Paper Info
Title
The art and science of mapping: computing geological categories from field data.
Abstract
Like many activities in the geosciences, geological mapping of surface bedrock involves the construction of a model for a geographic region via field-based surveys. Individuals interpret field evidence to constrain possible histories and explanations, and these are regularly under-determined by available theory and data, resulting in multiple valid explanatory models where selection of the optimal model is often described as being an art as well as a science. This study empirically investigates this artistry by evaluating the correlation between data collected in the field and the geological map unit concepts interpreted from these data. Several geologists’ data are selected from a completed field survey, and unsupervised and supervised categorization techniques provided by the self-organizing neural network are used to investigate the correlation between the selected data and interpreted concepts. Reported are results suggesting that the development of geological map unit concepts is influenced by theory, data, individuality and specific situations. Significant challenges in preparing largely qualitative data are also reported, and discussed are some broader implications related to the ability of computational techniques to capture and compute with the experiential knowledge of human agents in field-based situations.
Year
DOI
Venue
2004
10.1016/j.cageo.2004.05.001
Computers & Geosciences
Keywords
Field
DocType
Geological mapping,Field data,Classification,Self-organizing map
Data science,Data mining,Categorization,Data processing,Field data,Qualitative property,Computer science,Self-organizing map,Experiential knowledge,Artificial intelligence,Artificial neural network,Geologic map
Journal
Volume
Issue
ISSN
30
7
0098-3004
Citations 
PageRank 
References 
11
0.99
12
Authors
3
Name
Order
Citations
PageRank
Boyan Brodaric112914.10
Mark Gahegan257155.38
Rob Harrap3110.99