Calibration of SQUID vector magnetometers in full tensor gradiometry systems

Schiffler, Markus; Queitsch, Matthias; Stolz, R.; Chwala, A.; Krech, W.; Meyer, H.‐G.; Kukowski, Nina

doi:10.1093/gji/ggu173

Cited by 45 publications

(34 citation statements)

References 19 publications

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“…Superconducting quantum interference devices (SQUIDs) provide unprecedented sensitivity down to the sub-fT/√Hz range, a broad frequency range of >1 MHz and a dynamic range of up to ~120 dB [1]. SQUID-based NDE systems have been developed for the investigation of objects that have dimensions of nanometers (nanoSQUID microscopes [2]) to kilometers (nondestructive archeology or geomagnetic evaluation [3,4]). Related scanning methods vary from 3D piezo stages to airborne systems transported by planes or helicopters.…”

Section: Introductionmentioning

confidence: 99%

Superconducting Quantum Interferometers for Nondestructive Evaluation

Faley

Kostyurina

Kalashnikov

et al. 2017

Sensors

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We review stationary and mobile systems that are used for the nondestructive evaluation of room temperature objects and are based on superconducting quantum interference devices (SQUIDs). The systems are optimized for samples whose dimensions are between 10 micrometers and several meters. Stray magnetic fields from small samples (10 µm–10 cm) are studied using a SQUID microscope equipped with a magnetic flux antenna, which is fed through the walls of liquid nitrogen cryostat and a hole in the SQUID’s pick-up loop and returned sidewards from the SQUID back to the sample. The SQUID microscope does not disturb the magnetization of the sample during image recording due to the decoupling of the magnetic flux antenna from the modulation and feedback coil. For larger samples, we use a hand-held mobile liquid nitrogen minicryostat with a first order planar gradiometric SQUID sensor. Low-Tc DC SQUID systems that are designed for NDE measurements of bio-objects are able to operate with sufficient resolution in a magnetically unshielded environment. High-Tc DC SQUID magnetometers that are operated in a magnetic shield demonstrate a magnetic field resolution of ~4 fT/√Hz at 77 K. This sensitivity is improved to ~2 fT/√Hz at 77 K by using a soft magnetic flux antenna.

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Section: Introductionmentioning

confidence: 99%

Superconducting Quantum Interferometers for Nondestructive Evaluation

Faley

Kostyurina

Kalashnikov

et al. 2017

Sensors

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“…(), and Schiffler et al . (). This section shortly recapitulates the key figures of the sensors employed with the system which affect the magnetic and spatial resolution at the end of processing.…”

Section: Setup Of the Measurement Star Systemmentioning

confidence: 97%

“…This approach was proposed by Schiffler et al . (). The observed strong motion noise signature of the magnetic field components is caused by the insufficient performance of the used inertial measurement unit.…”

Section: Application Of the Transforms To A Full Tensor Magnetic Gradmentioning

confidence: 97%

“…After scalar calibration (Schiffler et al . ) of these signals in the body frame, they are rotated by

{boldB}_{italicNED} = {({boldD}_{NED}^{b})}^{T} {boldB}_{b}

into the NED frame and interpolated with the mentioned Delaunay triangulation. The minimisation parameters p as defined by equation obtained during calibration along with the initial and final standard deviations of the term

B - B^{italicref}

are listed in Table .…”

Section: Application Of the Transforms To A Full Tensor Magnetic Gradmentioning

confidence: 99%

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Application of Hilbert‐like transforms for enhanced processing of full tensor magnetic gradient data

Schiffler

Queitsch

Stolz

et al. 2017

Geophysical Prospecting

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Commonly, geomagnetic prospection is performed via scalar magnetometers that measure values of the total magnetic intensity. Recent developments of superconducting quantum interference devices have led to their integration in full tensor magnetic gradiometry systems consisting of planar‐type first‐order gradiometers and magnetometers fabricated in thin‐film technology. With these systems measuring directly the magnetic gradient tensor and field vector, a significantly higher magnetic and spatial resolution of the magnetic maps is yield than those produced via conventional magnetometers. In order to preserve the high data quality in this work, we develop a workflow containing all the necessary steps for generating the gradient tensor and field vector quantities from the raw measurement data up to their integration into highresolution, lownoise, and artefactless two‐dimensional maps of the magnetic field vector. The gradient tensor components are processed by superposition of the balanced gradiometer signals and rotation into an Earth‐centred Earth‐fixed coordinate frame. As the magnetometers have sensitivity lower than that of gradiometers and the total magnetic intensity is not directly recorded, we employ Hilbert‐like transforms, e.g., integration of the gradient tensor components or the conversion of the total magnetic intensity derived by calibrated magnetometer readings to obtain these values. This can lead to a better interpretation of the measured magnetic anomalies of the Earth's magnetic field that is possible from scalar total magnetic intensity measurements. Our conclusions are drawn from the application of these algorithms on a survey acquired in South Africa containing full tensor magnetic gradiometry data.

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“…Yin constructed linear calibration method of magnetic gradient tensor system and rotated the system in three mutually perpendicular axes to calibrate the misalignment error and transformed outputs of different magnetometers into the platform frame-orthogonal coordinate [27]. M. Schiffler have implemented and compared two calibration including Olsen method and Merayo method about their stability of rapid data processing and integration into a full tensor magnetic gradiometry, SQUID-based, system [28].…”

Section: Review Of Calibration Techniquesmentioning

confidence: 99%

Two-Step Complete Calibration of Magnetic Vector Gradiometer Based on Functional Link Artificial Neural Network and Least Squares

Huang

2016

IEEE Sensors J.

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Magnetic vector gradiometers are frequently used for the detection of ferrous metals, the detection of unexploded ordinance (UXO) and defense applications. A magnetic vector gradiometer, which is under consideration in this paper, consists of two tri-axial magnetometers (TAMs). It requires a calibration procedure in order to take into account the errors in the TAM(tri-axial magnetometer) itself and the spatial misalignment of the magnetometers that will deteriorate the measurement precision of the gradiometer. This paper reports a two-step calibration algorithm of magnetic vector gradiometer based on functional link artificial neural network (FLANN) and least squares according to its response to an external magnetic vector field. The procedure and steps to identify the coefficients related to the measurement error are given. The calibration algorithm with good convergence proved by the numerical simulations decreased the relative error of magnetic vector gradient, in the case when the TAM is used in earth field, from 6.2340 down to 0.0187 and the parameters can be identified with the maximum inaccuracy of 5.35%. The efficiency of two-step calibration is also validated through experimental tests of two TAMs of type Mag03-MSB100 strapped on a nonmagnetic turntable. The calibrated coefficients are a good match with those specified by the manufacturer of the TAMs; the standard deviations of the increments of magnetic vector components in x, y and z directions decrease from 74.655nT, 90.617nT and 162.39nT before calibration down to 18.793nT, 20.095nT and 13.671nT after calibration, respectively.

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Calibration of SQUID vector magnetometers in full tensor gradiometry systems

Cited by 45 publications

References 19 publications

Superconducting Quantum Interferometers for Nondestructive Evaluation

Superconducting Quantum Interferometers for Nondestructive Evaluation

Application of Hilbert‐like transforms for enhanced processing of full tensor magnetic gradient data

Two-Step Complete Calibration of Magnetic Vector Gradiometer Based on Functional Link Artificial Neural Network and Least Squares

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