enhanced.

[0114] In an exemplary embodiment of the present application, a super-high-density background noise deep buried pipeline imaging method is provided, comprising:

[0115] 1) Data acquisition: In the area to be detected, the measuring line is laid out, the direction of the measuring line is not parallel to the pipeline, the number of measuring lines is not less than 2, the measuring points are arranged in a concentric multiple circle array or a nested triangle array, the concentric multiple circle array uses at least 7 acquisition units, the nested triangle array uses at least 4 acquisition units, the radius of the array is the observation radius R, the maximum detection depth is generally 3-5 times the observation radius, the observation radius is set according to the actual situation, the distance between the measuring points is not greater than 1 / 2 of the diameter D of the detected pipeline, the sampling rate is 1-100 ms when collecting Rayleigh waves, the collection time is 10-40 min for each measuring point, and a vertical source is needed when collecting Love waves, and the main operation is to use a hammer to hit an iron plate at a certain distance to make it generate a source on the ground, and the corresponding information is collected on the array.

[0116] 2) Data processing: The following processing steps are performed on the data of each measuring point.

[0117] ① Raw data processing (remove instrument response, remove mean value, remove linear trend, band-pass filter, data segmentation and extraction, time domain normalization, frequency domain spectrum whitening);

[0118] ② Correlation calculation, extract the empirical Green function of the medium between the stations (extract the Love wave empirical Green function by using the tangential direction noise record between the arrays, and extract the Rayleigh wave empirical Green function by using the vertical direction noise record between the arrays);

[0119] ③ Frequency-wavenumber domain analysis is performed on the empirical Green function to obtain the dispersion curve of Rayleigh wave and Love wave (i.e. two-dimensional Fourier transform is performed on the data);

[0120] ④ The velocity structure of the detected area is calculated by using the conjugate gradient method; different surrounding rocks have different wave velocities; generally, when the lithology is artificial fill, the wave velocity is 90-180 m / s, when the lithology is silt, the wave velocity is 140-250 m / s, when the lithology is sand and gravel, the wave velocity is 350-450 m / s, when the lithology is strong weathered soft rock, the wave velocity is 450-750 m / s, and when the lithology is strong weathered hard rock, the wave velocity is 550-850 m / s.

[0121] ⑤ According to the velocity structure of different measuring points, a profile is arranged;

[0122] ⑥ According to the velocity anomaly area, the position and depth of the buried pipeline are divided; generally, the concrete material has high speed characteristics, and the plastic material has low speed characteristics, and the position and depth of the pipeline are determined according to the velocity anomaly area.

[0123] Compared with the prior art, the technical scheme of the present application has the advantages of: ground non-destructive detection can be realized; the requirement for the surrounding environment is low; the pipeline material has no requirement; the detection depth is large; the interference in the city is small; and the Rayleigh wave and Love wave are used for joint inversion, which can improve the constraint force of the result.

[0124] It should be understood that although the steps in the flowcharts involved in the embodiments described above are shown in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least some of the other steps or steps or stages in other steps.

[0125] Based on the same inventive concept, the embodiments of the present application also provide an ultrahigh-density background noise deep buried pipeline imaging device for implementing the above-mentioned ultrahigh-density background noise deep buried pipeline imaging method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more ultrahigh-density background noise deep buried pipeline imaging device embodiments provided below can refer to the limitations of the ultrahigh-density background noise deep buried pipeline imaging method described above, which will not be repeated here.

[0126] In one exemplary embodiment, as shown in Figure 6 An ultrahigh-density background noise deep buried pipeline imaging device 600 is provided, comprising:

[0127] The sensor data acquisition module 601 can be used to acquire original sensor data collected by the detection matrix sensor, and to pre-process the original sensor data to obtain ultrahigh-density background noise data and active source detection data.

[0128] The Green function generation module 602 can be used to extract Love wave signal data and Rayleigh wave signal data based on the ultrahigh-density background noise data and active source detection data corresponding to each sensor in the detection matrix sensor, and to perform cross-correlation calculation on the Love wave signal data and Rayleigh wave signal data, respectively, to obtain Love wave empirical Green function and Rayleigh wave empirical Green function.

[0129] The dispersion curve generation module 603 can be used to perform frequency-wavenumber domain analysis on the Love wave empirical Green function and the Rayleigh wave empirical Green function to generate Love wave dispersion curves and Rayleigh wave dispersion curves.

[0130] The imaging result generation module 604 can be configured to calculate and construct a velocity structure of a detection area of the sensor of the detection matrix sensor based on the Love wave dispersion curve and the Rayleigh wave dispersion curve by using a conjugate gradient method joint inversion algorithm, and generate deep buried pipeline imaging result data of each sensor of the detection matrix sensor.

[0131] In an optional embodiment of the present application, the imaging result generation module 604 can be further configured to:

[0132] Initialize the conjugate gradient method joint inversion model to obtain initial parameter data of the conjugate gradient method joint inversion model parameters of the detection area.

[0133] Based on the Love wave dispersion curve and the Rayleigh wave dispersion curve, the conjugate gradient method joint inversion model parameters are iteratively updated by using a conjugate gradient method combined with a Love wave-Rayleigh wave joint objective function, a Love wave-Rayleigh wave joint preprocessing operator and a Love wave-Rayleigh wave joint gradient operator.

[0134] If the iteration end condition is met, the velocity structure of the detection area is constructed based on the conjugate gradient method joint inversion model parameters, and the deep buried pipeline imaging result data is generated.

[0135] In an optional embodiment of the present application, the Green function generation module 602 can be further configured to:

[0136] Noise Rayleigh wave signal data is generated based on the ultra-high density background noise data of each peripheral sensor of the detection matrix sensor in the tangential direction of the circumferential of the circle with the center sensor as the center and the peripheral sensor as a point on the circumference, and noise Love wave signal data is generated based on the ultra-high density background noise data of each sensor of the detection matrix sensor in the vertical direction of the ground.

[0137] Active Rayleigh wave signal data is generated based on the active source detection data of each peripheral sensor of the detection matrix sensor in the tangential direction, and active Love wave signal data is generated based on the active source detection data of each sensor of the detection matrix sensor in the vertical direction of the ground.

[0138] Comprehensive calibration Love wave signal data is generated according to the noise Love wave signal data and the active Love wave signal data, and comprehensive calibration Rayleigh wave signal data is generated according to the noise Rayleigh wave signal data and the active Rayleigh wave signal data.

[0139] The conjugate correlation calculation is performed on the comprehensive calibration Love wave signal data and the comprehensive calibration Rayleigh wave signal data respectively to obtain the Love wave empirical Green function and the Rayleigh wave empirical Green function.

[0140] In an optional embodiment of the present application, the ultra-high density background noise deep buried pipeline imaging device 600 can be further configured to:

[0141] The imaging result data of the deep buried pipeline is matched and calibrated in position based on the position information of each sensor to obtain calibrated deep buried pipeline imaging result data.

[0142] The deep buried pipeline imaging profile is arranged according to the calibrated deep buried pipeline imaging result data of each sensor, and the deep buried pipeline imaging profile is used to represent the velocity structure of the detection area.

[0143] The deep buried pipeline position data of the deep buried pipeline is identified based on the deep buried pipeline imaging profile, and the deep buried pipeline position data includes horizontal position data and vertical position data.

[0144] In an optional embodiment of the present application, the detection matrix sensor adopts a nested triangular array structure, and the midpoints of the edges of the outer triangular array formed by the peripheral sensors are the vertices of the inner triangular array formed by the peripheral sensors, and each layer of triangular array formed by the peripheral sensors is an equilateral triangle with the center sensor as the center.

[0145] In an optional embodiment of the present application, the detection matrix sensor adopts a concentric multi-circle array structure, and each layer of circle formed by the peripheral sensors is a concentric circle with the center sensor as the center, and each layer of concentric circle formed by the peripheral sensors forms a radial sensor layout with the center sensor as the origin and covering three equal angles.

[0146] In an embodiment, a computer device is provided, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the ultra-high density background noise deep buried pipeline imaging method as described above when executing the computer program.

[0147] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts are described in the method embodiment. The device embodiments described above are merely illustrative, wherein the components described as separate components can be or can not be physically separated, and the components displayed as units can be or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Some or all modules can be selected to achieve the purpose of the present disclosure according to actual needs. Those skilled in the art can understand and implement it without creative labor.

[0148] The above-described embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the patent scope of the application. It should be pointed out that, for those skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are all within the protection scope of the present application.

Claims

1. An ultra-high density background noise deep buried pipeline imaging method, characterized in that, The method comprises the following steps: acquire original sensing data collected by a detection matrix sensor, and preprocess the original sensing data to obtain ultrahigh-density background noise data and active source detection data; extract Love wave signal data and Rayleigh wave signal data based on the ultrahigh-density background noise data and the active source detection data corresponding to each sensor in the detection matrix sensor, and perform cross-correlation calculation on the Love wave signal data and the Rayleigh wave signal data respectively to obtain Love wave empirical Green's function and Rayleigh wave empirical Green's function; perform frequency-wavenumber domain analysis on the Love wave empirical Green's function and the Rayleigh wave empirical Green's function to generate Love wave dispersion curves and Rayleigh wave dispersion curves; calculate and construct the velocity structure of the detection area of the detection matrix sensor based on the Love wave dispersion curves and the Rayleigh wave dispersion curves using a conjugate gradient joint inversion algorithm to generate deep buried pipeline imaging result data of each sensor of the detection matrix sensor.

2. The ultra-high density background noise deep buried pipeline imaging method according to claim 1, characterized in that, The method of calculating and constructing the velocity structure of the detection area of the detection matrix sensor based on the Love wave dispersion curves and the Rayleigh wave dispersion curves using the conjugate gradient joint inversion algorithm to generate deep buried pipeline imaging result data of each sensor of the detection matrix sensor comprises: initializing a conjugate gradient joint inversion model to obtain initial parameter data of conjugate gradient joint inversion model parameters of the detection area; iteratively updating the conjugate gradient joint inversion model parameters using a conjugate gradient method combined with a Love wave-Rayleigh wave joint objective function, a Love wave-Rayleigh wave joint preprocessing operator and a Love wave-Rayleigh wave joint gradient operator based on the Love wave dispersion curves and the Rayleigh wave dispersion curves; if the iteration end condition is met, constructing the velocity structure of the detection area based on the conjugate gradient joint inversion model parameters to generate the deep buried pipeline imaging result data.

3. The ultra-high density background noise deep buried pipeline imaging method according to claim 2, wherein, The expression of the conjugate gradient method combined with the Love wave-Rayleigh wave joint objective function is: In the formula, is the conjugate gradient method combined with the Love-Rayleigh wave joint objective function, α L , α R and γ are respectively the Love wave residual weight coefficient, the Rayleigh wave residual weight coefficient and the cross gradient constraint weight coefficient, and Φ C are respectively the Love wave residual term, the Rayleigh wave residual term and the cross gradient constraint term, f is the frequency of different frequency points of the dispersion curve, is the conjugate gradient method joint inversion model parameter of the kth iteration, is the observed Love wave phase velocity when the frequency is f, is the Love wave phase velocity obtained by the conjugate gradient method joint inversion model parameter of the kth iteration when the frequency is f, J is the total number of orders of Rayleigh waves, ω j is the weight coefficient of the jth order Rayleigh wave, is the observed Rayleigh wave phase velocity of the jth order Rayleigh wave when the frequency is f, is the Rayleigh wave phase velocity of the jth order Rayleigh wave obtained by the conjugate gradient method joint inversion model parameter of the kth iteration when the frequency is f, Ω is the detection area, is the S-wave velocity obtained based on the Rayleigh wave dispersion curve, is the S-wave velocity obtained based on the Love wave dispersion curve, ρ is the horizontal direction, and z is the vertical depth direction.

4. The ultra-high density background noise deep buried pipeline imaging method according to claim 2, characterized in that, The Love wave-Rayleigh wave joint gradient operator comprises a Love wave-Rayleigh wave joint gradient scalar operator and a Love wave-Rayleigh wave joint gradient direction operator; The expression of the Love wave-Rayleigh wave joint preprocessing operator corresponding to the conjugate gradient joint inversion model parameters of the kth iteration is: The expression of the Love wave-Rayleigh wave joint gradient scalar operator corresponding to the conjugate gradient joint inversion model parameters of the kth iteration is: The expression of the Love wave-Rayleigh wave joint gradient direction operator corresponding to the conjugate gradient joint inversion model parameters of the kth iteration is: where λ R , λ L , and λ δ are Rayleigh wave, Love wave, and regularization coefficients, respectively, , and are Rayleigh wave, Love wave, and identity matrices, respectively, is the gradient matrix of the conjugate gradient method combined with the Love-Rayleigh wave joint objective function corresponding to the conjugate gradient method joint inversion model parameters in the kth iteration.

5. The ultra-high density background noise deep buried pipeline imaging method of claim 1, wherein, The Love wave signal data includes noise Love wave signal data, active Love wave signal data and comprehensive calibration Love wave signal data, the Rayleigh wave signal data includes noise Rayleigh wave signal data, active Rayleigh wave signal data and comprehensive calibration Rayleigh wave signal data, the detection matrix sensor includes a central sensor and a peripheral sensor, the Love wave signal data and the Rayleigh wave signal data are extracted based on the ultra-high density background noise data and the active source detection data corresponding to each sensor in the detection matrix sensor, and cross-correlation calculation is performed on the Love wave signal data and the Rayleigh wave signal data respectively, so that Love wave empirical Green function and Rayleigh wave empirical Green function are obtained, comprising: Noise Rayleigh wave signal data is generated based on the ultra-high density background noise data of each peripheral sensor in the detection matrix sensor in the tangential direction of the circumference with the central sensor as the center and the peripheral sensor as a point on the circumference, and noise Love wave signal data is generated based on the ultra-high density background noise data of each sensor in the detection matrix sensor in the vertical direction of the ground; Active Rayleigh wave signal data is generated based on the active source detection data of each peripheral sensor in the detection matrix sensor in the tangential direction, and active Love wave signal data is generated based on the active source detection data of each sensor in the detection matrix sensor in the vertical direction of the ground; The comprehensive calibration Love wave signal data is generated according to the noise Love wave signal data and the active Love wave signal data, and the comprehensive calibration Rayleigh wave signal data is generated according to the noise Rayleigh wave signal data and the active Rayleigh wave signal data; Cross-correlation calculation is performed on the comprehensive calibration Love wave signal data and the comprehensive calibration Rayleigh wave signal data respectively, so that the Love wave empirical Green function and the Rayleigh wave empirical Green function are obtained.

6. The ultra-high density background noise deep buried pipeline imaging method of claim 5, wherein, The method further comprises: The deep buried pipeline imaging result data is position-matched and data-calibrated based on the position information of each sensor, so that calibrated deep buried pipeline imaging result data is obtained; A deep buried pipeline imaging profile is arranged according to the calibrated deep buried pipeline imaging result data of each sensor, and the deep buried pipeline imaging profile is used to represent the velocity structure of the detection area; The deep buried pipeline position data of the deep buried pipeline is identified based on the deep buried pipeline imaging profile, and the deep buried pipeline position data includes horizontal position data and vertical position data.

7. The ultra-high density background noise deep buried pipeline imaging method of claim 6, wherein, The detection matrix sensor adopts a nested triangular array structure, and the midpoint of the edge of the outer triangular array constructed by the peripheral sensors is the vertex of the inner triangular array constructed by the peripheral sensors, and each layer of triangular array constructed by the peripheral sensors is an equilateral triangle with the central sensor as the centroid.

8. The ultra-high density background noise deep buried pipeline imaging method of claim 6, wherein, The detection matrix sensor adopts a concentric multi-circle array structure, wherein each layer of circles formed by the peripheral sensors is a concentric circle with the center of the circle coinciding with the center sensor, and each layer of the concentric circles formed by the peripheral sensors forms a radial sensor layout with the center sensor as the origin and covering equal angles in three directions.

9. An ultra-high density background noise buried pipeline imaging apparatus, characterized by, Comprise: A sensing data acquisition module for acquiring original sensing data collected by a detection matrix sensor and preprocessing the original sensing data to obtain ultrahigh-density background noise data and active source detection data; A Green function generation module for extracting Love wave signal data and Rayleigh wave signal data based on the ultrahigh-density background noise data and the active source detection data corresponding to each sensor in the detection matrix sensor, and performing cross-correlation calculation on the Love wave signal data and the Rayleigh wave signal data, respectively, to obtain Love wave empirical Green function and Rayleigh wave empirical Green function; A dispersion curve generation module for frequency-wavenumber domain analysis of the Love wave empirical Green function and the Rayleigh wave empirical Green function to generate Love wave dispersion curves and Rayleigh wave dispersion curves; An imaging result generation module for calculating and constructing the velocity structure of the detection area of the detection matrix sensor based on the Love wave dispersion curves and the Rayleigh wave dispersion curves using a conjugate gradient method joint inversion algorithm to generate deep buried pipeline imaging result data of each sensor of the detection matrix sensor. 10.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-9. The processor executes the computer program to realize the ultrahigh-density background noise deep buried pipeline imaging method of any one of claims 1 to 8.

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