A Bayesian/geostatistical approach to the design of adaptive sampling programs

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Description

Traditional approaches to the delineation of subsurface contamination extent are costly and time consuming. Recent advances in field screening technologies present the possibility for adaptive sampling programs--programs that adapt or change to reflect sample results generated in the field. A coupled Bayesian/geostatistical methodology can be used to guide adaptive sampling programs. A Bayesian approach quantitatively combines ``soft`` information regarding contaminant location with ``hard`` sampling results. Soft information can include historical information, non-intrusive geophysical survey data, preliminary transport modeling results, past experience with similar sites, etc. Soft information is used to build an initial conceptual image of where contamination is likely ... continued below

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17 p.

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Johnson, R.L. June 1, 1995.

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This report is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 20 times . More information about this report can be viewed below.

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  • Johnson, R.L. Argonne National Lab., IL (United States). Environmental Assessment Div.

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Description

Traditional approaches to the delineation of subsurface contamination extent are costly and time consuming. Recent advances in field screening technologies present the possibility for adaptive sampling programs--programs that adapt or change to reflect sample results generated in the field. A coupled Bayesian/geostatistical methodology can be used to guide adaptive sampling programs. A Bayesian approach quantitatively combines ``soft`` information regarding contaminant location with ``hard`` sampling results. Soft information can include historical information, non-intrusive geophysical survey data, preliminary transport modeling results, past experience with similar sites, etc. Soft information is used to build an initial conceptual image of where contamination is likely to be. As samples are collected and analyzed, indicator kriging is used to update the initial conceptual image. New sampling locations are selected to minimize the uncertainty associated with contaminant extent. An example is provided that illustrates the methodology.

Physical Description

17 p.

Notes

OSTI as DE95013500

Source

  • Geostatistics for environmental and geotechnical applications, Phoenix, AZ (United States), 26-27 Jan 1995

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  • Other: DE95013500
  • Report No.: ANL/EA/CP--85472
  • Report No.: CONF-950159--2
  • Grant Number: W-31109-ENG-38
  • DOI: 10.2172/81017 | External Link
  • Office of Scientific & Technical Information Report Number: 81017
  • Archival Resource Key: ark:/67531/metadc739764

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Office of Scientific & Technical Information Technical Reports

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Creation Date

  • June 1, 1995

Added to The UNT Digital Library

  • Oct. 18, 2015, 6:40 p.m.

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  • Dec. 11, 2015, 6:47 p.m.

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Johnson, R.L. A Bayesian/geostatistical approach to the design of adaptive sampling programs, report, June 1, 1995; Illinois. (digital.library.unt.edu/ark:/67531/metadc739764/: accessed December 9, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.