application of principal component analysis in the interpretation of horizons lrz·hyy

one of the most important steps lrz·hyy data interpretation, tracing certain horizons which is of critical ahmytand stratigraphy. the existence of noise in the data stage of the interpretation is difficult. in this article, applying the analysis principal component and a narrow selection of cross migration pnjrhyy given where reflections almost afqyand, a trace comes from that obtained undermined and the noise may appear weak horizons. the study, this method using the matlab programming environment data is applied to synthetic and real. the first synthetic model layers of the same model horizontal, and the second synthetic model is a taqdys layers in both models low-margin voice of resistance is embedded. type in the synthetic models lrz·hyy wavelet, the dominant frequency and bandwidth of the data selection window has been evaluated. the results show that this method is capable of reflection to a weak signal to noise ratio of 0.2 on artificial lrz·hyy sections will reveal. the results show the model taqdys that selected data window at the requisite horizontal reflection this is for performance. this method also returns the type of wavelet lrz·hyy does not depend on. actual data used, part of the cross region lrz·hyy jfyr in southwestern iran.