Separation of regional and residual magnetic field data

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.

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

Geophysics ◽

10.1190/geo2012-0225.1 ◽

2013 ◽

Vol 78 (3) ◽

pp. J25-J32 ◽

Cited By ~ 40

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.

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Magnetization clustering inversion — Part 1: Building an automated numerical optimization algorithm

Geophysics ◽

10.1190/geo2017-0844.1 ◽

2018 ◽

Vol 83 (5) ◽

pp. J61-J73 ◽

Cited By ~ 9

Author(s):

Jiajia Sun ◽

Yaoguo Li

Keyword(s):

Field Data ◽

Learning Algorithm ◽

Search Algorithm ◽

Magnetic Data ◽

Optimization Strategy ◽

Inversion Algorithm ◽

Data Set ◽

Regularized Inversion ◽

Automatic Search ◽

Parameter Values

Magnetic data are among the most widely used geoscientific data for studying the earth interior in the oil and mining industries. However, interpreting magnetic data has been traditionally challenged by the presence of remanence. Recently, a new inversion algorithm, called magnetization clustering inversion (MCI), was developed by combining the classical Tikhonov regularized inversion with fuzzy [Formula: see text]-means clustering, an unsupervised machine-learning algorithm. This method has proven to be an effective tool for interpreting magnetic data complicated by remanence through synthetic and field data tests. However, the MCI algorithm in previous work requires users to specify the values of the weighting parameters for both smoothness regularization and clustering terms in the objective function. In practice, this entails many iterations of trial and error, and it consequently hinders the effective use of this inversion algorithm for iterative hypothesis testing and timely decision making. We have developed an automated strategy for determining the two weighting parameter values. Our algorithm of automatic search for optimal weighting parameters is based on an understanding of their roles and the complex interplay between them during an inversion. Our search algorithm works by alternately searching for one weighting parameter, whereas the other is fixed. A series of synthetic examples confirms the effectiveness of this automated optimization strategy. We also applied the automated inversion algorithm to a field data set from the Carajás Mineral Province in Brazil. The recovered magnetic anomalous features are highly consistent with known geology.

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

Geophysics ◽

10.1190/geo2013-0087.1 ◽

2014 ◽

Vol 79 (1) ◽

pp. IM1-IM9 ◽

Cited By ~ 20

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.2670 ◽

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.

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A fast methodology for large-scale focusing inversion of gravity and magnetic data using the structured model matrix and the 2-D fast Fourier transform

Geophysical Journal International ◽

10.1093/gji/ggaa372 ◽

2020 ◽

Vol 223 (2) ◽

pp. 1378-1397

Author(s):

Rosemary A Renaut ◽

Jarom D Hogue ◽

Saeed Vatankhah ◽

Shuang Liu

Keyword(s):

Fourier Transform ◽

Fast Fourier Transform ◽

Linear Systems ◽

Large Scale ◽

Surface Measurement ◽

Magnetic Data ◽

Uniform Grid ◽

Data Sets ◽

Inversion Algorithm ◽

Data Set

SUMMARY We discuss the focusing inversion of potential field data for the recovery of sparse subsurface structures from surface measurement data on a uniform grid. For the uniform grid, the model sensitivity matrices have a block Toeplitz Toeplitz block structure for each block of columns related to a fixed depth layer of the subsurface. Then, all forward operations with the sensitivity matrix, or its transpose, are performed using the 2-D fast Fourier transform. Simulations are provided to show that the implementation of the focusing inversion algorithm using the fast Fourier transform is efficient, and that the algorithm can be realized on standard desktop computers with sufficient memory for storage of volumes up to size n ≈ 106. The linear systems of equations arising in the focusing inversion algorithm are solved using either Golub–Kahan bidiagonalization or randomized singular value decomposition algorithms. These two algorithms are contrasted for their efficiency when used to solve large-scale problems with respect to the sizes of the projected subspaces adopted for the solutions of the linear systems. The results confirm earlier studies that the randomized algorithms are to be preferred for the inversion of gravity data, and for data sets of size m it is sufficient to use projected spaces of size approximately m/8. For the inversion of magnetic data sets, we show that it is more efficient to use the Golub–Kahan bidiagonalization, and that it is again sufficient to use projected spaces of size approximately m/8. Simulations support the presented conclusions and are verified for the inversion of a magnetic data set obtained over the Wuskwatim Lake region in Manitoba, Canada.

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PEMETAAN SEBARAN BATUAN PENYUSUN PAGAR CANDI DI SITUS CANDI LOSARI DUSUN LOSARI, DESA SALAM, KECAMATAN SALAM, KABUPATEN MAGELANG BERDASARKAN METODE MAGNETIK

Berkala Arkeologi ◽

10.30883/jba.v33i1.10 ◽

2013 ◽

Vol 33 (1) ◽

pp. 121-131

Author(s):

Novi Dwi Ariani ◽

Thaqibul Fikri Niyartama ◽

Nugroho Budi Wibowo

Keyword(s):

Magnetic Field ◽

Field Data ◽

Daily Variation ◽

Local Magnetic Field ◽

Magnetic Data ◽

Reference Field ◽

Magnetic Pole ◽

Local Anomaly ◽

Anomaly Pattern ◽

Anomaly Map

Mapping geophysics research was conducted by geomagnetic method to know anomaly pattern of magnetic pole and to know distribution location and depth of temple gate composing stone in Losari Temple Site by using magnetic data. Data collection used Proton Precessions Magnetometer (PPM) G-856AX by area width of 88 km x 40 km and measurement space of 3 meter used looping method. Field data was corrected by daily variation and IGRF (International Geomagnetics Reference Field) correction and then reduction to pole. The slice modeling was conducted on local anomaly map on height of 6 meter. The result of the local magnetic field anomalies incision then interpolated to get an idea of the spread and depth of rocks making up the fence Losari temple. Local anomaly map shows that anomaly position lies in southwest, southeast, and northeast from main temple. Based from interpolated distribution of magnetic pole anomaly is dominated in depth of 2 meter to 4 meter.

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The Earth's magnetic field variations at the Algerian magnetic observatory of Tamanrasset from 1932 to 2020.

10.5194/egusphere-egu21-2446 ◽

2021 ◽

Author(s):

Lemgharbi Abdenaceur ◽

Hamoudi Mohamed ◽

Abtout Abdeslam ◽

Abdelhamid Bendekken ◽

Ener Aganou ◽

...

Keyword(s):

Magnetic Field ◽

International Association ◽

Sampling Rate ◽

Magnetic Data ◽

Magnetic Observatory ◽

Earth’S Magnetic Field ◽

Data Set ◽

Secular Variations ◽

The World ◽

Earth's Magnetic Field

In order to understand the spatial and temporal behavior of the Earth's magnetic field, scientists, following C.F. Gauss initiative in 1838 have established observatories around the world. More than 200 observatories aiming to continuously record, the time variations of the magnetic field vector and to maintain the best standard of the accuracy and resolution of the measurements.This study focused on the acquisition and analysis of the magnetic data provided by the Algerian magnetic observatory of Tamanrasset (labelled TAM by the International Association of Geomagnetism and Aeronomy). This observatory is located in southern Algeria at 5.53&#176;E longitude, 22.79&#176;N Latitude. Its altitude is 1373 meters above msl. TAM is continuously running since 1932, using old brand variometers, like Mascart and La Cour with photographic recording at the very beginning. Nowadays modern electronic equipment are used in the framework of INTERMAGNET project. Very large geomagnetic database collected over a century is available. We will describe the history and the various improvement of the methods and instrumentation.Preliminary analysis of time series of the observatory data allowed to distinguish two kinds of data: the first type, with low resolution, collected between 1932 and 1992. This data set comes from the annual, monthly, daily and hourly means. The second one with high resolution is represented by minutes and seconds sampling rate since 1993 when TAM was integrated to the world observatory network, INTERMAGNET. Part of the second dataset contains many gaps. We try to fill these gaps thanks to mathematical methods. Absolute measurements and repeat station data allow better accuracy in the secular variations and an improved regional model.Keywords: TAM observatory, temporal variation, terrestrial magnetic field, secular variations, INTERMAGNET.

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Fast 3D magnetic inversion of a surface relief in the space domain

Geophysics ◽

10.1190/geo2018-0712.1 ◽

2019 ◽

Vol 84 (5) ◽

pp. J57-J67 ◽

Cited By ~ 2

Author(s):

Marlon C. Hidalgo-Gato ◽

Valéria C. F. Barbosa

Keyword(s):

Hessian Matrix ◽

Computational Cost ◽

Magnetization Vector ◽

Forward Modeling ◽

Magnetic Data ◽

Total Field ◽

Inversion Algorithm ◽

Sensitivity Matrix ◽

Magnetic Inversion ◽

Magnetic Basement

We have developed a fast 3D regularized magnetic inversion algorithm for depth-to-basement estimation based on an efficient way to compute the total-field anomaly produced by an arbitrary interface separating nonmagnetic sediments from a magnetic basement. We approximate the basement layer by a grid of 3D vertical prisms juxtaposed in the horizontal directions, in which the prisms’ tops represent the depths to the magnetic basement. To compute the total-field anomaly produced by the basement relief, the 3D integral of the total-field anomaly of a prism is simplified by a 1D integral along the prism thickness, which in turn is multiplied by the horizontal area of the prism. The 1D integral is calculated numerically using the Gauss-Legendre quadrature produced by dipoles located along the vertical axis passing through the prism center. This new magnetic forward modeling overcomes one of the main drawbacks of the nonlinear inverse problem for estimating the basement depths from magnetic data: the intense computational cost to calculate the total-field anomaly of prisms. The new sensitivity matrix is simpler and computationally faster than the one using classic magnetic forward modeling based on the 3D integrals of a set of prisms that parameterize the earth’s subsurface. To speed up the inversion at each iteration, we used the Gauss-Newton approximation for the Hessian matrix keeping the main diagonal only and adding the first-order Tikhonov regularization function. The large sparseness of the Hessian matrix allows us to construct and solve a linear system iteratively that is faster and demands less memory than the classic nonlinear inversion with prism-based modeling using 3D integrals. We successfully inverted the total-field anomaly of a simulated smoothing basement relief with a constant magnetization vector. Tests on field data from a portion of the Pará-Maranhão Basin, Brazil, retrieved a first depth-to-basement estimate that was geologically plausible.

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A comparison of methods for approximating the vertical gradient of one‐dimensional magnetic field data

Geophysics ◽

10.1190/1.1442221 ◽

1986 ◽

Vol 51 (9) ◽

pp. 1725-1735 ◽

Cited By ~ 5

Author(s):

J. W. Paine

Keyword(s):

Magnetic Field ◽

Fourier Transform ◽

Field Data ◽

Total Error ◽

Vertical Gradient ◽

Magnetic Data ◽

Total Field ◽

One Dimensional ◽

Convolution Filter ◽

Principal Factors

The vertical gradient of a one‐dimensional magnetic field is known to be a useful aid in interpretation of magnetic data. When the vertical gradient is required but has not been measured, it is necessary to approximate the gradient using the available total‐field data. An approximation is possible because a relationship between the total field and the vertical gradient can be established using Fourier analysis. After reviewing the theoretical basis of this relationship, a number of methods for approximating the vertical gradient are derived. These methods fall into two broad categories: methods based on the discrete Fourier transform, and methods based on discrete convolution filters. There are a number of choices necessary in designing such methods, each of which will affect the accuracy of the computed values in differing, and sometimes conflicting, ways. A comparison of the spatial and spectral accuracy of the methods derived here shows that it is possible to construct a filter which maintains a reasonable balance between the various components of the total error. Further, the structure of this filter is such that it is also computationally more efficient than methods based on fast Fourier transform techniques. The spacing and width of the convolution filter are identified as the principal factors which influence the accuracy and efficiency of the method presented here, and recommendations are made on suitable choices for these parameters.

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