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.

Interpretation of aeromagnetic data using eigenvector analysis of pseudogravity gradient tensor

Geophysics ◽  
2011 ◽  
Vol 76 (3) ◽  
pp. L1-L10 ◽  
Author(s):  
Majid Beiki ◽  
Laust B. Pedersen ◽  
Hediyeh Nazi
Keyword(s):  
Magnetic Field ◽  
Least Squares ◽  
Field Data ◽  
Random Noise ◽  
Gravity Gradient ◽  
Aeromagnetic Data ◽  
Gradient Tensor ◽  
Magnetization Direction ◽  
Magnetic Field Data ◽  
Robust Least Squares

This study has shown that the same properties of the gravity gradient tensor are valid for the pseudogravity gradient tensor derived from magnetic field data, assuming that the magnetization direction is known. Eigenvectors of the pseudogravity gradient tensor are used to estimate depth to the center of mass of geologic bodies. The strike directions of 2D geological structures are estimated from the eigenvectors corresponding to the smallest eigenvalues. For a set of data points enclosed by a square window, a robust least-squares procedure is used to estimate the source point which has the smallest sum of squared distances to the lines passing through the measurement points and parallel to the eigenvectors corresponding to the maximum eigenvalues. The dimensionality of the pseudogravity field is defined from the dimensionality indicator I, derived from the tensor components. In the case of quasi-2D sources, a rectangular window is used in the robust least-squares procedure to reduce the uncertainty of estimations.Based on synthetic data sets, the method was tested on synthetic models and found to be robust to random noise in magnetic field data. The application of the method was also tested on a pseudogravity gradient tensor derived from total magnetic field data over the Särna area in west-central Sweden. Combined with Euler deconvolution, the method provides useful complementary information for interpretation of aeromagnetic data.

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.

scholarly journals GALS – Gradient Analysis by Least Squares

2008 ◽  
Vol 26 (11) ◽  
pp. 3491-3499 ◽  
Author(s):  
M. Hamrin ◽  
K. Rönnmark ◽  
N. Börlin ◽  
J. Vedin ◽  
A. Vaivads
Keyword(s):  
Magnetic Field ◽  
Least Squares ◽  
Gradient Analysis ◽  
Least Squares Method ◽  
Field Measurements ◽  
Characteristic Size ◽  
Magnetic Field Data ◽  
User Input ◽  
Magnetic Field Measurements ◽  
Synthetic Magnetic Field

Abstract. We present a method, GALS (Gradient Analysis by Least Squares) for estimating the gradient of a physical field from multi-spacecraft observations. To obtain the best possible spatial resolution, the gradient is estimated in the frame of reference where structures in the field are essentially locally stationary. The estimates are refined iteratively by a least squares method. We show that GALS is not very sensitive to the spacecraft configuration and resolves structures much smaller than the characteristic size of the spacecraft distribution. Furthermore, GALS requires little user input. GALS has been tested on synthetic magnetic field data and data from the Cluster FGM instrument. GALS will also be useful for other types of data. The results indicate that GALS is robust and superior to the curlometer method for estimating the current from magnetic field measurements.

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.

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.

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.

Adaptive sampling of potential-field data: A direct approach to compressive inversion

Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. IM1-IM9 ◽  
Author(s):  
Nathan Leon Foks ◽  
Richard Krahenbuhl ◽  
Yaoguo Li
Keyword(s):  
Potential Field ◽  
Adaptive Sampling ◽  
Field Data ◽  
Computational Cost ◽  
Large Field ◽  
Magnetic Data ◽  
Data Sampling ◽  
Data Types ◽  
Data Set ◽  
Potential Field Data

Compressive inversion uses computational algorithms that decrease the time and storage needs of a traditional inverse problem. Most compression approaches focus on the model domain, and very few, other than traditional downsampling focus on the data domain for potential-field applications. To further the compression in the data domain, a direct and practical approach to the adaptive downsampling of potential-field data for large inversion problems has been developed. The approach is formulated to significantly reduce the quantity of data in relatively smooth or quiet regions of the data set, while preserving the signal anomalies that contain the relevant target information. Two major benefits arise from this form of compressive inversion. First, because the approach compresses the problem in the data domain, it can be applied immediately without the addition of, or modification to, existing inversion software. Second, as most industry software use some form of model or sensitivity compression, the addition of this adaptive data sampling creates a complete compressive inversion methodology whereby the reduction of computational cost is achieved simultaneously in the model and data domains. We applied the method to a synthetic magnetic data set and two large field magnetic data sets; however, the method is also applicable to other data types. Our results showed that the relevant model information is maintained after inversion despite using 1%–5% of the data.

Computer Application in Geosciences: Imaging of the Subsurface by Structural Coupled Inversion of Potential Field Data

2014 ◽  
Vol 644-650 ◽  
pp. 2670-2673
Author(s):  
Jun Wang ◽  
Xiao Hong Meng ◽  
Fang Li ◽  
Jun Jie Zhou
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Gravity Data ◽  
Computer Application ◽  
Magnetic Data ◽  
Imaging Method ◽  
Inversion Algorithm ◽  
Constant Property ◽  
Near Surface ◽  
Potential Field Data

With the continuing growth in influence of near surface geophysics, the research of the subsurface structure is of great significance. Geophysical imaging is one of the efficient computer tools that can be applied. This paper utilize the inversion of potential field data to do the subsurface imaging. Here, gravity data and magnetic data are inverted together with structural coupled inversion algorithm. The subspace (model space) is divided into a set of rectangular cells by an orthogonal 2D mesh and assume a constant property (density and magnetic susceptibility) value within each cell. The inversion matrix equation is solved as an unconstrained optimization problem with conjugate gradient method (CG). This imaging method is applied to synthetic data for typical models of gravity and magnetic anomalies and is tested on field data.

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
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