THE DIRECT APPROACH TO MAGNETIC INTERPRETATION AND ITS PRACTICAL APPLICATION

Geophysics ◽  
1949 ◽  
Vol 14 (3) ◽  
pp. 290-320 ◽  
Author(s):  
Leo J. Peters
Keyword(s):  
Field Data ◽  
Potential Problem ◽  
Scalar Potential ◽  
Direct Calculation ◽  
Magnetic Data ◽  
Practical Application ◽  
Magnetic Field Data ◽  
Magnetic Interpretation ◽  
Basement Rocks ◽  
Derivatives Of

This paper discusses the solution of the inverse potential problem and its practical application in the interpretation of field data which have a scalar potential distribution. The discussion will be in terms of the interpretation of magnetic data. Among the topics discussed are: the direct calculation of basement relief, the derivation of the potential and the horizontal components of the field from the vertical intensity, the continuation of the field upward, the continuation of the field downward towards its source, the calculation of derivatives of the vertical intensity with special attention to the second and fourth, and the estimation of depths to igneous basement rocks. The uses of these tools and the information of practical value which can be obtained by their use are discussed and illustrated. Methods of rapidly making calculations using magnetic field data are given.

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 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.

TWO‐DIMENSIONAL HARMONIC ANALYSIS AS A TOOL FOR MAGNETIC INTERPRETATION

Geophysics ◽  
1965 ◽  
Vol 30 (5) ◽  
pp. 829-857 ◽  
Author(s):  
B. K. Bhattacharyya
Keyword(s):  
Geomagnetic Field ◽  
Polarization Vector ◽  
Double Fourier Series ◽  
Fourier Series Expansion ◽  
Magnetic Data ◽  
Block Type ◽  
Total Field ◽  
Magnetic Pole ◽  
Magnetic Interpretation ◽  
Derivatives Of

The total magnetic field values over an area can be represented exactly by a double Fourier series expansion. In this analysis, such an expansion is used to evaluate very accurately the fields continued downward and upward from the plane of observation and the vertical derivatives of the total field. This harmonic expansion of the anomalous total field makes it possible to calculate, with exceptional accuracy, the field reduced to the magnetic pole and its second derivative. The results of the calculations are free from the effect of the inclination of the earth’s main geomagnetic field and that of the polarization vector, at all magnetic latitudes and for all possible directions of polarization. In order to determine the influence of remanence on the above field, a number of anomalies caused by rectangular block‐type bodies with known polarization are reduced to the magnetic pole, correcting only for the obliquity of the earth’s normal field. It is concluded from a study of these anomalies that the interpretation of magnetic data based on the assumption of rock magnetization due solely to induction in the earth’s field may yield erroneous results, particularly when remanence is important.

An analysis of 3-D analytic signal

Geophysics ◽  
1995 ◽  
Vol 60 (2) ◽  
pp. 531-536 ◽  
Author(s):  
N. L. Mohan ◽  
L. Anand Babu
Keyword(s):  
Magnetic Field ◽  
Andhra Pradesh ◽  
Field Data ◽  
Analytic Signal ◽  
Vertical Magnetic Field ◽  
Gridded Data ◽  
Magnetic Field Data ◽  
Derivatives Of ◽  
Analytic Signals ◽  
Godavari Basin

The mathematical and physical basis of defining the 3-D analytic function and the corresponding analytic signal is critically examined, and it is proved that 3-D analytic signals based on (1) scalar and (2) vector additions of the horizontal derivatives of the total magnetic fields are completely identical. Two sets of simulated gridded data are considered, and 3-D analytic signals are computed using both scalar and vector additions and are found to be identical. The equality of scalar and vector additions of 3-D analytic signals is further demonstrated with the help of gridded‐surface vertical magnetic field data from the Krishna‐Godavari Basin, Andhra Pradesh, India.

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.

Optimizing preventive maintenance over a finite planning horizon in a semi-Markov framework

2020 ◽  
Author(s):  
Antonio Sánchez Herguedas ◽  
Adolfo Crespo Márquez ◽  
Francisco Rodrigo Muñoz
Keyword(s):  
Preventive Maintenance ◽  
Recurrence Relations ◽  
Direct Calculation ◽  
Planning Horizon ◽  
Practical Implementation ◽  
Practical Application ◽  
Mathematical Solution ◽  
Accumulated Reward ◽  
Finite Planning Horizon

Abstract This paper describes the optimization of preventive maintenance (PM) over a finite planning horizon in a semi-Markov framework. In this framework, the asset may be operating, and providing income for the asset owner, or not operating and undergoing PM, or not operating and undergoing corrective maintenance following failure. PM is triggered when the asset has been operating for τ time units. A number m of transitions specifies the finite horizon. This system is described with a set of recurrence relations, and their z-transform is used to determine the value of τ that maximizes the average accumulated reward over the horizon. We study under what conditions a solution can be found, and for those specific cases the solution τ* is calculated. Despite the complexity of the mathematical solution, the result obtained allows the analyst to provide a quick and easy-to-use tool for practical application in many real-world cases. To demonstrate this, the method has been implemented for a case study, and its accuracy and practical implementation were tested using Monte Carlo simulation and direct calculation.

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.

Automatic 3-D interpretation of potential field data using analytic signal derivatives

Geophysics ◽  
1997 ◽  
Vol 62 (1) ◽  
pp. 87-96 ◽  
Author(s):  
Nicole Debeglia ◽  
Jacques Corpel
Keyword(s):  
Signal Amplitude ◽  
Vertical Gradient ◽  
Analytic Signal ◽  
Magnetic Data ◽  
Practical Implementation ◽  
Potential Field Data ◽  
Gravity And Magnetic ◽  
Second Derivatives ◽  
Gravity And Magnetic Data ◽  
Derivatives Of

A new method has been developed for the automatic and general interpretation of gravity and magnetic data. This technique, based on the analysis of 3-D analytic signal derivatives, involves as few assumptions as possible on the magnetization or density properties and on the geometry of the structures. It is therefore particularly well suited to preliminary interpretation and model initialization. Processing the derivatives of the analytic signal amplitude, instead of the original analytic signal amplitude, gives a more efficient separation of anomalies caused by close structures. Moreover, gravity and magnetic data can be taken into account by the same procedure merely through using the gravity vertical gradient. The main advantage of derivatives, however, is that any source geometry can be considered as the sum of only two types of model: contact and thin‐dike models. In a first step, depths are estimated using a double interpretation of the analytic signal amplitude function for these two basic models. Second, the most suitable solution is defined at each estimation location through analysis of the vertical and horizontal gradients. Practical implementation of the method involves accurate frequency‐domain algorithms for computing derivatives with an automatic control of noise effects by appropriate filtering and upward continuation operations. Tests on theoretical magnetic fields give good depth evaluations for derivative orders ranging from 0 to 3. For actual magnetic data with borehole controls, the first and second derivatives seem to provide the most satisfactory depth estimations.

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