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.

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>

Simultaneous 1D inversion of loop–loop electromagnetic data for magnetic susceptibility and electrical conductivity

Geophysics ◽  
2003 ◽  
Vol 68 (6) ◽  
pp. 1857-1869 ◽  
Author(s):  
Colin G. Farquharson ◽  
Douglas W. Oldenburg ◽  
Partha S. Routh
Keyword(s):  
Magnetic Susceptibility ◽  
Objective Function ◽  
Sum Of Squares ◽  
Magnetic Data ◽  
Data Sets ◽  
Low Frequencies ◽  
Simultaneous Inversion ◽  
Electromagnetic Data ◽  
Susceptibility Effects ◽  
Logarithmic Barrier

Magnetic susceptibility affects electromagnetic (EM) loop–loop observations in ways that cannot be replicated by conductive, nonsusceptible earth models. The most distinctive effects are negative in‐phase values at low frequencies. Inverting data contaminated by susceptibility effects for conductivity alone can give misleading models: the observations strongly influenced by susceptibility will be underfit, and those less strongly influenced will be overfit to compensate, leading to artifacts in the model. Simultaneous inversion for both conductivity and susceptibility enables reliable conductivity models to be constructed and can give useful information about the distribution of susceptibility in the earth. Such information complements that obtained from the inversion of static magnetic data because EM measurements are insensitive to remanent magnetization. We present an algorithm that simultaneously inverts susceptibility‐affected data for 1D conductivity and susceptibility models. The solution is obtained by minimizing an objective function comprised of a sum‐of‐squares measure of data misfit and sum‐of‐squares measures of the amounts of structure in the conductivity and susceptibility models. Positivity of the susceptibilities is enforced by including a logarithmic barrier term in the objective function. The trade‐off parameter is automatically estimated using the generalized cross validation (GCV) criterion. This enables an appropriate fit to the observations to be achieved even if good noise estimates are not available. As well as synthetic examples, we show the results of inverting airborne data sets from Australia and Heath Steele Stratmat, New Brunswick.

Mutagenic Potential of Direct Current Magnetic Fields.

1997 ◽  
Author(s):  
John W. Obringer ◽  
Tara E. Nolan ◽  
Brandon Horne ◽  
Brian Kelchner
Keyword(s):  
Magnetic Fields ◽  
Direct Current ◽  
Mutagenic Potential

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.

scholarly journals 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

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.

Inversion of airborne geophysics over the DO-27/DO-18 kimberlites — Part 1: Potential fields

2017 ◽  
Vol 5 (3) ◽  
pp. T299-T311 ◽  
Author(s):  
Sarah G. R. Devriese ◽  
Kristofer Davis ◽  
Douglas W. Oldenburg
Keyword(s):  
Magnetic Data ◽  
Gravity Gradiometry ◽  
3D Inversion ◽  
Potential Field Data ◽  
Electromagnetic Data ◽  
Magnetic Inversion ◽  
Geophysical Surveys ◽  
Airborne Geophysics ◽  
Joint Interpretation ◽  
Geologic Model

The Tli Kwi Cho (TKC) kimberlite complex contains two pipes, called DO-27 and DO-18, which were discovered during the Canadian diamond exploration rush in the 1990s. The complex has been used as a testbed for ground and airborne geophysics, and an abundance of data currently exist over the area. We have evaluated the historical and geologic background of the complex, the physical properties of interest for kimberlite exploration, and the geophysical surveys. We have carried out 3D inversion and joint interpretation of the potential field data. The magnetic data indicate high susceptibility at DO-18, and the magnetic inversion maps the horizontal extent of the pipe. DO-27 is more complicated. The northern part is highly magnetic and is contaminated with remanent magnetization; other parts of DO-27 have a low susceptibility. Low densities, obtained from the gravity and gravity gradiometry data, map the horizontal extents of DO-27 and DO-18. We combine the 3D density contrast and susceptibility models into a single geologic model that identifies three distinct kimberlite rock units that agree with drilling data. In further research, our density and magnetic susceptibility models are combined with information from electromagnetic data to provide a multigeophysical interpretation of the TKC kimberlite complex.

Insights into the behavioural responses of juvenile thornback ray Raja clavata to alternating and direct current magnetic fields

2021 ◽  
Author(s):  
Luana Albert ◽  
Frédéric Olivier ◽  
Aurélie Jolivet ◽  
Laurent Chauvaud ◽  
Sylvain Chauvaud
Keyword(s):  
Magnetic Fields ◽  
Direct Current ◽  
Behavioural Responses ◽  
Thornback Ray ◽  
Raja Clavata

Integrated Geophysical Interpretation of Kerio Valley Basin Stratigraphy, Kenya Rift

2016 ◽  
Author(s):  
Godfred Osukuku ◽  
Abiud Masinde ◽  
Bernard Adero ◽  
Edmond Wanjala ◽  
John Ego
Keyword(s):  
Gravity Data ◽  
Gravity Anomalies ◽  
Fault System ◽  
Magnetic Data ◽  
Data Sets ◽  
Seismic Profiles ◽  
The West ◽  
Seismic Reflection Data ◽  
Basement Rocks ◽  
Western Side

Abstract This research work attempts to map out the stratigraphic sequence of the Kerio Valley Basin using magnetic, gravity and seismic data sets. Regional gravity data consisting of isotactic, free-air and Bouguer anomaly grids were obtained from the International Gravity Bureau (BGI). Magnetic data sets were sourced from the Earth Magnetic Anomaly grid (EMAG2). The seismic reflection data was acquired in 1989 using a vibrating source shot into inline geophones. Gravity Isostacy data shows low gravity anomalies that depict a deeper basement. Magnetic tilt and seismic profiles show sediment thickness of 2.5-3.5 Km above the basement. The Kerio Valley Basin towards the western side is underlain by a deeper basement which are overlain by succession of sandstones/shales and volcanoes. At the very top are the mid Miocene phonolites (Uasin Gishu) underlain by mid Miocene sandstones/shales (Tambach Formation). There are high gravity anomalies in the western and southern parts of the basin with the sedimentation being constrained by two normal faults. The Kerio Valley Basin is bounded to the west by the North-South easterly dipping fault system. Gravity data was significantly of help in delineating the basement, scanning the lithosphere and the upper mantle according to the relative densities. The basement rocks as well as the upper cover of volcanoes have distinctively higher densities than the infilled sedimentary sections within the basin. From the seismic profiles, the frequency of the shaley rocks and compact sandstones increases with depths. The western side of the basin is characterized by the absence of reflections and relatively higher frequency content. The termination of reflectors and the westward dip of reflectors represent a fault (Elgeyo fault). The reflectors dip towards the west, marking the basin as an asymmetrical syncline, indicating that the extension was towards the east. The basin floor is characterized by a nearly vertical fault which runs parallel to the Elgeyo fault. The seismic reflectors show marked discontinuities which may be due to lava flows. The deepest reflector shows deep sedimentation in the basin and is in reasonable agreement with basement depths delineated from potential methods (gravity and magnetic). Basement rocks are deeper at the top of the uplift footwall of the Elgeyo Escarpment. The sediments are likely of a thickness of about 800 M which is an interbed of sandstones and shales above the basement.

1. What is geophysics?

2018 ◽  
pp. 1-5
Author(s):  
William Lowrie
Keyword(s):  
Magnetic Fields ◽  
Physical Properties ◽  
Complex Behaviour ◽  
Tectonic Plates ◽  
Natural Processes ◽  
Research Fields ◽  
The Earth ◽  
Deep Interior ◽  
Wide Range ◽  
Geophysical Investigations

Geophysics is a field of earth sciences that uses the methods of physics to investigate the complex physical properties of the Earth and the natural processes that have determined and continue to govern its evolution. ‘What is geophysics?’ explains how geophysical investigations cover a wide range of research fields—including planetary gravitational and magnetic fields and seismology—extending from surface changes that can be observed from Earth-orbiting satellites to complex behaviour in the Earth’s deep interior. The timescale of processes occurring in the Earth also has a very broad range, from earthquakes lasting a few seconds to the motions of tectonic plates that take place over tens of millions of years.

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