scholarly journals An Approximate Factorization Method for Inverse Acoustic Scattering with Phaseless Total-Field Data

2020 ◽  
Vol 80 (5) ◽  
pp. 2271-2298
Author(s):  
Bo Zhang ◽  
Haiwen Zhang
Keyword(s):  
Field Data ◽  
Acoustic Scattering ◽  
Factorization Method ◽  
Total Field ◽  
Approximate Factorization

scholarly journals Imaging of bi-anisotropic periodic structures from electromagnetic near-field data

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Dinh-Liem Nguyen ◽  
Trung Truong
Keyword(s):  
Inverse Scattering ◽  
Maxwell Equations ◽  
Field Data ◽  
Periodic Structures ◽  
Near Field ◽  
Scattering Problem ◽  
Three Dimensional ◽  
Inverse Scattering Problem ◽  
Factorization Method ◽  
Unique Determination

AbstractThis paper is concerned with the inverse scattering problem for the three-dimensional Maxwell equations in bi-anisotropic periodic structures. The inverse scattering problem aims to determine the shape of bi-anisotropic periodic scatterers from electromagnetic near-field data at a fixed frequency. The factorization method is studied as an analytical and numerical tool for solving the inverse problem. We provide a rigorous justification of the factorization method which results in the unique determination and a fast imaging algorithm for the periodic scatterer. Numerical examples for imaging three-dimensional periodic structures are presented to examine the efficiency of the method.

scholarly journals Phaseless Gauss-Newton Inversion for Microwave Imaging

2020 ◽  
Author(s):  
Chaitanya Narendra ◽  
Puyan Mojabi
Keyword(s):  
Field Data ◽  
Prior Information ◽  
Structural Information ◽  
Microwave Imaging ◽  
The Other ◽  
Total Field ◽  
Field Phase ◽  
Full Data ◽  
L2 Norm ◽  
Phase Data

<p>A phaseless Gauss-Newton inversion (GNI) algorithm is developed for microwave imaging applications. In contrast to full-data microwave imaging inversion that uses complex (magnitude and phase) scattered field data, the proposed phaseless GNI algorithm inverts phaseless (magnitude-only) total field data. This phaseless Gauss-Newton inversion (PGNI) algorithm is augmented with three different forms of regularization, originally developed for complex GNI. First, we use the standard weighted L2 norm total variation multiplicative regularizer which is appropriate when there is no prior information about the object being imaged. We then use two other forms of regularization operators to incorporate prior information about the object being imaged into the PGNI algorithm. The first one, herein referred to as SL-PGNI, incorporates prior information about the expected relative complex permittivity values of the object of interest. The other, referred to as SP-PGNI, incorporates spatial priors (structural information) about the objects being imaged. The use of prior information aims to compensate for the lack of total field phase data. The PGNI, SL-PGNI, and SP-PGNI inversion algorithms are then tested against synthetic and experimental phaseless total field data.</p>

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

Geophysics ◽  
1986 ◽  
Vol 51 (9) ◽  
pp. 1725-1735 ◽  
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.

The factorization method for inverse acoustic scattering in a layered medium

2013 ◽  
Vol 29 (4) ◽  
pp. 045010 ◽  
Author(s):  
Oleksandr Bondarenko ◽  
Andreas Kirsch ◽  
Xiaodong Liu
Keyword(s):  
Layered Medium ◽  
Acoustic Scattering ◽  
Factorization Method

Quantifying the error level in computed magnetic amplitude data for 3D magnetization inversion

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. J75-J84 ◽  
Author(s):  
Camriel Coleman ◽  
Yaoguo Li
Keyword(s):  
Field Data ◽  
Regularization Parameter ◽  
Three Dimensional ◽  
Inverse Model ◽  
Magnetic Data ◽  
Total Field ◽  
Noise Estimate ◽  
Amplitude Inversion ◽  
Amplitude Data ◽  
Magnetic Amplitude

Three-dimensional inversion plays an important role in the quantitative interpretation of magnetic data in exploration problems, and magnetic amplitude data can be an effective tool in cases in which remanently magnetized materials are present. Because amplitude data are typically calculated from total-field anomaly data, the error levels must be characterized for inversions. Lack of knowledge of the error in amplitude data hinders the ability to properly estimate the data misfit associated with an inverse model and, therefore, the selection of the appropriate regularization parameter for a final model. To overcome these challenges, we have investigated the propagation of errors from total-field anomaly to amplitude data. Using parametric bootstrapping, we find that the standard deviation of the noise in amplitude data is approximately equal to that of the noise in total-field anomaly data when the amplitude data are derived from the conversion of total-field data to three orthogonal components. We then illustrate how the equivalent source method can be used to estimate the error in total-field anomaly data when needed. The obtained noise estimate can be applied to amplitude inversion to recover an optimal inverse model by applying the discrepancy principle. We test this method on synthetic and field data and determine its effectiveness.

scholarly journals Aplikasi Metode Very Low Frequency Electromagnetic (VLF-EM) untuk Karakteristik Bawah Permukaan di Daerah Kapur Desa Melirang Kecamatan Bungah Kabupaten Gresik (Halaman 38 s.d. 41)

2015 ◽  
Vol 19 (56) ◽  
Author(s):  
Eko Hadi Purwanto ◽  
Eko Minarto ◽  
Ayi Syaeful Bahri
Keyword(s):  
Tilt Angle ◽  
Field Data ◽  
Low Frequency ◽  
Total Field ◽  
Very Low Frequency

Metode Very Low Frequency Electromagnetic (VLF-EM) merupakan metode geofisika yang cepat, ramah lingkungan dan metode elektromagnetik pasif yang bekerja pada pita frekuensi 15-30 kHz. Metode ini digunakan untuk mengkarakteristik bawah permukaan dan menentukan sebaran dolomit di daerah kapur Desa Melirang Kecamatan Bungah Kabupaten Gresik. Pengambilan data dilakukan pada enam lintasan dengan panjang 550 m dan spasi setiap lintasan sebesar 5 m. Hasil akuisisi data VLF-EM berupa nilai inphase, quadrature, tilt-angle, dan total field. Data tersebut diolah dengan filter NA-MEMD dan diinterpretasikan secara kualitatif dan kuantitatif. Analisa kualitatif menggunakan filter fraser dan karous-Hjelt untuk menentukan bentuk, jalur, dan kedalaman dolomit dengan tepat (nilai resistivitas 100-104 Ωm), sedangkan analisa kuantitatif dihasilkan nilai resistivitas 2-D dari hasil inversi data tripper (inphase dan quadrature) dengan software Inv2DVLF. Analisa kuantitatif menghasilkan kedalaman dolomit antara 0 – 30 m dan 50 – 70 m dengan nilai resitivitas antara 110 – 600 Ωm. Hasil estimasi ini menunjukkan jalur dolomit dari arah timur laut-barat daya, timur-barat, dan barat laut-tenggara. 

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