3D Magnetization Vector Inversion of Magnetic Data: Improving and Comparing Methods

Liu, Shuang; Hu, Xiangyun; Zhang, Henglei; Geng, Meixia

doi:10.1007/s00024-017-1654-3

Cited by 51 publications

(25 citation statements)

References 25 publications

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“…These include 2-D approaches assuming a constant magnetization of a layer and solving for the thickness of the layer (Parker, 1972) or assuming a constant layer thickness and solving for magnetization (Parker & Huestis, 1974). Three-dimensional approaches include solving for the full 3-D distribution of magnetization (either susceptibility or magnetization; Li & Oldenburg, 1996;Lelièvre & Oldenburg, 2009;Liu et al, 2017). Volcanic geology is very three-dimensional, and realistic slope stability models require detailed 3-D information on altered rock volumes (Ball et al, 2018); hence, 3-D inversion of magnetic data is required to achieve this.…”

Section: Magnetic Modelingmentioning

confidence: 99%

“…To address possible issues with remanence, we use a three-dimensional MVI implemented in the open source package SimPEG (Cockett et al, 2015), to invert our TMI data. Our MVI algorithm (see Fournier, 2019;Fournier & Oldenburg, 2019, for full details) simultaneously solves for both the direction and amplitude of magnetization building on the approaches described by Lelièvre and Oldenburg (2009) and Liu et al (2017). In the inversion the magnetization amplitude, a, is recast as an apparent susceptibility parameter, ⃗, by dividing the magnetization components by the amplitude of the Earth's field H o .…”

Section: Magnetic Modelingmentioning

confidence: 99%

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Three‐Dimensional Mapping of Mt. Ruapehu Volcano, New Zealand, From Aeromagnetic Data Inversion and Hyperspectral Imaging

et al. 2020

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Combining 3‐D inversion of high‐resolution aeromagnetic data with airborne hyperspectral imaging creates a new method to map buried structure and hydrothermal alteration, applied to Mt. Ruapehu volcano, New Zealand. Hyperspectral imaging is sensitive to surface mineralogy including alteration minerals, while magnetic vector inversion reveals the volumetric distribution of magnetic susceptibility from which we interpret buried geology. Probability assessment from multiple model regularizations provides an important model uncertainty estimate. At Ruapehu, hyperspectral imaging highlights two main regions of surface alteration: the Pinnacle Ridge and the southeast flanks. The magnetic model of Pinnacle Ridge shows that alteration seen at surface continues to depth, but strongly magnetic, unaltered dikes form the core of the ridge. On the southeast flanks, the magnetic model also shows alteration imaged on the surface continues to depth; however, a previously unknown, magnetized sill intrudes part of the flank. Several smaller demagnetized regions are modeled, unlike at neighboring Mt. Tongariro where the hydrothermal system created a large demagnetized core. We propose that these differences relate to spatially focused (Ruapehu) vs distributed (Tongariro) eruption vents, the degree of faulting of the edifice and its glaciation history. Lava‐ice interaction produces fine‐grained lavas with measured magnetic susceptibilities similar to some moderately altered lavas, illustrating that care must be taken in the interpretation of magnetic data in the absence of geological information. The combination of hyperspectral imaging and aeromagnetic data inversion distinguishes shallow surface weathering from deeper‐seated hydrothermally altered rock masses, with implications for the magnitude and probability of collapse events.

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Section: Magnetic Modelingmentioning

confidence: 99%

Section: Magnetic Modelingmentioning

confidence: 99%

Three‐Dimensional Mapping of Mt. Ruapehu Volcano, New Zealand, From Aeromagnetic Data Inversion and Hyperspectral Imaging

et al. 2020

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“…These techniques face great challenges when magnetic data are collected at low magnetic latitudes because RTP and component conversion in wavenumber domain are known to be unstable near the magnetic equator (Blakely, 1996; Pedersen, 1978). Researchers have also developed inversion‐based methods (Coleman, 2014; Ellis et al, 2012; Foss & McKenzie, 2011; Fournier, 2015; Fournier et al, 2020; Fullagar & Pears, 2015; Lelièvre & Oldenburg, 2009; Li & Sun, 2016; S. Liu et al, 2017; Pratt et al, 2012; Sun & Li, 2018, 2019) to estimate the magnetization directions. These methods all have their pros and cons and typically involve 3‐D inversions that are either procedurally or computationally complex.…”

Section: Introductionmentioning

confidence: 99%

Predicting Magnetization Directions Using Convolutional Neural Networks

Nurindrawati

Sun

2020

JGR Solid Earth

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Magnetic data have been widely used for understanding basin structures, mineral deposit systems, the formation history of various geological systems, and many others. Proper interpretation of magnetic data requires accurate knowledge of total magnetization directions of the source bodies in an area of study. Existing approaches for estimating magnetization directions involve either unstable data processing steps or computationally intensive processes such as 3-D inversions. In this study, we developed a new method of automatically predicting the magnetization direction of a magnetic source body using convolutional neural networks (CNNs). CNNs have achieved great success in many other applications such as computer vision and seismic image interpretation but have not been used to extract parameters from magnetic data. We simulated many magnetic data maps with different magnetization directions from a synthetic source body, all subject to the same background field. Two CNNs were trained separately, one for predicting the inclination and the other for predicting declination. We determined the optimal CNN architectures for predicting inclinations and declinations by systematically comparing 13 different CNN architectures. In addition, we investigated the effect of having different parameters such as magnetization magnitude, source body shape, location, and depth on the performance of our predictive models. We also tested the method using field data from Black Hill Norite, Australia, and Yeshan region, China, for which prior research results are available for comparison. Our study shows that machine learning provides an effective means of automatically predicting magnetization directions based on magnetic data maps.

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“…In this paper, we extend the sequential inversion method from 2D to 3D and propose a fast iteration method (M-IDI) to estimate the magnetization direction and to implement the magnetization vector inversion (Liu et al, 2017). We compare it with previous important methods: the magnetization vector inversion in Cartesian framework (MMM) and spherical framework (MID) proposed �� Lelièvre and �lden�urg (2009).…”

Section: Introductionmentioning

confidence: 99%

“…Synthetic example. �o test and compare the methods, we set a s�nthetic model that is consisted of one vertical cu�oid (i.e., A) and two dipping prisms (i.e., B and C) (Liu et al, 2017). �he inclination and declination of Earth's magnetic field are I 0 = 45° (horizontal to downward) and D 0 = 90° (east to north).…”

Section: Introductionmentioning

confidence: 99%

The Use of Continuous Wavelet Transform for Ground-Roll Attenuation

Tognarelli

2018

Proceedings

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Finito di stampare nel mese di novembre 2017 gngts.inogs.it IV GNGTS 2017 Prefazione Molte note (148) sono in lingua inglese: ciò permette una diffusione del presente volume anche all'estero. Delle 221 note in programma, ben 175 sono destinate alla presentazione orale. Anche quest'anno alcune sessioni del convegno (quelle di Geofisica Applicata) sono state organizzate in collaborazione con la Sezione Italiana EAGE-SEG, che realizza così il suo 17° Convegno Nazionale.Una segnalazione degna di nota va all'Associazione Geofisica Licio Cernobori, che ha scelto anche quest'anno il convegno GNGTS quale sede per l'attribuzione del premio in memoria di un caro collega ed amico prematuramente scomparso anni or sono.Un ringraziamento particolare va ai convener (

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3D Magnetization Vector Inversion of Magnetic Data: Improving and Comparing Methods

Cited by 51 publications

References 25 publications

Three‐Dimensional Mapping of Mt. Ruapehu Volcano, New Zealand, From Aeromagnetic Data Inversion and Hyperspectral Imaging

Three‐Dimensional Mapping of Mt. Ruapehu Volcano, New Zealand, From Aeromagnetic Data Inversion and Hyperspectral Imaging

Predicting Magnetization Directions Using Convolutional Neural Networks

The Use of Continuous Wavelet Transform for Ground-Roll Attenuation

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