Data spacing and uncertainty

• Author / Creator

  Wilde, Brandon Jesse

• Modeling spatial variables involves uncertainty. Uncertainty is affected by the degree to which a spatial variable has been sampled: decreased spacing between samples leads to decreased uncertainty. The reduction in uncertainty due to increased sampling is dependent on the properties of the variable being modeled. A densely sampled erratic variable may have a level of uncertainty similar to a sparsely sampled continuous variable. A simulation based approach is developed to quantify the relationship between uncertainty and data spacing. Reference realizations are simulated and sampled at different spacings. The samples are used to condition additional realizations from which uncertainty is quantified. A number of factors complicate the relationship between uncertainty and data spacing including the proportional effect, nonstationary variogram, classification threshold, number of realizations, data quality and modeling scale. A case study of the relationship between uncertainty and data density for bitumen thickness data from northern Alberta is presented.

• Subjects / Keywords

  • Data spacing
  • Sampling
  • Data density
  • Uncertainty
  • Sequential Gaussian simulation

• Graduation date

  Fall 2010

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3GH71

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.