scholarly journals A Bike Built for Magnetic Mapping

Eos ◽  
2017 ◽  
Author(s):  
Uri Schattner
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Low Cost ◽  
Magnetic Sensor ◽  
Rough Terrain ◽  
Magnetic Field Data ◽  
Ground Magnetic

Mounting a magnetic sensor on a bicycle offers an efficient, low-cost method of collecting ground magnetic field data over rough terrain where conventional vehicles dare not venture.

scholarly journals Development and validation of inversion technique for substorm current wedge using ground magnetic field data

2014 ◽  
Vol 119 (3) ◽  
pp. 1909-1924 ◽  
Author(s):  
Xiangning Chu ◽  
Tung-Shin Hsu ◽  
Robert L. McPherron ◽  
Vassilis Angelopoulos ◽  
Zuyin Pu ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Substorm Current Wedge ◽  
Magnetic Field Data ◽  
Inversion Technique ◽  
Ground Magnetic ◽  
Development And Validation ◽  
Current Wedge

scholarly journals MagIO: Magnetic Field Strength Based Indoor- Outdoor Detection with a Commercial Smartphone

Micromachines ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 534 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Magnetic Sensor ◽  
Training Data ◽  
Magnetic Data ◽  
Magnetic Field Data ◽  
Smartphone Sensors ◽  
Indoor And Outdoor

A wide range of localization techniques has been proposed recently that leverage smartphone sensors. Context awareness serves as the backbone of these localization techniques, which helps them to shift the localization technologies to improve efficiency and energy utilization. Indoor-outdoor (IO) context sensing plays a vital role for such systems, which serve both indoor and outdoor localization. IO systems work with collaborative technologies including the Global Positioning System (GPS), cellular tower signals, Wi-Fi, Bluetooth and a variety of smartphone sensors. GPS- and Wi-Fi-based systems are power hungry, and their accuracy is severed by limiting factors like multipath, shadowing, etc. On the other hand, various built-in smartphone sensors can be deployed for environmental sensing. Although these sensors can play a crucial role, yet they are very less studied. This research aims at investigating the use of ambient magnetic field data alone from a smartphone for IO detection. The research first investigates the feasibility of utilizing magnetic field data alone for IO detection and then extracts different features suitable for IO detection to be used in machine learning-based classifiers to discriminate between indoor and outdoor environments. The experiments are performed at three different places including a subway station, a shopping mall and Yeungnam University (YU), Korea. The training data are collected from one spot of the campus, and testing is performed with data from various locations of the above-mentioned places. The experiment involves Samsung Galaxy S8, LG G6 and Samsung Galaxy Round smartphones. The results show that the magnetic data from smartphone magnetic sensor embody enough information and can discriminate the indoor environment from the outdoor environment. Naive Bayes (NB) outperforms with a classification accuracy of 83.26%, as against Support vector machines (SVM), random induction (RI), gradient boosting machines (GBM), random forest (RF), k-nearest neighbor (kNN) and decision trees (DT), whose accuracies are 67.21%, 73.38%, 73.40%, 78.59%, 69.53% and 68.60%, respectively. kNN, SVM and DT do not perform well when noisy data are used for classification. Additionally, other dynamic scenarios affect the attitude of magnetic data and degrade the performance of SVM, RI and GBM. NB and RF prove to be more noise tolerant and environment adaptable and perform very well in dynamic scenarios. Keeping in view the performance of these classifiers, an ensemble-based stacking scheme is presented, which utilizes DT and RI as the base learners and naive Bayes as the ensemble classifier. This approach is able to achieve an accuracy of 85.30% using the magnetic data of the smartphone magnetic sensor. Moreover, with an increase in training data, the accuracy of the stacking scheme can be elevated by 0.83%. The performance of the proposed approach is compared with GPS-, Wi-Fi- and light sensor-based IO detection.

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

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.

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

1998 ◽  
Vol 25 (19) ◽  
pp. 3721-3724 ◽  
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

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

1972 ◽  
Vol 77 (4) ◽  
pp. 604-610 ◽  
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

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

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1489-1494 ◽  
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.

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

1988 ◽  
Vol 40 (9) ◽  
pp. 1103-1127 ◽  
Author(s):  
R. A. LANGEL ◽  
J. R. RIDGWAY ◽  
M. SUGIURA ◽  
K. MAEZAWA
Keyword(s):  
Magnetic Field ◽  
Geomagnetic Field ◽  
Field Data ◽  
Magnetic Field Data
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