%0 Journal Article
%T PRINCIPAL COMPONENT ANALYSIS APPLIED TO SEISMIC HORIZON INTERPRETATIONS
%J Sharif Journal of Civil Engineering
%I Sharif University of Technology
%Z 2676-4768
%A Sabeti, H.
%A Javaherian, A.
%A Araabi, B. N.
%D 2009
%\ 12/22/2009
%V vol. 25
%N 51.1
%P 69-76
%! PRINCIPAL COMPONENT ANALYSIS APPLIED TO SEISMIC HORIZON INTERPRETATIONS
%R
%X One of the most important stages in seismic interpretation is picking especial horizons in order to detect their underground downward and upward movements in an oilfield. Background noise, however, causes many dif- ficulties to this end. Considering a narrow window of a seismic section, whose re ectors are nearly horizontal, and applying a multivariate statistical method called the Principal Component Analysis, we find the largest eigenvalue that has the most contribution to the variance of data. Lower eigenvalues are subject to noise. Projecting data onto an eigenvector associated with the largest eigenvalue, we obtain a trace with sharper peaks and troughs. This method is applied to two synthetic models; horizontal re ectors and anticline. We, also, examine
the window length and dominant frequency of the seismic wavelet. Obtained trace with significantly attenuated noise can be used for tracking weak horizons in a seismic section with a signal-to-noise ratio of 0.2. Dominant frequency cannot change the result considerably. Optimum window length is the area in which re ectors are horizontal. It is also applied to the real data of an
oilfield in S.W. Iran. The obtained results were useful in picking some important horizons.
%U https://sjce.journals.sharif.edu/article_36_e2db1e64bc46f27bb1f50e6cb621bd35.pdf