Data compensation method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a data compensation method and device, equipment and a storage medium, and the method comprises the steps: building a target interference compensation model of a pod platform for a towed superconducting full-tensor magnetic gradient measurement system, the target interference compensation model comprising an SQUID magnetic gradiometer measurement item, an interference compensation item and a sensor self-interference correction item; wherein the interference compensation item excludes a residual magnetism interference item of the pod platform, and the interference compensation item comprises a magnetic induction interference item represented by a first-order derivative of an earth magnetic field component and an eddy current interference item represented by a second-order derivative of the earth magnetic field component; the self-interference correction item of the sensor is used for representing the self unbalance degree and unknown offset of the SQUID magnetic gradiometer; constructing a target function based on the target interference compensation model; inputting the measured value of the SQUID magnetic gradiometer and the measured value of the SQUID magnetometer into a target function, and solving by adopting a symmetric super-relaxation iteration method to obtain a compensation coefficient; and compensating the towed superconducting full-tensor magnetic gradient measurement system based on the compensation coefficient.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of geophysical and geochemical airborne magnetic exploration technology, and particularly to a data compensation method, apparatus, equipment, and storage medium. Background Technology

[0002] Superconducting quantum interference devices (SQUIDs), with their fT-level high sensitivity, have become a core technology in the field of airborne magnetic surveying. A superconducting airborne full-tensor magnetic gradient measurement system, constructed by combining multiple SQUIDs, can achieve high-precision measurements of all elements of the geomagnetic field. Its measurement accuracy is 1-2 orders of magnitude higher than traditional airborne magnetic techniques, providing a revolutionary means for deep resource exploration.

[0003] However, dynamic magnetic interference from aircraft severely restricts the performance of the system, and data without effective compensation will produce significant deviations, affecting the analysis of magnetic field source characteristics. Summary of the Invention

[0004] In view of this, embodiments of this application provide at least one data compensation method, apparatus, device, and storage medium.

[0005] The technical solution of this application embodiment is implemented as follows:

[0006] On one hand, embodiments of this application provide a data compensation method, the method comprising:

[0007] A target interference compensation model is established for the towed superconducting full-tensor magnetic gradient measurement system using a pod platform. This model includes at least: a SQUID magnetic gradiometer measurement term, an interference compensation term, and a sensor self-interference correction term. The interference compensation term excludes residual magnetic interference from the pod platform and includes: a magnetic interference term characterized by the first derivative of the Earth's magnetic field component and an eddy current interference term characterized by the second derivative of the Earth's magnetic field component. The sensor self-interference correction term characterizes the unbalance and unknown offset of the SQUID magnetic gradiometer itself.

[0008] Construct an objective function based on the aforementioned target interference compensation model;

[0009] The SQUID magnetic gradient meter measurement value and the SQUID magnetometer measurement value are input into the objective function, and the compensation coefficient is obtained by using the symmetric over-relaxation iterative method.

[0010] The dragged superconducting full tensor magnetic gradient measurement system is compensated based on the compensation coefficient.

[0011] On the other hand, embodiments of this application provide a data compensation device, the device comprising:

[0012] A construction module is used to establish a target interference compensation model for the towed superconducting full-tensor magnetic gradient measurement system by the pod platform. The target interference compensation model includes at least: a SQUID magnetic gradiometer measurement term, an interference compensation term, and a sensor self-interference correction term. The interference compensation term excludes the residual magnetic interference term of the pod platform, and includes: a magnetic interference term characterized by the first derivative of the Earth's magnetic field component and an eddy current interference term characterized by the second derivative of the Earth's magnetic field component. The sensor self-interference correction term is used to characterize the unbalance and unknown offset of the SQUID magnetic gradiometer itself. An objective function is constructed based on the target interference compensation model, wherein the input parameters of the objective function include the SQUID magnetic gradiometer measurement value and the SQUID magnetometer measurement value.

[0013] The compensation module is used to input the SQUID magnetic gradient meter measurement value and the SQUID magnetometer measurement value into the objective function, and use the symmetric over-relaxation iterative method to obtain the compensation coefficient; and to compensate the towed superconducting full tensor magnetic gradient measurement system based on the compensation coefficient.

[0014] In another aspect, embodiments of this application provide a computer device, including a memory and a processor. The memory stores a computer program that can run on the processor, and when the processor executes the program, it implements some or all of the steps in the above-described data compensation method.

[0015] In another aspect, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements some or all of the steps in the above-described data compensation method.

[0016] In another aspect, embodiments of this application provide a computer program including computer-readable code. When the computer-readable code is run in a computer device, the processor in the computer device executes some or all of the steps for implementing the above-described data compensation method.

[0017] In another aspect, embodiments of this application provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program. When the computer program is read and executed by a computer, it implements some or all of the steps in the above-described data compensation method.

[0018] In this embodiment, an interference compensation model specifically designed for a towed superconducting full tensor magnetic gradient measurement system is established. This interference compensation model accurately describes the interference mechanism unique to the SQUID magnetic gradient meter by excluding the residual magnetic interference term of the pod platform and introducing a magnetic interference term characterized by the first derivative of the Earth's magnetic field component and an eddy current interference term characterized by the second derivative of the Earth's magnetic field component. It adapts to the soft connection characteristics of the towed system, achieves high-precision compensation for superconducting magnetic gradient data, and significantly improves the quality and reliability of the measurement data.

[0019] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the technical solutions of this application. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with this application and, together with the specification, serve to explain the technical solutions of this application.

[0021] Figure 1 A schematic diagram of a SQUID magnetic gradient meter provided for an embodiment of this application;

[0022] Figure 2 A schematic diagram illustrating the implementation process of a data compensation method provided in this application embodiment;

[0023] Figure 3 A schematic diagram of a coordinate system with the center of the bottom surface of a hexagonal frustum as the origin, provided in an embodiment of this application;

[0024] Figure 4 This is a schematic diagram of the configuration of a pod system provided in an embodiment of this application;

[0025] Figure 5 A flight test track diagram provided for an embodiment of this application;

[0026] Figure 6 A comparison diagram of compensation results provided in an embodiment of this application;

[0027] Figure 7 This is a schematic diagram of the composition structure of a data compensation device provided in an embodiment of this application;

[0028] Figure 8 This is a schematic diagram of the hardware entity of a computer device provided in an embodiment of this application. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application are further described in detail below with reference to the accompanying drawings and embodiments. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0031] The terms “first / second / third” are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that “first / second / third” may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0032] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this application is for descriptive purposes only and is not intended to be limiting.

[0033] Superconducting quantum interference devices (SQUIDs), with their fT-level high sensitivity, have become a core technology in the field of airborne magnetic measurement. A superconducting airborne full-tensor magnetic gradient measurement system constructed by combining multiple SQUIDs can achieve high-precision measurement of all elements of the geomagnetic field.

[0034] The measurement accuracy is 1-2 orders of magnitude higher than traditional aeromagnetic technology, providing a revolutionary means for deep resource exploration and military reconnaissance. However, dynamic magnetic interference from aircraft severely restricts the system's performance, and data without effective compensation will produce significant deviations, affecting the analysis of magnetic field source characteristics.

[0035] Traditional aeromagnetic compensation techniques primarily employ the Tolles-Lawson model, which describes the residual magnetic field, induced field, and eddy current field interference of the aircraft through a system of linear equations. Compensation coefficients require specific maneuvering flight. Typical implementation methods include:

[0036] Airborne compensation: The magnetic gradient instrument is installed close to the fuselage and the parameters are solved by maneuvering in a figure-eight pattern or other maneuvers. However, the system is complex and the compensation accuracy is limited.

[0037] Towed ground calibration: Parameters are obtained by measuring the magnetic field around the fuselage on the ground, but the dynamic change of the pod position during actual flight leads to significant compensation errors.

[0038] Cross-modulation method: Triaxial compensation is achieved by alternately applying modulation magnetic fields, but the operation is complicated and the response speed is slow.

[0039] These methods suffer from the following fundamental drawbacks: 1) Limited connection method: The towed system uses a soft connection, making it unable to perform the complex maneuvers required for traditional compensation; 2) Mismatched principles: The SQUID magnetic gradient meter reflects the dynamic changes in the magnetic field by detecting the difference in magnetic flux between two superconducting rings, rather than measuring the background magnetic field. Figure 1 As shown, this difference in measurement principle makes it impossible for traditional magnetic compensation models to accurately describe its interference characteristics; 3) Insufficient signal processing: Superconducting magnetic gradient data belongs to the category of weak signal measurement, and the interference signal is comparable in magnitude to the effective signal, making it difficult for traditional methods to effectively separate them.

[0040] Traditional Tolles-Lawson compensation schemes require aircraft to perform ±10° multi-axis maneuvers (pitch / yaw / roll), but towed systems cannot perform such complex maneuvers due to the characteristics of soft connections. Furthermore, SQUIDs are prone to loss of lock during complex maneuvers (operating point loss rate >30%), leading to data acquisition interruptions. Traditional magnetic compensation models (remanence / magnetic induction / eddy currents) fail to closely integrate with the quantum measurement principle of SQUID magnetic sensors and lack sufficient analysis of system interference sources, resulting in a severe disconnect between the compensation model and actual operating conditions, and an inability to accurately characterize interference mechanisms in complex environments. Traditional least squares parameter solving methods degrade the signal-to-noise ratio under weak signal conditions and generate invalid fluctuations in non-magnetic source regions.

[0041] To address the aforementioned issues, this application proposes a dedicated magnetic compensation method for towed superconducting magnetic field measurement systems. Based on the measurement principle of the SQUID magnetic gradiometer, a dedicated compensation model is established to accurately describe the interference mechanism of the magnetic gradiometer. A compensation coefficient solution algorithm without complex maneuvers is developed to adapt to the constraints of towed systems. An extremely weak signal separation technique is designed to improve the accuracy of interference extraction. Through systematic model construction, parameter solving, and effect evaluation, a complete superconducting magnetic gradient data compensation technology system is formed, overcoming the limitations of existing methods in terms of accuracy and applicability. The implementation of this technology is expected to significantly improve the overall accuracy of superconducting magnetic measurement data, providing an efficient and reliable compensation solution for superconducting magnetic field measurement systems, promoting technological breakthroughs in this field in my country, and narrowing the gap with international advanced levels.

[0042] This application provides a data compensation method that can be executed by a processor of a computer device. The computer device refers to a server, laptop, tablet, desktop computer, smart TV, set-top box, mobile device (e.g., mobile phone, portable video player, personal digital assistant, dedicated messaging device, portable gaming device), or any device capable of drag-and-drop supernavigation space full tensor magnetic gradient data compensation. Figure 2This is a schematic diagram illustrating the implementation process of a data compensation method provided in an embodiment of this application, as shown below. Figure 2 As shown, the method includes:

[0043] Step 101: Establish a target interference compensation model for the towed superconducting full tensor magnetic gradient measurement system by the pod platform. The target interference compensation model includes at least: a SQUID magnetic gradiometer measurement term, an interference compensation term, and a sensor self-interference correction term. The interference compensation term excludes the residual magnetic interference term of the pod platform, and the interference compensation term includes: a magnetic interference term characterized by the first derivative of the Earth's magnetic field component and an eddy current interference term characterized by the second derivative of the Earth's magnetic field component. The sensor self-interference correction term is used to characterize the unbalance and unknown offset of the SQUID magnetic gradiometer itself. The SQUID magnetic gradient meter reflects the dynamic changes of the magnetic field by measuring the difference in magnetic flux changes in the superconducting ring. It is not sensitive to the remanence that remains stable for a short period of time, but it is extremely sensitive to the higher-order derivatives of the magnetic field. Therefore, the interference compensation model introduces higher-order derivative terms to accurately reflect the SQUID's sensitivity to dynamic magnetic fields and weakens stable interference terms such as remanence. That is, the interference model excludes the remanence interference term of the pod platform. The interference compensation terms include: magnetic interference term characterized by the first derivative of the Earth's magnetic field component, eddy current interference term characterized by the second derivative of the Earth's magnetic field component, and interference terms from the imbalance and unknown offset of the SQUID magnetic sensor itself.

[0044] In this embodiment, the pod platform refers to a device suspended below the aircraft to support the superconducting full-tensor magnetic gradient measurement system. The SQUID magnetic gradient meter is a flux-to-voltage converter based on superconducting quantum interference effects, capable of measuring the difference in magnetic flux changes between two superconducting rings. The target interference compensation model is a mathematical model used to describe and eliminate various interference factors in the measurement system. The induced magnetic interference term refers to the interference component generated due to the magnetization of the pod platform in the Earth's magnetic field. The eddy current interference term refers to the interference component caused by eddy currents induced by the pod platform when the Earth's magnetic field changes. The SQUID magnetometer is a superconducting quantum interference device used to measure the strength of the ambient magnetic field.

[0045] The system first establishes a target interference compensation model for a towed superconducting full-tensor magnetic gradient measurement system. This model comprises three main components: a SQUID magnetic gradiometer measurement term characterizing the gradiometer's measurements; an interference compensation term specifically modeling interference generated by the pod platform, excluding remanent magnetization interference because the flux quantization of the superconducting ring makes it insensitive to invariant remanent magnetization; and a sensor self-interference correction term using SQUID magnetometer measurements to correct for spurious gradient values ​​caused by the superconducting ring's asymmetry. During the modeling process, the system specifically considers the relationship between the magnetic flux interference term and the first derivative of the Earth's magnetic field component, as well as the relationship between the eddy current interference term and the second derivative of the Earth's magnetic field component, which is fundamentally different from the interference models of traditional magnetometers.

[0046] Step 102: Construct an objective function based on the target interference compensation model.

[0047] In this embodiment, the objective function is a mathematical expression used to optimize the process of solving the compensation coefficients. It quantifies the difference between the measured values ​​and the model predictions into an optimizable index. The symmetric over-relaxation iterative method is an iterative algorithm for solving linear equation systems, which accelerates convergence by introducing a relaxation factor.

[0048] The objective function comprises three main parts: a first-order derivative difference term between the measured signal and the model prediction, used to amplify the high-frequency components of the interference signal; adaptive weighting coefficients, used to dynamically adjust the contributions of different terms based on local magnetic field characteristics; and a variable norm order, used to enhance the model's robustness to outliers. When constructing the objective function, the system specifically considers the characteristic of SQUID magnetic gradient data as a weak signal measurement system, using time-domain differential operators to enhance the difference between the interference signal and the target signal, thereby improving signal separation performance.

[0049] Step 103: Input the SQUID magnetic gradient meter measurement value and the SQUID magnetometer measurement value into the objective function, and use the symmetric over-relaxation iterative method to solve for the compensation coefficient.

[0050] In this embodiment, the compensation coefficient refers to the model coefficient used to correct for interference effects in the measurement data. The SQUID magnetic gradiometer measurement value refers to the difference signal of magnetic flux change actually output by the magnetic gradiometer. The SQUID magnetometer measurement value refers to the three-component value of the ambient magnetic field measured by the magnetometer.

[0051] The system inputs the actual measured values ​​from the SQUID magnetometer and SQUID gradiometer into the constructed objective function. The system employs a symmetric over-relaxation iterative method to solve for the compensation coefficients. This algorithm is implemented through the following steps: First, initialize the coefficient matrix and initial values ​​of the compensation coefficients; then set the relaxation factor, maximum number of iterations, and stopping error threshold; next, perform iterative calculations, updating the compensation coefficients based on the current parameter values ​​in each iteration; finally, determine if the iteration meets the stopping condition. If it does, output the final compensation coefficients; otherwise, continue iterating until the maximum number of iterations is reached. This method ensures the stability of the solution under weak signal conditions.

[0052] Step 104: Compensate the towed superconducting full tensor magnetic gradient measurement system based on the compensation coefficient.

[0053] In this embodiment, the system uses the compensation coefficients obtained in step 103 to compensate the towed superconducting full tensor magnetic gradient measurement system. The compensation process includes: calculating the interference value of each data segment based on the compensation coefficients; subtracting the interference value from the original measured magnetic flux to obtain the compensated data; and stitching together the compensated data segments to obtain complete compensated magnetic gradient data. During the compensation process, the system uses different fitting orders and weighting coefficients based on the magnetic source region division results, increasing the proportion of signal derivative information in the low error region and increasing the proportion of data fitting degree in the high error region to achieve the optimal compensation effect.

[0054] The system in this application establishes an interference compensation model specifically for a towed superconducting full-tensor magnetic gradient measurement system. This interference compensation model accurately describes the interference mechanism unique to the SQUID magnetic gradient meter by excluding the residual magnetic interference term of the pod platform and introducing a magnetic interference term characterized by the first derivative of the Earth's magnetic field component and an eddy current interference term characterized by the second derivative of the Earth's magnetic field component. It adapts to the soft connection characteristics of the towed system, achieves high-precision compensation for superconducting magnetic gradient data, and significantly improves the quality and reliability of the measurement data.

[0055] In some embodiments, step 101 includes:

[0056] Step 1011: Obtain the traditional interference compensation model of the pod platform on the towed superconducting full tensor magnetic gradient measurement system. The traditional interference compensation model includes at least: residual magnetic interference coefficient, magnetic induction interference coefficient and eddy current interference coefficient.

[0057] In the embodiments of this application, the remanent magnetization interference coefficient is a parameter describing the constant magnetic field interference generated by the permanent magnetic material in the measurement system. The magnetic flux density interference coefficient is a parameter characterizing the interference generated by the magnetization of the measurement system in the geomagnetic field. The eddy current interference coefficient is a parameter reflecting the eddy current interference generated by the conductive material in the measurement system when the geomagnetic field changes.

[0058] The system first obtains a traditional interference compensation model for the towed superconducting full-tensor magnetic gradient measurement system caused by the pod platform. This traditional interference compensation model includes three main interference parameters: remanent magnetization interference coefficient, induced magnetization interference coefficient, and eddy current interference coefficient. The system establishes the mathematical expression of the traditional interference compensation model by analyzing the structural characteristics and material composition of the pod platform. The traditional interference compensation model is built upon traditional magnetic compensation theory, providing a basic framework for subsequent model optimization. The system treats the pod platform as an interference source and quantifies its impact on the superconducting magnetic gradient measurement system.

[0059] Step 1012: Remove the residual magnetic interference coefficient from the traditional interference compensation model, and derive the magnetic flux interference coefficient as a magnetic flux interference term characterized by the first derivative of the Earth's magnetic field component, and derive the eddy current interference coefficient as an eddy current interference term characterized by the second derivative of the Earth's magnetic field component.

[0060] In this embodiment, the first derivative of the Earth's magnetic field component refers to the rate of change of the Earth's magnetic field in three orthogonal directions, reflecting the characteristics of the magnetic field's variation with time or space. The second derivative of the Earth's magnetic field component refers to the rate of change of the Earth's magnetic field, characterizing the acceleration characteristics of the magnetic field's change.

[0061] The system makes two key optimizations to the traditional interference compensation model: First, based on the quantum measurement characteristics of the superconducting quantum interference device (SQUID), the system eliminates the remanent magnetization interference coefficient, as the flux quantization characteristic of the superconducting ring makes it insensitive to constant remanent magnetization. Second, the system derives the magnetic field interference term as the product of the traditional magnetic field interference coefficient and the first derivative of the Earth's magnetic field component, and the eddy current interference term as the product of the eddy current interference coefficient and the second derivative of the Earth's magnetic field component. This method accurately reflects the essential characteristic of the SQUID gradiometer, which measures the change in magnetic field rather than the strength of the magnetic field. Through this optimization, the system makes the interference model more consistent with the actual working principle of the superconducting magnetic gradiometer.

[0062] Step 1013: Introduce a sensor self-interference correction term characterized by the SQUID magnetometer measurement value into the conventional interference compensation model to obtain the target interference compensation model.

[0063] In this embodiment, the system introduces a sensor-based interference correction term into the optimized interference model. This correction term is constructed based on real-time measurements from a SQUID magnetometer. The system monitors changes in the ambient magnetic field using a triaxial SQUID magnetometer and establishes a correction matrix to eliminate spurious gradient signals caused by superconducting ring asymmetry and installation deviations. The system mathematically integrates the correction term with the optimized interference model to form a complete target interference compensation model. This model accurately describes the interference mechanism of the towed superconducting full-tensor magnetic gradient measurement system under actual operating conditions, providing a theoretical basis for subsequent compensation coefficient calculation and data processing.

[0064] This application embodiment establishes an interference compensation model specifically for superconducting magnetic gradient measurement systems through three key steps: obtaining a traditional interference compensation model, optimizing the model structure, and introducing a sensor's own interference correction term. This model overcomes the limitations of traditional magnetic compensation technology, accurately reflects the special working principle of superconducting quantum interference devices, and solves the technical problem that towed systems cannot perform complex maneuvering flight.

[0065] In some embodiments, step 102 includes:

[0066] Step 1021: Obtain the fitting term and derivative term of the target interference compensation model.

[0067] In this embodiment, the derivative term refers to the mathematical expression obtained by performing a first-order time derivative operation on the fitting term, used to enhance the difference between the interference signal and the target signal. The fitting term aims to make the model's prediction or estimation result as close as possible to the real data. It is one of the most fundamental components of the optimization objective function.

[0068] The system first extracts fitting terms and derivative terms from the established superconducting magnetic gradient compensation model. The derivative term is obtained by calculating the first-order time derivative of the measured signal, which can amplify the high-frequency components of the interference signal and suppress high-frequency noise in the target signal. The fitting term is extracted as a constraint condition to maintain the stability of the model during subsequent parameter solving.

[0069] Step 1022: Determine the adaptive weight coefficients and variable norm order corresponding to the fitting term and the derivative term, respectively.

[0070] In this embodiment, the adaptive weighting coefficient refers to a weighting parameter that can be dynamically adjusted according to the local magnetic field characteristics, used to balance the contributions of different terms in the objective function. The variable norm order refers to the norm calculation order that can be adjusted according to changes in signal characteristics, enhancing the model's robustness to outliers.

[0071] The system dynamically determines the adaptive weighting coefficients α and β for the fitting term and derivative term based on the magnetic field characteristics of the current measurement area. These coefficients are automatically adjusted by analyzing the local characteristics of the signal; α is increased in the high error region to improve the data fit, and β is increased in the low error region to preserve the signal fluctuation characteristics. Simultaneously, the system determines appropriate variable norm orders p and q for each term. These orders can be adjusted within the range of 1-2 according to the signal characteristics to enhance the model's adaptability to different types of interference.

[0072] Step 1023: Combine the fitting term and the derivative term with the corresponding adaptive weight coefficients and variable norm order to obtain the objective function.

[0073] In this embodiment, the system mathematically combines the acquired fitting term and derivative term with the determined adaptive weighting coefficients and variable norm order to construct a complete objective function. In this objective function, the derivative term DGm-DAx is used to capture the high-frequency characteristics and boundary features of the signal, while the fitting term is used to maintain the stability of the solution. Through this combination, the system achieves accurate separation of the weak magnetic signal, providing an optimized mathematical framework for subsequent parameter solving.

[0074] This application's embodiments, through systematically acquiring key model terms, dynamically determining optimization parameters, and constructing a complete objective function, effectively address the weak signal separation problem in superconducting magnetic gradient measurements. Compared to traditional methods, this technique based on derivative operations and adaptive parameter adjustment significantly improves the separability of interference and target signals, achieving high-precision magnetic compensation without the need for complex maneuvering flight.

[0075] In some embodiments, step 1022 includes:

[0076] Step 10221: Input the SQUID magnetic gradient meter measurement value and the SQUID magnetometer measurement value into the objective function, and use the symmetric over-relaxation iterative method to solve the objective function in each partitioned region to obtain the compensation coefficient of each partitioned region.

[0077] In this embodiment, the system first receives measurement inputs from a SQUID magnetic gradiometer and a SQUID magnetometer. The computer substitutes these measurements into a pre-constructed objective function, which includes derivative and fitting terms. The system employs a symmetric over-relaxation iterative method for partitioned solution. This method accelerates convergence while ensuring numerical stability by setting appropriate relaxation factors and iteration parameters. During the solution process, the system optimizes calculations for different regions using different norm orders and weighting coefficients based on the magnetic source region partitioning results. At each iteration, the system updates the estimated compensation coefficients and checks whether the convergence conditions are met. When the preset convergence accuracy or maximum number of iterations is reached, the system outputs the optimal compensation coefficient solution for the current partition.

[0078] The embodiments of this application achieve high-precision solution of partition parameters through the symmetric over-relaxation iterative method, ensuring local optimality, and ensure global consistency through the intelligent splicing algorithm. This technical solution of partitioning and then splicing the whole effectively solves the problem of insufficient adaptability of traditional methods in complex magnetic field environments.

[0079] In some embodiments, after 10221, the method further includes:

[0080] Step 10222: Use the compensation coefficient to compensate the magnetic field measurement value corresponding to each division zone to obtain the partition compensation data corresponding to each division zone.

[0081] Step 10223: The partition compensation data is spliced ​​together to obtain the compensation data of the dragged superconducting full tensor magnetic gradient measurement system.

[0082] In this embodiment, the system first uses a symmetric over-relaxation iterative method to solve for the compensation coefficient x of each region's data. Then, the magnetic gradient data of each partition is compensated using the compensation coefficient of each partition to obtain the compensated magnetic gradient data of each partition. Finally, the compensated magnetic gradient data of each partition are concatenated to obtain the compensated measurement data of the entire system.

[0083] This application embodiment first optimizes the measurement error using the gradient descent method, then realizes intelligent partitioning of the survey line based on error characteristics, adopts differentiated processing strategies in different regions, and finally integrates the partition parameters into a global compensation scheme, thereby significantly improving the compensation accuracy of superconducting magnetic gradient data. It is particularly suitable for towed systems that cannot perform complex maneuvering flight, solves the problem of poor compensation effect of traditional methods under weak signal conditions, and provides a more reliable data foundation for deep resource exploration.

[0084] In some embodiments, step 10221 includes:

[0085] Step 102211: Set the relaxation factor and stopping condition for the symmetric over-relaxation iteration method.

[0086] In this embodiment, the system first initializes the parameters of the symmetric over-relaxation iterative method, including setting the range of the relaxation factor ω (1 < ω < 2). The system selects an appropriate relaxation factor based on the characteristics of the magnetic compensation problem, typically determining the optimal value through experimentation or experience. Simultaneously, the system sets stopping condition parameters, including the maximum number of iterations Nmax and the allowable error threshold ε. These parameters control the termination timing of the iteration process, ensuring that computation stops when sufficient accuracy or maximum computational complexity is achieved.

[0087] Step 102212: Solve the interference compensation model in each partitioned region according to the relaxation factor to obtain the value of the objective function.

[0088] In this embodiment, the system performs symmetric over-relaxation iterative calculations on the coefficient matrix of the magnetic compensation model based on a set relaxation factor ω. In each iteration, the system calculates the residual of the current solution and adjusts the update step size using the relaxation factor. Simultaneously, the system calculates the objective function value, which includes the fitting error between the measured data and the model prediction, as well as the fitting term. Through iterative updates, the system gradually optimizes the compensation coefficients, causing the objective function value to converge towards the optimal solution.

[0089] Step 102213: Terminate the iteration when the value of the objective function satisfies the stopping condition, and obtain the partitioned magnetic gradient compensation value.

[0090] In this embodiment, the system checks the stopping condition after each iteration: whether the change in the objective function value is less than a preset threshold ε, or whether the number of iterations has reached Nmax. When either condition is met, the system terminates the iteration process and outputs the current optimal compensation coefficient. The system applies these parameters to the original measurement data to calculate the compensated magnetic gradient values ​​for each partition. Finally, the system concatenates the compensation results of all partitions to form a complete compensated magnetic gradient dataset.

[0091] This application's embodiments achieve efficient solutions to the magnetic compensation model by appropriately setting relaxation factors and stopping conditions. The symmetric over-relaxation iterative method ensures the stability and convergence speed of the solution process, while the partitioning strategy adapts to the differences in magnetic field characteristics in different regions.

[0092] In some embodiments, after step 104, the method further includes:

[0093] Step 201: Calculate the first standard deviation of the data before compensation and the second standard deviation of the data after compensation.

[0094] In this embodiment, the system first reads and stores the original magnetic gradient measurement data sequence before compensation, and performs arithmetic calculations on these data to obtain the first standard deviation of the data before compensation. Then, the system reads the data sequence processed by the compensation algorithm and similarly performs arithmetic calculations to obtain the second standard deviation of the data after compensation. The system stores the second standard deviation as the first key indicator for evaluating the compensation effect.

[0095] Step 202: The ratio of the first standard deviation to the second standard deviation is used as the compensation improvement ratio.

[0096] In this embodiment, the system uses the ratio of the first standard deviation to the second standard deviation as the compensation improvement ratio. A larger ratio indicates a better compensation effect and a more significant improvement in data quality. The system stores this ratio as a second key indicator for evaluating the compensation effect.

[0097] Step 203: Evaluate the compensation effect based on the compensation improvement ratio.

[0098] In this embodiment, the system compensation improvement ratio is used as an input parameter for comprehensive evaluation and analysis. The system establishes an evaluation model, performs weighted calculations on these two indicators, or establishes a scoring system to provide a quantitative score for the compensation effect. Simultaneously, the system can set threshold standards to determine whether the compensation effect has achieved the expected goal. The evaluation results can be used to optimize and adjust algorithm parameters, or as output content of a compensation quality report.

[0099] This application's embodiments quantify the degree to which the compensation algorithm improves the overall data quality by calculating the compensation improvement ratio; and evaluate the stability and consistency of the compensated data by calculating the standard deviation. This evaluation method is particularly suitable for high-precision weak signal detection scenarios such as superconducting magnetic gradient measurement systems, accurately capturing the improvement effect of compensation processing on extremely weak magnetic signals, and ensuring the reliability and accuracy of airborne magnetic measurement data.

[0100] In some embodiments, this application uses another data compensation method, as follows:

[0101] Step S1: Interference Source Analysis

[0102] For towed cryogenic superconducting full-tensor magnetic gradient measurement systems, when the distance between the aircraft and the pod is sufficiently large, the magnetic interference from the aircraft on the SQUID magnetic gradiometer inside the pod can be significantly reduced to a negligible level. This characteristic provides a reasonable basis for simplifying the magnetic compensation model, allowing the model to focus more on the interference from the pod's flight platform and the SQUID magnetic gradiometer itself. This section will derive the interference compensation model from the definition of tensors, the measurement principle of the SQUID magnetic gradiometer, and the structure of the superconducting magnetic probe.

[0103] Step S2: Analysis of the working principle of the SQUID gradient meter

[0104] The SQUID magnetic gradient meter is essentially a flux-to-voltage converter, comprising signal detection and a superconducting loop. Its core consists of a superconducting loop and a Josephson structure, such as... Figure 1 As shown. When the magnetic flux in the superconducting ring changes, the superconducting ring will induce a current. Therefore, SQUID actually measures the difference in the change of magnetic flux in the two superconducting rings. This difference represents the magnetic gradient field with direction. Therefore, the SQUID magnetic gradient meter measurement value G can be expressed as formula (1).

[0105]

[0106] Where Δφ1 and Δφ2 represent the magnetic flux of the two superconducting rings, S is the effective induction area of ​​the superconducting ring, L is the baseline length of the two superconducting rings, and ΔBi is the change in magnetic field strength in the i-th superconducting ring.

[0107] The low-temperature superconducting full-tensor magnetic gradient measurement system adopts a regular hexagonal truncated pyramid structure, with six SQUID magnetic gradient meters mounted on the six sides of the truncated pyramid, establishing a... Figure 3 The diagram shows a right-handed coordinate system with the center of the base of the hexagonal frustum as the origin. The z-axis points vertically upward, the angle between the side face and the base is α, and the azimuth angle of each face is β = 60° × n (n = 0, 1, ..., 5). Figure 3 As shown. Through the projection decomposition of the magnetic field components onto the superconducting ring plane, the relationship between the SQUID gradient meter output and the three-component magnetic field can be further expressed as Equation (2):

[0108]

[0109] Where, ΔB ix ,ΔB iy ,ΔB iz (i = 1, 2) represents the change in the three components of the magnetic field of the i-th superconducting ring in the three different directions (x, y, z). It directly reflects the dynamic change of the magnetic field in the three directions and thus affects the output signal of the SQUID magnetic gradient meter.

[0110] Step S3: Establish a magnetic compensation model

[0111] The supernavigation full tensor magnetic gradient measurement system adopts a soft-connection pod design (suspended at a distance of approximately 50 meters), effectively isolating the main magnetic interference from the aircraft platform. Therefore, the interference generated by the pod itself becomes the dominant factor affecting the SQUID magnetic gradient meter measurement. The interference field generated by the pod at each superconducting ring of the SQUID gradient meter can be modeled as a three-component form, as shown in formula (3):

[0112]

[0113] Where i = 1, 2 represent the two superconducting rings of the SQUID gradient meter, B j (j = x, y, z) are the three components of the Earth's magnetic field, B ij (j = x, y, z) are the three components of the pod interference at the i-th superconducting ring, P ij k ijl e ijl (j, l = x, y, z) are the remanence coefficient, magnetization coefficient, and eddy current interference coefficient at the i-th superconducting ring, respectively.

[0114] Unlike traditional magnetometers, the SQUID gradiometer's measurement mechanism results in a significantly specific response to disturbance fields:

[0115] 1) Residual magnetism exclusion effect: The flux quantization characteristic of the superconducting ring makes it sensitive only to changing flux. The short-term constant residual magnetism interference (dB / dt=0) will not generate a measurable signal. Therefore, the residual magnetism term does not need to be considered in formula (3).

[0116] 2) Dynamic coupling characteristics: For the two types of interference, namely magnetic induction and eddy current, special attention should be paid to the fact that the SQUID magnetic gradient meter measures the change. Therefore, in the interference model, the remanence is proportional to the first derivative of the magnetic field, and the eddy current interference is proportional to the second derivative of the magnetic field.

[0117] Based on the above analysis, the interference model of the i-th superconducting ring of the SQUID magnetic gradient meter from the pod platform can be expressed as Equation (4).

[0118]

[0119] Where △B ij (j=x,y,z) represents the three magnetic flux variation components of the pod disturbance at the i-th superconducting ring.

[0120] Combining the working principle formulas (2) and (4) of the SQUID gradiometer, the interference model of the SQUID magnetic gradiometer can be obtained as shown in formula (5). The essential difference between this model and the interference model of the traditional magnetometer is that the interference of the SQUID magnetic gradiometer does not include the residual magnetic field interference term, is unrelated to the magnetic field strength itself, but is closely related to the first and second derivative terms of the magnetic field.

[0121]

[0122] Where K represents k ijl The matrix formed, i.e. k yj =sinαcosβ(k 1yj -k 2yj );

[0123] E represents e ijl The burden of proof, namely e yj =sinαcosβ(e 1yj -e 2yj ); (j = x, y, z).

[0124] Due to limitations in manufacturing processes, it is difficult to achieve perfect symmetry between the two superconducting rings of a SQUID magnetic gradiometer, and the coil normals cannot be guaranteed to be absolutely parallel. This results in a false gradient value being output in a uniform magnetic field environment. In practical applications, a triaxial SQUID magnetometer is usually installed near the SQUID magnetic gradiometer to monitor the magnetic field environment in real time. The false gradient value is corrected by minimizing the standard deviation, as shown in formula (6). ri , (i = x, y, z) are the values ​​of the SQUID triaxial magnetometer.

[0125]

[0126] Among them G balance c represents the correction compensation value. i (i = x, y, z) represents the imbalance compensation coefficient of the three-component magnetic field.

[0127] Theoretically, the SQUID magnetic gradient meter should have a zero value at the lockout moment, but in actual measurements, an unknown offset will occur, which can be represented by 'o'. Therefore, by combining formulas (5) and (6), the SQUID magnetic gradient compensation model can be obtained, as shown in formula (7), G r G is the compensated magnetic gradient value. m The value measured by the gradient meter is here, and it is related to G. m Similar to the meaning of G in the above formula, A is a matrix composed of the first and second derivatives of the three-component magnetic field and the magnetometer measurement value, and m is the compensation coefficient.

[0128]

[0129] Step S4: Construct the objective function

[0130] SQUID magnetic gradient data is a typical weak signal measurement system, with the amplitude of its interference signal being similar in magnitude to that of the target signal. Theoretical analysis shows that the interference signal is significantly correlated with flight attitude parameters (such as pitch and roll angles) and overlaps with the target signal in the frequency domain. This spectral aliasing phenomenon leads to a significant reduction in the signal-to-noise ratio within the magnetic anomaly region, posing a challenge to target signal extraction.

[0131] To improve signal separability, this application proposes a method based on time-domain differential operators to perform first-order time derivative operations on the measurement signal and the target signal, thereby enhancing the separation effect by increasing the difference between the interference signal and the target signal. Based on this, the following adaptive objective function (8) is constructed:

[0132]

[0133] DG m-DAm represents the derivative information of the fitted data, which can effectively amplify the high-frequency components of the interference signal, suppress high-frequency noise in the target signal, and can also be used to capture the boundary characteristics of the signal.

[0134] α and β represent adaptive weighting coefficients, which can dynamically balance the contributions of the fitting term and the fitting term according to the local magnetic field characteristics;

[0135] p and q represent the variable norm order, and the variable order enhances the model's robustness to outliers.

[0136] Step S5: Magnetic source region division

[0137] The magnetic compensation method proposed in this application employs a two-step approach to directly solve for the compensation coefficient in different regions. The specific method is as follows:

[0138] 1) Calculate the initial compensation data G using the least squares fitting results. r0 However, the accuracy of the initial compensation result is low. Therefore, a data-driven error prediction model is introduced. By learning the systematic error v in the least squares calculation and correcting it, a more accurate initial compensation result can be obtained, as shown in formula (9) and formula (10).

[0139] v = G m -G r0 (9);

[0140] G corrected =G r0 +vh (10);

[0141] Where h is the correction coefficient, optimized using the gradient descent method. After multiple optimizations, the machine rate of change of error v is calculated. Constructing the feature matrix

[0142] 2) Set thresholds τ1 and τ2, divide the region according to the following formula, and use different fitting orders p, q and weight coefficients in different regions, as shown in the following formula (11):

[0143]

[0144] In the low error region, to preserve the amplitude and shape of signal fluctuations, β can be appropriately increased to increase the proportion of signal derivative information; while in the low error region, to reduce invalid fluctuations, α can be appropriately increased to increase the proportion of data fit.

[0145] Step S6: Solving for the compensation coefficient

[0146] To ensure the stability of the solution, the symmetric over-relaxation iterative method is used to solve for the compensation coefficient m. The solution steps are as follows:

[0147] 1) Initialize the coefficient matrix A = (a ij );

[0148] 2) Give the initial value m0, relaxation factor ω, maximum number of iterations k, and stopping error v;

[0149] 3) Perform iterative calculations according to the following formula (12);

[0150]

[0151] 4) Determine if the iteration has stopped. If it satisfies ||m k+1 -x k || / ||x k If || < ε, the iteration stops, where ε represents the preset loss value; otherwise, it returns 3), or the maximum number of iterations is reached.

[0152] 5) Calculate the compensated data based on the compensation coefficient m.

[0153] When the symmetric over-relaxation iterative method is used to solve for the compensation coefficient m of each data segment i Then calculate the corresponding compensated magnetic gradient data G. ri Finally, the compensated data segments are concatenated to obtain the compensated magnetic data G. r See formula (13), where n is the number of magnetic source regions.

[0154] G r ={G r1 G r2 ,…,G rn} (13);

[0155] Step S7: Evaluation of Compensation Effect

[0156] The improvement ratio (IR) and the first and second standard deviations (SD) are used to quantitatively evaluate the compensation results (the calculation methods are shown in formulas (14) and (15)). IR reflects the degree of improvement in signal quality before and after compensation, while SD characterizes the discrete characteristics of the data after compensation.

[0157]

[0158] Where, d 0 For the data before compensation, mean(d) 0 ) represents the average value of the data before compensation, d c For the compensated data, mean(d) c ) represents the average value of the compensated data, and N represents the number of data points.

[0159] In some embodiments, this application also provides another data compensation method, specifically including:

[0160] Step P1, System Configuration

[0161] To verify the effectiveness of the compensation method proposed in this application in actual flight measurements, a systematic flight experiment was designed and implemented. All components of the cryogenic supernavigation full-tensor magnetic gradient measurement system used in the experiment were integrated into a dedicated pod, which was suspended below the helicopter by cables for flight measurements. A schematic diagram of the pod system configuration is shown below. Figure 4 As shown. The core measurement device of the experiment is the SQUID sensor measurement probe, which integrates six SQUID gradiometers and one triaxial SQUID magnetometer. To ensure measurement accuracy, the SQUID magnetic gradiometers are encapsulated in a non-magnetic Dewar flask filled with liquid helium, allowing them to maintain a superconducting state at a temperature of 4.2K, thereby ensuring optimal measurement performance.

[0162] Step P2, Flight Data Acquisition Scheme

[0163] As a flight test area, the background magnetic field in this region is generally stable, but there are localized areas affected by human activities such as industrial facilities.

[0164] The activity caused a magnetic anomaly. Ten north-south survey lines were set up for the flight experiment, with a line spacing of 500 meters and a single line length of 10 kilometers, maintaining a flight altitude of 200 meters. To compare with traditional compensation methods, a square closed-loop flight was also conducted at an altitude of 1000 meters to obtain the parameter calculation data required by traditional compensation methods. The flight path was cut after invalid survey lines were removed, as shown below. Figure 5 As shown.

[0165] Step P3, Analysis of Compensation Results

[0166] This application employs both traditional compensation methods and the method proposed in this application to process flight data, and systematically evaluates their compensation effects. The traditional compensation method calculates compensation coefficients based on closed-loop flight path data at 1000 meters altitude and applies them to survey line data at 200 meters altitude. In contrast to the traditional method, the novel compensation method proposed in this application does not rely on high-altitude flight data but directly processes data based on 200-meter survey line data. Figure 6 The compensation effects of the two methods are visually demonstrated: the red curve represents the preprocessed magnetic gradient data, which still has obvious interference despite initial noise reduction; the green and blue curves correspond to the compensation results of the traditional method and the method of this application, respectively. It can be seen that the data fluctuation after compensation by the method of this application is significantly reduced, showing better stability and anti-interference ability.

[0167] Table 1 summarizes the compensation results of the two methods. Compared with the traditional method, the proposed method significantly improves the IR values ​​of each tensor component (by 2.53, 3.09, 1.94, 2.14, 2.13, and 1.87 times, respectively, with a mean of 2.28), while significantly reducing the SD values ​​(to 67.60%, 42.00%, 51.72%, 40.81%, 43.66%, and 63.08%, respectively, with a mean of 51.48%). These data fully demonstrate that the proposed method is superior to the traditional method in both improving data accuracy and compensation stability.

[0168] Table 1

[0169]

[0170] The magnetic compensation model established in this application, taking into account both measurement platform interference and the inherent interference of the SQUID gradiometer, demonstrates a high degree of innovation and practicality. Starting from the core measurement principle of the SQUID gradiometer, the model successfully eliminates the influence of stability interferences such as residual magnetism on the measurement accuracy of the SQUID gradiometer through comprehensive and in-depth derivation and optimization. Simultaneously, it cleverly introduces the first and second derivative terms of the magnetic field, more accurately reflecting the true situation of magnetic field changes. This is a significant breakthrough over traditional magnetic compensation techniques, providing a more reliable foundation for the subsequent processing and analysis of magnetic measurement data, and paving new paths for the further development and application of magnetic measurement technology.

[0171] This application proposes a novel magnetic compensation method that fully considers the operating characteristics of towed systems and the vulnerability of SQUID magnetic sensors to lock-up under complex maneuvers. The core innovation lies in overcoming the dependence of traditional magnetic compensation methods on complex flight maneuvers, achieving a significant reduction in flight costs, a substantial simplification of operational procedures, and a comprehensive improvement in the safety of field operations. To ensure the reliability of this method in practical applications, a weak signal separation technique is designed to significantly enhance the difference between interference signals and target signals, thereby significantly improving the efficiency of signal separation and extraction. Simultaneously, a corresponding compensation coefficient solution method is designed to achieve accurate calculation of the compensation coefficients without the constraints of complex flight maneuver data. This innovation provides a completely new magnetic compensation solution for towed super-navigational magnetic survey systems, significantly improving the system's engineering practicality and operational safety, demonstrating significant practical value in real-world applications.

[0172] Based on the foregoing embodiments, this application provides a data compensation device, which includes various units and modules included in each unit. It can be implemented by a processor in a computer device; of course, it can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0173] Figure 7 This is a schematic diagram of the composition structure of a data compensation device provided in an embodiment of this application, as shown below. Figure 7 As shown, the data compensation device includes:

[0174] Module 301 is used to establish a target interference compensation model for the towed superconducting full-tensor magnetic gradient measurement system by the pod platform. The target interference compensation model includes at least: a SQUID magnetic gradiometer measurement term, an interference compensation term, and a sensor self-interference correction term. The interference compensation term excludes the residual magnetic interference term of the pod platform, and includes: a magnetic interference term characterized by the first derivative of the Earth's magnetic field component and an eddy current interference term characterized by the second derivative of the Earth's magnetic field component. The sensor self-interference correction term is used to characterize the unbalance and unknown offset of the SQUID magnetic gradiometer itself. An objective function is constructed based on the target interference compensation model, wherein the input parameters of the objective function include the SQUID magnetic gradiometer measurement value and the SQUID magnetometer measurement value.

[0175] The compensation module 302 is used to input the SQUID magnetic gradient meter measurement value and the SQUID magnetometer measurement value into the objective function, and use the symmetric over-relaxation iterative method to solve for the compensation coefficient; and to compensate the towed superconducting full tensor magnetic gradient measurement system based on the compensation coefficient.

[0176] In some embodiments, the construction module 301 is further configured to: obtain a traditional interference compensation model for the towed superconducting full tensor magnetic gradient measurement system by the pod platform, wherein the traditional interference compensation model includes at least: a remanent magnetic interference coefficient, a magnetically induced interference coefficient, and an eddy current interference coefficient; exclude the remanent magnetic interference coefficient from the traditional interference compensation model, and derive the magnetically induced interference coefficient as a magnetically induced interference term characterized by the first derivative of the Earth's magnetic field component, and derive the eddy current interference coefficient as an eddy current interference term characterized by the second derivative of the Earth's magnetic field component.

[0177] By introducing a sensor self-interference correction term characterized by the SQUID magnetometer measurement value into the traditional interference compensation model, a target interference compensation model is obtained.

[0178] In some embodiments, the construction module 301 is further configured to: obtain the fitting term and derivative term of the target interference compensation model; determine the adaptive weight coefficients and variable norm order corresponding to the fitting term and the derivative term respectively; and combine the fitting term and the derivative term with the corresponding adaptive weight coefficients and variable norm order to obtain the objective function.

[0179] In some embodiments, the compensation module 302 is further configured to: input the SQUID magnetic gradient meter measurement value and the SQUID magnetometer measurement value into the objective function, and use the symmetric over-relaxation iterative method to solve the objective function in each partitioned region to obtain the compensation coefficient of each partitioned region.

[0180] In some embodiments, the compensation module 302 is further configured to: compensate the measurement values ​​corresponding to each division region using the compensation coefficient to obtain partition compensation data corresponding to each division region; and splice the partition compensation data to obtain the compensation data of the towed superconducting full tensor magnetic gradient measurement system.

[0181] The compensation coefficient is obtained by concatenating the partition compensation coefficients based on the partitioning results.

[0182] In some embodiments, the compensation module 302 is further configured to:

[0183] A relaxation factor and stopping condition are set for the symmetric over-relaxation iterative method; the interference compensation model is solved in each partitioned region according to the relaxation factor to obtain the value of the objective function; the iteration is terminated when the value of the objective function satisfies the stopping condition, and the compensation coefficients corresponding to each partitioned region are obtained.

[0184] In some embodiments, the compensation module 302 is further configured to: calculate a first standard deviation of the data before compensation and a second standard deviation of the data after compensation; use the ratio of the first standard deviation to the second standard deviation as the compensation improvement ratio; and evaluate the compensation effect based on the compensation improvement ratio.

[0185] This application embodiment establishes an interference compensation model specifically for a towed superconducting full tensor magnetic gradient measurement system. This interference compensation model accurately describes the interference mechanism unique to the SQUID magnetic gradient meter by excluding the residual magnetic interference term of the pod platform and introducing a magnetic interference term characterized by the first derivative of the Earth's magnetic field component and an eddy current interference term characterized by the second derivative of the Earth's magnetic field component. It adapts to the soft connection characteristics of the towed system, achieves high-precision compensation for superconducting magnetic gradient data, and significantly improves the quality and reliability of the measurement data.

[0186] The descriptions of the apparatus embodiments above are similar to those of the method embodiments above, and have similar beneficial effects. In some embodiments, the functions or modules included in the apparatus provided in this application can be used to perform the methods described in the method embodiments above. For technical details not disclosed in the apparatus embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0187] It should be noted that, in the embodiments of this application, if the above-described data compensation method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the related technology, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware, software, or firmware, or any combination of hardware, software, and firmware.

[0188] This application provides a computer device including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, it implements some or all of the steps in the above-described method.

[0189] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements some or all of the steps in the above-described method. The computer-readable storage medium can be transient or non-transient.

[0190] This application provides a computer program including computer-readable code, wherein when the computer-readable code is executed in a computer device, a processor in the computer device performs some or all of the steps in the above-described method.

[0191] This application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program. When the computer program is read and executed by a computer, it implements some or all of the steps in the above-described method. This computer program product can be implemented specifically through hardware, software, or a combination thereof. In some embodiments, the computer program product is specifically embodied as a computer storage medium; in other embodiments, the computer program product is specifically embodied as a software product, such as a software development kit (SDK), etc.

[0192] It should be noted that the descriptions of the various embodiments above tend to emphasize the differences between them, while their similarities or commonalities can be referred to interchangeably. The descriptions of the above embodiments of the device, storage medium, computer program, and computer program product are similar to the descriptions of the above method embodiments and have similar beneficial effects. For technical details not disclosed in the embodiments of the device, storage medium, computer program, and computer program product of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0193] It should be noted that, Figure 8 This is a schematic diagram of a hardware entity of a computer device in an embodiment of this application, such as... Figure 8 As shown, the hardware entity of the computer device 700 includes: one or more processors 701, a communication interface 702, and a memory 703, wherein:

[0194] Processor 701 typically controls the overall operation of computer device 700.

[0195] Communication interface 702 enables computer devices to communicate with other terminals or servers over a network.

[0196] The memory 703 is configured to store instructions and applications executable by the processor 701, and can also cache data to be processed or already processed (e.g., image data, audio data, voice communication data, and video communication data) in the processor 701 and various modules in the computer device 700. It can be implemented using flash memory or random access memory (RAM). Data transfer between the processor 701, the communication interface 702, and the memory 703 can be performed via bus 704. Only one processor is shown in the figure; each processor 100 includes one or more cores.

[0197] It should be noted that the computer device may include multiple processors 701, and each processor 701 can interact with each other through aggregation communication methods such as all-to-all, all-gather, or all-reduce. The processors 701 may be central processing units (CPUs), graphics processing units (GPUs), embedded neural network processing units (NPUs), tensor processing units (TPUs), dragged hypersonic space full tensor magnetic gradient data compensation units (DPUs), accelerated processing units (APUs), floating-point processing units (FPUs), or application-specific integrated circuits (ASICs). The processors may also be single-core or multi-core processors. The processor may consist of a CPU and hardware chips. The hardware chips may be ASICs, PLDs, or combinations thereof. The PLDs may be complex programmable logic devices (CPLDs), FPGAs, generic array logic (GALs), or any combination thereof. The processor can also be implemented using logic devices with built-in processing logic, such as FPGA or digital signal processor (DSP).

[0198] The communication interface 702 can be a wired interface or a wireless interface, used to communicate with other modules or devices. The wired interface can be an Ethernet interface, a local interconnect network (LIN), etc., and the wireless interface can be a cellular network interface or a wireless LAN interface, etc.

[0199] Memory 703 can be non-volatile memory, such as read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Memory 703 can also be volatile memory, which can be random access memory (RAM) used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus RAM (DRRAM), direct rambus DRAM (DRDRAM), and rambus DRAM.

[0200] The 704 bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc.

[0201] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above steps / processes do not imply a sequential order of execution; the execution order of each step / process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above embodiments of this application are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0202] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0203] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0204] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0205] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0206] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0207] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence or the part that contributes to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, magnetic disks, or optical disks.

[0208] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A data compensation method, characterized in that, The method includes: A target interference compensation model is established for the towed superconducting full-tensor magnetic gradient measurement system using a pod platform. This model includes at least: a SQUID magnetic gradiometer measurement term, an interference compensation term, and a sensor self-interference correction term. The interference compensation term excludes residual magnetic interference from the pod platform and includes: a magnetic interference term characterized by the first derivative of the Earth's magnetic field component and an eddy current interference term characterized by the second derivative of the Earth's magnetic field component. The sensor self-interference correction term characterizes the unbalance and unknown offset of the SQUID magnetic gradiometer itself. Construct an objective function based on the aforementioned target interference compensation model; The SQUID magnetic gradient meter measurement value and the SQUID magnetometer measurement value are input into the objective function, and the compensation coefficient is obtained by using the symmetric over-relaxation iterative method. The dragged superconducting full tensor magnetic gradient measurement system is compensated based on the compensation coefficient.

2. The method according to claim 1, characterized in that, The establishment of the target interference compensation model for the towed superconducting full tensor magnetic gradient measurement system by the pod platform includes: A traditional interference compensation model for the towed superconducting full tensor magnetic gradient measurement system is obtained from the pod platform. The traditional interference compensation model includes at least: remanent magnetic interference coefficient, magnetic induction interference coefficient and eddy current interference coefficient. The residual magnetic interference coefficient is excluded from the traditional interference compensation model, and the magnetic flux interference coefficient is derived as a magnetic flux interference term characterized by the first derivative of the Earth's magnetic field component. The eddy current interference coefficient is derived as an eddy current interference term characterized by the second derivative of the Earth's magnetic field component. By introducing a sensor self-interference correction term characterized by the SQUID magnetometer measurement value into the traditional interference compensation model, a target interference compensation model is obtained.

3. The method according to claim 1, characterized in that, The objective function constructed based on the target interference compensation model includes: Obtain the fitting term and derivative term of the target interference compensation model; Determine the adaptive weight coefficients and variable norm order corresponding to the fitting term and the derivative term, respectively; The objective function is obtained by combining the fitting term and the derivative term with the corresponding adaptive weight coefficients and the variable norm order.

4. The method according to claim 3, characterized in that, The step of inputting the SQUID magnetic gradient meter measurement value and the SQUID magnetometer measurement value into the objective function, and solving for the compensation coefficient using the symmetric over-relaxation iterative method, includes: The SQUID magnetometer measurement values ​​and the SQUID magnetometer measurement values ​​are input into the objective function. The objective function is solved in each partitioned region using the symmetric over-relaxation iterative method to obtain the compensation coefficients for each partitioned region.

5. The method according to claim 4, characterized in that, The compensation of the towed superconducting full tensor magnetic gradient measurement system based on the compensation coefficient includes: The compensation coefficient is used to compensate the measurement values ​​corresponding to each division zone to obtain the partition compensation data corresponding to each division zone; The compensation data of the dragged superconducting full tensor magnetic gradient measurement system is obtained by splicing the partition compensation data.

6. The method according to claim 4 or 5, characterized in that, The objective function is solved in each partitioned region using a symmetric over-relaxation iterative method to obtain the compensation coefficients for each partitioned region, including: Define the relaxation factor and stopping condition for the symmetric over-relaxation iterative method; The interference compensation model is solved in each partitioned region according to the relaxation factor to obtain the value of the objective function; The iteration terminates when the value of the objective function satisfies the stopping condition, and the compensation coefficients corresponding to each partition are obtained.

7. The method according to any one of claims 1 to 5, characterized in that, After compensating the towed superconducting full tensor magnetic gradient measurement system based on the compensation coefficient, the method further includes: Calculate the first standard deviation of the data before compensation and the second standard deviation of the data after compensation; The ratio of the first standard deviation to the second standard deviation is used as the compensation improvement ratio; The compensation effect is evaluated based on the aforementioned compensation-improvement ratio.

8. A data compensation device, characterized in that, include: A construction module is used to establish a target interference compensation model for the pod platform on the towed superconducting full-tensor magnetic gradient measurement system. The target interference compensation model includes at least: a SQUID magnetic gradiometer measurement term, an interference compensation term, and a sensor self-interference correction term. The interference compensation term excludes the residual magnetic interference term of the pod platform, and includes: a magnetically induced interference term characterized by the first derivative of the Earth's magnetic field component, and an eddy current interference term characterized by the second derivative of the Earth's magnetic field component. The sensor self-interference correction term characterizes the unbalance and unknown offset of the SQUID magnetic gradiometer itself. An objective function is constructed based on the target interference compensation model, wherein the input parameters of the objective function include the SQUID magnetic gradiometer measurement value and the SQUID magnetometer measurement value. The compensation module is used to input the SQUID magnetic gradient meter measurement value and the SQUID magnetometer measurement value into the objective function, and use the symmetric over-relaxation iterative method to obtain the compensation coefficient; and to compensate the towed superconducting full tensor magnetic gradient measurement system based on the compensation coefficient.

9. A computer device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the data compensation method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the data compensation method according to any one of claims 1 to 7.

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