Estimating water and ice content on planetary soils using neutron measurements: A neural network approach

A. Luciani, P. Panfili, R. Furfaro, B. D. Ganapol, D. Mostacci

Research output: Contribution to journalArticle

Abstract

A model-based neural network methodology to estimate water and ice content in planetary soils using neutron fluxes detected by in situ and/or airborne deployment of neutron detectors is proposed and shown to be effective. Focusing of epithermal and thermal energy regimes, the neutron fluxes are computed [Panfili, P.; Luciani, A.; Furfaro, R.; Ganapol, B.D.; Mostacci, D. Radiat. Eff. Defects Solids 2009, 164 (5-6), 340-344.] as functions of the medium physical properties and used to train neural networks in the inverse mode. For homogeneous soil, the model-based neural network shows satisfactory performances in retrieving the percentage of water. For soil modelled as layered, neural networks designed to retrieve both the depth and thickness of an ice layer beneath the soil surface provide good results only in a limited range of configurations. However, it has been found that training the two networks to independently retrieve the two parameter results more accurately. It has also been found that multiple measurements help improve the accuracy of the inversion for this configuration.

Original languageEnglish (US)
Pages (from-to)345-349
Number of pages5
JournalRadiation Effects and Defects in Solids
Volume164
Issue number5-6
DOIs
StatePublished - May 1 2009

Keywords

  • Inverse transport problem
  • Neural networks
  • Neutron flux
  • Neutron transport

ASJC Scopus subject areas

  • Radiation
  • Nuclear and High Energy Physics
  • Materials Science(all)
  • Condensed Matter Physics

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