Datapages, Inc.Print this page

 

Honoring Uncertainty in Mapping and Interpreting Large Volumes of Digital Spatial Data*

By

John D. Grace1

 

Search and Discovery Article #40197 (2006)

Posted July 3, 2006

 

*Oral presentation at AAPG Annual Convention, Houston, Texas, April 9-12, 2006

 

Click to view presentation in PDF format (0.7 mb).

 

1Earth Science Associates, Long Beach, CA (j[email protected])

 

Abstract 

The development of very large digital collections of spatial oil and gas data has permitted the easy extraction of information on individual variables (e.g., depth to top of a unit) or spatial interaction between variables (e.g., sediment accumulation rate and pore pressure). Yet improving access to digital data and the ease of mapping software have often created a confidence in the results that are justified neither by the underlying data nor the (usually deterministic) gridding and contouring algorithms applied to them.  

Geostatistical techniques afford an opportunity to reflect not only the anticipated value of a variable in space, but to estimate the probability of its occurrence and confidence intervals surrounding the estimate. A simple procedure is introduced for creation of masks. Their purpose is to eliminate map areas where confidence in the estimated surface falls below a threshold and to characterize the certainty for the remaining, mapped area. Estimated probabilities associated with individual surfaces in a multi-layer analysis can be consistently propagated through to final result maps.  

Systematic inclusion of uncertainty in mapping and spatial analysis is critical where risk arising from errors in assumptions is an explicit part of decision making. The procedure is applied within a geographic information system (GIS), the most common tool used for manipulation and analysis of large, digital, spatial data stores. Illustrations are based on very large data bases covering geologic and engineering variables from the offshore Gulf of Mexico.  

 

Click to view in sequence the following maps prepared for example: Mapping depth to “Ang-B” in Gulf of Mexico (Top of “Ang-B”, Prediction standard error, Mask map where error ≥ 1103, and Error-clipped “Ang-B” depth map)