Lithology classification using self-organising map

Author

Che Fung, Chun ; Wai Wong, Kok ; Eren, Halit ; Charlebois, Robert

Author_Institution

Sch. of Electr. & Comput. Eng., Curtin Univ. of Technol., Bentley, WA, Australia

Volume

1

fYear

1995

fDate

Nov/Dec 1995

Firstpage

526

Abstract

This paper reports the application of Kohonen´s self-organising map (SOM) network to the classification of lithology from well log data. The well log data are classified into nodes according to a pre-defined grid arrangement. The learning vector quantization (LVQ) algorithm is then applied to train the network under supervised learning. After the network is trained, it is used as the classification model for subsequent data. Results obtained from example studies using this proposed method have shown to be fast and accurate

Keywords

geology; geophysical prospecting; geophysical signal processing; pattern classification; self-organising feature maps; vector quantisation; LVQ; VQ; learning vector quantization; lithology classification; self-organising map; supervised learning; well log data; Application software; Artificial neural networks; Australia; Computer networks; Data engineering; Data processing; Instruments; Reservoirs; Supervised learning; Testing; Vector quantization; Well logging;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1995. Proceedings., IEEE International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-2768-3

Type

conf

DOI

10.1109/ICNN.1995.488233

Filename

488233