Geocomputing with Geological Field Data: Is there a 'ghost in the machine'?
BRODARIC, B.1,2, GAHEGAN, M.1, TAKATUSKA, M.1
and HARRAP, R.3
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:
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.
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;
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
changes over time; the history of rock sequences must be defined by extrapolating
present states into the past.