Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

(Pseudo-)3D Inversion of Geophysical Electromagnetic Induction Data by Using an Arbitrary Prior and Constrained to Ancillary Information

Zaru, Nicola ; Rossi, Matteo LU ; Vacca, Giuseppina and Vignoli, Giulio (2023) 23rd International Conference on Computational Science and Its Applications, ICCSA 2023 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 14111 LNCS. p.624-638
Abstract

Electromagnetic induction (EMI) methods are often used to map rapidly large areas with minimal logistical efforts. However, they are limited by a small number of frequencies and by their severe ill-posedness. On the other hand, electrical resistivity tomography (ERT) results are generally considered more reliable, with no need for specific calibration procedures and easy 2D/3D inversion. Still, ERT surveys are definitely more time-consuming, and, ideally, an approach with the advantages of both EMI and ERT would be optimal. The present research addresses this issue by incorporating realistic constraints into EMI inversion, going beyond simplistic spatial constraints like smooth or sharp regularization terms, while taking into... (More)

Electromagnetic induction (EMI) methods are often used to map rapidly large areas with minimal logistical efforts. However, they are limited by a small number of frequencies and by their severe ill-posedness. On the other hand, electrical resistivity tomography (ERT) results are generally considered more reliable, with no need for specific calibration procedures and easy 2D/3D inversion. Still, ERT surveys are definitely more time-consuming, and, ideally, an approach with the advantages of both EMI and ERT would be optimal. The present research addresses this issue by incorporating realistic constraints into EMI inversion, going beyond simplistic spatial constraints like smooth or sharp regularization terms, while taking into consideration the ancillary information already available about the investigated site. We demonstrate how additional pre-existing information, such as a reference model (i.e., an existing ERT section) can enhance the EMI inversion. The study verifies the results against observations from boreholes.

(Less)
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Electromagnetic Induction, Realistic Prior Distribution, Spatially Constrained Inversion
host publication
Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings
series title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
editor
Gervasi, Osvaldo ; Murgante, Beniamino ; Scorza, Francesco ; Rocha, Ana Maria A. C. ; Garau, Chiara ; Karaca, Yeliz and Torre, Carmelo M.
volume
14111 LNCS
pages
15 pages
publisher
Springer Science and Business Media B.V.
conference name
23rd International Conference on Computational Science and Its Applications, ICCSA 2023
conference location
Athens, Greece
conference dates
2023-07-03 - 2023-07-06
external identifiers
  • scopus:85164981334
ISSN
0302-9743
1611-3349
ISBN
9783031371257
DOI
10.1007/978-3-031-37126-4_40
language
English
LU publication?
yes
id
7834f87d-c997-4a32-8f09-698d19ffdd66
date added to LUP
2023-09-25 14:44:43
date last changed
2025-04-19 12:44:20
@inproceedings{7834f87d-c997-4a32-8f09-698d19ffdd66,
  abstract     = {{<p>Electromagnetic induction (EMI) methods are often used to map rapidly large areas with minimal logistical efforts. However, they are limited by a small number of frequencies and by their severe ill-posedness. On the other hand, electrical resistivity tomography (ERT) results are generally considered more reliable, with no need for specific calibration procedures and easy 2D/3D inversion. Still, ERT surveys are definitely more time-consuming, and, ideally, an approach with the advantages of both EMI and ERT would be optimal. The present research addresses this issue by incorporating realistic constraints into EMI inversion, going beyond simplistic spatial constraints like smooth or sharp regularization terms, while taking into consideration the ancillary information already available about the investigated site. We demonstrate how additional pre-existing information, such as a reference model (i.e., an existing ERT section) can enhance the EMI inversion. The study verifies the results against observations from boreholes.</p>}},
  author       = {{Zaru, Nicola and Rossi, Matteo and Vacca, Giuseppina and Vignoli, Giulio}},
  booktitle    = {{Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings}},
  editor       = {{Gervasi, Osvaldo and Murgante, Beniamino and Scorza, Francesco and Rocha, Ana Maria A. C. and Garau, Chiara and Karaca, Yeliz and Torre, Carmelo M.}},
  isbn         = {{9783031371257}},
  issn         = {{0302-9743}},
  keywords     = {{Electromagnetic Induction; Realistic Prior Distribution; Spatially Constrained Inversion}},
  language     = {{eng}},
  pages        = {{624--638}},
  publisher    = {{Springer Science and Business Media B.V.}},
  series       = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
  title        = {{(Pseudo-)3D Inversion of Geophysical Electromagnetic Induction Data by Using an Arbitrary Prior and Constrained to Ancillary Information}},
  url          = {{http://dx.doi.org/10.1007/978-3-031-37126-4_40}},
  doi          = {{10.1007/978-3-031-37126-4_40}},
  volume       = {{14111 LNCS}},
  year         = {{2023}},
}