The Mie representation for Mercury’s magnetic field

AbstractThe parameterization of the magnetospheric field contribution, generated by currents flowing in the magnetosphere is of major importance for the analysis of Mercury’s internal magnetic field. Using a combination of the Gauss and the Mie representation (toroidal–poloidal decomposition) for the parameterization of the magnetic field enables the analysis of magnetic field data measured in current carrying regions in the vicinity of Mercury. In view of the BepiColombo mission, the magnetic field resulting from the plasma interaction of Mercury with the solar wind is simulated with a hybrid simulation code and the internal Gauss coefficients for the dipole, quadrupole and octupole field are reconstructed from the data, evaluated along the prospective trajectories of the Mercury Planetary Orbiter (MPO) using Capon’s method. Especially, it turns out that a high-precision determination of Mercury’s octupole field is expectable from the future analysis of the magnetic field data measured by the magnetometer on board MPO. Furthermore, magnetic field data of the MESSENGER mission are analyzed and the reconstructed internal Gauss coefficients are in reasonable agreement with the results from more conventional methods such as the least-square fit.

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Using an induction coil sensor to indirectly measure the B-field response in the bandwidth of the transient electromagnetic method

Geophysics ◽

10.1190/1.1444837 ◽

2000 ◽

Vol 65 (5) ◽

pp. 1489-1494 ◽

Cited By ~ 34

Author(s):

Richard S. Smith ◽

A. Peter Annan

Keyword(s):

Magnetic Field ◽

Field Data ◽

Indirect Measurement ◽

Rate Of Change ◽

Induction Coil ◽

Voltage Response ◽

Magnetic Field Data ◽

Transient Electromagnetic ◽

Different Characteristics ◽

The Magnetic Field

The traditional sensor used in transient electromagnetic (EM) systems is an induction coil. This sensor measures a voltage response proportional to the time rate of change of the magnetic field in the EM bandwidth. By simply integrating the digitized output voltage from the induction coil, it is possible to obtain an indirect measurement of the magnetic field in the same bandwidth. The simple integration methodology is validated by showing that there is good agreement between synthetic voltage data integrated to a magnetic field and synthetic magnetic‐field data calculated directly. Further experimental work compares induction‐coil magnetic‐field data collected along a profile with data measured using a SQUID magnetometer. These two electromagnetic profiles look similar, and a comparison of the decay curves at a critical point on the profile shows that the two types of measurements agree within the bounds of experimental error. Comparison of measured voltage and magnetic‐field data show that the two sets of profiles have quite different characteristics. The magnetic‐field data is better for identifying, discriminating, and interpreting good conductors, while suppressing the less conductive targets. An induction coil is therefore a suitable sensor for the indirect collection of EM magnetic‐field data.

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Magnetic Fields in OH Maser Clouds

Symposium - International Astronomical Union ◽

10.1017/s0074180900026565 ◽

1974 ◽

Vol 60 ◽

pp. 275-292 ◽

Cited By ~ 1

Author(s):

R. D. Davies

Keyword(s):

Magnetic Field ◽

Angular Momentum ◽

Magnetic Fields ◽

Magnetic Flux ◽

Field Data ◽

Zeeman Splitting ◽

Galactic Rotation ◽

Magnetic Field Data ◽

The Magnetic Field ◽

Maser Sources

Observations of Class I OH maser sources show a range of features which are predicted on the basis of Zeeman splitting in a source magnetic field. Magnetic field strengths of 2 to 7 mG are derived for eight OH maser sources. The fields in all the clouds are directed in the sense of galactic rotation. A model of W3 OH is proposed which incorporates the magnetic field data. It is shown that no large amount of magnetic flux or angular momentum has been lost since the condensation from the interstellar medium began.

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Enhancing Performance of Magnetic Field Based Indoor Localization Using Magnetic Patterns from Multiple Smartphones

Sensors ◽

10.3390/s20092704 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2704 ◽

Cited By ~ 5

Author(s):

Imran Ashraf ◽

Soojung Hur ◽

Yongwan Park

Keyword(s):

Magnetic Field ◽

Geomagnetic Field ◽

Field Data ◽

Performance Comparison ◽

Magnetic Sensors ◽

Location Based Services ◽

Field Pattern ◽

Magnetic Field Data ◽

Localization Performance ◽

The Magnetic Field

Wide expansion of smartphones triggered a rapid demand for precise localization that can meet the requirements of location-based services. Although the global positioning system is widely used for outdoor positioning, it cannot provide the same accuracy for the indoor. As a result, many alternative indoor positioning technologies like Wi-Fi, Bluetooth Low Energy (BLE), and geomagnetic field localization have been investigated during the last few years. Today smartphones possess a rich variety of embedded sensors like accelerometer, gyroscope, and magnetometer that can facilitate estimating the current location of the user. Traditional geomagnetic field-based fingerprint localization, although it shows promising results, it is limited by the fact that various smartphones have embedded magnetic sensors from different manufacturers and the magnetic field strength that is measured from these smartphones vary significantly. Consequently, the localization performance from various smartphones is different even when the same localization approach is used. So devising an approach that can provide similar performance with various smartphones is a big challenge. Contrary to previous works that build the fingerprint database from the geomagnetic field data of a single smartphone, this study proposes using the geomagnetic field data collected from multiple smartphones to make the geomagnetic field pattern (MP) database. Many experiments are carried out to analyze the performance of the proposed approach with various smartphones. Additionally, a lightweight threshold technique is proposed that can detect user motion using the acceleration data. Results demonstrate that the localization performance for four different smartphones is almost identical when tested with the database made using the magnetic field data from multiple smartphones than that of which considers the magnetic field data from only one smartphone. Moreover, the performance comparison with previous research indicates that the overall performance of smartphones is improved.

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Solar flare forecasting model using 3D magnetic field data

10.5194/egusphere-egu2020-13298 ◽

2020 ◽

Author(s):

Xin Huang

Keyword(s):

Magnetic Field ◽

Deep Learning ◽

Solar Flare ◽

Solar Flares ◽

Field Data ◽

Active Regions ◽

Forecasting Model ◽

Magnetic Field Data ◽

The Magnetic Field

<p>Solar flares originate from the release of the energy stored in the magnetic field of solar active regions. Generally, the photospheric magnetograms of active regions are used as the input of the solar flare forecasting model. However, solar flares are considered to occur in the low corona. Therefore, the role of 3D magnetic field of active regions in the solar flare forecast should be explored. We extrapolate the 3D magnetic field using the potential model for all the active regions during 2010 to 2017, and then the deep learning method is applied to extract the precursors of solar flares in the 3D magnetic field data. We find that the 3D magnetic field of active regions is helpful to build a deep learning based forecasting model.</p>

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Identification of Complex Shear Modulus of MR Layer Placed in Three-Layer Beam – Part 2: Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.759.15 ◽

2015 ◽

Vol 759 ◽

pp. 15-25

Author(s):

Mateusz Romaszko ◽

Jacek Snamina ◽

Sebastian Pakuła

Keyword(s):

Magnetic Field ◽

Shear Modulus ◽

Field Data ◽

Free Vibrations ◽

Complex Shear Modulus ◽

Mr Fluid ◽

Magnetic Field Data ◽

Complex Modes ◽

Complex Shear ◽

The Magnetic Field

The paper presents the procedure of identification of a complex shear modulus which describes properties of MR fluid in the pre-yield regime as a function of magnetic field. Data necessary for identification were collected basing on measurements of free vibrations of a three-layered cantilever beam at a special laboratory stand. Magnetic field exerting on MR fluid placed in the beam was generated by electromagnet. In the next step, complex modes of beam vibrations for various places of applying the magnetic field and its strength were calculated.

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Interinstrument calibration using magnetic field data from the flux-gate magnetometer (FGM) and electron drift instrument (EDI) onboard Cluster

Geoscientific Instrumentation Methods and Data Systems ◽

10.5194/gi-3-1-2014 ◽

2014 ◽

Vol 3 (1) ◽

pp. 1-11 ◽

Cited By ~ 13

Author(s):

R. Nakamura ◽

F. Plaschke ◽

R. Teubenbacher ◽

L. Giner ◽

W. Baumjohann ◽

...

Keyword(s):

Magnetic Field ◽

Field Data ◽

Spin Axis ◽

Electron Drift ◽

Magnetic Field Data ◽

Field Magnitude ◽

Magnetic Field Magnitude ◽

Flux Gate ◽

Axis Offset ◽

The Magnetic Field

Abstract. We compare the magnetic field data obtained from the flux-gate magnetometer (FGM) and the magnetic field data deduced from the gyration time of electrons measured by the electron drift instrument (EDI) onboard Cluster to determine the spin-axis offset of the FGM measurements. Data are used from orbits with their apogees in the magnetotail, when the magnetic field magnitude was between about 20 and 500 nT. Offset determination with the EDI–FGM comparison method is of particular interest for these orbits, because no data from solar wind are available in such orbits to apply the usual calibration methods using the Alfvén waves. In this paper, we examine the effects of the different measurement conditions, such as direction of the magnetic field relative to the spin plane and field magnitude in determining the FGM spin-axis offset, and also take into account the time-of-flight offset of the EDI measurements. It is shown that the method works best when the magnetic field magnitude is less than about 128 nT and when the magnetic field is aligned near the spin-axis direction. A remaining spin-axis offset of about 0.4 ∼ 0.6 nT was observed for Cluster 1 between July and October 2003. Using multipoint multi-instrument measurements by Cluster we further demonstrate the importance of the accurate determination of the spin-axis offset when estimating the magnetic field gradient.

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3-D Modeling the Magnetic Field due to Ocean Tidal Flow O1

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.658.471 ◽

2013 ◽

Vol 658 ◽

pp. 471-474

Author(s):

Yan Xu ◽

Jun Xu ◽

Wei Hua Zhu ◽

Xia Feng ◽

Hai Yan Xie

Keyword(s):

Magnetic Field ◽

Field Data ◽

Tidal Flow ◽

Ocean Water ◽

Magnetic Field Data ◽

Tidal Motion ◽

The Earth ◽

Sea Bottom ◽

Tidal Constituents ◽

The Magnetic Field

The tidal motion of the ocean water through the ambient magnetic field, generates secondary electric and magnetic field. The magnetic fields generated by the diurnal (O1) ocean flow can be clearly detected. We simulate the magnetic signals for tidal constituents –diurnal (O1) tides. The idea of exploiting tidal signals for EM studies of the Earth is not new, but so far it was used only for interpretation of inland and transoceanic magnetic field data due to O1. Emphasis in this work is made on a discussion of sea bottom electric field of the same origin.

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Swarm Satellite Magnetic Field Data Analysis Prior to 2019 Mw = 7.1 Ridgecrest (California, USA) Earthquake

Geosciences ◽

10.3390/geosciences10120502 ◽

2020 ◽

Vol 10 (12) ◽

pp. 502

Author(s):

Dedalo Marchetti ◽

Angelo De Santis ◽

Saioa A. Campuzano ◽

Maurizio Soldani ◽

Alessandro Piscini ◽

...

Keyword(s):

Magnetic Field ◽

Field Data ◽

Geomagnetic Latitude ◽

Earthquake Forecasting ◽

Magnetic Data ◽

Epicentral Area ◽

Magnetic Field Data ◽

Preparation Phase ◽

Swarm Satellite ◽

The Magnetic Field

This work presents an analysis of the ESA Swarm satellite magnetic data preceding the Mw = 7.1 California Ridgecrest earthquake that occurred on 6 July 2019. In detail, we show the main results of a procedure that investigates the track-by-track residual of the magnetic field data acquired by the Swarm constellation from 1000 days before the event and inside the Dobrovolsky’s area. To exclude global geomagnetic perturbations, we select the data considering only quiet geomagnetic field time, defined by thresholds on Dst and ap geomagnetic indices, and we repeat the same analysis in two comparison areas at the same geomagnetic latitude of the Ridgecrest earthquake epicentre not affected by significant seismicity and in the same period here investigated. As the main result, we find some increases of the anomalies in the Y (East) component of the magnetic field starting from about 500 days before the earthquake. Comparing such anomalies with those in the validation areas, it seems that the geomagnetic activity over California from 222 to 168 days before the mainshock could be produced by the preparation phase of the seismic event. This anticipation time is compatible with the Rikitake empirical law, recently confirmed from Swarm satellite data. Furthermore, the Swarm Bravo satellite, i.e., that one at highest orbit, passed above the epicentral area 15 min before the earthquake and detected an anomaly mainly in the Y component. These analyses applied to the Ridgecrest earthquake not only intend to better understand the physical processes behind the preparation phase of the medium-large earthquakes in the world, but also demonstrate the usefulness of a satellite constellation to monitor the ionospheric activity and, in the future, to possibly make reliable earthquake forecasting.

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Corrigendum to "Substorm activity in Venus's magnetotail" published in Ann. Geophys., 27, 2321–2330, doi:10.5194/angeo-27-2321-2009, 2009

Annales Geophysicae ◽

10.5194/angeo-28-1877-2010 ◽

2010 ◽

Vol 28 (10) ◽

pp. 1877-1878 ◽

Cited By ~ 2

Author(s):

M. Volwerk ◽

M. Delva ◽

Y. Futaana ◽

A. Retinò ◽

Z. Vörös ◽

...

Keyword(s):

Magnetic Field ◽

Active Region ◽

Field Data ◽

Hall Current ◽

Magnetic Field Data ◽

Guide Field ◽

Substorm Activity ◽

Original Interpretation ◽

Current Signatures ◽

The Magnetic Field

Abstract. A re-evaluation of the reconnection event reported by Volwerk et al. (2009) shows that the original interpretation of the magnetic field data as quadrupolar Hall-current signatures around a reconnection site was mistaken. It could be interpreted as the signature of reconnection in the presence of a guide field. The path of VEX through the active region in Venus's magnetotail is re-evaluated and the strongly energized ions associated to this event are now in agreement with the magnetic field data.

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Inter-instrument calibration using magnetic field data from Flux Gate Magnetometer (FGM) and Electron Drift Instrument (EDI) onboard Cluster

Geoscientific Instrumentation Methods and Data Systems Discussions ◽

10.5194/gid-3-459-2013 ◽

2013 ◽

Vol 3 (2) ◽

pp. 459-487

Author(s):

R. Nakamura ◽

F. Plaschke ◽

R. Teubenbacher ◽

L. Giner ◽

W. Baumjohann ◽

...

Keyword(s):

Magnetic Field ◽

Field Data ◽

Spin Axis ◽

Electron Drift ◽

Magnetic Field Data ◽

Field Magnitude ◽

Magnetic Field Magnitude ◽

Flux Gate ◽

Axis Offset ◽

The Magnetic Field

Abstract. We compare the magnetic field data obtained from the Flux-Gate Magnetometer (FGM) and the magnetic field data deduced from the gyration time of electrons measured by the Electron Drift Instrument (EDI) onboard Cluster to determine the spin axis offset of the FGM measurements. Data are used from orbits with their apogees in the magnetotail, when the magnetic field magnitude was between about 20 nT and 500 nT. Offset determination with the EDI-FGM comparison method is of particular interest for these orbits, because no data from solar wind are available in such orbits to apply the usual calibration methods using the Alfvén waves. In this paper, we examine the effects of the different measurement conditions, such as direction of the magnetic field relative to the spin plane and field magnitude in determining the FGM spin-axis offset, and also take into account the time-of-flight offset of the EDI measurements. It is shown that the method works best when the magnetic field magnitude is less than about 128 nT and when the magnetic field is aligned near the spin-axis direction. A remaining spin-axis offset of about 0.4 ~ 0.6 nT was observed between July and October 2003. Using multi-point multi-instrument measurements by Cluster we further demonstrate the importance of the accurate determination of the spin-axis offset when estimating the magnetic field gradient.

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