Abstract
This thesis investigates the application of the Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) techniques in de-noising of marine seismic data. EMD decomposes the seismic dataset into sub-datasets called Intrinsic Mode Functions (IMFs); the sum of these IMFs produces the original seismic data. EMD is a proven technique to capture the non-stationarity and non linearity of a signal; therefore, the key idea behind EMD is its use as a non-stationary filter. Marine seismic noise, such as swell, cable
strum, reflected interference, ground roll, and refracted waves, causes non-stationary seismic data and represents a major challenge. Therefore, I propose to investigate in this thesis if seismic noise can be separated from target reflections by using the EMD/EEMD techniques.
The motivation of this work is to establish a reliable methodology to de-noise seismic data using the EMD/EEMD techniques. The main idea is that non-stationarity caused by noise is collected in just a few IMFs that do not carry the target (primary) reflections in the seismic
dataset. The filtered section can be obtained by subtracting these IMFs from the original data leading to signal-to-noise enhancement of the data. In order to benchmark the effectiveness of the method I will compare the filtered EMD/EEMD sections to filtered reference sections generated using the GeoCluster software propriety to CGGVeritas.