Mitigating remanent magnetization effects in magnetic data using the normalized source strength

Geophysics ◽  
2013 ◽  
Vol 78 (3) ◽  
pp. J25-J32 ◽  
Author(s):  
Mark Pilkington ◽  
Majid Beiki
Keyword(s):  
Magnetic Field ◽  
Remanent Magnetization ◽  
Field Data ◽  
Magnetic Data ◽  
Magnetization Distribution ◽  
Source Strength ◽  
Inversion Algorithm ◽  
Data Set ◽  
Magnetic Field Data ◽  
The Magnetic Field

We have developed an approach for the interpretation of magnetic field data that can be used when measured anomalies are affected by significant remanent magnetization components. The method deals with remanent effects by using the normalized source strength (NSS), a quantity calculated from the eigenvectors of the magnetic gradient tensor. The NSS is minimally affected by the direction of remanent magnetization present and compares well with other transformations of the magnetic field that are used for the same purpose. It therefore offers a way of inverting magnetic data containing the effects of remanent magnetizations, particularly when these are unknown and are possibly varying within a given data set. We use a standard 3D inversion algorithm to invert NSS data from an area where varying remanence directions are apparent, resulting in a more reliable image of the subsurface magnetization distribution than possible using the observed magnetic field data directly.

Automatic boundary extraction from magnetic field data using triangular meshes

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. J47-J60 ◽  
Author(s):  
Nathan Leon Foks ◽  
Yaoguo Li
Keyword(s):  
Magnetic Field ◽  
Synthetic Data ◽  
Magnetic Data ◽  
Lake Area ◽  
Boundary Extraction ◽  
Data Set ◽  
Magnetic Field Data ◽  
Line Segments ◽  
Extraction Algorithm ◽  
The Magnetic Field

Boundary extraction is a collective term that we use for the process of extracting the locations of faults, lineaments, and lateral boundaries between geologic units using geophysical observations, such as measurements of the magnetic field. The process typically begins with a preprocessing stage, where the data are transformed to enhance the visual clarity of pertinent features and hence improve the interpretability of the data. The majority of the existing methods are based on raster grid enhancement techniques, and the boundaries are extracted as a series of points or line segments. In contrast, we set out a methodology for boundary extraction from magnetic data, in which we represent the transformed data as a surface in 3D using a mesh of triangular facets. After initializing the mesh, we modify the node locations, such that the mesh smoothly represents the transformed data and that facet edges are aligned with features in the data that approximate the horizontal locations of subsurface boundaries. To illustrate our boundary extraction algorithm, we first apply it to a synthetic data set. We then apply it to identify boundaries in a magnetic data set from the McFaulds Lake area in Ontario, Canada. The extracted boundaries are in agreement with known boundaries and several of the regions that are completely enclosed by extracted boundaries coincide with regions of known mineralization.

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

Geosciences ◽  
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.

Separation of regional and residual magnetic field data

Geophysics ◽  
1998 ◽  
Vol 63 (2) ◽  
pp. 431-439 ◽  
Author(s):  
Yaoguo Li ◽  
Douglas W. Oldenburg
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Power Spectra ◽  
Magnetic Data ◽  
Inversion Algorithm ◽  
Large Area ◽  
Data Set ◽  
Regional Sources ◽  
Magnetic Inversion ◽  
Susceptibility Model

We present a method for separating regional and residual magnetic fields using a 3-D magnetic inversion algorithm. The separation is achieved by inverting the observed magnetic data from a large area to construct a regional susceptibility distribution. The magnetic field produced by the regional susceptibility model is then used as the regional field, and the residual data are obtained by simple subtraction. The advantages of this method of separation are that it introduces little distortion to the shape of the extracted anomaly and that it is not affected significantly by factors such as topography and the overlap of power spectra of regional and residual fields. The proposed method is tested using a synthetic example having varying relative positions between the local and regional sources and then using a field data set from Australia. Results show that the residual field extracted using this method enables good recovery of target susceptibility distribution from inversions.

scholarly journals Characteristics of the Taylor microscale in the solar wind/foreshock: magnetic field and electron velocity measurements

2013 ◽  
Vol 31 (11) ◽  
pp. 2063-2075 ◽  
Author(s):  
C. Gurgiolo ◽  
M. L. Goldstein ◽  
W. H. Matthaeus ◽  
A. Viñas ◽  
A. N. Fazakerley
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Fluid Velocity ◽  
Electron Velocity ◽  
Data Sets ◽  
Velocity Data ◽  
Data Set ◽  
Magnetic Field Data ◽  
Spacecraft Data ◽  
The Magnetic Field

Abstract. The Taylor microscale is one of the fundamental turbulence scales. Not easily estimated in the interplanetary medium employing single spacecraft data, it has generally been studied through two point correlations. In this paper we present an alternative, albeit mathematically equivalent, method for estimating the Taylor microscale (λT). We make two independent determinations employing multi-spacecraft data sets from the Cluster mission, one using magnetic field data and a second using electron velocity data. Our results using the magnetic field data set yields a scale length of 1538 ± 550 km, slightly less than, but within the same range as, values found in previous magnetic-field-based studies. During time periods where both magnetic field and electron velocity data can be used, the two values can be compared. Relative comparisons show λT computed from the velocity is often significantly smaller than that from the magnetic field data. Due to a lack of events where both measurements are available, the absolute λT based on the electron fluid velocity is not able to be determined.

scholarly journals Estimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networks

2020 ◽  
Vol 9 (4) ◽  
pp. 226
Author(s):  
Carlos E. Galván-Tejada ◽  
Laura A. Zanella-Calzada ◽  
Antonio García-Domínguez ◽  
Rafael Magallanes-Quintanar ◽  
Huizilopoztli Luna-García ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Area Under The Curve ◽  
Training Data ◽  
Spectral Evolution ◽  
Data Set ◽  
Magnetic Field Data ◽  
Indoor Location ◽  
Location Data ◽  
The Magnetic Field

Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (i.e., wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set.

scholarly journals KELURUSAN ANOMALI MAGNET BENDA X DI DAERAH Y DARI HASIL REDUKSI KE KUTUB

2016 ◽  
Vol 6 (2) ◽  
Author(s):  
Ketut Gede Aryawan ◽  
Subarsyah Subarsyah
Keyword(s):  
Magnetic Field ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Magnetic Anomaly ◽  
Field Data ◽  
Transformation Process ◽  
Gas Pipeline ◽  
Magnetic Data ◽  
Magnetic Field Data ◽  
The Magnetic Field

Kita mengalami kesulitan untuk mendeteksi anomali secara langsung dari data medan magnet karena mempunyai polaritas positif dan negatif. Untuk itu diperlukan teknik pemrosesan data magnet untuk memperoleh delineasi pipa yang lebih baik. Pada kasus delineasi pipa gas di laut daerah X, diterapkan teknik reduksi ke kutub (RTP) untuk mengolah data magnet total. Fast Fourier Transform (FFT) diterapkan pada proses transformasi RTP dalam 2-dimensi dan 3-dimensi menggunakan perangkat lunak Matlab dan Magpick. Hasilnya menunjukkan arah dari pipa utara-selatan dan memperlihatkan posisi dari pipa semakin jelas yang diperkirakan tepat berada di bawah puncak kurva anomali. Kata kunci: anomali magnet total, delineasi, reduksi ke kutub, transformasi fourier, klosur. We have the problem to detect anomaly directly from the magnetic field data because it have two polarities, positive and negative. We need a technique of data processing to detect magnetic anomaly better. In the case of gas pipeline delineation in X-area, Reduce to Pole (RTP) technique was applied to process total magnetic data. Fast Fourier Transform (FFT) was applied on RTP transformation process in 2-Dimension and 3-Dimension using Matlab and Magpick softwares. The result indicate that the gas pipeline is north-south direction and the position is under the peak of anomaly curve. Keywords: total magnetic anomaly, delineation, reduce to pole, fast fourier transform, closur.

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.

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 Magnetic Fields in OH Maser Clouds

1974 ◽  
Vol 60 ◽  
pp. 275-292 ◽  
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.

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

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2704 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document