2015
DOI: 10.1088/0266-5611/31/12/125004
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Electromagnetic 3D subsurface imaging with source sparsity for a synthetic object

Abstract: Abstract. This paper concerns electromagnetic 3D subsurface imaging in connection with sparsity of signal sources. We explored an imaging approach that can be implemented in situations that allow obtaining a large amount of data over a surface or a set of orbits but at the same time require sparsity of the signal sources. Characteristic to such a tomography scenario is that it necessitates the inversion technique to be genuinely three-dimensional: For example, slicing is not possible due to the low number of s… Show more

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Cited by 7 publications
(9 citation statements)
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“…Last but not the least, sensitivity analyses on each individual error source [19], [20] should be included in the future work. Following Pursiainen and Kaasalainen [19], error sources can be categorized into measurement error, model error, and inaccuracy in the a priori regularizer.…”
Section: Discussionmentioning
confidence: 99%
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“…Last but not the least, sensitivity analyses on each individual error source [19], [20] should be included in the future work. Following Pursiainen and Kaasalainen [19], error sources can be categorized into measurement error, model error, and inaccuracy in the a priori regularizer.…”
Section: Discussionmentioning
confidence: 99%
“…Note that, in (20), J is an underdetermined matrix, and the matrix inversion can be efficiently calculated using the matrix inversion lemma [24] as…”
Section: A Algorithm Derivationmentioning
confidence: 99%
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“…In order to ensure that a robust tomographic reconstruction can be produced, we use a 1.5-2.0 spatial oversampling rate with respect to the Nyquist criterion (NC) which was suggested for a monochromatic (travel-time) measurement in (Pursiainen and Kaasalainen, 2015). This means recording the radar signal for 5000 to 20 000 measurement positions, when extrapolating the measurement positions to the close proximity of a targeted 260 to 600 m diameter NEA.…”
Section: Radar Measurement and Mapping Orbitmentioning
confidence: 99%
“…The value of the parameter β determines the balance between the regularization matrices. A small value for β leads to inverse estimates with low total variation and larger values can be expected to result in well-localized estimates [15], [36], [37]. C. Test setup and scenario 1) Domain: An acoustic setup with one speaker and two microphones was utilized to gather the experimental data.…”
Section: B Inversion Proceduresmentioning
confidence: 99%