Underground Pipeline Detection System and Method Based on Trans-hole Seismic Wave CT Inspection Technology

Abstract

This invention proposes an underground pipeline detection system based on cross-hole seismic wave CT inspection technology, comprising a seismic wave emitting source device and a signal detection head assembly device extending into a first borehole and a second borehole respectively, which are connected to the underground pipeline. The system also includes multiple geophones, a seismograph, and a host computer. Furthermore, this invention proposes a detection method using the system described above, comprising: acquiring measured shear wave velocity and measured p-wave velocity; acquiring wave velocity imagery; preprocessing the surface wave data collected on-site; acquiring a dispersion curve that meets resolution requirements; constraining and limiting the shear wave velocity to obtain an inferred shear wave velocity; inverting the dispersion curve to obtain a relationship between shear wave velocity and depth; acquiring the final contour map, and then comparing and verifying the wave velocity imagery with the contour map; performing contour map analysis to obtain the location and cross-sectional dimensions of the underground pipeline. This invention can accurately and efficiently detect underground pipelines at extremely deep burial depths.

Description

Technical Field

[0001] This invention relates to the field of ultra-deep pipeline detection technology, specifically to an underground pipeline detection system and method based on cross-hole seismic wave CT inspection technology. Background Technology

[0002] With the increasing development and utilization of underground space resources, the safety hazards of underground construction are attracting more and more attention from the industry. Safety accidents caused by unclear pipeline detection occur frequently every year. According to the China Underground Pipeline Accident Report, the economic losses caused by underground pipeline accidents are increasing linearly each year, especially in recent years. For ultra-deep buried pipelines, especially complex non-metallic pipelines, their complex physical characteristics and great depth make conventional geophysical exploration methods inadequate, becoming a pain point and a difficult problem in the underground pipeline industry. Because underground space is invisible and intangible, and the geological composition is complex, the detection difficulty is even greater. Pipelines below three meters in depth are generally considered ultra-deep buried pipelines. Current technology lacks effective methods to solve the problem of ultra-deep burial. Shallow metallic pipelines can be detected using clamping methods, direct connection methods, or other electromagnetic wave methods with pipeline detectors. However, there is a lack of effective methods to solve the problem for metallic pipelines below three meters, and non-metallic pipelines below three meters are even more difficult, almost impossible to detect accurately. Currently, methods such as direct excavation, ground-penetrating radar, and acoustic detection are commonly used for non-metallic pipeline detection. However, these methods have a shallow detection depth and can occasionally solve the problem of detecting non-metallic pipelines above three meters. For non-metallic pipelines below three meters, it is currently difficult to detect them accurately. The industry urgently needs effective detection methods. Summary of the Invention

[0003] In view of this, it is necessary to propose an underground pipeline detection system and method based on cross-hole seismic wave CT inspection technology to address the above-mentioned problems, so as to overcome some of the shortcomings of the above-mentioned background technology and solve the technical problem of how to accurately and efficiently detect underground pipelines at ultra-deep burial depth.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] This invention proposes an underground pipeline detection system based on cross-hole seismic wave CT inspection technology, comprising a seismic wave emission source device and a signal detection head assembly. This underground pipeline detection system is applied to the surface layer where underground pipelines are located, with a first borehole and a second borehole drilled. The top-view projections of the first borehole and the second borehole are located on the left and right sides of the cross-sectional view of the underground pipeline, respectively. The signal detection head assembly and the seismic wave emission source device extend into the first and second boreholes, respectively. A virtual connecting line between the center of the first borehole opening and the center of the second borehole opening is a virtual connecting line between the two boreholes. The underground pipeline detection system also includes:

[0006] Multiple detectors are arranged in a linear array on the ground surface, forming a virtual straight line array of detectors virtually connected to each other. The virtual straight line array intersects the underground pipeline in the top-view projection direction. The virtual straight line array coincides with the virtual connection line between two holes, or the virtual straight line array is parallel to the virtual connection line between two holes and is located beside the virtual connection line between two holes.

[0007] The seismograph communicates with the signal detection head assembly; the seismograph is also physically connected to the control terminal of the seismic wave emission source device.

[0008] The host computer communicates with multiple geophones and seismographs. The host computer has an analysis program that analyzes the signals from the geophones and seismographs to obtain the location and relevant dimensions of underground pipelines.

[0009] Furthermore, the seismic wave emission source device is an electric spark source; the signal detection head assembly device is a tubular detection device, which includes a tube body and multiple hydrophones disposed inside the tube body, and the tube body does not obstruct the detection end of each hydrophone.

[0010] Furthermore, the length of the virtual straight line of the detector array is greater than the length of the virtual connecting line between the two holes; the number of detectors is 40-60; the center distance between two adjacent detectors is 0.3 meters-0.6 meters; the angle formed by the virtual connecting line between the two holes and the underground pipeline is 90°±α, where α is ≤9°; the drilling verticality of the first and second boreholes is 90°±β, where β is ≤1°; the length of the virtual connecting line between the two holes is 19 meters-21 meters; and the distance between the apex of the underground pipeline and the ground surface is greater than 3 meters.

[0011] Furthermore, the analysis program includes the following software modules:

[0012] The wave velocity image acquisition module acquires wave velocity images based on measured shear wave velocities and measured longitudinal wave velocities; the wave velocity image images are used to represent the internal structure and anomalies of the strata.

[0013] The data preprocessing module is used to preprocess the surface wave data collected on site;

[0014] The dispersion curve acquisition module is used to acquire dispersion curves that meet the resolution requirements.

[0015] The module for obtaining the estimated shear wave velocity is used to obtain the estimated shear wave velocity.

[0016] The inversion processing module is used to invert the dispersion curve to obtain the relationship between shear wave velocity and depth;

[0017] The profile drawing and processing module is used to obtain the final contour map, and then compare and verify the wave velocity image map with the contour map to effectively verify the internal structure and anomalies of the surface layer, distinguish them from the independent anomalies of the target pipeline, and accurately locate the target underground pipeline.

[0018] The contour map analysis module is used to perform contour map analysis and obtain data on the location and cross-sectional dimensions of underground pipelines.

[0019] This invention further proposes a detection method for an underground pipeline detection system based on cross-hole seismic wave CT inspection technology as described in any one of the above claims, comprising:

[0020] Step S1: Obtain the measured shear wave velocity and the measured longitudinal wave velocity;

[0021] The process of obtaining the measured shear wave velocity includes steps S111-S114:

[0022] Step S111, Drilling: Drill the first borehole and the second borehole in the surface layer where the underground pipeline is located. The top view projection of the first borehole and the top view projection of the second borehole are located on the left and right sides of the cross-section of the underground pipeline, respectively.

[0023] Step S112: Insert the signal detection head assembly and the seismic wave emission source device into the first borehole and the second borehole, respectively;

[0024] Step S113: The seismic wave emission source device emits multiple seismic wave signals sequentially from shallow to deep.

[0025] Step S114: The signal detection head assembly device collects seismic wave signals and transmits them to the host computer for internal processing.

[0026] The process of obtaining the measured longitudinal wave velocity is as follows:

[0027] Multiple detectors are arranged in a linear array on a virtual straight line of the detector array. The midpoint of the detector queue, which is composed of all the detectors arranged in the linear array, is used as the detection positioning point. Multiple test points with equal intervals are set between the two virtual boreholes. Each test point is tested by aligning the detection positioning point with the test point. For each test point, a standard penetrator, acting as the seismic source, strikes the ground multiple times at different positions and collects data through each detector. The center of the first borehole is the first test point on the virtual line connecting the two boreholes, and the center of the second borehole is the last test point on the virtual line connecting the two boreholes.

[0028] Step S3: Obtain a wave velocity image based on the measured shear wave velocity and measured longitudinal wave velocity; the wave velocity image is used to represent the internal structure and anomalies of the strata.

[0029] Step S4: Preprocess the surface wave data collected on site;

[0030] Step S5: Obtain the dispersion curve that meets the resolution requirements;

[0031] Step S6: Constrain and limit the shear wave velocity to obtain the estimated shear wave velocity;

[0032] Step S7: Invert the dispersion curve to obtain the relationship between shear wave velocity and depth;

[0033] Step S8: Generate a graph showing the relationship between shear wave velocity and depth for each detector array within the detection area, generate a shear wave velocity-depth profile, perform moving average smoothing on the profile to obtain the final contour map, and then compare and verify the wave velocity image map with the contour map.

[0034] Step S9: Perform contour map analysis to obtain data on the location and cross-sectional dimensions of underground pipelines.

[0035] Furthermore, during the process of obtaining the measured longitudinal wave velocity in step S1, when testing a test point, the ground surface located to the left of the underground pipeline is struck N times with a standard penetration test hammer, and the ground surface located to the right of the underground pipeline is struck N times again with a standard penetration test hammer.

[0036] Furthermore, step S3 includes the following steps:

[0037] Step S31, perform cross-aperture CT data processing: Select the first arrival time of the acquired cross-aperture CT data, and use the algebraic reconstruction iterative algorithm to obtain the longitudinal wave velocity value distribution of the grid cell with the first fixed size square by using the distance value between the two cross-apertures and a formula (1) and a formula (2). Then, obtain the high-precision longitudinal wave velocity value distribution of the grid cell with the second fixed size square by using the least squares difference and densification, so that the longitudinal wave velocity values ​​of the same coordinate in the wave velocity image are correlated one by one.

[0038] Formula (1) is:

[0039]

[0040] Formula (2) is:

[0041]

[0042] In formula (1), l nm S is the path length of the nth ray within the mth unit. m It is the slowness of the m-th unit, t n It is the travel time of the nth ray;

[0043] In formula (2), Si represents the slowness of the i-th element, and Vi represents the longitudinal wave velocity of the i-th element;

[0044] Step S32: Combine the longitudinal wave velocity values ​​with a second fixed size square interval within the detection range between the first borehole and the second borehole with the corresponding depth data to generate a wave velocity image map, and use different colors to represent different velocity ranges.

[0045] Further, step S4 includes the following steps:

[0046] Step S41: Calibrate the detector;

[0047] Step S42: Use a GNSS receiver to collect the coordinates and elevation of each detector, and add the coordinate and elevation data during data processing.

[0048] Further, step S5 includes the following steps:

[0049] Step S51: When the detector array composed of detectors arranged in a linear array collects the first set of waveform recording data for the first test point, the artificial source and natural source waveform recordings are extracted and separated at the strongest position in the recording diagram by the method of identifying the strongest noise; the first set of waveform recording data includes test data of the standard penetration test hammer being performed twice N (2N) times.

[0050] Step S52: Extract the artificial source waveform records and use wavelet transform to perform seismic inter-trace interpolation of the data;

[0051] Step S53: Use a τ-p transform algorithm to extract the dispersion energy map, and extract the strongest signal as the dispersion curve of the data set based on the dispersion energy map;

[0052] Step S54: When the detector queue, composed of detectors arranged in a linear array, collects the first test point, the detector queue is the first group of detectors. The artificial source surface wave data of N times (2N) separated from the first group of waveform recording data is processed to obtain the artificial source surface wave dispersion map and artificial source dispersion curve. The artificial source dispersion curves of the corresponding artificial source surface wave data of N times (2N) separated from the first group of waveform recording data are fused by energy weighting. The artificial source dispersion curves of N times (2N) are fused by weight, thereby completing the final dispersion map of the artificial source surface wave and the weighted fusion of the artificial source dispersion curve.

[0053] Simultaneously or in any order, the natural source waveform recording data separated from the first set of waveform recording data are used to extract the natural source surface wave dispersion energy map using the Extended Spatial Autocorrelation (ESPAC) method;

[0054] Step S55: Process the N times (2N) groups of natural source surface wave data separated from the first group of waveform recording data to obtain the natural source surface wave dispersion map and natural source dispersion curve. Perform energy equal weight fusion of the N times (2N) groups of natural source dispersion curves to complete the final energy equal weight fusion of the natural source surface wave dispersion map and the natural source dispersion curve.

[0055] Step S56: Integrate or sum the squares of the amplitudes of the artificial and natural source surface wave signals near frequency f to obtain energies E1(f) and E2(f). The dispersion curve of the artificial source surface wave is D1(f), and the dispersion curve of the natural source surface wave is D2(f). Calculate the energy proportions of the artificial and natural source surface waves at each frequency, expressed by weights: Artificial source surface wave weight:

[0056]

[0057] Natural source surface wave weights:

[0058]

[0059] Dispersion curve after fusion:

[0060] D(f)=P1(f)D1(f)+P2(f)D2(f) (5);

[0061] In step S5, the remaining test points that are not the first test point are executed in the same way as steps S51-S56, so that the fused dispersion curves of the remaining test points that are not the first test point are obtained one by one.

[0062] Further, step S6 includes the following steps:

[0063] Step S61: The range of Poisson's ratio and the measured P-wave velocity are used to constrain the range of the predicted S-wave velocity by formula (6); formula (6) is:

[0064]

[0065] In formula (6), V p It is the longitudinal wave velocity, V s Let v be the transverse wave velocity and v be Poisson's ratio. Porosity is calculated by injecting mercury into core samples taken from the first two boreholes, based on the amount of mercury entering the core pores under different pressures. Φ is the medium inhomogeneity coefficient, and the standard deviation σ is obtained using all P-wave velocities inverted via cross-hole CT. It is calculated based on the amount of mercury that enters the pores of the core after mercury is injected into the core samples taken from the two boreholes, under different pressures.

[0066] Step S62: Compare the range of the predicted shear wave velocity with the measured shear wave velocity. If the measured shear wave velocity does not meet the set requirements, resampling or correction is performed until the optimal dispersion curve is obtained.

[0067] Step S63: Based on the relationship between shear wave velocity and frequency, using λ=V / f and the half-wavelength empirical formula h=0.5λ, and employing the damped least squares method, the shear wave velocity and depth relationship is obtained by inverting the dispersion curve, thus obtaining the shear wave velocity and depth relationship diagram of the optimal dispersion curve; where λ is the wavelength, f is the frequency, and h is the depth.

[0068] In step S6, the remaining test points that are not the first test point are executed in the same manner as steps S61-S63, so as to obtain the relationship between the transverse wave velocity and depth of the optimal dispersion curve of the remaining test points that are not the first test point.

[0069] Furthermore, when the medium of the surface layer being probed is isotropic, the Poisson's ratio formula is set as follows:

[0070]

[0071] In formula (7), porosity The value is 1, the medium inhomogeneity coefficient Φ is 0; K is a constant: its value is between 1.5 and 2.5; v is Poisson's ratio.

[0072] The beneficial effects of this invention are as follows:

[0073] This invention can accurately and efficiently detect underground pipelines buried at extremely deep depths, thereby quickly obtaining accurate information on the burial depth, location, and diameter of the underground pipelines, solving the problem of effective detection of underground pipelines buried at extremely deep depths that is currently unsolvable in the industry. Attached Figure Description

[0074] The accompanying drawings are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the invention and, together with the description, serve to explain the principles of the invention. These drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0075] Figure 1 This invention relates to a three-dimensional structural schematic diagram of an underground pipeline detection system based on cross-hole seismic wave CT inspection technology, in conjunction with a first borehole, a second borehole, and a standard penetration test hammer.

[0076] Figure 2 This invention relates to a recording diagram of the original waveforms acquired by a array of detectors.

[0077] Figure 3 This is a diagram illustrating the relationship between the dispersion energy map and the dispersion curve of the artificial source surface wave involved in this invention.

[0078] Figure 4 This invention relates to a diagram showing the relationship between the dispersion energy map and the dispersion curve of a natural source surface wave.

[0079] Figure 5 This is a diagram illustrating the relationship between the fusion of high-resolution dispersion energy map and dispersion curve involved in this invention.

[0080] Figure 6 This is a graph showing the relationship between shear wave velocity and depth, as per the present invention.

[0081] Figure 7 This invention relates to a contour map of the detection depth and shear wave velocity of drainage pipe No. 12 in the flood control project of Jiangkou Town, Fengkai City, Guangdong Province, as per the present invention.

[0082] Figure 8 This invention relates to a contour map of the detection depth and shear wave velocity of drainage pipe No. 14 in the flood control project of Jiangkou Town, Fengkai City, Guangdong Province, as per the present invention.

[0083] Explanation of reference numerals in the attached figures:

[0084] 1. Seismic wave emission source device; 2. Signal detection head assembly device; 3. First borehole; 4. Second borehole; 5. Geophone; 6. Seismograph; 7. Host computer; 9. Virtual connection between the two boreholes; 8. Virtual straight line of the geophone array; 10. Standard penetration test hammer. Detailed Implementation

[0085] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be further described clearly and completely below in conjunction with the embodiments of this invention. It should be noted that the described embodiments are merely some embodiments of this invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0086] The terms “first,” “second,” “third,” and “fourth” are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, the use of “first,” “second,” “third,” and “fourth” to designate a feature may explicitly or implicitly include one or more of that feature.

[0087] The following is a detailed description of embodiments of the invention depicted in the accompanying drawings. The embodiments are detailed in order to clearly convey the invention. However, the amount of detail provided is not intended to limit the contemplative variations of the embodiments; rather, it is intended to cover all modifications, equivalents, and substitutions falling within the spirit and scope of the invention as defined by the appended claims.

[0088] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the invention. It will be apparent to those skilled in the art that embodiments of the invention may be practiced without some of these specific details.

[0089] Embodiments of the present invention include various steps, which will be described below. These steps may be performed by hardware components or may be contained in machine-executable instructions that can be used by a general-purpose or special-purpose processor programmed with those instructions to perform these steps. Alternatively, the steps may be performed by a combination of hardware, software, and firmware and / or by a human operator.

[0090] The various methods described herein can be practiced by combining one or more machine-readable storage media containing code according to the invention with suitable standard computer hardware to execute the code contained therein. Apparatus for implementing the various embodiments of the invention may include one or more computers (or one or more processors within a single computer) and a storage system containing or having network access to computer programs encoded according to the various methods described herein, and the method steps of the invention may be performed by modules, routines, subroutines, or sub-parts of a computer program product.

[0091] If the specification states that a component or feature "may", "can", "may" include or have the feature, then it is not necessary to include that particular component or feature or have that feature.

[0092] As used in this specification and the following claims, the words “a,” “an,” and “the” have the meaning of plural reference unless the context clearly indicates otherwise. Furthermore, as used in the description herein, unless the context clearly indicates otherwise, “in” has the meaning of both “in…” and “on…”.

[0093] Exemplary embodiments will now be described more fully below with reference to the accompanying drawings, which illustrate exemplary embodiments. These exemplary embodiments are provided for illustrative purposes only and to make the invention thorough and complete, and to fully convey the scope of the invention to those skilled in the art. However, the disclosed invention can be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Various modifications will be apparent to those skilled in the art. The general principles defined herein can be applied to other embodiments and applications without departing from the spirit and scope of the invention. Furthermore, all statements regarding embodiments of the invention and specific examples thereof described herein are intended to cover their structural and functional equivalents. Additionally, these equivalents are intended to include currently known equivalents as well as those developed in the future (i.e., any element developed that performs the same function, regardless of its structure). Moreover, the terminology and wording used are for the purpose of describing exemplary embodiments and should not be considered limiting. Therefore, the invention is to be endowed with the broadest scope, including various substitutions, modifications, and equivalents consistent with the disclosed principles and features. For clarity, details of technical materials known in the art related to this invention have not been described in detail so as not to unnecessarily obscure the invention.

[0094] Therefore, for example, those skilled in the art will understand that schematic diagrams, schematics, illustrations, etc., represent conceptual views or processes embodying the systems and methods of the present invention. The functionality of the various elements shown in the figures can be provided using dedicated hardware and hardware capable of executing the relevant software. Similarly, any switches shown in the figures are merely conceptual. Their functionality can be performed through the operation of program logic, through dedicated logic, through interaction between program control and dedicated logic, or even manually; specific techniques may be chosen by the entity implementing the invention. Those skilled in the art should further understand that the exemplary hardware, software, processes, methods, and / or operating systems described herein are for illustrative purposes and are therefore not intended to be limited to any particular named element.

[0095] Embodiments of the present invention may provide a computer program product that may include a machine-readable storage medium on which instructions are tangibly implemented, which may be used to program a computer (or other electronic device) to perform processing. The terms "machine-readable storage medium" or "computer-readable storage medium" include, but are not limited to, fixed (hardware) drives, magnetic tape, floppy disks, optical discs, optical disc read-only memory (CD-ROM) and magneto-optical discs, semiconductor memories such as ROMs, PROMs, random access memories (RAM), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other types of media / machine-readable media suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware). Machine-readable media may include non-transitory media in which data can be stored and does not include carrier waves and / or transient electronic signals propagated via wireless or wired connections. Examples of non-transitory media may include, but are not limited to, magnetic disks or magnetic tapes, optical storage media such as compact discs (CDs) or digital universal discs (DVDs), flash memory, memory, or memory devices. Computer program products may include code and / or machine-executable instructions, which may represent any combination of procedures, functions, subroutines, programs, routines, subroutines, modules, software packages, classes, or instructions, data structures, or program statements. Code segments may be coupled to other code segments or hardware circuitry by passing and / or receiving information, data, variables, parameters, or memory contents. Information, variables, parameters, data, etc., may be passed, forwarded, or transmitted by any suitable means, including memory sharing, message passing, token passing, network transmission, etc.

[0096] Furthermore, embodiments can be implemented using hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware, or microcode, program code or code segments (e.g., a computer program product) that perform the necessary tasks can be stored on a machine-readable medium. The processor can then perform the necessary tasks.

[0097] The systems depicted in the figures can be provided in various configurations. In some embodiments, the system can be configured as a distributed system, wherein one or more components of the system are distributed across one or more networks of a cloud computing system.

[0098] Each of the appended claims defines a separate invention, which, for infringement purposes, is considered to include the various elements or limited equivalents specified in the claims. Depending on the context, all references to "invention" below may refer only to certain specific embodiments in some cases. In other cases, it should be recognized that references to "invention" will refer to one or more, but not necessarily all, the subject matter described in the claims.

[0099] Unless otherwise stated herein or the context clearly contradicts it, all methods described herein may be performed in any suitable order. The use of any and all examples or exemplary language (e.g., “such as”) provided with respect to certain embodiments herein is intended only to better illustrate the invention and not to limit the scope of the claimed invention. No language in the specification should be construed as indicating any unclaimed element essential to the implementation of the invention.

[0100] The various terms used herein are as follows. Where no term is defined below as used in the claims, the broadest definition should be given, and those skilled in the art have provided the term as reflected in printed publications and granted patents at the time of filing.

[0101] Example 1

[0102] like Figure 1 As shown:

[0103] This embodiment proposes an underground pipeline detection system based on cross-hole seismic wave CT inspection technology, including a seismic wave emission source device 1 and a signal detection head assembly device 2. This underground pipeline detection system is applied to the surface layer where underground pipelines are located, with a first borehole 3 and a second borehole 4 drilled. The top-view projections of the first borehole 3 and the second borehole 4 are located on the left and right sides of the cross-sectional view of the underground pipeline, respectively. The signal detection head assembly device 2 and the seismic wave emission source device 1 extend into the first borehole 3 and the second borehole 4, respectively. The virtual connecting line between the center of the borehole opening of the first borehole 3 and the center of the borehole opening of the second borehole 4 is a virtual connecting line 9 between the two boreholes. The underground pipeline detection system also includes:

[0104] Multiple detectors 5 are arranged in a linear array on the ground surface, forming a virtual straight line 8 of detector array, which is virtually connected by the individual detectors 5. The virtual straight line 8 of detector array intersects the underground pipeline in the top-view projection direction. The virtual straight line 8 of detector array coincides with the virtual connection line 9 between two holes, or the virtual straight line 8 of detector array is parallel to the virtual connection line 9 between two holes and is located beside the virtual connection line 9 between two holes.

[0105] Seismograph 6 is connected to signal detection head assembly 2 via communication; seismograph 6 is also physically connected to the control terminal of seismic wave emission source device 1.

[0106] The host computer 7 communicates with multiple geophones 5 and seismographs 6. The host computer 7 is equipped with an analysis program, which is used to analyze the signals of geophones 5 and seismographs 6 to obtain the location and relevant dimensions of underground pipelines.

[0107] The underground pipeline detection system based on cross-hole seismic wave CT inspection technology in this embodiment can accurately and efficiently detect underground pipelines buried at depths of less than three meters.

[0108] Ideally, the seismic wave emitting source device 1 is an electric spark source; the signal detection head assembly device 2 is a tubular detection device, which includes a tube body and multiple hydrophones installed inside the tube body, and the tube body does not obstruct the detection end of each hydrophone.

[0109] Optimally, the length of the virtual straight line 8 of the detector array is greater than the length of the virtual connecting line 9 between the two holes; the number of detectors 5 is 40-60 (preferably 50); the center distance between two adjacent detectors 5 is 0.3-0.6 meters (preferably 0.5 meters); the angle formed by the virtual connecting line 9 between the two holes and the underground pipeline is 90°±α, where α is ≤9°; the drilling verticality of the first borehole 3 and the second borehole 4 is 90°±β, where β is ≤1°; the length of the virtual connecting line 9 between the two holes is 19-21 meters (preferably 20 meters); and the distance between the apex of the underground pipeline and the ground surface is greater than 3 meters.

[0110] Ideally, the analysis program includes the following software modules:

[0111] The wave velocity image acquisition module is used to represent the internal structure and anomalies of the strata;

[0112] The data preprocessing module is used to preprocess the surface wave data collected on site;

[0113] The dispersion curve acquisition module is used to acquire dispersion curves that meet the resolution requirements.

[0114] The module for obtaining the estimated shear wave velocity is used to obtain the estimated shear wave velocity.

[0115] The inversion processing module is used to invert the dispersion curve to obtain the relationship between shear wave velocity and depth;

[0116] The profile drawing and processing module is used to obtain the final contour map, and then compare and verify the wave velocity image map with the contour map to effectively verify the internal structure and anomalies of the surface layer, distinguish them from the independent anomalies of the target pipeline, and accurately locate the target underground pipeline.

[0117] The contour map analysis module is used to perform contour map analysis and obtain data on the location and cross-sectional dimensions of underground pipelines.

[0118] Specifically, the contour map analysis module analyzes the abrupt changes and anomalies within non-metallic pipelines in conjunction with the actual environment to obtain the location of underground pipelines and the cross-sectional dimensions of non-metallic pipelines.

[0119] In a further optimized manner, the data preprocessing module is used to perform resampling, instrument calibration, terrain correction, interference removal, straight-line trend removal, mean reduction, and filtering on the surface wave data collected on site.

[0120] Further optimized, the dispersion curve acquisition module is used to fuse the received P-wave velocity data by setting the Poisson ratio, constrain the range of the received S-wave velocity by the received P-wave velocity, perform multiple iterations to converge, and determine the relationship between S-wave velocity and depth that conforms to the optimal dispersion curve based on the mean square error transformation rate. Then, the above steps are repeated to obtain the relationship between S-wave velocity and depth for all groups of detector arrays in the detection area. Finally, based on the relationship between S-wave velocity and frequency, using λ=V / f and the half-wavelength empirical formula h=0.5λ, the damped least squares method is used to invert the dispersion curve to obtain the relationship between S-wave velocity and depth.

[0121] In a further optimized manner, the profile plotting and processing module generates a shear wave velocity-depth profile based on the relationship between shear wave velocity and depth at the center exploration point of each group of detectors in the detection area. The shear wave velocity-depth profile is then smoothed by moving average to generate Surfer plotting data, and the final contour map is plotted. Finally, the wave velocity image map is compared and verified with the contour map.

[0122] Further optimization reveals that non-metallic pipelines exhibit low-velocity anomalies, while non-metallic cement pipelines and metal pipelines, due to their hard surfaces, form high-velocity layered areas, exhibiting low-velocity anomalies within the pipes. The contour map analysis module, based on the abrupt anomalies and actual environmental analysis, identifies the location and relevant dimensions of underground pipelines.

[0123] Example 2

[0124] like Figures 1-8 As shown:

[0125] Figure 2 The horizontal axis represents the number of detectors, spaced 0.5m apart, and the vertical axis represents the arrival time of the surface wave.

[0126] Figure 3 , Figure 4 , Figure 5 , Figure 6 The horizontal axis represents the transverse wave velocity, and the vertical axis represents the frequency.

[0127] Figure 7 The profile indicates severe loosening and breakage of the strata in the actual area, manifested as a low-velocity anomaly in shear waves. Figure 7 The location circled in red indicates a high-speed transverse wave anomaly, which was identified as a concrete box culvert at a depth of approximately 3.5m. Further probing confirmed it to be the target drainage pipeline.

[0128] Figure 8The cross-section shows that hard strata exhibit higher shear wave velocities. Figure 8 The red box indicates the presence of a low-velocity transverse wave anomaly that contrasts sharply with the surrounding strata. At a depth of approximately 13.5m, it was identified as a target detection pipeline. Excavation and sealing confirmed that it was a drainage pipeline with a diameter of approximately 1m.

[0129] This embodiment further proposes a detection method for an underground pipeline detection system based on cross-hole seismic wave CT inspection technology, as described in any of the technical solutions in Embodiment 1, including sequentially executed steps S1-S9:

[0130] Step S1: Obtain the measured shear wave velocity and the measured longitudinal wave velocity;

[0131] The process of obtaining the measured shear wave velocity includes steps S111-S114 executed sequentially:

[0132] Step S111, Drilling: Drill the first borehole 3 and the second borehole 4 in the surface layer where the underground pipeline is located. The top view projection of the first borehole 3 and the top view projection of the second borehole 4 are located on the left and right sides of the cross-section of the underground pipeline, respectively.

[0133] Step S112: Insert the signal detection head assembly 2 and the seismic wave emission source 1 into the first borehole 3 and the second borehole 4, respectively.

[0134] Step S113: The seismic wave emitting source device 1 emits multiple seismic wave signals sequentially from shallow to deep.

[0135] Step S114: The signal detection head assembly device 2 collects seismic wave signals and transmits them to the host computer 7 for data processing.

[0136] The process of obtaining the measured longitudinal wave velocity is as follows:

[0137] Multiple detectors 5 are arranged in a linear array on the virtual linear line 8 of the detector array. The midpoint of the detector queue formed by the detectors 5 arranged in a linear array is used as the detection positioning point. Multiple test points with equal intervals are set between the two virtual connecting lines 9. Each test point is tested by aligning the detection positioning point with the test point. For each test point, a standard penetrator 10, which serves as the seismic source, is used to strike the ground multiple times at different positions and data is collected by each detector 5. The center of the opening of the first borehole 3 is the first test point on the virtual connecting line 9, and the center of the opening of the second borehole 4 is the last test point on the virtual connecting line 9.

[0138] Step S3: Obtain a wave velocity image based on the measured shear wave velocity and measured longitudinal wave velocity; the wave velocity image is used to represent the internal structure and anomalies of the strata.

[0139] Step S4: Preprocess the surface wave data collected on site;

[0140] Step S5: Obtain the dispersion curve that meets the resolution requirements;

[0141] Step S6: Constrain and limit the shear wave velocity to obtain the estimated shear wave velocity;

[0142] Step S7: Invert the dispersion curve to obtain the relationship between shear wave velocity and depth;

[0143] Step S8: Generate a graph of the relationship between shear wave velocity and depth for each group of detectors in the detection area, generate a shear wave velocity-depth profile, and perform moving average smoothing on the profile to obtain the final contour map. Then, compare and check the wave velocity image map with the contour map to make the internal structure and anomalies of the strata consistent, reduce interference and misjudgment, and distinguish between strata anomalies and target pipeline anomalies.

[0144] Step S9: Perform contour map analysis to obtain data on the location and cross-sectional dimensions of underground pipelines.

[0145] The detection method in this embodiment can solve the problem of accurate detection of underground pipelines buried at depths of less than 3 meters, accurately obtaining data such as spatial coordinates, depth, material, and size of the underground pipelines, providing basic information for design, layout, and project construction; it also has the following advantages:

[0146] Advantage 1: Based on experience, conventional ground-penetrating radar can detect pipeline locations when the ratio of its size to depth is between 1 / 5 and 1 / 3. However, in areas with high water content, radar waves are easily absorbed, making it difficult to detect pipelines deeper than 3 meters. When using acoustic detection, the leak point of the pipeline needs to be found first. Acoustic detection is easily affected by objective noise sources, resulting in shallow detection depths and difficulty in detecting deep pipelines. The detection method of this invention can accurately detect underground pipelines at extremely deep burial depths, is less affected by external objective conditions, and will not damage the pipeline being detected, thus belonging to non-destructive detection.

[0147] Advantage 2: For the first time, cross-hole seismic wave CT technology combined with the scientific theory of surface wave exploration stratigraphic boundary has been applied to the field of precise detection technology for ultra-deep underground pipelines, and has been successfully applied in engineering projects with good results, solving the problem of precise detection of ultra-deep underground pipelines.

[0148] Advantage 3: By constraining and limiting the P-wave velocity and Poisson's ratio obtained by cross-hole seismic wave CT inversion, the range of S-wave velocity can be estimated, reducing the drawbacks of direct S-wave velocity inversion by traditional methods and improving the accuracy of S-wave velocity inversion.

[0149] Advantage 4: By superimposing and fusing the dispersion curves of active and passive sources, not only is the vertical resolution of the dispersion curve improved, but the horizontal resolution is also improved, thereby improving the overall resolution of the dispersion curve.

[0150] Advantage 5: It adopts a node-type three-dimensional micro-motion acquisition station to collect natural source surface wave data from multiple angles. It integrates Beidou satellite synchronization and high-precision temperature-compensated pressure-controlled clock functions. The time error range between multiple devices is 25ns-100ns. The timekeeping error is less than 2μs in 1 hour without satellite signal. It also has a differential capacitor displacement force balance accelerometer function to avoid data distortion.

[0151] Advantage 6: In the inversion process, the mean square error transformation rate mathematical model can be used to intelligently determine the relationship between the transverse wave velocity and depth that conforms to the optimal dispersion curve, reducing the error of traditional manual identification of the best-fit dispersion curve.

[0152] In an optimized manner, during the process of obtaining the measured longitudinal wave velocity in step S1, when testing a test point, the ground surface located to the left of the underground pipeline is struck N times (preferably 3 times) by a standard penetration test hammer 10, and the ground surface located to the right of the underground pipeline is struck N times (preferably 3 times) by a standard penetration test hammer 10.

[0153] Optimally, step S3 includes the following steps:

[0154] Step S31, perform cross-aperture CT data processing: select the first arrival time for the acquired cross-aperture CT data, and use the algebraic reconstruction iterative algorithm to obtain the longitudinal wave velocity value distribution of 0.5m*0.5m grid cells by using the distance value between the two cross-apertures and a formula (1) and a formula (2). Then, obtain the high-precision longitudinal wave velocity value distribution of 0.1m*0.1m grid cells by using the least squares difference and densification, so that the longitudinal wave velocity values ​​of the same coordinates in the wave velocity image are correlated one by one.

[0155] Formula (1) is:

[0156]

[0157] Formula (2) is:

[0158]

[0159] In formula (1), l nm S is the path length of the nth ray within the mth unit. m It is the slowness of the m-th unit, t n It is the travel time of the nth ray;

[0160] In formula (2), Si represents the slowness of the i-th element, and Vi represents the longitudinal wave velocity of the i-th element;

[0161] Step S32: Combine the longitudinal wave velocity values ​​with a second fixed size square (specifically 0.1m*0.1m) interval within the detection range between the first borehole 3 and the second borehole 4 with the corresponding depth data to generate a wave velocity image map, using different colors to represent different velocity ranges.

[0162] Optimally, step S4 includes the following steps:

[0163] Step S41: Calibrate detector 5;

[0164] Step S42: Use a GNSS receiver to collect the coordinates and elevation of each detector 5, and add the coordinate and elevation data during data processing.

[0165] Optimally, step S5 includes the following steps:

[0166] Step S51: When the detector array 5, which is arranged in a linear array, collects the first set of waveform recording data of the first test point, the artificial source and natural source waveform recordings are extracted and separated at the strongest position in the recording diagram by the method of identifying the strongest noise; the first set of waveform recording data includes the test data of the standard penetration test hammer 10 after N times (2N) tests.

[0167] Step S52: Extract the artificial source waveform records and use wavelet transform to perform seismic inter-trace interpolation of the data;

[0168] Step S53: Use a τ-p transform algorithm to extract the dispersion energy map, and extract the strongest signal as the dispersion curve of the data set based on the dispersion energy map;

[0169] Step S54: When the detector queue composed of detectors 5 arranged in a linear array collects the first test point, the detector queue at this time is the first group of detectors 5. The artificial source surface wave data of N times (2N) separated from the first group of waveform recording data is processed to obtain the artificial source surface wave dispersion map and artificial source dispersion curve. The artificial source dispersion curves of the corresponding artificial source surface wave data of N times (2N) separated from the first group of waveform recording data are fused by energy weighting. The artificial source dispersion curves of N times (2N) are fused by weight, thereby completing the final dispersion map of the artificial source surface wave and the weighted fusion of the artificial source dispersion curve.

[0170] Simultaneously or in any order, the natural source waveform recording data separated from the first set of waveform recording data are used to extract the natural source surface wave dispersion energy map using the Extended Spatial Autocorrelation (ESPAC) method;

[0171] Step S55: Process the N times (2N) groups of natural source surface wave data separated from the first group of waveform recording data to obtain the natural source surface wave dispersion map and natural source dispersion curve. Perform energy equal weight fusion of the N times (2N) groups of natural source dispersion curves to complete the final energy equal weight fusion of the natural source surface wave dispersion map and the natural source dispersion curve.

[0172] Step S56: Integrate or sum the squares of the amplitudes of the artificial and natural source surface wave signals near frequency f to obtain energies E1(f) and E2(f). The dispersion curve of the artificial source surface wave is D1(f), and the dispersion curve of the natural source surface wave is D2(f). Calculate the energy proportions of the artificial and natural source surface waves at each frequency, expressed by weights: Artificial source surface wave weight:

[0173]

[0174] Natural source surface wave weights:

[0175]

[0176] Dispersion curve after fusion:

[0177] D(f)=P1(f)D1(f)+P2(f)D2(f) (5);

[0178] In step S5, the remaining test points that are not the first test point are executed in the same way as steps S51-S56, so that the fused dispersion curves of the remaining test points that are not the first test point are obtained one by one.

[0179] Optimally, step S6 includes the following steps:

[0180] Step S61: The range of Poisson's ratio and the measured P-wave velocity are used to constrain the range of the predicted S-wave velocity by formula (6); formula (6) is:

[0181]

[0182] In formula (6), V p It is the longitudinal wave velocity, V s Here, is the transverse wave velocity, and v is Poisson's ratio, typically between 0.2 and 0.4. Porosity is calculated by injecting mercury into core samples taken from the first two boreholes, based on the amount of mercury entering the core pores under different pressures. Φ is the medium inhomogeneity coefficient. The standard deviation σ is obtained using all P-wave velocities inverted via cross-hole CT. When σ is 0, it indicates a homogeneous medium, and Φ is 0. When 0 < σ ≤ 2, Φ takes a value of 0.1; when σ < 2, Φ takes a value of 0.3. K is a constant, ranging from 1.5 to 2.5 (preferably 2). It is calculated based on the amount of mercury entering the pores of the core after mercury is injected into the core samples taken from the two boreholes, under different pressures; specifically, Φ is defined as the medium inhomogeneity coefficient, and the standard deviation σ is calculated by inverting all longitudinal wave velocities through cross-hole CT. When σ is 0, it indicates that the medium is homogeneous and Φ is 0. When 0 < σ ≤ 2, Φ takes the value of 0.1, and when σ < 2, Φ takes the value of 0.3.

[0183] Step S62: Compare the range of the predicted shear wave velocity with the measured shear wave velocity. If the measured shear wave velocity does not meet the set requirements, resampling or correction is performed until the optimal dispersion curve is obtained.

[0184] Step S63: Based on the relationship between shear wave velocity and frequency, using λ=V / f and the half-wavelength empirical formula h=0.5λ, and employing the damped least squares method, the shear wave velocity and depth relationship is obtained by inverting the dispersion curve, thus obtaining the shear wave velocity and depth relationship diagram of the optimal dispersion curve; where λ is the wavelength, f is the frequency, and h is the depth.

[0185] In step S6, the remaining test points that are not the first test point are executed in the same manner as steps S61-S63, so as to obtain the relationship between the transverse wave velocity and depth of the optimal dispersion curve of the remaining test points that are not the first test point.

[0186] Ideally, when the medium of the surface layer being probed is isotropic, the Poisson's ratio formula is set as follows:

[0187]

[0188] In formula (7), porosity The value is 1, the medium non-uniformity coefficient Φ is 0, and the meanings of the other symbols in formula (7) are the same as those in formula (6).

[0189] Example 3

[0190] Example 3 is an optimized design of any one of the technical solutions in Example 2;

[0191] In step S112, after drilling is completed, PVC casing is placed in each hole. Then, a high-sensitivity multichannel hydrophone is placed in each hole at a spacing of 0.5m. The length of the hydrophone is from the ground to the bottom of the hole. An electric spark source is placed in one hole. The hydrophone is connected to a seismograph on the ground. The electric spark source is connected to an energy box (i.e., the control end of the seismic wave transmitting source device 1 described in Example 1). The other end of the energy box is connected to the seismograph. The seismograph is connected to a computer, and the data acquisition is controlled by a workstation. A virtual straight line is drawn between the centers of the two holes, and each end is extended by 20m.

[0192] In step S113, the electric spark source is fired every 0.2m from the ground downwards, and the hydrophone receives the seismic data until the electric spark source reaches the bottom of the hole, thus completing the data acquisition.

[0193] In step S114, after the above-mentioned cross-hole seismic wave CT has acquired the data, it performs indoor processing. The P-wave velocity value distribution between the two holes is inverted according to the 0.5m*0.5m grid unit. The least squares difference method is used to obtain the P-wave velocity value distribution of the 0.1m*0.1m grid unit.

[0194] In step S1, during the acquisition of the measured P-wave velocity, a standard penetration test (SPT) hammer weighing over 50 kg is used as the source to strike the ground to the left of the linear array detector. A metal plate is placed on the ground, and the angle between the line connecting the metal plate and the first detector and the line where the detector is located is as close to 0° as possible, ensuring it is less than -9° to 9°. The location of the hammer strike source is on the same straight line as the center of the detector, and the shot-detector distance is 3 meters. After acquiring the first set of hammer strike data, the metal plate is moved, and the shot-detector distance is 5 meters. After an interval of about 10 minutes, a second hammer strike is performed. The metal plate is moved again, and the distance between the detectors is 8 meters. After an interval of about 10 minutes, a third hammer strike is performed. The detector sampling interval is 0.1 ms, the number of sampling points is 200,000, and the total acquisition time is about 20 minutes, acquiring a total of 3 sets of data. The metal plate is then moved to the right side of the detector, again ensuring that the location of the hammer strike source is on the same straight line as the center of the detector, and the shot-detector distance is 3 meters. After acquiring the first set of hammer data, the iron plate was moved, with a shot-detector distance of 5 meters. A second hammering was performed approximately 10 minutes later. The iron plate was moved again, with a detector spacing of 8 meters. A third hammering was performed approximately 10 minutes later. The detector sampling interval was 0.1 ms, with 200,000 sampling points. The total acquisition time was approximately 20 minutes, acquiring 3 sets of data. A total of 6 sets of data were acquired throughout the process. Specifically, a linear array was arranged on a straight line, with 50 detectors placed. The detector offset distance was set to 0.5 meters, arranged sequentially from left to right, ensuring the array center overlapped with the left borehole center. This ensured the first set of waveform data acquired by the array was on the vertical line of the borehole center. Next, the leftmost detector was moved to the rightmost position, also 0.5 meters away. Step 2 was repeated to acquire data after moving the array. After acquiring one set of array data, the leftmost detector was continuously moved to the rightmost position in a rolling motion until the array center overlapped with the right borehole center.

[0195] In step S4, the surface wave data collected on site undergoes resampling, instrument calibration, terrain correction, interference elimination, delinearization, demeaning, and filtering. After a series of data preprocessing steps, the detection accuracy is effectively improved.

[0196] In step S5, the waveform records of artificial and natural sources are separated by identifying the strongest noise in each group of data collected by the first set of detectors. Using self-developed surface wave processing software, wavelet transform is first used to perform inter-trace interpolation of each group of original data, with an interpolation interval of 0.1m. Then, a τ-p transform algorithm is used to extract the dispersion energy map of each group of interpolated waveform data of the artificial source surface waves. Based on the dispersion energy map, the strongest signal is extracted as the dispersion curve of that group of data. All dispersion curves of the left source and all dispersion curves of the right source are fused to obtain the average dispersion curve of the artificial source surface waves. The extended spatial autocorrelation (ESPAC) method is used to extract the dispersion energy map of the natural source surface waves. Based on the dispersion energy map, the strongest signal is extracted as the dispersion curve of the natural source surface waves. The dispersion curves of the artificial and natural source surface waves are fused to form a high-resolution dispersion curve.

[0197] In step S9, the velocity image is compared and analyzed with the generated cross-hole CT image to more accurately determine the geological structure, distinguish it from the anomalies of underground pipelines, and reduce the possibility of misjudgment caused by geological anomalies. Underground pipelines have their own independent anomalies due to their physical properties, which do not interfere with each other and will be different from geological anomalies. Non-metallic pipelines show low-velocity anomalies, while non-metallic cement pipelines and metal pipelines will form high-velocity layered areas due to their hard surfaces, and show low-velocity anomalies inside the pipes. Based on the abnormality of pipeline changes and the analysis of the actual environment, the burial depth and size of underground pipelines can be accurately found.

[0198] In step S41, a site with relatively uniform geological structure is found, and all the geophones that have collected data are arranged in a circle. A standard penetration test hammer is used to excite the source at the center of the circle. According to theory, the distance from the source to each geophone should be the same, and the amplitude and phase of the wave received by each geophone should be the same. Geophones with inconsistent phases or amplitudes are corrected, and the correction values ​​are added to the data collected by that geophone for preprocessing.

[0199] In step S42, a GNSS receiver is used to collect the coordinates and elevation of each detector. When processing the data, the coordinate and elevation data are added instead of treating the elevation of all detectors as the same. This makes the recorded point positions more accurate and more in line with the actual situation, thus providing accurate basic data for the following steps.

[0200] In step S52, the interpolation interval is 0.1m.

[0201] Step S5 also includes step S57, which is executed after step S56: Based on energy-weighted fusion, an adaptive mechanism is introduced to dynamically adjust the weights according to the characteristics and quality of the data. Based on the weight adjustment function of indicators such as data signal-to-noise ratio and bandwidth, when the signal-to-noise ratio of a certain frequency band is high and the bandwidth is wide, its weight in that frequency band is automatically increased, and vice versa, so as to achieve high-precision fusion of dispersion curves.

[0202] In step S5, after all test points other than the first test point have been processed in the manner of steps S51-S56, the following step is performed: Based on energy-weighted fusion, an adaptive mechanism is introduced to dynamically adjust the weights according to the characteristics and quality of the data. A weight adjustment function based on indicators such as signal-to-noise ratio and bandwidth automatically increases the weight of a frequency band when the signal-to-noise ratio is high and the bandwidth is wide, and decreases the weight when the signal-to-noise ratio is low, thus achieving high-precision fusion of dispersion curves (e.g., ...). Figure 4 (As shown).

[0203] In formula (6) of step S6, the range of the shear wave velocity value is obtained by substituting the range of Poisson's ratio and the longitudinal wave velocity value; in step S62, after repeated calculations, when there is no unreasonable and obvious difference between the inverted shear wave velocity and the predicted shear wave velocity, the inversion result gradually approaches a certain fixed value. Each repeated calculation will produce a mean square error rate of change. By continuously observing the mean square error rate of the calculation results, the end of the inversion is determined. The mean square error rate of change represents the degree of overlap between the inverted measured dispersion curve and the original theoretical dispersion curve. When the mean square error rate of change approaches 0, it can be found that the original dispersion curve is infinitely close to the measured dispersion curve, and the optimal dispersion curve can be obtained.

[0204] Example 4

[0205] Example 4 is a further optimized design of Example 1;

[0206] The analysis program performs the following control steps:

[0207] Based on the measured shear wave velocity and measured longitudinal wave velocity, wave velocity image maps are obtained; wave velocity image maps are used to represent the internal structure and anomalies of the formation.

[0208] Preprocess the surface wave data collected on-site;

[0209] Obtain the dispersion curve that meets the resolution requirements;

[0210] By constraining and limiting the shear wave velocity, the inferred shear wave velocity can be obtained;

[0211] The dispersion curve is inverted to obtain the relationship between shear wave velocity and depth.

[0212] A graph based on the relationship between shear wave velocity and depth is generated for each group of detectors within the detection area. A shear wave velocity-depth profile is generated, and the profile is smoothed by moving average to obtain the final contour map. Then, the wave velocity image map and the contour map are compared and verified together.

[0213] Contour map analysis was performed to obtain data on the location and cross-sectional dimensions of underground pipelines.

[0214] In a further optimized manner, the specific implementation process of the above steps is the same as the corresponding steps of the method in Example 2.

[0215] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A detection method for an underground pipeline detection system using cross-hole seismic wave CT inspection technology, characterized in that, The underground pipeline detection system includes a seismic wave emission source device and a signal detection head assembly. This system is applied to the surface layer where underground pipelines are located, with a first borehole and a second borehole drilled. The top-view projections of the first and second boreholes are located on the left and right sides of the cross-section of the underground pipeline, respectively. The signal detection head assembly and the seismic wave emission source device extend into the first and second boreholes, respectively. A virtual connecting line between the center of the first borehole opening and the center of the second borehole opening is a virtual connecting line between the two boreholes. The system is characterized by further comprising: Multiple detectors are arranged in a linear array on the ground surface, forming a virtual straight line array of detectors. This virtual straight line array intersects the underground pipeline in the top-view projection direction and coincides with the virtual connection line between two holes. The seismograph communicates with the signal detection head assembly; the seismograph is also physically connected to the control terminal of the seismic wave emission source device. The host computer communicates with multiple geophones and seismographs; the host computer has an analysis program that analyzes the signals from the geophones and seismographs to obtain the location and relevant dimensions of underground pipelines. The detection method includes: Step S1: Obtain the measured shear wave velocity and the measured longitudinal wave velocity; The process of obtaining the measured shear wave velocity includes steps S111-S114: Step S111, Drilling: Drill the first borehole and the second borehole in the surface layer where the underground pipeline is located. The top view projection of the first borehole and the top view projection of the second borehole are located on the left and right sides of the cross-section of the underground pipeline, respectively. Step S112: Insert the signal detection head assembly and the seismic wave emission source device into the first borehole and the second borehole, respectively; Step S113: The seismic wave emission source device emits multiple seismic wave signals sequentially from shallow to deep. Step S114: The signal detection head assembly device collects seismic wave signals and transmits them to the host computer for internal processing. The process of obtaining the measured longitudinal wave velocity is as follows: Multiple detectors are arranged in a linear array on a virtual straight line of the detector array. The midpoint of the detector queue, which is composed of all the detectors arranged in the linear array, is used as the detection positioning point. Multiple test points with equal intervals are set between the two virtual boreholes. Each test point is tested by aligning the detection positioning point with the test point. For each test point, a standard penetrator, acting as the seismic source, strikes the ground multiple times at different positions and collects data through each detector. The center of the first borehole is the first test point on the virtual line connecting the two boreholes, and the center of the second borehole is the last test point on the virtual line connecting the two boreholes. Step S3: Obtain a wave velocity image based on the measured shear wave velocity and measured longitudinal wave velocity; the wave velocity image is used to represent the internal structure and anomalies of the strata. Step S4: Preprocess the surface wave data collected on site; Step S5: Obtain the dispersion curve that meets the resolution requirements; Step S6: Constrain and limit the shear wave velocity to obtain the estimated shear wave velocity; Step S7: Invert the dispersion curve to obtain the relationship between shear wave velocity and depth; Step S8: Generate a graph showing the relationship between shear wave velocity and depth for each detector array within the detection area, generate a shear wave velocity-depth profile, perform moving average smoothing on the profile to obtain the final contour map, and then compare and verify the wave velocity image map with the contour map. Step S9: Perform contour map analysis to obtain data on the location and cross-sectional dimensions of underground pipelines.

2. The detection method according to claim 1, characterized in that, The seismic wave emission source device is an electric spark source; the signal detection head assembly device is a tubular detection device, which includes a tube body and multiple hydrophones installed inside the tube body, and the tube body does not obstruct the detection end of each hydrophone.

3. The detection method according to claim 1, characterized in that, The length of the virtual straight line of the detector array is greater than the length of the virtual connecting line between the two holes; the number of detectors is 40-60; the center distance between two adjacent detectors is 0.3 meters-0.6 meters; the angle formed by the virtual connecting line between the two holes and the underground pipeline is 90°±α, where α is ≤9°; the drilling verticality of the first and second boreholes is 90°±β, where β is ≤1°; the length of the virtual connecting line between the two holes is 19 meters-21 meters; the distance between the apex of the underground pipeline and the ground surface is greater than 3 meters.

4. The detection method according to claim 1, characterized in that, The analysis program includes the following software modules: The wave velocity image acquisition module acquires wave velocity images based on measured shear wave velocities and measured longitudinal wave velocities; the wave velocity image images are used to represent the internal structure and anomalies of the strata. The data preprocessing module is used to preprocess the surface wave data collected on site; The dispersion curve acquisition module is used to acquire dispersion curves that meet the resolution requirements. The module for obtaining the estimated shear wave velocity is used to obtain the estimated shear wave velocity. The inversion processing module is used to invert the dispersion curve to obtain the relationship between shear wave velocity and depth; The profile drawing and processing module is used to obtain the final contour map, and then compare and verify the wave velocity image map with the contour map to effectively verify the internal structure and anomalies of the surface layer, distinguish them from the independent anomalies of the target pipeline, and accurately locate the target underground pipeline. The contour map analysis module is used to perform contour map analysis and obtain data on the location and cross-sectional dimensions of underground pipelines.

5. The detection method according to claim 1, characterized in that, In the process of obtaining the measured longitudinal wave velocity in step S1, when testing a test point, the ground surface located to the left of the underground pipeline is struck N times with a standard penetration test hammer, and the ground surface located to the right of the underground pipeline is struck N times again with a standard penetration test hammer.

6. The detection method according to claim 1, characterized in that, Step S3 includes the following steps: Step S31, perform cross-aperture CT data processing: select the first arrival time for the acquired cross-aperture CT data, and use the algebraic reconstruction iterative algorithm to obtain the longitudinal wave velocity value distribution of the grid cell with the first fixed size square by using the distance value between the two cross-apertures and a formula (1) and a formula (2). Then, obtain the high-precision longitudinal wave velocity value distribution of the grid cell with the second fixed size square by using the least squares difference and densification, so that the longitudinal wave velocity values ​​of the same coordinate in the wave velocity image are correlated one by one. Formula (1) is: = (1) Formula (2) is: (2) In formula (1), It is the path length of the nth ray within the mth unit. It is the slowness of the m-th unit. It is the travel time of the nth ray; In formula (2), Si represents the slowness of the i-th element, and Vi represents the longitudinal wave velocity of the i-th element; Step S32: Combine the longitudinal wave velocity values ​​with a second fixed size square interval within the detection range between the first borehole and the second borehole with the corresponding depth data to generate a wave velocity image map, and use different colors to represent different velocity ranges.

7. The detection method according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Calibrate the detector; Step S42: Use a GNSS receiver to collect the coordinates and elevation of each detector, and add the coordinate and elevation data during data processing.

8. The detection method according to claim 5, characterized in that, Step S5 includes the following steps: Step S51: When the detector array composed of detectors arranged in a linear array collects the first set of waveform recording data for the first test point, the artificial source and natural source waveform recordings are extracted and separated at the strongest position in the recording diagram using the method of identifying the strongest noise; the first set of waveform recording data includes the test data of the standard penetration test hammer performing N times twice; Step S52: Extract the artificial source waveform records and use wavelet transform to perform seismic inter-trace interpolation of the data; Step S53: Use the τ-p transform algorithm to extract the dispersion energy map, and extract the strongest signal as the dispersion curve of the data set based on the dispersion energy map; Step S54: When the detector queue, composed of detectors arranged in a linear array, collects the first test point, the detector queue is the first group of detectors. The N twice the group of artificial source surface wave data separated from the first group of waveform recording data is processed to obtain the artificial source surface wave dispersion map and artificial source dispersion curve. The artificial source dispersion curves corresponding to the N twice the group of artificial source surface wave data separated from the first group of waveform recording data are fused by energy weighting. The artificial source dispersion curves of the N twice the group are fused by weight, thereby completing the final dispersion map of the artificial source surface wave and the weighted fusion of the artificial source dispersion curve. Simultaneously or in any order, the natural source waveform recording data separated from the first set of waveform recording data are used to extract the natural source surface wave dispersion energy map using the Extended Spatial Autocorrelation (ESPAC) method; Step S55: Process the N-fold group of natural source surface wave data separated from the first group of waveform recording data to obtain the natural source surface wave dispersion map and the natural source dispersion curve. Perform energy equal weight fusion on the N-fold group of natural source dispersion curves to complete the final energy equal weight fusion of the natural source surface wave dispersion map and the natural source dispersion curve. Step S56: Integrate or sum the squares of the amplitudes of the artificial and natural source surface wave signals near frequency f to obtain the energy. The dispersion curve of the artificial source surface wave is The dispersion curve of the natural source surface wave is Calculate the energy proportions of artificial and natural source surface waves at each frequency, expressed using weights: Artificial source surface wave weights: (3) Natural source surface wave weights: (4) Dispersion curve after fusion: = (5)； In step S5, the remaining test points that are not the first test point are executed in the same way as steps S51-S56, so that the fused dispersion curves of the remaining test points that are not the first test point are obtained one by one.

9. The detection method according to claim 1, characterized in that, Step S6 includes the following steps: Step S61: The range of Poisson's ratio and the range of measured P-wave velocity are constrained by formula (6) to obtain the range of predicted S-wave velocity; formula (6) is: (6) In formula (6), It is the longitudinal wave velocity. For transverse wave velocity, Poisson's ratio, To determine porosity, core samples were taken from the first two boreholes, and mercury was injected into the core samples. The porosity was calculated based on the amount of mercury entering the core pores under different pressures. The inhomogeneity coefficient is obtained by calculating the standard deviation σ of all P-wave velocities inverted through cross-hole CT; porosity. It is calculated based on the amount of mercury entering the pores of the core samples taken from the two boreholes after mercury was injected into them, according to different pressures; K is a constant: its value is between 1.5 and 2.5; Step S62: Compare the range of the predicted shear wave velocity with the measured shear wave velocity. If the measured shear wave velocity does not meet the set requirements, resampling or correction is performed until the optimal dispersion curve is obtained. Step S63: Based on the relationship between shear wave velocity and frequency, using λ=V / f and the half-wavelength empirical formula h=0.5λ, and employing the damped least squares method, the dispersion curve is inverted to obtain the relationship between shear wave velocity and depth, thus obtaining the relationship diagram between shear wave velocity and depth of the optimal dispersion curve; where λ is the wavelength, f is the frequency, and h is the depth. In step S6, the remaining test points that are not the first test point are executed in the same manner as steps S61-S63, so as to obtain the relationship between the transverse wave velocity and depth of the optimal dispersion curve of the remaining test points that are not the first test point.

Images