Self-organizing maps for geoscientific data analysis: geological interpretation of multidimensional geophysical data
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Data interpretation is a common task in geoscientific disciplines. Interpretation difficulties occur especially if the data that have to be interpreted are of arbitrary dimension. This paper describes the application of a statistical method, called self-organizing mapping (SOM), to interpret multidimensional, non-linear, and highly noised geophysical data for purposes of geological prediction. The underlying theory is explained, and the method is applied to a six-dimensional seismic data set. Results of SOM classifications can be represented as two-dimensional images, called feature maps. Feature maps illustrate the complexity and demonstrate interrelations between single features or clusters of the complete feature space. SOM images can be visually described and easily interpreted. The advantage is that the SOM method considers interdependencies between all geophysical features at each instance. An application example of an automated geological interpretation based on the geophysical data is shown.
Keywordsgeological interpretation multidimensional geophysical data neural information processing self-organizing mapping
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- 1.Klose, C.D.: Engineering geological rock mass characterisation of granitic gneisses based on seismic in-situ measurements, Scientific Technical Report, GeoForschungsZentrum, Potsdam (2004). STR 04/08, http://www.gfz-potsdam.de/bib/pub/str0408/0408.htm
- 3.Garbin, H.D., Knopoff, L.: The compressional modulus of a material permeated by a random distribution of circular cracks. Q. Appl. Math. 30, 453–464 (1973)Google Scholar
- 4.Garbin, H.D., Knopoff, L.: The shear modulus of a material permeated by a random distribution of free circular cracks. Q. Appl. Math. 33, 296–300 (1975)Google Scholar
- 11.Bieniawski, Z.T.: Engineering Rock Mass Classification. Wiley, New York (1989)Google Scholar
- 12.Vesanto, J.: Data Exploration Process Based on Self-Organizing Map. PhD thesis, Helsinki University of Technology (2002) http://lib.tkk.fi/Diss/2002/isbn9512258978/
- 13.Silverman, B.W.: Density Estimation. London (1986)Google Scholar
- 14.LeCun, Y.: Effective Learning and Second-order Methods, A Tutorial at NIPS 93. Denver (1993)Google Scholar
- 16.Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons Inc., New York (1990)Google Scholar
- 17.Klose, C.D., Loew, S.: Engineering geological rock mass characterisation of granitic gneisses based on seismic in-situ measurements during tunnel excavations. In: Schubert, W. (ed.) Rock Engineering Theory and Practice, ISRM Regional Symposium EUROCK '04, pp. 120–126. VGE (2004)Google Scholar