GeoComputation 2000 HomeConference ProgrammeAlphabetical List of AuthorsPaper

Geocomputing with Geological Field Data: Is there a 'ghost in the machine'?

1 The Pennsylvania State University, USA
2 Geological Survey of Canada, Canada
3 Queen's University, Canada

Key words: Field Data, Geological Mapping, Self-Organising Map, Classification

Bedrock geological mapping involves the construction of a spatial and temporal model of a region via field-based surveys. The geologist interprets the field evidence to constrain possible geologic histories, and constructs hypotheses by combining the field constraints with extant geologic theory. Such geologic reasoning often leads to multiple valid hypotheses since the evidence from field and theory regularly underdetermines the history: there are many valid ways to explain limited data in the Earth's large open system. More specifically, the underdetermination results from:

  1. data scarcity, resulting from bedrock unavailability and from unknown underground conditions, i.e., observations are typically limited to areas of exposed bedrock at the Earth's surface, and must be extrapolated underground into the largely unseen third dimension;
  2. large variable space; only a limited subset of the variable space is available for observation, and a subset of this space is typically grasped by the observer, often from perspectives biased by education and experience; and
  3. changes over time; the history of rock sequences must be defined by extrapolating present states into the past.
Because multiple hypotheses can fit the facts, and because the facts themselves are contentious being somewhat subjective due to the variability of observation, geological mappers often regard their skill as an art as well as a science. The encroachment of computer technologies into the field mapping process, and the subsequent availability of digital geological field data, provides an opportunity to test these claims geocomputationally in order to evaluate the degree of artistry involved in geological mapping. This study specifically investigates the degree of correlation between field data and the geological classes generalized from them. A study area was chosen where several geologists' data and interpretations were compared, correlated, and contrasted using a self-organising neural map (SOM). Significant challenges in preparing largely qualitative data for the SOM were overcome and are reported. Also reported are correlation results that indicate the geological mapping process is indeed a mixture of abductive (i.e., intuitive) as well as deductive reasoning. These results lead to broader questions regarding the ability of geocomputational techniques to compute with, or capture, the experiential knowledge of agents in field based situations. The results and ensuing questions coincide with field geologists' intuitions that geological classification is partially dependent on the experiential nature of field-based geological surveying and cannot be wholly replicated by computational analogues. This highlights some challenges as well as opportunities for improved geocomputing with field data.