Method and system for detecting urban underground pipelines

Abstract

The application discloses a kind of detection method and system of urban underground pipeline, comprising: along the preset survey line, based on the synchronization acquisition of multicomponent electromagnetic response data and multicomponent seismic wave mapping data in the control instruction of co-time sequence, establish time correspondence;In the same acquisition point, extract multicomponent electromagnetic attenuation curve set from electromagnetic data and determine main energy azimuth, extract transverse wave splitting parameter including fast wave polarization azimuth and transverse wave reflection travel time from seismic wave data;Calculate the spatial azimuth deviation of main energy azimuth and fast wave polarization azimuth, when the deviation is less than the preset threshold and the electromagnetic attenuation curve set meets the preset anisotropic attenuation intensity condition, determine that candidate pipeline target exists, determine its depth and trend according to acquisition point coordinates, transverse wave reflection travel time and fast wave polarization azimuth and output.The directionality characteristics of the present application are associated and verified in spatial orientation by electromagnetic and elastic wave, reduce the risk of misjudgment in complex environment, improve detection accuracy.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, specifically to a method and system for detecting urban underground pipelines. Background Technology

[0002] Accurate detection of underground pipelines in cities is a crucial aspect of municipal construction, pipeline maintenance, and engineering projects. Electromagnetic induction has high sensitivity for detecting metallic pipelines, while elastic wave detection can reflect the geometry and spatial location of underground targets; each has its advantages. However, in complex urban environments such as those with iron-containing backfill, power line interference, and large-diameter non-metallic pipelines, both electromagnetic induction and elastic wave methods have limitations and are easily affected by non-target anomalies.

[0003] The aforementioned limitations arise because the response characteristics of metal pipelines under different detection methods have not been fully correlated and utilized. Existing detection methods struggle to comprehensively identify pipeline targets from the perspective of joint responses. Therefore, improving the accuracy of urban underground pipeline detection and reducing the misjudgment rate in complex environments is the technical problem this application aims to solve. Summary of the Invention

[0004] This application provides a method and system for detecting underground pipelines in cities, which at least addresses the problems existing in the prior art.

[0005] A first aspect of this application provides a method for detecting urban underground pipelines, comprising the following steps: S1. During the process of traveling along the preset survey line, based on the time-sequence control command, the transient electromagnetic detection device is triggered to collect multi-component electromagnetic response data, and the elastic wave detection device is triggered to collect multi-component seismic wave image data. S2. Based on the co-time sequence control command, establish the time correspondence between each sampling time in the multi-component electromagnetic response data and each sampling time in the multi-component seismic wave image data; S3. At the same acquisition point, based on the time correspondence, extract the set of multi-component electromagnetic attenuation curves from the multi-component electromagnetic response data and determine the principal energy azimuth of the anisotropy of the induced magnetic field. Extract the shear wave splitting parameters from the multi-component seismic wave image data. The shear wave splitting parameters include the fast wave polarization azimuth and the shear wave reflection travel time. S4. Calculate the azimuth difference between the main energy azimuth and the fast wave polarization azimuth, and use it as the spatial azimuth deviation. S5. When the spatial orientation deviation is less than the preset deviation threshold and the multi-component electromagnetic attenuation curve set satisfies the preset anisotropic attenuation intensity condition of the metal pipeline, the underground location below the collection point is determined to be a candidate pipeline target. S6. Based on the spatial coordinates of the acquisition point, the travel time of the transverse wave reflection, and the azimuth angle of the fast wave polarization, determine and output the burial depth and direction of the candidate pipeline target.

[0006] By synchronously acquiring multi-component electromagnetic response data and multi-component seismic wave image data at the same acquisition point using time-series control commands, the principal energy azimuth angle of the anisotropic induced magnetic field and the fast wave polarization azimuth angle of the shear wave splitting are extracted. The spatial azimuth deviation between the two is used as one of the core criteria, combined with the anisotropic attenuation intensity condition of the multi-component electromagnetic attenuation curve set, to determine whether there are candidate pipeline targets in the underground location. This method correlates and verifies the directional response characteristics of metal pipelines exhibited in transient electromagnetic detection with those exhibited in elastic wave detection in terms of spatial azimuth. This allows the two detection methods to corroborate and constrain each other, helping to distinguish metal pipelines from non-target interference objects with only single directional response characteristics. This reduces the risk of misjudgment in complex urban environments such as iron-containing backfill soil and large-diameter non-metallic pipelines, and improves the accuracy of pipeline detection results.

[0007] Furthermore, in S3, the shear wave splitting parameters are extracted, including: rotating the two horizontal components in the multi-component seismic wave image data to radial and transverse components; calculating the cross-correlation function between the radial and transverse components, determining the splitting time difference between the fast and slow shear waves based on the cross-correlation function, and extracting the polarization azimuth angle of the fast wave from the rotation angle; and determining the shear wave reflection travel time based on the arrival times of the fast and slow shear waves.

[0008] By performing coordinate rotation and cross-correlation analysis on the two horizontal components, fast and slow shear waves can be separated from multi-component seismic wave image data. The polarization azimuth angle and splitting time difference of the fast wave, which reflect the anisotropic characteristics of the medium, can be extracted, providing reliable input parameters for subsequent spatial azimuth correlation calculations with the azimuth angle of the electromagnetic anisotropic principal energy.

[0009] Furthermore, in S3, determining the principal energy azimuth angle of the anisotropy of the induced magnetic field includes: vector synthesis of the amplitudes of the components in different directions of the multi-component electromagnetic attenuation curve within a preset early time window to obtain the evolution trajectory of the horizontal vector of the induced magnetic field over time; extracting the principal energy direction of the evolution trajectory, and determining the angle pointed to by the principal energy direction as the principal energy azimuth angle.

[0010] By vector synthesis of the induced magnetic field components in different directions within an early time window, the principal energy direction of the anisotropy of the induced magnetic field can be extracted from the multi-component electromagnetic attenuation curve. This direction reflects the main spatial orientation of the induced eddy current field in the metal pipeline, providing a basis for subsequent azimuth comparison with the polarization azimuth of the fast wave.

[0011] Furthermore, in S5, determining whether the preset anisotropic attenuation intensity condition of the metal pipeline is met includes: calculating the amplitude ratio of the components in different directions of the multi-component electromagnetic attenuation curve within a preset early time window, as the anisotropic ratio; when the anisotropic ratio exceeds the preset intensity threshold, it is determined that the preset anisotropic attenuation intensity condition of the metal pipeline is met.

[0012] By calculating the amplitude ratio of different directional components in the early time channel as the anisotropy ratio and comparing it with a preset intensity threshold, the significance of the anisotropy of the induced magnetic field can be quantitatively evaluated, providing a quantitative criterion for distinguishing metal pipelines from interference bodies with indistinct anisotropic characteristics.

[0013] Furthermore, after S6, S7 and S8 are also included: S7: Obtain the confirmed pipeline target verified by excavation or drilling, and extract the spatial orientation deviation and anisotropy ratio corresponding to the confirmed pipeline target; S8: Add the extracted spatial orientation deviation and anisotropy ratio as new positive samples to the training sample set to retrain the classifier, so as to update the preset deviation threshold and preset intensity threshold.

[0014] By feeding back the confirmed pipeline target data from actual excavation verification into the classifier's training process, the discrimination threshold can be continuously optimized using real pipeline cases. This allows the preset deviation threshold and preset intensity threshold to gradually approach the characteristic distribution of the actual pipeline response as the detection work progresses, which helps to improve the accuracy and adaptability of the judgment in subsequent detections.

[0015] Furthermore, in S6, the burial depth of the candidate pipeline target is determined by: using the set of multi-component electromagnetic attenuation curves corresponding to the candidate pipeline target as observation data, using the preliminary burial depth obtained by transverse wave reflection travel time conversion as the initial model constraint, performing one-dimensional inversion on the multi-component electromagnetic response data to obtain the corrected burial depth and apparent conductivity; and querying the preset pipeline material comparison table according to the apparent conductivity to output the predicted material type of the candidate pipeline target.

[0016] By using the preliminary burial depth calculated from transverse wave reflection travel time as the initial model constraint for electromagnetic inversion, the search range for inversion solution can be narrowed, which helps to improve the stability and accuracy of burial depth estimation. At the same time, the apparent conductivity information obtained from the inversion can provide a reference for the prediction of pipeline material type.

[0017] Furthermore, the preset deviation threshold and preset intensity threshold are determined as follows: Multiple first samples are acquired and calculated according to steps S1 to S4 on a known metal pipeline section. Each first sample includes the corresponding spatial orientation deviation and anisotropy ratio, and is labeled as a positive sample. Multiple second samples are acquired and calculated according to steps S1 to S4 on a known non-pipeline interference section. Each second sample includes the corresponding spatial orientation deviation and anisotropy ratio, and is labeled as a negative sample. Using the positive and negative samples as training data, a classifier is trained in a two-dimensional feature space composed of the spatial orientation deviation and anisotropy ratio to obtain the decision boundary for dividing positive and negative samples. Based on the decision boundary, the preset deviation threshold and preset intensity threshold are determined.

[0018] By collecting and labeling samples in known pipeline sections and known interference sections, a classifier is trained in a two-dimensional feature space composed of spatial orientation deviation and anisotropy ratio. Based on the measured data, the discrimination boundary can be automatically determined, making the threshold setting more consistent with the pipeline response distribution characteristics and interference response distribution characteristics in the actual detection scenario.

[0019] Furthermore, S5 also includes: obtaining the spatial orientation deviation of multiple adjacent acquisition points along the preset survey line direction and the judgment results of the preset anisotropic attenuation intensity conditions of metal pipelines; when the spatial orientation deviation is less than the preset deviation threshold and the number of consecutive acquisition points that meet the preset anisotropic attenuation intensity conditions of metal pipelines exceeds the preset number threshold, the underground locations below the consecutive acquisition points are merged and judged as the same candidate pipeline target.

[0020] By performing spatial continuity analysis on the judgment results of adjacent collection points, isolated single-point anomaly judgments can be filtered out, and multiple points that meet the conditions and are continuously distributed can be merged into a coherent pipeline target. This helps to reduce misjudgments caused by local noise or accidental interference, making the judgment results of pipeline targets more continuous and reasonable in space.

[0021] Furthermore, when the elastic wave detection device acquires multi-component seismic wave image data, it uses a transverse wave source for excitation; the receiving coil of the transient electromagnetic detection device is a horizontal dual-component or triple-component induction coil, which is used to acquire the attenuated signals of the induced magnetic field components in the direction parallel to and perpendicular to the preset survey line, respectively.

[0022] By using a shear wave source for excitation, it is beneficial to generate shear wave splitting near the pipeline target, providing a physical basis for extracting the fast wave polarization azimuth angle; by using horizontal dual-component or triple-component induction coils to obtain induced magnetic field components in different directions, multi-component data sources are provided for calculating the principal energy azimuth angle of the anisotropy of the induced magnetic field.

[0023] Furthermore, the execution of S1 also includes: acquiring the spatial position data of the transient electromagnetic detection device and the elastic wave detection device in real time; comparing the spatial position data with the pre-stored pipeline geographic information base map; and generating a heading correction command when it is determined that the angle between the current direction of travel and the suspected pipeline direction recorded in the base map is less than the preset angle, so as to adjust the direction of travel of the preset survey line.

[0024] By comparing the direction of the detection platform with the suspected pipeline direction on the existing pipeline geographic information base map in real time, the direction of the detection line can be dynamically adjusted during the detection process. This helps to maintain a better geometric relationship between the detection line and the pipeline direction, providing operational support for obtaining clearer detection response signals.

[0025] Furthermore, after S5 determines that a candidate pipeline target exists, the process also includes: generating a location marker at the surface projection position of the candidate pipeline target; driving the detection platform to move to the location marker again, and using the direction perpendicular to the fast wave polarization azimuth angle as the new survey line direction to perform a second detection on the candidate pipeline target; obtaining the second spatial azimuth deviation and the second anisotropy ratio obtained from the second detection, and comparing the consistency with the corresponding data obtained from the first detection; if the consistency index exceeds a preset threshold value, the candidate pipeline target is confirmed as a confirmed pipeline target.

[0026] By changing the direction of the survey line at the location of the candidate pipeline target to conduct a secondary detection, and using the consistency of the detection results from different directions as the confirmation criterion, the judgment results of the first detection can be cross-validated, which helps to reduce misjudgments caused by accidental factors and improve the confidence of the final confirmed pipeline target.

[0027] A second aspect of this application provides a detection system for urban underground pipelines. The system includes: a detection platform integrating a transient electromagnetic detection device and an elastic wave detection device; a synchronization control unit for generating synchronous timing control commands to drive the transient electromagnetic detection device and the elastic wave detection device to synchronously collect data; a real-time positioning unit for acquiring real-time dynamic positioning information of the detection platform during its movement; and a data processing unit communicatively connected to the synchronization control unit, the real-time positioning unit, the transient electromagnetic detection device, and the elastic wave detection device, respectively. The data processing unit is configured to execute program instructions corresponding to the above-described method.

[0028] By integrating transient electromagnetic detection devices and elastic wave detection devices into the same detection platform and configuring a synchronous control unit, a real-time positioning unit, and a data processing unit, a hardware foundation can be provided for the implementation of the above detection methods. This enables the synchronous acquisition, temporal correlation, and joint processing of multi-component electromagnetic response data and multi-component seismic wave image data, which helps to improve the accuracy of pipeline detection in complex urban environments. Attached Figure Description

[0029] Figure 1 A flowchart illustrating a method for detecting urban underground pipelines provided in this application embodiment; Figure 2 A schematic diagram of the framework of an urban underground pipeline detection system provided in this application embodiment; Figure 3 A schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0030] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0031] Among the technologies related to urban underground pipeline detection, electromagnetic induction has high detection sensitivity for metal pipelines, while elastic wave detection can reflect the geometry and spatial location of underground targets; each has its advantages. However, in complex urban environments such as iron-containing backfill soil, power line interference, and large-diameter non-metallic pipelines, using either electromagnetic induction or elastic wave detection alone is easily affected by non-target anomalies, making it difficult to accurately reflect the true distribution of pipelines.

[0032] Analysis revealed that the shortcomings of the aforementioned detection methods in complex environments stem from the fact that the response signals generated by metal pipelines under different detection methods were treated in isolation and not fully utilized in a correlated manner. In complex urban environments, interfering objects such as iron-containing backfill soil may exhibit signal characteristics similar to metal pipelines under a single detection method. Relying solely on signal strength or travel time information is insufficient to distinguish them from the actual pipeline target. Therefore, it is necessary to seek a deeper understanding of the response characteristics to differentiate metal pipelines from interfering objects.

[0033] To address this issue, this application proposes a method and system for detecting underground pipelines in cities. It simultaneously acquires multi-component electromagnetic response data and shear wave multi-component seismic image data at the same acquisition point, extracting the principal energy azimuth angle of the induced magnetic field anisotropy and the fast wave polarization azimuth angle of the shear wave splitting from these data. The degree of spatial azimuth deviation between these two data points is used as one criterion, combined with the significant degree of electromagnetic anisotropy attenuation for joint determination. When the two directional characteristics tend to be consistent in spatial orientation and the electromagnetic anisotropy attenuation intensity meets preset conditions, it indicates that the underground target at that location simultaneously possesses electromagnetic and elastic wave directions consistent with those of the metal pipeline. Non-target interference objects typically do not simultaneously possess these two directional characteristics, or their azimuth directions are inconsistent. Through this dual-physical field directional characteristic correlation verification mechanism, the two detection methods are mutually verified and constrained in spatial orientation, helping to reduce the risk of misjudgment in complex urban environments and improve the accuracy of pipeline detection results.

[0034] The method and system for detecting urban underground pipelines provided in this application can be applied not only to the conventional detection of underground metal pipelines in cities, but also to the general survey and verification of underground pipelines in areas such as integrated utility tunnels, industrial plants, and airport runways. Detection targets can include not only metal pipelines for water supply, gas, and heating, but also underground facilities with conductive properties such as cables and communication lines. In practical applications, the method and system of this application can be used for as-built surveying of newly laid pipelines, as well as for location verification and hazard identification of existing pipelines, and are suitable for urban environments with varying geological conditions and pipeline distribution densities.

[0035] Please refer to Figure 1 , Figure 1 This application provides a method for detecting underground pipelines in cities, comprising the following steps: S1. During the process of traveling along the preset survey line, based on the time-sequence control command, the transient electromagnetic detection device is triggered to collect multi-component electromagnetic response data, and the elastic wave detection device is triggered to collect multi-component seismic wave image data. S2. Based on the co-time sequence control command, establish the time correspondence between each sampling time in the multi-component electromagnetic response data and each sampling time in the multi-component seismic wave image data; S3. At the same acquisition point, based on the time correspondence, extract the set of multi-component electromagnetic attenuation curves from the multi-component electromagnetic response data and determine the principal energy azimuth of the anisotropy of the induced magnetic field. Extract the shear wave splitting parameters from the multi-component seismic wave image data. The shear wave splitting parameters include the fast wave polarization azimuth and the shear wave reflection travel time. S4. Calculate the azimuth difference between the main energy azimuth and the fast wave polarization azimuth, and use it as the spatial azimuth deviation. S5. When the spatial orientation deviation is less than the preset deviation threshold and the multi-component electromagnetic attenuation curve set satisfies the preset anisotropic attenuation intensity condition of the metal pipeline, the underground location below the collection point is determined to be a candidate pipeline target. S6. Based on the spatial coordinates of the acquisition point, the travel time of the transverse wave reflection, and the azimuth angle of the fast wave polarization, determine and output the burial depth and direction of the candidate pipeline target.

[0036] In this embodiment, the co-temporal control command refers to a sequence of control signals generated by a synchronization control unit to coordinate the time-synchronous operation of the transient electromagnetic detection device and the elastic wave detection device. For example, this command can control the electromagnetic emission current to shut off and initiate data acquisition at a first moment, and trigger the elastic wave source excitation at a second moment with a preset lag time, causing the acquisition windows of the two detection devices to overlap in time. The time correspondence can be understood as establishing a time-series reference based on the co-temporal control command, linking each sampling moment in the multi-component electromagnetic response data with each sampling moment in the multi-component seismic wave image data along the time axis. Establishing this time correspondence allows the electromagnetic attenuation signal and the elastic wave reflection signal within the same time period to correspond to underground targets in the same acquisition period, providing a temporal alignment reference for subsequent data extraction.

[0037] It is understandable that a data acquisition point refers to a discrete detection location determined by a real-time positioning unit along a pre-set survey line. At each acquisition point, two sets of devices complete one synchronous data acquisition. The pre-set survey line can be a straight line, a broken line, or a curve, and is planned in advance according to the terrain conditions and detection requirements of the detection area. Electromagnetic data and elastic wave data acquired at the same acquisition point are considered to correspond to the same underground location area, and the acquisition point serves as the spatial reference for the correlation between the two types of data.

[0038] A multi-component electromagnetic attenuation curve set refers to the sequence of secondary field amplitudes of each received component across multiple time channels after the transmitting current is turned off, reflecting the attenuation process of the induced magnetic field in different directions over time. Taking a horizontal dual-component receiving coil as an example, the attenuation curves of the X component along the measurement line direction and the Y component perpendicular to the measurement line direction are recorded respectively, and the Z component in the vertical direction can also be included. When the pipeline direction forms an angle with the direction of a certain component, the attenuation curves of the two horizontal components will show amplitude differences in the early time channels. This difference is a manifestation of the anisotropy of the induced magnetic field.

[0039] The principal energy azimuth angle refers to the spatial pointing angle where the horizontal vector of the induced magnetic field has the strongest energy within a preset early time channel window. The preset early time channel window can be selected from a period relatively early after the transmission current is turned off, such as tens to hundreds of microseconds after the turn-off. The signal during this period mainly reflects the response of the near-surface high conductor. For example, the amplitudes of the X and Y components within the early time channel can be vector-synthesized to obtain the horizontal vector of each time channel, forming the evolution trajectory of the horizontal vector of the induced magnetic field over time. The direction with the highest energy proportion in this trajectory is then extracted as the principal energy azimuth angle.

[0040] Shear wave splitting parameters are a set of parameters extracted from multi-component shear wave signals to characterize the anisotropy of the medium, including the fast wave polarization azimuth and the shear wave reflection travel time. Shear wave splitting can be understood as the process by which a shear wave splits into a fast shear wave and a slow shear wave with orthogonal polarization directions and different propagation speeds when passing through anisotropic media. In pipeline detection scenarios, when there is a significant difference in wave impedance or stress concentration between the pipeline and the surrounding soil, shear waves passing through this area may produce an observable shear wave splitting effect. The fast wave polarization azimuth refers to the projection angle of the particle vibration direction of the fast shear wave onto the horizontal plane of the ground surface, which is physically consistent with the pipeline axis. The shear wave reflection travel time refers to the propagation time of the shear wave from the source, after reflection from the underground target, to the detector. It can be obtained by picking up the arrival time of the phase axis of the reflected wave from the multi-component record. In the case of shear wave splitting, the average of the fast and slow shear wave travel times can also be selected as a reference.

[0041] Spatial azimuth deviation refers to the angular difference between the principal energy azimuth angle and the fast wave polarization azimuth angle, used to measure the degree of consistency between electromagnetic directivity characteristics and elastic wave directivity characteristics in spatial orientation. For example, if the principal energy azimuth angle is 30 degrees and the fast wave polarization azimuth angle is 33 degrees, then the spatial azimuth deviation is 3 degrees; the smaller the deviation, the more consistent the two directional characteristics are. The preset deviation threshold is an angular threshold value for judging whether the spatial azimuth deviation is within the allowable range. Its specific value can be determined by collecting samples in known pipeline sections and known interference sections and training a classifier. The preset deviation threshold setting allows for a certain tolerance range to accommodate angular deviations caused by factors such as noise and medium inhomogeneity in actual detection.

[0042] The preset anisotropic attenuation intensity condition for metal pipelines refers to the condition for determining whether the multi-component electromagnetic attenuation curve set exhibits sufficiently significant anisotropic attenuation characteristics of the induced magnetic field. For example, this condition can be determined by comparing whether the amplitude ratio of different directional components within the early time window exceeds a preset intensity threshold. If the amplitude ratio of the X and Y components within the early time window significantly deviates from 1, it indicates a large difference in the attenuation degree of the induced magnetic field in different directions, which is consistent with the directional eddy current attenuation characteristics caused by the metal pipeline. Candidate pipeline targets refer to underground targets initially identified as metal pipelines based on joint criteria. These are intermediate determinations in the detection process, and their spatial location and orientation need to be further determined through subsequent steps.

[0043] Furthermore, the burial depth of a candidate pipeline target refers to the vertical distance from the top of the target to the ground surface. The burial depth can be determined by converting the shear wave reflection travel time, i.e., the two-way propagation time of the shear wave from the seismic source, through reflection from the target, and back to the detector, combined with the estimated shear wave velocity in the work area, to calculate the approximate burial depth of the target. For example, if the shear wave reflection travel time is 20 milliseconds and the estimated shear wave velocity is 200 meters per second, the initial burial depth is approximately 2 meters. For higher precision, this initial burial depth can be used as a constraint to invert the multi-component electromagnetic response data to obtain a corrected burial depth value. The orientation of the candidate pipeline target refers to its direction of extension in the horizontal plane. In pipeline detection scenarios, the fast wave polarization azimuth angle physically tends to coincide with the pipeline axis. Therefore, the fast wave polarization azimuth angle can be directly output as the angle value of the pipeline orientation, or the direction perpendicular to the fast wave polarization azimuth angle can be used as a reference direction for the pipeline orientation, depending on the actual geological conditions and data characteristics. For example, if the fast wave polarization azimuth angle is 35 degrees, the orientation of the candidate pipeline target can be determined to be 35 degrees east of north. "Determining and outputting" refers to the data processing unit providing the calculation results to the user or downstream processing module in the form of data records, graphic annotations, or reports after completing the aforementioned calculations of burial depth and orientation. The output may include the plane coordinates, burial depth, orientation angle, and corresponding acquisition point information of the candidate pipeline target, facilitating subsequent pipeline mapping, statistical analysis, or excavation verification.

[0044] In this embodiment of the application, during the implementation of steps S3 to S5, the characteristic quantities involved, such as the principal energy azimuth angle, spatial azimuth deviation, and anisotropy ratio, can be calculated and implemented in the data processing unit in the following manner.

[0045] Suppose that at a certain sampling point, the horizontal component of the multi-component electromagnetic attenuation curve concentrated along the survey line is... The horizontal component perpendicular to the survey line direction is ,in This represents the i-th sampling time within the early time channel window, which contains N time channels. The amplitude of each component has been standardized to voltage or magnetic field units.

[0046] First, vector synthesis is performed on the two horizontal components of each time channel to obtain the instantaneous azimuth angle. and composite amplitude : in, This is the arctangent function in the four quadrants, returning a value in radians. After conversion, the azimuth angle is obtained in degrees, and the range of values ​​can be normalized to [ ]. , Composite amplitude The unit and component amplitude are the same. This step converts the amplitudes of the two components at each time channel into a unified expression of direction and intensity.

[0047] Secondly, based on each time channel ( , The azimuth angle of the main energy is calculated using an amplitude-weighted average method. : The meaning of this formula is: the larger the amplitude, the greater the contribution of its azimuth angle to the average, and the extracted direction can reflect the spatial orientation where the induced magnetic field energy is dominant. The calculation result is in degrees.

[0048] Next, the azimuth angle of the main energy The fast wave polarization azimuth angle extracted from multi-component seismic image data Compare and calculate the spatial orientation deviation. : Considering the azimuth angle Periodicity, deviation from the minimum value of the angle between the two, the result range is [ , The value is in degrees. The smaller the value, the more consistent the electromagnetic directionality and elastic wave directionality are in spatial orientation.

[0049] To measure the significance of the anisotropic decay of the induced magnetic field, the anisotropy ratio R is calculated. It is the ratio of the maximum to the minimum amplitude of the two components within an early time window: in, This represents the global maximum value of the two component amplitudes across all time channels within the window. This represents the global minimum. The ratio R ≥ 1. The larger R is than 1, the greater the difference in the attenuation of the induced magnetic field in different directions, that is, the more significant the anisotropy.

[0050] Finally, the two features mentioned above are compared with preset thresholds respectively, and the joint judgment condition is: in, Preset deviation threshold (unit: degrees). This is a preset intensity threshold. When this logical expression is true, the location below the corresponding sampling point is determined to have a candidate pipeline target.

[0051] This application embodiment synchronously acquires multi-component electromagnetic response data and multi-component seismic wave image data at the same acquisition point through synchronous timing control commands. It extracts the principal energy azimuth angle of the anisotropic induced magnetic field and the fast wave polarization azimuth angle of the shear wave splitting from these data. The spatial azimuth deviation between these two is used as one of the core criteria, combined with the anisotropic attenuation intensity condition of the multi-component electromagnetic attenuation curve set, to determine whether a candidate pipeline target exists at an underground location. This method correlates and verifies the directional response characteristics of metal pipelines exhibited in transient electromagnetic detection with those exhibited in elastic wave detection in terms of spatial azimuth. This allows the two detection methods to mutually verify and constrain each other, helping to distinguish metal pipelines from non-target interference objects with only a single directional response characteristic. This reduces the risk of misjudgment in complex urban environments such as iron-containing backfill soil and large-diameter non-metallic pipelines, thereby improving the accuracy of pipeline detection results.

[0052] In some embodiments disclosed in this application, S3 extracts the shear wave splitting parameters, including: rotating two horizontal components in the multi-component seismic wave image data to radial and transverse components; calculating the cross-correlation function between the radial and transverse components, determining the splitting time difference between the fast and slow shear waves based on the cross-correlation function, and extracting the polarization azimuth angle of the fast wave from the rotation angle; and determining the shear wave reflection travel time based on the arrival times of the fast and slow shear waves.

[0053] It is understandable that the two horizontal components refer to the shear wave signal components recorded along two orthogonal horizontal directions in multi-component seismic wave imaging data. The radial component refers to the component aligned with the direction of the line connecting the source to the detector, and the transverse component refers to the horizontal component orthogonal to the radial component. Rotating the two horizontal components to the radial and transverse components can be achieved through coordinate rotation transformation, with the rotation angle scanning within a preset angle range. The cross-correlation function is a similarity measurement function between the radial and transverse components at different time shifts. The time shift corresponding to the maximum value of the cross-correlation function is the splitting time difference between the fast and slow shear waves, and the corresponding rotation angle is the polarization azimuth angle of the fast wave. The splitting time difference is the time difference between the arrival of the fast and slow shear waves at the detector, and its magnitude reflects the strength of the medium's anisotropy. The shear wave reflection travel time is determined based on the arrival times of the fast and slow shear waves. The average travel time of the fast and slow shear waves can be selected as a reference, or the start time of the reflected signal can be directly picked up when the splitting is not obvious. The above parameter extraction method provides a specific way to obtain shear wave splitting parameters for step S3, enabling the fast wave polarization azimuth and shear wave reflection travel time to be quantitatively extracted from the original multi-component seismic wave data.

[0054] This application embodiment, by performing coordinate rotation and cross-correlation analysis on two horizontal components, can separate fast and slow shear waves from multi-component seismic wave image data, and extract the fast wave polarization azimuth angle and splitting time difference, which reflect the anisotropic characteristics of the medium, providing reliable input parameters for subsequent spatial azimuth correlation calculation with the azimuth angle of the electromagnetic anisotropic principal energy.

[0055] In some embodiments disclosed in this application, determining the principal energy azimuth angle of the anisotropy of the induced magnetic field in S3 includes: vector synthesis of the amplitudes of the components in different directions of the multi-component electromagnetic attenuation curve within a preset early time window to obtain the evolution trajectory of the horizontal vector of the induced magnetic field over time; extracting the principal energy direction of the evolution trajectory, and determining the angle pointed to by the principal energy direction as the principal energy azimuth angle.

[0056] The preset early time window refers to an earlier time interval selected after the transmitting current is turned off. The secondary field signal during this period mainly reflects the electromagnetic response of high conductors near the Earth's surface. Vector synthesis refers to the operation of combining the amplitudes of different directional components on the same time channel as orthogonal components into a single composite vector. For example, the amplitudes of the X and Y components are used as two orthogonal components of the vector to calculate the vector azimuth and magnitude corresponding to each time channel. The evolution trajectory of the horizontal vector of the induced magnetic field over time refers to the path formed by the composite vector of each time channel in a polar or rectangular coordinate system, reflecting the change in the direction of the induced magnetic field over time. The principal energy direction of the evolution trajectory can be understood as the direction with the largest vector magnitude in the trajectory, or the statistical principal direction of the vector direction distribution.

[0057] This application embodiment performs vector synthesis of induced magnetic field components in different directions within an early time window, which enables the extraction of the principal energy direction of the anisotropy of the induced magnetic field from the multi-component electromagnetic attenuation curve. This direction reflects the main spatial orientation of the induced eddy current field of the metal pipeline, providing a basis for subsequent azimuth comparison with the polarization azimuth angle of the fast wave.

[0058] In some embodiments disclosed in this application, determining whether the preset anisotropic attenuation intensity condition of the metal pipeline is met in step S5 includes: calculating the amplitude ratio of the components in different directions of the multi-component electromagnetic attenuation curve in a preset early time window, as the anisotropic ratio value; when the anisotropic ratio value exceeds the preset intensity threshold, it is determined that the preset anisotropic attenuation intensity condition of the metal pipeline is met.

[0059] The anisotropy ratio refers to the ratio of the amplitudes of the components in different directions within a preset early time window of the multi-component electromagnetic attenuation curve. For example, the amplitude ratios of the X and Y components within each time channel of the early time window can be calculated, and the maximum or average value can be taken as the anisotropy ratio at that sampling point. When a metal pipeline exists and its direction forms an angle with the direction of a certain component, the attenuation amplitude of the induced magnetic field on the two components differs, and the anisotropy ratio will deviate from 1. The preset intensity threshold is a numerical limit for determining whether the anisotropy is sufficiently significant. When the anisotropy ratio exceeds this threshold, the anisotropic attenuation characteristics of the induced magnetic field are considered to match the response mode of the metal pipeline. The above method provides a quantitative criterion for determining whether the electromagnetic anisotropic attenuation intensity condition is met in step S5.

[0060] This application embodiment calculates the amplitude ratio of different directional components in the early time channel as the anisotropy ratio and compares it with a preset intensity threshold. This can quantitatively evaluate the significance of the anisotropy of the induced magnetic field and provide a quantitative criterion for distinguishing metal pipelines from interference bodies with indistinct anisotropic characteristics.

[0061] In some embodiments disclosed in this application, after S6, S7 and S8 are also included: S7, obtaining the confirmed pipeline target verified by excavation or drilling, and extracting the spatial orientation deviation and anisotropy ratio corresponding to the confirmed pipeline target; S8, adding the extracted spatial orientation deviation and anisotropy ratio as new positive samples to the training sample set to retrain the classifier, so as to update the preset deviation threshold and preset intensity threshold.

[0062] In this context, "confirmed pipeline target" refers to a known pipeline that has been verified through excavation or drilling, whose type, location, and orientation have been confirmed, and can serve as a reference sample for real pipelines. Excavation verification refers to the method of excavating the ground to expose the pipeline, while drilling confirmation refers to the method of confirming the existence and attributes of the pipeline through core drilling or borehole exploration. "Added positive samples" refers to using the spatial orientation deviation and anisotropy ratio corresponding to the confirmed pipeline target as newly added training data for real pipelines. The training sample set refers to the set of samples used to train the classifier, including both positive and negative samples. The classifier is a mathematical model used to delineate the decision boundaries between positive and negative samples in the feature space, such as a support vector machine classifier. Steps S7 and S8 constitute a closed-loop feedback process. Step S7 is responsible for acquiring the feature data of the confirmed pipeline target, and step S8 is responsible for incorporating new samples into the training and updating the discrimination threshold, which helps to gradually improve the accuracy of the judgment in subsequent detections.

[0063] This application embodiment feeds back the confirmed pipeline target data verified by actual excavation to the classifier training process, which can continuously optimize the discrimination threshold using real pipeline cases. This allows the preset deviation threshold and preset intensity threshold to gradually approach the characteristic distribution of the actual pipeline response as the detection work progresses, which helps to improve the accuracy and adaptability of the judgment in subsequent detection.

[0064] In some embodiments disclosed in this application, determining the burial depth of the candidate pipeline target in step S6 includes: using the set of multi-component electromagnetic attenuation curves corresponding to the candidate pipeline target as observation data, using the preliminary burial depth obtained by transverse wave reflection travel time conversion as the initial model constraint, performing one-dimensional inversion on the multi-component electromagnetic response data to obtain the corrected burial depth and apparent conductivity; querying a preset pipeline material comparison table based on the apparent conductivity, and outputting the predicted material type of the candidate pipeline target.

[0065] The preliminary burial depth refers to the estimated burial depth obtained by multiplying the transverse wave reflection travel time by the estimated transverse wave velocity, providing an initial reference for electromagnetic inversion. Initial model constraints refer to using the preliminary burial depth as a known condition or constraint range during the electromagnetic inversion process, limiting the search space for the inversion solution. One-dimensional inversion refers to performing inversion calculations on electromagnetic data along the depth direction to solve for the resistivity or apparent conductivity parameters of each depth layer. Corrected burial depth refers to the burial depth value corrected after electromagnetic inversion. Apparent conductivity refers to the equivalent conductivity parameter of the underground medium obtained through inversion; different pipeline materials have different apparent conductivity ranges. The pipeline material comparison table is a pre-set table recording the correspondence between different pipeline materials and typical apparent conductivity ranges; for example, steel pipes correspond to a higher apparent conductivity range, cast iron pipes to a medium apparent conductivity range, and non-metallic pipes to a lower apparent conductivity range.

[0066] This embodiment of the application uses the preliminary burial depth calculated by transverse wave reflection travel time as the initial model constraint for electromagnetic inversion, which can narrow the search range of the inversion solution and help improve the stability and accuracy of burial depth estimation. At the same time, the apparent conductivity information obtained by inversion can provide a reference for the prediction of pipeline material type.

[0067] In some embodiments disclosed in this application, the preset deviation threshold and preset intensity threshold are determined in the following manner: Multiple first samples are acquired and calculated according to steps S1 to S4 on a known metal pipeline section. Each first sample includes a corresponding spatial orientation deviation and anisotropy ratio, and is labeled as a positive sample. Multiple second samples are acquired and calculated according to steps S1 to S4 on a known non-pipeline interference section. Each second sample includes a corresponding spatial orientation deviation and anisotropy ratio, and is labeled as a negative sample. A classifier is trained in a two-dimensional feature space composed of spatial orientation deviation and anisotropy ratio using the positive and negative samples as training data to obtain the decision boundary for dividing positive and negative samples. The preset deviation threshold and preset intensity threshold are determined based on the decision boundary.

[0068] In this study, known metal pipeline sections refer to areas where the type, location, and direction of pipelines have been reliably confirmed, and these can be used as sources for positive samples. Known non-pipeline interference sections refer to areas where metal pipelines are confirmed to be absent but other interfering factors exist, such as areas with iron-containing backfill soil, areas with large-diameter non-metallic pipelines, or areas with dense power lines, and these can be used as sources for negative samples. The first and second samples refer to data pairs obtained by collecting and calculating the spatial orientation deviation and anisotropy ratio from the above two types of sections according to steps S1 to S4, respectively. Positive and negative samples are fundamental concepts in machine learning, representing training instances belonging to and not belonging to the target category, respectively. The two-dimensional feature space refers to a planar space constructed with the spatial orientation deviation as one coordinate axis and the anisotropy ratio as the other coordinate axis. The decision boundary refers to the dividing line or interface learned by the classifier in the feature space for distinguishing between positive and negative samples. The above method provides a definite approach based on measured data training for setting the preset deviation threshold and preset intensity threshold, ensuring that the threshold setting is based on sample data.

[0069] This application embodiment collects and labels samples on known pipeline sections and known interference sections respectively, and trains a classifier in a two-dimensional feature space composed of spatial orientation deviation and anisotropy ratio. It can automatically determine the discrimination boundary based on the measured data, so that the threshold setting is more in line with the pipeline response distribution characteristics and interference response distribution characteristics in the actual detection scenario.

[0070] In some embodiments disclosed in this application, S5 further includes: obtaining the spatial orientation deviation of multiple adjacent acquisition points along the preset survey line direction and the judgment result of the preset anisotropic attenuation intensity condition of the metal pipeline; when the spatial orientation deviation is less than the preset deviation threshold and the number of consecutive acquisition points that meet the preset anisotropic attenuation intensity condition of the metal pipeline exceeds the preset number threshold, the underground locations below the consecutive acquisition points are merged and determined as the same candidate pipeline target.

[0071] In this context, "adjacent acquisition points along the preset survey line direction" refers to a series of acquisition points that are spatially adjacent to each other along the survey line's direction of travel. "Number of consecutive acquisition points" refers to the number of adjacent acquisition points along the survey line that satisfy both a spatial orientation deviation less than a preset deviation threshold and a preset anisotropic attenuation intensity condition for the metal pipeline. "Preset number threshold" refers to the minimum number of consecutive acquisition points required to determine whether a continuous anomaly constitutes an independent pipeline target. "Same candidate pipeline target" refers to merging the underground locations beneath multiple acquisition points that meet the continuity condition, considering them as anomalies caused by the same pipeline.

[0072] This application embodiment performs spatial continuity analysis on the determination results of adjacent collection points, which can filter out isolated single-point anomaly determinations and merge multiple points that meet the conditions and are continuously distributed into a coherent pipeline target. This helps to reduce misjudgments caused by local noise or accidental interference, making the determination results of pipeline targets more continuous and reasonable in space.

[0073] In some embodiments disclosed in this application, when the elastic wave detection device acquires multi-component seismic wave image data, it uses a transverse wave source for excitation; the receiving coil of the transient electromagnetic detection device is a horizontal dual-component or triple-component induction coil, which is used to acquire the attenuation signal of the induced magnetic field components in the direction parallel to and perpendicular to the preset survey line, respectively.

[0074] Among them, shear wave source excitation refers to the elastic wave detection device generating shear waves with particle vibration direction perpendicular to the propagation direction through a shear wave source. Shear waves are sensitive to changes in the shear modulus of the medium during propagation, which helps to generate a shear wave splitting effect near the pipeline target, providing a physical basis for extracting shear wave splitting parameters in step S3. A horizontal dual-component induction coil refers to a receiving coil that can simultaneously receive induced magnetic field components in two orthogonal horizontal directions; a three-component induction coil adds the reception of a vertical component. The preset induced magnetic field components in the parallel and perpendicular directions of the measurement line provide the necessary multi-component data source for calculating the multi-component electromagnetic attenuation curve set and extracting the principal energy azimuth angle in step S3.

[0075] The embodiments of this application employ transverse wave source excitation, which is beneficial for generating transverse wave splitting near the pipeline target, providing a physical basis for extracting the fast wave polarization azimuth angle; and use horizontal dual-component or triple-component induction coils to obtain induced magnetic field components in different directions, providing a multi-component data source for calculating the principal energy azimuth angle of the anisotropy of the induced magnetic field.

[0076] In some embodiments disclosed in this application, the process of executing S1 further includes: acquiring the spatial position data of the transient electromagnetic detection device and the elastic wave detection device in real time; comparing the spatial position data with a pre-stored pipeline geographic information base map; and when it is determined that the angle between the current direction of travel and the suspected pipeline direction recorded in the base map is less than a preset angle, generating a heading correction command to adjust the direction of travel of the preset survey line.

[0077] Spatial location data refers to the spatial coordinates of the detection platform, acquired through real-time positioning units. Pre-stored pipeline geographic information base maps refer to pre-stored geographic information data containing the approximate direction and distribution area of ​​known pipelines, which can be derived from historical pipeline archives or preliminary survey results. Suspected pipeline direction refers to the approximate extension direction of known pipelines recorded on the base map. Preset angle refers to the angle threshold value used to determine whether the current direction of travel needs adjustment. Heading correction commands are control commands used to adjust the direction of travel of the preset survey line, increasing the angle between the detection platform's direction of travel and the suspected pipeline direction, making it more perpendicular. Since a clearer response signal is usually obtained when the survey line is perpendicular to the pipeline direction, the above heading correction steps help provide auxiliary support for obtaining high-quality detection data during the S1 step operation.

[0078] This application embodiment compares the direction of the detection platform with the suspected pipeline direction on the existing pipeline geographic information base map in real time, and dynamically adjusts the direction of the detection line during the detection process. This helps to maintain a better geometric relationship between the detection line and the pipeline direction, and provides operational-level auxiliary support for obtaining clearer detection response signals.

[0079] In some embodiments disclosed in this application, after determining the existence of a candidate pipeline target in S5, the method further includes: generating a position mark at the surface projection position of the candidate pipeline target; driving the detection platform to move to the position mark again, and using the direction perpendicular to the fast wave polarization azimuth angle as the new survey line direction to perform a second detection on the candidate pipeline target; obtaining the second spatial azimuth deviation and the second anisotropy ratio obtained from the second detection, and comparing the consistency with the corresponding data obtained from the first detection; if the consistency index exceeds a preset threshold value, then the candidate pipeline target is confirmed as a confirmed pipeline target.

[0080] The surface projection location refers to the point on the ground corresponding to the vertical projection of the candidate pipeline target's position in the underground space onto the ground surface. The location marker can be a physical marker or a virtual coordinate marker, used to indicate the location requiring secondary detection. The new survey line direction refers to the direction of travel perpendicular to the fast wave polarization azimuth angle. When conducting secondary detection in this direction, the survey line is parallel or substantially parallel to the pipeline's direction, and the response characteristics of electromagnetic induction and elastic waves differ from those during the initial detection. The second spatial azimuth deviation and the second anisotropy ratio refer to the spatial azimuth deviation and anisotropy ratio recalculated during the secondary detection process. The consistency index is a quantitative indicator measuring the similarity between the two detection results; for example, it can be the difference between the two spatial azimuth deviations, the relative deviation of the two anisotropy ratios, or a comprehensive score of both. The preset threshold value is the threshold for determining whether the two detection results are sufficiently consistent. Confirmed pipeline target refers to the pipeline determination result with higher credibility after secondary detection consistency verification. The above-mentioned secondary detection confirmation step provides a cross-validation method for the determination result in S5, verifying the candidate pipeline target through repeated detection in different survey line directions.

[0081] This application embodiment performs secondary detection by changing the direction of the survey line at the candidate pipeline target location, and uses the consistency of the detection results in different directions as the confirmation basis. This can cross-verify the judgment results of the first detection, which helps to reduce misjudgments caused by accidental factors and improve the confidence of the final confirmed pipeline target.

[0082] Please continue to refer to this. Figure 2 , Figure 2 This application provides a detection system for urban underground pipelines. The system includes: a detection platform 21, which integrates a transient electromagnetic detection device and an elastic wave detection device; a synchronization control unit 22, which generates synchronous timing control commands to drive the transient electromagnetic detection device and the elastic wave detection device to synchronously collect data; a real-time positioning unit 23, which acquires real-time dynamic positioning information of the detection platform during its movement; and a data processing unit 24, which is communicatively connected to the synchronization control unit 22, the real-time positioning unit 23, the transient electromagnetic detection device, and the elastic wave detection device, and is configured to execute the program instructions corresponding to the above method.

[0083] The detection platform 21 refers to the carrier that carries the detection device and moves along the preset survey line. It can be a towed platform, i.e., a platform towed by a vehicle or other traction equipment, or a self-propelled platform, i.e., a mobile platform with its own power drive. The detection platform integrates a transient electromagnetic detection device and an elastic wave detection device. The relative spatial position relationship between the two devices is pre-calibrated during installation, so that the spatial offset between the electromagnetic data receiving point and the elastic wave data receiving point at the same acquisition point is a known quantity, which facilitates spatial reduction in subsequent data processing. The detection platform 21 provides a mobile carrier for moving along the preset survey line and for the synchronous acquisition of data by the two devices in step S1.

[0084] Transient electromagnetic detection devices are equipment that utilize the principle of electromagnetic induction to detect differences in the electrical properties of underground media. They may include a transmitting coil, a multi-component receiving coil, and an electromagnetic data acquisition module. The transmitting coil transmits a primary excitation magnetic field underground, the multi-component receiving coil acquires secondary field attenuation signals in different directions after the transmitting current is turned off, and the electromagnetic data acquisition module converts analog signals into digital signals and records them. Elastic wave detection devices are equipment that utilize the propagation and reflection characteristics of elastic waves underground to detect differences in the mechanical properties of underground media. They may include a controlled shear wave source, a multi-component shear wave detector, and a seismic data acquisition module. The controlled shear wave source excites horizontal shear waves at the surface, the multi-component shear wave detector receives reflected shear wave signals returned from the underground media, and the seismic data acquisition module performs analog-to-digital conversion and records the signals.

[0085] The synchronization control unit 22 is a hardware module used to generate synchronous timing control commands and drive the two devices to operate synchronously under a preset timing relationship. The synchronization control unit 22 can have a built-in high-precision clock source, such as a temperature-compensated crystal oscillator or a Global Navigation Satellite System (GNSS) timing module, to ensure the accuracy and stability of the timing control. The synchronization control unit 22 is electrically connected to both the transient electromagnetic detection device and the elastic wave detection device, and the output synchronous timing control commands are used to coordinate the timing relationship between the electromagnetic emission current cutoff time and the elastic wave source excitation time. The synchronization control unit 22 provides the hardware foundation for the synchronous acquisition in step S1 and the establishment of the time correspondence in step S2.

[0086] The real-time positioning unit 23 refers to a positioning device used to acquire the spatial position information of the detection platform 21 in real time. For example, the real-time positioning unit 23 can be a Global Navigation Satellite System receiver, which calculates the three-dimensional spatial coordinates of the detection platform by receiving satellite signals. The real-time positioning unit 23 is installed on the detection platform 21 and moves synchronously with it. During the detection process, it continuously outputs real-time dynamic positioning information to determine the spatial coordinates of each acquisition point, providing a source of position data for determining the spatial position of candidate pipeline targets in step S6.

[0087] The data processing unit 24 refers to a computing device capable of receiving, storing, and processing data, and may include a processor, a memory, and corresponding input / output interfaces. The processor may be a central processing unit, a digital signal processor, or a field-programmable gate array (FPGA) or other device with program execution capabilities. The memory stores executable program instructions and detection data. The data processing unit 24 is communicatively connected to the synchronization control unit 22, the real-time positioning unit 23, the transient electromagnetic detection device, and the elastic wave detection device, respectively. The communication connection may be wired or wireless. The data processing unit 24 receives synchronization clock information, positioning information, electromagnetic response data, and seismic wave data from each unit. When executing program instructions, it implements the data processing and judgment logic of steps S2 to S6, as well as the processing functions corresponding to other additional steps.

[0088] This embodiment integrates the transient electromagnetic detection device and the elastic wave detection device on the same detection platform 21, and configures a synchronous control unit 22, a real-time positioning unit 23 and a data processing unit 24. This provides a hardware foundation for the implementation of the above detection method, and enables the synchronous acquisition, temporal correlation and joint processing of multi-component electromagnetic response data and multi-component seismic wave image data. This helps to improve the accuracy of pipeline detection in complex urban environments.

[0089] Please continue to refer to this. Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to execute the aforementioned method for detecting underground urban pipelines.

[0090] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0091] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0092] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0093] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation methods described in any embodiment of the urban underground pipeline detection method provided in the embodiments of this application, or they can execute the implementation methods of the electronic devices described in the embodiments of this application, which will not be repeated here.

[0094] In another embodiment of this application, an electronic device is provided. The electronic device stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the urban underground pipeline detection method described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in an electronic device, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0095] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0096] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0097] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or units, or it may be an electrical, mechanical, or other form of connection.

[0099] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0100] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0101] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting underground pipelines in cities, characterized in that, Includes the following steps: S1. During the process of traveling along the preset survey line, based on the time-sequence control command, the transient electromagnetic detection device is triggered to collect multi-component electromagnetic response data, and the elastic wave detection device is triggered to collect multi-component seismic wave image data. S2. According to the co-time sequence control command, establish the time correspondence between each sampling time in the multi-component electromagnetic response data and each sampling time in the multi-component seismic wave image data; S3. At the same acquisition point, based on the time correspondence, extract the multi-component electromagnetic attenuation curve set from the multi-component electromagnetic response data and determine the principal energy azimuth of the anisotropic induced magnetic field. Extract the shear wave splitting parameters from the multi-component seismic wave image data. The shear wave splitting parameters include the fast wave polarization azimuth and the shear wave reflection travel time. S4. Calculate the azimuth difference between the main energy azimuth and the fast wave polarization azimuth, and use it as the spatial azimuth deviation. S5. When the spatial orientation deviation is less than the preset deviation threshold and the multi-component electromagnetic attenuation curve set satisfies the preset anisotropic attenuation intensity condition of the metal pipeline, the underground location below the collection point is determined to be a candidate pipeline target. S6. Based on the spatial coordinates of the acquisition point, the travel time of the transverse wave reflection, and the polarization azimuth of the fast wave, determine and output the burial depth and orientation of the candidate pipeline target.

2. The method according to claim 1, characterized in that, Extracting the shear wave splitting parameters in step S3 further includes: Rotate the two horizontal components in the multi-component seismic wave imaging data to the radial and transverse components; Calculate the cross-correlation function between the radial component and the transverse component, determine the splitting time difference between the fast and slow transverse waves based on the cross-correlation function, and extract the polarization azimuth angle of the fast wave from the rotation angle; The travel time of the transverse wave reflection is determined based on the arrival times of the fast and slow transverse waves.

3. The method according to claim 1, characterized in that, Determining the principal energy azimuth angle of the anisotropy of the induced magnetic field in step S3 further includes: The amplitudes of the components in different directions of the multi-component electromagnetic decay curve within a preset early time window are vector-synthesized to obtain the evolution trajectory of the horizontal vector of the induced magnetic field over time. Extract the main energy direction of the evolution trajectory, and determine the angle pointed to by the main energy direction as the main energy azimuth angle.

4. The method according to claim 1, characterized in that, The step S5, determining whether the preset anisotropic attenuation strength condition of the metal pipeline is met, further includes: The amplitude ratio of the components in different directions of the multi-component electromagnetic attenuation curve in the preset early time window is calculated as the anisotropy ratio value. When the anisotropy ratio exceeds a preset strength threshold, it is determined that the preset anisotropy attenuation strength condition of the metal pipeline is met.

5. The method according to claim 4, characterized in that, Following S6, it also includes: S7. Obtain confirmed pipeline targets verified by excavation or drilling, and extract the spatial orientation deviation and anisotropy ratio corresponding to the confirmed pipeline targets; S8. The extracted spatial orientation deviation and anisotropy ratio are used as new positive samples and added to the training sample set to retrain the classifier, so as to update the preset deviation threshold and the preset intensity threshold.

6. The method according to claim 1, characterized in that, Determining the burial depth of the candidate pipeline target in step S6 further includes: Using the set of multi-component electromagnetic attenuation curves corresponding to the candidate pipeline target as observation data, and the preliminary burial depth obtained by the transverse wave reflection travel time conversion as the initial model constraint, a one-dimensional inversion is performed on the multi-component electromagnetic response data to obtain the corrected burial depth and apparent conductivity. Based on the apparent conductivity, a preset pipeline material lookup table is queried, and the predicted material type of the candidate pipeline target is output.

7. The method according to claim 4, characterized in that, The preset deviation threshold and the preset intensity threshold are determined in the following way: Multiple first samples are collected and calculated according to steps S1 to S4 on a known metal pipeline section. Each first sample includes the corresponding spatial orientation deviation and the anisotropy ratio, and is labeled as a positive sample. In a known non-pipeline interference section, multiple second samples are collected and calculated according to steps S1 to S4. Each second sample includes the corresponding spatial orientation deviation and the anisotropy ratio, and is labeled as a negative sample. Using the positive and negative samples as training data, a classifier is trained in a two-dimensional feature space composed of the spatial orientation deviation and the anisotropy ratio to obtain the decision boundary for dividing positive and negative samples. Based on the decision boundary, the preset deviation threshold and the preset intensity threshold are determined.

8. The method according to claim 1, characterized in that, S5 further includes: The spatial orientation deviation and the judgment results of the preset anisotropic attenuation intensity condition of the metal pipeline are obtained for multiple adjacent acquisition points along the preset survey line direction. When the spatial orientation deviation is less than the preset deviation threshold and the number of continuous acquisition points that meet the preset anisotropic attenuation intensity condition of the metal pipeline exceeds the preset number threshold, the underground locations below the continuous acquisition points are merged and determined as the same candidate pipeline target.

9. The method according to any one of claims 1 to 8, characterized in that, When the elastic wave detection device acquires the multi-component seismic wave image data, it uses a shear wave source for excitation; the receiving coil of the transient electromagnetic detection device is a horizontal dual-component or triple-component induction coil, which is used to acquire the attenuated signals of the induced magnetic field components in the direction parallel to and perpendicular to the preset survey line, respectively.

10. A detection system for urban underground pipelines, used to implement the method according to any one of claims 1 to 9, characterized in that, include: The detection platform integrates transient electromagnetic detection devices and elastic wave detection devices; A synchronization control unit is used to generate the synchronous timing control command to drive the transient electromagnetic detection device and the elastic wave detection device to synchronously acquire data. A real-time positioning unit is used to acquire real-time dynamic positioning information of the detection platform during its movement. The data processing unit is communicatively connected to the synchronization control unit, the real-time positioning unit, the transient electromagnetic detection device, and the elastic wave detection device, respectively, and is configured to execute the program instructions corresponding to the method.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for detecting urban underground pipelines as described in any one of claims 1 to 9.

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