Mapping method for monitoring the condition and / or geolocating a buried, semi-buried or submerged structure containing a metallic or magnetic material
The method automates the correction of magnetic data anomalies and ensures accurate geolocation of buried structures by comparing simulated and measured data, enhancing precision and consistency in mapping.
Patent Information
- Application Number
- FR2024001260
- Authority / Receiving Office
- FR · FR
- Patent Type
- Patents
- Current Assignee / Owner
- Filing Date
- 2024-02-08
- Publication Date
- 2026-01-30
- Estimated Expiration
- 2044-02-08
AI Technical Summary
Existing mapping methods for buried or submerged structures using magnetic data lack precision and require human intervention to correct anomalies, fail to automatically validate inconsistencies, and do not allow comparison with simulated data.
A method involving spatialized magnetic data acquisition, provisional point generation, simulation of magnetic values, comparison with measured data, and selection of points based on scores to create a coherent geolocation map without human intervention.
Improves geolocation accuracy by automating the correction of anomalies and ensuring consistency between measured and simulated data, preventing the generation of inaccurate maps.
Smart Images
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Abstract
Description
Title of the invention: Mapping method for monitoring the condition and / or geolocating a buried, semi-buried, or submerged structure containing a metallic or magnetic material. Technical field
[0001] The present invention relates to a mapping method for the condition monitoring and / or geolocation of a buried, semi-buried, or submerged structure made of a metallic or magnetic material. The invention will find application in the location of pipelines, particularly those used for transferring liquids or gases. The invention can also be used for the detection and geolocation of other types of structures or in geophysical surveys. Furthermore, the invention can be used in the external monitoring of structures, both for verifying the integrity of the structure and for assessing the magnetic connection between two adjacent structures. Technological background
[0002] Various detection methods using magnetometers for the detection of non-visible structures are known, as well as various associated methods.
[0003] Processes using various technologies such as radio detection, radar, and lidar are also known. In each of these known techniques, measurements are taken, notably by scanning the area to be monitored. Then, a 2D or 3D geolocation map is generated from the collected data, combining the measurements and their positioning. However, the generation of these maps does not always verify measurement anomalies, which can originate from various causes, including sensor failure or interference from nearby structures. Consequently, these generated maps generally lack precision and may also contain inaccuracies.
[0004] The applicant filed application WO2023148057 which addresses some of the aforementioned drawbacks. This application describes a method for generating an interactive magnetic card from collected magnetic data. From this interactive magnetic card, an operator can add or remove magnetic data to correct detection anomalies or to interpolate data based on neighboring magnetic data. In the method described in this application, the data and successive processing or filtering operations generally yield good results; however, this method requires human intervention to correct a number of anomalies. This operator intervention requires considerable experience and is time-consuming. Furthermore, this This type of process does not allow for the automatic invalidation of an erroneous or inconsistent layout or structure positioning, whether the source of these errors originates from the collected magnetic data or from a poor model selection. This type of process also does not allow for the comparison of real data with data from simulation models.
[0005] Technical problem to be solved
[0006] A technical problem that the present invention aims to solve is to provide a new mapping method in which magnetic data are compared with simulated data in order to improve the accuracy of the location of the structure.
[0007] Another problem that the present invention aims to solve is to provide a method for verifying and / or correcting the results obtained from the simulation and the data collected without human intervention.
[0008] Another problem that the present invention aims to solve is to prevent the generation of the geolocation map in case of inconsistency or aberration.
[0009] Another problem that the present invention aims to solve is to propose a simple method to implement and to test several hypotheses until a coherent geolocation map is obtained. Summary of the invention
[0010] The present invention relates to a mapping method for the condition control and / or geolocation of a buried, semi-buried or submerged structure comprising a metallic or magnetic material, this method comprises the steps described below.
[0011] The mapping process includes a step of acquiring spatialized magnetic data obtained by magnetic sensors at different measurement points of the area to be controlled, after injecting a current into the structure.
[0012] The method further includes a step of generating a provisional segment, comprising a provisional set of points (Pi), and a volume (Vi) around each provisional point (Pi), the volume (Vi) comprising a cloud of points.
[0013] The method further includes a simulation step, for each point of each volume (Vi), allowing the calculation of the simulated magnetic values (VMS) of the points of the cloud at the level of all or part of the measurement points (Pm).
[0014] The method further includes a step of comparing the simulated magnetic values (VMS) of the cloud points of each volume (Vi) and the spatialized magnetic values (VM) to assign a score to each cloud point of each volume (Vi),
[0015] The process further includes a step of selecting, for each volume (Vi), a point of the cloud presenting the best score, the set of selected points (Si) replacing the set of provisional points (Pi) of the provisional segment.
[0016] The process further includes a step of creating a magnetic card containing all the selected points (Si).
[0017] Definitions
[0018] According to the present invention, the expression magnetic or metallic material, in this application, refers to any type of conductive material generating a magnetic field after the injection of a current, and in particular includes ferromagnetic materials.
[0019] According to the present invention, the expression point cloud, in this application, refers to an unlimited set of points distributed in an ordered or random manner in a volume. Brief description of the figures
[0020] Other features and advantages of the present invention will become apparent from the description of the specific and non-limiting embodiments of the present invention below, with reference to the attached Figures 1 to 4, in which:
[0021] [Fig. 1] schematically illustrates part of the steps of the process according to the invention,
[0022] [Fig. 2] schematically illustrates an example of a trace generated by the implementation of the process according to the invention,
[0023] [Fig.3] represents an example of a map obtained by implementing the process according to the invention,
[0024] [Fig.4] represents an example of the implementation of the process in the form of a diagram showing the different steps of the process according to the invention. Detailed description
[0025] Figure 1 illustrates in general terms different stages of the process. More specifically, the process includes a stage of acquiring spatialized magnetic data obtained by magnetic sensors at different measurement points of the area to be monitored, after injecting a current into the structure. These measurement points are schematically represented by the set of points Pm in the upper part of Figure 1.
[0026] The example in Figures 1 and 2 is a simplified example, therefore it includes a limited number of measurement points Pm at average altitudes, this being the principle of measurement and the process steps are identical for the processing of a real case even if the number of measurements is greater.
[0027] The magnetic measurement of the measurement points is advantageously carried out by magnetometers, in particular those placed on a mobile device such as a vehicle or aircraft, and in particular by a drone equipped with magnetometer arrays. The device mobile moves vertically above the area to be controlled, advantageously along bands substantially parallel to the supposed positioning of the buried structure or along a grid.
[0028] When the measurement points Pm have been obtained, the magnetic data can be processed so that they can be compared in the subsequent steps of the process.
[0029] According to a first example, in order to collect magnetic data, a step of current injection into the structure and a signal processing step are carried out, of the electrical component of the signal emitted by the structure in response to the signal injection allowing comparison with the simulated magnetic values.
[0030] According to a second advantageous example, this spatialized magnetic data acquisition step includes a step of injecting an alternating current into the structure and a step of processing the magnetic measurements via a bandpass filter and a Hilbert filter.
[0031] The process then includes a step of generating a provisional segment, comprising a set of provisional points Pi. In the simplified example, the provisional segment 1 comprises ten provisional points PI to P10.
[0032] The positioning of its points PI to P10 can be carried out according to several options depending on the actual case to be addressed. According to a first embodiment, it can be based on field data, particularly if one or more positioning points of the structure are known. It is also possible to position this provisional segment 1 using existing cartographic data, even if this data is imperfect or even imprecise. In the absence of data, it is also possible to place the points based on hypotheses about the location of the buried structure or even hypotheses about its general shape.
[0033] The distance between the provisional points Pi can also vary, particularly depending on the accuracy required for geolocation. Advantageously, the length of a subsegment between two successive provisional points is between 5 and 15% of the expected length of the smallest dimension of the structure.
[0034] In this step of generating the provisional segment 1, the generation of a volume Vi around each provisional point Pi is also planned.
[0035] In the example of Figures 1 and 2, a single volume Vi containing a point cloud is represented. This volume Vi is arranged around the provisional point P4. The process generates a volume Vi around each provisional point Pi to allow the comparison steps described below.
[0036] In the example of Figures 1 and 2, this volume Vi around a point Pi is a cube, centered on Pi, whose edge length corresponds to the length of the subsegment between two successive points Pi. The set of volumes Vi thus forms a volume incorporating the entire provisional segment 1. However, in other embodiments, one It is possible to provide for volumes with different geometries and, for example, spheres around the points Pi. Also, in other embodiments, it is possible to provide for successive volumes to overlap or, conversely, not to be in contact.
[0037] Referring to [Fig. 1], a simplified example of the distribution of the point cloud in a volume Vi is shown. In an advantageous mode, the cloud points are distributed homogeneously throughout the volume Vi. However, other distribution modes are also conceivable, such as a random distribution of points in the volume or increasing or decreasing concentrations or densities of points around the provisional point Pi.
[0038] According to another embodiment, additional cloud points of a volume Vi are also created successively during the simulation step and positioned in the volume Vi according to the scores of the cloud points simulated in the simulation step.
[0039] According to the invention, the method further consists of carrying out a simulation step, for each point of each volume Vi, allowing the calculation of the simulated magnetic values (VMS) of the points of the cloud at the level of all or part of the measurement points.
[0040] In the example of Figures 1 and 2, the magnetic value of each point in the cloud of each volume Vi is advantageously simulated at the level of the set of measurement points Pm. However, this simulation requires a very large computational volume when the cloud points, on the one hand, and the measurement points, on the other, are numerous. To limit the computational requirements, the magnetic value of each point in the cloud can also be simulated on only a subset of the measurement points Pm.
[0041] By way of example, the magnetic value (VMS) of a cloud point can be simulated only at measurement points located at a distance less than a value D of said point. This limitation reduces computational requirements while retaining the simulated magnetic values at the most significant measurement points Pm for the cloud point in question.
[0042] By way of example, it is also provided that the simulation step, for each point of each volume (Vi), allows the calculation of the simulated magnetic values (VMS) of the points of the cloud at the level of a number N of measurement points among the set of measurement points and corresponding to the N measurement points closest to the point Pi of the volume (Vi) considered.
[0043] The simulation of the magnetic value of a point in the volume on all or part of the measurement points Pm is obtained by applying the Biot and Savart formula defining the magnetic field vector as a function of the set of infinitesimal functions of the sections of the pipelines.
[0044]
[0045]
[0046]
[0047]
[0048]
[0049]
[0050]
[0051]
[0052] The following formula is applied for the simulation of the simulated magnetic values with the variables listed in the table below: [Formula 1] b( T ) = V > 471C [Tables 1] Variable Meaning B(r) The magnetic field at the point considered with coordinates x, y, z. Ho 4tt A constant specific to the homogeneous medium considered. (Magnetic permeability of free space). ï The direction of the direct current in the infinitesimal section of the pipeline. dT Generating the section considered having a length of cross-section is a unit vector in three dimensions. r Vector defining the oriented distance between the origin of the considered coordinate system and the point of measurement. f Vector defining the oriented distance between the origin of the considered coordinate system and the center of the infinitesimal section of the section. Line integral. This formula is then adapted to a computer simulation in which infinitesimal pipeline sections are considered as short-distance segments. For each section, the following formula is obtained: [Formula 2] dB(r) = IM with g(r) = ^cdB(r) This second formula allows the creation of a function to simulate magnetic values at measurement points (VMS) based on a cross-section configuration. for cloud points.
[0053] When the simulation step is completed with the calculation of the simulated magnetic values (VMS), the method further consists of performing a comparison step between the simulated magnetic values (VMS) of the cloud points of each volume Vi and the spatialized magnetic data to assign a score to each cloud point of each volume Vi.
[0054] Advantageously, the score assigned to a point in a point cloud is a function of the comparison between the simulated magnetic values (VMS) of that point at the measurement points Pm and the measured values (VM) at the level of these measurement points Pm, i.e. the measured values (VM) obtained by the magnetometers, possibly corrected or processed so that they can be compared to the simulated magnetic values (VMS).
[0055] In general, the best score will be assigned to the point in the cloud whose VMS are closest to the VM of the measurement points.
[0056] At this level, different types of scoring algorithms can be considered, including algorithms using different convergence methods such as those listed in the table below:
[0057] [Tables2] Method Explanation Nelder-Mead The Nelder-Mead method is a numerically optimized method for minimizing nonlinear problems where the derivative is unknown. It is a search method heuristic that can converge to nonstationary points. Powell The Powell method is an iterative minimization method for score-least-squares problems. This method uses the descending gradient to search for parameter sets. CG The conjugate gradient method finds the local minimum closest to the minimization by performing multidimensional gradient descent. BFGS The Broyden-Fletcher-Goldfarb-Shanno method is an unconstrained nonlinear minimization method. This method relies on the analysis of successive gradients without constructing Hessian matrices. This method assumes a solution at the optimum that is quadratic around the optimum.L-BFGS-B This BFGS-based method adds a memory size limitation and constraints on the minimization parameters to guide gradient descent and prevent solution divergence. SLSQP The sequential programmed least-squares optimization method based on SQP is a method for quasi-Newtonian problems to model the local parameter problem as a quadratic hyperplane to find the global minimum.
[0058] Of course, other convergence methods can also be considered for calculating the score; depending on the application, one of the minimization methods will be selected and associated with a score calculation function.
[0059] The score calculation may, according to a first embodiment, be based on the errors at the measurement points between the actual measurement VM and the simulated value VMS. Among the various functions allowing this score calculation, a function from the following non-exhaustive list may be used: f (sum of squared errors between VMS and VM), f (mean of squared errors between VMS and VM), f (median of squared errors between VMS and VM), f (90th percentile of squared errors between VMS and VM), f (maximum squared errors between VMS and VM).
[0060] The score calculation may also, according to a second embodiment, be based on the variation at the measurement points between the actual measurement and the simulated value. Among the various functions enabling this score calculation, a function from the following non-exhaustive list of functions, presented in the table below with their descriptions, may be used.
[0061] [Tables3] Function Description Log_pearson _ / W 103 2 - pearsonmag . mag A 1 y °measure' '-synthetic / / equation allows for a minimum when the correlation is maximal Sum_square_diff_normali ze V / , - / This L mag - normalize mag fl r "y cmeasure y °syntnetic / / equation looks at the normalized squared error, in order to remove the initialization problem in C. Log_spearman Ios2-spearman(mag , mag n This 1 y ^measure' “synthetic / / equation allows for a minimum when the correlation is maximal Variance Minimization of the variance mag -mag z \ °measure ^synthetic / variance STD Minimization of the variance mag -mag . z] y '-measure Dsyntnet)c / type
[0062] The choice of the minimization method and the calculation of the score allows the operator to highlight different parameters such as the speed of convergence towards the point of the cloud to be selected, the convergence towards a global optimum, or even the precision of the convergence when the convergence is stopped.
[0063] When the comparison step is completed with the calculation of the scores of the points in the cloud, the process further consists of carrying out a selection step, for each volume Vi, of a point in the cloud with the best score, the set of selected points Si replacing the set of provisional points Pi of the provisional segment 1.
[0064] Referring to [Fig.2], we see the final segment referenced as 2. Compared to the provisional segment, only point PI is retained, the final segment 2 being positioned slightly below the provisional segment.
[0065] According to an advantageous embodiment of the invention, the selection step consists of keeping the point in the cloud with the best score and preventing the generation of the magnetic card if a selected point If at least one of the sub-segments presents an insufficient score compared to a predetermined score value.
[0066] This feature is particularly important since it makes it possible to prevent the generation of geolocation maps exhibiting aberrations or anomalies.
[0067] According to this characteristic, if the score is insufficient, corresponding to too great a discrepancy between the simulated magnetic values (VMS) and the measured values (VM), several possibilities are conceivable. According to one option, the process generates an error message for the operator. The operator can then analyze the reasons for the card generation failure and restart the process.
[0068] According to a second option, if a selected point Si has an insufficient score, the process restarts at the first step of generating a segment with other assumptions, including a new set of magnetic data, another numerical simulation model, another scenario for an underground structure, or another initial positioning of a provisional segment. This procedure can advantageously be restarted until an assumption makes it possible to obtain points Si with sufficient scores.
[0069] Of course, in the method according to the invention, it is not necessary to go through display steps of the positions of the measurement points Pm, of the provisional segment 1 or even of the final segment 2. These display steps can however be provided as an alternative embodiment to improve the understanding of the results for the operator.
[0070] The method according to the invention makes it possible to determine, by calculation and simulation, the selected points Si. This determination of the points Si will make it possible to carry out the step of creating a magnetic card containing all the selected points Si.
[0071] Referring to [Fig.3] we can see represented an example of a magnetic card 4 according to the invention.
[0072] In the example of [Fig. 3], it can be seen that the segment corresponding to the structure has smoothed angles. According to an advantageous feature of the invention, a smoothing step is provided during the creation of the magnetic card 4, allowing the angles between two successive sub-segments to be reduced. This step refines the overall shape of the final segment to make it compatible with the expected overall shape of the structure to be geolocated.
[0073] According to an advantageous embodiment, the magnetic creation step allows for the display of zones of varying precision depending on the scores of each sub-segment and / or the final segment score 2. These different zones will allow an operator to quickly visualize the zones according to the estimated precision of the Location. In the example in [Fig. 3], the gray levels allow the accuracy of one area relative to another to be identified. However, in other embodiments, the accuracy of the areas may be indicated differently, for example by a color code or numerical values.
[0074] Referring now to [Fig. 4], an example of an embodiment of the process is shown in diagram form. The various steps of the mapping process are summarized in a diagram that allows the operation and advantages of the invention to be understood, particularly in relation to the prior art.
[0075] Of course, other features within the reach of a person skilled in the art could also have been considered without going out of the scope of the invention as defined in the following claims.
Claims
Demands
1. Mapping method for the condition monitoring and / or geolocation of a buried, semi-buried or submerged structure comprising a metallic or magnetic material, characterized in that it comprises: - a step of acquiring spatialized magnetic data obtained by magnetic sensors at different measurement points of the area to be monitored, after injecting a current into the structure, - a step of generating a provisional segment, comprising a set of provisional points (Pi), and a volume (Vi) around each provisional point (Pi), the volume (Vi) comprising a point cloud, - a simulation step, for each point of each volume (Vi), allowing the calculation of the simulated magnetic values (VMS) of the points of the cloud at the level of all or part of the measurement points (Pm),- a step comparing the simulated magnetic values (VMS) of the cloud points in each volume (Vi) with the magnetic values (VM) of the spatialized magnetic data to assign a score to each cloud point in each volume (Vi), - a step selecting, for each volume (Vi), a cloud point with the best score, the set of selected points (Si) replacing the set of provisional points (Pi) of the provisional segment, - a step creating a magnetic map containing the set of selected points (Si).
2. Mapping method according to claim 1 wherein the volume (Vi) around a provisional point (Pi) is a cube, centered on (Pi), whose edge length corresponds to the length of the subsegment between two successive provisional points (Pi).
3. Mapping method according to any one of the preceding claims wherein the cloud points are distributed homogeneously in the volume (Vi)
4. Mapping method according to any one of the preceding claims wherein the simulated magnetic value (SMV) of a cloud point at a measurement point is obtained by applying the following formula: dB(θ) = 3(θ) = fcdB(T)
5. Mapping method according to any one of the preceding claims wherein the score assigned to a point in a point cloud is a function of the comparison between the simulated magnetic values (VMS) of that point at the measurement points and the measured values (VM) at those measurement points (PM).
6. Mapping method according to claim 5 wherein the calculation of the score of a cloud point is based on the errors at the measurement points between the measured magnetic value (VM) and the simulated magnetic value (VMS).
7. Mapping method according to claim 5 wherein the calculation of the score of a cloud point is based on the variation at measurement points between the measured magnetic value (VM) and the simulated magnetic value (VMS).
8. Mapping method according to any one of the preceding claims wherein the simulation step, for each point of each volume (Vi), allows the calculation of the simulated magnetic values (VMS) of the points of the cloud at the level of a number N of measurement points among the set of measurement points and corresponding to the N measurement points closest to the point Pi of the volume (Vi) considered.
9. Mapping method according to any one of the preceding claims wherein additional cloud points of a volume (Vi) are created successively during the simulation step and positioned in the volume (Vi) according to the scores of the simulated cloud points in the simulation step.
10. Mapping method according to any one of the preceding claims wherein the selection step consists of keeping the point in the cloud with the best score and preventing the generation of the magnetic map if a selected point (Si) of at least one of the subsegments has an insufficient score compared to a predetermined score value.
11. Mapping method according to any one of the preceding claims wherein if a selected point (Si) has an insufficient score, the method resumes at the first step of generating a segment with a new set of magnetic data, or another numerical simulation model, another buried structure scenario, or another initial provisional segment positioning.
12. A mapping method according to any one of the preceding claims, wherein the magnetic creation step enables a display of zones of variable precision depending on the scores of each final sub-segment (2) and / or the score of the final segment (2).
13. A mapping method according to any one of the preceding claims, wherein said creation step comprises a smoothing step for limiting the angles between two successive final subsegments (2)
14. Mapping method according to any one of the preceding claims wherein the spatialized magnetic data acquisition step comprises: - a current injection step into the structure, - a signal processing step of the electrical component of the signal emitted by the structure in response to the signal injection allowing comparison with the simulated magnetic values.
15. Mapping method according to any one of the preceding claims wherein the spatialized magnetic data acquisition step comprises: - an alternating current injection step into the structure, - a magnetic measurement processing step via a bandpass filter and a Hilbert filter.