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Area of Science:

  • Environmental Science
  • Nuclear Science
  • Geospatial Analysis

Background:

  • A probabilistic method for a priori scan MDC calculation exists.
  • Existing methods lack robust a posteriori validation for geospatial data.

Purpose of the Study:

  • Validate the use of statistical minimum detectable count rate (MDCR) for retrospective scan MDC estimation.
  • Develop a technical basis for assessing sensitivity metrics in geospatially rendered gamma survey datasets.

Main Methods:

  • Utilized nearest-neighbor averaging on geospatial gamma survey data.
  • Quantified retrospective MDCR by analyzing variance in background and source data.
  • Modeled detection efficiencies using a probabilistic method for various parameters.

Main Results:

  • Demonstrated the validity of using statistical MDCR for retrospective scan MDC calculations.
  • Established a method for a posteriori estimation of MDCR and scan MDC sensitivity.
  • Showcased the application for diverse radionuclides, source dimensions, detector heights, and scan speeds.

Conclusions:

  • Retrospective MDCR estimation provides a valid approach for assessing scan MDC sensitivity.
  • The developed methods enhance the analysis of existing gamma radiation survey data.
  • This work supports more accurate environmental radionuclide contamination assessments.