Data mining of 3D poststack seismic attribute volumes using Kohonen self-organizing maps

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 (Carr et al., 2001). Data classification occurs when similar data are captured within a Euclidean distance of a neural node, thus providing data clusters or classes as an output data set. In this paper, an unsupervised artificial neural network using four different suites of poststack seismic attributes is employed to classify a 3D seismic data volume from Lafourche Parish, South Louisiana. Figure 1 identifies Cretaceous through Holocene paleoshelf distribution of major Cenozoic depocenters. The star indicates the study area which is associated with play trends of Miocene age. Figure 2 outlines a 3D seismic survey near Thibodaux, Lafourche Parish, South Louisiana, that was acquired and processed in 2001. This survey encompasses approximately 72 mi2 with a bin spacing of 33.5 m. Densely spaced lease acreage positions crossing the survey are those of the Atchafalaya river levee system.