In the petroleum industry, stacking, one of the principal steps of conventional seismic signal processing, plays an important role in enhancing events and cancelling random and coherent noises by utilizing the predesigned redundancy in the seismic data. This paper demonstrates that by applying an alternative technique, factor analysis, to the same dataset, better subsurface image of the earth can be obtained. Contrary to stacking, it takes into consideration the scaling of the latent signal and makes explicit use of the second order statistics, obtaining higher signal-to-noise ratio. Moreover, factor analysis is compared with principal component analysis and independent component analysis, which can both be realized by neural networks, in processing the synthetic Marmousi dataset.
['Zhenhai Wang', 'Caihe Chen']
Factor Analysis for Geophysical Signal Processing with Seismic Profiles