Algorithm development of Self Organizing Maps in Fortran 90

1. Algorithm development of Self Organizing Maps in Fortran 90 Atish Roy , Marcilo Matos - University of Oklahoma, Norman, Oklahoma Attribute-Assisted Seismic Processing and Interpretation http://geology.ou.edu/aaspi 1. INTRODUCTION When there is incomplete or nonexistence geological information, seismic facies analysis is called nonsupervised and is performed through unsupervised learning or clustering algorithms (Duda et al.,2001). The SOM (Kohonen,2001) is one of the most effective pattern recognition techniques commonly used tool for non-supervised seismic facies analysis. It can also be associated with 1D and 2D color maps to help in seismic interpretation(Matos ,2009). 3.Application of 1D SOM and 2D SOM to identify different seismic facies 1D and 2D SOM have been applied to a flattened time migrated seismic data set from Osage County. The input data set consists of 20 samples around the flattened Oswego horizon. After training of the SOM prototype vectors they are projected using Principle Component Analysis (PCA) and the HSV color model is then applied to the SOM units. Classification of the input data is made by cross-correlation of each input trace with the SOM prototype vectors and assigning it the color of the closest prototype vector. Presently the codes used here have been written in Matlab. 4. Workflow in Fortran 90 Our major goal is to implement SOM in our AASPI software. The workflow is simplified in the following flowchart. 2.Kohonen Self Organizing Maps (SOMs) The SOM clusters data such that the statistical relationship between multidimensional data is converted into 2D geometrical relationship between the data points. Mathematically each SOM unit in the 2D output data set preserves the metric relationships and topologies of the multidimensional input data. Each SOM unit is called a prototype vector and are connected to its neighbor by either hexagonal or rectangular structural maps. The basic idea and the workflow of Kohonen Self Organizing Maps (SOM) has been explained with the help of the following figures. a) b) c) d) 2D SOM after color coding using PCA projection From the 2D SOM after PCA projection we can see some better delineation of facies in the river channel on the left. A possible levee bank on the river channel in the NE is properly seen. Also a possible deltaic deposit can be visible in the south east of the map. e) f) 6. ACKNOWLEDGEMENT Special thanks and regards to Dr. Kurt Marfurt for inspiring this work and thanks for the support by the AASPI Consortium, Schlumberger Petrel software and SOM toolbox in Matlab; http:/www.cis.hut.fi/projects/somtoolbox/. 7. REFERENCES Kohonen,T.,2001,Self Organizing Maps,3rd ed.: Springer-Verlag Matos, M.C./Osorio, P.L.M and P.R.S. Johanan,2007, Unsupervised seismic facies analysis using wavelet transform and self-organizing maps: Geophysics,72,P9-P21 5. CONCLUSION Color-coding the SOM helps greatly in visualization and interpretation of different seismic facies. Other projection algorithm such as Sammon projection can also be used for color coding. Preferably different seismic attributes should be chosen while making the SOM to confirm the final results.