Testing Sedimentary Basins Using Adaptive Resonance Theory

摘要

The objective of this paper is to present identification and recognition of Magneto-telluric data for sedimentary basins using Adaptive Resonance Theory (ART2).The ART is an unsupervised learning algorithm where the network is provided with inputs but not with desired outputs. The system itself must then 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. 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. Various techniques used in this experiment are creating the pattern files, normalizing the files, training the neural network, adjustment of weights and parameters, network file creation and finally testing of the field data for the pattern identification. The recognition rate in the proposed system lies between 95% and 100%.