2.5D forward modeling and inversion of frequency-domain airborne electromagnetic data

Li, Wen-Ben; Zeng, Zhaofa; Li, Jing; Chen, Xiong; Wang, Kun; Zhao, Xin

doi:10.1007/s11770-016-0548-y

Cited by 15 publications

(9 citation statements)

References 26 publications

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“…The original electromagnetic field can be transformed in the wave domain by the Dufort-Frankel difference approach: (18) and (19), as shown at the top of the next page, where µ,σ , and represent magnetic permeability, average conductivity, and step size, respectively. The forward model is divided into several small rectangular elements ( Figure 2) by a nonuniform grid to improve the accuracy and efficiency of the electromagnetic field.…”

Section: 5-d Mine Tem Methods a 25-d Forward Calculationmentioning

confidence: 99%

“…Moreover, the iterative process of this method can invert the optimal parameters quantitatively and obtain an accurate inversion result. The damped least squares method is widely used in geophysical inverse problems and can be expressed as follows [18], [23]- [25]:…”

Section: 5-d Damped Least Squares Inversionmentioning

confidence: 99%

“…Furthermore, Kirkegaard (2015) proposed a parallel inversion method to accelerate the computation speed [17]. Li (2016) proposed a 2.5-D airborne TEM inversion method in the frequency domain based on the singular value decomposition and least squares methods [18]. Qiang (2016) proposed a 2.5-D airborne TEM inversion method by NLCG and obtained good results [19].…”

Section: Introductionmentioning

confidence: 99%

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2.5-D Inversion of Advanced Detection Transient Electromagnetic Method in Full Space

et al. 2020

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The transient electromagnetic method (TEM) can be applied in detecting coal mining heading face with multiple angles. However, a 1-D inversion scheme cannot fit this kind of multi-angle data-receiving system well, nor reducing detection accuracy. Developing a 2.5-D inversion method is necessary for TEM multi-angle data. The expressions of the vertical and horizontal magnetic dipole source at any position in the layered medium are derived on the basis of the potential function and the boundary conditions of the electromagnetic field. Then, the electromagnetic field of the magnetic dipole source with multiple angles can be obtained through vector stacking. A low-resistance anomalous body above the roof in front of a roadway excavation was identified by applying a 2.5-D least square inversion method in mine TEM-advanced detection to verify the effectiveness of the inversion method. Moreover, the method was used to detect water inrush from one side of the roadway in a coal mine in Shandong Province, China. Furthermore, the inversion results were verified by the information of boreholes and excavation. The proposed 2.5-D inversion method has a higher interpretation accuracy and resolution than the 1-D inversion method of the model and the measured data. INDEX TERMS Mine transient electromagnetic method, advanced detection, arbitrary angle field source, 2.5-dimensional inversion, damped least squares method.

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Section: 5-d Mine Tem Methods a 25-d Forward Calculationmentioning

confidence: 99%

Section: 5-d Damped Least Squares Inversionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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2.5-D Inversion of Advanced Detection Transient Electromagnetic Method in Full Space

et al. 2020

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“…I N modelling the responses of electromagnetic (EM) surveys for geophysical exploration, it is usually necessary to separate the EM fields into the primary (background) field in the subsurface background (i.e., the subsurface without the specific scatterers) and the secondary (scattered) field due to the responses of the specific scatterers (i.e., anomalies) in the subsurface background. The secondary fields are widely employed in various EM exploration methods such as multicomponent induction tools [1], controlled-source electromagnetic (CSEM) method [2], the marine controlled-source electromagnetic (MCSEM) method [3], inversion of airborne electromagnetic data [4], helicopter-borne electromagnetic (HEM) measurements [5], semi-airborne electromagnetic method (SAEM) [6], the ground-airborne frequency-domain electromagnetic (GAFDEM) survey [7], ground penetrating radar [8], [9] and other detection methods [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Mixed Spectral Element Method for Electromagnetic Secondary Fields in Stratified Inhomogeneous Anisotropic Media

et al. 2021

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The spectral element method (SEM) is widely used for analyzing high frequency electromagnetic wave propagation and scattering in complex structures. At low frequencies, however, the primary (direct) fields are usually much larger than the secondary fields caused by scattering, thus making it much more difficult to accurately simulate the secondary (scattered) fields because of the source singularity in the primary fields. To more effectively and accurately simulate the low-frequency secondary fields in subsurface structures, in this work the vector Helmholtz equation for the scattered fields is used to directly obtain the secondary field data in inhomogeneous anisotropic media. The computational method for the secondary electromagnetic fields is based on the mixed spectral element method (mixed SEM) for anisotropic objects in a layered anisotropic background medium. The electromagnetic field is separated into the background fields that are evaluated analytically using the dyadic Green's functions (DGFs) of a layered uniaxially anisotropic medium, and the secondary fields caused by anomalous bodies with arbitrary shapes are numerically computed by the mixed SEM. This avoids the source singularity, and allows the transmitters to be located outside the computational domain. By enforcing Gauss' law as the constraint condition, the mixed SEM efficiently eliminates the low frequency breakdown problem. Numerical results verify the proposed method over the traditional SEM in low-frequency scattering from objects in subsurface layered anisotropic media.INDEX TERMS Mixed spectral element method, secondary field, anisotropic media, source singularity.

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“…Considering this and the inherent computational burden of doing full 3D inversions, it is clear that there are areas where it is sufficient and even desirable to operate within a 2D formulation. Several 2D inversion algorithms have been presented over the years: Mitsuhata and Uchida (2002), Wilson et al (2006), Li et al (2016), and Key and Ovall (2011) develop 2D finite-element algorithms, whereas Abubakar et al (2008) use a finite-difference approach and Yu and Haber (2012) present a finite-volume approach. In general, finite-difference approaches are considered the most simple and inaccurate of the three approaches, but they can sometimes be justified due to their superior parallel scaling.…”

Section: Introductionmentioning

confidence: 99%

An efficient 2D inversion scheme for airborne frequency-domain data

et al. 2018

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In many cases, inversion in 2D gives a better description of the subsurface compared with 1D inversion, but, computationally, 2D inversion is expensive, and it can be hard to use for large-scale surveys. We have developed an efficient hybrid 2D airborne frequency-domain electromagnetic inversion algorithm. Our hybrid scheme combines 1D and 2D inversions in a three-stage process, in which each step is progressively more accurate and computationally more expensive than the previous one. This results in an approximately 2x − 6x speedup compared with full 2D inversions, and with only minor changes to the inversion results. Our inversion structure is based on a regular grid, in which each sounding is discretized individually. The 1D modeling code uses layered models with derivatives derived through the finite-difference method, whereas our 2D modeling code uses an adaptive finiteelement mesh, and it uses the adjoint-state method to calculate the derivatives. By incorporating the inversion grid structure into the 2D finite-element mesh, interpolation between the different meshes becomes trivial. Large surveys are handled by using local meshing to split large surveys into small sections, which retains the 2D information. The algorithm is heavily optimized and parallelized over the frequencies and sections, with good scalability even on nonuniform memory architecture systems, on which it is generally hard to achieve a satisfactory scaling. The algorithm has been tested successfully with various synthetic studies as well as field examples, of which results from two synthetic studies and a field example are shown.

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2.5D forward modeling and inversion of frequency-domain airborne electromagnetic data

Cited by 15 publications

References 26 publications

2.5-D Inversion of Advanced Detection Transient Electromagnetic Method in Full Space

2.5-D Inversion of Advanced Detection Transient Electromagnetic Method in Full Space

Mixed Spectral Element Method for Electromagnetic Secondary Fields in Stratified Inhomogeneous Anisotropic Media

An efficient 2D inversion scheme for airborne frequency-domain data

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