Multi-component magnetic method detection system based on seabed crawler

Abstract

The invention provides a multi-component magnetic method detection system based on a seabed crawler, and belongs to the technical field of geophysics, the multi-component magnetic method detection system comprises an investigation ship, the seabed crawler, a total field magnetometer, a vector magnetometer, a self-contained magnetometer and a positioning beacon, a dragging cable is arranged at the tail part of the seabed crawler, the total field magnetometer, the vector magnetometer, the self-contained magnetometer and the positioning beacon are fixed on the dragging cable, the total field magnetometer, the vector magnetometer and the self-contained magnetometer are combined to form a magnetic sensor array, and a power supply and a data acquisition preprocessing chip are arranged in the seabed crawler. The power supply is used for supplying power to the magnetic sensor array, the data acquisition preprocessing chip is connected through a data cable and communicates with the magnetic sensor array, and the data acquisition preprocessing chip is used for acquiring real-time data of the magnetic sensor array and sending a control command to the magnetic sensor array.

Description

Technical Field

[0001] The present invention belongs to the field of geophysical technology, and in particular, relates to a multi-component magnetic detection system based on a seafloor crawler. Background Art

[0002] As one of the most basic and important physical fields of the Earth, the measurement of the Earth's magnetic field is of great significance to many disciplines, including geodesy, space science, geophysics, geodynamics, ocean science, resource exploration, and modern military. The precise measurement of the Earth's magnetic field not only provides key data for these disciplines, but also provides an important basis for solving many practical problems.

[0003] At present, there are four main methods for measuring the Earth's magnetic field: ground magnetic measurement, aerial magnetic measurement, marine magnetic measurement and satellite magnetic measurement. Each measurement method has its specific application scenarios and advantages. Among them, marine magnetic measurement has always been the focus of research due to its unique measurement environment and importance. Marine magnetic measurement mainly includes two methods: surface towed magnetic measurement and deep towed magnetic measurement.

[0004] Sea surface towed magnetic measurement is a widely used technology. In this measurement method, the survey ship tows one or more magnetic sensors, and the magnetic acquisition control module is located in the laboratory of the survey ship. In order to minimize the influence of the survey ship's magnetic field on the magnetic sensor, the tow cable length is usually greater than three times the ship length. This measurement method can use a total field type magnetic sensor to measure the total geomagnetic field, or a vector type magnetic sensor to measure the geomagnetic vector field. However, since the magnetic sensor is towed at the stern of the ship and is severely affected by wind, waves and currents, the sensor attitude is in an uncontrollable state, and its pitch, roll, and yaw are all uncontrollable. Therefore, sea surface towed magnetic measurement is usually limited to the total geomagnetic field measurement.

[0005] In addition, the sea surface towed magnetic measurement faces another important problem. The extremely thick seawater layer can be regarded as a huge low-pass filter. The high-frequency information of the seabed magnetic signal will rapidly decay or even disappear as the observation distance increases. This means that in deep sea areas, the sea surface towed magnetic measurement cannot detect the high-frequency part of the earth's magnetic signal, resulting in the resolution and accuracy of the measurement results being limited. Despite this, after data processing such as diurnal correction, the accuracy of the sea surface towed magnetic measurement can still reach about a few nanoteslas (nT).

[0006] Another important marine magnetic measurement method is deep towed magnetic measurement. This measurement method uses a survey ship to tow one or more towed bodies, and the magnetic acquisition control module is located in the laboratory of the survey ship. The laboratory of the survey ship and the towed body are connected by a composite cable, and the length of the composite cable can reach several thousand meters. Deep towed magnetic measurement can simultaneously measure the total geomagnetic field and the geomagnetic vector field. This is because the deep towed body avoids the influence of wind and wave currents on the sea surface, and the towed body itself generally integrates attitude sensors to measure the pitch, roll, and yaw angles of the towed body.

[0007] However, deep-towed magnetic measurement also faces a significant problem. Since the deep-towed towed body generally contains a large amount of ferromagnetic materials, the magnetic sensor integrated on the towed body will be disturbed by the magnetic field of the towed body. This interference will significantly affect the accuracy of the measurement. Even after completing data processing such as magnetic interference compensation and diurnal correction, the accuracy of deep-towed magnetic measurement can only reach tens of nanoT, which is lower than the accuracy of surface towed magnetic measurement.

[0008] Whether it is a surface towed magnetic survey or a deep towed magnetic survey, at least one geomagnetic diurnal variation station buoy needs to be deployed to obtain geomagnetic diurnal variation data and make diurnal variation corrections to the magnetic survey data. However, the deployment and recovery process of the geomagnetic diurnal variation station buoy is relatively complicated, which not only consumes a lot of manpower and material resources, but also takes up precious ship time. This further increases the cost and difficulty of marine magnetic surveys.

[0009] In summary, the existing marine magnetic measurement technology has the following main problems: 1) It is difficult to obtain vector magnetic information by towed magnetic measurement on the sea surface, and the thick seawater layer will weaken or even completely cover the high-frequency information of the geomagnetic field; 2) Although deep-towed magnetic measurement can obtain total field and vector magnetic field information, the magnetic sensor will be disturbed by the magnetic field of the towed body, resulting in low magnetic measurement accuracy. In other words, the current existing marine magnetic measurement technology has the technical problem of insufficient data accuracy. Summary of the invention

[0010] In view of this, the present invention provides a multi-component magnetic detection system based on a seafloor crawler, which can solve the technical problem of insufficient data acquisition accuracy in the current existing marine magnetic measurement technology.

[0011] The present invention is achieved in that:

[0012] The first aspect of the present invention provides a multi-component magnetic detection system based on a seabed crawler, which includes: a survey ship, a seabed crawler, a total field magnetometer, a vector magnetometer, a self-contained magnetometer and a data acquisition preprocessing chip, wherein the survey ship is connected to the seabed crawler through a towing rope, a towing cable is provided at the tail of the seabed crawler, the total field magnetometer, the vector magnetometer, and the self-contained magnetometer are fixed on the towing cable, the total field magnetometer, the vector magnetometer, and the self-contained magnetometer are combined to form a magnetic sensor array, the data acquisition preprocessing chip is connected through a data cable and communicates with the magnetic sensor array, including collecting real-time data of the magnetic sensor array and sending control commands to the magnetic sensor array, wherein the real-time data includes real-time status data of the magnetic sensor array and real-time electromagnetic data collected by the magnetic sensor array; an acquisition control module is provided in the data acquisition preprocessing chip, the acquisition control module is used to realize the autonomous crawling of the crawler and collect and generate magnetic field distribution data of the entire detection area, and the data acquisition preprocessing chip is provided with a data transmission device to send the magnetic field distribution data to the survey ship.

[0013] On the basis of the above technical solution, the multi-component magnetic detection system based on the seafloor crawler of the present invention can also be improved as follows:

[0014] Wherein, the seabed crawler is a crawler-type seabed crawler.

[0015] Furthermore, it also includes a positioning beacon, which is fixed on the towing cable and is used to position the self-contained magnetometer.

[0016] Furthermore, a power supply is provided in the seafloor crawler, and the power supply is used to supply power to the total field magnetometer and the vector magnetometer; the self-contained magnetometer has its own power supply.

[0017] Furthermore, there are one or more self-contained magnetometers, and each self-contained magnetometer is equipped with a positioning beacon.

[0018] Furthermore, the mechanical module of the seafloor crawler includes a manipulator for placing the self-contained magnetometer and its configured positioning beacon on the seafloor surface.

[0019] Furthermore, the bottoms of the total field magnetometer, the vector magnetometer, the self-contained magnetometer and the positioning beacon are all provided with non-magnetic sled boards.

[0020] Furthermore, the data transmission device sends the magnetic field distribution data to the survey vessel via a data cable or a wireless channel.

[0021] Furthermore, the survey vessel is equipped with a satellite navigation system, an underwater positioning system, and a power supply unit.

[0022] The step S90 specifically includes:

[0023] a) For each outlier point, calculate the difference between the new and old data, including the differences between the total field magnetic data, the three-component magnetic data and the magnetic field gradient data;

[0024] b) Based on the pre-set difference threshold, the outliers are divided into three categories: significant difference points, slight difference points and consistent points;

[0025] c) For points with significant differences, new measurement data are preferred, but old data are retained as a reference. At the same time, additional sampling is performed in the surrounding area to verify the reliability of the new data;

[0026] d) For points with slight differences, the weighted average of the new and old data is used, and the weight is dynamically adjusted according to the measurement conditions (such as the stability of the seafloor crawler, environmental noise, etc.);

[0027] e) For consistent points, the original data are retained, but they are marked as verified high-confidence data points in the magnetic field distribution data;

[0028] f) Using the updated data set, recalculate the magnetic field distribution data of the entire detection area, including the total field magnetic data matrix, the three-component data matrix and the magnetic field gradient data matrix.

[0029] Compared with the prior art, the multi-component magnetic detection system based on the seafloor crawler provided by the present invention has the following beneficial effects:

[0030] First, the present invention uses a seabed crawler as a carrier for magnetic detection, which effectively solves the problem of uncontrollable sensor posture in sea surface towed magnetic measurement. The seabed crawler can move stably on the seabed, greatly reducing the impact of wind and wave currents on the measurement. This not only improves the stability of the measurement, but also makes it possible to obtain vector magnetic information. Compared with traditional sea surface towed magnetic measurement, the present invention can simultaneously obtain total field magnetic data and vector magnetic data, greatly enriching the dimension of magnetic field information.

[0031] Secondly, since the seafloor crawler conducts measurements directly on the seafloor, the distance between the sensor and the seafloor is significantly shortened. This effectively overcomes the attenuation effect of the seawater layer on the high-frequency magnetic field signal, making it possible to obtain high-frequency magnetic field information. Compared with the towed magnetic measurement on the sea surface, the present invention can detect more high-frequency magnetic field signals and greatly improve the resolution and accuracy of magnetic field measurement. This is of great significance for detecting small-scale magnetic anomalies, such as seafloor hydrothermal deposits, paleomagnetic records, etc.

[0032] Furthermore, the present invention adopts a combination of multiple magnetometers, including a total field magnetometer, a vector magnetometer and a self-contained magnetometer, to form a comprehensive magnetic sensor array. This multi-sensor configuration not only improves the comprehensiveness of the measurement, but also increases the redundancy of the data, which is beneficial to the later data processing and error correction. In particular, the addition of the self-contained magnetometer makes it possible to measure the magnetic field gradient, further increasing the richness of the magnetic field information.

[0033] In addition, the system design of the present invention overcomes the problem of interference of the towed body with the magnetic sensor in deep-towed magnetic measurement. By fixing the magnetic sensor array on the towed cable instead of directly installing it on the seafloor crawler, the interference of the crawler body with the magnetic field measurement is effectively reduced. This design greatly improves the accuracy of magnetic field measurement, making it possible to exceed the measurement accuracy of traditional deep-towed magnetic measurement.

[0034] The present invention also realizes autonomous crawling and data collection of the seabed crawler through the design of the acquisition control module. This intelligent design not only improves the efficiency of measurement, but also makes it possible to make refined measurements under complex terrain. The acquisition control module can dynamically adjust the sampling strategy according to the magnetic field data obtained in real time, realize key sampling of abnormal areas, and thus obtain more refined and reliable magnetic field distribution data.

[0035] Finally, the system design of the present invention eliminates the need to deploy geomagnetic diurnal variation station buoys. Through the long-term continuous operation of the seabed crawler, the system can obtain a sufficiently long time series of magnetic field data, thereby realizing its own diurnal variation correction. This not only simplifies the measurement process, but also significantly reduces the measurement cost and improves the measurement efficiency.

[0036] In summary, the solution of the present invention solves the technical problem of insufficient data acquisition accuracy in the current existing marine magnetic measurement technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of a system provided by the present invention;

[0038] Figure 2 is a specific system schematic diagram of Embodiment 2 of the present invention;

[0039] Figure 3 It is a specific system diagram of Example 3 of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0041] like Figure 1As shown, it is a schematic diagram of a multi-component magnetic detection system based on a seabed crawler provided by the present invention. The system includes: a survey ship, a seabed crawler, a total field magnetometer, a vector magnetometer, a self-contained magnetometer and a data acquisition preprocessing chip, wherein the survey ship is connected to the seabed crawler through a towing rope, a towing cable is provided at the tail of the seabed crawler, the total field magnetometer, the vector magnetometer, and the self-contained magnetometer are fixed on the towing cable, the total field magnetometer, the vector magnetometer, and the self-contained magnetometer are combined to form a magnetic sensor array, the data acquisition preprocessing chip is connected through a data cable and communicates with the magnetic sensor array, including collecting real-time data of the magnetic sensor array and sending control commands to the magnetic sensor array, wherein the real-time data includes real-time status data of the magnetic sensor array and real-time electromagnetic data collected by it; an acquisition control module is provided in the data acquisition preprocessing chip, the acquisition control module is used to realize the autonomous crawling of the crawler and collect and generate magnetic field distribution data of the entire detection area, and the data acquisition preprocessing chip is provided with a data transmission device to send the magnetic field distribution data to the survey ship.

[0042] Specifically, the present invention provides a multi-component magnetic detection system based on a seafloor crawler, which mainly includes components such as a survey ship, a seafloor crawler, a total field magnetometer, a vector magnetometer, a self-contained magnetometer, and a positioning beacon. The overall structure and working principle of the system are as follows:

[0043] The survey ship is the command center and data processing center of the entire detection system, and is connected to the seafloor crawler through a towing rope. The towing rope not only serves as a physical connection, but also can be used as a carrier for power transmission and data communication. This cable connection method ensures the stability and real-time performance of the system. The seafloor crawler is the core carrier of this system, and it has the ability to walk autonomously in complex seafloor terrain. The crawler is equipped with a power supply and data acquisition preprocessing chip to provide power and data processing capabilities for the entire system. A towing cable is connected to the tail of the crawler to tow the magnetic sensor array. The magnetic sensor array consists of a total field magnetometer, a vector magnetometer and a self-contained magnetometer, which are fixed on the towing cable. This combination design can simultaneously obtain multiple magnetic field data, improving the comprehensiveness and accuracy of detection. The positioning beacon is used in conjunction with the self-contained magnetometer to accurately locate the position of the self-contained magnetometer, and also provides underwater positioning information for the entire system.

[0044] The built-in data acquisition preprocessing chip of the seabed crawler is equipped with an acquisition control module, which can realize the autonomous crawling of the crawler. The crawler can autonomously complete the magnetic field detection task of the entire area according to the preset detection area range and path planning. The total field magnetometer and vector magnetometer are connected to the seabed crawler through a towing cable, and collect magnetic field data as the crawler moves. These magnetometers are powered by the crawler and transmit real-time data with the crawler. The self-contained magnetometer is deployed at a specific location on the seabed for long-term continuous collection of geomagnetic daily variation data. The data acquisition preprocessing chip in the seabed crawler communicates with the magnetic sensor array through a data cable to collect magnetic field data and status information in real time. The acquisition control module in the chip performs preliminary processing on the data to generate magnetic field distribution data for the entire detection area. Subsequently, these data are sent to the survey ship through the wireless transmission equipment on the crawler. The seabed crawler is equipped with a manipulator for deploying and recovering the self-contained magnetometer and its positioning beacon. Before the detection begins, the crawler places the self-contained magnetometer at the predetermined location; after the detection is completed, the crawler reaches the designated location again based on the location information of the positioning beacon and recovers the equipment.

[0045] The seafloor crawler is an underwater robot that can walk autonomously in complex seafloor terrain. Its main features include autonomous navigation capability, power system, data acquisition preprocessing chip, manipulator and wireless transmission equipment. Through the built-in navigation system and sensors, it can accurately locate and move in complex terrain. The high-capacity battery can support long-term operation. At the same time, it can also obtain continuous power from the survey ship through the towing rope. The data acquisition preprocessing chip is used to process and store the collected magnetic field data in real time and control the entire detection process. The manipulator is used to deploy and recover the self-contained magnetometer and positioning beacon. The wireless transmission equipment is used for real-time data communication with the survey ship.

[0046] The magnetic sensor array consists of several types of magnetometers, including total field magnetometers, vector magnetometers, and self-contained magnetometers. Total field magnetometers measure the total strength of the geomagnetic field. Vector magnetometers measure the three components (X, Y, Z) of the geomagnetic field. Self-contained magnetometers work independently and collect geomagnetic daily variation data for a long time. These magnetometers are equipped with non-magnetic sleds to ensure that the sensor body is 0.5m above the seabed surface to avoid interference from strong magnetic objects near the seabed surface. The combined use of multiple magnetometers can obtain richer magnetic field information, including magnetic field gradient and magnetic field gradient tensor data.

[0047] Each self-contained magnetometer is equipped with a positioning beacon. The main function of the positioning beacon is to provide precise positioning for the self-contained magnetometer, send underwater hydroacoustic positioning information to the survey ship and crawler, and assist the crawler to accurately find and recover the self-contained magnetometer after the detection. The data acquisition preprocessing chip is the "brain" of the entire system. Its main functions include real-time data acquisition of the magnetic sensor array, sending control commands to the magnetic sensor array, preliminary processing and storage of the collected data, controlling the autonomous navigation of the crawler, and generating magnetic field distribution data for the entire detection area. The acquisition control module is the core component of the data acquisition preprocessing chip. It is mainly responsible for planning and controlling the movement path of the crawler, coordinating the working status of each magnetometer, real-time monitoring of the system operation status, and adjusting the acquisition parameters according to the detection needs.

[0048] The workflow of the system includes the preparation phase, the detection phase, the data processing phase and the recovery phase. In the preparation phase, the survey ship arrives at the designated sea area and puts down the seafloor crawler. The crawler descends to the seafloor and starts autonomous navigation. The crawler uses a manipulator to deploy a self-contained magnetometer and its positioning beacon at the predetermined location. In the detection phase, the self-contained magnetometer begins to continuously collect geomagnetic daily variation data, with a sampling interval of no more than 1 minute. The seafloor crawler tows the total field magnetometer and the vector magnetometer to detect along the predetermined path. The data acquisition preprocessing chip receives and processes the magnetic field data in real time. The processed data is sent to the survey ship through a wireless transmission device. In the data processing phase, the survey ship receives real-time data from the crawler. The scientific researchers on board further analyze and process the data. The detection path or parameters of the crawler can be adjusted in real time as needed. In the recovery phase, after the detection mission is completed, the survey ship sends a recovery instruction to the crawler. The crawler finds and recovers the self-contained magnetometer and its positioning beacon based on the location information of the positioning beacon. The crawler returns to the survey ship to complete the entire detection mission.

[0049] This system has many advantages. First, by directly dragging the magnetic sensor array on the seabed, more high-frequency components and more information-rich magnetic field data can be obtained, which has significant advantages over traditional surface towing or deep towing magnetic detection, and achieves high-precision detection. Secondly, the design of the seabed crawler enables the system to work in areas with drastic terrain fluctuations, greatly expanding the application scope of magnetic detection and adapting to complex terrain. Thirdly, through the combination of cable connection and wireless transmission, the system can realize real-time collection, processing and transmission of magnetic field data, providing scientific researchers with timely detection results. In addition, the system can receive commands to start or stop crawling at any time, flexibly adjust the detection strategy, and improve detection efficiency and accuracy. Finally, through the combined use of total field magnetometers, vector magnetometers and self-contained magnetometers, the system can simultaneously obtain multiple types of magnetic field data, providing rich data support for comprehensive analysis of geomagnetic field characteristics, and realizing multi-dimensional data collection.

[0050] The acquisition control module is one of the core components of this system, which is used to perform a series of steps to achieve efficient and accurate seabed magnetic field detection. First, the acquisition control module controls the seabed crawler to start from the center of the seabed to be measured, and moves at a uniform speed in an equidistant diffusion spiral manner, and controls the total field magnetometer to measure, and obtains the total field basic magnetic data set and the acquisition point of each total field basic magnetic data obtained, which is recorded as the total field acquisition point. This spiral path can ensure uniform coverage of the entire area to be measured while minimizing repeated measurements.

[0051] Next, the acquisition control module divides the seabed area to be detected into a plane triangular grid with each total field acquisition point as a vertex, obtains multiple grid cells, and establishes a set of grid cell vertices and a set of center points, which are recorded as vertex set and center point set respectively. This grid division method can effectively discretize the entire detection area, laying the foundation for subsequent detailed detection and data analysis. Then, the module calculates the pairwise similarity of the total field basic magnetic data of the three vertices of each grid cell, and takes the minimum similarity as the similarity index of the grid cell. The purpose of this step is to preliminarily identify areas where anomalies or large changes may exist.

[0052] Based on the calculated similarity index, the acquisition control module divides the seabed into high-similarity areas and low-similarity areas. For high-similarity areas, vector magnetometers and self-contained magnetometers are used only at the center points contained therein; while for low-similarity areas, vector magnetometers and self-contained magnetometers are used to measure all the vertices and center points contained therein. This differentiated measurement strategy can improve the overall detection efficiency while ensuring detection accuracy.

[0053] During the actual measurement process, the acquisition control module controls the seafloor crawler to move along the preset path to each point that needs to be measured. At each measurement point, the following operations are performed: first stop moving and wait for the system to stabilize; then start the total field magnetometer to measure and obtain the total field magnetic data; then start the vector magnetometer to measure in a completely static state to obtain three-component magnetic data; finally start the self-contained magnetometer, also in a completely static state to measure and obtain the magnetic field gradient data. This rigorous measurement process ensures the acquisition of high-quality magnetic field data.

[0054] For each grid unit, the acquisition control module uses the interpolation algorithm to calculate the magnetic field distribution in the entire grid unit based on the measurement data of its vertices and center points, including total magnetic field data, three-component magnetic data, and magnetic field gradient data, and obtains the total magnetic field data matrix, three-component data matrix, and magnetic field gradient data matrix respectively, and merges them into the magnetic field distribution data of the entire detection area. This step converts discrete measurement point data into continuous magnetic field distribution information, providing a basis for subsequent analysis and interpretation.

[0055] In order to further improve the data quality and reliability, the acquisition control module will conduct an in-depth analysis of the obtained data matrix. First, Bayesian analysis is performed on the total field magnetic data matrix, the three-component data matrix, and the magnetic field gradient data matrix, respectively, to identify the abnormal points of each matrix, which are recorded as the first abnormal point set. At the same time, according to the relationship between the three magnetic data of each vertex or center point, the consistency index of the total field magnetic data and the three-component magnetic data, the covariance index of the three-component magnetic data and the magnetic field gradient data, and the correlation index of the total field magnetic data and the magnetic field gradient data are calculated to obtain the abnormal points of consistency, covariance and correlation between the three magnetic data, which are recorded as the second abnormal point set.

[0056] The acquisition control module takes the combination of the first abnormal point set and the second abnormal point set as the abnormal point set, performs path traversal planning for these abnormal points, and controls the seabed crawler according to the planned path, repeats the previous measurement steps, and obtains new measurement data for each abnormal point. This repeated measurement of abnormal points can effectively verify the accuracy of the data and eliminate possible measurement errors or temporary anomalies.

[0057] Finally, the acquisition control module compares and analyzes the new measurement data of the abnormal point with the original data, and updates the magnetic field distribution data of the entire detection area. This step specifically includes the following sub-steps: First, for each abnormal point, the difference between the new and old data is calculated, including the differences between the total field magnetic data, the three-component magnetic data, and the magnetic field gradient data. Then, based on the pre-set difference threshold, the abnormal points are divided into three categories: significant difference points, slight difference points, and consistent points. For significant difference points, new measurement data are preferred, but the old data are retained as a reference. At the same time, additional sampling is performed in the surrounding area to verify the reliability of the new data. For slight difference points, the weighted average of the new and old data is used, and the weight is dynamically adjusted according to the measurement conditions (such as the stability of the seabed crawler, environmental noise, etc.). For consistent points, the original data is retained, but it is marked as a verified high-confidence data point in the magnetic field distribution data. Finally, the magnetic field distribution data of the entire detection area is recalculated using the updated data set, including the total field magnetic data matrix, the three-component data matrix, and the magnetic field gradient data matrix.

[0058] The reason why the multi-component magnetic detection system based on the seafloor crawler of the present invention can effectively solve the problems existing in the existing marine magnetic measurement technology is that its technical principle can be analyzed from the following aspects:

[0059] 1. Principle of measuring platform stability:

[0060] The present invention uses a seabed crawler as a measurement platform, and this choice is based on the principles of fluid mechanics and motion control. Compared with the sea surface, the seabed environment is less disturbed and the water flow speed is usually lower. The seabed crawler adopts a crawler design, which increases the contact area with the seabed and increases the friction, thereby enhancing the stability of the platform. According to Newton's third law, the action and reaction force between the crawler and the seabed can effectively offset the disturbance of the water flow on the crawler. In addition, the center of gravity design of the crawler also follows the principle of torque balance, which further enhances the stability of the platform. This stable measurement platform provides the basic conditions for high-precision magnetic field measurement.

[0061] 2. Near-source measurement principle:

[0062] The measurement system of the present invention directly measures on the seabed, greatly shortening the distance between the sensor and the magnetic field source. According to the magnetic dipole model, the magnetic field strength is inversely proportional to the cube of the distance:

[0063]

[0064] Where B is the magnetic induction intensity, μ 0 is the vacuum magnetic permeability, m is the magnetic dipole moment, r is the distance, and θ is the angle with the dipole axis. It can be seen that the reduction of distance can significantly enhance the intensity of magnetic field signals, especially high-frequency signals. This explains why the present invention can detect more high-frequency magnetic field information.

[0065] 3. Multi-sensor fusion principle:

[0066] The present invention uses a combination of a total field magnetometer, a vector magnetometer and a self-contained magnetometer. This design is based on data fusion theory. Different types of magnetometers provide complementary information: the total field magnetometer provides the absolute value of the magnetic field strength, the vector magnetometer provides the direction information of the magnetic field, and the self-contained magnetometer provides the magnetic field gradient information. Through data fusion algorithms such as Kalman filtering, this information can be organically combined to obtain a more comprehensive and accurate description of the magnetic field. For example, the following fusion model can be used:

[0067]

[0068] in, is the fused magnetic field estimate, B t , B v and are the total field, vector field and gradient field measurements, respectively, K 1 , K 2 and K 3 is the weight coefficient, which can be optimized by minimizing the estimation error.

[0069] 4. Principle of magnetic interference suppression:

[0070] The present invention effectively reduces the interference of the crawler body on the magnetic field measurement by fixing the magnetic sensor array on the towing cable instead of directly installing it on the seabed crawler. This design is based on the magnetic field superposition principle and the magnetic dipole model. According to the magnetic field superposition principle, the total magnetic field at the measuring point is the vector sum of the earth's magnetic field and the interfering magnetic field generated by the crawler. By increasing the distance between the sensor and the crawler, the influence of the interfering magnetic field can be significantly reduced. Assuming that the crawler can be simplified as a magnetic dipole, the intensity of the interfering magnetic field it generates is inversely proportional to the cube of the distance. Therefore, even if the distance is moderately increased, the interference intensity can be significantly reduced.

[0071] In order to better understand and implement the acquisition control module in the present invention, a specific embodiment 1 of the acquisition control module is provided below. The steps of the embodiment 1 specifically include the following:

[0072] S10, control the seafloor crawler to move in an equidistant diffuse spiral manner. In this step, it is necessary to define the motion trajectory of the seafloor crawler. The equidistant diffuse spiral path can be expressed in a polar coordinate system:

[0073] r = aθ;

[0074] in:

[0075] r represents the distance from the starting point to the current position (unit: meter)

[0076] θ represents the rotation angle (unit: radians)

[0077] a represents the expansion rate of the spiral (unit: meter / radian)

[0078] The position (x, y) of the seafloor crawler can be calculated by the following formula:

[0079] x = r cos θ = a θ cos θ;

[0080] y = rsinθ = aθsinθ;

[0081] To ensure equal spacing, the crawler speed needs to be controlled. The tangential speed v should be kept constant:

[0082]

[0083] Therefore, the angular velocity ω should vary with time:

[0084]

[0085] In actual implementation, the continuous spiral path can be discretized into a series of sampling points. Assuming the total number of sampling points is N, the angle θ of the i-th sampling point is i It can be expressed as:

[0086]

[0087] This discretization method ensures that the sampling points are evenly distributed throughout the area.

[0088] S20, plane triangulation: based on the sampling points obtained in S10, plane triangulation needs to be performed. Here, the Delaunay triangulation algorithm can be used. The characteristic of Delaunay triangulation is to maximize the minimum angle and avoid the appearance of narrow and long triangles.

[0089] Define point set P = {p 1 ,p 2 ,...,p N}, where p i =(x i ,y i ) represents the coordinates of the i-th sampling point.

[0090] The constraint of Delaunay triangulation is that for any triangle, its circumscribed circle does not contain any points other than the vertices of the triangle.

[0091] An incremental algorithm can be used to construct a Delaunay triangulation:

[0092] Initialization: Select three non-collinear points to form an initial triangle.

[0093] For each newly added point p k :

[0094] a. Find the content containing p k The triangle or p k Intersecting edges.

[0095] b. If p k Inside a triangle, split the triangle into three new triangles.

[0096] c. If p k On a certain edge, split each of the two adjacent triangles into two new triangles.

[0097] d. Perform local optimization on the newly generated triangles and check whether they meet the Delaunay conditions. If not, perform edge flipping.

[0098] Edge flipping criterion: For two adjacent triangles ABC and ACD, if point D is inside the circumcircle of triangle ABC, then the flipped edge AC is BD.

[0099] After completing the triangulation, a triangular mesh G = (V, E, F) is obtained, where:

[0100] V={v1 ,v 2 ,...,v N} is the vertex set;

[0101] E={e 1 ,e 2 ,...,e M} is the edge set;

[0102] F={f 1 ,f 2 ,...,f K} is a set of triangle faces;

[0103] For each triangle f i , calculate its centroid as the center point:

[0104]

[0105] Where (x a ,y a )、(x b ,y b )、(x c ,y c ) are the coordinates of the three vertices of the triangle.

[0106] In this way, we get the vertex set V and the center point set C = {c 1 ,c 2 ,...,c K}.

[0107] S30, calculating the similarity index of the grid unit: for each triangular grid unit, it is necessary to calculate the pairwise similarity of the total field basic magnetic data of its three vertices. Assume that the triangle f i The total magnetic field data corresponding to the three vertices are B 1 , B 2 and B 3 .

[0108] The cosine similarity can be used to measure the similarity between two magnetic field data:

[0109]

[0110] Where · represents the dot product and ‖·‖ represents the magnitude of the vector.

[0111] Calculate the similarity between three pairs of vertices: s 12 =similarity(B 1 ,B 2 );s 23 =similarity(B 2 ,B 3 );s31 =similarity(B 3 ,B 1 ); triangle f i The similarity index is defined as:

[0112] SI(f i )=min(s 12 ,s 23 ,s 31 );

[0113] This definition ensures that the similarity index reflects the part of the triangle where the magnetic field changes the most.

[0114] S40, distinguishing high-similarity regions from low-similarity regions: In order to distinguish high-similarity regions from low-similarity regions, a threshold τ needs to be set. This threshold can be determined empirically or learned from data using statistical methods.

[0115] Define an indicator function I(f i ):

[0116]

[0117] Where I(f i )=1 indicates a high similarity region, I(f i )=0 indicates a low similarity area.

[0118] For high-similarity areas, measurements need only be performed at the center point of the triangle; for low-similarity areas, measurements need to be performed at all vertices and the center point of the triangle.

[0119] Define the measurement point set M:

[0120]

[0121] This set contains all the points that need to be measured.

[0122] S50, perform measurement operation: for each point in the set M, the following measurement operation needs to be performed:

[0123] Total field magnetometer measurement: B t =measure total (p);

[0124] Vector magnetometer measurements:

[0125] Self-contained magnetometer measurement:

[0126] Among them, measure total , measure vectorand measure gradient Represent the measurement functions of the three magnetometers respectively.

[0127] In order to ensure the accuracy of the measurement, it is necessary to wait for the system to stabilize before each measurement. A stability function stability(t) can be defined. When its value is less than a certain threshold ∈, the system is considered stable:

[0128] stability(t)<∈;

[0129] Where t represents the waiting time, and stability(t) can be a function based on an acceleration sensor or other stability indicators.

[0130] S60, calculating magnetic field distribution: for each triangular mesh unit, interpolating the measured data of its vertices and center points to obtain the magnetic field distribution in the entire mesh unit. Here, the barycentric coordinate interpolation method can be used.

[0131] Assume that triangle f i The coordinates of the three vertices are (x 1 ,y 1 )、(x 2 ,y 2 )、(x 3 ,y 3 ), the corresponding magnetic field data is B 1 , B 2 , B 3 , the center point coordinates are (x c ,y c ), the corresponding magnetic field data is B c .

[0132] For any point (x, y) in the triangle, its centroid coordinates (λ 1 ,λ 2 ,λ 3 )satisfy:

[0133]

[0134] Solving this system of equations, we can get λ 1 , 2 , 3 expression.

[0135] Then, interpolation can be performed using the following formula:

[0136] B(x,y)=λ 1 B 1 +λ 2 B 2 +λ 3 B 3 +λ cB c ;

[0137] where λ c =1-(λ 1 +λ 2 +λ 3 ) is the weight of the center point.

[0138] Applying this interpolation method to each triangle can obtain the magnetic field distribution data of the entire detection area, including the total field magnetic data matrix B t (x,y), three-component data matrix and the magnetic field gradient data matrix

[0139] S70, Bayesian analysis and outlier identification: For the obtained magnetic field distribution data, Bayesian analysis is first performed to identify outliers. Assuming that the magnetic field data obeys Gaussian distribution, a local outlier factor (LOF) algorithm can be used to detect outliers.

[0140] Define the k-distance of a point p:

[0141] k-distance(p)=d(p,o);

[0142] where o is the kth nearest neighbor of p and d(·,·) is the Euclidean distance.

[0143] Define the reachable distance:

[0144] reach-dist k (p,o)=max{k-distance(o),d(p,o)};

[0145] Define the local reachability density:

[0146]

[0147] Where N k (p) is the set of k nearest neighbors of p.

[0148] Finally, define the local anomaly factor:

[0149]

[0150] If LOF k (p)>α (α is the preset threshold), then point p is considered to be an abnormal point.

[0151] For the total field magnetic data matrix B t (x,y), three-component data matrix and the magnetic field gradient data matrix Apply the LOF algorithm respectively to obtain the first outlier point set A 1 .

[0152] Next, the relationship indicators between the three magnetic data are calculated:

[0153] The consistency index between total field magnetic data and three-component magnetic data:

[0154]

[0155] Covariance index of three-component magnetic data and magnetic field gradient data:

[0156]

[0157] Correlation index between total field magnetic data and magnetic field gradient data:

[0158]

[0159] Apply the LOF algorithm to these indicators and get the second outlier set A 2 .

[0160] The final set of outliers is:

[0161] A=A 1 ∪A 2 ;

[0162] S80, re-measurement of outlier points: For the outlier point set A, path planning is required to re-measure these points. A greedy algorithm can be used to solve the approximate solution of this traveling salesman problem:

[0163] Select a starting point s (can be the current crawler position).

[0164] Repeat the following steps until all outliers are visited:

[0165] a. Find the unvisited outlier point closest to the current point.

[0166] b. Move to that point and take a measurement.

[0167] c. Mark the point as visited.

[0168] This process can be expressed as:

[0169] path = [s];

[0170] unvisited = A;

[0171]

[0172] path.append(p next );

[0173] unvisited=unvisited(p next );

[0174] For each point in the path, repeat the measurement steps in S50 to obtain new measurement data:

[0175]

[0176] To improve the reliability of the measurement, multiple measurements can be made at each point and then the average value can be taken:

[0177]

[0178] Where N is the number of repeated measurements, usually 3-5 times.

[0179] During the measurement process, it is also necessary to record the timestamp of each measurement. i and the crawler's attitude information i ,This information will be used for subsequent data analysis and correction:

[0180] T(p)={t 1 ,t 2 ,...,t N};

[0181] Attitude(p)={attitude 1 ,attitude 2 ,...,attitude N};

[0182] Among them, attitude i =(φ i ,θ i ,ψ i ) represents the pitch angle, roll angle and heading angle of the crawler at the i-th measurement.

[0183] S90, data comparison analysis and update:

[0184] a) Calculate the difference between new and old data

[0185] For each abnormal point p∈A, the difference between the total field magnetic data, three-component magnetic data and magnetic field gradient data is calculated respectively:

[0186] Total field magnetic data difference:

[0187]

[0188] Difference of three-component magnetic data:

[0189]

[0190] Magnetic field gradient data difference:

[0191]

[0192] The superscripts new and old represent new measurement data and original data, respectively.

[0193] b) Outlier classification

[0194] According to the pre-set difference threshold τ 1 and τ 2 (τ 1 <τ 2 ), and divide the outliers into three categories:

[0195] Significant difference point set: S 1 ={p∈A|max(D t (p),D v (p),D g (p))≥τ 2};

[0196] Slightly different point set: S 2 ={p∈A|τ 1 ≤max(D t (p),D v (p),D g (p))<τ 2};

[0197] Consistent point set: S 3 ={p∈A|max(D t (p),D v (p),D g (p))<τ 1};

[0198] c) Processing of significant differences;

[0199] For p∈S 1 , the new measurement data is adopted first, but the old data is retained as a reference. At the same time, additional sampling of the surrounding area is required to verify the reliability of the new data.

[0200] Define a neighborhood centered at p:

[0201] N(p)={q|d(p,q)≤r}

[0202] Where r is the preset neighborhood radius and d(p,q) is the Euclidean distance between two points.

[0203] For each point q in N(p), additional measurements are made to obtain new data and

[0204] Then, calculate the weighted average of the new and old data:

[0205]

[0206] where w 1 、w 2 and w 3 is the weight coefficient, satisfying w 1 +w 2 +w 3 = 1. These weights can be adjusted dynamically based on the measurement conditions.

[0207] d) Handling of minor differences

[0208] For p∈S 2 , using the weighted average of new and old data. The weights are dynamically adjusted according to the measurement conditions. Define a reliability index RI(p):

[0209] RI(p)=f(stability(p),noise(p),accuracy(p))

[0210] Where stability(p) represents the stability of the seafloor crawler at the measurement point p, noise(p) represents the environmental noise level, and accuracy(p) represents the accuracy of the magnetometer. Function f can be a weighted sum of these parameters or other suitable combination functions.

[0211] Then, we can define the weights for new and old data:

[0212]

[0213] w old (p) = 1-w new (p)

[0214] Among them, RI old (p) is the reliability index at the time of original measurement.

[0215] The updated data is:

[0216]

[0217] e) Consistency point processing;

[0218] For p∈S 3 , retain the original data, but mark them as verified high-confidence data points in the magnetic field distribution data. A confidence index CI(p) can be introduced:

[0219]

[0220] The value range of this indicator is (0,1]. When the difference is 0, it takes the maximum value of 1 and decreases as the difference increases.

[0221] f) Update magnetic field distribution data

[0222] Using the updated dataset, it is necessary to recalculate the magnetic field distribution data of the entire detection area. Here, Kriging interpolation (Kriging) can be used to generate a continuous magnetic field distribution.

[0223] First, we need to construct a semivariogram. For total field magnetic data, the semivariogram is defined as:

[0224]

[0225] Where h is the distance and N(h) is the number of pairs of sample points with a distance of h.

[0226] Then, fit a theoretical semivariogram model, such as a spherical model:

[0227]

[0228] Among them C 0 is the nugget effect, C is the sill value, and a is the range.

[0229] Using ordinary kriging, we can estimate the 0 The total magnetic field strength is:

[0230]

[0231] where λ i is the weight coefficient, satisfying The weight coefficients are obtained by solving the following system of equations:

[0232]

[0233] where h ij is the distance between point i and point j, and μ is the Lagrange multiplier.

[0234] For three-component magnetic data and magnetic field gradient data, similar methods can be used, but the characteristics of vectors or tensors need to be considered. For example, for three-component magnetic data, the covariance function can be used instead of the semivariance function:

[0235] C ij (h)=E[(B i (x)-μ i )(B j (x+h)-μj )];

[0236] where i,j∈{x,y,z},μ i and μ j B i and B j The mean of .

[0237] Then, cokriging can be used to estimate the three components simultaneously:

[0238]

[0239] where λ ijk are weight coefficients, which can be obtained by solving a system of equations similar to ordinary kriging.

[0240] For magnetic field gradient data, a similar approach can be used, but the properties of the tensor need to be taken into account.

[0241] Finally, the updated total field magnetic data matrix is ​​obtained Three-component data matrix and the magnetic field gradient data matrix

[0242] To evaluate the effect of the update, you can calculate the root mean square error (RMSE) before and after the update:

[0243]

[0244] Where N is the total number of measurement points, B measured is the actual measured value.

[0245] In addition, spatial autocorrelation indicators before and after the update can also be calculated, such as the Moran's I index:

[0246]

[0247] where w ij are the elements of the spatial weight matrix, is the average value of the magnetic field strength.

[0248] The value range of Moran's I index is [-1,1], positive value indicates positive correlation, negative value indicates negative correlation, and 0 indicates random distribution. By comparing the Moran's I index before and after the update, it can be evaluated whether the update has improved the spatial structure of the magnetic field distribution.

[0249] In order to further improve the quality of magnetic field distribution data, the following aspects can be considered:

[0250] Adaptive sampling: Dynamically adjust the sampling density according to the complexity of the magnetic field distribution. Increase the sampling points in areas where the magnetic field changes dramatically, and reduce the sampling points in areas where the changes are gentle. A complexity index C(x,y) can be defined:

[0251]

[0252] in is the Laplace operator of the magnetic field. Increase the number of sampling points in the region where C(x,y) is large.

[0253] Multi-scale analysis: Use wavelet transform to decompose magnetic field data into multiple scales and process features of different scales separately. For example, for total field magnetic data, two-dimensional discrete wavelet transform can be used:

[0254]

[0255] where ψ j,k,l (x,y) is the wavelet function, φ J,k,l (x,y) is the scaling function, d j,k,l and a J,k,l They are detail coefficient and approximation coefficient respectively.

[0256] Noise filtering: Use appropriate filtering techniques to remove noise from the measured data. For example, Wiener filtering can be used:

[0257]

[0258] Where H(f) is the system transfer function, S B (f) and S N (f) are the power spectral densities of signal and noise, respectively, B noisy (f) is the observed signal with noise.

[0259] In order to better understand and implement the present invention, the following provides Example 2 of a specific application scenario of the present invention: Example 2 describes a specific application scenario of a multi-component magnetic detection system based on a seabed crawler for seabed magnetic field exploration in a certain sea area of ​​the Yellow Sea. The system is mainly composed of a survey ship, a seabed crawler, a magnetic sensor array and a self-contained magnetometer. By comprehensively utilizing a variety of magnetic field measurement technologies, high-precision and high-resolution detection of the seabed magnetic field is achieved.

[0260] 1. System composition

[0261] 1.1 Survey vessel

[0262] The survey ship used in this embodiment is the "Ocean Explorer", a research vessel specially used for marine geophysical exploration. The ship is 110 meters long, 18 meters wide, has a displacement of 6,000 tons, and a maximum speed of 15 knots. Figure 2 As shown, the ship is equipped with the following key equipment:

[0263] a) Satellite navigation system: It adopts dual-frequency GPS / Beidou combined navigation system with positioning accuracy better than 0.5 meters.

[0264] b) Underwater positioning system: It adopts ultra-short baseline (USBL) acoustic positioning system, and the positioning accuracy is better than 0.2% of the water depth at a water depth of 3000 meters.

[0265] c) Integrated monitoring host: A high-performance workstation equipped with dual Intel Xeon Gold 6258R processors, 384 GB RAM, and 20 TB SSD storage.

[0266] d) Power supply unit: provides stable 400V / 50Hz three-phase AC power with a maximum output power of 500kW.

[0267] e) Composite cable interface unit: It uses photoelectric composite cable, which can transmit power and data signals at the same time, with a maximum transmission distance of 5000 meters.

[0268] 1.2 Seafloor Crawler

[0269] The submarine crawler model used in this embodiment is "Kunlong 500", which has the following main features:

[0270] a) Dimensions: 3.5 meters long, 2.5 meters wide, 1.8 meters high (excluding the robotic arm); b) Weight: 500 kg in water; c) Maximum operating depth: 5,000 meters; d) Maximum crawling speed: 2 km / h; e) Endurance: 50 hours (under full load working conditions);

[0271] The seafloor crawler is equipped with the following key modules:

[0272] Composite cable interface unit: matches with the composite cable interface unit of the survey ship to realize two-way transmission of power and data. Power supply module: receives 400V AC from the survey ship and converts it into the DC voltage required by each module. Sensor module: includes depth sensor, attitude sensor, acoustic rangefinder, etc. Underwater platform controller: adopts industrial-grade embedded computer, responsible for the control and data processing of the entire crawler. Crawler module: includes track drive system, hydraulic system and robotic arm. Magnetic acquisition control module: specially used to control the data acquisition and preprocessing of magnetic sensor array.

[0273] 1.3 Magnetic sensor array

[0274] The magnetic sensor array consists of the following components:

[0275] a) Total field magnetometer: It uses an optically pumped cesium magnetometer with a sensitivity of 0.001nT and a sampling rate of 10Hz.

[0276] b) Vector magnetometer: A three-axis fluxgate magnetometer is used with a measuring range of ±100000nT, a resolution of 0.01nT, and a sampling rate of 100Hz.

[0277] c) Self-contained magnetometer: 5 units in total, using optically pumped cesium magnetometers, with a sensitivity of 0.001nT, a sampling rate of 1Hz, and a built-in lithium battery that can work continuously for 30 days.

[0278] d) Positioning beacon: Equipped with self-contained magnetometer, 5 in total, operating frequency 10-30kHz, maximum effective distance 8000 meters.

[0279] The magnetic sensor array is connected to the seafloor crawler by a 30-meter-long towed cable.

[0280] 2. System workflow

[0281] 2.1 System Deployment

[0282] (1) After the survey ship arrives at the designated sea area, it first uses the ship-borne multi-beam bathymetric system to quickly scan the seabed topography to determine the location suitable for deploying the self-contained magnetometer. (2) Based on the bathymetric results, five relatively flat and evenly distributed locations are selected as the deployment points of the self-contained magnetometer. These five points form a rough pentagon that covers the entire exploration area. (3) The survey ship uses the winch system to lower the seabed crawler to a position about 50 meters from the seabed, and then starts the crawler's autonomous diving mode. (4) After the seabed crawler reaches the seabed, it first conducts a short-distance crawling test to ensure that all systems are working properly. (5) The crawler follows the preset path and goes to the five self-contained magnetometer deployment points in turn. At each deployment point, the crawler performs the following operations:

[0283] a) Stop moving and wait for the system to stabilize (about 2 minutes).

[0284] b) Use the robotic arm to grab the self-contained magnetometer.

[0285] c) Gently place the self-contained magnetometer on the seabed, making sure it remains vertical.

[0286] d) Place the corresponding positioning beacon about 2 meters away from the magnetometer.

[0287] e) Use the onboard camera system to record image data of the deployment location.

[0288] (6) After completing the deployment of the five self-contained magnetometers, the crawler returns to the predetermined starting position and prepares to begin formal magnetic field detection work.

[0289] 2.2 Magnetic field detection

[0290] In this embodiment, the detection area is a square area with a side length of about 5 kilometers. The acquisition control module performs the detection task according to the following steps:

[0291] S10: Control the seafloor crawler to start from the center of the seafloor to be measured, move at a constant speed in an equidistant diffuse spiral, and control the total field magnetometer to measure. The spiral line spacing is 50 meters; the crawler moving speed is 1km / h; the total field magnetometer sampling interval is 1 second; at this stage, the crawler takes about 25 hours to complete the preliminary scan of the entire area. The total field basic magnetic data set obtained contains about 90,000 data points.

[0292] S20: Taking each total field collection point as a vertex, the seabed area to be detected is divided into a plane triangular grid. Among them, the side length of the grid unit is about 50 meters; the number of grid units is about 40,000; the number of vertex sets is about 90,000; the number of center point sets is about 40,000;

[0293] S30: Calculate the pairwise pre-similarity of the total field basic magnetic data of the three vertices of each grid cell.

[0294] S40: According to the similarity index of each grid cell, the detected seabed is divided into a high-similarity area and a low-similarity area.

[0295] The high similarity threshold is 0.99, and the low similarity threshold is 0.95. After calculation, about 70% of the area is divided into high similarity areas, and 30% is divided into low similarity areas.

[0296] S50: Control the seafloor crawler to move along the preset path to each point that needs to be measured for detailed measurement. In the high similarity area, only the center point is measured, about 28,000 measurement points; in the low similarity area, all vertices and center points are measured, about 36,000 measurement points; the total number of measurement points: about 64,000;

[0297] At each measurement point, the crawler performs the following operations:

[0298] a) Stop moving and wait for the system to stabilize (about 1 minute);

[0299] b) Start total field magnetometer measurement (last 10 seconds and take the average value);

[0300] c) Start vector magnetometer measurement (last 10 seconds and take the average value);

[0301] d) Calculation of magnetic field gradients (using total field magnetometer data at different locations);

[0302] Taking into account the crawler movement time and measurement time, this phase took approximately 80 hours to complete.

[0303] S60: For each grid cell, use the interpolation algorithm to calculate the magnetic field distribution within the entire grid cell. Use the Kriging interpolation method to calculate and obtain the following data matrices: total field magnetic data matrix: 5000x5000; three-component data matrix: 5000x5000x3; magnetic field gradient data matrix: 5000x5000x3;

[0304] S70: Perform Bayesian analysis and magnetic data relationship analysis. Specifically: the first abnormal point set: about 2000 points; the second abnormal point set: about 1500 points; the abnormal point collection: about 3000 points;

[0305] S80: Perform path traversal planning for the collection of outlier points and re-measure.

[0306] S90: Compare and analyze new and old data, and update magnetic field distribution data.

[0307] a) Calculate the difference between new and old data: Total field magnetic data difference threshold: 5nT; Three-component magnetic data difference threshold: 3nT / component; Magnetic field gradient data difference threshold: 0.1nT / m;

[0308] b) Outlier classification results: significant difference points: about 500; slight difference points: about 1500; consistent points: about 1000;

[0309] c) For points with significant differences, additional sampling was performed at 5 surrounding points.

[0310] d) Weighted average of data with slight differences: weight of new data: 0.7; weight of old data: 0.3;

[0311] e) The consistent points are marked as high-confidence data points in the magnetic field distribution data.

[0312] f) Recalculate the magnetic field distribution data of the entire detection area using the updated data set.

[0313] The following is an example 3 of a specific application scenario of the present invention. In this example, the survey ship and the seabed crawling platform communicate using hydroacoustic sound: This example 3 describes a multi-component magnetic detection system based on a seabed crawler, which is used to perform seabed magnetic field detection tasks in the northern slope area of ​​the South China Sea. The detection area is located between 20°30' and 21°30' north latitude and 114°00' and 115°00' east longitude, with a water depth ranging from 200 meters to 1500 meters. The terrain in this area is complex, with multiple seabed canyons and steep slopes, and it is an area that is difficult to cover with traditional towed magnetic detection.

[0314] 1. System composition

[0315] 1.1 Survey vessel

[0316] The survey ship used in this embodiment is the "Ocean Explorer", a multi-purpose oceanographic survey ship with a length of 98 meters, a width of 16 meters, and a draft of 5.8 meters. The ship has a displacement of about 4,500 tons, a maximum speed of 16 knots, and a cruising range of 15,000 nautical miles. Figure 3 The following key equipment is installed on board the vessel shown:

[0317] a) Satellite navigation system: It adopts dual-frequency GPS / Beidou combined navigation system with positioning accuracy better than 0.5 meters.

[0318] b) Underwater positioning system: It adopts ultra-short baseline (USBL) acoustic positioning system with an operating frequency of 18-36kHz and a positioning accuracy within 1% of the water depth.

[0319] c) Integrated monitoring host: uses a high-performance workstation equipped with dual Intel Xeon Gold 6258R processors, 384GB RAM, and 20TB SSD storage.

[0320] d) Power supply unit: Provides stable 400V / 50Hz three-phase AC power with a maximum output power of 200kW.

[0321] e) Underwater acoustic communication interface unit: The operating frequency of the underwater acoustic communication interface unit is 300kHz-500kHz, the transmission power is 10-100W, the communication distance can reach 100-2000m, the data transmission rate is 1200-19200bps, the receiving sensitivity is -90 to -120dBV / μPa, it has a certain directivity, can work normally in a water depth of 100-2000m, the waterproof level reaches IP68, and supports common underwater acoustic communication protocols such as MATS, WHOIMicro-Modem, AUVSI, etc.

[0322] 1.2 Seafloor Crawler

[0323] The seafloor crawler used in this embodiment is a "deep sea spider" type, and its specific parameters are as follows:

[0324] a) Dimensions: 3.5 meters long, 2.2 meters wide, and 1.8 meters high;

[0325] b) Weight: 4.5 tons in air, adjustable underwater weight;

[0326] c) Maximum operating depth: 3000 meters;

[0327] d) Maximum crawling speed: 2 km / h;

[0328] d) Maximum climbing ability: 35 degrees;

[0329] The main components on the seafloor crawler include:

[0330] a) Hydroacoustic communication interface unit: corresponding to the survey vessel.

[0331] b) Power module: receives 400V AC power and converts it into 48V DC power for internal use, while also powering the magnetic sensor array.

[0332] c) Sensor module: Depth sensor: accuracy ±0.1% FS; Altitude sensor: resolution 1cm; Attitude sensor: pitch / roll accuracy 0.1°, heading accuracy 0.5°; Multi-beam depth sounding system: coverage angle 120°, resolution 1cm;

[0333] d) Underwater platform controller: uses industrial-grade embedded computer, Intel Core i7-10700T processor, 32GB RAM, 2TB SSD storage.

[0334] e) Crawler module: adopts crawler design, driven by 4 independent motors on each side.

[0335] f) Magnetic acquisition control module: FPGA-based real-time data acquisition and preprocessing system with a sampling rate of up to 10kHz and 24-bit ADC.

[0336] g) Manipulator: 7-DOF manipulator with a maximum load of 50 kg, used for deploying and retrieving self-contained magnetometers.

[0337] 1.3 Magnetic sensor array

[0338] The magnetic sensor array is connected to the seafloor crawler via a 10-meter towed cable and contains the following sensors:

[0339] a) Total field magnetometer: optically pumped cesium magnetometer with a sensitivity of 0.003nT / √Hz@1Hz and a measuring range of 20,000 to 100,000nT.

[0340] b) Vector magnetometer: Three-axis fluxgate magnetometer, noise level <10pT / √Hz@1Hz, range ±100,000nT.

[0341] c) Self-contained magnetometer: 5 optically pumped rubidium magnetometers, sensitivity 0.01nT / √Hz@1Hz, range 20,000to120,000nT, sampling rate 10Hz, storage capacity 32GB, battery life 30 days.

[0342] d) Positioning beacons: 5, one-to-one corresponding to the self-contained magnetometer, operating frequency 8-14kHz, battery life 60 days.

[0343] 2. System workflow

[0344] 2.1 Task Preparation

[0345] (1) Pre-voyage inspection: Check the status of each system of the survey ship; test the functions of each module of the seafloor crawler; calibrate all magnetometers; check the battery status and storage space of the self-contained magnetometer;

[0346] (2) Route planning: According to the terrain characteristics of the detection area, a zigzag survey line is designed; the survey line spacing is 500 meters, with a total of 20 parallel survey lines; each survey line is about 60 kilometers long, and the total flight distance is about 1,200 kilometers;

[0347] (3) System deployment: After the survey vessel reaches the predetermined location, it lowers the seafloor crawler to the seafloor. During the lowering process, the composite cable is released at the same time. After the crawler touches the bottom, the system self-checks and reports the status.

[0348] 2.2 Magnetic field detection process

[0349] (1) Deployment of self-contained magnetometers: The seafloor crawler moves along the predetermined path to five deployment points. At each deployment point, a manipulator is used to place the self-contained magnetometer and its positioning beacon on the seafloor. The deployment points are spaced approximately 10 kilometers apart, forming a "cross" layout.

[0350] (2) Main detection process:

[0351] a. The seafloor crawler crawls along the predetermined survey line at a speed of 1.5 km / h

[0352] b. Magnetic sensor array continuously collects data:

[0353] Total field magnetometer: sampling rate 10Hz;

[0354] Vector magnetometer: sampling rate 100Hz;

[0355] Self-contained magnetometer: sampling rate 10Hz (independent operation);

[0356] c. The magnetic acquisition control module processes data in real time: denoises and filters the raw data; calculates the magnetic field gradient; and performs preliminary anomaly detection;

[0357] d. The underwater platform controller performs the following tasks: real-time navigation and path control; monitoring crawler status; data compression and upload;

[0358] e. The survey vessel receives real-time data and conducts preliminary analysis;

[0359] (3) Detailed detection of abnormal areas: If a magnetic anomaly area is detected, the crawler will slow down to 0.5 km / h; perform "S"-shaped reciprocating scanning in the abnormal area, and reduce the survey line spacing to 100 meters; increase the vector magnetometer sampling rate to 200 Hz to improve data resolution;

[0360] 2.3 Data processing and analysis

[0361] (1) Real-time processing:

[0362] a. The magnetic acquisition control module performs the following steps:

[0363] S10: Control the seafloor crawler to start from the center of the seafloor to be measured (21°00'N, 114°30'E) and move at a constant speed in an equidistant diffusion spiral manner. Control the total field magnetometer to measure and obtain the total field basic magnetic data set and the collection point of each total field basic magnetic data (total field collection point).

[0364] S20: Taking each total field acquisition point as a vertex, the seabed area to be detected is divided into a plane triangular grid to obtain a plurality of grid units. In this embodiment, the detection area is divided into a grid of 1000 meters × 1000 meters, with a total of 3600 grid units. A set of grid unit vertices (vertex set) and a set of center points (center point set) are established.

[0365] S30: Calculate the pairwise similarities of the total field basic magnetic data of the three vertices of each grid unit, and use the minimum similarity as the cosine similarity index of the grid unit.

[0366] S40: According to the similarity index of each grid unit, the detected seabed is divided into a high-similarity area and a low-similarity area. In this embodiment, the area with a similarity index greater than 0.95 is defined as a high-similarity area, and the area with a similarity index less than or equal to 0.95 is defined as a low-similarity area. The high-similarity area is measured only at the center point contained in it using a vector magnetometer or a self-contained magnetometer, and the low-similarity area needs to be measured at all the vertices and center points contained in it using a vector magnetometer or a self-contained magnetometer.

[0367] S50: Control the seafloor crawler to move along the preset path to each point that needs to be measured. At each measurement point, perform the following operations: stop moving and wait for the system to stabilize (about 30 seconds); start the total field magnetometer for measurement, collect 60 seconds of data, and take the average value as the total field magnetic data; start the vector magnetometer, measure for 120 seconds in a completely static state, and obtain three-component magnetic data; start the self-contained magnetometer, also measure for 120 seconds in a completely static state, and obtain magnetic field gradient data.

[0368] S60: For each grid cell, the Kriging interpolation algorithm is used to calculate the magnetic field distribution within the entire grid cell based on the measurement data of its vertices and center points, and the total field magnetic data matrix, three-component data matrix and magnetic field gradient data matrix are obtained respectively, and merged into the magnetic field distribution data of the entire detection area.

[0369] S70: Perform Bayesian analysis on the total magnetic field data matrix, the three-component data matrix, and the magnetic field gradient data matrix, respectively, and identify the abnormal points of each matrix, which are recorded as the first abnormal point set. An abnormal point is defined as a point that deviates from the mean by more than 3 standard deviations.

[0370] Based on the relationship between the three magnetic data of each vertex or center point, calculate:

[0371] The consistency index between total field magnetic data and three-component magnetic data:

[0372]

[0373] Covariance index of three-component magnetic data and magnetic field gradient data:

[0374]

[0375] Correlation index between total field magnetic data and magnetic field gradient data:

[0376]

[0377] Among them, B t is the total field magnetic data, B x , B y , B z is the three-component magnetic data, is the magnetic field gradient.

[0378] Points with consistency index less than 0.95, covariance index less than 0.7, or correlation index less than 0.6 are recorded as the second abnormal point set.

[0379] S80: Taking the combination of the first abnormal point set and the second abnormal point set as the abnormal point set, a path traversal planning is performed on the abnormal point set. The improved ant colony algorithm is used to optimize the path to minimize the total travel distance. The seabed crawler is controlled according to the planned path, and the steps of S50-S60 are repeated to obtain new measurement data for each abnormal point.

[0380] S90: Compare and analyze the new measurement data of the abnormal point with the original data, and update the magnetic field distribution data of the entire detection area. The specific steps are as follows:

[0381] a) For each outlier, calculate the difference between the new and old data:

[0382] Total field magnetic data difference:

[0383] Three-component magnetic data difference:

[0384] Magnetic field gradient data differences:

[0385] b) Based on the pre-set difference threshold, the outliers are divided into three categories:

[0386] Significant difference: any difference is greater than 10%; slight difference: all differences are between 5% and 10%; consistent point: all differences are less than 5%;

[0387] c) For the significant difference points: give priority to the new measurement data; keep the old data in the database as a reference; perform additional sampling within the range of 5 grid cells around the significant difference point, and perform one measurement at the center of each grid cell; use the new sampling data to update the magnetic field distribution in the area;

[0388] d) For minor differences:

[0389] The weighted average of new and old data is used, and the weights are dynamically adjusted according to the measurement conditions:

[0390] B final =w new B new +w old B old ;

[0391] Among them, w_new+w_old=1;

[0392] The weight calculation takes into account the following factors: the stability of the seafloor crawler (assessed based on the attitude sensor data); the environmental noise level (assessed based on the magnetometer background noise); the current conditions during the measurement (assessed based on the acoustic Doppler current profiler data)

[0393] Weight calculation formula:

[0394] w new =0.6+0.1×(S stability +S noise +S current );

[0395] Among them, S stability ,S noise ,S current They are stability score, noise score and current score, and each score ranges from 0 to 1.

[0396] e) For consistent points: retain the original data; mark them as verified high-confidence data points in the magnetic field distribution data; these points will serve as key reference points in subsequent geological interpretation

[0397] f) Use the updated data set to recalculate the magnetic field distribution data of the entire detection area: use the improved Kriging interpolation algorithm, taking into account the credibility of the data points; generate updated total field magnetic data matrix, three-component data matrix and magnetic field gradient data matrix; resolution: 10m × 10m × 1m (vertical direction); matrix size: 6000 × 6000 × 150.

[0398] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A multi-component magnetic detection system based on a seafloor crawler, characterized in that: include: A survey ship, a seabed crawler, a total field magnetometer, a vector magnetometer, a self-contained magnetometer and a data acquisition preprocessing chip, wherein the survey ship is connected to the seabed crawler through a towing rope, a towing cable is provided at the tail of the seabed crawler, the total field magnetometer, the vector magnetometer and the self-contained magnetometer are fixed on the towing cable, the total field magnetometer, the vector magnetometer and the self-contained magnetometer are combined to form a magnetic sensor array, the data acquisition preprocessing chip is connected through a data cable and communicates with the magnetic sensor array, including collecting real-time data of the magnetic sensor array and sending control commands to the magnetic sensor array, wherein the real-time data includes real-time status data of the magnetic sensor array and real-time electromagnetic data collected by the magnetic sensor array; an acquisition control module is provided in the data acquisition preprocessing chip, the acquisition control module is used to realize the autonomous crawling of the crawler and collect and generate magnetic field distribution data of the entire detection area, and the data acquisition preprocessing chip is provided with a data transmission device to send the magnetic field distribution data to the survey ship.

2. The multi-component magnetic detection system based on a seafloor crawler according to claim 1 is characterized in that: The seabed crawler is a crawler-type seabed crawler.

3. The multi-component magnetic detection system based on a seafloor crawler according to claim 2 is characterized in that: It also includes a positioning beacon, which is fixed on the towing cable and is used to position the self-contained magnetometer.

4. The multi-component magnetic detection system based on a seafloor crawler according to claim 3 is characterized in that: The seafloor crawler is provided with a power supply, and the power supply is used to supply power to the total field magnetometer and the vector magnetometer; the self-contained magnetometer has its own power supply.

5. The multi-component magnetic detection system based on the seafloor crawler according to claim 4 is characterized in that: The self-contained magnetometer is one or more, and each self-contained magnetometer is equipped with a positioning beacon.

6. The multi-component magnetic detection system based on the seafloor crawler according to claim 5 is characterized in that: The mechanical module of the seafloor crawler includes a manipulator for placing the self-contained magnetometer and its configured positioning beacon on the seafloor surface.

7. The multi-component magnetic detection system based on a seafloor crawler according to claim 6 is characterized in that: The bottoms of the total field magnetometer, the vector magnetometer, the self-contained magnetometer and the positioning beacon are all provided with non-magnetic sled boards.

8. The multi-component magnetic detection system based on the seafloor crawler according to claim 7 is characterized in that: The data transmission device sends the magnetic field distribution data to the survey ship via a data cable or a wireless channel.

9. The multi-component magnetic detection system based on a seafloor crawler according to claim 8, characterized in that: The survey vessel is equipped with a satellite navigation system, an underwater positioning system, and a power supply unit.

10. A multi-component magnetic detection system based on a seafloor crawler according to any one of claims 1 to 9, characterized in that: The acquisition control module is used to perform the following steps: S10, controlling the seafloor crawler to take the center of the seafloor to be measured as the starting point, moving at a uniform speed in an equidistant diffusion spiral manner, and controlling the total field magnetometer to perform measurement, to obtain a total field basic magnetic data set and a collection point of each item of total field basic magnetic data obtained, which is recorded as a total field collection point; S20, taking each of the total field acquisition points as vertices, dividing the seabed area to be detected into a plane triangular grid to obtain a plurality of grid units, and establishing a set of grid unit vertices and a set of center points, which are respectively recorded as a vertex set and a center point set; S30, calculating the pairwise similarities of the total field basic magnetic data of the three vertices of each grid unit, and taking the minimum similarity as the similarity index of the grid unit; S40, according to the similarity index of each grid unit, the detected seabed is divided into a high-similarity area and a low-similarity area, wherein the high-similarity area is measured only at the center point thereof using a vector magnetometer or a self-contained magnetometer, and the low-similarity area needs to be measured at all vertices and the center point thereof using a vector magnetometer or a self-contained magnetometer; S50, control the seafloor crawler to move along a preset path to each point that needs to be measured, and at each measuring point, perform the following operations: first stop moving and wait for the system to stabilize; then start the total field magnetometer to measure and obtain total field magnetic data; then start the vector magnetometer to measure in a completely static state and obtain three-component magnetic data; finally start the self-contained magnetometer to measure in a completely static state and obtain magnetic field gradient data; S60, for each grid unit, according to the measurement data of its vertices and center points, including total field magnetic data, three-component magnetic data and magnetic field gradient data, use an interpolation algorithm to calculate the magnetic field distribution in the entire grid unit, and obtain a total field magnetic data matrix, a three-component data matrix and a magnetic field gradient data matrix respectively, and merge them into the magnetic field distribution data of the entire detection area; S70, performing Bayesian analysis on the total field magnetic data matrix, the three-component data matrix and the magnetic field gradient data matrix respectively, identifying the abnormal points of each matrix and recording them as the first abnormal point set; and calculating the consistency index of the total field magnetic data and the three-component magnetic data, the covariance index of the three-component magnetic data and the magnetic field gradient data, and the correlation index of the total field magnetic data and the magnetic field gradient data according to the relationship between the three magnetic data of each vertex or center point, and obtaining the abnormal points of consistency, covariance and correlation between the three magnetic data, which are recorded as the second abnormal point set; S80, taking the combination of the first abnormal point set and the second abnormal point set as the abnormal point set, performing path traversal planning for the abnormal point set, and controlling the seabed crawler according to the planned path, repeating the steps of S50-S60, and obtaining new measurement data of each abnormal point; S90, comparing and analyzing the new measurement data of the abnormal point with the original data, and updating the magnetic field distribution data of the entire detection area.

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