This paper proposed a new compensation method of underwater magnetic vector measurement system which innovatively using the geomagnetic total field as benchmark. Moreover, this method does not require the use of heading angle data in the calculation of the compensation parameters, which avoids errors caused by insufficient accuracy of underwater heading angle. The implementation of these features relies on the new objective function constructed according to the characteristics of the spatial projection of the magnetic vector field on the horizontal plane of the geographical coordinate. The core matrix of the geomagnetic vector ellipsoid model is used as a constraint to determine the range of function solutions. The Levenberg-Marquardt algorithm is used to find the optimal solution to the new objective function. Three experiments were carried out to verify the proposed method. In simulation experiment, the deviation between the calculated value and the preset value is less than 0.01%. In field experiment, the standard deviation value of x, y, z magnetic components and the root mean square error of magnetic total field after compensation are reduced from 1527.38nT, 1397.35nT, 1529.16nT, 574.94nT to 9.86nT (0.65%), 9.99nT (0.72%), 5.63nT (0.38%), 2.14nT (0.37%). The proposed method is finally verified by marine experiment. The standard deviation value of x, y, z magnetic components and the root mean square error of magnetic total field after compensation are reduced from 575.02 nT, 830.60 nT, 620.95 nT, 1702.35 nT to 17.43 nT (1.11%), 14.14 nT (1.70%), 25.17nT (4.10%), 7.18 nT (0.42%).

Autonomous Underwater Vehicle (AUV) with towed Magnetic Anomaly Detection (MAD) system is a reliable technique for detecting underwater magnetic targets. However, arranging too many magnetometers in the towed MAD system is not suitable, and the key lies in their reasonable arrangement. As a result, this paper proposes a new MAD system equipped with two scalar and one vector magnetometers, along with the corresponding multi-parameter magnetic target localization method. By using two scalar magnetometers and the geometric relationship with vector magnetometer, the time when the vector magnetometer reaches the closest point approach (CPA) on the trajectory and the value range of the shortest distance are obtained. The value range of the shortest distance is cleverly set as the search boundary for the improved particle swarm optimization algorithm in this dimension, which greatly mitigates the randomness of the optimization algorithm and enhances the accuracy of the solution. Subsequently, the unique solution for the magnetic target parameters can be easily obtained by using the vector magnetometer. The combination of scalar and vector magnetometers proves to be both economical and effective. Numerical simulation and field experiments show that this method provides a new idea for static magnetic target detection.

In full tensor magnetic gradient measurement system based on superconducting quantum interference devices (SQUIDs), six first-order planar-type SQUID gradiometers mounted on the six sloping facets of a hexagonal pyramid can form the probe and determine all independent components of the magnetic gradient tensor with the help of triaxial SQUID magnetometers placed near the probe. The geometrical parameters of probe, such as the inclination angle of sloping facets and x-axis orientation in the probe coordinate system o-xyz, seriously impact on the sensitivities for magnetic gradient tensor invariants. We deduced that the invariants' sensitivities depend on the maximum among five independent elements' sensitivities. Based on the functional relationship between each independent element's relative sensitivity and the geometrical parameters, we designed the optimal probe geometry structure with the inclination being 58.7° and the angle of the x-axis relative to the projection of the first gradiometer's baseline on the xoy plane being ±15°, when the maximum among the relative sensitivities for the five independent elements achieved the minimum. Finally, we generalized three types of geometrical inaccuracies of the gradiometers arising in assembling the probe and evaluated the influences of each type of them on the measurement accuracy for each independent element.

To obtain a precise 2D/3D image of fissure or karst water, multi-channel magnetic resonance sounding (MRS) systems using edge-to-edge or overlapping receiving coils are needed. Thus, acquiring a nano-volt signal for a small amount of the aquifer and suppressing the mutual coupling between adjacent coils are two important issues for the design of the signal acquisition module in the system. In the present study, we propose to use a passive low pass filter, consisted of a resistance (R) and capacitance (C), to inhibit the mutual coupling effects of the coils. Four low-noise operational amplifiers LT1028, OPA124, AD745, and OP27 were compared with respect to achieving the lowest system noise. As a result, 3 pieces of LT1028 were chosen and connected in parallel to serve as preamplifier, with a sensitivity of 1.4 nV/√Hz at 2 kHz. Experimental results are presented for 2D MRS groundwater investigations conducted in the suburb of Changchun, China. The inversion result is consistent with the result of drilling log, suggesting that the signal acquisition module is well developed.

In order to calibrate the misalignment error between inertial navigation system and tri-axial magnetometer in the three-component magnetic measurement system, a novel calibration method is proposed in the paper. Considering that the magnetometer error is inevitable, the calibration of which is the prerequisite to misalignment error calibration. Therefore, the calibration models of magnetometer error and misalignment error are established firstly, based on which, the objective function is constructed to calibrate the misalignment error. The magnetometer error calibration is based on the principle that the norm of the magnetic survey data keeps constant during the process of solving the objective function. Hence, the objective function solving problem is considered as the equality constrained nonlinear optimization problem, that is, the MATLAB’s FMINCOM function is used to solve the objective function. The feasibility and validity of the proposed method were verified by simulation and field experiment, the results of which indicate high accuracy in misalignment error calibration. In the field experiment, the range of x, y, z components are decreased to 1.34%, 1.75% and 1.77% while the absolute errors are reduced to 2.13%, 2.11% and 3.47% respectively after calibration. Method comparisons indicate that the proposed method is superior to traditional methods, which can guarantee the accuracy for three-component magnetic measurement.

The improving quality of ultra-sensitive superconducting quantum interference devices has led to the construction of advanced full-tensor magnetic gradiometers (FTMGs) integrated on mobile carriers such as aircraft, vehicles and ships. FTMGs measure the smallest spatial magnetic gradients, possessing many practical advantages compared to conventional scalar surveys and three-component surveys. However, the magnetic interference field caused by the magnetic sources associated with the carrier significantly decreases the precision of measurement. In this paper, a novel method is proposed to compensate for the magnetic interference field. Specifically, a mathematical model containing compensation coefficients of the mobile FTMG system is constructed by extending the Tolles–Lawson model. By formulating the task into nonlinear problems, unknown compensation coefficients in the model can be solved by the flower pollination algorithm, which has the advantages of insensitivity to iterative initial values, less parameter tuning and strong global search abilities. Both the finite-element simulation results based on COMSOL Multiphysics and the results of a field experiment show that the proposed method provides a useful way for the optimal estimation of compensation coefficients and the reduction of the magnetic interference field.