A multi-component magnetic detection system based on seabed crawler

Abstract

The present invention provides a multi-component magnetic detection system based on a seabed crawler, which belongs to the field of geophysical technology and includes: a survey ship, a seabed crawler, a total field magnetometer, a vector magnetometer, a self-contained magnetometer and a positioning beacon, 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, the self-contained magnetometer and the positioning beacon 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, a power supply and a data acquisition preprocessing chip are provided in the seabed crawler, the power supply is used to power the magnetic sensor array, and the data acquisition preprocessing chip is connected to and communicates with the magnetic sensor array through a data cable, including collecting real-time data of the magnetic sensor array and sending control commands 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 Earth's most fundamental and important physical fields, the measurement of the Earth's magnetic field is of great significance to numerous disciplines, including geodesy, space science, geophysics, geodynamics, oceanography, resource exploration, and modern military science. Precise measurements of the Earth's magnetic field not only provide critical data for these disciplines but also offer a crucial basis for solving numerous practical problems.

[0003] Currently, Earth's magnetic field is measured using four main methods: ground-based magnetics, airborne magnetics, marine magnetics, and satellite magnetics. Each method has its own specific application scenarios and advantages. Marine magnetics, due to its unique measurement environment and importance, have long been a research focus. Marine magnetics primarily include two methods: surface towed magnetics and deep towed magnetics.

[0004] Towed magnetic surveying is a widely used technique. In this method, a survey vessel tows one or more magnetic sensors, with the magnetic acquisition control module located in the vessel's laboratory. To minimize the influence of the survey vessel's magnetic field on the sensors, the tow cable is typically longer than three times the vessel's length. This method can measure the total geomagnetic field using total-field magnetic sensors or the vector field using vector-field magnetic sensors. However, because the magnetic sensors are towed behind the vessel, they are severely affected by wind, waves, and currents, rendering the sensor's attitude uncontrollable: pitch, roll, and yaw. Therefore, towed magnetic surveying is typically limited to measuring the total geomagnetic field.

[0005] Furthermore, surface-drag magnetic measurements face another significant challenge. The thick seawater layer can be considered a massive low-pass filter, rapidly attenuating or even disappearing the high-frequency information in the seafloor magnetic signal as the observation distance increases. This means that in deep waters, surface-drag magnetic measurements are unable to detect the high-frequency components of the Earth's magnetic signal, limiting the resolution and accuracy of the measurements. Despite this, after data processing such as diurnal variation corrections, the accuracy of surface-drag magnetic measurements can still reach around a few nanoT.

[0006] Another important marine magnetic survey method is deep-towed magnetic surveying. This method uses a survey vessel towing one or more towed bodies, with the magnetic acquisition control module located in the vessel's laboratory. A composite cable, which can be several kilometers long, connects the vessel's laboratory to the towed bodies. Deep-towed magnetic surveying allows for simultaneous measurement of both the total and vector magnetic fields. This is because the towed bodies are shielded from the influence of surface winds, waves, and currents, and they typically incorporate attitude sensors to measure their pitch, roll, and yaw angles.

[0007] However, deep-towed magnetic measurements also face a significant challenge. Because deep-towed bodies typically contain a significant amount of ferromagnetic material, the magnetic sensors integrated into the body are subject to interference from the magnetic field of the body itself. This interference significantly affects measurement accuracy. Even after data processing such as magnetic interference compensation and diurnal variation correction, the accuracy of deep-towed magnetic measurements can only reach tens of nanoT, which is lower than the accuracy of surface-towed magnetic measurements.

[0008] Both surface-towed and deep-towed magnetic surveys require the deployment of at least one diurnal geomagnetic variation station buoy to obtain diurnal geomagnetic variation data for diurnal correction of the magnetic measurement data. However, the deployment and recovery of diurnal geomagnetic variation station buoys is complex, consuming significant manpower and resources and requiring valuable vessel time. This further increases the cost and difficulty of marine magnetic surveys.

[0009] In summary, existing marine magnetic measurement technology has the following major issues: 1) Surface towed magnetic measurements have difficulty acquiring vector magnetic information, and thick seawater layers can weaken or even completely obscure high-frequency information from the Earth's magnetic field. 2) While deep-towed magnetic measurements can acquire total and vector magnetic field information, the magnetic sensor is subject to interference from the magnetic field of the towed body, resulting in low magnetic measurement accuracy. In other words, current marine magnetic measurement technology suffers from 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 seabed 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] A 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 by 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 to and communicates with the magnetic sensor array via a data cable, 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; the data acquisition preprocessing chip is provided with an acquisition control module, the acquisition control module is used to realize 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 seabed 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 seabed crawler, and the power supply is used to power 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 seabed crawler includes a manipulator for placing the self-contained magnetometer and its configured positioning beacon on the seabed surface.

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

[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 a pre-set difference threshold, outliers are classified into three categories: significant difference points, slight difference points, and consistent points;

[0025] c) For points with significant differences, new measurement data is used first, but the old data is 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, a weighted average of the old and new data is used, with the weights dynamically adjusted based on measurement conditions (such as the stability of the seabed 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 seabed crawler provided by the present invention has the following beneficial effects:

[0030] First, the present invention uses a seafloor crawler as a magnetic detection vehicle, effectively resolving the issue of uncontrollable sensor posture in surface-towed magnetic measurements. The seafloor crawler can stably move along the seabed, significantly reducing the impact of wind, waves, and currents on measurements. This not only improves measurement stability but also enables the acquisition of vector magnetic information. Compared to traditional surface-towed magnetic measurements, the present invention can simultaneously acquire both total-field and vector magnetic data, significantly enriching the dimensionality of magnetic field information.

[0031] Secondly, because 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 of high-frequency magnetic field signals by the seawater layer, making it possible to obtain high-frequency magnetic field information. Compared to surface-drag magnetic measurements, this method can detect more high-frequency magnetic field signals, significantly improving the resolution and accuracy of magnetic field measurements. This is of great significance for detecting small-scale magnetic anomalies, such as seafloor hydrothermal deposits and paleomagnetic records.

[0032] Furthermore, the present invention utilizes 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 measurements but also increases data redundancy, facilitating subsequent data processing and error correction. In particular, the inclusion of the self-contained magnetometer enables the measurement of magnetic field gradients, further increasing the richness of magnetic field information.

[0033] Furthermore, the system design of this invention overcomes the problem of interference from the towed body on the magnetic sensors during deep-towed magnetic measurements. By attaching the magnetic sensor array to the towed cable, rather than directly mounting it on the seafloor crawler, the crawler's interference with magnetic field measurements is effectively reduced. This design significantly improves the accuracy of magnetic field measurements, potentially exceeding that of conventional deep-towed magnetic measurements.

[0034] This invention also enables autonomous crawling and data collection for the submarine crawler through the design of an acquisition control module. This intelligent design not only improves measurement efficiency but also enables refined measurements in complex terrain. The acquisition control module dynamically adjusts the sampling strategy based on real-time magnetic field data, focusing sampling on abnormal areas and obtaining more detailed and reliable magnetic field distribution data.

[0035] Finally, the system design of this invention eliminates the need for deploying diurnal geomagnetic variation stations. By continuously operating the seafloor crawler over a long period of time, the system can acquire sufficiently long-term magnetic field data series to achieve its own diurnal variation correction. This not only simplifies the measurement process, but also significantly reduces measurement costs and improves 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 the system provided by the present invention;

[0038] Figure 2 is a specific system diagram of Example 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 solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0041] like Figure 1The figure shows 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 and the seabed crawler are connected by 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 and communicates with the magnetic sensor array through a data cable, 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 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 exploration system based on a seafloor crawler. The system mainly includes a survey vessel, 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 vessel serves as the command and data processing center for the entire detection system and is connected to the seafloor crawler via a towline. The towline not only provides a physical connection but also serves as a carrier for power transmission and data communication. This cabled connection ensures system stability and real-time performance. The seafloor crawler is the core component of the system, capable of autonomous navigation through complex seafloor terrain. The crawler is equipped with a power supply and data acquisition and pre-processing chips, providing power and data processing for the entire system. A towline is connected to the rear of the crawler, which tows the magnetic sensor array. The magnetic sensor array consists of a total field magnetometer, a vector magnetometer, and a self-contained magnetometer, all attached to the towline. This combination allows for simultaneous acquisition of multiple magnetic field data, enhancing the comprehensiveness and accuracy of the detection. A positioning beacon, used in conjunction with the self-contained magnetometer, accurately locates its position and also provides underwater positioning information for the entire system.

[0044] The data acquisition and pre-processing chip built into the seafloor crawler houses an acquisition control module, which enables the crawler's autonomous crawling. The crawler can autonomously complete magnetic field surveys throughout the entire area, based on a pre-set detection area and route planning. A total-field magnetometer and a vector magnetometer are connected to the seafloor crawler via a towed cable, collecting magnetic field data as the crawler moves. These magnetometers are powered by the crawler and transmit data to it in real time. A self-contained magnetometer is deployed at a specific location on the seafloor, primarily to collect magnetic field gradient data and provide a magnetic field reference. The data collected is used to calibrate and optimize the measurements of the total-field magnetometer and vector magnetometer. The data acquisition and pre-processing chip within the seafloor crawler communicates with the magnetic sensor array via a data cable, collecting magnetic field data and status information in real time. The acquisition control module within the chip performs preliminary data processing to generate magnetic field distribution data for the entire detection area. This data is then transmitted to the survey vessel via wireless transmission equipment on the crawler. The seafloor crawler is equipped with a manipulator for deploying and retrieving the self-contained magnetometer and its positioning beacon. Before the detection begins, the crawler places the self-contained magnetometer at a predetermined location; after the detection is completed, the crawler reaches the designated location again based on the position information of the positioning beacon and recovers the equipment.

[0045] The seafloor crawler is an underwater robot capable of autonomously navigating complex seafloor terrain. Its key features include autonomous navigation, a power system, a data acquisition and pre-processing chip, a manipulator, and wireless transmission equipment. Its built-in navigation system and sensors enable accurate positioning and movement in complex terrain. It uses a high-capacity battery for extended operation. It also receives continuous power from a survey vessel via a tow rope. The data acquisition and pre-processing chip processes and stores collected magnetic field data in real time and controls the entire exploration process. The manipulator deploys and retrieves the self-contained magnetometer and positioning beacon. The wireless transmission equipment enables real-time data communication with the survey vessel.

[0046] The magnetic sensor array consists of various types of magnetometers, including total-field magnetometers, vector magnetometers, and self-contained magnetometers. Total-field magnetometers measure the total strength of the Earth's magnetic field. Vector magnetometers measure the three components of the Earth's magnetic field (X, Y, and Z). Self-contained magnetometers operate independently, collecting long-term data on diurnal variations in the Earth's magnetic field. These magnetometers are equipped with non-magnetic sleds to ensure that the sensor bodies are 0.5 meters above the seafloor to avoid interference from strong magnetic objects near the seafloor surface. The combined use of multiple magnetometers can provide 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 positioning beacon's primary function is to provide precise positioning for the self-contained magnetometer, transmit underwater acoustic positioning information to the survey vessel and crawler, and assist the crawler in accurately locating and recovering the self-contained magnetometer after survey completion. The data acquisition and pre-processing chip is the "brain" of the entire system. Its main functions include real-time data acquisition from the magnetic sensor array, sending control commands to the magnetic sensor array, performing preliminary processing and storage of the collected data, controlling the crawler's autonomous navigation, and generating magnetic field distribution data for the entire survey area. The acquisition control module is the core component of the data acquisition and pre-processing chip. It is primarily responsible for planning and controlling the crawler's movement path, coordinating the operating status of each magnetometer, monitoring system operation in real time, and adjusting acquisition parameters based on survey requirements.

[0048] The system's workflow consists of preparation, detection, data processing, and recovery. During the preparation phase, the survey vessel arrives at the designated sea area and deploys the seafloor crawler. The crawler descends to the seafloor and begins autonomous navigation. The crawler uses a manipulator to deploy a self-contained magnetometer and its positioning beacon at the predetermined location. During the detection phase, the self-contained magnetometer begins continuously collecting diurnal geomagnetic data, with sampling intervals no longer than one minute. The seafloor crawler tows a total field magnetometer and a vector magnetometer, conducting detection along a predetermined route. A data acquisition and pre-processing chip receives and processes magnetic field data in real time. The processed data is transmitted to the survey vessel via wireless transmission. During the data processing phase, the survey vessel receives real-time data from the crawler. Onboard researchers further analyze and process the data. The crawler's detection path or parameters can be adjusted in real time as needed. During the recovery phase, upon completion of the detection mission, the survey vessel sends a recovery command to the crawler. Based on the location information from the positioning beacon, the crawler locates and recovers the self-contained magnetometer and its positioning beacon. The crawler returns to the survey vessel, completing the detection mission.

[0049] This system has several advantages. First, by towing the magnetic sensor array directly on the seabed, it is possible to obtain magnetic field data with more high-frequency components and richer information. This has significant advantages over traditional surface towing or deep towing magnetic detection, and achieves high-precision detection. Second, the design of the seabed crawler enables the system to operate in areas with drastic terrain fluctuations, greatly expanding the application range of magnetic detection and adapting to complex terrain. Third, through the combination of cable connection and wireless transmission, the system can realize real-time collection, processing and transmission of magnetic field data, providing researchers with timely detection results. In addition, the system can receive commands to start or stop crawling at any time, flexibly adjust detection strategies, 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 a core component of the system, executing a series of steps to achieve efficient and accurate seafloor magnetic field detection. First, the acquisition control module controls the seafloor crawler, starting from the center of the seafloor to be measured, and moves at a constant speed using an equidistant, diffuse spiral pattern. It also controls the total-field magnetometer to perform measurements, generating a total-field basic magnetic data set and the acquisition point for each piece of total-field basic magnetic data, known as a total-field acquisition point. This spiral path ensures uniform coverage of the entire survey area while minimizing duplicate measurements.

[0051] Next, the acquisition control module divides the seabed area to be surveyed into a planar triangular grid using each total field acquisition point as a vertex, obtaining multiple grid cells. A set of grid cell vertices and a set of center points are established, recorded as the vertex set and the center point set, respectively. This grid division method can effectively discretize the entire survey area, laying the foundation for subsequent detailed exploration and data analysis. The module then calculates the pairwise similarity of the total field basic magnetic data of the three vertices of each grid cell, with the minimum similarity being used as the similarity index of the grid cell. The purpose of this step is to preliminarily identify areas where anomalies or significant changes may exist.

[0052] Based on the calculated similarity index, the acquisition control module divides the seabed into high-similarity and low-similarity areas. For high-similarity areas, measurements are performed only at the center point within the area using a vector magnetometer or self-contained magnetometer. For low-similarity areas, measurements are performed at all vertices and the center point. This differentiated measurement strategy improves overall detection efficiency while ensuring accuracy.

[0053] During the actual measurement process, the acquisition control module controls the seafloor crawler along a pre-set path to each measurement point. At each measurement point, the following steps are performed: first, the crawler stops and waits for the system to stabilize; then, the total-field magnetometer is activated to obtain total magnetic field data; then, the vector magnetometer is activated, remaining completely stationary, to obtain three-component magnetic data; and finally, the self-contained magnetometer is activated, also remaining completely stationary, to obtain magnetic field gradient data. This rigorous measurement process ensures high-quality magnetic field data.

[0054] For each grid cell, the acquisition control module uses interpolation algorithms to calculate the magnetic field distribution within the entire grid cell based on the measurement data at its vertices and center points, including total magnetic field data, three-component magnetic data, and magnetic field gradient data. This generates a total magnetic field data matrix, a three-component data matrix, and a magnetic field gradient data matrix, which are then combined to form the magnetic field distribution data for 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] To further improve data quality and reliability, the acquisition control module conducts an in-depth analysis of the resulting 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, identifying outliers in each matrix, which are recorded as the first outlier point set. At the same time, based on the relationship between the three magnetic data at each vertex or center point, the consistency index between the total field magnetic data and the three-component magnetic data, the covariance index between the three-component magnetic data and the magnetic field gradient data, and the correlation index between the total field magnetic data and the magnetic field gradient data are calculated. This yields outliers in terms of consistency, covariance, and correlation between the three magnetic data, which are recorded as the second outlier point set.

[0056] The acquisition control module uses the combination of the first and second outlier point sets as the outlier set, plans a path traversal for these outlier points, and controls the seafloor crawler according to the planned path, repeating the previous measurement steps to obtain new measurement data for each outlier point. This repeated measurement of outlier points effectively verifies the accuracy of the data and eliminates possible measurement errors or temporary anomalies.

[0057] Finally, the acquisition control module compares and analyzes the new measurement data at the outlier point with the existing data to update the magnetic field distribution data for the entire survey area. This step specifically includes the following substeps: First, for each outlier point, the difference between the new and old data is calculated, including the differences in the total magnetic field data, three-component magnetic data, and magnetic field gradient data. Then, based on a pre-set difference threshold, the outliers are classified into three categories: significant difference points, slight difference points, and consistent points. For significant difference points, the new measurement data is preferred, while the old data is 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, a weighted average of the new and old data is used, with the weights dynamically adjusted based on measurement conditions (such as the stability of the seafloor crawler and ambient noise). For consistent points, the original data is retained but marked as verified high-confidence data points in the magnetic field distribution data. Finally, using the updated dataset, the magnetic field distribution data for the entire survey area is recalculated, including the total magnetic field data matrix, three-component data matrix, and magnetic field gradient data matrix.

[0058] The reason why the multi-component magnetic detection system based on the seabed 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, a choice based on the principles of fluid mechanics and motion control. Compared to the sea surface, the seabed environment is less disturbed and the water flow velocity is generally lower. The seabed crawler adopts a crawler design, which increases the contact area with the seabed and improves friction, thereby enhancing the stability of the platform. According to Newton's third law, the action and reaction forces 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, further enhancing the stability of the platform. This stable measurement platform provides the basic conditions for high-precision magnetic field measurements.

[0061] 2. Near-source measurement principle:

[0062] The measurement system of the present invention measures directly 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] in, is the magnetic induction intensity, is the vacuum permeability, is the magnetic dipole moment, It's distance, is the angle with the dipole axis. Thus, reducing the distance can significantly enhance the strength of the magnetic field signal, especially the high-frequency signal. 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 of the magnetic field, and the self-contained magnetometer provides the magnetic field gradient. 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, 、 and are the total field, vector field and gradient field measurements, respectively, 、 and is the weight coefficient, which can be optimized by minimizing the estimation error.

[0069] 4. Magnetic interference suppression principle:

[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 tow cable instead of directly installing it on the seabed crawler. This design is based on the principle of magnetic field superposition and the magnetic dipole model. According to the principle of magnetic field superposition, the total magnetic field at the measurement 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 impact 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 of the present invention, a specific embodiment 1 of the acquisition control module is provided below. The steps of this embodiment 1 specifically include the following:

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

[0073] ;

[0074] in:

[0075] Indicates the distance from the starting point to the current location (unit: meter)

[0076] Indicates the rotation angle (unit: radians)

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

[0078] Location of the seafloor crawler It can be calculated by the following formula:

[0079] ;

[0080] ;

[0081] In order to ensure equal distance, the speed of the crawler needs to be controlled. Should remain constant:

[0082] ;

[0083] Therefore, the angular velocity Should change over time:

[0084] ;

[0085] In practical implementation, the continuous spiral path can be discretized into a series of sampling points. Assume that the total number of sampling points is , then The angle of the sampling point 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 is performed. The Delaunay triangulation algorithm can be used here. The characteristic of Delaunay triangulation is to maximize the minimum angle and avoid the formation of narrow triangles.

[0089] Define point set ,in Indicates the The coordinates of the sampling points.

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

[0091] The Delaunay triangulation can be constructed using an incremental algorithm:

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

[0093] For each newly added point :

[0094] a. Find the file containing The triangle or Intersecting edges.

[0095] b. If Within a triangle, split the triangle into three new triangles.

[0096] c. If 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 condition. If not, perform edge flipping.

[0098] Edge flipping criteria: For two adjacent triangles and If you click In the triangle If the circumcircle of for .

[0099] After triangulation is completed, a triangular mesh is obtained ,in:

[0100] is the set of vertices;

[0101] is an edge set;

[0102] is a set of triangle faces;

[0103] For each triangle , calculate its center of gravity as the center point:

[0104] ;

[0105] in 、 、 are the coordinates of the three vertices of the triangle.

[0106] In this way, we get the vertex set and the center point set .

[0107] S30, calculate 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 The total magnetic field data corresponding to the three vertices are 、 and .

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

[0109] ;

[0110] in represents the dot product, Represents the magnitude of a vector.

[0111] Compute the similarity between three pairs of vertices: ; ; ;triangle The similarity index is defined as:

[0112] ;

[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 areas from low similarity areas: In order to distinguish high similarity areas from low similarity areas, a threshold needs to be set. This threshold can be determined empirically or learned from data using statistical methods.

[0115] Define an indicator function :

[0116] ;

[0117] in Indicates high similarity areas, Indicates low similarity areas.

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

[0119] Define a measurement point set :

[0120] ;

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

[0122] S50, perform measurement operation: for the set For each point in , the following measurement operations need to be performed:

[0123] Total field magnetometer measurements: ;

[0124] Vector magnetometer measurements: ;

[0125] Self-contained magnetometer measurement: ;

[0126] in 、 and 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 can be defined , when its value is less than a certain threshold The system is considered stable when:

[0128] ;

[0129] in Indicates the waiting time, It can be a function based on an accelerometer or other stability indicator.

[0130] S60, calculate magnetic field distribution: For each triangular grid unit, use the measured data of its vertices and center points to interpolate and obtain the magnetic field distribution within the entire grid unit. Here, the barycentric coordinate interpolation method can be used.

[0131] Hypothetical triangle The coordinates of the three vertices are 、 、 , the corresponding magnetic field data is 、 、 , the center point coordinates are , the corresponding magnetic field data is .

[0132] For any point in the triangle , its center of gravity coordinates satisfy:

[0133] ;

[0134] Solving this system of equations, we can get 、 、 expression.

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

[0136] ;

[0137] in 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 , 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 follows a Gaussian distribution, the Local Outlier Factor (LOF) algorithm can be used to detect outliers.

[0140] Definition Point of -distance:

[0141] ;

[0142] in yes No. nearest neighbor points, is the Euclidean distance.

[0143] Define reachable distance:

[0144] ;

[0145] Define the local reachability density:

[0146] ;

[0147] in yes of Neighbor set.

[0148] Finally, define the local outlier factor:

[0149] ;

[0150] if ( is the preset threshold), then the point It is an outlier.

[0151] Total field magnetic data matrix , three-component data matrix and the magnetic field gradient data matrix Apply the LOF algorithm separately to obtain the first abnormal point set .

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

[0153] 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 abnormal point set .

[0160] The final set of outliers is:

[0161] ;

[0162] S80, outlier retest: for outlier collection , path planning is needed to re-measure these points. A greedy algorithm can be used to find an approximate solution to this traveling salesman problem:

[0163] Choose a starting point (Can be 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 the point and take a measurement.

[0167] c. Mark the point as visited.

[0168] This process can be expressed as:

[0169] ;

[0170] ;

[0171] while :

[0172] ;

[0173] ;

[0174] ;

[0175] for For each point in , repeat the measurement steps in S50 to obtain new measurement data:

[0176] ;

[0177] ;

[0178] ;

[0179] To improve the reliability of the measurement, multiple measurements can be taken at each point and then the average value is taken:

[0180] ;

[0181] ;

[0182] ;

[0183] in It is the number of repeated measurements, usually 3-5 times.

[0184] During the measurement process, the timestamp of each measurement needs to be recorded. and the crawler's posture information ,This information will be used for subsequent data analysis and correction:

[0185] ;

[0186] ;

[0187] in Indicates that the crawler is in The pitch angle, roll angle and heading angle at the time of the measurement.

[0188] S90, data comparison analysis and update:

[0189] a) Calculate the difference between new and old data

[0190] For each outlier , calculate the differences of total field magnetic data, three-component magnetic data and magnetic field gradient data respectively:

[0191] Total field magnetic data difference:

[0192] ;

[0193] Difference of three-component magnetic data:

[0194] ;

[0195] Magnetic field gradient data difference:

[0196] ;

[0197] Among them, the superscript and Represent the new measurement data and the original data respectively.

[0198] b) Outlier classification

[0199] According to the pre-set difference threshold and ( ), the outliers are divided into three categories:

[0200] Significantly different point set: ;

[0201] Slightly different set of points: ;

[0202] Consistent point set: ;

[0203] c) Processing of significant differences;

[0204] for , 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.

[0205] Defined by The neighborhood centered on:

[0206]

[0207] in is the preset neighborhood radius, is the Euclidean distance between two points.

[0208] for Each point in , make additional measurements and get new data 、 and .

[0209] Then, calculate the weighted average of the new and old data:

[0210] ;

[0211] ;

[0212] ;

[0213] in 、 and is the weight coefficient, satisfying These weights can be adjusted dynamically based on the measurement conditions.

[0214] d) Handling of minor differences

[0215] for , using the weighted average of new and old data. The weights are dynamically adjusted according to the measurement conditions. Define a reliability index :

[0216]

[0217] in Indicates that the seabed crawler is at the measurement point stability, Indicates the ambient noise level, Indicates the accuracy of the magnetometer. Function It can be a weighted sum of these parameters or other suitable combination function.

[0218] Then, we can define the weights for new and old data:

[0219] ;

[0220]

[0221] in is the reliability index of the original measurement.

[0222] The updated data is:

[0223] ;

[0224] ;

[0225] ;

[0226] e) consistent point processing;

[0227] for , retain the original data, but mark them as verified high-confidence data points in the magnetic field distribution data. A confidence index can be introduced :

[0228] ;

[0229] The range of this indicator is , takes the maximum value of 1 when the difference is 0, and decreases as the difference increases.

[0230] f) Update magnetic field distribution data

[0231] Using the updated dataset, it is necessary to recalculate the magnetic field distribution data for the entire detection area. Kriging can be used here to generate a continuous magnetic field distribution.

[0232] First, we need to construct a semivariogram. For total field magnetic data, the semivariogram is defined as:

[0233] ;

[0234] in It's distance, The distance is The number of sample points.

[0235] Then, fit a theoretical semivariogram model, such as a spherical model:

[0236] ;

[0237] in It's the nugget effect. is the sill value, It is a variable process.

[0238] Ordinary Kriging can be used to estimate the value of any unknown point The total magnetic field strength is:

[0239]

[0240] in is the weight coefficient, satisfying The weight coefficients are obtained by solving the following equations:

[0241] ;

[0242] in Yes and point The distance between is the Lagrange multiplier.

[0243] 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:

[0244] ;

[0245] in , and They are and The mean of .

[0246] Then, cokriging can be used to estimate the three components simultaneously:

[0247] ;

[0248] in are weight coefficients, which can be obtained by solving a system of equations similar to ordinary kriging.

[0249] For magnetic field gradient data, a similar approach can be used, but the properties of the tensor need to be taken into account.

[0250] Finally, the updated total field magnetic data matrix is ​​obtained , three-component data matrix and the magnetic field gradient data matrix .

[0251] To evaluate the effect of the update, you can calculate the root mean square error (RMSE) before and after the update:

[0252] ;

[0253] in is the total number of measurement points, is the actual measured value.

[0254] In addition, spatial autocorrelation indicators before and after the update can be calculated, such as the Moran's I index:

[0255] ;

[0256] in are the elements of the spatial weight matrix, is the average value of the magnetic field strength.

[0257] The Moran's I index ranges from -1 to 1, with positive values ​​indicating positive correlation, negative values ​​indicating negative correlation, and 0 indicating random distribution. By comparing the Moran's I index before and after the update, we can assess whether the update has improved the spatial structure of the magnetic field distribution.

[0258] In order to further improve the quality of magnetic field distribution data, the following aspects can be considered:

[0259] Adaptive sampling: Dynamically adjust the sampling density according to the complexity of the magnetic field distribution. Increase the number of sampling points in areas where the magnetic field changes dramatically, and reduce the number of sampling points in areas where the changes are gentle. You can define a complexity index :

[0260] ;

[0261] in is the Laplace operator of the magnetic field. For larger areas, increase the number of sampling points.

[0262] Multi-scale analysis: Use wavelet transform to decompose magnetic field data into multiple scales and process features at different scales separately. For example, for total field magnetic data, two-dimensional discrete wavelet transform can be used:

[0263] ;

[0264] in is the wavelet function, is the scaling function, and are the detail coefficient and the approximation coefficient respectively.

[0265] Noise filtering: Use appropriate filtering techniques to remove noise from the measured data. For example, Wiener filtering can be used:

[0266] ;

[0267] in is the system transfer function, and are the power spectral densities of the signal and noise, respectively, is the observed signal containing noise.

[0268] To better understand and implement the present invention, Example 2, a specific application scenario, is provided below. This Example 2 describes the use of a multi-component magnetic exploration system based on a seafloor crawler for seafloor magnetic field exploration in a specific area of ​​the Yellow Sea. The system primarily consists of a survey vessel, a seafloor crawler, a magnetic sensor array, and a self-contained magnetometer. By integrating multiple magnetic field measurement technologies, it achieves high-precision, high-resolution detection of the seafloor magnetic field.

[0269] 1. System composition

[0270] 1.1 Survey vessel

[0271] The survey vessel used in this example is the Ocean Explorer, a research vessel specifically designed for marine geophysical exploration. The vessel 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:

[0272] a) Satellite navigation system: It adopts dual-frequency GPS / Beidou combined navigation system with positioning accuracy better than 0.5 meters.

[0273] b) Underwater positioning system: It adopts an ultra-short baseline (USBL) acoustic positioning system, with a positioning accuracy better than 0.2% of the water depth at a water depth of 3,000 meters.

[0274] c) Integrated monitoring host: uses a high-performance workstation equipped with dual Intel Xeon Gold 6258R processors, 384GB RAM, and 20TB SSD storage.

[0275] d) Power supply unit: provides stable 400V / 50Hz three-phase AC power with a maximum output power of 500kW.

[0276] e) Composite cable interface unit: It uses optical-electrical composite cable, which can transmit power and data signals simultaneously, with a maximum transmission distance of 5000 meters.

[0277] 1.2 Seabed crawler

[0278] The submarine crawler used in this example is the Kunlong 500, which has the following main features:

[0279] a) Dimensions: 3.5 meters long, 2.5 meters wide, and 1.8 meters high (excluding the robotic arm); b) Weight: 500 kg underwater; c) Maximum operating depth: 5,000 meters; d) Maximum crawling speed: 2 km / h; e) Endurance: 50 hours (under full load conditions);

[0280] The seabed crawler is equipped with the following key modules:

[0281] Composite cable interface unit: matches the composite cable interface unit of the survey vessel to achieve two-way transmission of power and data. Power supply module: receives 400V AC power from the survey vessel 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: specifically used to control the data acquisition and preprocessing of the magnetic sensor array.

[0282] 1.3 Magnetic Sensor Array

[0283] The magnetic sensor array consists of the following components:

[0284] a) Total field magnetometer: uses an optically pumped cesium magnetometer with a sensitivity of 0.001nT and a sampling rate of 10Hz.

[0285] b) Vector magnetometer: A three-axis fluxgate magnetometer is used with a measuring range of ±100,000 nT, a resolution of 0.01 nT, and a sampling rate of 100 Hz.

[0286] 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 operate continuously for 30 days.

[0287] d) Positioning beacon: equipped with self-contained magnetometer, 5 in total, operating frequency 10-30kHz, maximum effective distance 8000 meters.

[0288] The magnetic sensor array is connected to the seafloor crawler by a 30-meter-long towed cable.

[0289] 2. System workflow

[0290] 2.1 System Deployment

[0291] (1) After the survey vessel arrives at the designated sea area, it first uses the ship-borne multi-beam bathymetry system to quickly scan the seabed topography to determine the location suitable for deploying the self-contained magnetometer. (2) Based on the bathymetry 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 vessel lowers the seabed crawler to a position about 50 meters from the seabed through the winch system, and then starts the crawler's autonomous diving mode. (4) After the seabed crawler arrives at 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:

[0292] a) Stop moving and wait for the system to stabilize (about 2 minutes).

[0293] b) Use the robotic arm to grab the self-contained magnetometer.

[0294] c) Gently place the self-contained magnetometer on the seabed, ensuring it remains vertical.

[0295] d) Place the corresponding positioning beacon about 2 meters away from the magnetometer.

[0296] e) Use the onboard camera system to record image data of the deployment location.

[0297] (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.

[0298] 2.2 Magnetic field detection

[0299] 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:

[0300] S10: The crawler is controlled to move at a constant speed, starting from the center of the seabed to be measured, in an equidistant, spreading spiral pattern. The total-field magnetometer is also controlled to conduct measurements. The spiral spacing is 50 meters, the crawler speed is 1 km / h, and the total-field magnetometer sampling interval is 1 second. At this stage, the crawler takes approximately 25 hours to complete a preliminary scan of the entire area. The resulting total-field basic magnetic dataset contains approximately 90,000 data points.

[0301] S20: Divide the seabed area to be surveyed into a plane triangular grid with each total field acquisition point as a vertex. The grid unit side length is about 50 meters; the number of grid units is about 40,000; the number of vertex sets is about 90,000; and the number of center point sets is about 40,000.

[0302] S30: Calculate the pairwise pre-similarity of the total field basic magnetic data of the three vertices of each grid cell.

[0303] 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.

[0304] 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.

[0305] S50: Control the seabed crawler to move along the preset path to each point to be measured, and perform detailed measurements. In high-similarity areas, measurements are performed only at the center point, totaling approximately 28,000 measurement points. In low-similarity areas, measurements are performed at all vertices and the center point, totaling approximately 36,000 measurement points. The total number of measurement points is approximately 64,000.

[0306] At each measurement point, the crawler performs the following operations:

[0307] a) Stop moving and wait for the system to stabilize (about 1 minute);

[0308] b) Start total field magnetometer measurement (last 10 seconds and take the average value);

[0309] c) Start vector magnetometer measurement (last 10 seconds and take the average value);

[0310] d) Calculate magnetic field gradients (using total field magnetometer data at different locations);

[0311] Taking into account the crawler movement time and measurement time, this phase took approximately 80 hours to complete.

[0312] 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 magnetic field data matrix: 5000x5000; three-component data matrix: 5000x5000x3; magnetic field gradient data matrix: 5000x5000x3;

[0313] S70: Perform Bayesian analysis and magnetic data relationship analysis. Specifically: first abnormal point set: about 2000 points; second abnormal point set: about 1500 points; abnormal point collection: about 3000 points;

[0314] S80: Perform path traversal planning on the set of outlier points and re-measure.

[0315] S90: Compare and analyze new and old data, and update magnetic field distribution data.

[0316] a) Calculate the difference between new and old data: the difference threshold of total magnetic data is 5nT; the difference threshold of three-component magnetic data is 3nT / component; the difference threshold of magnetic field gradient data is 0.1nT / m;

[0317] b) Outlier classification results: significant difference points: about 500; slight difference points: about 1500; consistent points: about 1000;

[0318] c) For points with significant differences, additional sampling was performed at 5 surrounding points.

[0319] d) Weighted average of data with slight differences: new data weight: 0.7; old data weight: 0.3;

[0320] e) The consistent points are marked as high-confidence data points in the magnetic field distribution data.

[0321] f) Recalculate the magnetic field distribution data of the entire detection area using the updated data set.

[0322] The following provides Example 3 of a specific application scenario of the present invention. In this example, the survey vessel and the seafloor crawler communicate using hydroacoustic sounding. This Example 3 describes a multi-component magnetic survey system based on a seafloor crawler. This system was used to conduct seafloor magnetic field surveys in the northern continental slope of the South China Sea. The survey area lies between 20°30' and 21°30'N latitude and 114°00' and 115°00'E longitude, with water depths ranging from 200 to 1500 meters. This area has complex terrain, with numerous submarine canyons and steep slopes, making it difficult for traditional towed magnetic surveys to cover.

[0323] 1. System composition

[0324] 1.1 Survey vessel

[0325] The survey vessel used in this example is the Ocean Explorer, a multifunctional oceanographic survey vessel with a length of 98 meters, a width of 16 meters, and a draft of 5.8 meters. The vessel has a displacement of approximately 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:

[0326] a) Satellite navigation system: It adopts dual-frequency GPS / Beidou combined navigation system with positioning accuracy better than 0.5 meters.

[0327] b) Underwater positioning system: It uses an ultra-short baseline (USBL) acoustic positioning system with an operating frequency of 18-36kHz and a positioning accuracy within 1% of the water depth.

[0328] c) Integrated monitoring host: uses a high-performance workstation equipped with dual Intel Xeon Gold 6258R processors, 384GB RAM, and 20TB SSD storage.

[0329] d) Power supply unit: Provides stable 400V / 50Hz three-phase AC power with a maximum output power of 200kW.

[0330] e) Underwater Acoustic Communication Interface Unit: This unit operates at a frequency of 300kHz-500kHz, has a transmit power of 10-100W, a communication range of 100-2000m, a data transmission rate of 1200-19200bps, a receiving sensitivity of -90 to -120dBV / μPa, and has a certain degree of directivity. It can operate normally in water depths of 100-2000m, has an IP68 waterproof rating, and supports common underwater acoustic communication protocols such as MATS, WHOI Micro-Modem, and AUVSI.

[0331] 1.2 Seabed crawler

[0332] The seabed crawler used in this embodiment is a "deep sea spider" type, and its specific parameters are as follows:

[0333] a) Dimensions: 3.5 meters long, 2.2 meters wide, and 1.8 meters high;

[0334] b) Weight: 4.5 tons in air, adjustable underwater weight;

[0335] c) Maximum operating depth: 3000 meters;

[0336] d) Maximum crawling speed: 2 km / h;

[0337] d) Maximum climbing ability: 35 degrees;

[0338] The main components of the seafloor crawler include:

[0339] a) Underwater acoustic communication interface unit: corresponding to the survey vessel.

[0340] b) Power module: Receives 400V AC power and converts it into 48V DC power for internal use, while also powering the magnetic sensor array.

[0341] c) Sensor module: Depth sensor: accuracy ±0.1% FS; Altitude sensor: resolution 1 cm; Attitude sensor: pitch / roll accuracy 0.1°, heading accuracy 0.5°; Multi-beam bathymetry system: coverage angle 120°, resolution 1 cm;

[0342] d) Underwater platform controller: uses an industrial-grade embedded computer with an Intel Core i7-10700T processor, 32GB RAM, and 2TB SSD storage.

[0343] e) Crawler module: adopts crawler design, driven by 4 independent motors on each side.

[0344] f) Magnetic acquisition control module: FPGA-based real-time data acquisition and preprocessing system with a sampling rate of up to 10kHz and a 24-bit ADC.

[0345] g) Manipulator: A 7-DOF manipulator with a maximum load of 50 kg, used for deploying and retrieving self-contained magnetometers.

[0346] 1.3 Magnetic Sensor Array

[0347] The magnetic sensor array is connected to the seafloor crawler via a 10-meter towed cable and contains the following sensors:

[0348] a) Total field magnetometer: uses an optically pumped cesium magnetometer with a sensitivity of 0.003nT / √Hz@1Hz and a measuring range of 20,000 to 100,000nT.

[0349] b) Vector magnetometer: Three-axis fluxgate magnetometer, noise level <10pT / √Hz@1Hz, range ±100,000nT.

[0350] c) Self-contained magnetometers: 5 optically pumped rubidium magnetometers, with a sensitivity of 0.01nT / √Hz@1Hz, a range of 20,000 to 120,000nT, a sampling rate of 10Hz, a storage capacity of 32GB, and a battery life of 30 days.

[0351] d) Positioning beacons: 5, one corresponding to each self-contained magnetometer, operating frequency 8-14kHz, battery life 60 days.

[0352] 2. System workflow

[0353] 2.1 Task Preparation

[0354] (1) Pre-voyage inspection: Check the status of each system of the survey vessel; test the functions of each module of the seabed crawler; calibrate all magnetometers; check the battery status and storage space of the self-contained magnetometer;

[0355] (2) Route planning: Based on the terrain characteristics of the detection area, a zigzag survey line was designed; the survey line spacing was 500 meters, with a total of 20 parallel survey lines; each survey line was about 60 kilometers long, and the total flight distance was about 1,200 kilometers;

[0356] (3) System deployment: After the survey vessel arrives at the predetermined location, the seabed crawler is lowered to the seabed; during the lowering process of the seabed crawler, the composite cable is released at the same time; after the crawler touches the bottom, the system self-checks and status reports are performed;

[0357] 2.2 Magnetic field detection process

[0358] (1) Deployment of self-contained magnetometers: The seafloor crawler moves along a predetermined path to five deployment points. At each deployment point, a manipulator places the self-contained magnetometer and its positioning beacon on the seafloor. The deployment points are spaced approximately 10 kilometers apart, forming a "cross" layout.

[0359] (2) Main detection process:

[0360] a. The seabed crawler crawls along the predetermined survey line at a speed of 1.5 km / h

[0361] b. Magnetic sensor array continuously collects data:

[0362] Total field magnetometer: sampling rate 10Hz;

[0363] Vector magnetometer: sampling rate 100Hz;

[0364] Self-contained magnetometer: sampling rate 10Hz (independent operation);

[0365] c. The magnetic acquisition control module processes data in real time: denoising and filtering the raw data; calculating the magnetic field gradient; and performing preliminary anomaly detection;

[0366] d. The underwater platform controller performs the following tasks: real-time navigation and path control; monitoring crawler status; data compression and upload;

[0367] e. The survey vessel receives real-time data and conducts preliminary analysis;

[0368] (3) Detailed detection of abnormal areas: If a magnetic anomaly is detected, the crawler will slow down to 0.5 km / h; perform an S-shaped reciprocating scan 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;

[0369] 2.3 Data processing and analysis

[0370] (1) Real-time processing:

[0371] a. The magnetic acquisition control module performs the following steps:

[0372] S10: Control the seafloor crawler to move at a constant speed using an equidistant, diffuse spiral pattern, starting from the center of the seafloor to be measured (21°00'N, 114°30'E). Control the total-field magnetometer to perform measurements, obtaining a total-field basic magnetic data set and the collection points for each total-field basic magnetic data item (total-field collection points).

[0373] S20: Using each total field acquisition point as a vertex, the seafloor area to be surveyed is divided into a planar triangular mesh to obtain a plurality of grid cells. In this embodiment, the survey area is divided into a 1000 m x 1000 m grid, for a total of 3600 grid cells. A set of grid cell vertices (vertex set) and a set of center points (center point set) are established.

[0374] S30: Calculate the pairwise similarity of the total field basic magnetic data of the three vertices of each grid cell, and use the minimum similarity as the cosine similarity index of the grid cell.

[0375] S40: Based on the similarity index of each grid cell, the detected seabed is divided into high-similarity areas and low-similarity areas. In this embodiment, areas with a similarity index greater than 0.95 are defined as high-similarity areas, and areas with a similarity index less than or equal to 0.95 are defined as low-similarity areas. High-similarity areas are measured only at their center points using a vector magnetometer or self-contained magnetometer. Low-similarity areas require measurement at all vertices and the center point using a vector magnetometer or self-contained magnetometer.

[0376] S50: Control the submarine crawler to move along the preset path to each point to be measured. At each measurement point, perform the following operations: stop moving and wait for the system to stabilize (approximately 30 seconds); start the total field magnetometer for measurement, collect 60 seconds of data, and take the average as the total field magnetic data; start the vector magnetometer, measure for 120 seconds while completely stationary, and obtain three-component magnetic data; start the self-contained magnetometer, also measure for 120 seconds while completely stationary, and obtain magnetic field gradient data.

[0377] 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 vertex and center point, 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.

[0378] S70: Perform Bayesian analysis on the total magnetic field data matrix, the three-component data matrix, and the magnetic field gradient data matrix, respectively, to identify outliers in each matrix, which are recorded as the first outlier point set. Outliers are defined as points that deviate from the mean by more than 3 standard deviations.

[0379] Based on the relationship between the three magnetic data of each vertex or center point, calculate:

[0380] Consistency index between total field magnetic data and three-component magnetic data:

[0381]

[0382] Covariance index of three-component magnetic data and magnetic field gradient data:

[0383] ;

[0384] Correlation index between total field magnetic data and magnetic field gradient data:

[0385] ;

[0386] in, is the total field magnetic data, 、 、 is the three-component magnetic data, and ∇B is the magnetic field gradient.

[0387] 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.

[0388] S80: Path traversal planning is performed for the combined outlier point set, using the first and second outlier point sets as the outlier point set. An improved ant colony algorithm is used to optimize the path to minimize the total travel distance. The submarine crawler is controlled according to the planned path, and steps S50-S60 are repeated to obtain new measurement data for each outlier point.

[0389] 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:

[0390] a) For each outlier, calculate the difference between the new and old data:

[0391] Total field magnetic data difference: ;

[0392] Three-component magnetic data differences: ;

[0393] Magnetic field gradient data differences: ;

[0394] b) Based on the pre-set difference threshold, the outliers are divided into three categories:

[0395] 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%;

[0396] c) For points with significant differences: prioritize new measurement data; retain old data in the database as a reference; perform additional sampling within 5 grid cells around the point with significant differences, with one measurement at the center of each grid cell; and use the new sampling data to update the magnetic field distribution in that area.

[0397] d) For minor differences:

[0398] The weighted average of new and old data is used, and the weights are dynamically adjusted according to the measurement conditions:

[0399] ;

[0400] Among them, w_new+w_old=1;

[0401] The weight calculation takes into account the following factors: the stability of the seafloor crawler (assessed based on attitude sensor data); the ambient noise level (assessed based on magnetometer background noise); and the current conditions during measurement (assessed based on acoustic Doppler current profiler data).

[0402] Weight calculation formula:

[0403] ;

[0404] in, , , They are stability score, noise score and current score, and each score ranges from 0 to 1.

[0405] 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

[0406] f) Use the updated dataset 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 the updated total field magnetic data matrix, three-component data matrix and magnetic field gradient data matrix; resolution: 10 meters × 10 meters × 1 meter (vertical direction); matrix size: 6000 × 6000 × 150.

[0407] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A multi-component magnetic detection system based on a seafloor crawler, characterized in that: include: A survey vessel, a seabed crawler, a total field magnetometer, a vector magnetometer, a self-contained magnetometer, and a data acquisition preprocessing chip, wherein the survey vessel 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 and the vector magnetometer are fixed on the towing cable, the mechanical module of the seabed crawler includes a manipulator, the self-contained magnetometer is placed on the seabed surface by the manipulator of the seabed crawler, the total field magnetometer, the vector magnetometer, and the self-contained magnetometer are combined to form a magnetic sensor array, and the data acquisition preprocessing chip It is connected to and communicates with the magnetic sensor array through a data cable, 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 the real-time status data of the magnetic sensor array and the real-time electromagnetic data collected by it; an acquisition control module is provided in the data acquisition preprocessing chip, and 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. 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 seabed crawler according to claim 1, characterized in that: The seabed crawler is a crawler-type seabed crawler.

3. The multi-component magnetic detection system based on a seabed crawler according to claim 2, 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, characterized in that: The seabed crawler is provided with a power supply, which 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 a seafloor crawler according to claim 4, characterized in that: There are one or more self-contained magnetometers, and each self-contained magnetometer is equipped with a positioning beacon.

6. The multi-component magnetic detection system based on a seafloor crawler according to claim 5, 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.

7. The multi-component magnetic detection system based on a seafloor crawler according to claim 6, 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.

8. The multi-component magnetic detection system based on a seafloor crawler according to claim 7, characterized in that: The survey vessel is equipped with a satellite navigation system, an underwater positioning system, and a power supply unit.

9. A multi-component magnetic detection system based on a seafloor crawler according to any one of claims 1 to 8, 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 constant speed in an equidistant diffusion spiral manner, and controlling the total field magnetometer to perform measurement, thereby obtaining a total field basic magnetic data set and a collection point for each item of total field basic magnetic data, which are recorded as total field collection points; S20, dividing the seabed area to be detected into a planar triangular grid with each total field acquisition point as a vertex to obtain a plurality of grid cells, and establishing a set of grid cell vertices and a set of center points, which are respectively recorded as a vertex set and a center point set; S30, calculating the pairwise similarity 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. Divide the detected seabed into high-similarity areas and low-similarity areas based on the similarity index of each grid cell. The high-similarity areas are measured only at the center point thereof using a vector magnetometer or a self-contained magnetometer. The low-similarity areas are measured at all vertices and the center point thereof using a vector magnetometer or a self-contained magnetometer. S50, controlling the seafloor crawler to move along a preset path to each point to be measured. At each measurement point, the following operations are performed: first, the crawler stops moving and waits for the system to stabilize; then, the total field magnetometer is started to perform measurements to obtain total field magnetic data; then, the vector magnetometer is started to perform measurements in a completely stationary state to obtain three-component magnetic data; finally, the self-contained magnetometer is started to perform measurements in a completely stationary state to obtain magnetic field gradient data; S60. For each grid cell, based on 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 within the entire grid cell, and obtain a total field magnetic data matrix, a three-component data matrix, and a magnetic field gradient data matrix, and merge them into magnetic field distribution data for 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 outliers in each matrix and recording them as a first outlier point set; and calculating, based on the relationship between the three magnetic data at each vertex or center point, a consistency index between the total field magnetic data and the three-component magnetic data, a covariance index between the three-component magnetic data and the magnetic field gradient data, and a correlation index between the total field magnetic data and the magnetic field gradient data, to obtain outliers of consistency, covariance, and correlation between the three magnetic data, which are recorded as a second outlier 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 on the abnormal point set, controlling the seabed crawler according to the planned path, and repeating steps S50-S60 to obtain new measurement data for each abnormal point; 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.

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