For several decades, artificial neural networks have assisted in data reduction processes through classifications applied to a wide spectrum of aspects—from traffic solutions and medicinal purposes to geophysical interpretations. Here we use an unsupervised approach where the neural network is free to search, to recognize, and to classify structural patterns in an n-dimensional vector field spanning the entire 3D input seismic attribute data set (Taner et al., 2001; Walls et al., 2002). Within the data set, each data sample is defined by a unique combination of physical, geometric, and hybrid attributes and is treated as an n-dimensional vector...

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