Artificial Bee Colony Algorithm with Adaptive Parameter Space Dimension: A Promising Tool for Geophysical Electromagnetic Induction Inversion

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
  • Dennis Wilken
  • Moritz Mercker
  • Peter Fischer
  • Andreas Voett
  • Ercan Erkul
  • Erica Corradini
  • Natalie Pickartz
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:001160230200001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.3390/rs16030470
eISSN
2072-4292
Externe Identifier
  • Clarivate Analytics Document Solution ID: HN6U6
Ausgabe der Veröffentlichung
3
Zeitschrift
REMOTE SENSING
Schlüsselwörter
  • FDEMI
  • inversion
  • artificial bee colony
  • Markov chain Monte Carlo
  • electromagnetic induction
  • geophysics
Artikelnummer
ARTN 470
Datum der Veröffentlichung
2024
Status
Published
Titel
Artificial Bee Colony Algorithm with Adaptive Parameter Space Dimension: A Promising Tool for Geophysical Electromagnetic Induction Inversion
Sub types
  • Article
Ausgabe der Zeitschrift
16

Datenquelle: Web of Science (Lite)

Andere Metadatenquellen:
Abstract
<jats:p>Frequency-domain electromagnetic induction (FDEMI) methods are frequently used in non-invasive, area-wise mapping of the subsurface electromagnetic soil properties. A crucial part of data analysis is the geophysical inversion of the data, resulting in either conductivity and/or magnetic susceptibility subsurface distributions. We present a novel 1D stochastic optimization approach that combines dimension-adapting reversible jump Markov chain Monte Carlo (MCMC) with artificial bee colony (ABC) optimization for geophysical inversion, with specific application to frequency-domain electromagnetic induction (FDEMI) data. Several solution models of simplified model geometry and a variable number of model knots, which are found by the inversion method, are used to create re-sampled resulting average models. We present synthetic test inversions using conductivity models based on 14 direct-push (DP) EC logs from Greece, Italy, and Germany, as well as field data applications using multi-coil FDEMI devices from three sites in Azerbaijan and Germany. These examples show that the method can effectively lead to solutions that resemble the known DP input models or image reasonable stratigraphic and archaeological features in the field data. Neighboring 1D solutions on field data examples show high coherence along profiles even though each 1D inversion is independently handled. The computational effort for one 1D inversion is less than 120,000 forward calculations, which is much less than usually needed in MCMC inversions, whereas the resulting models show more plausible solutions due to the dimension-adapting properties of the inversion method.</jats:p>
Autoren
  • Dennis Wilken
  • Moritz Mercker
  • Peter Fischer
  • Andreas Vött
  • Ercan Erkul
  • Erica Corradini
  • Natalie Pickartz
DOI
10.3390/rs16030470
eISSN
2072-4292
Ausgabe der Veröffentlichung
3
Zeitschrift
Remote Sensing
Sprache
en
Online publication date
2024
Paginierung
470 - 470
Status
Published online
Herausgeber
MDPI AG
Herausgeber URL
http://dx.doi.org/10.3390/rs16030470
Datum der Datenerfassung
2024
Titel
Artificial Bee Colony Algorithm with Adaptive Parameter Space Dimension: A Promising Tool for Geophysical Electromagnetic Induction Inversion
Ausgabe der Zeitschrift
16

Datenquelle: Crossref

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