HR: 1340h
AN: NG43A-0437 [Abstracts]
TI: A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets
AU: * Porwal, A
EM: porwal@itc.nl
AF: State Department of Mines and Geology, Government of Rajasthan, Shastri Circle, Udaipur, Raj 313001
India
AU: * Porwal, A
EM: porwal@itc.nl
AF: International Institute for Geo-information Science and Earth Observation (ITC), Henegelosestraat 99,
Enschede, 7500AA
Netherlands
AU: Carranza, J
EM: carranza@itc.nl
AF: International Institute for Geo-information Science and Earth Observation (ITC), Henegelosestraat 99,
Enschede, 7500AA
Netherlands
AU: Hale, M
EM: hale@itc.nl
AF: International Institute for Geo-information Science and Earth Observation (ITC), Henegelosestraat 99,
Enschede, 7500AA
Netherlands
AB:
A GIS-based hybrid neuro-fuzzy approach to integration of large earth-science datasets for mineral prospectivity mapping is
described. It implements a Takagi-Sugeno type fuzzy inference system in the framework of a four-layered feed-forward
adaptive neural network. Each unique combination of the datasets is considered a feature vector whose components are derived
by knowledge-based ordinal encoding of the constituent datasets. A subset of feature vectors with a known output target
vector (i.e., unique conditions known to be associated with either a mineralized or a barren location) is used for the
training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive
neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector
with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors.
The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized
class or the barren class. These values are used to generate a prospectivity map. The procedure is demonstrated by an
application to regional-scale base metal prospectivity mapping in a study area located in the Aravalli metallogenic province
(western India). A comparison of the hybrid neuro-fuzzy approach with pure knowledge-driven fuzzy and pure data-driven neural
network approaches indicates that the former offers a superior method for integrating large earth-science datasets for
predictive spatial mathematical modelling.
DE: 3200 MATHEMATICAL GEOPHYSICS (New field)
DE: 3210 Modeling
DE: 3230 Numerical solutions
DE: 3299 General or miscellaneous
SC: Nonlinear Geophysics [NG]
MN: 2004 AGU Fall Meeting