Mapping Ground-Level Radiation Fields with DREO's Airborne Gamma-Ray Spectrometer

Abstract

An algorithm is developed and tested that infers a ground contamination pattern from the dose rate patterns measured by DREO's Airborne Gamma-ray Spectrometer. This algorithm is based on a least-squares minimization, and uses Micro shield calculations of dose rates as a function of altitude over a patch of contaminated ground. The algorithm is successful in that it correctly identifies regions of high and low contamination, which would permit a commander to identify areas to avoid, or paths to follow through a non-uniformly contaminated region. However, the contamination pattern predicted by this algorithm is not a high-fidelity facsimile of the actual distribution. The reason for this deficiency is likely that the problem of calculating ground-level contamination patterns from airborne measurements is inherently underdetermined, and evidence is presented to this effect. These results demonstrate clearly the utility of airborne survey for military purposes, and a method of analyzing the data from such a platform.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2000
Accession Number
ADA386135

Entities

People

  • D. S. Haslip
  • Ph. Bouteilloux
  • T. A. Cousins
  • T. A. Jones
  • Thomas Cousins

Organizations

  • Defence Research and Development Canada

Tags

Communities of Interest

  • Air Platforms
  • Counter WMD
  • Human Systems
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Altitude
  • Classification
  • Detectors
  • Dose Rate
  • Engineering
  • Gamma Ray Spectroscopy
  • Gamma Rays
  • Global Positioning Systems
  • Ground Level
  • Measurement
  • Military Commanders
  • Military Operations
  • Radiation Effects
  • Reliability
  • Security
  • Spectrometers
  • Spectroscopy

Fields of Study

  • Environmental science

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Computational Modeling and Simulation
  • Solar Physics

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms