Global and local high-resolution magnetic field inversion using spherical harmonic models of individual sources

2020 ◽  
Author(s):  
Eldar Baykiev ◽  
Jörg Ebbing
Keyword(s):  
High Resolution ◽  
Spherical Harmonic ◽  
Magnetic Data ◽  
Data Sets ◽  
Projected Gradient Method ◽  
Magnetic Inversion ◽  
Starting Point ◽  
Synthetic Test ◽  
Susceptibility Model ◽  
Global And Local

<p>Inverting satellite and airborne magnetic data with a common model is challenging due to the spectral gap between the data sets, but needed to provide meaningful models of lithospheric magnetisation.</p><p>Here, we present a step-wise approach, where first spherical prisms (tesseroids) are used for global magnetic inversion of satellite-acquired lithospheric field models and second airborne data re inverted in their suitable spectral range for added details. For the synthetic test, the susceptibility model of Hemant (2003) was used as a starting point to calculate the spherical harmonic model of each tesseroid in the model. The resulting spherical harmonic coefficients were inverted for magnetic susceptibility in the global model, where the geometry is based on seismic or gravity observations. The projected gradient method is used to avoid negative susceptibilities in the result. After the global inversion, high-resolution local tile-wise inversion together with synthetic airborne data within a different wavelength range is performed for even higher resolution results.</p><p>The approach is applied to the Swarm-derived LCS-1 field model and for selected areas with high-resolution aeromagnetic coverage.</p>

scholarly journals Global High-Resolution Magnetic Field Inversion Using Spherical Harmonic Representation of Tesseroids as Individual Sources

Geosciences ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 147 ◽  
Author(s):  
Eldar Baykiev ◽  
Dilixiati Yixiati ◽  
Jörg Ebbing
Keyword(s):  
Magnetic Field ◽  
High Resolution ◽  
Spectral Range ◽  
Gradient Method ◽  
Spherical Harmonic ◽  
Projected Gradient Method ◽  
Projected Gradient ◽  
Magnetic Inversion ◽  
Harmonic Representation ◽  
The Magnetic Field

In this study, we present a novel approach combining the advantages of tesseroids in representing geophysical structures though their voxel-like discretization features with a spherical harmonic representation of the magnetic field. Modelling of the Earth lithospheric magnetic field is challenging since part of the spectra is hidden by the core field and the forward modeled field of a lithospheric magnetization is always biased by the spectral range used. In our approach, a spherical harmonic representation of the magnetic field of spherical prisms (tesseroids) is used for high-resolution magnetic inversion of lithospheric field models. The use of filtered spherical harmonic models of the magnetic field of each tesseroid ensures that the resulting field matches the spectral range of the input data. For the inversion, we use the projected gradient method. The projected gradient method easily allows us to assign an initial guess (i.e., a-priori assumption) for the inversion and avoids negative values of susceptibilities. The latter is providing more plausible models since induced magnetization is assumed to be dominant over the continents and, for the oceans, a remanence model can be subtracted. We show an application of the technique to a synthetic dataset and a satellite-derived lithospheric field model where the model geometry is based on seismic information. We also demonstrate a proof-of-concept for high-resolution tile-wise inversion for the Bangui anomaly in Africa.

scholarly journals Exploring nonlinear inversions: A 1D magnetotelluric example

2017 ◽  
Vol 36 (8) ◽  
pp. 696-699 ◽  
Author(s):  
Seogi Kang ◽  
Lindsey J. Heagy ◽  
Rowan Cockett ◽  
Douglas W. Oldenburg
Keyword(s):  
Magnetic Fields ◽  
Physical Properties ◽  
Direct Current ◽  
Magnetic Data ◽  
Data Sets ◽  
Electromagnetic Data ◽  
Problem Finding ◽  
Deconvolution Problem ◽  
Susceptibility Model ◽  
Multicomponent Data

At some point in many geophysical workflows, an inversion is a necessary step for answering the geoscientific question at hand, whether it is recovering a reflectivity series from a seismic trace in a deconvolution problem, finding a susceptibility model from magnetic data, or recovering conductivity from an electromagnetic survey. This is particularly true when working with data sets where it may not even be clear how to plot the data: 3D direct current resistivity and induced polarization surveys (it is not necessarily clear how to organize data into a pseudosection) or multicomponent data, such as electromagnetic data (we can measure three spatial components of electric and/or magnetic fields through time over a range of frequencies). Inversion is a tool for translating these data into a model we can interpret. The goal of the inversion is to find a “model” — some description of the earth's physical properties — that is consistent with both the data and geologic knowledge.

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 Probing 3-d Electrical Conductivity of the Mantle Using 6 Years of Swarm, Cryosat-2 and Observatory Magnetic Data and Exploiting Matrix Q-responses Approach

2020 ◽  
Author(s):  
Alexey Kuvshinov ◽  
Alexander Grayver ◽  
Lars Toffner-Clausen ◽  
Nils Olsen
Keyword(s):  
Electrical Conductivity ◽  
Spherical Harmonic ◽  
Geomagnetic Field ◽  
Input Data ◽  
Transfer Functions ◽  
Three Dimensional ◽  
Magnetic Potential ◽  
Magnetic Data ◽  
Joint Analysis ◽  
Data Sets

Abstract This study presents results of mapping three-dimensional (3-D) variations of the electrical conductivity in a depths range from 400 to 1200 km using six years of magnetic data from the Swarm and CryoSat-2 satellites as well as from ground observatories. The approach involves the 3-D inversion of matrix Q-responses (transfer functions) that relate spherical harmonic coefficients of external (inducing) and internal (induced) origin of the magnetic potential. Transfer functions were estimated from geomagnetic field variations at periods ranging from 2 to 40 days. We study the effect of different combinations of input data sets on the transfer functions. We also present a new global 1-D conductivity prole based on a joint analysis of satellite tidal signals and global magnetospheric Q-responses.

Joint inversion of surface and borehole magnetic data: A level-set approach

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. J15-J32 ◽  
Author(s):  
Wenbin Li ◽  
Jianliang Qian ◽  
Yaoguo Li
Keyword(s):  
Level Set ◽  
Joint Inversion ◽  
Complex Structure ◽  
Effective Means ◽  
Spatial Separation ◽  
Depth Resolution ◽  
Magnetic Data ◽  
Magnetic Inversion ◽  
Susceptibility Model ◽  
Level Set Approach

The need to improve the depth resolution of the magnetic susceptibility model recovered from surface magnetic data is a well-known challenge, and it becomes increasingly important as exploration moves to regions under cover and at great depths. Incorporating borehole magnetic data can be an effective means to achieve increased model resolution at depth. The recently developed level-set method for magnetic inversion provides a novel means for constructing the geometric shape of causative bodies and opens a new avenue for the joint inversion of surface and borehole magnetic data for the purpose of achieving improved depth resolution. We have developed an extension of the algorithm to the joint inversion and find that the level-set algorithm can resolve the configuration and spatial separation of complex magnetic sources using the information in the magnetic data from sparse boreholes. We further examine the use of borehole intersection information in estimating the crucially important susceptibility values within the context of level-set inversion and find that the susceptibility value can also be used as a probing parameter to assess the uncertainty in the spatial extent of the causative bodies. We determine that the modified level-set inversion leads to an effective means to image and delineate magnetic causative bodies with complex structure by combining the information from surface magnetic data, borehole magnetic data, and sparse drillhole intersection data.

scholarly journals Probing 3-D electrical conductivity of the mantle using 6 years of Swarm, CryoSat-2 and observatory magnetic data and exploiting matrix Q-responses approach

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Alexey Kuvshinov ◽  
Alexander Grayver ◽  
Lars Tøffner-Clausen ◽  
Nils Olsen
Keyword(s):  
Electrical Conductivity ◽  
Spherical Harmonic ◽  
Geomagnetic Field ◽  
Input Data ◽  
Transfer Functions ◽  
Three Dimensional ◽  
Magnetic Data ◽  
Joint Analysis ◽  
Data Sets ◽  
Conductivity Profile

AbstractThis study presents results of mapping three-dimensional (3-D) variations of the electrical conductivity in depths ranging from 400 to 1200 km using 6 years of magnetic data from the Swarm and CryoSat-2 satellites as well as from ground observatories. The approach involves the 3-D inversion of matrix Q-responses (transfer functions) that relate spherical harmonic coefficients of external (inducing) and internal (induced) origin of the magnetic potential. Transfer functions were estimated from geomagnetic field variations at periods ranging from 2 to 40 days. We study the effect of different combinations of input data sets on the transfer functions. We also present a new global 1-D conductivity profile based on a joint analysis of satellite tidal signals and global magnetospheric Q-responses.

High-Resolution Seismic-Reflection and Marine Magnetic Data Along the Hosgri Fault Zone, Central California

2009 ◽  
Author(s):  
Ray W. Sliter ◽  
Peter J. Triezenberg ◽  
Patrick E. Hart ◽  
Janet T. Watt ◽  
Samuel Y. Johnson ◽  
...  
Keyword(s):  
High Resolution ◽  
Fault Zone ◽  
Seismic Reflection ◽  
Magnetic Data ◽  
Central California

Airborne geophysical surveys in Greenland in 1998

1999 ◽  
pp. 34-38 ◽  
Author(s):  
Thorkild M. Rasmussen ◽  
Leif Thorning
Keyword(s):  
High Resolution ◽  
Geophysical Data ◽  
Digital Data ◽  
Geological Survey ◽  
Magnetic Data ◽  
Magnetic Survey ◽  
Original Series ◽  
Geophysical Surveys ◽  
The Government

NOTE: This article was published in a former series of GEUS Bulletin. Please use the original series name when citing this article, for example: Rasmussen, T. M., & Thorning, L. (1999). Airborne geophysical surveys in Greenland in 1998. Geology of Greenland Survey Bulletin, 183, 34-38. https://doi.org/10.34194/ggub.v183.5202 _______________ Airborne geophysical surveying in Greenland during 1998 consisted of a magnetic project referred to as ‘Aeromag 1998’ and a combined electromagnetic and magnetic project referred to as ‘AEM Greenland 1998’. The Government of Greenland financed both with administration managed by the Geological Survey of Denmark and Greenland (GEUS). With the completion of the two projects, approximately 305 000 line km of regional high-resolution magnetic data and approximately 75 000 line km of detailed multiparameter data (electromagnetic, magnetic and partly radiometric) are now available from government financed projects. Figure 1 shows the location of the surveyed areas with highresolution geophysical data together with the area selected for a magnetic survey in 1999. Completion of the two projects was marked by the release of data on 1 March, 1999. The data are included in the geoscientific databases at the Survey for public use; digital data and maps may be purchased from the Survey.

scholarly journals A Review of Geophysical Modeling Based on Particle Swarm Optimization

2021 ◽  
Author(s):  
Francesca Pace ◽  
Alessandro Santilano ◽  
Alberto Godio
Keyword(s):  
Particle Swarm Optimization ◽  
Inverse Modeling ◽  
Critical Evaluation ◽  
Particle Swarm ◽  
Geophysical Data ◽  
Magnetic Data ◽  
Data Sets ◽  
Swarm Optimization ◽  
Geophysical Modeling ◽  
Original Works

AbstractThis paper reviews the application of the algorithm particle swarm optimization (PSO) to perform stochastic inverse modeling of geophysical data. The main features of PSO are summarized, and the most important contributions in several geophysical fields are analyzed. The aim is to indicate the fundamental steps of the evolution of PSO methodologies that have been adopted to model the Earth’s subsurface and then to undertake a critical evaluation of their benefits and limitations. Original works have been selected from the existing geophysical literature to illustrate successful PSO applied to the interpretation of electromagnetic (magnetotelluric and time-domain) data, gravimetric and magnetic data, self-potential, direct current and seismic data. These case studies are critically described and compared. In addition, joint optimization of multiple geophysical data sets by means of multi-objective PSO is presented to highlight the advantage of using a single solver that deploys Pareto optimality to handle different data sets without conflicting solutions. Finally, we propose best practices for the implementation of a customized algorithm from scratch to perform stochastic inverse modeling of any kind of geophysical data sets for the benefit of PSO practitioners or inexperienced researchers.

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