Data mining of 3D poststack seismic attribute volumes using Kohonen self-organizing maps
| Autor: | Richard Uden, Uwe Strecker |
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| Rok vydání: | 2002 |
| Předmět: | |
| Zdroj: | The Leading Edge. 21:1032-1037 |
| ISSN: | 1938-3789 1070-485X |
| DOI: | 10.1190/1.1518442 |
| Popis: | 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 (Carr et al., 2001). Data classification occurs when similar data are captured with 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 ... |
| Databáze: | OpenAIRE |
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