The geomagnetic field at 1982 from DE-2 and other magnetic field data.

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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An Augmented Iteratively Re-weighted and Refined Least Squares Method for lp Minimization Problem in Inverse Modeling of Potential Field Magnetic Data

Journal of Geological Research ◽

10.30564/jgr.v1i3.1316 ◽

2020 ◽

Vol 1 (3) ◽

Author(s):

Maysam Abedi

Keyword(s):

Magnetic Field ◽

Magnetic Susceptibility ◽

Least Squares ◽

Minimization Problem ◽

Potential Field ◽

Field Data ◽

Least Squares Method ◽

Porphyry Copper ◽

Magnetic Data ◽

Magnetic Field Data

The presented work examines application of an Augmented Iteratively Re-weighted and Refined Least Squares method (AIRRLS) to construct a 3D magnetic susceptibility property from potential field magnetic anomalies. This algorithm replaces an lp minimization problem by a sequence of weighted linear systems in which the retrieved magnetic susceptibility model is successively converged to an optimum solution, while the regularization parameter is the stopping iteration numbers. To avoid the natural tendency of causative magnetic sources to concentrate at shallow depth, a prior depth weighting function is incorporated in the original formulation of the objective function. The speed of lp minimization problem is increased by inserting a pre-conditioner conjugate gradient method (PCCG) to solve the central system of equation in cases of large scale magnetic field data. It is assumed that there is no remanent magnetization since this study focuses on inversion of a geological structure with low magnetic susceptibility property. The method is applied on a multi-source noise-corrupted synthetic magnetic field data to demonstrate its suitability for 3D inversion, and then is applied to a real data pertaining to a geologically plausible porphyry copper unit. The real case study located in Semnan province of Iran consists of an arc-shaped porphyry andesite covered by sedimentary units which may have potential of mineral occurrences, especially porphyry copper. It is demonstrated that such structure extends down at depth, and consequently exploratory drilling is highly recommended for acquiring more pieces of information about its potential for ore-bearing mineralization.

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A reexamination of “interstellar ion waves” previously identified in Pioneer 10 magnetic field data

Geophysical Research Letters ◽

10.1029/98gl02802 ◽

1998 ◽

Vol 25 (19) ◽

pp. 3721-3724 ◽

Cited By ~ 3

Author(s):

Neil Murphy ◽

Edward J. Smith ◽

Joyce Wolf ◽

Devrie S. Intriligator

Keyword(s):

Magnetic Field ◽

Field Data ◽

Magnetic Field Data ◽

Ion Waves ◽

Pioneer 10

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Ogo 5 magnetic-field data near the Earth's bow shock: A correlation with theory

Journal of Geophysical Research Atmospheres ◽

10.1029/ja077i004p00604 ◽

1972 ◽

Vol 77 (4) ◽

pp. 604-610 ◽

Cited By ~ 19

Author(s):

J. K. Guha ◽

D. L. Judge ◽

J. H. Marburger

Keyword(s):

Magnetic Field ◽

Field Data ◽

Bow Shock ◽

Magnetic Field Data ◽

Earth’S Bow Shock

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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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