ABSTRACT

Associative Memory (AM) research covers technologies enabling implementation of associative memory which enables thought process and links previous experience to novel situations. Each neural network system requires a memory for storing and retrieval of associated concepts, based on a combination of the base concept and the context. Adaptive Resonance Theory is a kind of associative neural memory model as unsupervised neural network model. The aim of this article is to present identification and recognition of Magneto-telluric data for sedimentary basins using associative neural memory with Adaptive Resonance Theory (ART2).The ART2 is an unsupervised learning algorithm where the network is provided with inputs but not with desired outputs. The system itself to decide what features it will use to group the input data. Several sets of data consisting of 17 phases and 17 apparent resistivity values and their respective tag values are given. These sets of data are used for training the network, and other sets of data are used to test the network for clustering. The testing will result in the approximate identification of the data patterns with tag value of 1 where there is sediment of hydrocarbon and a tag value of 0 where there is no sediment of hydrocarbon in the given data set. The recognition rate in the proposed system lies between 90% and 100%.